Repository: microsoft/data-formulator Branch: main Commit: 676e7225c060 Files: 273 Total size: 11.9 MB Directory structure: gitextract_rufwyicb/ ├── .devcontainer/ │ └── devcontainer.json ├── .github/ │ ├── dependabot.yml │ └── workflows/ │ └── python-build.yml ├── .gitignore ├── .python-version ├── .vscode/ │ ├── launch.json │ └── tasks.json ├── CODESPACES.md ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── DEVELOPMENT.md ├── LICENSE ├── MANIFEST.in ├── README.md ├── SECURITY.md ├── SUPPORT.md ├── embed/ │ ├── README.md │ ├── dynamic.html │ ├── index.html │ └── postMessageTest.html ├── eslint.config.js ├── index.html ├── local_server.bat ├── local_server.sh ├── package.json ├── public/ │ ├── df_gas_prices.json │ ├── df_global_energy.json │ ├── df_movies.json │ ├── df_stock_prices_live.json │ ├── df_unemployment.json │ ├── manifest.json │ └── robots.txt ├── py-src/ │ └── data_formulator/ │ ├── __init__.py │ ├── __main__.py │ ├── agent_routes.py │ ├── agents/ │ │ ├── __init__.py │ │ ├── agent_chart_insight.py │ │ ├── agent_code_explanation.py │ │ ├── agent_data_clean_stream.py │ │ ├── agent_data_load.py │ │ ├── agent_data_rec.py │ │ ├── agent_data_transform.py │ │ ├── agent_interactive_explore.py │ │ ├── agent_report_gen.py │ │ ├── agent_sort_data.py │ │ ├── agent_utils.py │ │ ├── agent_utils_sql.py │ │ ├── client_utils.py │ │ ├── data_agent.py │ │ ├── semantic_types.py │ │ └── web_utils.py │ ├── app.py │ ├── auth.py │ ├── code_signing.py │ ├── data_loader/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── athena_data_loader.py │ │ ├── azure_blob_data_loader.py │ │ ├── bigquery_data_loader.py │ │ ├── external_data_loader.py │ │ ├── kusto_data_loader.py │ │ ├── mongodb_data_loader.py │ │ ├── mssql_data_loader.py │ │ ├── mysql_data_loader.py │ │ ├── postgresql_data_loader.py │ │ └── s3_data_loader.py │ ├── datalake/ │ │ ├── __init__.py │ │ ├── azure_blob_workspace.py │ │ ├── cache_manager.py │ │ ├── cached_azure_blob_workspace.py │ │ ├── file_manager.py │ │ ├── metadata.py │ │ ├── parquet_utils.py │ │ └── workspace.py │ ├── demo_stream_routes.py │ ├── example_datasets_config.py │ ├── sandbox/ │ │ ├── __init__.py │ │ ├── base.py │ │ ├── docker_sandbox.py │ │ ├── local_sandbox.py │ │ └── not_a_sandbox.py │ ├── session_routes.py │ ├── tables_routes.py │ ├── workflows/ │ │ ├── __init__.py │ │ ├── chart_semantics.py │ │ └── create_vl_plots.py │ └── workspace_factory.py ├── pyproject.toml ├── requirements.txt ├── src/ │ ├── app/ │ │ ├── App.tsx │ │ ├── chartCache.ts │ │ ├── chartRecommendation.ts │ │ ├── dfSlice.tsx │ │ ├── identity.ts │ │ ├── store.ts │ │ ├── tableThunks.ts │ │ ├── tokens.ts │ │ ├── useDataRefresh.tsx │ │ ├── useFormulateData.ts │ │ └── utils.tsx │ ├── assets/ │ │ └── icon-design.md │ ├── components/ │ │ ├── ChartTemplates.tsx │ │ ├── ComponentType.tsx │ │ ├── FunComponents.tsx │ │ ├── RotatingTextBlock.tsx │ │ └── chartUtils.ts │ ├── data/ │ │ ├── column.ts │ │ ├── table.ts │ │ ├── types.ts │ │ └── utils.ts │ ├── icons.tsx │ ├── index.css │ ├── index.tsx │ ├── lib/ │ │ └── agents-chart/ │ │ ├── README.md │ │ ├── chartjs/ │ │ │ ├── README.md │ │ │ ├── assemble.ts │ │ │ ├── index.ts │ │ │ ├── instantiate-spec.ts │ │ │ ├── recommendation.ts │ │ │ └── templates/ │ │ │ ├── area.ts │ │ │ ├── bar.ts │ │ │ ├── histogram.ts │ │ │ ├── index.ts │ │ │ ├── line.ts │ │ │ ├── pie.ts │ │ │ ├── radar.ts │ │ │ ├── rose.ts │ │ │ ├── scatter.ts │ │ │ └── utils.ts │ │ ├── core/ │ │ │ ├── compute-layout.ts │ │ │ ├── decisions.ts │ │ │ ├── field-semantics.ts │ │ │ ├── filter-overflow.ts │ │ │ ├── index.ts │ │ │ ├── recommendation.ts │ │ │ ├── resolve-semantics.ts │ │ │ ├── semantic-types.ts │ │ │ ├── type-registry.ts │ │ │ └── types.ts │ │ ├── docs/ │ │ │ ├── README.md │ │ │ ├── design-semantics.md │ │ │ ├── design-stretch-model.md │ │ │ └── test_plan.md │ │ ├── echarts/ │ │ │ ├── README.md │ │ │ ├── assemble.ts │ │ │ ├── facet.ts │ │ │ ├── index.ts │ │ │ ├── instantiate-spec.ts │ │ │ ├── recommendation.ts │ │ │ └── templates/ │ │ │ ├── area.ts │ │ │ ├── bar.ts │ │ │ ├── boxplot.ts │ │ │ ├── candlestick.ts │ │ │ ├── density.ts │ │ │ ├── funnel.ts │ │ │ ├── gauge.ts │ │ │ ├── heatmap.ts │ │ │ ├── histogram.ts │ │ │ ├── index.ts │ │ │ ├── jitter.ts │ │ │ ├── line.ts │ │ │ ├── lollipop.ts │ │ │ ├── pie.ts │ │ │ ├── pyramid.ts │ │ │ ├── radar.ts │ │ │ ├── ranged-dot.ts │ │ │ ├── rose.ts │ │ │ ├── sankey.ts │ │ │ ├── scatter.ts │ │ │ ├── streamgraph.ts │ │ │ ├── sunburst.ts │ │ │ ├── treemap.ts │ │ │ ├── utils.ts │ │ │ └── waterfall.ts │ │ ├── gofish/ │ │ │ ├── README.md │ │ │ ├── assemble.ts │ │ │ ├── index.ts │ │ │ ├── recommendation.ts │ │ │ └── templates/ │ │ │ ├── area.ts │ │ │ ├── bar.ts │ │ │ ├── index.ts │ │ │ ├── line.ts │ │ │ ├── pie.ts │ │ │ ├── scatter.ts │ │ │ ├── scatterpie.ts │ │ │ └── utils.ts │ │ ├── index.ts │ │ ├── test-data/ │ │ │ ├── area-tests.ts │ │ │ ├── bar-tests.ts │ │ │ ├── chartjs-tests.ts │ │ │ ├── date-tests.ts │ │ │ ├── debug-tests.ts │ │ │ ├── discrete-axis-tests.ts │ │ │ ├── distribution-tests.ts │ │ │ ├── echarts-tests.ts │ │ │ ├── facet-tests.ts │ │ │ ├── gas-pressure-tests.ts │ │ │ ├── generators.ts │ │ │ ├── gofish-tests.ts │ │ │ ├── index.ts │ │ │ ├── line-area-stretch-tests.ts │ │ │ ├── line-area-tests.ts │ │ │ ├── line-tests.ts │ │ │ ├── scatter-tests.ts │ │ │ ├── semantic-tests.ts │ │ │ ├── specialized-tests.ts │ │ │ ├── stress-tests.ts │ │ │ └── types.ts │ │ └── vegalite/ │ │ ├── README.md │ │ ├── assemble.ts │ │ ├── index.ts │ │ ├── instantiate-spec.ts │ │ ├── recommendation.ts │ │ └── templates/ │ │ ├── area.ts │ │ ├── bar.ts │ │ ├── bump.ts │ │ ├── candlestick.ts │ │ ├── custom.ts │ │ ├── density.ts │ │ ├── index.ts │ │ ├── jitter.ts │ │ ├── line.ts │ │ ├── lollipop.ts │ │ ├── map.ts │ │ ├── pie.ts │ │ ├── radar.ts │ │ ├── rose.ts │ │ ├── scatter.ts │ │ ├── utils.ts │ │ └── waterfall.ts │ ├── scss/ │ │ ├── App.scss │ │ ├── DataView.scss │ │ ├── DraggableCard.scss │ │ ├── EncodingShelf.scss │ │ └── VisualizationView.scss │ ├── types.d.ts │ └── views/ │ ├── About.tsx │ ├── AgentRulesDialog.tsx │ ├── ChartGallery.tsx │ ├── ChartRecBox.tsx │ ├── ChartRenderService.tsx │ ├── ChartifactDialog.tsx │ ├── ChatDialog.tsx │ ├── ChatThreadView.tsx │ ├── DBTableManager.tsx │ ├── DataFormulator.tsx │ ├── DataLoadingChat.tsx │ ├── DataLoadingThread.tsx │ ├── DataThread.tsx │ ├── DataThreadCards.tsx │ ├── DataView.tsx │ ├── EncodingBox.tsx │ ├── EncodingShelfCard.tsx │ ├── EncodingShelfThread.tsx │ ├── ExampleSessions.tsx │ ├── ExplComponents.tsx │ ├── MessageSnackbar.tsx │ ├── ModelSelectionDialog.tsx │ ├── MultiTablePreview.tsx │ ├── OperatorCard.tsx │ ├── ReactTable.tsx │ ├── RefreshDataDialog.tsx │ ├── ReportView.tsx │ ├── SelectableDataGrid.tsx │ ├── SimpleChartRecBox.tsx │ ├── TableSelectionView.tsx │ ├── TestPanel.tsx │ ├── UnifiedDataUploadDialog.tsx │ ├── ViewUtils.tsx │ ├── VisualizationView.tsx │ └── useFormulateData.ts ├── tsconfig.json └── vite.config.ts ================================================ FILE CONTENTS ================================================ ================================================ FILE: .devcontainer/devcontainer.json ================================================ // For format details, see https://aka.ms/devcontainer.json. For config options, see the // README at: https://github.com/devcontainers/templates/tree/main/src/python { "name": "Data Formulator Dev", // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye", // Features to add to the dev container. More info: https://containers.dev/features. "features": { "ghcr.io/devcontainers/features/node:1": { "version": "18" }, "ghcr.io/devcontainers/features/azure-cli:1": {}, "ghcr.io/astral-sh/uv:1": {} }, // Use 'forwardPorts' to make a list of ports inside the container available locally. "forwardPorts": [5000, 5173], // Use 'postCreateCommand' to run commands after the container is created. "postCreateCommand": "cd /workspaces/data-formulator && npm install && npm run build && uv sync && uv run data_formulator" // Configure tool-specific properties. // "customizations": {}, // Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root. // "remoteUser": "root" } ================================================ FILE: .github/dependabot.yml ================================================ # To get started with Dependabot version updates, you'll need to specify which # package ecosystems to update and where the package manifests are located. # Please see the documentation for more information: # https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates # https://containers.dev/guide/dependabot version: 2 updates: - package-ecosystem: "devcontainers" directory: "/" schedule: interval: weekly ================================================ FILE: .github/workflows/python-build.yml ================================================ # This workflow will install Python dependencies, run tests and lint with a variety of Python versions # For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python name: build on: push: branches: [ "main" ] pull_request: branches: [ "main" ] # Global permissions required for OIDC authentication permissions: id-token: write contents: read jobs: build: runs-on: ubuntu-latest strategy: fail-fast: false steps: - uses: actions/checkout@v4 - name: Set Node.js 20 uses: actions/setup-node@v4 with: node-version: 20 cache: 'yarn' - name: Set up uv uses: astral-sh/setup-uv@v4 with: python-version: 3.12 - name: Install node dependencies run: yarn install - name: Build frontend run: yarn build - name: Build python artifact run: uv build - name: Archive production artifacts uses: actions/upload-artifact@v4 with: name: release-dist path: dist pypi-publish: runs-on: ubuntu-latest needs: - build if: github.event_name == 'push' && contains(github.event.head_commit.message, '[deploy]') environment: name: pypi url: https://pypi.org/p/data-formulator permissions: id-token: write steps: - name: Retrieve release distributions uses: actions/download-artifact@v4 with: name: release-dist path: dist/ - name: Publish package distributions to PyPI uses: pypa/gh-action-pypi-publish@release/v1 ================================================ FILE: .gitignore ================================================ *env .venv/ .env **/*.ipynb_checkpoints/ .DS_Store build/ dist/ experiment_data/ ## Ignore Visual Studio temporary files, build results, and ## files generated by popular Visual Studio add-ons. ## ## Get latest from https://github.com/github/gitignore/blob/main/VisualStudio.gitignore # User-specific files *.rsuser *.suo *.user *.userosscache *.sln.docstates # User-specific files (MonoDevelop/Xamarin Studio) *.userprefs # Mono auto generated files mono_crash.* # Build results [Dd]ebug/ [Dd]ebugPublic/ [Rr]elease/ [Rr]eleases/ x64/ x86/ [Ww][Ii][Nn]32/ [Aa][Rr][Mm]/ [Aa][Rr][Mm]64/ bld/ [Bb]in/ [Oo]bj/ [Ll]og/ [Ll]ogs/ # Visual Studio 2015/2017 cache/options directory .vs/ # Uncomment if you have tasks that create the project's static files in wwwroot #wwwroot/ # Visual Studio 2017 auto generated files Generated\ Files/ # MSTest test Results [Tt]est[Rr]esult*/ [Bb]uild[Ll]og.* # NUnit *.VisualState.xml TestResult.xml nunit-*.xml # Build Results of an ATL Project [Dd]ebugPS/ [Rr]eleasePS/ dlldata.c # Benchmark Results BenchmarkDotNet.Artifacts/ # .NET Core project.lock.json project.fragment.lock.json artifacts/ # ASP.NET Scaffolding ScaffoldingReadMe.txt # StyleCop StyleCopReport.xml # Files built by Visual Studio *_i.c *_p.c *_h.h *.ilk *.meta *.obj *.iobj *.pch *.pdb *.ipdb *.pgc *.pgd *.rsp *.sbr *.tlb *.tli *.tlh *.tmp *.tmp_proj *_wpftmp.csproj *.log *.tlog *.vspscc *.vssscc .builds *.pidb *.svclog *.scc # Chutzpah Test files _Chutzpah* # Visual C++ cache files ipch/ *.aps *.ncb *.opendb *.opensdf *.sdf *.cachefile *.VC.db *.VC.VC.opendb # Visual Studio profiler *.psess *.vsp *.vspx *.sap # Visual Studio Trace Files *.e2e # TFS 2012 Local Workspace $tf/ # Guidance Automation Toolkit *.gpState # ReSharper is a .NET coding add-in _ReSharper*/ *.[Rr]e[Ss]harper *.DotSettings.user # TeamCity is a build add-in _TeamCity* # DotCover is a Code Coverage Tool *.dotCover # AxoCover is a Code Coverage Tool .axoCover/* !.axoCover/settings.json # Coverlet is a free, cross platform Code Coverage Tool coverage*.json coverage*.xml coverage*.info # Visual Studio code coverage results *.coverage *.coveragexml # NCrunch _NCrunch_* .*crunch*.local.xml nCrunchTemp_* # MightyMoose *.mm.* AutoTest.Net/ # Web workbench (sass) .sass-cache/ # Installshield output folder [Ee]xpress/ # DocProject is a documentation generator add-in DocProject/buildhelp/ DocProject/Help/*.HxT DocProject/Help/*.HxC DocProject/Help/*.hhc DocProject/Help/*.hhk DocProject/Help/*.hhp DocProject/Help/Html2 DocProject/Help/html # Click-Once directory publish/ # Publish Web Output *.[Pp]ublish.xml *.azurePubxml # Note: Comment the next line if you want to checkin your web deploy settings, # but database connection strings (with potential passwords) will be unencrypted *.pubxml *.publishproj # Microsoft Azure Web App publish settings. Comment the next line if you want to # checkin your Azure Web App publish settings, but sensitive information contained # in these scripts will be unencrypted PublishScripts/ # NuGet Packages *.nupkg # NuGet Symbol Packages *.snupkg # The packages folder can be ignored because of Package Restore **/[Pp]ackages/* # except build/, which is used as an MSBuild target. !**/[Pp]ackages/build/ # Uncomment if necessary however generally it will be regenerated when needed #!**/[Pp]ackages/repositories.config # NuGet v3's project.json files produces more ignorable files *.nuget.props *.nuget.targets # Microsoft Azure Build Output csx/ *.build.csdef # Microsoft Azure Emulator ecf/ rcf/ # Windows Store app package directories and files AppPackages/ BundleArtifacts/ Package.StoreAssociation.xml _pkginfo.txt *.appx *.appxbundle *.appxupload # Visual Studio cache files # files ending in .cache can be ignored *.[Cc]ache # but keep track of directories ending in .cache !?*.[Cc]ache/ # Others ClientBin/ ~$* *~ *.dbmdl *.dbproj.schemaview *.jfm *.pfx *.publishsettings orleans.codegen.cs # Including strong name files can present a security risk # (https://github.com/github/gitignore/pull/2483#issue-259490424) #*.snk # Since there are multiple workflows, uncomment next line to ignore bower_components # (https://github.com/github/gitignore/pull/1529#issuecomment-104372622) #bower_components/ # RIA/Silverlight projects Generated_Code/ # Backup & report files from converting an old project file # to a newer Visual Studio version. Backup files are not needed, # because we have git ;-) _UpgradeReport_Files/ Backup*/ UpgradeLog*.XML UpgradeLog*.htm ServiceFabricBackup/ *.rptproj.bak # SQL Server files *.mdf *.ldf *.ndf # Business Intelligence projects *.rdl.data *.bim.layout *.bim_*.settings *.rptproj.rsuser *- [Bb]ackup.rdl *- [Bb]ackup ([0-9]).rdl *- [Bb]ackup ([0-9][0-9]).rdl # Microsoft Fakes FakesAssemblies/ # GhostDoc plugin setting file *.GhostDoc.xml # Node.js Tools for Visual Studio .ntvs_analysis.dat node_modules/ # Visual Studio 6 build log *.plg # Visual Studio 6 workspace options file *.opt # Visual Studio 6 auto-generated workspace file (contains which files were open etc.) *.vbw # Visual Studio 6 auto-generated project file (contains which files were open etc.) *.vbp # Visual Studio 6 workspace and project file (working project files containing files to include in project) *.dsw *.dsp # Visual Studio 6 technical files *.ncb *.aps # Visual Studio LightSwitch build output **/*.HTMLClient/GeneratedArtifacts **/*.DesktopClient/GeneratedArtifacts **/*.DesktopClient/ModelManifest.xml **/*.Server/GeneratedArtifacts **/*.Server/ModelManifest.xml _Pvt_Extensions # Paket dependency manager .paket/paket.exe paket-files/ # FAKE - F# Make .fake/ # CodeRush personal settings .cr/personal # Python Tools for Visual Studio (PTVS) __pycache__/ *.pyc # Cake - Uncomment if you are using it # tools/** # !tools/packages.config # Tabs Studio *.tss # Telerik's JustMock configuration file *.jmconfig # BizTalk build output *.btp.cs *.btm.cs *.odx.cs *.xsd.cs # OpenCover UI analysis results OpenCover/ # Azure Stream Analytics local run output ASALocalRun/ # MSBuild Binary and Structured Log *.binlog # NVidia Nsight GPU debugger configuration file *.nvuser # MFractors (Xamarin productivity tool) working folder .mfractor/ # Local History for Visual Studio .localhistory/ # Visual Studio History (VSHistory) files .vshistory/ # BeatPulse healthcheck temp database healthchecksdb # Backup folder for Package Reference Convert tool in Visual Studio 2017 MigrationBackup/ # Ionide (cross platform F# VS Code tools) working folder .ionide/ # Fody - auto-generated XML schema FodyWeavers.xsd # VS Code files for those working on multiple tools .vscode/* !.vscode/settings.json !.vscode/tasks.json !.vscode/launch.json !.vscode/extensions.json *.code-workspace # Local History for Visual Studio Code .history/ # Windows Installer files from build outputs *.cab *.msi *.msix *.msm *.msp # JetBrains Rider *.sln.iml venv \.\NUL NUL ================================================ FILE: .python-version ================================================ 3.11 ================================================ FILE: .vscode/launch.json ================================================ { "version": "0.2.0", "configurations": [ { "name": "Flask", "type": "debugpy", "request": "launch", "program": "${workspaceFolder}/py-src/data_formulator/app.py", "args": ["run", "--no-debugger", "--no-reload"], "env": { "FLASK_APP": "app.py", "FLASK_ENV": "development", "FLASK_RUN_PORT": "5000", "FLASK_RUN_HOST": "0.0.0.0" }, "preLaunchTask": "Activate Virtual Environment", } ] } ================================================ FILE: .vscode/tasks.json ================================================ { "version": "2.0.0", "tasks": [ { "label": "Activate Virtual Environment", "type": "shell", "command": "source /workspaces/data-formulator/venv/bin/activate", "problemMatcher": [], "group": { "kind": "test", "isDefault": true }, "presentation": { "reveal": "always", "panel": "new" } } ] } ================================================ FILE: CODESPACES.md ================================================ # Set up Data Formulator on Codespaces Use a preconfigured Codespace to get Data Formulator up and running quickly. ## Prerequisites You will need a GitHub account and to be logged in to use Codespaces. ### Step 1: Create a Codespace (~5 minutes) [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/microsoft/data-formulator?quickstart=1) image ### Step 2: Run the app The codespace is a VSCode development environment in the cloud. Once the Codespace is created, start Data Formuator with the following steps: * A toast about port forwarding will appear, click the **Open in Browser** button. * You will see the Data Formulator app! image ### Usage See the [Usage section on the README.md page](README.md#usage). ================================================ FILE: CODE_OF_CONDUCT.md ================================================ # Microsoft Open Source Code of Conduct This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). Resources: - [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/) - [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) - Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns ================================================ FILE: CONTRIBUTING.md ================================================ # Contributing This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com. When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repositories using our CLA. This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. ================================================ FILE: DEVELOPMENT.md ================================================ # Set up a local Data Formulator development environment How to set up your local machine. ## Prerequisites * Python >= 3.11 * Node.js * Yarn * [uv](https://docs.astral.sh/uv/) (recommended) or pip ## Backend (Python) ### Option 1: With uv (recommended) uv is faster and provides reproducible builds via lockfile. ```bash uv sync # Creates .venv and installs all dependencies uv run data_formulator # Run app (opens browser automatically) uv run data_formulator --dev # Run backend only (for frontend development) ``` **Which command to use:** - **End users / testing the full app**: `uv run data_formulator` - starts server and opens browser to http://localhost:5567 - **Frontend development**: `uv run data_formulator --dev` - starts backend server only, then run `yarn start` separately for the Vite dev server on http://localhost:5173 ### Option 2: With pip (fallback) - **Create a Virtual Environment** ```bash python -m venv venv source venv/bin/activate # Unix # or .\venv\Scripts\activate # Windows ``` - **Install Dependencies** ```bash pip install -r requirements.txt ``` - **Configure environment variables (optional)** - copy `.env.template` to `.env` and fill in your values: - **API keys**: set `{PROVIDER}_ENABLED=true`, `{PROVIDER}_API_KEY=...`, and `{PROVIDER}_MODELS=...` for each LLM provider you want to use. See the [LiteLLM setup](https://docs.litellm.ai/docs#litellm-python-sdk) guide for provider-specific fields. - **Server settings**: `DISABLE_DISPLAY_KEYS`, `SANDBOX`, etc. - **Azure Blob workspace** (optional): see [Azure Blob Storage Workspace](#azure-blob-storage-workspace) below. - this lets Data Formulator automatically load API keys at startup so you don't need to enter them in the UI. - **Run the app** ```bash # Unix ./local_server.sh # Windows .\local_server.bat # Or directly data_formulator # Opens browser automatically data_formulator --dev # Backend only (for frontend development) ``` ## Frontend (TypeScript) - **Install NPM packages** ```bash yarn ``` - **Development mode** First, start the backend server (in a separate terminal): ```bash uv run data_formulator --dev # or ./local_server.sh ``` Then, run the frontend in development mode with hot reloading: ```bash yarn start ``` Open [http://localhost:5173](http://localhost:5173) to view it in the browser. The page will reload if you make edits. You will also see any lint errors in the console. ## Build for Production - **Build the frontend and then the backend** Compile the TypeScript files and bundle the project: ```bash yarn build ``` This builds the app for production to the `py-src/data_formulator/dist` folder. Then, build python package: ```bash # With uv uv build # Or with pip pip install build python -m build ``` This will create a python wheel in the `dist/` folder. The name would be `data_formulator--py3-none-any.whl` - **Test the artifact** You can then install the build result wheel (testing in a virtual environment is recommended): ```bash # replace with the actual build version. pip install dist/data_formulator--py3-none-any.whl ``` Once installed, you can run Data Formulator with: ```bash data_formulator ``` or ```bash python -m data_formulator ``` Open [http://localhost:5567](http://localhost:5567) to view it in the browser. ## Sandbox AI-generated Python code runs inside a **sandbox** to isolate it from the main server process. Two backends are available: | Backend | Flag | How it works | Overhead | |---------|------|--------------|----------| | **local** (default) | `--sandbox local` | Persistent warm subprocess with pre-imported pandas/numpy/duckdb. Audit hooks block file writes and dangerous operations (subprocess, shutil, etc.). | ~1 ms | | **docker** | `--sandbox docker` | Each execution runs in a disposable `docker run --rm` container. Workspace is mounted read-only; output is returned via a bind-mounted parquet file. Memory/CPU/PID limits enforced. | ~700 ms | ```bash # Use the default local sandbox python -m data_formulator # Use Docker sandbox (requires Docker daemon) python -m data_formulator --sandbox docker ``` The Docker sandbox image is built from `py-src/data_formulator/sandbox/Dockerfile.sandbox`: ```bash docker build -t data-formulator-sandbox -f py-src/data_formulator/sandbox/Dockerfile.sandbox . ``` Source: [`py-src/data_formulator/sandbox/`](py-src/data_formulator/sandbox/) ## Azure Blob Storage Workspace By default, workspace data (uploaded files, parquet tables, metadata) is stored on the **local filesystem** under `~/.data_formulator/workspaces/`. For cloud deployments you can switch to **Azure Blob Storage** so all workspace data lives in a blob container instead. ### Quick start (local dev with connection string) 1. **Install extra dependencies:** ```bash pip install azure-storage-blob # or with uv: uv pip install azure-storage-blob ``` 2. **Create a storage account & container** (one-time setup): ```bash az storage account create -n -g -l eastus --sku Standard_LRS az storage container create -n data-formulator --account-name ``` 3. **Get the connection string:** ```bash az storage account show-connection-string -n -g -o tsv ``` 4. **Add to `.env`:** ```env WORKSPACE_BACKEND=azure_blob AZURE_BLOB_CONNECTION_STRING=DefaultEndpointsProtocol=https;AccountName=... # AZURE_BLOB_CONTAINER=data-formulator # default, change if needed ``` 5. **Run normally:** ```bash uv run data_formulator --dev ``` Or pass as CLI flags: ```bash data_formulator --workspace-backend azure_blob \ --azure-blob-connection-string "DefaultEndpointsProtocol=https;AccountName=..." ``` ### Production setup with Entra ID (no secrets) In production (Azure App Service, AKS, etc.) you can authenticate the app to blob storage via **Managed Identity** instead of a connection string. This eliminates secrets entirely. 1. **Install extra dependencies:** ```bash pip install azure-storage-blob azure-identity ``` 2. **Assign a role to the app's Managed Identity:** ```bash # Get the App Service's principal ID PRINCIPAL_ID=$(az webapp identity show -n -g --query principalId -o tsv) # Grant it "Storage Blob Data Contributor" on the storage account az role assignment create \ --assignee "$PRINCIPAL_ID" \ --role "Storage Blob Data Contributor" \ --scope "/subscriptions//resourceGroups//providers/Microsoft.Storage/storageAccounts/" ``` 3. **Set environment variables** (no secrets needed): ```env WORKSPACE_BACKEND=azure_blob AZURE_BLOB_ACCOUNT_URL=https://.blob.core.windows.net # AZURE_BLOB_CONTAINER=data-formulator ``` The app uses [`DefaultAzureCredential`](https://learn.microsoft.com/python/api/azure-identity/azure.identity.defaultazurecredential), which automatically picks up the Managed Identity. 4. **For local development** with the same Entra ID path, log in with the Azure CLI: ```bash az login # Grant your user the same "Storage Blob Data Contributor" role az role assignment create \ --assignee "" \ --role "Storage Blob Data Contributor" \ --scope "/subscriptions//resourceGroups//providers/Microsoft.Storage/storageAccounts/" ``` Then set: ```env WORKSPACE_BACKEND=azure_blob AZURE_BLOB_ACCOUNT_URL=https://.blob.core.windows.net ``` `DefaultAzureCredential` will use your `az login` session. ### Authentication methods summary | Method | Env var | When to use | |--------|---------|-------------| | **Connection string** | `AZURE_BLOB_CONNECTION_STRING` | Local dev, quick tests | | **Entra ID (Managed Identity)** | `AZURE_BLOB_ACCOUNT_URL` | Azure App Service, AKS — no secrets | | **Entra ID (az login)** | `AZURE_BLOB_ACCOUNT_URL` | Local dev without secrets | | **Entra ID (service principal)** | `AZURE_BLOB_ACCOUNT_URL` + `AZURE_CLIENT_ID` / `AZURE_TENANT_ID` / `AZURE_CLIENT_SECRET` | CI/CD pipelines | If both `AZURE_BLOB_CONNECTION_STRING` and `AZURE_BLOB_ACCOUNT_URL` are set, the connection string takes precedence. ### Blob layout All workspace data is stored under `//` inside the container: ``` data-formulator/ ← container workspaces/ ← datalake_root (default) browser_550e8400.../ ← anonymous user workspace workspace.yaml sales_data.parquet user_alice_example_com/ ← authenticated user workspace workspace.yaml quarterly_report.parquet ``` ### CLI flags reference | Flag | Env var | Default | Description | |------|---------|---------|-------------| | `--workspace-backend` | `WORKSPACE_BACKEND` | `local` | `local` or `azure_blob` | | `--azure-blob-connection-string` | `AZURE_BLOB_CONNECTION_STRING` | — | Shared-key connection string | | `--azure-blob-account-url` | `AZURE_BLOB_ACCOUNT_URL` | — | Account URL for Entra ID auth | | `--azure-blob-container` | `AZURE_BLOB_CONTAINER` | `data-formulator` | Blob container name | ## Security Considerations for Production Deployment ⚠️ **IMPORTANT SECURITY WARNING FOR PRODUCTION DEPLOYMENT** When deploying Data Formulator to production, please be aware of the following security considerations: ### Database and Data Storage Security 1. **Workspace and table data**: Table data is stored in per-identity workspaces (e.g. parquet files). DuckDB is used only in-memory per request when needed (e.g. for SQL mode); no persistent DuckDB database files are created by the app. 2. **Identity Management**: - Each user's data is isolated by a namespaced identity key (e.g., `user:alice@example.com` or `browser:550e8400-...`) - Anonymous users get a browser-based UUID stored in localStorage - Authenticated users get their verified user ID from the auth provider 3. **Data persistence**: User data may be written to workspace storage (e.g. parquet) on the server. In multi-tenant deployments, ensure workspace directories are isolated and access-controlled. ### Recommended Security Measures For production deployment, consider: 1. **Use `--disable-database` flag** to disable table-connector routes when you do not need external or uploaded table support 2. **Implement proper authentication, authorization, and other security measures** as needed for your specific use case, for example: - User authentication (OAuth, JWT tokens, etc.) - Role-based access control - API rate limiting - HTTPS/TLS encryption - Input validation and sanitization ### Configuration for Production ```bash # For stateless deployment (recommended for public hosting) python -m data_formulator.app --disable-database ``` ## Authentication Architecture Data Formulator supports a **hybrid identity system** that supports both anonymous and authenticated users. ### Identity Flow Overview ``` ┌─────────────────────────────────────────────────────────────────────┐ │ Frontend Request │ ├─────────────────────────────────────────────────────────────────────┤ │ Headers: │ │ X-Identity-Id: "browser:550e8400-..." (namespace sent by client) │ │ Authorization: Bearer (if custom auth implemented) │ │ (Azure also adds X-MS-CLIENT-PRINCIPAL-ID automatically) │ └─────────────────────────────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────────┐ │ Backend Identity Resolution │ │ (auth.py: get_identity_id) │ ├─────────────────────────────────────────────────────────────────────┤ │ Priority 1: Azure X-MS-CLIENT-PRINCIPAL-ID → "user:" │ │ Priority 2: JWT Bearer token (if implemented) → "user:" │ │ Priority 3: X-Identity-Id header → ALWAYS "browser:" │ │ (client-provided namespace is IGNORED for security) │ └─────────────────────────────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────────┐ │ Storage Isolation │ ├─────────────────────────────────────────────────────────────────────┤ │ "user:alice@example.com" → alice's DuckDB file (ONLY via auth) │ │ "browser:550e8400-..." → anonymous user's DuckDB file │ └─────────────────────────────────────────────────────────────────────┘ ``` ### Security Model **Critical Security Rule:** The backend NEVER trusts the namespace prefix from the client-provided `X-Identity-Id` header. Even if a client sends `X-Identity-Id: "user:alice@..."`, the backend strips the prefix and forces `browser:alice@...`. Only verified authentication (Azure headers or JWT) can result in a `user:` prefixed identity. The key security principle is **namespaced isolation with forced prefixing**: | Scenario | X-Identity-Id Sent | Backend Resolution | Storage Key | |----------|-------------------|-------------------|-------------| | Anonymous user | `browser:550e8400-...` | Strips prefix, forces `browser:` | `browser:550e8400-...` | | Azure logged-in user | `browser:550e8400-...` | Uses Azure header (priority 1) | `user:alice@...` | | Attacker spoofing | `user:alice@...` (forged) | No valid auth, strips & forces `browser:` | `browser:alice@...` | **Why this is secure:** An attacker sending `X-Identity-Id: user:alice@...` gets `browser:alice@...` as their storage key, which is completely separate from the real `user:alice@...` that only authenticated Alice can access. ### Implementing Custom Authentication To add JWT-based authentication: 1. **Backend** (`tables_routes.py`): Uncomment and configure the JWT verification code in `get_identity_id()` 2. **Frontend** (`utils.tsx`): Implement `getAuthToken()` to retrieve the JWT from your auth context 3. **Add JWT secret** to Flask config: `current_app.config['JWT_SECRET']` ### Azure App Service Authentication When deployed to Azure with EasyAuth enabled: - Azure automatically adds `X-MS-CLIENT-PRINCIPAL-ID` header to authenticated requests - The backend reads this header first (highest priority) - No frontend changes needed - Azure handles the auth flow ### Frontend Identity Management The frontend (`src/app/identity.ts`) manages identity as follows: ```typescript // Identity is always initialized with browser ID identity: { type: 'browser', id: getBrowserId() } // If user logs in (e.g., via Azure), it's updated to: identity: { type: 'user', id: userInfo.userId } // All API requests send namespaced identity: // X-Identity-Id: "browser:550e8400-..." or "user:alice@..." ``` This ensures: 1. **Anonymous users**: Work immediately with localStorage-based browser ID 2. **Logged-in users**: Get their verified user ID from the auth provider 3. **Cross-tab consistency**: Browser ID is shared via localStorage across all tabs ## Usage See the [Usage section on the README.md page](README.md#usage). ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) Microsoft Corporation. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE ================================================ FILE: MANIFEST.in ================================================ include py-src/data_formulator/dist/* include py-src/data_formulator/dist/assets/* global-exclude .DS_Store exclude py-src/examples ================================================ FILE: README.md ================================================

Data Formulator icon  Data Formulator: AI-powered Data Visualization

🪄 Explore data with visualizations, powered by AI agents.

Try Online Demo   Install Locally

arXiv  License: MIT  YouTube  build  Discord

## News 🔥🔥🔥 [03-02-2026] **Data Formulator 0.7 (alpha)** — More charts, new experience, enterprise-ready - 📊 **30 chart types** with a new semantic chart engine (area, streamgraph, candlestick, pie, radar, maps, and more). - 💬 **Hybrid chat + data thread** — chat woven into the exploration timeline with lineage, previews, and reasoning. - 🤖 **Unified `DataAgent`** replacing four separate agents, plus new recommendation and insight agents. - 🏗️ **Workspace / Data Lake** — persistent, identity-based data management with local and Azure Blob backends. - 🔒 **Security hardening** — code signing, sandboxed execution, authentication, and rate limiting. - 📦 **UV-first build** — reproducible builds via `uv.lock`; `uv sync` + `uv run data_formulator`. - 📝 Detailed writeup on the new architecture coming soon — stay tuned! ## Previous Updates Here are milestones that lead to the current design: - **v0.6** ([Demo](https://github.com/microsoft/data-formulator/releases/tag/0.6)): Real-time insights from live data — connect to URLs and databases with automatic refresh - **uv support**: Faster installation with [uv](https://docs.astral.sh/uv/) — `uvx data_formulator` or `uv pip install data_formulator` - **v0.5.1** ([Demo](https://github.com/microsoft/data-formulator/pull/200#issue-3635408217)): Community data loaders, US Map & Pie Chart, editable reports, snappier UI - **v0.5**: Vibe with your data, in control — agent mode, data extraction, reports - **v0.2.2** ([Demo](https://github.com/microsoft/data-formulator/pull/176)): Goal-driven exploration with agent recommendations and performance improvements - **v0.2.1.3/4** ([Readme](https://github.com/microsoft/data-formulator/tree/main/py-src/data_formulator/data_loader) | [Demo](https://github.com/microsoft/data-formulator/pull/155)): External data loaders (MySQL, PostgreSQL, MSSQL, Azure Data Explorer, S3, Azure Blob) - **v0.2** ([Demos](https://github.com/microsoft/data-formulator/releases/tag/0.2)): Large data support with DuckDB integration - **v0.1.7** ([Demos](https://github.com/microsoft/data-formulator/releases/tag/0.1.7)): Dataset anchoring for cleaner workflows - **v0.1.6** ([Demo](https://github.com/microsoft/data-formulator/releases/tag/0.1.6)): Multi-table support with automatic joins - **Model Support**: OpenAI, Azure, Ollama, Anthropic via [LiteLLM](https://github.com/BerriAI/litellm) ([feedback](https://github.com/microsoft/data-formulator/issues/49)) - **Python Package**: Easy local installation ([try it](#get-started)) - **Visualization Challenges**: Test your skills ([challenges](https://github.com/microsoft/data-formulator/issues/53)) - **Data Extraction**: Parse data from images and text ([demo](https://github.com/microsoft/data-formulator/pull/31#issuecomment-2403652717)) - **Initial Release**: [Blog](https://www.microsoft.com/en-us/research/blog/data-formulator-exploring-how-ai-can-help-analysts-create-rich-data-visualizations/) | [Video](https://youtu.be/3ndlwt0Wi3c)
View detailed update history - [07-10-2025] Data Formulator 0.2.2: Start with an analysis goal - Some key frontend performance updates. - You can start your exploration with a goal, or, tab and see if the agent can recommend some good exploration ideas for you. [Demo](https://github.com/microsoft/data-formulator/pull/176) - [05-13-2025] Data Formulator 0.2.1.3/4: External Data Loader - We introduced external data loader class to make import data easier. [Readme](https://github.com/microsoft/data-formulator/tree/main/py-src/data_formulator/data_loader) and [Demo](https://github.com/microsoft/data-formulator/pull/155) - Current data loaders: MySQL, Azure Data Explorer (Kusto), Azure Blob and Amazon S3 (json, parquet, csv). - [07-01-2025] Updated with: Postgresql, mssql. - Call for action [link](https://github.com/microsoft/data-formulator/issues/156): - Users: let us know which data source you'd like to load data from. - Developers: let's build more data loaders. - [04-23-2025] Data Formulator 0.2: working with *large* data 📦📦📦 - Explore large data by: 1. Upload large data file to the local database (powered by [DuckDB](https://github.com/duckdb/duckdb)). 2. Use drag-and-drop to specify charts, and Data Formulator dynamically fetches data from the database to create visualizations (with ⚡️⚡️⚡️ speeds). 3. Work with AI agents: they generate SQL queries to transform the data to create rich visualizations! 4. Anchor the result / follow up / create a new branch / join tables; let's dive deeper. - Checkout the demos at [[https://github.com/microsoft/data-formulator/releases/tag/0.2]](https://github.com/microsoft/data-formulator/releases/tag/0.2) - Improved overall system performance, and enjoy the updated derive concept functionality. - [03-20-2025] Data Formulator 0.1.7: Anchoring ⚓︎ - Anchor an intermediate dataset, so that followup data analysis are built on top of the anchored data, not the original one. - Clean a data and work with only the cleaned data; create a subset from the original data or join multiple data, and then go from there. AI agents will be less likely to get confused and work faster. ⚡️⚡️ - Check out the demos at [[https://github.com/microsoft/data-formulator/releases/tag/0.1.7]](https://github.com/microsoft/data-formulator/releases/tag/0.1.7) - Don't forget to update Data Formulator to test it out! - [02-20-2025] Data Formulator 0.1.6 released! - Now supports working with multiple datasets at once! Tell Data Formulator which data tables you would like to use in the encoding shelf, and it will figure out how to join the tables to create a visualization to answer your question. 🪄 - Checkout the demo at [[https://github.com/microsoft/data-formulator/releases/tag/0.1.6]](https://github.com/microsoft/data-formulator/releases/tag/0.1.6). - Update your Data Formulator to the latest version to play with the new features. - [02-12-2025] More models supported now! - Now supports OpenAI, Azure, Ollama, and Anthropic models (and more powered by [LiteLLM](https://github.com/BerriAI/litellm)); - Models with strong code generation and instruction following capabilities are recommended (gpt-4o, claude-3-5-sonnet etc.); - You can store API keys in `.env` to avoid typing them every time (copy `.env.template` to `.env` and fill in your keys). - Let us know which models you have good/bad experiences with, and what models you would like to see supported! [[comment here]](https://github.com/microsoft/data-formulator/issues/49) - [11-07-2024] Minor fun update: data visualization challenges! - We added a few visualization challenges with the sample datasets. Can you complete them all? [[try them out!]](https://github.com/microsoft/data-formulator/issues/53#issue-2641841252) - Comment in the issue when you did, or share your results/questions with others! [[comment here]](https://github.com/microsoft/data-formulator/issues/53) - [10-11-2024] Data Formulator python package released! - You can now install Data Formulator using Python and run it locally, easily. [[check it out]](#get-started). - Our Codespaces configuration is also updated for fast start up ⚡️. [[try it now!]](https://codespaces.new/microsoft/data-formulator?quickstart=1) - New experimental feature: load an image or a messy text, and ask AI to parse and clean it for you(!). [[demo]](https://github.com/microsoft/data-formulator/pull/31#issuecomment-2403652717) - [10-01-2024] Initial release of Data Formulator, check out our [[blog]](https://www.microsoft.com/en-us/research/blog/data-formulator-exploring-how-ai-can-help-analysts-create-rich-data-visualizations/) and [[video]](https://youtu.be/3ndlwt0Wi3c)!
## Overview **Data Formulator** is a Microsoft Research prototype for data exploration with visualizations powered by AI agents. Data Formulator enables analysts to iteratively explore and visualize data. Started with data in any format (screenshot, text, csv, or database), users can work with AI agents with a novel blended interface that combines *user interface interactions (UI)* and *natural language (NL) inputs* to communicate their intents, control branching exploration directions, and create reports to share their insights. ## Get Started Play with Data Formulator with one of the following options: - **Option 1: Install via uv (recommended)** [uv](https://docs.astral.sh/uv/) is an extremely fast Python package manager. If you have uv installed, you can run Data Formulator directly without any setup: ```bash # Run data formulator directly (no install needed) uvx data_formulator ``` Or install it in a project/virtual environment: ```bash # Install data_formulator uv pip install data_formulator # Run data formulator python -m data_formulator ``` Data Formulator will be automatically opened in the browser at [http://localhost:5567](http://localhost:5567). - **Option 2: Install via pip** Use pip for installation (recommend: install it in a virtual environment). ```bash # install data_formulator pip install data_formulator # Run data formulator with this command python -m data_formulator ``` Data Formulator will be automatically opened in the browser at [http://localhost:5567](http://localhost:5567). *you can specify the port number (e.g., 8080) by `python -m data_formulator --port 8080` if the default port is occupied.* - **Option 3: Codespaces (5 minutes)** You can also run Data Formulator in Codespaces; we have everything pre-configured. For more details, see [CODESPACES.md](CODESPACES.md). [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/microsoft/data-formulator?quickstart=1) - **Option 4: Working in the developer mode** You can build Data Formulator locally if you prefer full control over your development environment and develop your own version on top. For detailed instructions, refer to [DEVELOPMENT.md](DEVELOPMENT.md). ## Using Data Formulator ### Load Data Besides uploading csv, tsv or xlsx files that contain structured data, you can ask Data Formulator to extract data from screenshots, text blocks or websites, or load data from databases use connectors. Then you are ready to explore. image ### Explore Data There are four levels to explore data based depending on whether you want more vibe or more control: - Level 1 (most control): Create charts with UI via drag-and-drop, if all fields to be visualized are already in the data. - Level 2: Specify chart designs with natural language + NL. Describe how new fields should be visualized in your chart, AI will automatically transform data to realize the design. - Level 3: Get recommendations: Ask AI agents to recommend charts directly from NL descriptions, or even directly ask for exploration ideas. - Level 4 (most vibe): In agent mode, provide a high-level goal and let AI agents automatically plan and explore data in multiple turns. Exploration threads will be created automatically. https://github.com/user-attachments/assets/164aff58-9f93-4792-b8ed-9944578fbb72 - Level 5: In practice, leverage all of them to keep up with both vibe and control! ### Create Reports Use the report builder to compose a report of the style you like, based on selected charts. Then share the reports to others! ## Developers' Guide Follow the [developers' instructions](DEVELOPMENT.md) to build your new data analysis tools on top of Data Formulator. Help wanted: * Add more database connectors (https://github.com/microsoft/data-formulator/issues/156) * Scaling up messy data extractor: more document types and larger files. * Adding more chart templates (e.g., maps). * other ideas? ## Research Papers * [Data Formulator 2: Iteratively Creating Rich Visualizations with AI](https://arxiv.org/abs/2408.16119) ``` @article{wang2024dataformulator2iteratively, title={Data Formulator 2: Iteratively Creating Rich Visualizations with AI}, author={Chenglong Wang and Bongshin Lee and Steven Drucker and Dan Marshall and Jianfeng Gao}, year={2024}, booktitle={ArXiv preprint arXiv:2408.16119}, } ``` * [Data Formulator: AI-powered Concept-driven Visualization Authoring](https://arxiv.org/abs/2309.10094) ``` @article{wang2023data, title={Data Formulator: AI-powered Concept-driven Visualization Authoring}, author={Wang, Chenglong and Thompson, John and Lee, Bongshin}, journal={IEEE Transactions on Visualization and Computer Graphics}, year={2023}, publisher={IEEE} } ``` ## Contributing This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com. When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repositories using our CLA. This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. ## Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies. ================================================ FILE: SECURITY.md ================================================ ## Security Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/). If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/opensource/security/definition), please report it to us as described below. ## Reporting Security Issues **Please do not report security vulnerabilities through public GitHub issues.** Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/opensource/security/create-report). If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/opensource/security/pgpkey). You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://aka.ms/opensource/security/msrc). Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue: * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.) * Full paths of source file(s) related to the manifestation of the issue * The location of the affected source code (tag/branch/commit or direct URL) * Any special configuration required to reproduce the issue * Step-by-step instructions to reproduce the issue * Proof-of-concept or exploit code (if possible) * Impact of the issue, including how an attacker might exploit the issue This information will help us triage your report more quickly. If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://aka.ms/opensource/security/bounty) page for more details about our active programs. ## Preferred Languages We prefer all communications to be in English. ## Policy Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/opensource/security/cvd). ================================================ FILE: SUPPORT.md ================================================ # Support ## How to file issues and get help This project uses GitHub Issues to track bugs and feature requests. Please search the existing issues before filing new issues to avoid duplicates. For new issues, file your bug or feature request as a new Issue. ## Microsoft Support Policy Support for this project is limited to the resources listed above. ================================================ FILE: embed/README.md ================================================ # Embed Data Formulator First you'll need to build the bundle: ``` yarn build ``` This puts the complete js file in the `dist` folder. ## Test bundle Next you can test to see the complete Data Formulator app by opening `/embed/index.html` in your browser. You can do this by double-clicking in your file explorer (this would use the `file://` protocol). To test cross-frame messaging, launch `postMessageTest.html` which hosts the app in an iframe, and has buttons to send commands such as `load data`. ## Use in Fabric Notebook You willl need to enable access to your `dist` from the cloud. There are 2 ways to do this: * Publish the `dist` (e.g. pip, npm, or other) * Create a tunnel to your localhost ### Tunnel to localhost One way is to install [local-web-server](https://www.npmjs.com/package/local-web-server). This will serve a local folder as a website on http://localhost:8000. Next, you can set up a tunnel such as [ngrok](https://ngrok.com/download) which can provide a cloud-accesible url proxy to your local server. Copy the python function in a notebook cell: ```py def dfviz(df, tableName, serverUrl): # df is a PySpark DataFrame import json from datetime import date, datetime # Custom function to convert datetime objects to string def json_serial(obj): if isinstance(obj, (datetime, date)): return obj.isoformat() raise TypeError ("Type %s not serializable" % type(obj)) # Convert DataFrame rows to dictionaries and collect them into a list data = [row.asDict() for row in df.collect()] # Convert list of dictionaries to a single JSON array using the custom function json_data = json.dumps(data, default=json_serial) displayHTML(f""" """ ) ``` Get a dataframe and pass it to the `dfviz` function: ```py df = spark.sql("SELECT * FROM Sample_lakehouse_475.publicholidays LIMIT 100") display(df) dfviz(df, 'Holidays', 'https://') ``` ================================================ FILE: embed/dynamic.html ================================================ Data Formulator embedded dynamically ================================================ FILE: embed/index.html ================================================ Data Formulator embedded
================================================ FILE: embed/postMessageTest.html ================================================ Data Formulator embedded iframe
================================================ FILE: eslint.config.js ================================================ import globals from "globals"; import pluginJs from "@eslint/js"; import tseslint from "typescript-eslint"; import pluginReact from "eslint-plugin-react"; import jsxA11y from "eslint-plugin-jsx-a11y"; /** @type {import('eslint').Linter.Config[]} */ export default [ { languageOptions: { globals: globals.browser }, settings: { react: { version: "detect", // Automatically detect the React version }, }, }, pluginJs.configs.recommended, ...tseslint.configs.recommended, pluginReact.configs.flat.recommended, { plugins: { 'jsx-a11y': jsxA11y, }, rules: { "@typescript-eslint/ban-ts-comment": "off", "@typescript-eslint/no-non-null-asserted-optional-chain": "off", "@typescript-eslint/no-empty-object-type": "off", "@typescript-eslint/no-explicit-any": "off", "@typescript-eslint/no-unused-expressions": "off", "@typescript-eslint/no-unused-vars": "off", "no-empty-pattern": "off", "no-empty": "off", "no-unsafe-optional-chaining": "off", "no-useless-escape": "off", "prefer-const": "off", "react/no-unescaped-entities": "off", } } ]; ================================================ FILE: index.html ================================================ Data Formulator
================================================ FILE: local_server.bat ================================================ :: Copyright (c) Microsoft Corporation. :: Licensed under the MIT License. @echo off :: Set HTTP proxy environment variables only (Flask doesn't support SOCKS5) :: set http_proxy=http://127.0.0.1:7890 :: set https_proxy=http://127.0.0.1:7890 set FLASK_RUN_PORT=5567 :: Use uv if available, otherwise fall back to python where uv >nul 2>nul if %ERRORLEVEL% EQU 0 ( uv run data_formulator --port %FLASK_RUN_PORT% --dev ) else ( python -m data_formulator.app --port %FLASK_RUN_PORT% --dev ) ================================================ FILE: local_server.sh ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. # Set HTTP proxy environment variables only (Flask doesn't support SOCKS5) # export http_proxy=http://127.0.0.1:7890 # export https_proxy=http://127.0.0.1:7890 export FLASK_RUN_PORT=5567 # Use uv if available, otherwise fall back to python if command -v uv &> /dev/null; then uv run data_formulator --port ${FLASK_RUN_PORT} --dev else python -m data_formulator.app --port ${FLASK_RUN_PORT} --dev fi ================================================ FILE: package.json ================================================ { "name": "data-formulator", "type": "module", "version": "0.1.0", "private": true, "dependencies": { "@emotion/react": "^11.14.0", "@emotion/styled": "^11.14.0", "@fontsource/roboto": "^4.5.5", "@mui/icons-material": "^7.1.1", "@mui/lab": "^7.0.1-beta.18", "@mui/material": "^7.1.1", "@reduxjs/toolkit": "^1.8.6", "@types/dompurify": "^3.0.5", "@types/validator": "^13.12.2", "allotment": "^1.20.4", "canvas": "^3.2.1", "chart.js": "^4.5.1", "d3": "^7.3.0", "dompurify": "^3.2.4", "echarts": "^6.0.0", "exceljs": "^4.4.0", "gofish-graphics": "^0.0.22", "html2canvas": "^1.4.1", "js-yaml": "^4.1.1", "katex": "^0.16.22", "localforage": "^1.10.0", "lodash": "^4.17.23", "markdown-to-jsx": "^7.4.0", "mui-markdown": "^2.0.3", "prettier": "^2.8.3", "prism-react-renderer": "^1.3.5", "prismjs": "^1.30.0", "prop-types": "^15.8.1", "react": "^18.2.0", "react-animate-height": "^3.0.4", "react-animate-on-change": "^2.2.0", "react-dnd": "^16.0.1", "react-dnd-html5-backend": "^16.0.1", "react-dom": "^18.2.0", "react-katex": "^3.1.0", "react-redux": "^8.0.4", "react-router-dom": "^6.22.0", "react-selectable-fast": "^3.4.0", "react-simple-code-editor": "^0.13.1", "react-vega": "^7.6.0", "react-virtuoso": "^4.3.10", "redux": "^4.2.0", "redux-persist": "^6.0.0", "typescript": "^4.9.5", "validator": "^13.15.20", "vega": "^6.2.0", "vega-embed": "^6.21.0", "vega-lite": "6.4.1", "vm-browserify": "^1.1.2" }, "scripts": { "lint": "eslint -c eslint.config.js src/**/*.{ts,tsx} --fix", "start": "vite", "build": "vite build" }, "browserslist": { "production": [ ">0.2%", "not dead", "not op_mini all" ], "development": [ "last 1 chrome version", "last 1 firefox version", "last 1 safari version" ] }, "devDependencies": { "@eslint/js": "^9.15.0", "@types/d3": "^7.4.3", "@types/lodash": "^4.17.7", "@types/node": "^20.14.10", "@types/prismjs": "^1.26.0", "@types/react": "^18.3.3", "@types/react-dom": "^18.3.0", "@types/react-katex": "^3.0.4", "@typescript-eslint/eslint-plugin": "^8.16.0", "@typescript-eslint/parser": "^8.16.0", "@vitejs/plugin-react-swc": "^3.7.0", "eslint": "^9.15.0", "eslint-plugin-jsx-a11y": "^6.10.2", "eslint-plugin-react": "^7.37.2", "globals": "^15.12.0", "sass": "^1.77.6", "typescript-eslint": "^8.16.0", "vite": "^5.4.21" } } ================================================ FILE: public/df_gas_prices.json ================================================ {"tables":[{"id":"weekly_gas_prices","displayId":"gas-prices","names":["date","fuel","grade","formulation","price"],"metadata":{"date":{"type":"date","semanticType":"Date"},"fuel":{"type":"string","semanticType":"String"},"grade":{"type":"string","semanticType":"String","levels":["all","regular","midgrade","premium","low_sulfur","ultra_low_sulfur"]},"formulation":{"type":"string","semanticType":"String","levels":["all","conventional","reformulated","NA"]},"price":{"type":"number","semanticType":"Number"}},"rows":[{"date":"1990-08-20","fuel":"gasoline","grade":"regular","formulation":"all","price":1.191},{"date":"1990-08-20","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.191},{"date":"1990-08-27","fuel":"gasoline","grade":"regular","formulation":"all","price":1.245},{"date":"1990-08-27","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.245},{"date":"1990-09-03","fuel":"gasoline","grade":"regular","formulation":"all","price":1.242},{"date":"1990-09-03","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.242},{"date":"1990-09-10","fuel":"gasoline","grade":"regular","formulation":"all","price":1.252},{"date":"1990-09-10","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.252},{"date":"1990-09-17","fuel":"gasoline","grade":"regular","formulation":"all","price":1.266},{"date":"1990-09-17","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.266},{"date":"1990-09-24","fuel":"gasoline","grade":"regular","formulation":"all","price":1.272},{"date":"1990-09-24","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.272},{"date":"1990-10-01","fuel":"gasoline","grade":"regular","formulation":"all","price":1.321},{"date":"1990-10-01","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.321},{"date":"1990-10-08","fuel":"gasoline","grade":"regular","formulation":"all","price":1.333},{"date":"1990-10-08","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.333},{"date":"1990-10-15","fuel":"gasoline","grade":"regular","formulation":"all","price":1.339},{"date":"1990-10-15","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.339},{"date":"1990-10-22","fuel":"gasoline","grade":"regular","formulation":"all","price":1.345},{"date":"1990-10-22","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.345},{"date":"1990-10-29","fuel":"gasoline","grade":"regular","formulation":"all","price":1.339},{"date":"1990-10-29","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.339},{"date":"1990-11-05","fuel":"gasoline","grade":"regular","formulation":"all","price":1.334},{"date":"1990-11-05","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.334},{"date":"1990-11-12","fuel":"gasoline","grade":"regular","formulation":"all","price":1.328},{"date":"1990-11-12","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.328},{"date":"1990-11-19","fuel":"gasoline","grade":"regular","formulation":"all","price":1.323},{"date":"1990-11-19","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.323},{"date":"1990-11-26","fuel":"gasoline","grade":"regular","formulation":"all","price":1.311},{"date":"1990-11-26","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.311},{"date":"1990-12-03","fuel":"gasoline","grade":"regular","formulation":"all","price":1.341},{"date":"1990-12-03","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.341},{"date":"1991-01-21","fuel":"gasoline","grade":"regular","formulation":"all","price":1.192},{"date":"1991-01-21","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.192},{"date":"1991-01-28","fuel":"gasoline","grade":"regular","formulation":"all","price":1.168},{"date":"1991-01-28","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.168},{"date":"1991-02-04","fuel":"gasoline","grade":"regular","formulation":"all","price":1.139},{"date":"1991-02-04","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.139},{"date":"1991-02-11","fuel":"gasoline","grade":"regular","formulation":"all","price":1.106},{"date":"1991-02-11","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.106},{"date":"1991-02-18","fuel":"gasoline","grade":"regular","formulation":"all","price":1.078},{"date":"1991-02-18","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.078},{"date":"1991-02-25","fuel":"gasoline","grade":"regular","formulation":"all","price":1.054},{"date":"1991-02-25","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.054},{"date":"1991-03-04","fuel":"gasoline","grade":"regular","formulation":"all","price":1.025},{"date":"1991-03-04","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.025},{"date":"1991-03-11","fuel":"gasoline","gr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pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n","source":["weekly_gas_prices"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhat are the major **price trends** across all fuel types from 1990 to 2025?"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to visualize the major price trends across all fuel types from 1990 to 2025 to understand how fuel prices have evolved over time.\",\n \"display_instruction\": \"Show **price** trends over time for different **fuel** types\",\n \"recommendation\": \"To visualize price trends over time across fuel types, we should aggregate the data by date and fuel type. Since there are multiple grades and formulations for each fuel type, we'll calculate the average price for each fuel type on each date. A line chart with date on the x-axis, average price on the y-axis, and different colored lines for each fuel type will clearly show the trends and allow easy comparison.\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n```"}],"trigger":{"tableId":"weekly_gas_prices","sourceTableIds":["weekly_gas_prices"],"instruction":"What are the major **price trends** across all fuel types from 1990 to 2025?","displayInstruction":"Show **price** trends over time for different **fuel** types","chart":{"id":"chart-1760744901532","chartType":"Auto","encodingMap":{},"tableRef":"weekly_gas_prices","saved":false,"source":"trigger","unread":true},"resultTableId":"table-904593"},"explanation":{"agent":"CodeExplanationAgent","code":"The code transforms weekly gas price data by performing the following steps:\n\n- **Groups** the data by `date` and `fuel` type (gasoline or diesel)\n- **Calculates** the mean `price` across all `grade` categories (regular, premium, midgrade, etc.) and `formulation` types (conventional, reformulated, etc.) for each date-fuel combination\n- **Renames** the aggregated price column to `avg_price` for clarity\n- **Sorts** the resulting data chronologically by `date` to ensure proper time-series visualization","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThe code transforms weekly gas price data by performing the following steps:\n\n- **Groups** the data by `date` and `fuel` type (gasoline or diesel)\n- **Calculates** the mean `price` across all `grade` categories (regular, premium, midgrade, etc.) and `formulation` types (conventional, reformulated, etc.) for each date-fuel combination\n- **Renames** the aggregated price column to `avg_price` for clarity\n- **Sorts** the resulting data chronologically by `date` to ensure proper time-series visualization\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-906894","displayId":"gas-price-prem","names":["date","fuel","grade","price_premium"],"rows":[{"date":"1993-04-05","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-04-12","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-04-19","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-04-26","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-05-03","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-05-10","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-05-17","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-05-24","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-05-31","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-06-07","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-06-14","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-06-21","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-06-28","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-07-05","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-07-12","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-07-19","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-07-26","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-08-02","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-08-09","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-08-16","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-08-23","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-08-30","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-09-06","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-09-13","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-09-20","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-09-27","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-10-04","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-10-11","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-10-18","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-10-25","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-11-01","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-11-08","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-11-15","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-11-22","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-11-29","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-12-06","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-12-13","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-12-20","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-12-27","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-01-03","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-01-10","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-01-17","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-01-24","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-01-31","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-02-07","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-02-14","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-02-21","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-02-28","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-03-07","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-03-14","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-03-21","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-03-28","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-04-04","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-04-11","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-04-18","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-04-25","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-05-02","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-05-09","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-05-16","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-05-23","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-05-30","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-06-06","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-06-13","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-06-20","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-06-27","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-07-04","fuel":"gasoline","grade":"all","price_premium":0},{"date":"199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4","fuel":"gasoline","grade":"midgrade","price_premium":0.581},{"date":"2025-02-24","fuel":"gasoline","grade":"premium","price_premium":0.95},{"date":"2025-03-03","fuel":"gasoline","grade":"all","price_premium":0.128},{"date":"2025-03-03","fuel":"gasoline","grade":"midgrade","price_premium":0.576},{"date":"2025-03-03","fuel":"gasoline","grade":"premium","price_premium":0.944},{"date":"2025-03-10","fuel":"gasoline","grade":"all","price_premium":0.128},{"date":"2025-03-10","fuel":"gasoline","grade":"midgrade","price_premium":0.571},{"date":"2025-03-10","fuel":"gasoline","grade":"premium","price_premium":0.936},{"date":"2025-03-17","fuel":"gasoline","grade":"all","price_premium":0.126},{"date":"2025-03-17","fuel":"gasoline","grade":"midgrade","price_premium":0.569},{"date":"2025-03-17","fuel":"gasoline","grade":"premium","price_premium":0.931},{"date":"2025-03-24","fuel":"gasoline","grade":"all","price_premium":0.125},{"date":"2025-03-24","fuel":"gasoline","grade":"midgrade","price_premium":0.559},{"date":"2025-03-24","fuel":"gasoline","grade":"premium","price_premium":0.917},{"date":"2025-03-31","fuel":"gasoline","grade":"all","price_premium":0.126},{"date":"2025-03-31","fuel":"gasoline","grade":"midgrade","price_premium":0.568},{"date":"2025-03-31","fuel":"gasoline","grade":"premium","price_premium":0.931},{"date":"2025-04-07","fuel":"gasoline","grade":"all","price_premium":0.127},{"date":"2025-04-07","fuel":"gasoline","grade":"midgrade","price_premium":0.572},{"date":"2025-04-07","fuel":"gasoline","grade":"premium","price_premium":0.931},{"date":"2025-04-14","fuel":"gasoline","grade":"all","price_premium":0.127},{"date":"2025-04-14","fuel":"gasoline","grade":"midgrade","price_premium":0.575},{"date":"2025-04-14","fuel":"gasoline","grade":"premium","price_premium":0.935},{"date":"2025-04-21","fuel":"gasoline","grade":"all","price_premium":0.127},{"date":"2025-04-21","fuel":"gasoline","grade":"midgrade","price_premium":0.577},{"date":"2025-04-21","fuel":"gasoline","grade":"premium","price_premium":0.933},{"date":"2025-04-28","fuel":"gasoline","grade":"all","price_premium":0.128},{"date":"2025-04-28","fuel":"gasoline","grade":"midgrade","price_premium":0.58},{"date":"2025-04-28","fuel":"gasoline","grade":"premium","price_premium":0.938},{"date":"2025-05-05","fuel":"gasoline","grade":"all","price_premium":0.126},{"date":"2025-05-05","fuel":"gasoline","grade":"midgrade","price_premium":0.568},{"date":"2025-05-05","fuel":"gasoline","grade":"premium","price_premium":0.925},{"date":"2025-05-12","fuel":"gasoline","grade":"all","price_premium":0.129},{"date":"2025-05-12","fuel":"gasoline","grade":"midgrade","price_premium":0.583},{"date":"2025-05-12","fuel":"gasoline","grade":"premium","price_premium":0.948},{"date":"2025-05-19","fuel":"gasoline","grade":"all","price_premium":0.129},{"date":"2025-05-19","fuel":"gasoline","grade":"midgrade","price_premium":0.583},{"date":"2025-05-19","fuel":"gasoline","grade":"premium","price_premium":0.943},{"date":"2025-05-26","fuel":"gasoline","grade":"all","price_premium":0.128},{"date":"2025-05-26","fuel":"gasoline","grade":"midgrade","price_premium":0.58},{"date":"2025-05-26","fuel":"gasoline","grade":"premium","price_premium":0.942},{"date":"2025-06-02","fuel":"gasoline","grade":"all","price_premium":0.129},{"date":"2025-06-02","fuel":"gasoline","grade":"midgrade","price_premium":0.585},{"date":"2025-06-02","fuel":"gasoline","grade":"premium","price_premium":0.942},{"date":"2025-06-09","fuel":"gasoline","grade":"all","price_premium":0.127},{"date":"2025-06-09","fuel":"gasoline","grade":"midgrade","price_premium":0.579},{"date":"2025-06-09","fuel":"gasoline","grade":"premium","price_premium":0.929},{"date":"2025-06-16","fuel":"gasoline","grade":"all","price_premium":0.126},{"date":"2025-06-16","fuel":"gasoline","grade":"midgrade","price_premium":0.572},{"date":"2025-06-16","fuel":"gasoline","grade":"premium","price_premium":0.923},{"date":"2025-06-23","fuel":"gasoline","grade":"all","price_premium":0.125},{"date":"2025-06-23","fuel":"gasoline","grade":"midgrade","price_premium":0.562},{"date":"2025-06-23","fuel":"gasoline","grade":"premium","price_premium":0.915}],"metadata":{"date":{"type":"date","semanticType":"Date"},"fuel":{"type":"string","semanticType":"String"},"grade":{"type":"string","semanticType":"String","levels":["all","midgrade","premium"]},"price_premium":{"type":"number","semanticType":"Number"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas):\n # Filter for 'all' formulation to avoid duplicates and focus on overall prices\n df_filtered = df_gas[df_gas['formulation'] == 'all'].copy()\n \n # Get regular grade prices as baseline\n df_regular = df_filtered[df_filtered['grade'] == 'regular'][['date', 'fuel', 'price']].rename(columns={'price': 'regular_price'})\n \n # Get all non-regular grades\n df_premium_grades = df_filtered[df_filtered['grade'] != 'regular'][['date', 'fuel', 'grade', 'price']].copy()\n \n # Merge to calculate premium\n df_merged = df_premium_grades.merge(df_regular, on=['date', 'fuel'], how='left')\n \n # Calculate price premium\n df_merged['price_premium'] = df_merged['price'] - df_merged['regular_price']\n \n # Select output fields\n transformed_df = df_merged[['date', 'fuel', 'grade', 'price_premium']].copy()\n \n # Remove rows with missing premium values\n transformed_df = transformed_df.dropna(subset=['price_premium'])\n \n return transformed_df\n","source":["weekly_gas_prices"],"dialog":[{"content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhich **fuel grade** commands the highest **price premium** and how has this premium evolved?","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"Analyze which fuel grade commands the highest price premium compared to regular grade, and visualize how this premium has evolved over time.\",\n \"display_instruction\": \"Compare **price premium** across **fuel grades** over time\",\n \"recommendation\": \"Calculate the price premium for each fuel grade by comparing it to the regular grade baseline for the same date and fuel type. This will show both the absolute premium values and their trends over time.\",\n \"output_fields\": [\"date\", \"fuel\", \"grade\", \"price_premium\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"price_premium\",\n \"color\": \"grade\",\n \"facet\": \"fuel\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas):\n # Filter for 'all' formulation to avoid duplicates and focus on overall prices\n df_filtered = df_gas[df_gas['formulation'] == 'all'].copy()\n \n # Get regular grade prices as baseline\n df_regular = df_filtered[df_filtered['grade'] == 'regular'][['date', 'fuel', 'price']].rename(columns={'price': 'regular_price'})\n \n # Get all non-regular grades\n df_premium_grades = df_filtered[df_filtered['grade'] != 'regular'][['date', 'fuel', 'grade', 'price']].copy()\n \n # Merge to calculate premium\n df_merged = df_premium_grades.merge(df_regular, on=['date', 'fuel'], how='left')\n \n # Calculate price premium\n df_merged['price_premium'] = df_merged['price'] - df_merged['regular_price']\n \n # Select output fields\n transformed_df = df_merged[['date', 'fuel', 'grade', 'price_premium']].copy()\n \n # Remove rows with missing premium values\n transformed_df = transformed_df.dropna(subset=['price_premium'])\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"weekly_gas_prices","sourceTableIds":["weekly_gas_prices"],"instruction":"Which **fuel grade** commands the highest **price premium** and how has this premium evolved?","displayInstruction":"Compare **price premium** across **fuel grades** over time","chart":{"id":"chart-1760744911829","chartType":"Auto","encodingMap":{},"tableRef":"weekly_gas_prices","saved":false,"source":"trigger","unread":true},"resultTableId":"table-906894"},"explanation":{"agent":"CodeExplanationAgent","code":"- Filter the dataset to only include entries where **formulation** is `'all'` to avoid duplicate records and focus on overall price data\n- Extract **regular grade** prices as a baseline, creating a reference table with **date**, **fuel**, and **regular_price**\n- Extract all **non-regular grades** (such as **premium**, **midgrade**, etc.) with their corresponding **date**, **fuel**, **grade**, and **price**\n- Merge the non-regular grades with the regular grade baseline by matching on **date** and **fuel** type\n- Calculate the **price_premium** for each non-regular grade by subtracting the **regular_price** from the grade's **price**\n- Select the final output fields: **date**, **fuel**, **grade**, and **price_premium**\n- Remove any rows with missing **price_premium** values to ensure data completeness","concepts":[{"explanation":"The additional cost of a specific fuel grade compared to regular grade fuel. Calculated as: \\( \\text{price\\_premium} = \\text{price}_{\\text{grade}} - \\text{price}_{\\text{regular}} \\). This metric indicates how much more consumers pay for higher-quality fuel grades (premium, midgrade, etc.) relative to the baseline regular grade on the same date and for the same fuel type.","field":"price_premium"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas):\n # Filter for 'all' formulation to avoid duplicates and focus on overall prices\n df_filtered = df_gas[df_gas['formulation'] == 'all'].copy()\n \n # Get regular grade prices as baseline\n df_regular = df_filtered[df_filtered['grade'] == 'regular'][['date', 'fuel', 'price']].rename(columns={'price': 'regular_price'})\n \n # Get all non-regular grades\n df_premium_grades = df_filtered[df_filtered['grade'] != 'regular'][['date', 'fuel', 'grade', 'price']].copy()\n \n # Merge to calculate premium\n df_merged = df_premium_grades.merge(df_regular, on=['date', 'fuel'], how='left')\n \n # Calculate price premium\n df_merged['price_premium'] = df_merged['price'] - df_merged['regular_price']\n \n # Select output fields\n transformed_df = df_merged[['date', 'fuel', 'grade', 'price_premium']].copy()\n \n # Remove rows with missing premium values\n transformed_df = transformed_df.dropna(subset=['price_premium'])\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Filter the dataset to only include entries where **formulation** is `'all'` to avoid duplicate records and focus on overall price data\n- Extract **regular grade** prices as a baseline, creating a reference table with **date**, **fuel**, and **regular_price**\n- Extract all **non-regular grades** (such as **premium**, **midgrade**, etc.) with their corresponding **date**, **fuel**, **grade**, and **price**\n- Merge the non-regular grades with the regular grade baseline by matching on **date** and **fuel** type\n- Calculate the **price_premium** for each non-regular grade by subtracting the **regular_price** from the grade's **price**\n- Select the final output fields: **date**, **fuel**, **grade**, and **price_premium**\n- Remove any rows with missing **price_premium** values to ensure data completeness\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"price_premium\",\n \"explanation\": \"The additional cost of a specific fuel grade compared to regular grade fuel. Calculated as: \\\\( \\\\text{price\\\\_premium} = \\\\text{price}_{\\\\text{grade}} - \\\\text{price}_{\\\\text{regular}} \\\\). This metric indicates how much more consumers pay for higher-quality fuel grades (premium, midgrade, etc.) relative to the baseline regular grade on the same date and for the same fuel type.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-922023","displayId":"fuel-price-yoy","names":["date","fuel","current_price","yoy_price","absolute_change","percentage_change"],"rows":[{"date":"1995-03-20","fuel":"diesel","current_price":1.085,"yoy_price":1.106,"absolute_change":-0.021,"percentage_change":-1.8987341772},{"date":"1995-03-27","fuel":"diesel","current_price":1.088,"yoy_price":1.107,"absolute_change":-0.019,"percentage_change":-1.7163504968},{"date":"1995-04-03","fuel":"diesel","current_price":1.094,"yoy_price":1.109,"absolute_change":-0.015,"percentage_change":-1.3525698828},{"date":"1995-04-10","fuel":"diesel","current_price":1.101,"yoy_price":1.108,"absolute_change":-0.007,"percentage_change":-0.6317689531},{"date":"1995-04-17","fuel":"diesel","current_price":1.106,"yoy_price":1.105,"absolute_change":0.001,"percentage_change":0.0904977376},{"date":"1995-04-24","fuel":"diesel","current_price":1.115,"yoy_price":1.106,"absolute_change":0.009,"percentage_change":0.8137432188},{"date":"1995-05-01","fuel":"diesel","current_price":1.119,"yoy_price":1.104,"absolute_change":0.015,"percentage_change":1.3586956522},{"date":"1995-05-08","fuel":"diesel","current_price":1.126,"yoy_price":1.101,"absolute_change":0.025,"percentage_change":2.2706630336},{"date":"1995-05-15","fuel":"diesel","current_price":1.126,"yoy_price":1.099,"absolute_change":0.027,"percentage_change":2.4567788899},{"date":"1995-05-22","fuel":"diesel","current_price":1.124,"yoy_price":1.099,"absolute_change":0.025,"percentage_change":2.2747952684},{"date":"1995-05-29","fuel":"diesel","current_price":1.13,"yoy_price":1.098,"absolute_change":0.032,"percentage_change":2.9143897996},{"date":"1995-06-05","fuel":"diesel","current_price":1.124,"yoy_price":1.101,"absolute_change":0.023,"percentage_change":2.0890099909},{"date":"1995-06-12","fuel":"diesel","current_price":1.122,"yoy_price":1.098,"absolute_change":0.024,"percentage_change":2.1857923497},{"date":"1995-06-19","fuel":"diesel","current_price":1.117,"yoy_price":1.103,"absolute_change":0.014,"percentage_change":1.2692656392},{"date":"1995-06-26","fuel":"diesel","current_price":1.112,"yoy_price":1.108,"absolute_change":0.004,"percentage_change":0.3610108303},{"date":"1995-07-03","fuel":"diesel","current_price":1.106,"yoy_price":1.109,"absolute_change":-0.003,"percentage_change":-0.2705139766},{"date":"1995-07-10","fuel":"diesel","current_price":1.103,"yoy_price":1.11,"absolute_change":-0.007,"percentage_change":-0.6306306306},{"date":"1995-07-17","fuel":"diesel","current_price":1.099,"yoy_price":1.111,"absolute_change":-0.012,"percentage_change":-1.0801080108},{"date":"1995-07-24","fuel":"diesel","current_price":1.098,"yoy_price":1.111,"absolute_change":-0.013,"percentage_change":-1.1701170117},{"date":"1995-07-31","fuel":"diesel","current_price":1.093,"yoy_price":1.116,"absolute_change":-0.023,"percentage_change":-2.0609318996},{"date":"1995-08-07","fuel":"diesel","current_price":1.099,"yoy_price":1.127,"absolute_change":-0.028,"percentage_change":-2.4844720497},{"date":"1995-08-14","fuel":"diesel","current_price":1.106,"yoy_price":1.127,"absolute_change":-0.021,"percentage_change":-1.8633540373},{"date":"1995-08-21","fuel":"diesel","current_price":1.106,"yoy_price":1.124,"absolute_change":-0.018,"percentage_change":-1.6014234875},{"date":"1995-08-28","fuel":"diesel","current_price":1.109,"yoy_price":1.122,"absolute_change":-0.013,"percentage_change":-1.1586452763},{"date":"1995-09-04","fuel":"diesel","current_price":1.115,"yoy_price":1.126,"absolute_change":-0.011,"percentage_change":-0.9769094139},{"date":"1995-09-11","fuel":"diesel","current_price":1.119,"yoy_price":1.128,"absolute_change":-0.009,"percentage_change":-0.7978723404},{"date":"1995-09-18","fuel":"diesel","current_price":1.122,"yoy_price":1.126,"absolute_change":-0.004,"percentage_change":-0.3552397869},{"date":"1995-09-25","fuel":"diesel","current_price":1.121,"yoy_price":1.12,"absolute_change":0.001,"percentage_change":0.0892857143},{"date":"1995-10-02","fuel":"diesel","current_price":1.117,"yoy_price":1.118,"absolute_change":-0.001,"percentage_change":-0.0894454383},{"date":"1995-10-09","fuel":"diesel","current_price":1.117,"yoy_price":1.117,"absolute_change":0,"percentage_change":0},{"date":"1995-10-16","fuel":"diesel","current_price":1.117,"yoy_price":1.119,"absolute_change":-0.002,"percentage_change":-0.1787310098},{"date":"1995-10-23","fuel":"diesel","current_price":1.114,"yoy_price":1.122,"absolute_change":-0.008,"percentage_change":-0.7130124777},{"date":"1995-10-30","fuel":"diesel","current_price":1.11,"yoy_price":1.133,"absolute_change":-0.023,"percentage_change":-2.0300088261},{"date":"1995-11-06","fuel":"diesel","current_price":1.118,"yoy_price":1.133,"absolute_change":-0.015,"percentage_change":-1.3239187996},{"date":"1995-11-13","fuel":"diesel","current_price":1.118,"yoy_price":1.135,"absolute_change":-0.017,"percentage_change":-1.4977973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pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for proper sorting and manipulation\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Sort by fuel and date\n df_agg = df_agg.sort_values(['fuel', 'date'])\n \n # Calculate year-over-year changes (52 weeks ago for weekly data)\n df_agg['yoy_price'] = df_agg.groupby('fuel')['price'].shift(52)\n df_agg['absolute_change'] = df_agg['price'] - df_agg['yoy_price']\n df_agg['percentage_change'] = ((df_agg['price'] - df_agg['yoy_price']) / df_agg['yoy_price']) * 100\n \n # Rename columns for clarity\n transformed_df = df_agg.rename(columns={'price': 'current_price'})\n \n # Convert date back to string format for visualization\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n # Keep only rows where we have year-over-year data (remove first 52 weeks)\n transformed_df = transformed_df.dropna(subset=['yoy_price'])\n \n # Select and order output columns\n transformed_df = transformed_df[['date', 'fuel', 'current_price', 'yoy_price', 'absolute_change', 'percentage_change']]\n \n return transformed_df\n","source":["weekly_gas_prices"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhat are the major **price trends** across all fuel types from 1990 to 2025?"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to visualize the major price trends across all fuel types from 1990 to 2025 to understand how fuel prices have evolved over time.\",\n \"display_instruction\": \"Show **price** trends over time for different **fuel** types\",\n \"recommendation\": \"To visualize price trends over time across fuel types, we should aggregate the data by date and fuel type. Since there are multiple grades and formulations for each fuel type, we'll calculate the average price for each fuel type on each date. A line chart with date on the x-axis, average price on the y-axis, and different colored lines for each fuel type will clearly show the trends and allow easy comparison.\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date fuel avg_price\n0 1990-08-20 gasoline 1.191\n1 1990-08-27 gasoline 1.245\n2 1990-09-03 gasoline 1.242\n3 1990-09-10 gasoline 1.252\n4 1990-09-17 gasoline 1.266\n5 1990-09-24 gasoline 1.272\n6 1990-10-01 gasoline 1.321\n7 1990-10-08 gasoline 1.333\n8 1990-10-15 gasoline 1.339\n9 1990-10-22 gasoline 1.345\n......\n\nUpdate the code above based on the following instruction:\n\nCalculate year-over-year price changes to identify the most dramatic price spikes for both diesel and gasoline, showing which periods had the largest absolute and percentage increases with their peak prices."},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to identify the most dramatic price spikes by calculating year-over-year price changes for both diesel and gasoline, showing periods with the largest absolute and percentage increases along with their peak prices.\",\n \"display_instruction\": \"Identify periods with largest **year-over-year price changes** (absolute and percentage) and **peak prices**\",\n \"recommendation\": \"To analyze year-over-year price changes, we need to calculate the price difference and percentage change compared to the same period one year prior. We'll compute both absolute change and percentage change, then identify the periods with the most dramatic spikes. The output should include the date, fuel type, current price, previous year's price, absolute change, and percentage change to provide a comprehensive view of price volatility.\",\n \"output_fields\": [\"date\", \"fuel\", \"current_price\", \"yoy_price\", \"absolute_change\", \"percentage_change\"],\n \"chart_type\": \"point\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"absolute_change\",\n \"color\": \"fuel\",\n \"size\": \"percentage_change\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for proper sorting and manipulation\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Sort by fuel and date\n df_agg = df_agg.sort_values(['fuel', 'date'])\n \n # Calculate year-over-year changes (52 weeks ago for weekly data)\n df_agg['yoy_price'] = df_agg.groupby('fuel')['price'].shift(52)\n df_agg['absolute_change'] = df_agg['price'] - df_agg['yoy_price']\n df_agg['percentage_change'] = ((df_agg['price'] - df_agg['yoy_price']) / df_agg['yoy_price']) * 100\n \n # Rename columns for clarity\n transformed_df = df_agg.rename(columns={'price': 'current_price'})\n \n # Convert date back to string format for visualization\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n # Keep only rows where we have year-over-year data (remove first 52 weeks)\n transformed_df = transformed_df.dropna(subset=['yoy_price'])\n \n # Select and order output columns\n transformed_df = transformed_df[['date', 'fuel', 'current_price', 'yoy_price', 'absolute_change', 'percentage_change']]\n \n return transformed_df\n```"}],"trigger":{"tableId":"table-904593","sourceTableIds":["weekly_gas_prices"],"instruction":"Calculate year-over-year price changes to identify the most dramatic price spikes for both diesel and gasoline, showing which periods had the largest absolute and percentage increases with their peak prices.","displayInstruction":"Identify periods with largest **year-over-year price changes** (absolute and percentage) and **peak prices**","chart":{"id":"chart-1760744928220","chartType":"Auto","encodingMap":{},"tableRef":"weekly_gas_prices","saved":false,"source":"trigger","unread":true},"resultTableId":"table-922023"},"explanation":{"agent":"CodeExplanationAgent","code":"The code analyzes **year-over-year price trends** for different fuel types by performing the following steps:\n\n- Aggregates the data by **date** and **fuel** type, calculating the **average price** across all grades and formulations for each fuel type on each date\n- Converts **date** field to datetime format and sorts the data chronologically by **fuel** and **date**\n- Calculates **year-over-year comparisons** by comparing each week's price to the price from **52 weeks ago** (one year prior)\n- Computes both **absolute change** (dollar difference) and **percentage change** between current prices and prices from one year ago\n- Removes the first **52 weeks** of data where year-over-year comparisons are not available\n- Returns a dataset containing **current prices**, **year-ago prices**, and their **changes** for each fuel type over time","concepts":[{"explanation":"The average fuel price from exactly 52 weeks (one year) prior to the current date, used as a baseline for year-over-year comparison. This represents the historical price point from the same week in the previous year.","field":"yoy_price"},{"explanation":"The dollar amount difference between the current week's price and the price from one year ago, calculated as: \\( \\text{absolute\\_change} = \\text{current\\_price} - \\text{yoy\\_price} \\). A positive value indicates price increase, while a negative value indicates price decrease.","field":"absolute_change"},{"explanation":"The year-over-year price change expressed as a percentage, calculated as: \\( \\text{percentage\\_change} = \\frac{\\text{current\\_price} - \\text{yoy\\_price}}{\\text{yoy\\_price}} \\times 100 \\). This metric normalizes price changes to allow comparison across different price levels and time periods.","field":"percentage_change"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for proper sorting and manipulation\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Sort by fuel and date\n df_agg = df_agg.sort_values(['fuel', 'date'])\n \n # Calculate year-over-year changes (52 weeks ago for weekly data)\n df_agg['yoy_price'] = df_agg.groupby('fuel')['price'].shift(52)\n df_agg['absolute_change'] = df_agg['price'] - df_agg['yoy_price']\n df_agg['percentage_change'] = ((df_agg['price'] - df_agg['yoy_price']) / df_agg['yoy_price']) * 100\n \n # Rename columns for clarity\n transformed_df = df_agg.rename(columns={'price': 'current_price'})\n \n # Convert date back to string format for visualization\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n # Keep only rows where we have year-over-year data (remove first 52 weeks)\n transformed_df = transformed_df.dropna(subset=['yoy_price'])\n \n # Select and order output columns\n transformed_df = transformed_df[['date', 'fuel', 'current_price', 'yoy_price', 'absolute_change', 'percentage_change']]\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThe code analyzes **year-over-year price trends** for different fuel types by performing the following steps:\n\n- Aggregates the data by **date** and **fuel** type, calculating the **average price** across all grades and formulations for each fuel type on each date\n- Converts **date** field to datetime format and sorts the data chronologically by **fuel** and **date**\n- Calculates **year-over-year comparisons** by comparing each week's price to the price from **52 weeks ago** (one year prior)\n- Computes both **absolute change** (dollar difference) and **percentage change** between current prices and prices from one year ago\n- Removes the first **52 weeks** of data where year-over-year comparisons are not available\n- Returns a dataset containing **current prices**, **year-ago prices**, and their **changes** for each fuel type over time\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"yoy_price\",\n \"explanation\": \"The average fuel price from exactly 52 weeks (one year) prior to the current date, used as a baseline for year-over-year comparison. This represents the historical price point from the same week in the previous year.\"\n },\n {\n \"field\": \"absolute_change\",\n \"explanation\": \"The dollar amount difference between the current week's price and the price from one year ago, calculated as: \\\\( \\\\text{absolute\\\\_change} = \\\\text{current\\\\_price} - \\\\text{yoy\\\\_price} \\\\). A positive value indicates price increase, while a negative value indicates price decrease.\"\n },\n {\n \"field\": \"percentage_change\",\n \"explanation\": \"The year-over-year price change expressed as a percentage, calculated as: \\\\( \\\\text{percentage\\\\_change} = \\\\frac{\\\\text{current\\\\_price} - \\\\text{yoy\\\\_price}}{\\\\text{yoy\\\\_price}} \\\\times 100 \\\\). This metric normalizes price changes to allow comparison across different price levels and time periods.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-937785","displayId":"fuel-price-stats","names":["period","fuel","avg_price","std_dev","coefficient_of_variation"],"rows":[{"period":"Post-2000 (2000-2025)","fuel":"diesel","avg_price":2.933,"std_dev":0.982,"coefficient_of_variation":33.48},{"period":"Post-2000 (2000-2025)","fuel":"gasoline","avg_price":2.847,"std_dev":0.838,"coefficient_of_variation":29.43},{"period":"Pre-2000 (1990-1999)","fuel":"diesel","avg_price":1.138,"std_dev":0.087,"coefficient_of_variation":7.65},{"period":"Pre-2000 (1990-1999)","fuel":"gasoline","avg_price":1.171,"std_dev":0.106,"coefficient_of_variation":9.09}],"metadata":{"period":{"type":"string","semanticType":"TimeRange","levels":["Pre-2000 (1990-1999)","Post-2000 (2000-2025)"]},"fuel":{"type":"string","semanticType":"String"},"avg_price":{"type":"number","semanticType":"Number"},"std_dev":{"type":"number","semanticType":"Number"},"coefficient_of_variation":{"type":"number","semanticType":"Number"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for period classification\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Extract year for period classification\n df_agg['year'] = df_agg['date'].dt.year\n \n # Classify into pre-2000 and post-2000 periods\n df_agg['period'] = df_agg['year'].apply(lambda x: 'Pre-2000 (1990-1999)' if x < 2000 else 'Post-2000 (2000-2025)')\n \n # Calculate volatility metrics by period and fuel\n volatility_stats = df_agg.groupby(['period', 'fuel']).agg({\n 'price': ['mean', 'std']\n }).reset_index()\n \n # Flatten column names\n volatility_stats.columns = ['period', 'fuel', 'avg_price', 'std_dev']\n \n # Calculate coefficient of variation (CV = std_dev / mean * 100)\n volatility_stats['coefficient_of_variation'] = (volatility_stats['std_dev'] / volatility_stats['avg_price']) * 100\n \n # Round values for readability\n volatility_stats['avg_price'] = volatility_stats['avg_price'].round(3)\n volatility_stats['std_dev'] = volatility_stats['std_dev'].round(3)\n volatility_stats['coefficient_of_variation'] = volatility_stats['coefficient_of_variation'].round(2)\n \n transformed_df = volatility_stats[['period', 'fuel', 'avg_price', 'std_dev', 'coefficient_of_variation']]\n \n return transformed_df\n","source":["weekly_gas_prices"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhat are the major **price trends** across all fuel types from 1990 to 2025?"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to visualize the major price trends across all fuel types from 1990 to 2025 to understand how fuel prices have evolved over time.\",\n \"display_instruction\": \"Show **price** trends over time for different **fuel** types\",\n \"recommendation\": \"To visualize price trends over time across fuel types, we should aggregate the data by date and fuel type. Since there are multiple grades and formulations for each fuel type, we'll calculate the average price for each fuel type on each date. A line chart with date on the x-axis, average price on the y-axis, and different colored lines for each fuel type will clearly show the trends and allow easy comparison.\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date fuel avg_price\n0 1990-08-20 gasoline 1.191\n1 1990-08-27 gasoline 1.245\n2 1990-09-03 gasoline 1.242\n3 1990-09-10 gasoline 1.252\n4 1990-09-17 gasoline 1.266\n5 1990-09-24 gasoline 1.272\n6 1990-10-01 gasoline 1.321\n7 1990-10-08 gasoline 1.333\n8 1990-10-15 gasoline 1.339\n9 1990-10-22 gasoline 1.345\n......\n\nUpdate the code above based on the following instruction:\n\nCalculate year-over-year price changes to identify the most dramatic price spikes for both diesel and gasoline, showing which periods had the largest absolute and percentage increases with their peak prices."},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to identify the most dramatic price spikes by calculating year-over-year price changes for both diesel and gasoline, showing periods with the largest absolute and percentage increases along with their peak prices.\",\n \"display_instruction\": \"Identify periods with largest **year-over-year price changes** (absolute and percentage) and **peak prices**\",\n \"recommendation\": \"To analyze year-over-year price changes, we need to calculate the price difference and percentage change compared to the same period one year prior. We'll compute both absolute change and percentage change, then identify the periods with the most dramatic spikes. The output should include the date, fuel type, current price, previous year's price, absolute change, and percentage change to provide a comprehensive view of price volatility.\",\n \"output_fields\": [\"date\", \"fuel\", \"current_price\", \"yoy_price\", \"absolute_change\", \"percentage_change\"],\n \"chart_type\": \"point\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"absolute_change\",\n \"color\": \"fuel\",\n \"size\": \"percentage_change\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for proper sorting and manipulation\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Sort by fuel and date\n df_agg = df_agg.sort_values(['fuel', 'date'])\n \n # Calculate year-over-year changes (52 weeks ago for weekly data)\n df_agg['yoy_price'] = df_agg.groupby('fuel')['price'].shift(52)\n df_agg['absolute_change'] = df_agg['price'] - df_agg['yoy_price']\n df_agg['percentage_change'] = ((df_agg['price'] - df_agg['yoy_price']) / df_agg['yoy_price']) * 100\n \n # Rename columns for clarity\n transformed_df = df_agg.rename(columns={'price': 'current_price'})\n \n # Convert date back to string format for visualization\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n # Keep only rows where we have year-over-year data (remove first 52 weeks)\n transformed_df = transformed_df.dropna(subset=['yoy_price'])\n \n # Select and order output columns\n transformed_df = transformed_df[['date', 'fuel', 'current_price', 'yoy_price', 'absolute_change', 'percentage_change']]\n \n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date fuel current_price yoy_price absolute_change percentage_change\n0 1995-03-20 diesel 1.085 1.106 -0.021 -1.898734\n1 1995-03-27 diesel 1.088 1.107 -0.019 -1.716350\n2 1995-04-03 diesel 1.094 1.109 -0.015 -1.352570\n3 1995-04-10 diesel 1.101 1.108 -0.007 -0.631769\n4 1995-04-17 diesel 1.106 1.105 0.001 0.090498\n5 1995-04-24 diesel 1.115 1.106 0.009 0.813743\n6 1995-05-01 diesel 1.119 1.104 0.015 1.358696\n7 1995-05-08 diesel 1.126 1.101 0.025 2.270663\n8 1995-05-15 diesel 1.126 1.099 0.027 2.456779\n9 1995-05-22 diesel 1.124 1.099 0.025 2.274795\n......\n\nUpdate the code above based on the following instruction:\n\nCompare price volatility (standard deviation and coefficient of variation) between pre-2000 (1990-1999) and post-2000 (2000-2025) periods for both fuel types to show how price stability has changed over time."},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to compare price volatility between pre-2000 (1990-1999) and post-2000 (2000-2025) periods for both fuel types by calculating standard deviation and coefficient of variation to understand how price stability has changed over time.\",\n \"display_instruction\": \"Compare **price volatility** (standard deviation and coefficient of variation) between **pre-2000** and **post-2000** periods\",\n \"recommendation\": \"To analyze price volatility across different time periods, we need to segment the data into pre-2000 (1990-1999) and post-2000 (2000-2025) periods, then calculate volatility metrics including standard deviation and coefficient of variation for each fuel type in each period. A grouped bar chart comparing these metrics across periods and fuel types will clearly show how price stability has evolved.\",\n \"output_fields\": [\"period\", \"fuel\", \"avg_price\", \"std_dev\", \"coefficient_of_variation\"],\n \"chart_type\": \"group_bar\",\n \"chart_encodings\": {\n \"x\": \"period\",\n \"y\": \"coefficient_of_variation\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for period classification\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Extract year for period classification\n df_agg['year'] = df_agg['date'].dt.year\n \n # Classify into pre-2000 and post-2000 periods\n df_agg['period'] = df_agg['year'].apply(lambda x: 'Pre-2000 (1990-1999)' if x < 2000 else 'Post-2000 (2000-2025)')\n \n # Calculate volatility metrics by period and fuel\n volatility_stats = df_agg.groupby(['period', 'fuel']).agg({\n 'price': ['mean', 'std']\n }).reset_index()\n \n # Flatten column names\n volatility_stats.columns = ['period', 'fuel', 'avg_price', 'std_dev']\n \n # Calculate coefficient of variation (CV = std_dev / mean * 100)\n volatility_stats['coefficient_of_variation'] = (volatility_stats['std_dev'] / volatility_stats['avg_price']) * 100\n \n # Round values for readability\n volatility_stats['avg_price'] = volatility_stats['avg_price'].round(3)\n volatility_stats['std_dev'] = volatility_stats['std_dev'].round(3)\n volatility_stats['coefficient_of_variation'] = volatility_stats['coefficient_of_variation'].round(2)\n \n transformed_df = volatility_stats[['period', 'fuel', 'avg_price', 'std_dev', 'coefficient_of_variation']]\n \n return transformed_df\n```"}],"trigger":{"tableId":"table-922023","sourceTableIds":["weekly_gas_prices"],"instruction":"Compare price volatility (standard deviation and coefficient of variation) between pre-2000 (1990-1999) and post-2000 (2000-2025) periods for both fuel types to show how price stability has changed over time.","displayInstruction":"Compare **price volatility** (standard deviation and coefficient of variation) between **pre-2000** and **post-2000** periods","chart":{"id":"chart-1760744940045","chartType":"Auto","encodingMap":{},"tableRef":"weekly_gas_prices","saved":false,"source":"trigger","unread":true},"resultTableId":"table-937785"},"explanation":{"agent":"CodeExplanationAgent","code":"This code performs a statistical analysis of **gas price volatility** across two time periods:\n\n1. **Aggregate prices** by computing the mean **price** for each **date** and **fuel** type, averaging across all **grades** and **formulations**\n2. **Convert dates** to datetime format and extract the **year** from each date\n3. **Classify data** into two time periods: **Pre-2000 (1990-1999)** and **Post-2000 (2000-2025)** based on the year\n4. **Calculate volatility metrics** for each **period** and **fuel** type combination:\n - Compute the **mean price** (average price level)\n - Compute the **standard deviation** (absolute price variability)\n - Calculate the **coefficient of variation** to measure relative volatility\n5. **Round numerical values** for improved readability (**avg_price** and **std_dev** to 3 decimals, **coefficient_of_variation** to 2 decimals)\n6. **Return summary statistics** showing volatility characteristics for **diesel** and **gasoline** in each time period","concepts":[{"explanation":"A normalized measure of price volatility that indicates the relative variability of prices independent of their absolute level. Calculated as \\( \\frac{\\text{std\\_dev}}{\\text{avg\\_price}} \\times 100 \\), this metric allows for fair comparison of price volatility between different time periods or fuel types that may have substantially different average price levels. A higher coefficient indicates more relative price instability.","field":"coefficient_of_variation"},{"explanation":"This analysis employs a **comparative volatility assessment** framework to examine fuel price stability across two distinct time periods. The approach uses **temporal aggregation** (grouping by date and fuel) followed by **period-based stratification** (pre-2000 vs. post-2000) to compute **descriptive volatility statistics** (mean, standard deviation, and coefficient of variation). This methodology allows for direct comparison of both absolute and relative price fluctuations across time periods and fuel types. **Alternative approaches** could include: (1) time series decomposition to separate trend, seasonal, and irregular components; (2) GARCH models to capture time-varying volatility; (3) moving window analysis to track volatility changes over shorter intervals; or (4) regime-switching models to identify distinct volatility states.","field":"Statistical Analysis"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for period classification\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Extract year for period classification\n df_agg['year'] = df_agg['date'].dt.year\n \n # Classify into pre-2000 and post-2000 periods\n df_agg['period'] = df_agg['year'].apply(lambda x: 'Pre-2000 (1990-1999)' if x < 2000 else 'Post-2000 (2000-2025)')\n \n # Calculate volatility metrics by period and fuel\n volatility_stats = df_agg.groupby(['period', 'fuel']).agg({\n 'price': ['mean', 'std']\n }).reset_index()\n \n # Flatten column names\n volatility_stats.columns = ['period', 'fuel', 'avg_price', 'std_dev']\n \n # Calculate coefficient of variation (CV = std_dev / mean * 100)\n volatility_stats['coefficient_of_variation'] = (volatility_stats['std_dev'] / volatility_stats['avg_price']) * 100\n \n # Round values for readability\n volatility_stats['avg_price'] = volatility_stats['avg_price'].round(3)\n volatility_stats['std_dev'] = volatility_stats['std_dev'].round(3)\n volatility_stats['coefficient_of_variation'] = volatility_stats['coefficient_of_variation'].round(2)\n \n transformed_df = volatility_stats[['period', 'fuel', 'avg_price', 'std_dev', 'coefficient_of_variation']]\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThis code performs a statistical analysis of **gas price volatility** across two time periods:\n\n1. **Aggregate prices** by computing the mean **price** for each **date** and **fuel** type, averaging across all **grades** and **formulations**\n2. **Convert dates** to datetime format and extract the **year** from each date\n3. **Classify data** into two time periods: **Pre-2000 (1990-1999)** and **Post-2000 (2000-2025)** based on the year\n4. **Calculate volatility metrics** for each **period** and **fuel** type combination:\n - Compute the **mean price** (average price level)\n - Compute the **standard deviation** (absolute price variability)\n - Calculate the **coefficient of variation** to measure relative volatility\n5. **Round numerical values** for improved readability (**avg_price** and **std_dev** to 3 decimals, **coefficient_of_variation** to 2 decimals)\n6. **Return summary statistics** showing volatility characteristics for **diesel** and **gasoline** in each time period\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"coefficient_of_variation\",\n \"explanation\": \"A normalized measure of price volatility that indicates the relative variability of prices independent of their absolute level. Calculated as \\\\( \\\\frac{\\\\text{std\\\\_dev}}{\\\\text{avg\\\\_price}} \\\\times 100 \\\\), this metric allows for fair comparison of price volatility between different time periods or fuel types that may have substantially different average price levels. A higher coefficient indicates more relative price instability.\"\n },\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"This analysis employs a **comparative volatility assessment** framework to examine fuel price stability across two distinct time periods. The approach uses **temporal aggregation** (grouping by date and fuel) followed by **period-based stratification** (pre-2000 vs. post-2000) to compute **descriptive volatility statistics** (mean, standard deviation, and coefficient of variation). This methodology allows for direct comparison of both absolute and relative price fluctuations across time periods and fuel types. **Alternative approaches** could include: (1) time series decomposition to separate trend, seasonal, and irregular components; (2) GARCH models to capture time-varying volatility; (3) moving window analysis to track volatility changes over shorter intervals; or (4) regime-switching models to identify distinct volatility states.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-34","displayId":"fuel-price-forecast","names":["avg_price","date","fuel","is_predicted"],"rows":[{"avg_price":1.106,"date":"1994-03-21","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0292206988,"date":"1994-03-21","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.107,"date":"1994-03-28","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0311478914,"date":"1994-03-28","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.109,"date":"1994-04-04","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0330750841,"date":"1994-04-04","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.108,"date":"1994-04-11","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0350022767,"date":"1994-04-11","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.105,"date":"1994-04-18","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0369294694,"date":"1994-04-18","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.106,"date":"1994-04-25","fuel":"diesel","is_predicted":"original"},{"avg_price":1.038856662,"date":"1994-04-25","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.104,"date":"1994-05-02","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0407838547,"date":"1994-05-02","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.101,"date":"1994-05-09","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0427110473,"date":"1994-05-09","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.099,"date":"1994-05-16","fuel":"diesel","is_predicted":"original"},{"avg_price":1.04463824,"date":"1994-05-16","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.099,"date":"1994-05-23","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0465654326,"date":"1994-05-23","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.098,"date":"1994-05-30","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0484926253,"date":"1994-05-30","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.101,"date":"1994-06-06","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0504198179,"date":"1994-06-06","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.098,"date":"1994-06-13","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0523470106,"date":"1994-06-13","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.103,"date":"1994-06-20","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0542742032,"date":"1994-06-20","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.108,"date":"1994-06-27","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0562013959,"date":"1994-06-27","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.109,"date":"1994-07-04","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0581285885,"date":"1994-07-04","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.11,"date":"1994-07-11","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0600557812,"date":"1994-07-11","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.111,"date":"1994-07-18","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0619829738,"date":"1994-07-18","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.111,"date":"1994-07-25","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0639101665,"date":"1994-07-25","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.116,"date":"1994-08-01","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0658373591,"date":"1994-08-01","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.127,"date":"1994-08-08","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0677645518,"date":"1994-08-08","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.127,"date":"1994-08-15","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0696917444,"date":"1994-08-15","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.124,"date":"1994-08-22","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0716189371,"date":"1994-08-22","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.122,"date":"1994-08-29","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0735461297,"date":"1994-08-29","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.126,"date":"1994-09-05","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0754733223,"date":"1994-09-05","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.128,"date":"1994-09-12","fuel":"diesel","is_predicted":"original"},{"avg_price":1.077400515,"date":"1994-09-12","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.126,"date":"1994-09-19","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0793277076,"date":"1994-09-19","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.12,"date":"1994-09-26","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0812549003,"date":"19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erive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train linear regression model\n model = LinearRegression()\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n","source":["weekly_gas_prices"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhat are the major **price trends** across all fuel types from 1990 to 2025?","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to visualize the major price trends across all fuel types from 1990 to 2025 to understand how fuel prices have evolved over time.\",\n \"display_instruction\": \"Show **price** trends over time for different **fuel** types\",\n \"recommendation\": \"To visualize price trends over time across fuel types, we should aggregate the data by date and fuel type. Since there are multiple grades and formulations for each fuel type, we'll calculate the average price for each fuel type on each date. A line chart with date on the x-axis, average price on the y-axis, and different colored lines for each fuel type will clearly show the trends and allow easy comparison.\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n date fuel avg_price\n0 1990-08-20 gasoline 1.191\n1 1990-08-27 gasoline 1.245\n2 1990-09-03 gasoline 1.242\n3 1990-09-10 gasoline 1.252\n4 1990-09-17 gasoline 1.266\n5 1990-09-24 gasoline 1.272\n6 1990-10-01 gasoline 1.321\n7 1990-10-08 gasoline 1.333\n8 1990-10-15 gasoline 1.339\n9 1990-10-22 gasoline 1.345\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"forecast the price trend for the next 5 years with some propert model!\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"column\": \"fuel\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Build upon the previous price trends visualization by adding forecasted prices for the next 5 years (2025-2030) using a time series forecasting model. The output should include both historical actual prices and forecasted prices, with an indicator to distinguish between them.\",\n \"display_instruction\": \"Forecast **price** trends for the next 5 years for each **fuel** type\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\", \"is_predicted\"],\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"facet\": \"is_predicted\"\n },\n \"reason\": \"The user wants to extend the price trend analysis with forecasting for the next 5 years. Following the guidelines for forecasting, the output should include a 'is_predicted' column to distinguish between actual historical data, regression on historical data, and forecasted future values. The chart should be a line chart showing both historical and predicted values for each fuel type.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train linear regression model\n model = LinearRegression()\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-904593","sourceTableIds":["weekly_gas_prices"],"instruction":"forecast the price trend for the next 5 years with some propert model!","displayInstruction":"Forecast **price** trends for the next 5 years for each **fuel** type","chart":{"id":"chart-1760745146503","chartType":"Line Chart","encodingMap":{"x":{"fieldID":"original--weekly_gas_prices--date"},"y":{"fieldID":"concept-avg_price-1760744859365"},"color":{"fieldID":"original--weekly_gas_prices--fuel"},"opacity":{"channel":"opacity","bin":false},"column":{"fieldID":"original--weekly_gas_prices--fuel"},"row":{"channel":"row","bin":false}},"tableRef":"table-904593","saved":false,"source":"trigger","unread":false},"resultTableId":"table-34"},"explanation":{"agent":"CodeExplanationAgent","code":"This code performs **time series analysis and forecasting** on historical gas prices:\n\n1. **Aggregate historical prices** by calculating the mean `price` across all `grade` and `formulation` values for each `date` and `fuel` type combination, creating an `avg_price` metric.\n\n2. **Prepare temporal features** by converting `date` to datetime format and calculating `days_since_start` (the number of days elapsed since the earliest date) for each `fuel` type separately.\n\n3. **Train linear regression models** for each `fuel` type (diesel, gasoline) using `days_since_start` as the predictor variable and `avg_price` as the target variable.\n\n4. **Generate three data series** for each fuel type:\n - **Original**: Historical `avg_price` values with label `is_predicted = 'original'`\n - **Regression**: Fitted values from the linear model for historical dates with label `is_predicted = 'regression'`\n - **Forecasting**: Predicted prices for the next **5 years** (260 weekly periods) beyond the latest historical date with label `is_predicted = 'forecasting'`\n\n5. **Combine and format** all series into a single dataset sorted by `fuel` and `date`, with dates converted back to string format.","concepts":[{"explanation":"This analysis uses **linear regression modeling** to capture long-term price trends and forecast future gas prices. The model treats time as a continuous predictor variable (days_since_start) to predict average price. Each fuel type is modeled independently to account for different pricing dynamics. The approach assumes a linear relationship between time and price, which captures overall trend but may not account for cyclical patterns, seasonality, or market shocks. **Alternative modeling approaches** could include: (1) ARIMA/SARIMA models to capture autocorrelation and seasonality, (2) exponential smoothing methods (Holt-Winters) for trend and seasonal components, (3) polynomial regression for non-linear trends, or (4) more advanced machine learning methods like LSTM neural networks for complex temporal patterns.","field":"Statistical Analysis"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train linear regression model\n model = LinearRegression()\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThis code performs **time series analysis and forecasting** on historical gas prices:\n\n1. **Aggregate historical prices** by calculating the mean `price` across all `grade` and `formulation` values for each `date` and `fuel` type combination, creating an `avg_price` metric.\n\n2. **Prepare temporal features** by converting `date` to datetime format and calculating `days_since_start` (the number of days elapsed since the earliest date) for each `fuel` type separately.\n\n3. **Train linear regression models** for each `fuel` type (diesel, gasoline) using `days_since_start` as the predictor variable and `avg_price` as the target variable.\n\n4. **Generate three data series** for each fuel type:\n - **Original**: Historical `avg_price` values with label `is_predicted = 'original'`\n - **Regression**: Fitted values from the linear model for historical dates with label `is_predicted = 'regression'`\n - **Forecasting**: Predicted prices for the next **5 years** (260 weekly periods) beyond the latest historical date with label `is_predicted = 'forecasting'`\n\n5. **Combine and format** all series into a single dataset sorted by `fuel` and `date`, with dates converted back to string format.\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"This analysis uses **linear regression modeling** to capture long-term price trends and forecast future gas prices. The model treats time as a continuous predictor variable (days_since_start) to predict average price. Each fuel type is modeled independently to account for different pricing dynamics. The approach assumes a linear relationship between time and price, which captures overall trend but may not account for cyclical patterns, seasonality, or market shocks. **Alternative modeling approaches** could include: (1) ARIMA/SARIMA models to capture autocorrelation and seasonality, (2) exponential smoothing methods (Holt-Winters) for trend and seasonal components, (3) polynomial regression for non-linear trends, or (4) more advanced machine learning methods like LSTM neural networks for complex temporal patterns.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-22","displayId":"fuel-price-forecast1","names":["avg_price","date","fuel","is_predicted"],"rows":[{"avg_price":1.106,"date":"1994-03-21","fuel":"diesel","is_predicted":"original"},{"avg_price":0.5951388141,"date":"1994-03-21","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.107,"date":"1994-03-28","fuel":"diesel","is_predicted":"original"},{"avg_price":0.5986628745,"date":"1994-03-28","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.109,"date":"1994-04-04","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6021849755,"date":"1994-04-04","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.108,"date":"1994-04-11","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6057051172,"date":"1994-04-11","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.105,"date":"1994-04-18","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6092232996,"date":"1994-04-18","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.106,"date":"1994-04-25","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6127395226,"date":"1994-04-25","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.104,"date":"1994-05-02","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6162537863,"date":"1994-05-02","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.101,"date":"1994-05-09","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6197660906,"date":"1994-05-09","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.099,"date":"1994-05-16","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6232764356,"date":"1994-05-16","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.099,"date":"1994-05-23","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6267848212,"date":"1994-05-23","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.098,"date":"1994-05-30","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6302912475,"date":"1994-05-30","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.101,"date":"1994-06-06","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6337957144,"date":"1994-06-06","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.098,"date":"1994-06-13","fuel":"diesel","is_predicted":"original"},{"avg_price":0.637298222,"date":"1994-06-13","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.103,"date":"1994-06-20","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6407987703,"date":"1994-06-20","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.108,"date":"1994-06-27","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6442973591,"date":"1994-06-27","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.109,"date":"1994-07-04","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6477939887,"date":"1994-07-04","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.11,"date":"1994-07-11","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6512886589,"date":"1994-07-11","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.111,"date":"1994-07-18","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6547813697,"date":"1994-07-18","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.111,"date":"1994-07-25","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6582721212,"date":"1994-07-25","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.116,"date":"1994-08-01","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6617609134,"date":"1994-08-01","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.127,"date":"1994-08-08","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6652477462,"date":"1994-08-08","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.127,"date":"1994-08-15","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6687326197,"date":"1994-08-15","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.124,"date":"1994-08-22","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6722155338,"date":"1994-08-22","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.122,"date":"1994-08-29","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6756964886,"date":"1994-08-29","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.126,"date":"1994-09-05","fuel":"diesel","is_predicted":"original"},{"avg_price":0.679175484,"date":"1994-09-05","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.128,"date":"1994-09-12","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6826525201,"date":"1994-09-12","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.126,"date":"1994-09-19","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6861275968,"date":"1994-09-19","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.12,"date":"1994-09-26","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6896007142,"date":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ive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.pipeline import make_pipeline\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train polynomial regression model (degree 2 for non-linear trends)\n model = make_pipeline(PolynomialFeatures(degree=2), LinearRegression())\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n","source":["weekly_gas_prices"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhat are the major **price trends** across all fuel types from 1990 to 2025?","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to visualize the major price trends across all fuel types from 1990 to 2025 to understand how fuel prices have evolved over time.\",\n \"display_instruction\": \"Show **price** trends over time for different **fuel** types\",\n \"recommendation\": \"To visualize price trends over time across fuel types, we should aggregate the data by date and fuel type. Since there are multiple grades and formulations for each fuel type, we'll calculate the average price for each fuel type on each date. A line chart with date on the x-axis, average price on the y-axis, and different colored lines for each fuel type will clearly show the trends and allow easy comparison.\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n date fuel avg_price\n0 1990-08-20 gasoline 1.191\n1 1990-08-27 gasoline 1.245\n2 1990-09-03 gasoline 1.242\n3 1990-09-10 gasoline 1.252\n4 1990-09-17 gasoline 1.266\n5 1990-09-24 gasoline 1.272\n6 1990-10-01 gasoline 1.321\n7 1990-10-08 gasoline 1.333\n8 1990-10-15 gasoline 1.339\n9 1990-10-22 gasoline 1.345\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"forecast the price trend for the next 5 years with some propert model!\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"column\": \"fuel\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Build upon the previous price trends visualization by adding forecasted prices for the next 5 years (2025-2030) using a time series forecasting model. The output should include both historical actual prices and forecasted prices, with an indicator to distinguish between them.\",\n \"display_instruction\": \"Forecast **price** trends for the next 5 years for each **fuel** type\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\", \"is_predicted\"],\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"facet\": \"is_predicted\"\n },\n \"reason\": \"The user wants to extend the price trend analysis with forecasting for the next 5 years. Following the guidelines for forecasting, the output should include a 'is_predicted' column to distinguish between actual historical data, regression on historical data, and forecasted future values. The chart should be a line chart showing both historical and predicted values for each fuel type.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train linear regression model\n model = LinearRegression()\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n avg_price date fuel is_predicted\n0 1.106000 1994-03-21 diesel original\n1 1.029221 1994-03-21 diesel regression\n2 1.107000 1994-03-28 diesel original\n3 1.031148 1994-03-28 diesel regression\n4 1.109000 1994-04-04 diesel original\n5 1.033075 1994-04-04 diesel regression\n6 1.108000 1994-04-11 diesel original\n7 1.035002 1994-04-11 diesel regression\n8 1.105000 1994-04-18 diesel original\n9 1.036929 1994-04-18 diesel regression\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"use some more complex model\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"is_predicted\",\n \"column\": \"fuel\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Improve the forecasting model by using a more sophisticated time series model (polynomial regression with degree 2) instead of simple linear regression to better capture non-linear trends in fuel price data. The output should still include historical actual prices, regression fitted values, and forecasted prices for the next 5 years.\",\n \"display_instruction\": \"Forecast **price** trends using a more complex model for each **fuel** type\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\", \"is_predicted\"],\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"is_predicted\",\n \"facet\": \"fuel\"\n },\n \"reason\": \"The user wants to use a more complex forecasting model to potentially capture non-linear trends better. We'll use polynomial regression (degree 2) which can model curved trends while still being interpretable. The chart encodings are updated to color by prediction type and facet by fuel type as specified by the user.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.pipeline import make_pipeline\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train polynomial regression model (degree 2 for non-linear trends)\n model = make_pipeline(PolynomialFeatures(degree=2), LinearRegression())\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-34","sourceTableIds":["weekly_gas_prices"],"instruction":"use some more complex model","displayInstruction":"Forecast **price** trends using a more complex model for each **fuel** type","chart":{"id":"chart-1760745227478","chartType":"Line Chart","encodingMap":{"x":{"fieldID":"original--weekly_gas_prices--date"},"y":{"fieldID":"concept-avg_price-1760744859365"},"color":{"fieldID":"concept-is_predicted-1760745153175","sortBy":"[\"original\",\"regression\",\"forecasting\"]"},"opacity":{"channel":"opacity","bin":false},"column":{"fieldID":"original--weekly_gas_prices--fuel"},"row":{"channel":"row","bin":false}},"tableRef":"table-34","saved":false,"source":"trigger","unread":false},"resultTableId":"table-22"},"explanation":{"agent":"CodeExplanationAgent","code":"This code performs **price trend analysis and forecasting** for different fuel types:\n\n1. **Aggregates** historical gas prices by calculating the **mean price** across all **grades** and **formulations** for each **date** and **fuel** type\n2. **Prepares** time series data by converting **dates** to datetime format and creating a **days_since_start** numeric variable (days elapsed since the first recorded date)\n3. **Trains** a **polynomial regression model** (degree 2) separately for each **fuel type** to capture non-linear price trends over time\n4. **Generates** three types of price records for each fuel:\n - **Original**: actual historical average prices with label `'original'`\n - **Regression**: model-fitted prices for historical dates with label `'regression'`\n - **Forecasting**: predicted prices for the **next 5 years** (260 weekly periods) with label `'forecasting'`\n5. **Combines** all records and sorts by **fuel** type and **date**","concepts":[{"explanation":"A numeric time variable representing the number of days elapsed since the first recorded date in the dataset. This transforms calendar dates into a continuous numeric scale suitable for regression modeling: \\( \\text{days\\_since\\_start} = (\\text{current\\_date} - \\text{min\\_date}) \\)","field":"days_since_start"},{"explanation":"A categorical label indicating the source and nature of the price data: 'original' for actual historical observations, 'regression' for model-fitted values on historical dates (representing the polynomial trend), and 'forecasting' for future price predictions beyond the observed data range.","field":"is_predicted"},{"explanation":"This analysis models fuel price trends using **polynomial regression (degree 2)** to capture non-linear patterns in price evolution over time. The model treats time (days since start) as the independent variable and average price as the dependent variable, fitted separately for each fuel type. The quadratic polynomial allows the model to capture acceleration or deceleration in price trends (e.g., prices rising faster over time or stabilizing). Alternative approaches could include: **ARIMA models** for capturing autocorrelation and seasonality, **exponential smoothing** for weighted recent observations, **Prophet** for handling multiple seasonality patterns and holidays, or **LSTM neural networks** for complex non-linear temporal dependencies.","field":"Statistical Analysis"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.pipeline import make_pipeline\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train polynomial regression model (degree 2 for non-linear trends)\n model = make_pipeline(PolynomialFeatures(degree=2), LinearRegression())\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThis code performs **price trend analysis and forecasting** for different fuel types:\n\n1. **Aggregates** historical gas prices by calculating the **mean price** across all **grades** and **formulations** for each **date** and **fuel** type\n2. **Prepares** time series data by converting **dates** to datetime format and creating a **days_since_start** numeric variable (days elapsed since the first recorded date)\n3. **Trains** a **polynomial regression model** (degree 2) separately for each **fuel type** to capture non-linear price trends over time\n4. **Generates** three types of price records for each fuel:\n - **Original**: actual historical average prices with label `'original'`\n - **Regression**: model-fitted prices for historical dates with label `'regression'`\n - **Forecasting**: predicted prices for the **next 5 years** (260 weekly periods) with label `'forecasting'`\n5. **Combines** all records and sorts by **fuel** type and **date**\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"days_since_start\",\n \"explanation\": \"A numeric time variable representing the number of days elapsed since the first recorded date in the dataset. This transforms calendar dates into a continuous numeric scale suitable for regression modeling: \\\\( \\\\text{days\\\\_since\\\\_start} = (\\\\text{current\\\\_date} - \\\\text{min\\\\_date}) \\\\)\"\n },\n {\n \"field\": \"is_predicted\",\n \"explanation\": \"A categorical label indicating the source and nature of the price data: 'original' for actual historical observations, 'regression' for model-fitted values on historical dates (representing the polynomial trend), and 'forecasting' for future price predictions beyond the observed data range.\"\n },\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"This analysis models fuel price trends using **polynomial regression (degree 2)** to capture non-linear patterns in price evolution over time. The model treats time (days since start) as the independent variable and average price as the dependent variable, fitted separately for each fuel type. The quadratic polynomial allows the model to capture acceleration or deceleration in price trends (e.g., prices rising faster over time or stabilizing). Alternative approaches could include: **ARIMA models** for capturing autocorrelation and seasonality, **exponential smoothing** for weighted recent observations, **Prophet** for handling multiple seasonality patterns and holidays, or **LSTM neural networks** for complex non-linear temporal dependencies.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""}],"charts":[{"id":"chart-1760745227133","chartType":"Line Chart","encodingMap":{"x":{"fieldID":"original--weekly_gas_prices--date"},"y":{"fieldID":"concept-avg_price-1760744859365"},"color":{"fieldID":"concept-is_predicted-1760745153175","sortBy":"[\"original\",\"regression\",\"forecasting\"]"},"opacity":{"channel":"opacity","bin":false},"column":{"fieldID":"original--weekly_gas_prices--fuel"},"row":{"channel":"row","bin":false}},"tableRef":"table-22","saved":false,"source":"user","unread":false},{"id":"chart-1760745149814","chartType":"Line 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Diesel consistently priced higher than gasoline throughout most of the period.\n\n• **Year-over-year volatility**: The scatter plot reveals several dramatic price spikes, with the largest absolute changes (~2.5) and percentage changes (70-75%) occurring in recent years (2021-2025), indicating major price disruptions in the post-pandemic period.\n\n• **Price stability comparison**: Price volatility increased dramatically post-2000, with coefficient of variation rising from ~7-9% (pre-2000) to ~29-33% (post-2000) for both fuel types, indicating prices became 3-4x more volatile in recent decades. Diesel shows slightly higher volatility than gasoline in both periods.","status":"completed","hidden":false,"lastUpdate":1760744954367}],"dataCleanBlocks":[],"cleanInProgress":false,"generatedReports":[{"id":"report-1760831021256-7310","content":"# Fuel Price Volatility and Premium Trends: A Three-Decade Analysis\n\nThe U.S. fuel market has undergone dramatic shifts in pricing dynamics over the past 35 years, with post-2000 prices showing significantly higher volatility and expanding premium differentials across gasoline grades.\n\n[IMAGE(chart-1760744944490)]\n\n**Price volatility has intensified dramatically in the modern era.** The coefficient of variation—a measure of price instability relative to average costs—reveals that both diesel and gasoline experienced roughly **four-fold increases** in volatility post-2000. Diesel prices showed particularly heightened instability with a coefficient of variation of 33.48% (2000-2025) compared to just 7.65% in the 1990s. This shift reflects increased sensitivity to global crude oil market fluctuations, geopolitical tensions, and refining capacity constraints. Average prices also more than doubled across both fuel types, with diesel climbing from $1.14 to $2.93 per gallon.\n\n[IMAGE(chart-1760744910486)]\n\n**Premium gasoline pricing has steadily diverged from regular grades over time.** The price premium commanded by higher-octane gasoline grades has expanded substantially, with premium gasoline reaching spreads of **$0.90-$0.95 above regular** by 2025—nearly five times the $0.18-$0.20 premium observed in the mid-1990s. Midgrade gasoline follows a similar trajectory, reaching $0.55-$0.60 premiums. This widening gap suggests evolving refining economics, increased consumer segmentation, and potentially higher margins on premium products.\n\n**In summary**, the fuel market has transitioned from relative price stability in the 1990s to an era characterized by substantial volatility and expanding price differentiation. Consumers and businesses face both higher absolute costs and greater uncertainty in budgeting for fuel expenses. Key questions for further investigation include: What specific market mechanisms drive the premium expansion? How do regional variations affect these national trends? What role do alternative fuels play in moderating future volatility?","style":"executive summary","selectedChartIds":["chart-1760744944490","chart-1760744910486"],"createdAt":1760831035718},{"id":"report-1760830985775-9885","content":"# Fuel Prices Set to Continue Rising Through 2030\n\nHistorical data reveals a clear upward trend in both diesel and gasoline prices since the 1990s. Our polynomial regression model shows prices have increased nearly sixfold over three decades, with notable volatility around 2008 and 2022 reflecting global economic shocks.\n\n[IMAGE(chart-1760745227133)]\n\nThe forecast suggests this upward trajectory will persist, with both fuel types projected to reach approximately $3.80-$3.90 per gallon by 2030. While short-term fluctuations are inevitable, the long-term trend remains consistently upward.\n\n**In summary**, fuel prices have demonstrated sustained growth over 35 years and are expected to continue rising. Consumers and businesses should anticipate higher energy costs in coming years and consider efficiency improvements or alternative energy sources.","style":"short note","selectedChartIds":["chart-1760745227133"],"createdAt":1760830993075}],"currentReport":{"id":"report-1760750575650-2619","content":"# Hollywood's Billion-Dollar Hitmakers\n\n*Avatar* stands alone—earning over $2.5B in profit, dwarfing all competition. Action and Adventure films dominate the most profitable titles, with franchises like *Jurassic Park*, *The Dark Knight*, and *Lord of the Rings* proving blockbuster formulas work.\n\n\"Chart\"\n\nSteven Spielberg leads all directors with $7.2B in total profit across his career, showcasing remarkable consistency with hits spanning decades—from *Jurassic Park* to *E.T.* His nearest competitors trail by billions, underlining his unmatched commercial impact.\n\n\"Chart\"\n\n**In summary**, mega-budget Action and Adventure films generate extraordinary returns when they succeed, and a handful of elite directors—led by Spielberg—have mastered the formula for sustained box office dominance.","style":"short note","selectedChartIds":["chart-1760743347871","chart-1760743768741"],"chartImages":{},"createdAt":1760750584189,"title":"Report - 10/17/2025"},"activeChallenges":[],"agentWorkInProgress":[],"_persist":{"version":-1,"rehydrated":true}} ================================================ FILE: 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source names to be more readable\n transformed_df['source'] = transformed_df['source'].str.replace('Electricity from ', '').str.replace(' (TWh)', '')\n \n return transformed_df\n","source":["global-energy-20-small.csv"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 158029.9988, 190729.9957, 209229.9957, ..., 6993180.176, 9861099.609, 9874660.156, 10096009.77\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 90.14, 91.79, 92.54, 804.95\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Transform the data into a long format where different energy sources (fossil fuels, nuclear, renewables) are unpivoted into separate rows. Each row should contain the year, entity (country), energy source type, and the corresponding energy value in TWh. This will enable visualization of energy generation trends over time by source and country.\",\n \"display_instruction\": \"Show **energy generation** trends by **source** and **Entity** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"source\", \"energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n },\n \"reason\": \"The user wants to visualize energy data across different sources with a line chart faceted by source. The original data has energy sources in separate columns (fossil fuels, nuclear, renewables), so we need to unpivot these columns into a long format with 'source' and 'energy' fields. The chart_encodings provided by the user are appropriate for this visualization goal, using faceting (column) to separate different energy sources.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Unpivot the energy source columns into long format\n transformed_df = pd.melt(\n df_energy,\n id_vars=['Year', 'Entity'],\n value_vars=[\n 'Electricity from fossil fuels (TWh)',\n 'Electricity from nuclear (TWh)',\n 'Electricity from renewables (TWh)'\n ],\n var_name='source',\n value_name='energy'\n )\n \n # Clean up the source names to be more readable\n transformed_df['source'] = transformed_df['source'].str.replace('Electricity from ', '').str.replace(' (TWh)', '')\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"global-energy-20-small.csv","sourceTableIds":["global-energy-20-small.csv"],"instruction":"","displayInstruction":"Show **energy generation** trends by **source** and **Entity** over **Year**","chart":{"id":"chart-1760738394155","chartType":"Line Chart","encodingMap":{"x":{"fieldID":"original--global-energy-20-small.csv--Year"},"y":{"channel":"y","bin":false,"fieldID":"concept-1760738385163"},"color":{"fieldID":"original--global-energy-20-small.csv--Entity"},"opacity":{"channel":"opacity","bin":false},"column":{"channel":"column","bin":false,"fieldID":"concept-1760738389404"},"row":{"channel":"row","bin":false}},"tableRef":"global-energy-20-small.csv","saved":false,"source":"trigger","unread":false},"resultTableId":"table-82"},"explanation":{"agent":"CodeExplanationAgent","code":"The code performs a data transformation to restructure the energy data from a wide format to a long format:\n\n1. **Unpivot energy columns**: Uses `pd.melt()` to transform the three energy source columns (**Electricity from fossil fuels**, **Electricity from nuclear**, and **Electricity from renewables**) from separate columns into rows, while keeping **Year** and **Entity** as identifier columns.\n\n2. **Create source classification**: Generates a new **source** column that captures which energy type each row represents, and stores the corresponding energy values in an **energy** column.\n\n3. **Clean source labels**: Removes the prefix \"Electricity from \" and suffix \" (TWh)\" from the source names, resulting in cleaner labels: **fossil fuels**, **nuclear**, and **renewables**.\n\n4. **Return restructured data**: The final dataset has each country-year-energy source combination as a separate row, making it easier to analyze and visualize energy mix across countries and time periods.","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 190729.9957, 227580.0018, 233600.0061, ..., 5736319.824, 5738290.039, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 90.14, 91.79, 92.54, nan\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Unpivot the energy source columns into long format\n transformed_df = pd.melt(\n df_energy,\n id_vars=['Year', 'Entity'],\n value_vars=[\n 'Electricity from fossil fuels (TWh)',\n 'Electricity from nuclear (TWh)',\n 'Electricity from renewables (TWh)'\n ],\n var_name='source',\n value_name='energy'\n )\n \n # Clean up the source names to be more readable\n transformed_df['source'] = transformed_df['source'].str.replace('Electricity from ', '').str.replace(' (TWh)', '')\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThe code performs a data transformation to restructure the energy data from a wide format to a long format:\n\n1. **Unpivot energy columns**: Uses `pd.melt()` to transform the three energy source columns (**Electricity from fossil fuels**, **Electricity from nuclear**, and **Electricity from renewables**) from separate columns into rows, while keeping **Year** and **Entity** as identifier columns.\n\n2. **Create source classification**: Generates a new **source** column that captures which energy type each row represents, and stores the corresponding energy values in an **energy** column.\n\n3. **Clean source labels**: Removes the prefix \"Electricity from \" and suffix \" (TWh)\" from the source names, resulting in cleaner labels: **fossil fuels**, **nuclear**, and **renewables**.\n\n4. **Return restructured data**: The final dataset has each country-year-energy source combination as a separate row, making it easier to analyze and visualize energy mix across countries and time periods.\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-45","displayId":"renewable-energy","names":["Entity","Year","renewable_percentage"],"rows":[{"Entity":"Australia","Year":2000,"renewable_percentage":8.6344368187},{"Entity":"Australia","Year":2001,"renewable_percentage":8.2180135078},{"Entity":"Australia","Year":2002,"renewable_percentage":8.0833022736},{"Entity":"Australia","Year":2003,"renewable_percentage":8.6598324205},{"Entity":"Australia","Year":2004,"renewable_percentage":8.7013045232},{"Entity":"Australia","Year":2005,"renewable_percentage":9.1562355123},{"Entity":"Australia","Year":2006,"renewable_percentage":9.6357600837},{"Entity":"Australia","Year":2007,"renewable_percentage":9.1190310213},{"Entity":"Australia","Year":2008,"renewable_percentage":8.0548900022},{"Entity":"Australia","Year":2009,"renewable_percentage":7.8043793133},{"Entity":"Australia","Year":2010,"renewable_percentage":9.0442152121},{"Entity":"Australia","Year":2011,"renewable_percentage":11.3454273735},{"Entity":"Australia","Year":2012,"renewable_percentage":11.4105750279},{"Entity":"Australia","Year":2013,"renewable_percentage":14.8708583355},{"Entity":"Australia","Year":2014,"renewable_percentage":14.9621290509},{"Entity":"Australia","Year":2015,"renewable_percentage":14.3476000693},{"Entity":"Australia","Year":2016,"renewable_percentage":15.6093794449},{"Entity":"Australia","Year":2017,"renewable_percentage":16.3138729943},{"Entity":"Australia","Year":2018,"renewable_percentage":17.145938174},{"Entity":"Australia","Year":2019,"renewable_percentage":21.3759705435},{"Entity":"Australia","Year":2020,"renewable_percentage":25.5031684668},{"Entity":"Brazil","Year":2000,"renewable_percentage":90.1307723743},{"Entity":"Brazil","Year":2001,"renewable_percentage":84.6953615744},{"Entity":"Brazil","Year":2002,"renewable_percentage":86.0883364189},{"Entity":"Brazil","Year":2003,"renewable_percentage":87.4561159097},{"Entity":"Brazil","Year":2004,"renewable_percentage":86.4260041451},{"Entity":"Brazil","Year":2005,"renewable_percentage":87.6781562721},{"Entity":"Brazil","Year":2006,"renewable_percentage":87.2842473236},{"Entity":"Brazil","Year":2007,"renewable_percentage":88.7252098726},{"Entity":"Brazil","Year":2008,"renewable_percentage":84.8072313004},{"Entity":"Brazil","Year":2009,"renewable_percentage":89.4172280725},{"Entity":"Brazil","Year":2010,"renewable_percentage":85.3576882415},{"Entity":"Brazil","Year":2011,"renewable_percentage":87.6618820986},{"Entity":"Brazil","Year":2012,"renewable_percentage":83.1164558813},{"Entity":"Brazil","Year":2013,"renewable_percentage":77.5240022006},{"Entity":"Brazil","Year":2014,"renewable_percentage":74.0418657409},{"Entity":"Brazil","Year":2015,"renewable_percentage":75.0231817625},{"Entity":"Brazil","Year":2016,"renewable_percentage":81.0938046902},{"Entity":"Brazil","Year":2017,"renewable_percentage":79.9091472228},{"Entity":"Brazil","Year":2018,"renewable_percentage":82.9198505403},{"Entity":"Brazil","Year":2019,"renewable_percentage":82.8548799017},{"Entity":"Brazil","Year":2020,"renewable_percentage":84.6411771408},{"Entity":"Canada","Year":2000,"renewable_percentage":61.8095917882},{"Entity":"Canada","Year":2001,"renewable_percentage":59.3287558747},{"Entity":"Canada","Year":2002,"renewable_percentage":61.1477403113},{"Entity":"Canada","Year":2003,"renewable_percentage":60.0789685174},{"Entity":"Canada","Year":2004,"renewable_percentage":59.6967771845},{"Entity":"Canada","Year":2005,"renewable_percentage":60.8208155391},{"Entity":"Canada","Year":2006,"renewable_percentage":60.8271602855},{"Entity":"Canada","Year":2007,"renewable_percentage":61.2460642446},{"Entity":"Canada","Year":2008,"renewable_percentage":62.6520720838},{"Entity":"Canada","Year":2009,"renewable_percentage":63.8919227732},{"Entity":"Canada","Year":2010,"renewable_percentage":62.9421470558},{"Entity":"Canada","Year":2011,"renewable_percentage":64.0922915917},{"Entity":"Canada","Year":2012,"renewable_percentage":65.098730952},{"Entity":"Canada","Year":2013,"renewable_percentage":65.4320794066},{"Entity":"Canada","Year":2014,"renewable_percentage":64.791145907},{"Entity":"Canada","Year":2015,"renewable_percentage":65.2946239925},{"Entity":"Canada","Year":2016,"renewable_percentage":66.1890584295},{"Entity":"Canada","Year":2017,"renewable_percentage":67.5399410579},{"Entity":"Canada","Year":2018,"renewable_percentage":67.3685700357},{"Entity":"Canada","Year":2019,"renewable_percentage":67.1741623137},{"Entity":"Canada","Year":2020,"renewable_percentage":68.7796436354},{"Entity":"China","Year":2000,"renewable_percentage":16.639126586},{"Entity":"China","Year":2001,"renewable_percentage":18.9581237042},{"Entity":"China","Year":2002,"renewable_percentage":17.6185006046},{"Entity":"China","Year":2003,"renewable_percentage":15.0362717081},{"Entity":"China","Year":2004,"renewable_percentage":16.2224108273},{"Entity":"China","Year":2005,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Africa","Year":2020,"renewable_percentage":5.780581212},{"Entity":"Spain","Year":2000,"renewable_percentage":15.6119862394},{"Entity":"Spain","Year":2001,"renewable_percentage":21.1524434719},{"Entity":"Spain","Year":2002,"renewable_percentage":13.8260180901},{"Entity":"Spain","Year":2003,"renewable_percentage":21.667314419},{"Entity":"Spain","Year":2004,"renewable_percentage":18.3190206468},{"Entity":"Spain","Year":2005,"renewable_percentage":14.8597342333},{"Entity":"Spain","Year":2006,"renewable_percentage":17.6623992413},{"Entity":"Spain","Year":2007,"renewable_percentage":19.3347262296},{"Entity":"Spain","Year":2008,"renewable_percentage":20.0051501593},{"Entity":"Spain","Year":2009,"renewable_percentage":25.4107639008},{"Entity":"Spain","Year":2010,"renewable_percentage":32.7922186819},{"Entity":"Spain","Year":2011,"renewable_percentage":30.0408415417},{"Entity":"Spain","Year":2012,"renewable_percentage":29.6047928652},{"Entity":"Spain","Year":2013,"renewable_percentage":39.5850357054},{"Entity":"Spain","Year":2014,"renewable_percentage":40.1032952644},{"Entity":"Spain","Year":2015,"renewable_percentage":34.9899091826},{"Entity":"Spain","Year":2016,"renewable_percentage":38.5818061138},{"Entity":"Spain","Year":2017,"renewable_percentage":32.220593624},{"Entity":"Spain","Year":2018,"renewable_percentage":38.2080329557},{"Entity":"Spain","Year":2019,"renewable_percentage":37.280815091},{"Entity":"Spain","Year":2020,"renewable_percentage":43.8108805298},{"Entity":"Thailand","Year":2000,"renewable_percentage":7.1261029822},{"Entity":"Thailand","Year":2001,"renewable_percentage":7.061527212},{"Entity":"Thailand","Year":2002,"renewable_percentage":7.9444772593},{"Entity":"Thailand","Year":2003,"renewable_percentage":7.6718362852},{"Entity":"Thailand","Year":2004,"renewable_percentage":6.5163549406},{"Entity":"Thailand","Year":2005,"renewable_percentage":6.0325203252},{"Entity":"Thailand","Year":2006,"renewable_percentage":7.5988547551},{"Entity":"Thailand","Year":2007,"renewable_percentage":7.7085852479},{"Entity":"Thailand","Year":2008,"renewable_percentage":6.5625458278},{"Entity":"Thailand","Year":2009,"renewable_percentage":6.6263303689},{"Entity":"Thailand","Year":2010,"renewable_percentage":5.7085828343},{"Entity":"Thailand","Year":2011,"renewable_percentage":8.039961941},{"Entity":"Thailand","Year":2012,"renewable_percentage":8.5396118358},{"Entity":"Thailand","Year":2013,"renewable_percentage":7.6765035487},{"Entity":"Thailand","Year":2014,"renewable_percentage":8.395728489},{"Entity":"Thailand","Year":2015,"renewable_percentage":7.9949619145},{"Entity":"Thailand","Year":2016,"renewable_percentage":8.9840234023},{"Entity":"Thailand","Year":2017,"renewable_percentage":10.9570957096},{"Entity":"Thailand","Year":2018,"renewable_percentage":14.1900054915},{"Entity":"Thailand","Year":2019,"renewable_percentage":14.7001731284},{"Entity":"Thailand","Year":2020,"renewable_percentage":13.7963737796},{"Entity":"Ukraine","Year":2000,"renewable_percentage":6.5860921352},{"Entity":"Ukraine","Year":2001,"renewable_percentage":6.9729761009},{"Entity":"Ukraine","Year":2002,"renewable_percentage":5.5597165409},{"Entity":"Ukraine","Year":2003,"renewable_percentage":5.1442841287},{"Entity":"Ukraine","Year":2004,"renewable_percentage":6.4718162839},{"Entity":"Ukraine","Year":2005,"renewable_percentage":6.6698940347},{"Entity":"Ukraine","Year":2006,"renewable_percentage":6.68633235},{"Entity":"Ukraine","Year":2007,"renewable_percentage":5.3380238605},{"Entity":"Ukraine","Year":2008,"renewable_percentage":6.1377090041},{"Entity":"Ukraine","Year":2009,"renewable_percentage":6.980762585},{"Entity":"Ukraine","Year":2010,"renewable_percentage":7.0914098083},{"Entity":"Ukraine","Year":2011,"renewable_percentage":5.7450628366},{"Entity":"Ukraine","Year":2012,"renewable_percentage":5.6614236741},{"Entity":"Ukraine","Year":2013,"renewable_percentage":7.8003200661},{"Entity":"Ukraine","Year":2014,"renewable_percentage":5.5885262117},{"Entity":"Ukraine","Year":2015,"renewable_percentage":4.3924771096},{"Entity":"Ukraine","Year":2016,"renewable_percentage":5.6797249171},{"Entity":"Ukraine","Year":2017,"renewable_percentage":7.0457194664},{"Entity":"Ukraine","Year":2018,"renewable_percentage":8.228528092},{"Entity":"Ukraine","Year":2019,"renewable_percentage":7.7754487096},{"Entity":"Ukraine","Year":2020,"renewable_percentage":11.8440577364},{"Entity":"United Kingdom","Year":2000,"renewable_percentage":2.6657406913},{"Entity":"United Kingdom","Year":2001,"renewable_percentage":2.5001961451},{"Entity":"United Kingdom","Year":2002,"renewable_percentage":2.8939157566},{"Entity":"United Kingdom","Year":2003,"renewable_percentage":2.6854802003},{"Entity":"United Kingdom","Year":2004,"renewable_percentage":3.6136880575},{"Entity":"United Kingdom","Year":2005,"renewable_percentage":4.2815234434},{"Entity":"United Kingdom","Year":2006,"renewable_percentage":4.6029890199},{"Entity":"United Kingdom","Year":2007,"renewable_percentage":5.0104331009},{"Entity":"United Kingdom","Year":2008,"renewable_percentage":5.6776842324},{"Entity":"United Kingdom","Year":2009,"renewable_percentage":6.7679854187},{"Entity":"United Kingdom","Year":2010,"renewable_percentage":6.9092924441},{"Entity":"United Kingdom","Year":2011,"renewable_percentage":9.6422505889},{"Entity":"United Kingdom","Year":2012,"renewable_percentage":11.4273047189},{"Entity":"United Kingdom","Year":2013,"renewable_percentage":14.9727052732},{"Entity":"United Kingdom","Year":2014,"renewable_percentage":19.2476358104},{"Entity":"United Kingdom","Year":2015,"renewable_percentage":24.6227709191},{"Entity":"United Kingdom","Year":2016,"renewable_percentage":24.6788390627},{"Entity":"United Kingdom","Year":2017,"renewable_percentage":29.4986571173},{"Entity":"United Kingdom","Year":2018,"renewable_percentage":33.2919818457},{"Entity":"United Kingdom","Year":2019,"renewable_percentage":37.4568630499},{"Entity":"United Kingdom","Year":2020,"renewable_percentage":42.8603962651},{"Entity":"United States","Year":2000,"renewable_percentage":9.2298992662},{"Entity":"United States","Year":2001,"renewable_percentage":7.5132056541},{"Entity":"United States","Year":2002,"renewable_percentage":8.749216358},{"Entity":"United States","Year":2003,"renewable_percentage":9.0252110397},{"Entity":"United States","Year":2004,"renewable_percentage":8.7334100887},{"Entity":"United States","Year":2005,"renewable_percentage":8.7494640631},{"Entity":"United States","Year":2006,"renewable_percentage":9.4184742052},{"Entity":"United States","Year":2007,"renewable_percentage":8.3984096829},{"Entity":"United States","Year":2008,"renewable_percentage":9.180943292},{"Entity":"United States","Year":2009,"renewable_percentage":10.547689996},{"Entity":"United States","Year":2010,"renewable_percentage":10.3180892283},{"Entity":"United States","Year":2011,"renewable_percentage":12.4665249812},{"Entity":"United States","Year":2012,"renewable_percentage":12.1841179804},{"Entity":"United States","Year":2013,"renewable_percentage":12.8311154179},{"Entity":"United States","Year":2014,"renewable_percentage":13.3503092033},{"Entity":"United States","Year":2015,"renewable_percentage":13.6261352256},{"Entity":"United States","Year":2016,"renewable_percentage":15.2880936304},{"Entity":"United States","Year":2017,"renewable_percentage":17.4515276472},{"Entity":"United States","Year":2018,"renewable_percentage":17.4499886946},{"Entity":"United States","Year":2019,"renewable_percentage":18.2946243489},{"Entity":"United States","Year":2020,"renewable_percentage":20.3156921037}],"metadata":{"Entity":{"type":"string","semanticType":"Location"},"Year":{"type":"number","semanticType":"Year"},"renewable_percentage":{"type":"number","semanticType":"Percentage"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage']].copy()\n \n return transformed_df\n","source":["global-energy-20-small.csv"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 158029.9988, 190729.9957, 209229.9957, ..., 6993180.176, 9861099.609, 9874660.156, 10096009.77\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 90.14, 91.79, 92.54, 804.95\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Transform the data into a long format where different energy sources (fossil fuels, nuclear, renewables) are unpivoted into separate rows. Each row should contain the year, entity (country), energy source type, and the corresponding energy value in TWh. This will enable visualization of energy generation trends over time by source and country.\",\n \"display_instruction\": \"Show **energy generation** trends by **source** and **Entity** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"source\", \"energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n },\n \"reason\": \"The user wants to visualize energy data across different sources with a line chart faceted by source. The original data has energy sources in separate columns (fossil fuels, nuclear, renewables), so we need to unpivot these columns into a long format with 'source' and 'energy' fields. The chart_encodings provided by the user are appropriate for this visualization goal, using faceting (column) to separate different energy sources.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Unpivot the energy source columns into long format\n transformed_df = pd.melt(\n df_energy,\n id_vars=['Year', 'Entity'],\n value_vars=[\n 'Electricity from fossil fuels (TWh)',\n 'Electricity from nuclear (TWh)',\n 'Electricity from renewables (TWh)'\n ],\n var_name='source',\n value_name='energy'\n )\n \n # Clean up the source names to be more readable\n transformed_df['source'] = transformed_df['source'].str.replace('Electricity from ', '').str.replace(' (TWh)', '')\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Entity Year energy source\n0 Australia 2000 181.05 fossil fuels\n1 Australia 2001 194.33 fossil fuels\n2 Australia 2002 197.29 fossil fuels\n3 Australia 2003 195.13 fossil fuels\n4 Australia 2004 203.66 fossil fuels\n5 Australia 2005 195.95 fossil fuels\n6 Australia 2006 198.72 fossil fuels\n7 Australia 2007 208.59 fossil fuels\n8 Australia 2008 211.06 fossil fuels\n9 Australia 2009 216.42 fossil fuels\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show renewable percentage for each country\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage for each country and year. The renewable percentage is computed as the ratio of renewable energy to total energy (sum of fossil fuels, nuclear, and renewables) multiplied by 100. The output should contain year, entity (country), and the renewable percentage value.\",\n \"display_instruction\": \"Show **renewable percentage** for each **Entity** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"renewable_percentage\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"renewable_percentage\",\n \"color\": \"Entity\"\n },\n \"reason\": \"The user wants to see renewable percentage trends, which requires calculating the proportion of renewable energy relative to total energy generation. This is a single metric per country per year, so we don't need the 'column' faceting by source anymore. The chart_encodings are updated to reflect that we're now showing a percentage metric rather than absolute energy values by source.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage']].copy()\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-82","sourceTableIds":["global-energy-20-small.csv"],"instruction":"show renewable percentage for each country","displayInstruction":"Show **renewable percentage** for each **Entity** over **Year**","chart":{"id":"chart-1760738416626","chartType":"Line Chart","encodingMap":{"x":{"fieldID":"original--global-energy-20-small.csv--Year"},"y":{"fieldID":"concept-1760738385163"},"color":{"fieldID":"original--global-energy-20-small.csv--Entity"},"opacity":{"channel":"opacity","bin":false},"column":{"fieldID":"concept-1760738389404","sortBy":"[\"fossil fuels\",\"nuclear\",\"renewables\"]"},"row":{"channel":"row","bin":false}},"tableRef":"table-82","saved":false,"source":"trigger","unread":false},"resultTableId":"table-45"},"explanation":{"agent":"CodeExplanationAgent","code":"The code transforms global energy data to analyze renewable energy adoption:\n\n- Calculates **total_energy** by summing electricity generation from three sources: **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n- Computes **renewable_percentage** by dividing **Electricity from renewables (TWh)** by **total_energy** and multiplying by **100**\n- Handles edge cases by replacing infinite values (from division by zero) with **NaN**\n- Filters the dataset to retain only **Year**, **Entity** (country), and **renewable_percentage** columns","concepts":[{"explanation":"The proportion of a country's total electricity generation that comes from renewable sources, expressed as a percentage. Calculated as: \\( \\frac{\\text{Electricity from renewables}}{\\text{Total electricity generation}} \\times 100 \\). This metric indicates the degree of renewable energy adoption in each country's energy mix.","field":"renewable_percentage"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5738290.039, 9861099.609, 10502929.69, 10707219.73\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., nan, nan, nan, nan\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage']].copy()\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThe code transforms global energy data to analyze renewable energy adoption:\n\n- Calculates **total_energy** by summing electricity generation from three sources: **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n- Computes **renewable_percentage** by dividing **Electricity from renewables (TWh)** by **total_energy** and multiplying by **100**\n- Handles edge cases by replacing infinite values (from division by zero) with **NaN**\n- Filters the dataset to retain only **Year**, **Entity** (country), and **renewable_percentage** columns\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"renewable_percentage\",\n \"explanation\": \"The proportion of a country's total electricity generation that comes from renewable sources, expressed as a percentage. Calculated as: \\\\( \\\\frac{\\\\text{Electricity from renewables}}{\\\\text{Total electricity generation}} \\\\times 100 \\\\). This metric indicates the degree of renewable energy adoption in each country's energy mix.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-78","displayId":"renewable-energy-rank","names":["Entity","Year","rank","renewable_percentage"],"rows":[{"Entity":"Australia","Year":2000,"rank":11,"renewable_percentage":8.6344368187},{"Entity":"Australia","Year":2001,"rank":10,"renewable_percentage":8.2180135078},{"Entity":"Australia","Year":2002,"rank":11,"renewable_percentage":8.0833022736},{"Entity":"Australia","Year":2003,"rank":11,"renewable_percentage":8.6598324205},{"Entity":"Australia","Year":2004,"rank":12,"renewable_percentage":8.7013045232},{"Entity":"Australia","Year":2005,"rank":11,"renewable_percentage":9.1562355123},{"Entity":"Australia","Year":2006,"rank":11,"renewable_percentage":9.6357600837},{"Entity":"Australia","Year":2007,"rank":11,"renewable_percentage":9.1190310213},{"Entity":"Australia","Year":2008,"rank":12,"renewable_percentage":8.0548900022},{"Entity":"Australia","Year":2009,"rank":12,"renewable_percentage":7.8043793133},{"Entity":"Australia","Year":2010,"rank":12,"renewable_percentage":9.0442152121},{"Entity":"Australia","Year":2011,"rank":11,"renewable_percentage":11.3454273735},{"Entity":"Australia","Year":2012,"rank":12,"renewable_percentage":11.4105750279},{"Entity":"Australia","Year":2013,"rank":11,"renewable_percentage":14.8708583355},{"Entity":"Australia","Year":2014,"rank":11,"renewable_percentage":14.9621290509},{"Entity":"Australia","Year":2015,"rank":12,"renewable_percentage":14.3476000693},{"Entity":"Australia","Year":2016,"rank":11,"renewable_percentage":15.6093794449},{"Entity":"Australia","Year":2017,"rank":12,"renewable_percentage":16.3138729943},{"Entity":"Australia","Year":2018,"rank":12,"renewable_percentage":17.145938174},{"Entity":"Australia","Year":2019,"rank":8,"renewable_percentage":21.3759705435},{"Entity":"Australia","Year":2020,"rank":8,"renewable_percentage":25.5031684668},{"Entity":"Brazil","Year":2000,"rank":1,"renewable_percentage":90.1307723743},{"Entity":"Brazil","Year":2001,"rank":1,"renewable_percentage":84.6953615744},{"Entity":"Brazil","Year":2002,"rank":1,"renewable_percentage":86.0883364189},{"Entity":"Brazil","Year":2003,"rank":1,"renewable_percentage":87.4561159097},{"Entity":"Brazil","Year":2004,"rank":1,"renewable_percentage":86.4260041451},{"Entity":"Brazil","Year":2005,"rank":1,"renewable_percentage":87.6781562721},{"Entity":"Brazil","Year":2006,"rank":1,"renewable_percentage":87.2842473236},{"Entity":"Brazil","Year":2007,"rank":1,"renewable_percentage":88.7252098726},{"Entity":"Brazil","Year":2008,"rank":1,"renewable_percentage":84.8072313004},{"Entity":"Brazil","Year":2009,"rank":1,"renewable_percentage":89.4172280725},{"Entity":"Brazil","Year":2010,"rank":1,"renewable_percentage":85.3576882415},{"Entity":"Brazil","Year":2011,"rank":1,"renewable_percentage":87.6618820986},{"Entity":"Brazil","Year":2012,"rank":1,"renewable_percentage":83.1164558813},{"Entity":"Brazil","Year":2013,"rank":1,"renewable_percentage":77.5240022006},{"Entity":"Brazil","Year":2014,"rank":1,"renewable_percentage":74.0418657409},{"Entity":"Brazil","Year":2015,"rank":1,"renewable_percentage":75.0231817625},{"Entity":"Brazil","Year":2016,"rank":1,"renewable_percentage":81.0938046902},{"Entity":"Brazil","Year":2017,"rank":1,"renewable_percentage":79.9091472228},{"Entity":"Brazil","Year":2018,"rank":1,"renewable_percentage":82.9198505403},{"Entity":"Brazil","Year":2019,"rank":1,"renewable_percentage":82.8548799017},{"Entity":"Brazil","Year":2020,"rank":1,"renewable_percentage":84.6411771408},{"Entity":"Canada","Year":2000,"rank":2,"renewable_percentage":61.8095917882},{"Entity":"Canada","Year":2001,"rank":2,"renewable_percentage":59.3287558747},{"Entity":"Canada","Year":2002,"rank":2,"renewable_percentage":61.1477403113},{"Entity":"Canada","Year":2003,"rank":2,"renewable_percentage":60.0789685174},{"Entity":"Canada","Year":2004,"rank":2,"renewable_percentage":59.6967771845},{"Entity":"Canada","Year":2005,"rank":2,"renewable_percentage":60.8208155391},{"Entity":"Canada","Year":2006,"rank":2,"renewable_percentage":60.8271602855},{"Entity":"Canada","Year":2007,"rank":2,"renewable_percentage":61.2460642446},{"Entity":"Canada","Year":2008,"rank":2,"renewable_percentage":62.6520720838},{"Entity":"Canada","Year":2009,"rank":2,"renewable_percentage":63.8919227732},{"Entity":"Canada","Year":2010,"rank":2,"renewable_percentage":62.9421470558},{"Entity":"Canada","Year":2011,"rank":2,"renewable_percentage":64.0922915917},{"Entity":"Canada","Year":2012,"rank":2,"renewable_percentage":65.098730952},{"Entity":"Canada","Year":2013,"rank":2,"renewable_percentage":65.4320794066},{"Entity":"Canada","Year":2014,"rank":2,"renewable_percentage":64.791145907},{"Entity":"Canada","Year":2015,"rank":2,"renewable_percentage":65.2946239925},{"Entity":"Canada","Year":2016,"rank":2,"renewable_percentage":66.1890584295},{"Entity":"Canada","Year":2017,"rank":2,"renewable_percentage":67.5399410579},{"Entity":"Canada","Year":20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Africa","Year":2020,"rank":17,"renewable_percentage":5.780581212},{"Entity":"Spain","Year":2000,"rank":6,"renewable_percentage":15.6119862394},{"Entity":"Spain","Year":2001,"rank":3,"renewable_percentage":21.1524434719},{"Entity":"Spain","Year":2002,"rank":6,"renewable_percentage":13.8260180901},{"Entity":"Spain","Year":2003,"rank":3,"renewable_percentage":21.667314419},{"Entity":"Spain","Year":2004,"rank":3,"renewable_percentage":18.3190206468},{"Entity":"Spain","Year":2005,"rank":7,"renewable_percentage":14.8597342333},{"Entity":"Spain","Year":2006,"rank":4,"renewable_percentage":17.6623992413},{"Entity":"Spain","Year":2007,"rank":3,"renewable_percentage":19.3347262296},{"Entity":"Spain","Year":2008,"rank":4,"renewable_percentage":20.0051501593},{"Entity":"Spain","Year":2009,"rank":3,"renewable_percentage":25.4107639008},{"Entity":"Spain","Year":2010,"rank":3,"renewable_percentage":32.7922186819},{"Entity":"Spain","Year":2011,"rank":3,"renewable_percentage":30.0408415417},{"Entity":"Spain","Year":2012,"rank":4,"renewable_percentage":29.6047928652},{"Entity":"Spain","Year":2013,"rank":3,"renewable_percentage":39.5850357054},{"Entity":"Spain","Year":2014,"rank":4,"renewable_percentage":40.1032952644},{"Entity":"Spain","Year":2015,"rank":4,"renewable_percentage":34.9899091826},{"Entity":"Spain","Year":2016,"rank":3,"renewable_percentage":38.5818061138},{"Entity":"Spain","Year":2017,"rank":5,"renewable_percentage":32.220593624},{"Entity":"Spain","Year":2018,"rank":4,"renewable_percentage":38.2080329557},{"Entity":"Spain","Year":2019,"rank":6,"renewable_percentage":37.280815091},{"Entity":"Spain","Year":2020,"rank":4,"renewable_percentage":43.8108805298},{"Entity":"Thailand","Year":2000,"rank":12,"renewable_percentage":7.1261029822},{"Entity":"Thailand","Year":2001,"rank":12,"renewable_percentage":7.061527212},{"Entity":"Thailand","Year":2002,"rank":12,"renewable_percentage":7.9444772593},{"Entity":"Thailand","Year":2003,"rank":13,"renewable_percentage":7.6718362852},{"Entity":"Thailand","Year":2004,"rank":13,"renewable_percentage":6.5163549406},{"Entity":"Thailand","Year":2005,"rank":14,"renewable_percentage":6.0325203252},{"Entity":"Thailand","Year":2006,"rank":13,"renewable_percentage":7.5988547551},{"Entity":"Thailand","Year":2007,"rank":13,"renewable_percentage":7.7085852479},{"Entity":"Thailand","Year":2008,"rank":13,"renewable_percentage":6.5625458278},{"Entity":"Thailand","Year":2009,"rank":15,"renewable_percentage":6.6263303689},{"Entity":"Thailand","Year":2010,"rank":16,"renewable_percentage":5.7085828343},{"Entity":"Thailand","Year":2011,"rank":15,"renewable_percentage":8.039961941},{"Entity":"Thailand","Year":2012,"rank":15,"renewable_percentage":8.5396118358},{"Entity":"Thailand","Year":2013,"rank":16,"renewable_percentage":7.6765035487},{"Entity":"Thailand","Year":2014,"rank":15,"renewable_percentage":8.395728489},{"Entity":"Thailand","Year":2015,"rank":15,"renewable_percentage":7.9949619145},{"Entity":"Thailand","Year":2016,"rank":15,"renewable_percentage":8.9840234023},{"Entity":"Thailand","Year":2017,"rank":15,"renewable_percentage":10.9570957096},{"Entity":"Thailand","Year":2018,"rank":14,"renewable_percentage":14.1900054915},{"Entity":"Thailand","Year":2019,"rank":15,"renewable_percentage":14.7001731284},{"Entity":"Thailand","Year":2020,"rank":15,"renewable_percentage":13.7963737796},{"Entity":"Ukraine","Year":2000,"rank":13,"renewable_percentage":6.5860921352},{"Entity":"Ukraine","Year":2001,"rank":13,"renewable_percentage":6.9729761009},{"Entity":"Ukraine","Year":2002,"rank":14,"renewable_percentage":5.5597165409},{"Entity":"Ukraine","Year":2003,"rank":14,"renewable_percentage":5.1442841287},{"Entity":"Ukraine","Year":2004,"rank":14,"renewable_percentage":6.4718162839},{"Entity":"Ukraine","Year":2005,"rank":13,"renewable_percentage":6.6698940347},{"Entity":"Ukraine","Year":2006,"rank":14,"renewable_percentage":6.68633235},{"Entity":"Ukraine","Year":2007,"rank":14,"renewable_percentage":5.3380238605},{"Entity":"Ukraine","Year":2008,"rank":14,"renewable_percentage":6.1377090041},{"Entity":"Ukraine","Year":2009,"rank":13,"renewable_percentage":6.980762585},{"Entity":"Ukraine","Year":2010,"rank":13,"renewable_percentage":7.0914098083},{"Entity":"Ukraine","Year":2011,"rank":16,"renewable_percentage":5.7450628366},{"Entity":"Ukraine","Year":2012,"rank":16,"renewable_percentage":5.6614236741},{"Entity":"Ukraine","Year":2013,"rank":15,"renewable_percentage":7.8003200661},{"Entity":"Ukraine","Year":2014,"rank":16,"renewable_percentage":5.5885262117},{"Entity":"Ukraine","Year":2015,"rank":16,"renewable_percentage":4.3924771096},{"Entity":"Ukraine","Year":2016,"rank":16,"renewable_percentage":5.6797249171},{"Entity":"Ukraine","Year":2017,"rank":16,"renewable_percentage":7.0457194664},{"Entity":"Ukraine","Year":2018,"rank":16,"renewable_percentage":8.228528092},{"Entity":"Ukraine","Year":2019,"rank":16,"renewable_percentage":7.7754487096},{"Entity":"Ukraine","Year":2020,"rank":16,"renewable_percentage":11.8440577364},{"Entity":"United Kingdom","Year":2000,"rank":15,"renewable_percentage":2.6657406913},{"Entity":"United Kingdom","Year":2001,"rank":15,"renewable_percentage":2.5001961451},{"Entity":"United Kingdom","Year":2002,"rank":15,"renewable_percentage":2.8939157566},{"Entity":"United Kingdom","Year":2003,"rank":15,"renewable_percentage":2.6854802003},{"Entity":"United Kingdom","Year":2004,"rank":15,"renewable_percentage":3.6136880575},{"Entity":"United Kingdom","Year":2005,"rank":15,"renewable_percentage":4.2815234434},{"Entity":"United Kingdom","Year":2006,"rank":15,"renewable_percentage":4.6029890199},{"Entity":"United Kingdom","Year":2007,"rank":15,"renewable_percentage":5.0104331009},{"Entity":"United Kingdom","Year":2008,"rank":15,"renewable_percentage":5.6776842324},{"Entity":"United Kingdom","Year":2009,"rank":14,"renewable_percentage":6.7679854187},{"Entity":"United Kingdom","Year":2010,"rank":15,"renewable_percentage":6.9092924441},{"Entity":"United Kingdom","Year":2011,"rank":13,"renewable_percentage":9.6422505889},{"Entity":"United Kingdom","Year":2012,"rank":11,"renewable_percentage":11.4273047189},{"Entity":"United Kingdom","Year":2013,"rank":10,"renewable_percentage":14.9727052732},{"Entity":"United Kingdom","Year":2014,"rank":8,"renewable_percentage":19.2476358104},{"Entity":"United Kingdom","Year":2015,"rank":6,"renewable_percentage":24.6227709191},{"Entity":"United Kingdom","Year":2016,"rank":7,"renewable_percentage":24.6788390627},{"Entity":"United Kingdom","Year":2017,"rank":6,"renewable_percentage":29.4986571173},{"Entity":"United Kingdom","Year":2018,"rank":6,"renewable_percentage":33.2919818457},{"Entity":"United Kingdom","Year":2019,"rank":5,"renewable_percentage":37.4568630499},{"Entity":"United Kingdom","Year":2020,"rank":5,"renewable_percentage":42.8603962651},{"Entity":"United States","Year":2000,"rank":10,"renewable_percentage":9.2298992662},{"Entity":"United States","Year":2001,"rank":11,"renewable_percentage":7.5132056541},{"Entity":"United States","Year":2002,"rank":10,"renewable_percentage":8.749216358},{"Entity":"United States","Year":2003,"rank":10,"renewable_percentage":9.0252110397},{"Entity":"United States","Year":2004,"rank":11,"renewable_percentage":8.7334100887},{"Entity":"United States","Year":2005,"rank":12,"renewable_percentage":8.7494640631},{"Entity":"United States","Year":2006,"rank":12,"renewable_percentage":9.4184742052},{"Entity":"United States","Year":2007,"rank":12,"renewable_percentage":8.3984096829},{"Entity":"United States","Year":2008,"rank":11,"renewable_percentage":9.180943292},{"Entity":"United States","Year":2009,"rank":10,"renewable_percentage":10.547689996},{"Entity":"United States","Year":2010,"rank":11,"renewable_percentage":10.3180892283},{"Entity":"United States","Year":2011,"rank":9,"renewable_percentage":12.4665249812},{"Entity":"United States","Year":2012,"rank":10,"renewable_percentage":12.1841179804},{"Entity":"United States","Year":2013,"rank":12,"renewable_percentage":12.8311154179},{"Entity":"United States","Year":2014,"rank":12,"renewable_percentage":13.3503092033},{"Entity":"United States","Year":2015,"rank":14,"renewable_percentage":13.6261352256},{"Entity":"United States","Year":2016,"rank":12,"renewable_percentage":15.2880936304},{"Entity":"United States","Year":2017,"rank":9,"renewable_percentage":17.4515276472},{"Entity":"United States","Year":2018,"rank":11,"renewable_percentage":17.4499886946},{"Entity":"United States","Year":2019,"rank":13,"renewable_percentage":18.2946243489},{"Entity":"United States","Year":2020,"rank":12,"renewable_percentage":20.3156921037}],"metadata":{"Entity":{"type":"string","semanticType":"Location"},"Year":{"type":"number","semanticType":"Year"},"rank":{"type":"number","semanticType":"Number"},"renewable_percentage":{"type":"number","semanticType":"Percentage"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Rank countries by renewable percentage within each year (highest percentage = rank 1)\n df_energy['rank'] = df_energy.groupby('Year')['renewable_percentage'].rank(\n method='dense', \n ascending=False\n )\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage', 'rank']].copy()\n \n return transformed_df\n","source":["global-energy-20-small.csv"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 158029.9988, 190729.9957, 209229.9957, ..., 6993180.176, 9861099.609, 9874660.156, 10096009.77\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 90.14, 91.79, 92.54, 804.95\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Transform the data into a long format where different energy sources (fossil fuels, nuclear, renewables) are unpivoted into separate rows. Each row should contain the year, entity (country), energy source type, and the corresponding energy value in TWh. This will enable visualization of energy generation trends over time by source and country.\",\n \"display_instruction\": \"Show **energy generation** trends by **source** and **Entity** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"source\", \"energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n },\n \"reason\": \"The user wants to visualize energy data across different sources with a line chart faceted by source. The original data has energy sources in separate columns (fossil fuels, nuclear, renewables), so we need to unpivot these columns into a long format with 'source' and 'energy' fields. The chart_encodings provided by the user are appropriate for this visualization goal, using faceting (column) to separate different energy sources.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Unpivot the energy source columns into long format\n transformed_df = pd.melt(\n df_energy,\n id_vars=['Year', 'Entity'],\n value_vars=[\n 'Electricity from fossil fuels (TWh)',\n 'Electricity from nuclear (TWh)',\n 'Electricity from renewables (TWh)'\n ],\n var_name='source',\n value_name='energy'\n )\n \n # Clean up the source names to be more readable\n transformed_df['source'] = transformed_df['source'].str.replace('Electricity from ', '').str.replace(' (TWh)', '')\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Entity Year energy source\n0 Australia 2000 181.05 fossil fuels\n1 Australia 2001 194.33 fossil fuels\n2 Australia 2002 197.29 fossil fuels\n3 Australia 2003 195.13 fossil fuels\n4 Australia 2004 203.66 fossil fuels\n5 Australia 2005 195.95 fossil fuels\n6 Australia 2006 198.72 fossil fuels\n7 Australia 2007 208.59 fossil fuels\n8 Australia 2008 211.06 fossil fuels\n9 Australia 2009 216.42 fossil fuels\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show renewable percentage for each country\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage for each country and year. The renewable percentage is computed as the ratio of renewable energy to total energy (sum of fossil fuels, nuclear, and renewables) multiplied by 100. The output should contain year, entity (country), and the renewable percentage value.\",\n \"display_instruction\": \"Show **renewable percentage** for each **Entity** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"renewable_percentage\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"renewable_percentage\",\n \"color\": \"Entity\"\n },\n \"reason\": \"The user wants to see renewable percentage trends, which requires calculating the proportion of renewable energy relative to total energy generation. This is a single metric per country per year, so we don't need the 'column' faceting by source anymore. The chart_encodings are updated to reflect that we're now showing a percentage metric rather than absolute energy values by source.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage']].copy()\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Entity Year renewable_percentage\n0 Australia 2000 8.634437\n1 Australia 2001 8.218014\n2 Australia 2002 8.083302\n3 Australia 2003 8.659832\n4 Australia 2004 8.701305\n5 Australia 2005 9.156236\n6 Australia 2006 9.635760\n7 Australia 2007 9.119031\n8 Australia 2008 8.054890\n9 Australia 2009 7.804379\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"rank countries by their renewable percentage\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"renewable_percentage\",\n \"color\": \"Entity\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage for each country and year, then rank countries based on their renewable percentage. The ranking should be computed for each year, showing which countries have the highest to lowest renewable percentage. The output should contain year, entity (country), renewable percentage, and the rank of each country within that year.\",\n \"display_instruction\": \"Show **countries ranked** by **renewable percentage** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"renewable_percentage\", \"rank\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"renewable_percentage\",\n \"color\": \"Entity\"\n },\n \"reason\": \"The user wants to rank countries by their renewable percentage, which requires adding a rank field that orders countries within each year based on their renewable percentage (highest percentage = rank 1). This allows comparison of countries' relative positions in renewable energy adoption over time. The chart_encodings remain the same as the previous step since we're still visualizing renewable percentage trends, but now with ranking information available.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Rank countries by renewable percentage within each year (highest percentage = rank 1)\n df_energy['rank'] = df_energy.groupby('Year')['renewable_percentage'].rank(\n method='dense', \n ascending=False\n )\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage', 'rank']].copy()\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-45","sourceTableIds":["global-energy-20-small.csv"],"instruction":"rank countries by their renewable percentage","displayInstruction":"Show **countries ranked** by **renewable percentage** over **Year**","chart":{"id":"chart-1760738441793","chartType":"Line Chart","encodingMap":{"x":{"fieldID":"original--global-energy-20-small.csv--Year"},"y":{"fieldID":"concept-renewable_percentage-1760738424337"},"color":{"fieldID":"original--global-energy-20-small.csv--Entity"},"opacity":{"channel":"opacity","bin":false},"column":{},"row":{"channel":"row","bin":false}},"tableRef":"table-45","saved":false,"source":"trigger","unread":false},"resultTableId":"table-78"},"explanation":{"agent":"CodeExplanationAgent","code":"- Calculate **total_energy** for each country-year by summing electricity generated from **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n- Compute **renewable_percentage** by dividing **Electricity from renewables (TWh)** by **total_energy** and multiplying by **100**\n- Replace any infinite values in **renewable_percentage** with NaN to handle cases where total energy is zero\n- Assign a **rank** to each country within each **Year** based on **renewable_percentage**, where rank **1** represents the highest renewable percentage (using dense ranking method)\n- Return a dataset containing **Year**, **Entity** (country name), **renewable_percentage**, and **rank**","concepts":[{"explanation":"The proportion of a country's total electricity generation that comes from renewable sources, expressed as a percentage. Calculated as: \\[ \\text{renewable\\_percentage} = \\frac{\\text{Electricity from renewables (TWh)}}{\\text{total\\_energy}} \\times 100 \\]","field":"renewable_percentage"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 190729.9957, 227580.0018, 233600.0061, ..., 9282549.805, 9861099.609, 9874660.156, 10096009.77\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 88.76, 90.14, 91.79, 92.54\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Rank countries by renewable percentage within each year (highest percentage = rank 1)\n df_energy['rank'] = df_energy.groupby('Year')['renewable_percentage'].rank(\n method='dense', \n ascending=False\n )\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage', 'rank']].copy()\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Calculate **total_energy** for each country-year by summing electricity generated from **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n- Compute **renewable_percentage** by dividing **Electricity from renewables (TWh)** by **total_energy** and multiplying by **100**\n- Replace any infinite values in **renewable_percentage** with NaN to handle cases where total energy is zero\n- Assign a **rank** to each country within each **Year** based on **renewable_percentage**, where rank **1** represents the highest renewable percentage (using dense ranking method)\n- Return a dataset containing **Year**, **Entity** (country name), **renewable_percentage**, and **rank**\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"renewable_percentage\",\n \"explanation\": \"The proportion of a country's total electricity generation that comes from renewable sources, expressed as a percentage. Calculated as: \\\\[ \\\\text{renewable\\\\_percentage} = \\\\frac{\\\\text{Electricity from renewables (TWh)}}{\\\\text{total\\\\_energy}} \\\\times 100 \\\\]\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-97","displayId":"renewable-elec","names":["Electricity from renewables (TWh)","Entity","Year"],"rows":[{"Electricity from renewables (TWh)":17.11,"Entity":"Australia","Year":"2000"},{"Electricity from renewables (TWh)":63.99,"Entity":"Australia","Year":"2020"},{"Electricity from renewables (TWh)":308.77,"Entity":"Brazil","Year":"2000"},{"Electricity from renewables (TWh)":520.01,"Entity":"Brazil","Year":"2020"},{"Electricity from renewables (TWh)":363.7,"Entity":"Canada","Year":"2000"},{"Electricity from renewables (TWh)":429.24,"Entity":"Canada","Year":"2020"},{"Electricity from renewables (TWh)":225.56,"Entity":"China","Year":"2000"},{"Electricity from renewables (TWh)":2184.94,"Entity":"China","Year":"2020"},{"Electricity from renewables (TWh)":67.83,"Entity":"France","Year":"2000"},{"Electricity from renewables (TWh)":125.28,"Entity":"France","Year":"2020"},{"Electricity from renewables (TWh)":35.47,"Entity":"Germany","Year":"2000"},{"Electricity from renewables (TWh)":251.48,"Entity":"Germany","Year":"2020"},{"Electricity from renewables (TWh)":80.27,"Entity":"India","Year":"2000"},{"Electricity from renewables (TWh)":315.76,"Entity":"India","Year":"2020"},{"Electricity from renewables (TWh)":19.6,"Entity":"Indonesia","Year":"2000"},{"Electricity from renewables (TWh)":52.91,"Entity":"Indonesia","Year":"2020"},{"Electricity from renewables (TWh)":50.87,"Entity":"Italy","Year":"2000"},{"Electricity from renewables (TWh)":116.9,"Entity":"Italy","Year":"2020"},{"Electricity from renewables (TWh)":104.16,"Entity":"Japan","Year":"2000"},{"Electricity from renewables (TWh)":205.6,"Entity":"Japan","Year":"2020"},{"Electricity from renewables (TWh)":7.53,"Entity":"Kazakhstan","Year":"2000"},{"Electricity from renewables (TWh)":11.94,"Entity":"Kazakhstan","Year":"2020"},{"Electricity from renewables (TWh)":44.51,"Entity":"Mexico","Year":"2000"},{"Electricity from renewables (TWh)":69.19,"Entity":"Mexico","Year":"2020"},{"Electricity from renewables (TWh)":2.33,"Entity":"Poland","Year":"2000"},{"Electricity from renewables (TWh)":28.23,"Entity":"Poland","Year":"2020"},{"Electricity from renewables (TWh)":0,"Entity":"Saudi Arabia","Year":"2000"},{"Electricity from renewables (TWh)":0.21,"Entity":"Saudi Arabia","Year":"2020"},{"Electricity from renewables (TWh)":1.79,"Entity":"South Africa","Year":"2000"},{"Electricity from renewables (TWh)":12.83,"Entity":"South Africa","Year":"2020"},{"Electricity from renewables (TWh)":34.49,"Entity":"Spain","Year":"2000"},{"Electricity from renewables (TWh)":113.79,"Entity":"Spain","Year":"2020"},{"Electricity from renewables (TWh)":6.38,"Entity":"Thailand","Year":"2000"},{"Electricity from renewables (TWh)":24.73,"Entity":"Thailand","Year":"2020"},{"Electricity from renewables (TWh)":11.28,"Entity":"Ukraine","Year":"2000"},{"Electricity from renewables (TWh)":17.56,"Entity":"Ukraine","Year":"2020"},{"Electricity from renewables (TWh)":9.98,"Entity":"United Kingdom","Year":"2000"},{"Electricity from renewables (TWh)":131.74,"Entity":"United Kingdom","Year":"2020"},{"Electricity from renewables (TWh)":350.93,"Entity":"United States","Year":"2000"},{"Electricity from renewables (TWh)":821.4,"Entity":"United States","Year":"2020"}],"metadata":{"Electricity from renewables (TWh)":{"type":"number","semanticType":"Number"},"Entity":{"type":"string","semanticType":"Location"},"Year":{"type":"number","semanticType":"Year"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n","source":["global-energy-20-small.csv"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5736319.824, 5738290.039, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 61.99, 62.21, 63.02, 63.71\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"compare each countries renewable between 2000 and 2020\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Energy\",\n \"color\": \"Year\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Compare renewable energy (Electricity from renewables) for each country between the years 2000 and 2020. Filter the data to include only years 2000 and 2020, and prepare the output with country names, year, and renewable energy values for visualization as a grouped bar chart.\",\n \"display_instruction\": \"Compare **renewable energy** by **country** between **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Electricity from renewables (TWh)\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to compare renewable energy across countries between 2000 and 2020. The output field 'Renewable Energy' mentioned in the original chart_encodings maps to 'Electricity from renewables (TWh)' in the dataset. To create a grouped bar chart comparing these two years, we need to filter the data to only include years 2000 and 2020, and output the Entity (country), Year, and the renewable energy values. The chart_encodings are updated to use the actual column name from the dataset.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"global-energy-20-small.csv","sourceTableIds":["global-energy-20-small.csv"],"instruction":"compare each countries renewable between 2000 and 2020","displayInstruction":"Compare **renewable energy** by **country** between **2000** and **2020**","chart":{"id":"chart-1760738769634","chartType":"Grouped Bar Chart","encodingMap":{"x":{"fieldID":"original--global-energy-20-small.csv--Entity"},"y":{"fieldID":"concept-1760738743125"},"color":{"channel":"color","bin":false,"fieldID":"original--global-energy-20-small.csv--Year"},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"global-energy-20-small.csv","saved":false,"source":"trigger","unread":false},"resultTableId":"table-97"},"explanation":{"agent":"CodeExplanationAgent","code":"The code performs a straightforward comparison analysis of renewable energy generation across countries:\n\n- **Filters** the dataset to include only data from years **2000** and **2020**, creating a 20-year comparison window\n- **Selects** three key columns: `Entity` (country name), `Year`, and `Electricity from renewables (TWh)` (renewable energy generation)\n- **Converts** the `Year` field to string format to treat it as a categorical variable for visualization purposes\n- **Sorts** the results by `Entity` and `Year` to organize countries alphabetically with their respective year data grouped together\n- **Returns** a cleaned dataset showing how renewable electricity generation has changed for each country between 2000 and 2020","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 10006669.92, 10502929.69, 10707219.73, nan\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 56.18, 61.99, 63.02, 63.71\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThe code performs a straightforward comparison analysis of renewable energy generation across countries:\n\n- **Filters** the dataset to include only data from years **2000** and **2020**, creating a 20-year comparison window\n- **Selects** three key columns: `Entity` (country name), `Year`, and `Electricity from renewables (TWh)` (renewable energy generation)\n- **Converts** the `Year` field to string format to treat it as a categorical variable for visualization purposes\n- **Sorts** the results by `Entity` and `Year` to organize countries alphabetically with their respective year data grouped together\n- **Returns** a cleaned dataset showing how renewable electricity generation has changed for each country between 2000 and 2020\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-27","displayId":"renewable-energy1","names":["Entity","Renewable Percentage","Year"],"rows":[{"Entity":"Australia","Renewable Percentage":8.6344368187,"Year":"2000"},{"Entity":"Australia","Renewable Percentage":25.5031684668,"Year":"2020"},{"Entity":"Brazil","Renewable Percentage":90.1307723743,"Year":"2000"},{"Entity":"Brazil","Renewable Percentage":84.6411771408,"Year":"2020"},{"Entity":"Canada","Renewable Percentage":61.8095917882,"Year":"2000"},{"Entity":"Canada","Renewable Percentage":68.7796436354,"Year":"2020"},{"Entity":"China","Renewable Percentage":16.639126586,"Year":"2000"},{"Entity":"China","Renewable Percentage":28.2464606924,"Year":"2020"},{"Entity":"France","Renewable Percentage":12.7117691154,"Year":"2000"},{"Entity":"France","Renewable Percentage":23.7610241821,"Year":"2020"},{"Entity":"Germany","Renewable Percentage":6.1977983575,"Year":"2000"},{"Entity":"Germany","Renewable Percentage":44.3324048937,"Year":"2020"},{"Entity":"Global Average","Renewable Percentage":16.4213212559,"Year":"2000"},{"Entity":"Global Average","Renewable Percentage":29.2955247263,"Year":"2020"},{"Entity":"India","Renewable Percentage":14.0481982534,"Year":"2000"},{"Entity":"India","Renewable Percentage":20.2059243238,"Year":"2020"},{"Entity":"Indonesia","Renewable Percentage":null,"Year":"2000"},{"Entity":"Indonesia","Renewable Percentage":null,"Year":"2020"},{"Entity":"Italy","Renewable Percentage":18.900241501,"Year":"2000"},{"Entity":"Italy","Renewable Percentage":42.0397741576,"Year":"2020"},{"Entity":"Japan","Renewable Percentage":10.5382436261,"Year":"2000"},{"Entity":"Japan","Renewable Percentage":21.324925062,"Year":"2020"},{"Entity":"Kazakhstan","Renewable Percentage":null,"Year":"2000"},{"Entity":"Kazakhstan","Renewable Percentage":null,"Year":"2020"},{"Entity":"Mexico","Renewable Percentage":22.9291160107,"Year":"2000"},{"Entity":"Mexico","Renewable Percentage":21.2552224134,"Year":"2020"},{"Entity":"Poland","Renewable Percentage":1.6273222517,"Year":"2000"},{"Entity":"Poland","Renewable Percentage":17.9648720886,"Year":"2020"},{"Entity":"Saudi Arabia","Renewable Percentage":null,"Year":"2000"},{"Entity":"Saudi Arabia","Renewable Percentage":null,"Year":"2020"},{"Entity":"South Africa","Renewable Percentage":0.9110805721,"Year":"2000"},{"Entity":"South Africa","Renewable Percentage":5.780581212,"Year":"2020"},{"Entity":"Spain","Renewable Percentage":15.6119862394,"Year":"2000"},{"Entity":"Spain","Renewable Percentage":43.8108805298,"Year":"2020"},{"Entity":"Thailand","Renewable Percentage":7.1261029822,"Year":"2000"},{"Entity":"Thailand","Renewable Percentage":13.7963737796,"Year":"2020"},{"Entity":"Ukraine","Renewable Percentage":6.5860921352,"Year":"2000"},{"Entity":"Ukraine","Renewable Percentage":11.8440577364,"Year":"2020"},{"Entity":"United Kingdom","Renewable Percentage":2.6657406913,"Year":"2000"},{"Entity":"United Kingdom","Renewable Percentage":42.8603962651,"Year":"2020"},{"Entity":"United States","Renewable Percentage":9.2298992662,"Year":"2000"},{"Entity":"United States","Renewable Percentage":20.3156921037,"Year":"2020"}],"metadata":{"Entity":{"type":"string","semanticType":"Name"},"Renewable Percentage":{"type":"number","semanticType":"Percentage"},"Year":{"type":"number","semanticType":"Year"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n df_filtered = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Calculate total electricity generation\n df_filtered['Total Electricity'] = (\n df_filtered['Electricity from fossil fuels (TWh)'] + \n df_filtered['Electricity from nuclear (TWh)'] + \n df_filtered['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_filtered['Renewable Percentage'] = (\n df_filtered['Electricity from renewables (TWh)'] / df_filtered['Total Electricity'] * 100\n )\n \n # Select required columns for individual countries\n country_data = df_filtered[['Entity', 'Year', 'Renewable Percentage']].copy()\n \n # Calculate global average for each year\n global_avg = df_filtered.groupby('Year').apply(\n lambda x: pd.Series({\n 'Entity': 'Global Average',\n 'Year': x['Year'].iloc[0],\n 'Renewable Percentage': (\n x['Electricity from renewables (TWh)'].sum() / x['Total Electricity'].sum() * 100\n )\n })\n ).reset_index(drop=True)\n \n # Combine country data with global average\n transformed_df = pd.concat([country_data, global_avg], ignore_index=True)\n \n # Convert Year to string for better visualization\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n","source":["global-energy-20-small.csv"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5736319.824, 5738290.039, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 61.99, 62.21, 63.02, 63.71\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"compare each countries renewable between 2000 and 2020\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Energy\",\n \"color\": \"Year\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Compare renewable energy (Electricity from renewables) for each country between the years 2000 and 2020. Filter the data to include only years 2000 and 2020, and prepare the output with country names, year, and renewable energy values for visualization as a grouped bar chart.\",\n \"display_instruction\": \"Compare **renewable energy** by **country** between **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Electricity from renewables (TWh)\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to compare renewable energy across countries between 2000 and 2020. The output field 'Renewable Energy' mentioned in the original chart_encodings maps to 'Electricity from renewables (TWh)' in the dataset. To create a grouped bar chart comparing these two years, we need to filter the data to only include years 2000 and 2020, and output the Entity (country), Year, and the renewable energy values. The chart_encodings are updated to use the actual column name from the dataset.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Electricity from renewables (TWh) Entity Year\n0 17.11 Australia 2000\n1 63.99 Australia 2020\n2 308.77 Brazil 2000\n3 520.01 Brazil 2020\n4 363.70 Canada 2000\n5 429.24 Canada 2020\n6 225.56 China 2000\n7 2184.94 China 2020\n8 67.83 France 2000\n9 125.28 France 2020\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show their renewable percentage instead, and add an entity to show global average\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage (proportion of renewables to total electricity generation from all sources) for each country between 2000 and 2020. Add a 'Global Average' entity that represents the worldwide average renewable percentage for each year. The output should include Entity (with 'Global Average' added), Year, and the renewable percentage.\",\n \"display_instruction\": \"Show **renewable energy percentage** by **country** with **global average** for **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Renewable Percentage\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Percentage\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to see renewable percentage instead of absolute values, and wants to add a global average entity for comparison. Renewable percentage is calculated as the ratio of renewable energy to total electricity generation (fossil fuels + nuclear + renewables). A 'Global Average' entity needs to be computed by aggregating all countries' data for each year. The y-axis encoding is updated to reflect the new metric 'Renewable Percentage'.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n df_filtered = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Calculate total electricity generation\n df_filtered['Total Electricity'] = (\n df_filtered['Electricity from fossil fuels (TWh)'] + \n df_filtered['Electricity from nuclear (TWh)'] + \n df_filtered['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_filtered['Renewable Percentage'] = (\n df_filtered['Electricity from renewables (TWh)'] / df_filtered['Total Electricity'] * 100\n )\n \n # Select required columns for individual countries\n country_data = df_filtered[['Entity', 'Year', 'Renewable Percentage']].copy()\n \n # Calculate global average for each year\n global_avg = df_filtered.groupby('Year').apply(\n lambda x: pd.Series({\n 'Entity': 'Global Average',\n 'Year': x['Year'].iloc[0],\n 'Renewable Percentage': (\n x['Electricity from renewables (TWh)'].sum() / x['Total Electricity'].sum() * 100\n )\n })\n ).reset_index(drop=True)\n \n # Combine country data with global average\n transformed_df = pd.concat([country_data, global_avg], ignore_index=True)\n \n # Convert Year to string for better visualization\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-97","sourceTableIds":["global-energy-20-small.csv"],"instruction":"show their renewable percentage instead, and add an entity to show global average","displayInstruction":"Show **renewable energy percentage** by **country** with **global average** for **2000** and **2020**","chart":{"id":"chart-1760738813383","chartType":"Grouped Bar Chart","encodingMap":{"x":{"fieldID":"original--global-energy-20-small.csv--Entity"},"y":{"fieldID":"original--global-energy-20-small.csv--Electricity from renewables (TWh)"},"color":{"fieldID":"original--global-energy-20-small.csv--Year"},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"table-97","saved":false,"source":"trigger","unread":false},"resultTableId":"table-27"},"explanation":{"agent":"CodeExplanationAgent","code":"The code performs the following transformation steps:\n\n1. **Filter data** to include only records from **2000** and **2020**\n2. Calculate **Total Electricity** generation by summing three sources: **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n3. Compute **Renewable Percentage** for each country and year by dividing **Electricity from renewables** by **Total Electricity** and multiplying by 100\n4. Extract relevant columns: **Entity** (country name), **Year**, and **Renewable Percentage**\n5. Calculate **Global Average** renewable percentage for each year by:\n - Summing all countries' **renewable electricity** generation\n - Dividing by the sum of all countries' **total electricity** generation\n - Multiplying by 100 to get percentage\n6. Combine individual country data with the **Global Average** statistics\n7. Convert **Year** values to string format\n8. Sort results by **Entity** name and **Year**","concepts":[{"explanation":"The sum of electricity generated from all three sources (fossil fuels, nuclear, and renewables) measured in terawatt-hours (TWh). This represents the total electricity production capacity for each country.","field":"Total Electricity"},{"explanation":"The proportion of electricity generated from renewable sources relative to total electricity production, expressed as a percentage: \\( \\text{Renewable Percentage} = \\frac{\\text{Electricity from renewables}}{\\text{Total Electricity}} \\times 100 \\). This metric indicates the extent of renewable energy adoption in a country's electricity mix.","field":"Renewable Percentage"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 10096009.77, 10502929.69, 10707219.73, nan\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., nan, nan, nan, nan\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n df_filtered = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Calculate total electricity generation\n df_filtered['Total Electricity'] = (\n df_filtered['Electricity from fossil fuels (TWh)'] + \n df_filtered['Electricity from nuclear (TWh)'] + \n df_filtered['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_filtered['Renewable Percentage'] = (\n df_filtered['Electricity from renewables (TWh)'] / df_filtered['Total Electricity'] * 100\n )\n \n # Select required columns for individual countries\n country_data = df_filtered[['Entity', 'Year', 'Renewable Percentage']].copy()\n \n # Calculate global average for each year\n global_avg = df_filtered.groupby('Year').apply(\n lambda x: pd.Series({\n 'Entity': 'Global Average',\n 'Year': x['Year'].iloc[0],\n 'Renewable Percentage': (\n x['Electricity from renewables (TWh)'].sum() / x['Total Electricity'].sum() * 100\n )\n })\n ).reset_index(drop=True)\n \n # Combine country data with global average\n transformed_df = pd.concat([country_data, global_avg], ignore_index=True)\n \n # Convert Year to string for better visualization\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThe code performs the following transformation steps:\n\n1. **Filter data** to include only records from **2000** and **2020**\n2. Calculate **Total Electricity** generation by summing three sources: **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n3. Compute **Renewable Percentage** for each country and year by dividing **Electricity from renewables** by **Total Electricity** and multiplying by 100\n4. Extract relevant columns: **Entity** (country name), **Year**, and **Renewable Percentage**\n5. Calculate **Global Average** renewable percentage for each year by:\n - Summing all countries' **renewable electricity** generation\n - Dividing by the sum of all countries' **total electricity** generation\n - Multiplying by 100 to get percentage\n6. Combine individual country data with the **Global Average** statistics\n7. Convert **Year** values to string format\n8. Sort results by **Entity** name and **Year**\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Total Electricity\",\n \"explanation\": \"The sum of electricity generated from all three sources (fossil fuels, nuclear, and renewables) measured in terawatt-hours (TWh). This represents the total electricity production capacity for each country.\"\n },\n {\n \"field\": \"Renewable Percentage\",\n \"explanation\": \"The proportion of electricity generated from renewable sources relative to total electricity production, expressed as a percentage: \\\\( \\\\text{Renewable Percentage} = \\\\frac{\\\\text{Electricity from renewables}}{\\\\text{Total Electricity}} \\\\times 100 \\\\). This metric indicates the extent of renewable energy adoption in a country's electricity mix.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-81","displayId":"energy-source1","names":["Energy","Energy Source","Year"],"rows":[{"Energy":7160.71,"Energy Source":"Fossil Fuels","Year":2000},{"Energy":1996.65,"Energy Source":"Nuclear","Year":2000},{"Energy":1742.56,"Energy Source":"Renewables","Year":2000},{"Energy":7273.89,"Energy Source":"Fossil Fuels","Year":2001},{"Energy":2037.54,"Energy Source":"Nuclear","Year":2001},{"Energy":1690.11,"Energy Source":"Renewables","Year":2001},{"Energy":7621.07,"Energy Source":"Fossil Fuels","Year":2002},{"Energy":2042.18,"Energy Source":"Nuclear","Year":2002},{"Energy":1757.63,"Energy Source":"Renewables","Year":2002},{"Energy":8043.86,"Energy Source":"Fossil Fuels","Year":2003},{"Energy":1998.52,"Energy Source":"Nuclear","Year":2003},{"Energy":1804.52,"Energy Source":"Renewables","Year":2003},{"Energy":8399.72,"Energy Source":"Fossil Fuels","Year":2004},{"Energy":2095,"Energy Source":"Nuclear","Year":2004},{"Energy":1952.72,"Energy Source":"Renewables","Year":2004},{"Energy":8828.43,"Energy Source":"Fossil Fuels","Year":2005},{"Energy":2094.4,"Energy Source":"Nuclear","Year":2005},{"Energy":2025.26,"Energy Source":"Renewables","Year":2005},{"Energy":9183.05,"Energy Source":"Fossil Fuels","Year":2006},{"Energy":2120.38,"Energy Source":"Nuclear","Year":2006},{"Energy":2165.94,"Energy Source":"Renewables","Year":2006},{"Energy":9853.09,"Energy Source":"Fossil Fuels","Year":2007},{"Energy":2067.04,"Energy Source":"Nuclear","Year":2007},{"Energy":2256.79,"Energy Source":"Renewables","Year":2007},{"Energy":9817.15,"Energy Source":"Fossil Fuels","Year":2008},{"Energy":2043.94,"Energy Source":"Nuclear","Year":2008},{"Energy":2496.03,"Energy Source":"Renewables","Year":2008},{"Energy":9686.86,"Energy Source":"Fossil Fuels","Year":2009},{"Energy":2017.25,"Energy Source":"Nuclear","Year":2009},{"Energy":2563.95,"Energy Source":"Renewables","Year":2009},{"Energy":10427.03,"Energy Source":"Fossil Fuels","Year":2010},{"Energy":2083.37,"Energy Source":"Nuclear","Year":2010},{"Energy":2802.89,"Energy Source":"Renewables","Year":2010},{"Energy":10974.83,"Energy Source":"Fossil Fuels","Year":2011},{"Energy":1956,"Energy Source":"Nuclear","Year":2011},{"Energy":2997.29,"Energy Source":"Renewables","Year":2011},{"Energy":11277.49,"Energy Source":"Fossil Fuels","Year":2012},{"Energy":1788.26,"Energy Source":"Nuclear","Year":2012},{"Energy":3226.1,"Energy Source":"Renewables","Year":2012},{"Energy":11561.86,"Energy Source":"Fossil Fuels","Year":2013},{"Energy":1813,"Energy Source":"Nuclear","Year":2013},{"Energy":3473.9,"Energy Source":"Renewables","Year":2013},{"Energy":11761.51,"Energy Source":"Fossil Fuels","Year":2014},{"Energy":1847.87,"Energy Source":"Nuclear","Year":2014},{"Energy":3753.03,"Energy Source":"Renewables","Year":2014},{"Energy":11653.61,"Energy Source":"Fossil Fuels","Year":2015},{"Energy":1886.61,"Energy Source":"Nuclear","Year":2015},{"Energy":3903.7,"Energy Source":"Renewables","Year":2015},{"Energy":11785.35,"Energy Source":"Fossil Fuels","Year":2016},{"Energy":1906.92,"Energy Source":"Nuclear","Year":2016},{"Energy":4186.83,"Energy Source":"Renewables","Year":2016},{"Energy":12003.24,"Energy Source":"Fossil Fuels","Year":2017},{"Energy":1942.94,"Energy Source":"Nuclear","Year":2017},{"Energy":4500.22,"Energy Source":"Renewables","Year":2017},{"Energy":12445.24,"Energy Source":"Fossil Fuels","Year":2018},{"Energy":2016.82,"Energy Source":"Nuclear","Year":2018},{"Energy":4834.65,"Energy Source":"Renewables","Year":2018},{"Energy":12355.91,"Energy Source":"Fossil Fuels","Year":2019},{"Energy":2073.87,"Energy Source":"Nuclear","Year":2019},{"Energy":5146.11,"Energy Source":"Renewables","Year":2019},{"Energy":12032.75,"Energy Source":"Fossil Fuels","Year":2020},{"Energy":1974.46,"Energy Source":"Nuclear","Year":2020},{"Energy":5497.73,"Energy Source":"Renewables","Year":2020}],"metadata":{"Energy":{"type":"number","semanticType":"Number"},"Energy Source":{"type":"string","semanticType":"String","levels":["Fossil Fuels","Nuclear","Renewables"]},"Year":{"type":"number","semanticType":"Year"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Group by Year and sum electricity generation from all countries\n global_energy = df_energy.groupby('Year').agg({\n 'Electricity from fossil fuels (TWh)': 'sum',\n 'Electricity from nuclear (TWh)': 'sum',\n 'Electricity from renewables (TWh)': 'sum'\n }).reset_index()\n \n # Rename columns for clarity\n global_energy.columns = ['Year', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n global_energy,\n id_vars=['Year'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Year and Energy Source\n transformed_df = transformed_df.sort_values(['Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n","source":["global-energy-20-small.csv"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5736319.824, 5738290.039, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 61.99, 62.21, 63.02, 63.71\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"compare each countries renewable between 2000 and 2020\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Energy\",\n \"color\": \"Year\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Compare renewable energy (Electricity from renewables) for each country between the years 2000 and 2020. Filter the data to include only years 2000 and 2020, and prepare the output with country names, year, and renewable energy values for visualization as a grouped bar chart.\",\n \"display_instruction\": \"Compare **renewable energy** by **country** between **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Electricity from renewables (TWh)\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to compare renewable energy across countries between 2000 and 2020. The output field 'Renewable Energy' mentioned in the original chart_encodings maps to 'Electricity from renewables (TWh)' in the dataset. To create a grouped bar chart comparing these two years, we need to filter the data to only include years 2000 and 2020, and output the Entity (country), Year, and the renewable energy values. The chart_encodings are updated to use the actual column name from the dataset.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Electricity from renewables (TWh) Entity Year\n0 17.11 Australia 2000\n1 63.99 Australia 2020\n2 308.77 Brazil 2000\n3 520.01 Brazil 2020\n4 363.70 Canada 2000\n5 429.24 Canada 2020\n6 225.56 China 2000\n7 2184.94 China 2020\n8 67.83 France 2000\n9 125.28 France 2020\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show their renewable percentage instead, and add an entity to show global average\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage (proportion of renewables to total electricity generation from all sources) for each country between 2000 and 2020. Add a 'Global Average' entity that represents the worldwide average renewable percentage for each year. The output should include Entity (with 'Global Average' added), Year, and the renewable percentage.\",\n \"display_instruction\": \"Show **renewable energy percentage** by **country** with **global average** for **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Renewable Percentage\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Percentage\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to see renewable percentage instead of absolute values, and wants to add a global average entity for comparison. Renewable percentage is calculated as the ratio of renewable energy to total electricity generation (fossil fuels + nuclear + renewables). A 'Global Average' entity needs to be computed by aggregating all countries' data for each year. The y-axis encoding is updated to reflect the new metric 'Renewable Percentage'.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n df_filtered = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Calculate total electricity generation\n df_filtered['Total Electricity'] = (\n df_filtered['Electricity from fossil fuels (TWh)'] + \n df_filtered['Electricity from nuclear (TWh)'] + \n df_filtered['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_filtered['Renewable Percentage'] = (\n df_filtered['Electricity from renewables (TWh)'] / df_filtered['Total Electricity'] * 100\n )\n \n # Select required columns for individual countries\n country_data = df_filtered[['Entity', 'Year', 'Renewable Percentage']].copy()\n \n # Calculate global average for each year\n global_avg = df_filtered.groupby('Year').apply(\n lambda x: pd.Series({\n 'Entity': 'Global Average',\n 'Year': x['Year'].iloc[0],\n 'Renewable Percentage': (\n x['Electricity from renewables (TWh)'].sum() / x['Total Electricity'].sum() * 100\n )\n })\n ).reset_index(drop=True)\n \n # Combine country data with global average\n transformed_df = pd.concat([country_data, global_avg], ignore_index=True)\n \n # Convert Year to string for better visualization\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Entity Renewable Percentage Year\n0 Australia 8.634437 2000\n1 Australia 25.503168 2020\n2 Brazil 90.130772 2000\n3 Brazil 84.641177 2020\n4 Canada 61.809592 2000\n5 Canada 68.779644 2020\n6 China 16.639127 2000\n7 China 28.246461 2020\n8 France 12.711769 2000\n9 France 23.761024 2020\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show global energy composition over time\",\n \"chart_type\": \"Custom Area\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Show the global energy composition over time by aggregating electricity generation from all countries for each year, broken down by energy source (fossil fuels, nuclear, and renewables). The output should be in long format with Year, Energy Source, and Energy values to support a stacked area chart visualization.\",\n \"display_instruction\": \"Show global **energy composition** by **energy source** over time\",\n \"output_fields\": [\"Year\", \"Energy Source\", \"Energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\"\n },\n \"reason\": \"The user wants to visualize global energy composition over time, which requires aggregating electricity generation data across all countries for each year and breaking it down by energy source type. The data needs to be transformed into long format where each row represents a year-energy source combination, with the total energy value for that source globally. This follows up from the previous renewable percentage analysis by showing the overall energy mix trends rather than focusing on specific countries or percentages.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Group by Year and sum electricity generation from all countries\n global_energy = df_energy.groupby('Year').agg({\n 'Electricity from fossil fuels (TWh)': 'sum',\n 'Electricity from nuclear (TWh)': 'sum',\n 'Electricity from renewables (TWh)': 'sum'\n }).reset_index()\n \n # Rename columns for clarity\n global_energy.columns = ['Year', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n global_energy,\n id_vars=['Year'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Year and Energy Source\n transformed_df = transformed_df.sort_values(['Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-27","sourceTableIds":["global-energy-20-small.csv"],"instruction":"show global energy composition over time","displayInstruction":"Show global **energy composition** by **energy source** over time","chart":{"id":"chart-1760738942326","chartType":"Custom Area","encodingMap":{"x":{"channel":"x","bin":false,"fieldID":"original--global-energy-20-small.csv--Year"},"y":{"channel":"y","bin":false,"fieldID":"concept-1760738917347"},"x2":{"channel":"x2","bin":false},"y2":{"channel":"y2","bin":false},"color":{"channel":"color","bin":false,"fieldID":"concept-1760738921927"},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"table-27","saved":false,"source":"trigger","unread":false},"resultTableId":"table-81"},"explanation":{"agent":"CodeExplanationAgent","code":"- Group the energy data by **Year** and aggregate electricity generation values across all countries by summing **Electricity from fossil fuels (TWh)**, **Electricity from nuclear (TWh)**, and **Electricity from renewables (TWh)**\n- Rename the aggregated columns to simplified labels: **Fossil Fuels**, **Nuclear**, and **Renewables**\n- Transform the data from wide format to long format by unpivoting the three energy source columns into two columns: **Energy Source** (containing the type of energy) and **Energy** (containing the generation value in TWh)\n- Sort the resulting dataset by **Year** and **Energy Source** for consistent ordering","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 158029.9988, 190729.9957, 209229.9957, ..., nan, 4956060.059, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., nan, nan, nan, nan\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Group by Year and sum electricity generation from all countries\n global_energy = df_energy.groupby('Year').agg({\n 'Electricity from fossil fuels (TWh)': 'sum',\n 'Electricity from nuclear (TWh)': 'sum',\n 'Electricity from renewables (TWh)': 'sum'\n }).reset_index()\n \n # Rename columns for clarity\n global_energy.columns = ['Year', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n global_energy,\n id_vars=['Year'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Year and Energy Source\n transformed_df = transformed_df.sort_values(['Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Group the energy data by **Year** and aggregate electricity generation values across all countries by summing **Electricity from fossil fuels (TWh)**, **Electricity from nuclear (TWh)**, and **Electricity from renewables (TWh)**\n- Rename the aggregated columns to simplified labels: **Fossil Fuels**, **Nuclear**, and **Renewables**\n- Transform the data from wide format to long format by unpivoting the three energy source columns into two columns: **Energy Source** (containing the type of energy) and **Energy** (containing the generation value in TWh)\n- Sort the resulting dataset by **Year** and **Energy Source** for consistent ordering\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-10","displayId":"energy-source2","names":["Energy","Energy Source","Entity","Year"],"rows":[{"Energy":1113.3,"Energy Source":"Fossil Fuels","Entity":"China","Year":2000},{"Energy":16.74,"Energy Source":"Nuclear","Entity":"China","Year":2000},{"Energy":225.56,"Energy Source":"Renewables","Entity":"China","Year":2000},{"Energy":1182.59,"Energy Source":"Fossil Fuels","Entity":"China","Year":2001},{"Energy":17.47,"Energy Source":"Nuclear","Entity":"China","Year":2001},{"Energy":280.73,"Energy Source":"Renewables","Entity":"China","Year":2001},{"Energy":1337.46,"Energy Source":"Fossil Fuels","Entity":"China","Year":2002},{"Energy":25.13,"Energy Source":"Nuclear","Entity":"China","Year":2002},{"Energy":291.41,"Energy Source":"Renewables","Entity":"China","Year":2002},{"Energy":1579.96,"Energy Source":"Fossil Fuels","Entity":"China","Year":2003},{"Energy":43.34,"Energy Source":"Nuclear","Entity":"China","Year":2003},{"Energy":287.28,"Energy Source":"Renewables","Entity":"China","Year":2003},{"Energy":1795.41,"Energy Source":"Fossil Fuels","Entity":"China","Year":2004},{"Energy":50.47,"Energy Source":"Nuclear","Entity":"China","Year":2004},{"Energy":357.43,"Energy Source":"Renewables","Entity":"China","Year":2004},{"Energy":2042.8,"Energy Source":"Fossil Fuels","Entity":"China","Year":2005},{"Energy":53.09,"Energy Source":"Nuclear","Entity":"China","Year":2005},{"Energy":404.37,"Energy Source":"Renewables","Entity":"China","Year":2005},{"Energy":2364.16,"Energy Source":"Fossil Fuels","Entity":"China","Year":2006},{"Energy":54.84,"Energy Source":"Nuclear","Entity":"China","Year":2006},{"Energy":446.72,"Energy Source":"Renewables","Entity":"China","Year":2006},{"Energy":2718.7,"Energy Source":"Fossil Fuels","Entity":"China","Year":2007},{"Energy":62.13,"Energy Source":"Nuclear","Entity":"China","Year":2007},{"Energy":500.71,"Energy Source":"Renewables","Entity":"China","Year":2007},{"Energy":2762.29,"Energy Source":"Fossil Fuels","Entity":"China","Year":2008},{"Energy":68.39,"Energy Source":"Nuclear","Entity":"China","Year":2008},{"Energy":665.08,"Energy Source":"Renewables","Entity":"China","Year":2008},{"Energy":2980.2,"Energy Source":"Fossil Fuels","Entity":"China","Year":2009},{"Energy":70.05,"Energy Source":"Nuclear","Entity":"China","Year":2009},{"Energy":664.39,"Energy Source":"Renewables","Entity":"China","Year":2009},{"Energy":3326.19,"Energy Source":"Fossil Fuels","Entity":"China","Year":2010},{"Energy":74.74,"Energy Source":"Nuclear","Entity":"China","Year":2010},{"Energy":786.38,"Energy Source":"Renewables","Entity":"China","Year":2010},{"Energy":3811.77,"Energy Source":"Fossil Fuels","Entity":"China","Year":2011},{"Energy":87.2,"Energy Source":"Nuclear","Entity":"China","Year":2011},{"Energy":792.38,"Energy Source":"Renewables","Entity":"China","Year":2011},{"Energy":3869.38,"Energy Source":"Fossil Fuels","Entity":"China","Year":2012},{"Energy":98.32,"Energy Source":"Nuclear","Entity":"China","Year":2012},{"Energy":999.56,"Energy Source":"Renewables","Entity":"China","Year":2012},{"Energy":4203.77,"Energy Source":"Fossil Fuels","Entity":"China","Year":2013},{"Energy":111.5,"Energy Source":"Nuclear","Entity":"China","Year":2013},{"Energy":1093.37,"Energy Source":"Renewables","Entity":"China","Year":2013},{"Energy":4345.86,"Energy Source":"Fossil Fuels","Entity":"China","Year":2014},{"Energy":133.22,"Energy Source":"Nuclear","Entity":"China","Year":2014},{"Energy":1289.23,"Energy Source":"Renewables","Entity":"China","Year":2014},{"Energy":4222.76,"Energy Source":"Fossil Fuels","Entity":"China","Year":2015},{"Energy":171.38,"Energy Source":"Nuclear","Entity":"China","Year":2015},{"Energy":1393.66,"Energy Source":"Renewables","Entity":"China","Year":2015},{"Energy":4355,"Energy Source":"Fossil Fuels","Entity":"China","Year":2016},{"Energy":213.18,"Energy Source":"Nuclear","Entity":"China","Year":2016},{"Energy":1522.79,"Energy Source":"Renewables","Entity":"China","Year":2016},{"Energy":4643.1,"Energy Source":"Fossil Fuels","Entity":"China","Year":2017},{"Energy":248.1,"Energy Source":"Nuclear","Entity":"China","Year":2017},{"Energy":1667.06,"Energy Source":"Renewables","Entity":"China","Year":2017},{"Energy":4990.28,"Energy Source":"Fossil Fuels","Entity":"China","Year":2018},{"Energy":295,"Energy Source":"Nuclear","Entity":"China","Year":2018},{"Energy":1835.32,"Energy Source":"Renewables","Entity":"China","Year":2018},{"Energy":5098.22,"Energy Source":"Fossil Fuels","Entity":"China","Year":2019},{"Energy":348.7,"Energy Source":"Nuclear","Entity":"China","Year":2019},{"Energy":2014.57,"Energy Source":"Renewables","Entity":"China","Year":2019},{"Energy":5184.13,"Energy Source":"Fossil Fuels","Entity":"China","Year":2020},{"Energy":366.2,"Energy Source":"Nuclear","Entity":"China","Year":2020},{"Energy":2184.94,"Energy Source":"Renewables","Entity":"China","Year":2020},{"Energy":475.35,"Energy Source":"Fossil Fuels","Entity":"India","Year":2000},{"Energy":15.77,"Energy Source":"Nuclear","Entity":"India","Year":2000},{"Energy":80.27,"Energy Source":"Renewables","Entity":"India","Year":2000},{"Energy":491.01,"Energy Source":"Fossil Fuels","Entity":"India","Year":2001},{"Energy":18.89,"Energy Source":"Nuclear","Entity":"India","Year":2001},{"Energy":76.19,"Energy Source":"Renewables","Entity":"India","Year":2001},{"Energy":517.51,"Energy Source":"Fossil Fuels","Entity":"India","Year":2002},{"Energy":19.35,"Energy Source":"Nuclear","Entity":"India","Year":2002},{"Energy":72.78,"Energy Source":"Renewables","Entity":"India","Year":2002},{"Energy":545.36,"Energy Source":"Fossil Fuels","Entity":"India","Year":2003},{"Energy":18.14,"Energy Source":"Nuclear","Entity":"India","Year":2003},{"Energy":74.63,"Energy Source":"Renewables","Entity":"India","Year":2003},{"Energy":567.86,"Energy Source":"Fossil Fuels","Entity":"India","Year":2004},{"Energy":21.26,"Energy Source":"Nuclear","Entity":"India","Year":2004},{"Energy":109.2,"Energy Source":"Renewables","Entity":"India","Year":2004},{"Energy":579.32,"Energy Source":"Fossil Fuels","Entity":"India","Year":2005},{"Energy":17.73,"Energy Source":"Nuclear","Entity":"India","Year":2005},{"Energy":107.47,"Energy Source":"Renewables","Entity":"India","Year":2005},{"Energy":599.24,"Energy Source":"Fossil Fuels","Entity":"India","Year":2006},{"Energy":17.63,"Energy Source":"Nuclear","Entity":"India","Year":2006},{"Energy":127.56,"Energy Source":"Renewables","Entity":"India","Year":2006},{"Energy":636.68,"Energy Source":"Fossil Fuels","Entity":"India","Year":2007},{"Energy":17.83,"Energy Source":"Nuclear","Entity":"India","Year":2007},{"Energy":141.75,"Energy Source":"Renewables","Entity":"India","Year":2007},{"Energy":674.27,"Energy Source":"Fossil Fuels","Entity":"India","Year":2008},{"Energy":15.23,"Energy Source":"Nuclear","Entity":"India","Year":2008},{"Energy":138.91,"Energy Source":"Renewables","Entity":"India","Year":2008},{"Energy":728.56,"Energy Source":"Fossil Fuels","Entity":"India","Year":2009},{"Energy":16.82,"Energy Source":"Nuclear","Entity":"India","Year":2009},{"Energy":134.33,"Energy Source":"Renewables","Entity":"India","Year":2009},{"Energy":771.78,"Energy Source":"Fossil Fuels","Entity":"India","Year":2010},{"Energy":23.08,"Energy Source":"Nuclear","Entity":"India","Year":2010},{"Energy":142.61,"Energy Source":"Renewables","Entity":"India","Year":2010},{"Energy":828.16,"Energy Source":"Fossil Fuels","Entity":"India","Year":2011},{"Energy":32.22,"Energy Source":"Nuclear","Entity":"India","Year":2011},{"Energy":173.62,"Energy Source":"Renewables","Entity":"India","Year":2011},{"Energy":893.45,"Energy Source":"Fossil Fuels","Entity":"India","Year":2012},{"Energy":33.14,"Energy Source":"Nuclear","Entity":"India","Year":2012},{"Energy":165.25,"Energy Source":"Renewables","Entity":"India","Year":2012},{"Energy":924.93,"Energy Source":"Fossil Fuels","Entity":"India","Year":2013},{"Energy":33.31,"Energy Source":"Nuclear","Entity":"India","Year":2013},{"Energy":187.9,"Energy Source":"Renewables","Entity":"India","Year":2013},{"Energy":1025.29,"Energy Source":"Fossil Fuels","Entity":"India","Year":2014},{"Energy":34.69,"Energy Source":"Nuclear","Entity":"India","Year":2014},{"Energy":202.04,"Energy Source":"Renewables","Entity":"India","Year":2014},{"Energy":1080.44,"Energy Source":"Fossil Fuels","Entity":"India","Year":2015},{"Energy":38.31,"Energy Source":"Nuclear","Entity":"India","Year":2015},{"Energy":203.21,"Energy Source":"Renewables","Entity":"India","Year":2015},{"Energy":1155.52,"Energy Source":"Fossil Fuels","Entity":"India","Year":2016},{"Energy":37.9,"Energy Source":"Nuclear","Entity":"India","Year":2016},{"Energy":208.21,"Energy Source":"Renewables","Entity":"India","Year":2016},{"Energy":1198.85,"Energy Source":"Fossil Fuels","Entity":"India","Year":2017},{"Energy":37.41,"Energy Source":"Nuclear","Entity":"India","Year":2017},{"Energy":234.9,"Energy Source":"Renewables","Entity":"India","Year":2017},{"Energy":1276.32,"Energy Source":"Fossil Fuels","Entity":"India","Year":2018},{"Energy":39.05,"Energy Source":"Nuclear","Entity":"India","Year":2018},{"Energy":263.61,"Energy Source":"Renewables","Entity":"India","Year":2018},{"Energy":1273.59,"Energy Source":"Fossil Fuels","Entity":"India","Year":2019},{"Energy":45.16,"Energy Source":"Nuclear","Entity":"India","Year":2019},{"Energy":303.16,"Energy Source":"Renewables","Entity":"India","Year":2019},{"Energy":1202.34,"Energy Source":"Fossil Fuels","Entity":"India","Year":2020},{"Energy":44.61,"Energy Source":"Nuclear","Entity":"India","Year":2020},{"Energy":315.76,"Energy Source":"Renewables","Entity":"India","Year":2020},{"Energy":2697.28,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2000},{"Energy":753.89,"Energy Source":"Nuclear","Entity":"United States","Year":2000},{"Energy":350.93,"Energy Source":"Renewables","Entity":"United States","Year":2000},{"Energy":2678.68,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2001},{"Energy":768.83,"Energy Source":"Nuclear","Entity":"United States","Year":2001},{"Energy":280.06,"Energy Source":"Renewables","Entity":"United States","Year":2001},{"Energy":2727.83,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2002},{"Energy":780.06,"Energy Source":"Nuclear","Entity":"United States","Year":2002},{"Energy":336.34,"Energy Source":"Renewables","Entity":"United States","Year":2002},{"Energy":2756.03,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2003},{"Energy":763.73,"Energy Source":"Nuclear","Entity":"United States","Year":2003},{"Energy":349.18,"Energy Source":"Renewables","Entity":"United States","Year":2003},{"Energy":2818.28,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2004},{"Energy":788.53,"Energy Source":"Nuclear","Entity":"United States","Year":2004},{"Energy":345.14,"Energy Source":"Renewables","Entity":"United States","Year":2004},{"Energy":2899.96,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2005},{"Energy":781.99,"Energy Source":"Nuclear","Entity":"United States","Year":2005},{"Energy":353.04,"Energy Source":"Renewables","Entity":"United States","Year":2005},{"Energy":2878.56,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2006},{"Energy":787.22,"Energy Source":"Nuclear","Entity":"United States","Year":2006},{"Energy":381.16,"Energy Source":"Renewables","Entity":"United States","Year":2006},{"Energy":2988.24,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2007},{"Energy":806.42,"Energy Source":"Nuclear","Entity":"United States","Year":2007},{"Energy":347.91,"Energy Source":"Renewables","Entity":"United States","Year":2007},{"Energy":2924.21,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2008},{"Energy":806.21,"Energy Source":"Nuclear","Entity":"United States","Year":2008},{"Energy":377.11,"Energy Source":"Renewables","Entity":"United States","Year":2008},{"Energy":2725.41,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2009},{"Energy":798.85,"Energy Source":"Nuclear","Entity":"United States","Year":2009},{"Energy":415.56,"Energy Source":"Renewables","Entity":"United States","Year":2009},{"Energy":2882.49,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2010},{"Energy":806.97,"Energy Source":"Nuclear","Entity":"United States","Year":2010},{"Energy":424.48,"Energy Source":"Renewables","Entity":"United States","Year":2010},{"Energy":2788.93,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2011},{"Energy":790.2,"Energy Source":"Nuclear","Entity":"United States","Year":2011},{"Energy":509.74,"Energy Source":"Renewables","Entity":"United States","Year":2011},{"Energy":2779.02,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2012},{"Energy":769.33,"Energy Source":"Nuclear","Entity":"United States","Year":2012},{"Energy":492.32,"Energy Source":"Renewables","Entity":"United States","Year":2012},{"Energy":2746.21,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2013},{"Energy":789.02,"Energy Source":"Nuclear","Entity":"United States","Year":2013},{"Energy":520.38,"Energy Source":"Renewables","Entity":"United States","Year":2013},{"Energy":2752.01,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2014},{"Energy":797.17,"Energy Source":"Nuclear","Entity":"United States","Year":2014},{"Energy":546.83,"Energy Source":"Renewables","Entity":"United States","Year":2014},{"Energy":2730.32,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2015},{"Energy":797.18,"Energy Source":"Nuclear","Entity":"United States","Year":2015},{"Energy":556.49,"Energy Source":"Renewables","Entity":"United States","Year":2015},{"Energy":2656.96,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2016},{"Energy":805.69,"Energy Source":"Nuclear","Entity":"United States","Year":2016},{"Energy":624.91,"Energy Source":"Renewables","Entity":"United States","Year":2016},{"Energy":2540.17,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2017},{"Energy":804.95,"Energy Source":"Nuclear","Entity":"United States","Year":2017},{"Energy":707.19,"Energy Source":"Renewables","Entity":"United States","Year":2017},{"Energy":2661.3,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2018},{"Energy":807.08,"Energy Source":"Nuclear","Entity":"United States","Year":2018},{"Energy":733.17,"Energy Source":"Renewables","Entity":"United States","Year":2018},{"Energy":2588.21,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2019},{"Energy":809.41,"Energy Source":"Nuclear","Entity":"United States","Year":2019},{"Energy":760.76,"Energy Source":"Renewables","Entity":"United States","Year":2019},{"Energy":2431.9,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2020},{"Energy":789.88,"Energy Source":"Nuclear","Entity":"United States","Year":2020},{"Energy":821.4,"Energy Source":"Renewables","Entity":"United States","Year":2020}],"metadata":{"Energy":{"type":"number","semanticType":"Number"},"Energy Source":{"type":"string","semanticType":"String","levels":["Fossil Fuels","Nuclear","Renewables"]},"Entity":{"type":"string","semanticType":"Location"},"Year":{"type":"number","semanticType":"Year"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total CO2 emissions by country across all years\n total_co2_by_country = df_energy.groupby('Entity')['Value_co2_emissions_kt_by_country'].sum().reset_index()\n \n # Get top 3 CO2 emitting countries\n top3_countries = total_co2_by_country.nlargest(3, 'Value_co2_emissions_kt_by_country')['Entity'].tolist()\n \n # Filter data for top 3 countries\n df_top3 = df_energy[df_energy['Entity'].isin(top3_countries)].copy()\n \n # Select relevant columns and rename for clarity\n df_top3 = df_top3[['Year', 'Entity', 'Electricity from fossil fuels (TWh)', \n 'Electricity from nuclear (TWh)', 'Electricity from renewables (TWh)']].copy()\n \n df_top3.columns = ['Year', 'Entity', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n df_top3,\n id_vars=['Year', 'Entity'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Entity, Year, and Energy Source\n transformed_df = transformed_df.sort_values(['Entity', 'Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n","source":["global-energy-20-small.csv"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5736319.824, 5738290.039, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 61.99, 62.21, 63.02, 63.71\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"compare each countries renewable between 2000 and 2020\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Energy\",\n \"color\": \"Year\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Compare renewable energy (Electricity from renewables) for each country between the years 2000 and 2020. Filter the data to include only years 2000 and 2020, and prepare the output with country names, year, and renewable energy values for visualization as a grouped bar chart.\",\n \"display_instruction\": \"Compare **renewable energy** by **country** between **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Electricity from renewables (TWh)\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to compare renewable energy across countries between 2000 and 2020. The output field 'Renewable Energy' mentioned in the original chart_encodings maps to 'Electricity from renewables (TWh)' in the dataset. To create a grouped bar chart comparing these two years, we need to filter the data to only include years 2000 and 2020, and output the Entity (country), Year, and the renewable energy values. The chart_encodings are updated to use the actual column name from the dataset.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Electricity from renewables (TWh) Entity Year\n0 17.11 Australia 2000\n1 63.99 Australia 2020\n2 308.77 Brazil 2000\n3 520.01 Brazil 2020\n4 363.70 Canada 2000\n5 429.24 Canada 2020\n6 225.56 China 2000\n7 2184.94 China 2020\n8 67.83 France 2000\n9 125.28 France 2020\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show their renewable percentage instead, and add an entity to show global average\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage (proportion of renewables to total electricity generation from all sources) for each country between 2000 and 2020. Add a 'Global Average' entity that represents the worldwide average renewable percentage for each year. The output should include Entity (with 'Global Average' added), Year, and the renewable percentage.\",\n \"display_instruction\": \"Show **renewable energy percentage** by **country** with **global average** for **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Renewable Percentage\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Percentage\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to see renewable percentage instead of absolute values, and wants to add a global average entity for comparison. Renewable percentage is calculated as the ratio of renewable energy to total electricity generation (fossil fuels + nuclear + renewables). A 'Global Average' entity needs to be computed by aggregating all countries' data for each year. The y-axis encoding is updated to reflect the new metric 'Renewable Percentage'.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n df_filtered = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Calculate total electricity generation\n df_filtered['Total Electricity'] = (\n df_filtered['Electricity from fossil fuels (TWh)'] + \n df_filtered['Electricity from nuclear (TWh)'] + \n df_filtered['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_filtered['Renewable Percentage'] = (\n df_filtered['Electricity from renewables (TWh)'] / df_filtered['Total Electricity'] * 100\n )\n \n # Select required columns for individual countries\n country_data = df_filtered[['Entity', 'Year', 'Renewable Percentage']].copy()\n \n # Calculate global average for each year\n global_avg = df_filtered.groupby('Year').apply(\n lambda x: pd.Series({\n 'Entity': 'Global Average',\n 'Year': x['Year'].iloc[0],\n 'Renewable Percentage': (\n x['Electricity from renewables (TWh)'].sum() / x['Total Electricity'].sum() * 100\n )\n })\n ).reset_index(drop=True)\n \n # Combine country data with global average\n transformed_df = pd.concat([country_data, global_avg], ignore_index=True)\n \n # Convert Year to string for better visualization\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Entity Renewable Percentage Year\n0 Australia 8.634437 2000\n1 Australia 25.503168 2020\n2 Brazil 90.130772 2000\n3 Brazil 84.641177 2020\n4 Canada 61.809592 2000\n5 Canada 68.779644 2020\n6 China 16.639127 2000\n7 China 28.246461 2020\n8 France 12.711769 2000\n9 France 23.761024 2020\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show global energy composition over time\",\n \"chart_type\": \"Custom Area\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Show the global energy composition over time by aggregating electricity generation from all countries for each year, broken down by energy source (fossil fuels, nuclear, and renewables). The output should be in long format with Year, Energy Source, and Energy values to support a stacked area chart visualization.\",\n \"display_instruction\": \"Show global **energy composition** by **energy source** over time\",\n \"output_fields\": [\"Year\", \"Energy Source\", \"Energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\"\n },\n \"reason\": \"The user wants to visualize global energy composition over time, which requires aggregating electricity generation data across all countries for each year and breaking it down by energy source type. The data needs to be transformed into long format where each row represents a year-energy source combination, with the total energy value for that source globally. This follows up from the previous renewable percentage analysis by showing the overall energy mix trends rather than focusing on specific countries or percentages.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Group by Year and sum electricity generation from all countries\n global_energy = df_energy.groupby('Year').agg({\n 'Electricity from fossil fuels (TWh)': 'sum',\n 'Electricity from nuclear (TWh)': 'sum',\n 'Electricity from renewables (TWh)': 'sum'\n }).reset_index()\n \n # Rename columns for clarity\n global_energy.columns = ['Year', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n global_energy,\n id_vars=['Year'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Year and Energy Source\n transformed_df = transformed_df.sort_values(['Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Energy Energy Source Year\n0 7160.71 Fossil Fuels 2000\n1 1996.65 Nuclear 2000\n2 1742.56 Renewables 2000\n3 7273.89 Fossil Fuels 2001\n4 2037.54 Nuclear 2001\n5 1690.11 Renewables 2001\n6 7621.07 Fossil Fuels 2002\n7 2042.18 Nuclear 2002\n8 1757.63 Renewables 2002\n9 8043.86 Fossil Fuels 2003\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show the composition of top 3 CO2 emitting countries\",\n \"chart_type\": \"Custom Area\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Identify the top 3 CO2 emitting countries based on total CO2 emissions across all years, then show the energy composition (fossil fuels, nuclear, and renewables) for each of these countries over time. The output should be in long format with Year, Entity (country), Energy Source, and Energy values to support faceted area charts.\",\n \"display_instruction\": \"Show **energy composition** of **top 3 CO2 emitting countries** over time\",\n \"output_fields\": [\"Year\", \"Entity\", \"Energy Source\", \"Energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\",\n \"facet\": \"Entity\"\n },\n \"reason\": \"The user wants to see the energy composition specifically for the top 3 CO2 emitting countries over time, following up from the global energy composition view. We need to identify which countries have the highest total CO2 emissions, filter the data for those countries, and transform it into long format by energy source. A facet encoding is added to show separate area charts for each of the top 3 countries, allowing comparison of their individual energy compositions over time.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total CO2 emissions by country across all years\n total_co2_by_country = df_energy.groupby('Entity')['Value_co2_emissions_kt_by_country'].sum().reset_index()\n \n # Get top 3 CO2 emitting countries\n top3_countries = total_co2_by_country.nlargest(3, 'Value_co2_emissions_kt_by_country')['Entity'].tolist()\n \n # Filter data for top 3 countries\n df_top3 = df_energy[df_energy['Entity'].isin(top3_countries)].copy()\n \n # Select relevant columns and rename for clarity\n df_top3 = df_top3[['Year', 'Entity', 'Electricity from fossil fuels (TWh)', \n 'Electricity from nuclear (TWh)', 'Electricity from renewables (TWh)']].copy()\n \n df_top3.columns = ['Year', 'Entity', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n df_top3,\n id_vars=['Year', 'Entity'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Entity, Year, and Energy Source\n transformed_df = transformed_df.sort_values(['Entity', 'Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-81","sourceTableIds":["global-energy-20-small.csv"],"instruction":"show the composition of top 3 CO2 emitting countries","displayInstruction":"Show **energy composition** of **top 3 CO2 emitting countries** over time","chart":{"id":"chart-1760739075465","chartType":"Custom Area","encodingMap":{"x":{"channel":"x","bin":false,"fieldID":"original--global-energy-20-small.csv--Year"},"y":{"channel":"y","bin":false,"fieldID":"concept-1760738917347"},"x2":{},"y2":{"channel":"y2","bin":false},"color":{"fieldID":"concept-1760738921927","sortBy":"[\"Fossil Fuels\",\"Nuclear\",\"Renewables\"]"},"column":{},"row":{"channel":"row","bin":false}},"tableRef":"table-81","saved":false,"source":"trigger","unread":false},"resultTableId":"table-10"},"explanation":{"agent":"CodeExplanationAgent","code":"The code performs the following high-level transformations:\n\n1. **Identifies top polluters**: Calculates the total `Value_co2_emissions_kt_by_country` across all years for each `Entity` (country), then selects the **top 3** countries with the highest cumulative CO2 emissions.\n\n2. **Filters the dataset**: Retains only records for the **top 3 CO2-emitting countries** from the original energy data.\n\n3. **Simplifies column structure**: Extracts the relevant energy production columns (`Electricity from fossil fuels (TWh)`, `Electricity from nuclear (TWh)`, `Electricity from renewables (TWh)`) and renames them to **Fossil Fuels**, **Nuclear**, and **Renewables** for clarity.\n\n4. **Reshapes data to long format**: Transforms the data from wide format (separate columns for each energy source) to long format, where each row represents a specific `Year`, `Entity`, and `Energy Source` combination with its corresponding `Energy` production value.\n\n5. **Sorts the output**: Orders the final dataset by `Entity`, `Year`, and `Energy Source` for systematic presentation.","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5593029.785, 5736319.824, 5738290.039, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 62.21, 63.02, 63.71, 439.73\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total CO2 emissions by country across all years\n total_co2_by_country = df_energy.groupby('Entity')['Value_co2_emissions_kt_by_country'].sum().reset_index()\n \n # Get top 3 CO2 emitting countries\n top3_countries = total_co2_by_country.nlargest(3, 'Value_co2_emissions_kt_by_country')['Entity'].tolist()\n \n # Filter data for top 3 countries\n df_top3 = df_energy[df_energy['Entity'].isin(top3_countries)].copy()\n \n # Select relevant columns and rename for clarity\n df_top3 = df_top3[['Year', 'Entity', 'Electricity from fossil fuels (TWh)', \n 'Electricity from nuclear (TWh)', 'Electricity from renewables (TWh)']].copy()\n \n df_top3.columns = ['Year', 'Entity', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n df_top3,\n id_vars=['Year', 'Entity'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Entity, Year, and Energy Source\n transformed_df = transformed_df.sort_values(['Entity', 'Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThe code performs the following high-level transformations:\n\n1. **Identifies top polluters**: Calculates the total `Value_co2_emissions_kt_by_country` across all years for each `Entity` (country), then selects the **top 3** countries with the highest cumulative CO2 emissions.\n\n2. **Filters the dataset**: Retains only records for the **top 3 CO2-emitting countries** from the original energy data.\n\n3. **Simplifies column structure**: Extracts the relevant energy production columns (`Electricity from fossil fuels (TWh)`, `Electricity from nuclear (TWh)`, `Electricity from renewables (TWh)`) and renames them to **Fossil Fuels**, **Nuclear**, and **Renewables** for clarity.\n\n4. **Reshapes data to long format**: Transforms the data from wide format (separate columns for each energy source) to long format, where each row represents a specific `Year`, `Entity`, and `Energy Source` combination with its corresponding `Energy` production value.\n\n5. **Sorts the output**: Orders the final dataset by `Entity`, `Year`, and `Energy Source` for systematic presentation.\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""}],"charts":[{"id":"chart-1760739084455","chartType":"Custom Area","encodingMap":{"x":{"fieldID":"original--global-energy-20-small.csv--Year"},"y":{"fieldID":"concept-1760738917347"},"x2":{},"y2":{"channel":"y2","bin":false},"color":{"fieldID":"concept-1760738921927"},"column":{"fieldID":"original--global-energy-20-small.csv--Entity"},"row":{"channel":"row","bin":false}},"tableRef":"table-10","saved":false,"source":"user","unread":false},{"id":"chart-1760739021201","chartType":"Custom Area","encodingMap":{"x":{"channel":"x","bin":false,"fieldID":"original--global-energy-20-small.csv--Year"},"y":{"channel":"y","bin":false,"fieldID":"concept-1760738917347"},"x2":{},"y2":{"channel":"y2","bin":false},"color":{"fieldID":"concept-1760738921927","sortBy":"[\"Fossil 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Global renewable electricity nearly doubled from 16% to 29% of total generation, signaling meaningful progress in the energy transition.\n\n[IMAGE(chart-1760738819387)]\n\nThe data reveals striking regional variations. Brazil maintained renewable leadership above 84%, while countries like Australia, Germany, and Italy dramatically expanded their renewable capacity—Australia tripling from 9% to 26%. However, some nations like Mexico experienced declining renewable shares, highlighting uneven progress across different energy systems.\n\n**In summary**, while the 20-year period shows encouraging momentum toward cleaner energy, the pace and direction vary significantly by country, suggesting that achieving global renewable energy goals will require sustained, coordinated efforts tailored to each nation's unique energy landscape and policy environment.","style":"short note","selectedChartIds":["chart-1760738819387"],"createdAt":1760831163718},{"id":"report-1760831130105-4063","content":"# The Global Renewable Energy Revolution: Two Decades of Transformation\n\nThe world's energy landscape has undergone a remarkable transformation between 2000 and 2020, with renewable electricity generation emerging as a critical component of the global energy mix. This shift reflects both technological advancement and growing commitment to sustainable energy solutions.\n\n[IMAGE(chart-1760738770100)]\n\nLooking at renewable energy adoption across major economies, the growth has been nothing short of extraordinary. China leads the pack with a staggering increase from 225.56 TWh in 2000 to 2,184.94 TWh in 2020—nearly a tenfold expansion. The United States more than doubled its renewable output from 335.45 TWh to 821.40 TWh, while Brazil grew from 308.77 TWh to 520.01 TWh. Notably, countries like Australia, India, and Germany also demonstrated significant gains, with Australia jumping from just 17.11 TWh to 63.99 TWh during this period.\n\n[IMAGE(chart-1760739084455)]\n\nWhen examining the energy portfolios of the three largest CO2 emitters—China, India, and the United States—a complex picture emerges. While China's total energy consumption has grown exponentially, with fossil fuels still dominating, the renewable sector (shown in red) has expanded substantially. The United States shows a more stable total energy consumption, with renewables gradually claiming a larger share. India's energy growth, though significant, remains heavily reliant on fossil fuels, though renewable adoption is accelerating.\n\n**In summary**, the past two decades reveal a global energy transition in progress. While renewable energy has achieved impressive growth worldwide, fossil fuels continue to dominate electricity generation in major economies. Key questions remain: Can this momentum accelerate sufficiently to meet climate goals? What policies will drive faster renewable adoption in emerging economies?","style":"blog post","selectedChartIds":["chart-1760739084455","chart-1760738770100"],"createdAt":1760831142289},{"id":"report-1760831094231-2424","content":"# Global Renewable Energy: A Tale of Leaders and Laggards\n\nThe global energy landscape has undergone significant transformation over the past two decades, with renewable energy emerging as a critical player in the electricity mix. Analyzing data from 21 major economies between 2000 and 2020 reveals striking disparities in how nations have embraced clean energy alternatives.\n\n[IMAGE(chart-1760738423852)]\n\nThe first visualization reveals a fascinating divergence in renewable energy adoption. **Brazil** stands out as a consistent leader, maintaining renewable electricity percentages between 75-90% throughout the entire period, thanks largely to its robust hydroelectric infrastructure. **Canada** follows a similar trajectory, steadily increasing from about 60% to nearly 70% by 2020. Meanwhile, **Germany, Spain, Italy, and the United Kingdom** show remarkable growth trajectories, climbing from under 20% in the early 2000s to over 40% by 2020—demonstrating that nations can dramatically reshape their energy portfolios within two decades.\n\nOn the opposite end of the spectrum, **South Africa** remains nearly flat at the bottom, showing minimal renewable adoption despite global trends. **Saudi Arabia** and **Poland** also lag significantly, though both show modest upticks in recent years.\n\n[IMAGE(chart-1760738436615)]\n\nThe ranking chart illustrates the competitive dynamics of renewable energy leadership. **Brazil and Canada** maintain their dominance at ranks 1-2 throughout most years, while European nations like **Germany, Spain, and the UK** engage in a dynamic competition for the 3rd-5th positions, particularly after 2010. The volatility in middle rankings reflects the rapid changes in energy policy and investment across different nations, with countries like **China** climbing from lower ranks to break into the top 7 by 2020.\n\n**In summary**, the data reveals a bifurcated global energy transition: a group of progressive nations have successfully scaled renewable electricity to 40-90% of their mix, while others remain heavily dependent on fossil fuels. These patterns suggest that political will, natural resource endowment, and infrastructure investment are key determinants of renewable energy success. Important follow-up questions include: What policy mechanisms enabled top performers to achieve such high renewable percentages? Can lagging nations replicate these successes, or do geographic and economic constraints create insurmountable barriers?","style":"blog post","selectedChartIds":["chart-1760738423852","chart-1760738436615"],"createdAt":1760831110064}],"currentReport":{"id":"report-1760750575650-2619","content":"# Hollywood's Billion-Dollar Hitmakers\n\n*Avatar* stands alone—earning over $2.5B in profit, dwarfing all competition. Action and Adventure films dominate the most profitable titles, with franchises like *Jurassic Park*, *The Dark Knight*, and *Lord of the Rings* proving blockbuster formulas work.\n\n\"Chart\"\n\nSteven Spielberg leads all directors with $7.2B in total profit across his career, showcasing remarkable consistency with hits spanning decades—from *Jurassic Park* to *E.T.* His nearest competitors trail by billions, underlining his unmatched commercial impact.\n\n\"Chart\"\n\n**In summary**, mega-budget Action and Adventure films generate extraordinary returns when they succeed, and a handful of elite directors—led by Spielberg—have mastered the formula for sustained box office dominance.","style":"short note","selectedChartIds":["chart-1760743347871","chart-1760743768741"],"chartImages":{},"createdAt":1760750584189,"title":"Report - 10/17/2025"},"activeChallenges":[],"agentWorkInProgress":[],"_persist":{"version":-1,"rehydrated":true}} ================================================ FILE: 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III: Revenge of the Sith","Worldwide Gross":848998877},{"Production Budget":75000000,"Profit":711686679,"Title":"The Lost World: Jurassic Park","Worldwide Gross":786686679},{"Production Budget":79300000,"Profit":704539505,"Title":"The Lion King","Worldwide Gross":783839505},{"Production Budget":250000000,"Profit":687499905,"Title":"Harry Potter and the Half-Blood Prince","Worldwide Gross":937499905},{"Production Budget":139000000,"Profit":682708551,"Title":"Spider-Man","Worldwide Gross":821708551},{"Production Budget":130000000,"Profit":665538952,"Title":"Harry Potter and the Prisoner of Azkaban","Worldwide Gross":795538952},{"Production Budget":300000000,"Profit":660996492,"Title":"Pirates of the Caribbean: At World's End","Worldwide Gross":960996492},{"Production Budget":50000000,"Profit":652623634,"Title":"The Twilight Saga: New Moon","Worldwide Gross":702623634},{"Production Budget":160000000,"Profit":638958162,"Title":"Shrek the Third","Worldwide Gross":798958162}],"metadata":{"Production Budget":{"type":"number","semanticType":"Number"},"Profit":{"type":"number","semanticType":"Number"},"Title":{"type":"string","semanticType":"Name"},"Worldwide Gross":{"type":"number","semanticType":"Number"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title'])\n \n # Sort by profit in descending order and select top 30\n df_top30 = df_filtered.nlargest(30, 'Profit')\n \n # Select only the required output fields\n transformed_df = df_top30[['Title', 'Production Budget', 'Worldwide Gross', 'Profit']].reset_index(drop=True)\n \n return transformed_df\n","source":["movies"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 380270577.0, 402111870.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: 33619971.0, 140689412.0, nan, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 237477.0, 417703.0, 519541.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"show top 30 movies based on their profit\",\n \"chart_type\": \"Scatter Plot\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Worldwide Gross\",\n \"color\": \"Title\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the profit for each movie (Worldwide Gross minus Production Budget), then select the top 30 movies with the highest profit. Display these movies with their Production Budget, Worldwide Gross, and Title to visualize the relationship between budget and revenue for the most profitable films.\",\n \"display_instruction\": \"Show top 30 movies by **profit** with their **Production Budget** and **Worldwide Gross**\",\n \"output_fields\": [\"Title\", \"Production Budget\", \"Worldwide Gross\", \"Profit\"],\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Worldwide Gross\",\n \"color\": \"Title\"\n },\n \"reason\": \"The user wants to show top 30 movies based on profit, which requires calculating profit as the difference between Worldwide Gross and Production Budget. The chart encodings provided are sufficient to visualize the most profitable movies, showing the relationship between their budget and gross revenue. An intermediate field 'Profit' is added to output_fields to enable the top 30 selection, though it won't be directly visualized in the chart.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title'])\n \n # Sort by profit in descending order and select top 30\n df_top30 = df_filtered.nlargest(30, 'Profit')\n \n # Select only the required output fields\n transformed_df = df_top30[['Title', 'Production Budget', 'Worldwide Gross', 'Profit']].reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"movies","sourceTableIds":["movies"],"instruction":"show top 30 movies based on their profit","displayInstruction":"Show top 30 movies by **profit** with their **Production Budget** and **Worldwide Gross**","chart":{"id":"chart-1760743154092","chartType":"Scatter Plot","encodingMap":{"x":{"fieldID":"original--movies--Production Budget"},"y":{"fieldID":"original--movies--Worldwide Gross"},"color":{"fieldID":"original--movies--Title"},"size":{"channel":"size","bin":false},"opacity":{"channel":"opacity","bin":false},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"movies","saved":false,"source":"trigger","unread":false},"resultTableId":"table-78"},"explanation":{"agent":"CodeExplanationAgent","code":"The code identifies the most financially successful movies by analyzing their profitability:\n\n- Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Filter out movies with missing values in **Profit**, **Production Budget**, **Worldwide Gross**, or **Title** fields\n- Sort all movies by **Profit** in descending order and select the **top 30** most profitable movies\n- Return a simplified dataset containing only **Title**, **Production Budget**, **Worldwide Gross**, and **Profit** for these top performers","concepts":[{"explanation":"The net financial gain from a movie, calculated as the difference between worldwide box office revenue and production costs: \\( \\text{Profit} = \\text{Worldwide\\_Gross} - \\text{Production\\_Budget} \\). This metric represents the pure theatrical profitability before accounting for marketing costs, distribution fees, or ancillary revenue streams like DVD sales.","field":"Profit"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 441226247.0, 533345358.0, 600788188.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 1065659812.0, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: 2638319.0, 3907625.0, 4653193.0, ..., 124058348.0, 153389976.0, 234119058.0, 320830925.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: 46.0, 72.0, 74.0, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.5, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 261439.0, 292562.0, 417703.0, 519541.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title'])\n \n # Sort by profit in descending order and select top 30\n df_top30 = df_filtered.nlargest(30, 'Profit')\n \n # Select only the required output fields\n transformed_df = df_top30[['Title', 'Production Budget', 'Worldwide Gross', 'Profit']].reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThe code identifies the most financially successful movies by analyzing their profitability:\n\n- Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Filter out movies with missing values in **Profit**, **Production Budget**, **Worldwide Gross**, or **Title** fields\n- Sort all movies by **Profit** in descending order and select the **top 30** most profitable movies\n- Return a simplified dataset containing only **Title**, **Production Budget**, **Worldwide Gross**, and **Profit** for these top performers\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Profit\",\n \"explanation\": \"The net financial gain from a movie, calculated as the difference between worldwide box office revenue and production costs: \\\\( \\\\text{Profit} = \\\\text{Worldwide\\\\_Gross} - \\\\text{Production\\\\_Budget} \\\\). This metric represents the pure theatrical profitability before accounting for marketing costs, distribution fees, or ancillary revenue streams like DVD sales.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-77","displayId":"movie-profits1","names":["Major Genre","Production Budget","Profit","Title","Worldwide Gross"],"rows":[{"Major Genre":"Action","Production Budget":237000000,"Profit":2530891499,"Title":"Avatar","Worldwide Gross":2767891499},{"Major Genre":"Action","Production Budget":63000000,"Profit":860067947,"Title":"Jurassic Park","Worldwide Gross":923067947},{"Major Genre":"Action","Production Budget":185000000,"Profit":837345358,"Title":"The Dark Knight","Worldwide Gross":1022345358},{"Major Genre":"Adventure","Production Budget":94000000,"Profit":1039027325,"Title":"The Lord of the Rings: The Return of the King","Worldwide Gross":1133027325},{"Major Genre":"Adventure","Production Budget":125000000,"Profit":851457891,"Title":"Harry Potter and the Sorcerer's Stone","Worldwide Gross":976457891},{"Major Genre":"Adventure","Production Budget":70000000,"Profit":849838758,"Title":"Shrek 2","Worldwide Gross":919838758},{"Major Genre":"Black Comedy","Production Budget":37000000,"Profit":126415735,"Title":"Burn After Reading","Worldwide Gross":163415735},{"Major Genre":"Black Comedy","Production Budget":10000000,"Profit":73593107,"Title":"Snatch","Worldwide Gross":83593107},{"Major Genre":"Black Comedy","Production Budget":28000000,"Profit":43430876,"Title":"The Royal Tenenbaums","Worldwide Gross":71430876},{"Major Genre":"Comedy","Production Budget":28000000,"Profit":476050219,"Title":"Aladdin","Worldwide Gross":504050219},{"Major Genre":"Comedy","Production Budget":150000000,"Profit":470495432,"Title":"Ratatouille","Worldwide Gross":620495432},{"Major Genre":"Comedy","Production Budget":110000000,"Profit":464480841,"Title":"Night at the Museum","Worldwide Gross":574480841},{"Major Genre":"Concert/Performance","Production Budget":6500000,"Profit":64781781,"Title":"Hannah Montana/Miley Cyrus: Best of Both Worlds Concert Tour","Worldwide Gross":71281781},{"Major Genre":"Concert/Performance","Production Budget":3000000,"Profit":35236338,"Title":"The Original Kings of Comedy","Worldwide Gross":38236338},{"Major Genre":"Concert/Performance","Production Budget":3000000,"Profit":16184820,"Title":"Martin Lawrence Live: RunTelDat","Worldwide Gross":19184820},{"Major Genre":"Documentary","Production Budget":6000000,"Profit":216414517,"Title":"Fahrenheit 9/11","Worldwide Gross":222414517},{"Major Genre":"Documentary","Production Budget":3400000,"Profit":126037223,"Title":"La marche de l'empereur","Worldwide Gross":129437223},{"Major Genre":"Documentary","Production Budget":3000000,"Profit":55576018,"Title":"Bowling for Columbine","Worldwide Gross":58576018},{"Major Genre":"Drama","Production Budget":10500000,"Profit":782410554,"Title":"ET: The Extra-Terrestrial","Worldwide Gross":792910554},{"Major Genre":"Drama","Production Budget":50000000,"Profit":652623634,"Title":"The Twilight Saga: New Moon","Worldwide Gross":702623634},{"Major Genre":"Drama","Production Budget":55000000,"Profit":624400525,"Title":"Forrest Gump","Worldwide Gross":679400525},{"Major Genre":"Horror","Production Budget":12000000,"Profit":458700000,"Title":"Jaws","Worldwide Gross":470700000},{"Major Genre":"Horror","Production Budget":150000000,"Profit":435055701,"Title":"I am Legend","Worldwide Gross":585055701},{"Major Genre":"Horror","Production Budget":12000000,"Profit":390500000,"Title":"The Exorcist","Worldwide Gross":402500000},{"Major Genre":"Musical","Production Budget":20000000,"Profit":383476931,"Title":"Beauty and the Beast","Worldwide Gross":403476931},{"Major Genre":"Musical","Production Budget":8200000,"Profit":278014286,"Title":"The Sound of Music","Worldwide Gross":286214286},{"Major Genre":"Musical","Production Budget":30000000,"Profit":277687518,"Title":"Chicago","Worldwide Gross":307687518},{"Major Genre":"Romantic Comedy","Production Budget":14000000,"Profit":449400000,"Title":"Pretty Woman","Worldwide Gross":463400000},{"Major Genre":"Romantic Comedy","Production Budget":5000000,"Profit":363744044,"Title":"My Big Fat Greek Wedding","Worldwide Gross":368744044},{"Major Genre":"Romantic Comedy","Production Budget":22000000,"Profit":338099999,"Title":"There's Something About Mary","Worldwide Gross":360099999},{"Major Genre":"Thriller/Suspense","Production Budget":200000000,"Profit":1642879955,"Title":"Titanic","Worldwide Gross":1842879955},{"Major Genre":"Thriller/Suspense","Production Budget":40000000,"Profit":632806292,"Title":"The Sixth Sense","Worldwide Gross":672806292},{"Major Genre":"Thriller/Suspense","Production Budget":125000000,"Profit":632236138,"Title":"The Da Vinci Code","Worldwide Gross":757236138},{"Major Genre":"Western","Production Budget":19000000,"Profit":405200000,"Title":"Dances with Wolves","Worldwide Gross":424200000},{"Major Genre":"Western","Production Budget":6000000,"Profit":96308900,"Title":"Butch Cassidy and the Sundance Kid","Worldwide Gross":102308900},{"Major Genre":"Western","Production Budget":26000000,"Profit":42293719,"Title":"Open Range","Worldwide Gross":68293719}],"metadata":{"Major Genre":{"type":"string","semanticType":"String"},"Production Budget":{"type":"number","semanticType":"Number"},"Profit":{"type":"number","semanticType":"Number"},"Title":{"type":"string","semanticType":"Name"},"Worldwide Gross":{"type":"number","semanticType":"Number"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title', 'Major Genre'])\n \n # Group by Major Genre and select top 3 most profitable movies from each genre\n transformed_df = df_filtered.groupby('Major Genre', group_keys=False).apply(\n lambda x: x.nlargest(3, 'Profit')\n ).reset_index(drop=True)\n \n # Select only the required output fields\n transformed_df = transformed_df[['Title', 'Major Genre', 'Profit', 'Production Budget', 'Worldwide Gross']]\n \n return transformed_df\n","source":["movies"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 380270577.0, 402111870.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: 33619971.0, 140689412.0, nan, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 237477.0, 417703.0, 519541.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"show top 30 movies based on their profit\",\n \"chart_type\": \"Scatter Plot\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Worldwide Gross\",\n \"color\": \"Title\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the profit for each movie (Worldwide Gross minus Production Budget), then select the top 30 movies with the highest profit. Display these movies with their Production Budget, Worldwide Gross, and Title to visualize the relationship between budget and revenue for the most profitable films.\",\n \"display_instruction\": \"Show top 30 movies by **profit** with their **Production Budget** and **Worldwide Gross**\",\n \"output_fields\": [\"Title\", \"Production Budget\", \"Worldwide Gross\", \"Profit\"],\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Worldwide Gross\",\n \"color\": \"Title\"\n },\n \"reason\": \"The user wants to show top 30 movies based on profit, which requires calculating profit as the difference between Worldwide Gross and Production Budget. The chart encodings provided are sufficient to visualize the most profitable movies, showing the relationship between their budget and gross revenue. An intermediate field 'Profit' is added to output_fields to enable the top 30 selection, though it won't be directly visualized in the chart.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title'])\n \n # Sort by profit in descending order and select top 30\n df_top30 = df_filtered.nlargest(30, 'Profit')\n \n # Select only the required output fields\n transformed_df = df_top30[['Title', 'Production Budget', 'Worldwide Gross', 'Profit']].reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Production Budget Profit Title Worldwide Gross\n0 237000000 2530891499 Avatar 2767891499\n1 200000000 1642879955 Titanic 1842879955\n2 94000000 1039027325 The Lord of the Rings: The Return of the King 1133027325\n3 63000000 860067947 Jurassic Park 923067947\n4 125000000 851457891 Harry Potter and the Sorcerer's Stone 976457891\n5 70000000 849838758 Shrek 2 919838758\n6 200000000 846340665 Toy Story 3 1046340665\n7 225000000 840659812 Pirates of the Caribbean: Dead Man's Chest 1065659812\n8 185000000 837345358 The Dark Knight 1022345358\n9 94000000 832284377 The Lord of the Rings: The Two Towers 926284377\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show the top 3 most profitable movies from each genre\",\n \"chart_type\": \"Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Title\",\n \"y\": \"Profit\",\n \"color\": \"Production Budget\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the profit for each movie (Worldwide Gross minus Production Budget), then group movies by their Major Genre and select the top 3 most profitable movies from each genre. Display these movies with their Title, Profit, Production Budget, Major Genre, and Worldwide Gross.\",\n \"display_instruction\": \"Show top 3 most profitable movies from each **Major Genre**\",\n \"output_fields\": [\"Title\", \"Major Genre\", \"Profit\", \"Production Budget\", \"Worldwide Gross\"],\n \"chart_encodings\": {\n \"x\": \"Title\",\n \"y\": \"Profit\",\n \"color\": \"Major Genre\"\n },\n \"reason\": \"The user wants to see the top 3 most profitable movies from each genre, which builds upon the previous profit calculation. The chart encodings are updated to use 'Major Genre' for color instead of 'Production Budget' because when showing top movies by genre, it's more meaningful to distinguish them by genre in the visualization. The Major Genre field is added to output_fields to enable grouping and visualization by genre.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title', 'Major Genre'])\n \n # Group by Major Genre and select top 3 most profitable movies from each genre\n transformed_df = df_filtered.groupby('Major Genre', group_keys=False).apply(\n lambda x: x.nlargest(3, 'Profit')\n ).reset_index(drop=True)\n \n # Select only the required output fields\n transformed_df = transformed_df[['Title', 'Major Genre', 'Profit', 'Production Budget', 'Worldwide Gross']]\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-78","sourceTableIds":["movies"],"instruction":"show the top 3 most profitable movies from each genre","displayInstruction":"Show top 3 most profitable movies from each **Major Genre**","chart":{"id":"chart-1760743349594","chartType":"Bar Chart","encodingMap":{"x":{"fieldID":"original--movies--Title"},"y":{"fieldID":"concept-Profit-1760742653681"},"color":{"fieldID":"original--movies--Production Budget"},"opacity":{"channel":"opacity","bin":false},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"table-78","saved":false,"source":"trigger","unread":false},"resultTableId":"table-77"},"explanation":{"agent":"CodeExplanationAgent","code":"This code performs a profitability analysis to identify the most successful movies in each genre:\n\n- **Calculate Profit**: Computes the **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- **Filter Data**: Removes movies with missing values in **Profit**, **Production Budget**, **Worldwide Gross**, **Title**, or **Major Genre** to ensure complete data for analysis\n- **Group by Genre**: Groups all movies by their **Major Genre** (e.g., Action, Comedy, Drama)\n- **Select Top Performers**: Within each genre, identifies the **top 3** movies with the highest **Profit**\n- **Format Output**: Returns a focused dataset containing only **Title**, **Major Genre**, **Profit**, **Production Budget**, and **Worldwide Gross** for these top-performing movies","concepts":[{"explanation":"The net financial gain from a movie's theatrical release, calculated as the difference between worldwide box office revenue and production costs: \\( \\text{Profit} = \\text{Worldwide\\_Gross} - \\text{Production\\_Budget} \\). This metric indicates the commercial success of a film before accounting for marketing costs and revenue sharing.","field":"Profit"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: nan, 140689412.0, nan, ..., 44145849.0, nan, nan, 46260220.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 5000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.5, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 237477.0, 417703.0, nan, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title', 'Major Genre'])\n \n # Group by Major Genre and select top 3 most profitable movies from each genre\n transformed_df = df_filtered.groupby('Major Genre', group_keys=False).apply(\n lambda x: x.nlargest(3, 'Profit')\n ).reset_index(drop=True)\n \n # Select only the required output fields\n transformed_df = transformed_df[['Title', 'Major Genre', 'Profit', 'Production Budget', 'Worldwide Gross']]\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThis code performs a profitability analysis to identify the most successful movies in each genre:\n\n- **Calculate Profit**: Computes the **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- **Filter Data**: Removes movies with missing values in **Profit**, **Production Budget**, **Worldwide Gross**, **Title**, or **Major Genre** to ensure complete data for analysis\n- **Group by Genre**: Groups all movies by their **Major Genre** (e.g., Action, Comedy, Drama)\n- **Select Top Performers**: Within each genre, identifies the **top 3** movies with the highest **Profit**\n- **Format Output**: Returns a focused dataset containing only **Title**, **Major Genre**, **Profit**, **Production Budget**, and **Worldwide Gross** for these top-performing movies\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Profit\",\n \"explanation\": \"The net financial gain from a movie's theatrical release, calculated as the difference between worldwide box office revenue and production costs: \\\\( \\\\text{Profit} = \\\\text{Worldwide\\\\_Gross} - \\\\text{Production\\\\_Budget} \\\\). This metric indicates the commercial success of a film before accounting for marketing costs and revenue sharing.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-770727","displayId":"director-profit","names":["Director","Profit","Title","Total_Director_Profit"],"rows":[{"Director":"Steven Spielberg","Profit":860067947,"Title":"Jurassic Park","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":782410554,"Title":"ET: The Extra-Terrestrial","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":711686679,"Title":"The Lost World: Jurassic Park","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":601558145,"Title":"Indiana Jones and the Kingdom of the Crystal Skull","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":459745532,"Title":"The War of the Worlds","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":458700000,"Title":"Jaws","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":426171806,"Title":"Indiana Jones and the Last Crusade","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":416635085,"Title":"Saving Private Ryan","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":366800358,"Title":"Raiders of the Lost Ark","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":317700000,"Title":"Close Encounters of the Third Kind","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":305080271,"Title":"Indiana Jones and the Temple of Doom","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":299106800,"Title":"Catch Me if You Can","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":296200000,"Title":"Schindler's List","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":256824714,"Title":"Minority Report","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":230854823,"Title":"Hook","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":145900000,"Title":"Artificial Intelligence: AI","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":143673959,"Title":"The Terminal","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":78589701,"Title":"The Color Purple","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":62875000,"Title":1941,"Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":55279090,"Title":"Munich","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":19500000,"Title":"Twilight Zone: The Movie","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":4212592,"Title":"Amistad","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":-130000000,"Title":"The Adventures of Tintin: Secret of the Unicorn","Total_Director_Profit":7169573056},{"Director":"James Cameron","Profit":2530891499,"Title":"Avatar","Total_Director_Profit":5078066216},{"Director":"James Cameron","Profit":1642879955,"Title":"Titanic","Total_Director_Profit":5078066216},{"Director":"James Cameron","Profit":416816151,"Title":"Terminator 2: Judgment Day","Total_Director_Profit":5078066216},{"Director":"James Cameron","Profit":265300000,"Title":"True Lies","Total_Director_Profit":5078066216},{"Director":"James Cameron","Profit":166316455,"Title":"Aliens","Total_Director_Profit":5078066216},{"Director":"James Cameron","Profit":71619031,"Title":"The Terminator","Total_Director_Profit":5078066216},{"Director":"James Cameron","Profit":-15756875,"Title":"The Abyss","Total_Director_Profit":5078066216},{"Director":"Chris Columbus","Profit":851457891,"Title":"Harry Potter and the Sorcerer's Stone","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":778987880,"Title":"Harry Potter and the Chamber of Secrets","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":461684675,"Title":"Home Alone","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":416286003,"Title":"Mrs. Doubtfire","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":338994850,"Title":"Home Alone 2: Lost in New York","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":131435277,"Title":"Percy Jackson & the Olympians: The Lightning Thief","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":69709917,"Title":"Stepmom","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":-1617462,"Title":"I Love You, Beth Cooper","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":-2579224,"Title":"Bicentennial Man","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":-8329380,"Title":"Rent","Total_Director_Profit":3036030427},{"Director":"George Lucas","Profit":809288297,"Title":"Star Wars Ep. I: The Phantom Menace","Total_Director_Profit":3011105789},{"Director":"George Lucas","Profit":786900000,"Title":"Star Wars Ep. IV: A New Hope","Total_Director_Profit":3011105789},{"Director":"George Lucas","Profit":733998877,"Title":"Star Wars Ep. III: Revenge of the Sith","Total_Director_Profit":3011105789},{"Director":"George Lucas","Profit":541695615,"Title":"Star Wars Ep. II: Attack of the Clones","Total_Director_Profit":3011105789},{"Director":"George Lucas","Profit":139223000,"Title":"American Graffiti","Total_Director_Profit":3011105789},{"Director":"Peter Jackson","Profit":1039027325,"Title":"The Lord of the Rings: The Return of the King","Total_Director_Profit":3001395936},{"Director":"Peter Jackson","Profit":832284377,"Title":"The Lord of the Rings: The Two Towers","Total_Director_Profit":3001395936},{"Director":"Peter Jackson","Profit":759621686,"Title":"The Lord of the Rings: The Fellowship of the Ring","Total_Director_Profit":3001395936},{"Director":"Peter Jackson","Profit":343517357,"Title":"King Kong","Total_Director_Profit":3001395936},{"Director":"Peter Jackson","Profit":29702568,"Title":"The Lovely Bones","Total_Director_Profit":3001395936},{"Director":"Peter Jackson","Profit":-2757377,"Title":"Braindead","Total_Director_Profit":3001395936},{"Director":"Robert Zemeckis","Profit":624400525,"Title":"Forrest Gump","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":362109762,"Title":"Back to the Future","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":342230516,"Title":"Cast Away","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":292000000,"Title":"Back to the Future Part II","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":281500000,"Title":"Who Framed Roger Rabbit?","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":203700000,"Title":"Back to the Future Part III","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":198693989,"Title":"What Lies Beneath","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":135420597,"Title":"The Polar Express","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":133743744,"Title":"Disney's A Christmas Carol","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":94022650,"Title":"Death Becomes Her","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":75900000,"Title":"Contact","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":44995215,"Title":"Beowulf","Total_Director_Profit":2788716998},{"Director":"Michael Bay","Profit":626303693,"Title":"Transformers: Revenge of the Fallen","Total_Director_Profit":2461192847},{"Director":"Michael Bay","Profit":557272592,"Title":"Transformers","Total_Director_Profit":2461192847},{"Director":"Michael Bay","Profit":414600000,"Title":"Armageddon","Total_Director_Profit":2461192847},{"Director":"Michael Bay","Profit":297739855,"Title":"Pearl Harbor","Total_Director_Profit":2461192847},{"Director":"Michael Bay","Profit":261069511,"Title":"The Rock","Total_Director_Profit":2461192847},{"Director":"Michael Bay","Profit":142940870,"Title":"Bad Boys II","Total_Director_Profit":2461192847},{"Director":"Michael Bay","Profit":118247413,"Title":"Bad Boys","Total_Director_Profit":2461192847},{"Director":"Michael Bay","Profit":43018913,"Title":"The Island","Total_Director_Profit":2461192847},{"Director":"Roland Emmerich","Profit":742400878,"Title":"Independence Day","Total_Director_Profit":2390416794},{"Director":"Roland Emmerich","Profit":566812167,"Title":2012,"Total_Director_Profit":2390416794},{"Director":"Roland Emmerich","Profit":419272402,"Title":"The Day After Tomorrow","Total_Director_Profit":2390416794},{"Director":"Roland Emmerich","Profit":251000000,"Title":"Godzilla","Total_Director_Profit":2390416794},{"Director":"Roland Emmerich","Profit":164065678,"Title":"10,000 B.C.","Total_Director_Profit":2390416794},{"Director":"Roland Emmerich","Profit":141565669,"Title":"Stargate","Total_Director_Profit":2390416794},{"Director":"Roland Emmerich","Profit":105300000,"Title":"The Patriot","Total_Director_Profit":2390416794},{"Director":"Gore Verbinski","Profit":840659812,"Title":"Pirates of the Caribbean: Dead Man's Chest","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Profit":660996492,"Title":"Pirates of the Caribbean: At World's End","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Profit":530011224,"Title":"Pirates of the Caribbean: The Curse of the Black Pearl","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Profit":201094024,"Title":"The Ring","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Profit":107808615,"Title":"The Mexican","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Profit":23894591,"Title":"Mouse Hunt","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Profit":-4533039,"Title":"The Weather Man","Total_Director_Profit":2359931719},{"Director":"Tim Burton","Profit":823291110,"Title":"Alice in Wonderland","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":376348924,"Title":"Batman","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":324459076,"Title":"Charlie and the Chocolate Factory","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":262211740,"Title":"Planet of the Apes","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":186822354,"Title":"Batman Returns","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":137068340,"Title":"Sleepy Hollow","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":87359111,"Title":"The Corpse Bride","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":58326666,"Title":"Beetle Juice","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":53432867,"Title":"Big Fish","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":33976987,"Title":"Edward Scissorhands","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":21371017,"Title":"Mars Attacks!","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":-12171534,"Title":"Ed Wood","Total_Director_Profit":2352496658},{"Director":"Andrew Adamson","Profit":849838758,"Title":"Shrek 2","Total_Director_Profit":2047535219},{"Director":"Andrew Adamson","Profit":568806957,"Title":"The Chronicles of Narnia: The Lion, the Witch and the Wardrobe","Total_Director_Profit":2047535219},{"Director":"Andrew Adamson","Profit":434399218,"Title":"Shrek","Total_Director_Profit":2047535219},{"Director":"Andrew Adamson","Profit":194490286,"Title":"The Chronicles of Narnia: Prince Caspian","Total_Director_Profit":2047535219},{"Director":"Sam Raimi","Profit":682708551,"Title":"Spider-Man","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":632871626,"Title":"Spider-Man 3","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":583705001,"Title":"Spider-Man 2","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":55724728,"Title":"Drag Me To Hell","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":34567606,"Title":"The Gift","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":29025000,"Title":"The Evil Dead","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":10502976,"Title":"Army of Darkness","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":2423044,"Title":"Evil Dead II","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":-683727,"Title":"A Simple Plan","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":-3887360,"Title":"For Love of the Game","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":-13447540,"Title":"The Quick and the Dead","Total_Director_Profit":2013509905},{"Director":"Ron Howard","Profit":632236138,"Title":"The Da Vinci Code","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":335975846,"Title":"Angels & Demons","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":269100000,"Title":"Apollo 13","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":238708996,"Title":"A Beautiful Mind","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":238700000,"Title":"Ransom","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":222141403,"Title":"How the Grinch Stole Christmas","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":54599495,"Title":"Splash","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":20539911,"Title":"Cinderella Man","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":-855414,"Title":"Frost/Nixon","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":-24680311,"Title":"EDtv","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":-26746567,"Title":"The Missing","Total_Director_Profit":1959719497},{"Director":"Christopher Nolan","Profit":837345358,"Title":"The Dark Knight","Total_Director_Profit":1823751815},{"Director":"Christopher Nolan","Profit":593830280,"Title":"Inception","Total_Director_Profit":1823751815},{"Director":"Christopher Nolan","Profit":222353017,"Title":"Batman Begins","Total_Director_Profit":1823751815},{"Director":"Christopher Nolan","Profit":67896006,"Title":"The Prestige","Total_Director_Profit":1823751815},{"Director":"Christopher Nolan","Profit":67622499,"Title":"Insomnia","Total_Director_Profit":1823751815},{"Director":"Christopher Nolan","Profit":34665950,"Title":"Memento","Total_Director_Profit":1823751815},{"Director":"Christopher Nolan","Profit":38705,"Title":"Following","Total_Director_Profit":1823751815},{"Director":"M. Night Shyamalan","Profit":632806292,"Title":"The Sixth Sense","Total_Director_Profit":1575120870},{"Director":"M. Night Shyamalan","Profit":337563071,"Title":"Signs","Total_Director_Profit":1575120870},{"Director":"M. Night Shyamalan","Profit":188514545,"Title":"The Village","Total_Director_Profit":1575120870},{"Director":"M. Night Shyamalan","Profit":174856037,"Title":"Unbreakable","Total_Director_Profit":1575120870},{"Director":"M. Night Shyamalan","Profit":140191957,"Title":"The Last Airbender","Total_Director_Profit":1575120870},{"Director":"M. Night Shyamalan","Profit":103403799,"Title":"The Happening","Total_Director_Profit":1575120870},{"Director":"M. Night Shyamalan","Profit":-2214831,"Title":"Lady in the Water","Total_Director_Profit":1575120870},{"Director":"David Yates","Profit":788468864,"Title":"Harry Potter and the Order of the Phoenix","Total_Director_Profit":1475968769},{"Director":"David Yates","Profit":687499905,"Title":"Harry Potter and the Half-Blood Prince","Total_Director_Profit":1475968769},{"Director":"John Lasseter","Profit":394966906,"Title":"Toy Story 2","Total_Director_Profit":1436948978},{"Director":"John Lasseter","Profit":391923762,"Title":"Cars","Total_Director_Profit":1436948978},{"Director":"John Lasseter","Profit":331948825,"Title":"Toy Story","Total_Director_Profit":1436948978},{"Director":"John Lasseter","Profit":318109485,"Title":"A Bug's Life","Total_Director_Profit":1436948978},{"Director":"Carlos Saldanha","Profit":796685941,"Title":"Ice Age: Dawn of the Dinosaurs","Total_Director_Profit":1373585223},{"Director":"Carlos Saldanha","Profit":576899282,"Title":"Ice Age: The Meltdown","Total_Director_Profit":1373585223},{"Director":"Andy Wachowski","Profit":611576929,"Title":"The Matrix Reloaded","Total_Director_Profit":1296311080},{"Director":"Andy Wachowski","Profit":395279930,"Title":"The Matrix","Total_Director_Profit":1296311080},{"Director":"Andy Wachowski","Profit":314259759,"Title":"The Matrix Revolutions","Total_Director_Profit":1296311080},{"Director":"Andy Wachowski","Profit":1800000,"Title":"Bound","Total_Director_Profit":1296311080},{"Director":"Andy Wachowski","Profit":-26605538,"Title":"Speed Racer","Total_Director_Profit":1296311080},{"Director":"Ridley Scott","Profit":354683805,"Title":"Gladiator","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":263100280,"Title":"Hannibal","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":194630630,"Title":"Alien","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":165697825,"Title":"American Gangster","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":101398413,"Title":"Kingdom of Heaven","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":100885538,"Title":"Robin Hood","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":78638745,"Title":"Black Hawk Down","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":40894666,"Title":"Body of Lies","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":15892212,"Title":"Black Rain","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":7064105,"Title":"A Good Year","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":5139618,"Title":"Blade Runner","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":-1830844,"Title":"G.I.Jane","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":-9497888,"Title":"Legend","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":-27770700,"Title":"White Squall","Total_Director_Profit":1288926405}],"metadata":{"Director":{"type":"string","semanticType":"Name"},"Profit":{"type":"number","semanticType":"Number"},"Title":{"type":"string","semanticType":"Name"},"Total_Director_Profit":{"type":"number","semanticType":"Number"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director or Profit values\n df = df.dropna(subset=['Director', 'Profit'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Add total director profit to each movie row\n df_filtered = df_filtered.merge(director_profit, on='Director', how='left')\n \n # Select and order columns\n transformed_df = df_filtered[['Director', 'Title', 'Profit', 'Total_Director_Profit']].copy()\n \n # Sort by total director profit (descending) and then by movie profit (descending)\n transformed_df = transformed_df.sort_values(['Total_Director_Profit', 'Profit'], ascending=[False, False])\n \n return transformed_df\n","source":["movies"],"dialog":[{"content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 441226247.0, 533345358.0, 600788188.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: nan, 1978111.0, 140689412.0, ..., 100178981.0, 104578578.0, 234119058.0, 320830925.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 15000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.5, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 245271.0, 261439.0, 326950.0, 417703.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\nshow top 20 directories based on profit breakdown by their movies","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"Show top 20 directors based on total profit, with a breakdown of profit by their individual movies\",\n \"display_instruction\": \"Show top 20 **directors** by total **profit** with movie breakdown\",\n \"recommendation\": \"Calculate profit (Worldwide Gross - Production Budget) for each movie, then aggregate by director to find top 20 directors by total profit. Display individual movie profits for each director to show the breakdown.\",\n \"output_fields\": [\"Director\", \"Title\", \"Profit\", \"Total_Director_Profit\"],\n \"chart_type\": \"group_bar\",\n \"chart_encodings\": {\n \"x\": \"Director\",\n \"y\": \"Profit\",\n \"color\": \"Title\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director or Profit values\n df = df.dropna(subset=['Director', 'Profit'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Add total director profit to each movie row\n df_filtered = df_filtered.merge(director_profit, on='Director', how='left')\n \n # Select and order columns\n transformed_df = df_filtered[['Director', 'Title', 'Profit', 'Total_Director_Profit']].copy()\n \n # Sort by total director profit (descending) and then by movie profit (descending)\n transformed_df = transformed_df.sort_values(['Total_Director_Profit', 'Profit'], ascending=[False, False])\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"movies","sourceTableIds":["movies"],"instruction":"show top 20 directories based on profit breakdown by their movies","displayInstruction":"Show top 20 **directors** by total **profit** with movie breakdown","chart":{"id":"chart-1760743765735","chartType":"Auto","encodingMap":{},"tableRef":"movies","saved":false,"source":"trigger","unread":true},"resultTableId":"table-770727"},"explanation":{"agent":"CodeExplanationAgent","code":"This code performs a director profitability analysis:\n\n1. Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n\n2. Remove movies with missing **Director** or **Profit** information to ensure clean analysis\n\n3. Aggregate movies by **Director** to compute **Total_Director_Profit** (sum of all profits from their movies)\n\n4. Identify the **top 20 directors** ranked by their total profit across all films\n\n5. Filter the dataset to include only movies directed by these **top 20 directors**\n\n6. Enrich each movie record with its director's **Total_Director_Profit** for comparison context\n\n7. Sort results first by **Total_Director_Profit** (descending) to group directors by overall success, then by individual movie **Profit** (descending) to rank films within each director's portfolio","concepts":[{"explanation":"Net financial return for each movie, calculated as: \\( \\text{Profit} = \\text{Worldwide\\_Gross} - \\text{Production\\_Budget} \\). Positive values indicate profitable films, while negative values represent financial losses.","field":"Profit"},{"explanation":"Cumulative profit generated by a director across all their films in the dataset, computed as: \\( \\text{Total\\_Director\\_Profit} = \\sum \\text{Profit}_{\\text{all movies by director}} \\). This metric measures a director's overall commercial track record and financial value to studios.","field":"Total_Director_Profit"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 402111870.0, 403706375.0, 600788188.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: 893953.0, 2638319.0, 3507046.0, ..., 104578578.0, 124058348.0, 234119058.0, 320830925.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: 46.0, 72.0, 74.0, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: nan, 1.5, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 212985.0, 237477.0, 417703.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director or Profit values\n df = df.dropna(subset=['Director', 'Profit'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Add total director profit to each movie row\n df_filtered = df_filtered.merge(director_profit, on='Director', how='left')\n \n # Select and order columns\n transformed_df = df_filtered[['Director', 'Title', 'Profit', 'Total_Director_Profit']].copy()\n \n # Sort by total director profit (descending) and then by movie profit (descending)\n transformed_df = transformed_df.sort_values(['Total_Director_Profit', 'Profit'], ascending=[False, False])\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThis code performs a director profitability analysis:\n\n1. Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n\n2. Remove movies with missing **Director** or **Profit** information to ensure clean analysis\n\n3. Aggregate movies by **Director** to compute **Total_Director_Profit** (sum of all profits from their movies)\n\n4. Identify the **top 20 directors** ranked by their total profit across all films\n\n5. Filter the dataset to include only movies directed by these **top 20 directors**\n\n6. Enrich each movie record with its director's **Total_Director_Profit** for comparison context\n\n7. Sort results first by **Total_Director_Profit** (descending) to group directors by overall success, then by individual movie **Profit** (descending) to rank films within each director's portfolio\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Profit\",\n \"explanation\": \"Net financial return for each movie, calculated as: \\\\( \\\\text{Profit} = \\\\text{Worldwide\\\\_Gross} - \\\\text{Production\\\\_Budget} \\\\). Positive values indicate profitable films, while negative values represent financial losses.\"\n },\n {\n \"field\": \"Total_Director_Profit\",\n \"explanation\": \"Cumulative profit generated by a director across all their films in the dataset, computed as: \\\\( \\\\text{Total\\\\_Director\\\\_Profit} = \\\\sum \\\\text{Profit}_{\\\\text{all movies by director}} \\\\). This metric measures a director's overall commercial track record and financial value to studios.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-89","displayId":"director-genre-profit","names":["Director","Genre_Profit","Major Genre","Total_Director_Profit"],"rows":[{"Director":"Steven Spielberg","Genre_Profit":2288324872,"Major Genre":"Action","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Genre_Profit":2222007781,"Major Genre":"Drama","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Genre_Profit":2118165403,"Major Genre":"Adventure","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Genre_Profit":478200000,"Major Genre":"Horror","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Genre_Profit":62875000,"Major Genre":"Comedy","Total_Director_Profit":7169573056},{"Director":"James Cameron","Genre_Profit":3435186261,"Major Genre":"Action","Total_Director_Profit":5078066216},{"Director":"James Cameron","Genre_Profit":1642879955,"Major Genre":"Thriller/Suspense","Total_Director_Profit":5078066216},{"Director":"Chris Columbus","Genre_Profit":1761881048,"Major Genre":"Adventure","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Genre_Profit":1215348066,"Major Genre":"Comedy","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Genre_Profit":67130693,"Major Genre":"Drama","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Genre_Profit":-8329380,"Major Genre":"Musical","Total_Director_Profit":3036030427},{"Director":"George Lucas","Genre_Profit":2871882789,"Major Genre":"Adventure","Total_Director_Profit":3011105789},{"Director":"George Lucas","Genre_Profit":139223000,"Major Genre":"Comedy","Total_Director_Profit":3011105789},{"Director":"Peter Jackson","Genre_Profit":2974450745,"Major Genre":"Adventure","Total_Director_Profit":3001395936},{"Director":"Peter Jackson","Genre_Profit":29702568,"Major Genre":"Drama","Total_Director_Profit":3001395936},{"Director":"Peter Jackson","Genre_Profit":-2757377,"Major Genre":"Horror","Total_Director_Profit":3001395936},{"Director":"Robert Zemeckis","Genre_Profit":1176274785,"Major Genre":"Drama","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Genre_Profit":1038225574,"Major Genre":"Adventure","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Genre_Profit":375522650,"Major Genre":"Comedy","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Genre_Profit":198693989,"Major Genre":"Thriller/Suspense","Total_Director_Profit":2788716998},{"Director":"Michael Bay","Genre_Profit":2046592847,"Major Genre":"Action","Total_Director_Profit":2461192847},{"Director":"Michael Bay","Genre_Profit":414600000,"Major Genre":"Adventure","Total_Director_Profit":2461192847},{"Director":"Roland Emmerich","Genre_Profit":1325738958,"Major Genre":"Adventure","Total_Director_Profit":2390416794},{"Director":"Roland Emmerich","Genre_Profit":959377836,"Major Genre":"Action","Total_Director_Profit":2390416794},{"Director":"Roland Emmerich","Genre_Profit":105300000,"Major Genre":"Drama","Total_Director_Profit":2390416794},{"Director":"Gore Verbinski","Genre_Profit":2031667528,"Major Genre":"Adventure","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Genre_Profit":201094024,"Major Genre":"Horror","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Genre_Profit":107808615,"Major Genre":"Action","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Genre_Profit":23894591,"Major Genre":"Comedy","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Genre_Profit":-4533039,"Major Genre":"Drama","Total_Director_Profit":2359931719},{"Director":"Tim Burton","Genre_Profit":1172861961,"Major Genre":"Adventure","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Genre_Profit":563171278,"Major Genre":"Action","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Genre_Profit":425962212,"Major Genre":"Comedy","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Genre_Profit":137068340,"Major Genre":"Horror","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Genre_Profit":53432867,"Major Genre":"Drama","Total_Director_Profit":2352496658},{"Director":"Andrew Adamson","Genre_Profit":2047535219,"Major Genre":"Adventure","Total_Director_Profit":2047535219},{"Director":"Sam Raimi","Genre_Profit":1899285178,"Major Genre":"Adventure","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Genre_Profit":97675748,"Major Genre":"Horror","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Genre_Profit":34567606,"Major Genre":"Thriller/Suspense","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Genre_Profit":-4571087,"Major Genre":"Drama","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Genre_Profit":-13447540,"Major Genre":"Western","Total_Director_Profit":2013509905},{"Director":"Ron Howard","Genre_Profit":968211984,"Major Genre":"Thriller/Suspense","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Genre_Profit":527493493,"Major Genre":"Drama","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Genre_Profit":252060587,"Major Genre":"Comedy","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Genre_Profit":238700000,"Major Genre":"Action","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Genre_Profit":-26746567,"Major Genre":"Western","Total_Director_Profit":1959719497},{"Director":"Christopher Nolan","Genre_Profit":1059698375,"Major Genre":"Action","Total_Director_Profit":1823713110},{"Director":"Christopher Nolan","Genre_Profit":729348785,"Major Genre":"Thriller/Suspense","Total_Director_Profit":1823713110},{"Director":"Christopher Nolan","Genre_Profit":34665950,"Major Genre":"Drama","Total_Director_Profit":1823713110},{"Director":"M. Night Shyamalan","Genre_Profit":1437143744,"Major Genre":"Thriller/Suspense","Total_Director_Profit":1575120870},{"Director":"M. Night Shyamalan","Genre_Profit":140191957,"Major Genre":"Adventure","Total_Director_Profit":1575120870},{"Director":"M. Night Shyamalan","Genre_Profit":-2214831,"Major Genre":"Drama","Total_Director_Profit":1575120870},{"Director":"David Yates","Genre_Profit":1475968769,"Major Genre":"Adventure","Total_Director_Profit":1475968769},{"Director":"John Lasseter","Genre_Profit":1045025216,"Major Genre":"Adventure","Total_Director_Profit":1436948978},{"Director":"John Lasseter","Genre_Profit":391923762,"Major Genre":"Comedy","Total_Director_Profit":1436948978},{"Director":"Carlos Saldanha","Genre_Profit":1373585223,"Major Genre":"Adventure","Total_Director_Profit":1373585223},{"Director":"Andy Wachowski","Genre_Profit":1294511080,"Major Genre":"Action","Total_Director_Profit":1296311080},{"Director":"Andy Wachowski","Genre_Profit":1800000,"Major Genre":"Thriller/Suspense","Total_Director_Profit":1296311080},{"Director":"Ridley Scott","Genre_Profit":534208088,"Major Genre":"Action","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Genre_Profit":457730910,"Major Genre":"Horror","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Genre_Profit":170931086,"Major Genre":"Drama","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Genre_Profit":64129825,"Major Genre":"Adventure","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Genre_Profit":61926496,"Major Genre":"Thriller/Suspense","Total_Director_Profit":1288926405}],"metadata":{"Director":{"type":"string","semanticType":"Name"},"Genre_Profit":{"type":"number","semanticType":"Number"},"Major Genre":{"type":"string","semanticType":"String"},"Total_Director_Profit":{"type":"number","semanticType":"Number"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director, Profit, or Major Genre values\n df = df.dropna(subset=['Director', 'Profit', 'Major Genre'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Aggregate profit by Director and Major Genre\n genre_breakdown = df_filtered.groupby(['Director', 'Major Genre'])['Profit'].sum().reset_index()\n genre_breakdown.columns = ['Director', 'Major Genre', 'Genre_Profit']\n \n # Add total director profit to each row\n genre_breakdown = genre_breakdown.merge(director_profit, on='Director', how='left')\n \n # Sort by total director profit (descending) and then by genre profit (descending)\n transformed_df = genre_breakdown.sort_values(['Total_Director_Profit', 'Genre_Profit'], ascending=[False, False])\n \n return transformed_df\n","source":["movies"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 441226247.0, 533345358.0, 600788188.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: nan, 1978111.0, 140689412.0, ..., 100178981.0, 104578578.0, 234119058.0, 320830925.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 15000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.5, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 245271.0, 261439.0, 326950.0, 417703.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\nshow top 20 directories based on profit breakdown by their movies","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"Show top 20 directors based on total profit, with a breakdown of profit by their individual movies\",\n \"display_instruction\": \"Show top 20 **directors** by total **profit** with movie breakdown\",\n \"recommendation\": \"Calculate profit (Worldwide Gross - Production Budget) for each movie, then aggregate by director to find top 20 directors by total profit. Display individual movie profits for each director to show the breakdown.\",\n \"output_fields\": [\"Director\", \"Title\", \"Profit\", \"Total_Director_Profit\"],\n \"chart_type\": \"group_bar\",\n \"chart_encodings\": {\n \"x\": \"Director\",\n \"y\": \"Profit\",\n \"color\": \"Title\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director or Profit values\n df = df.dropna(subset=['Director', 'Profit'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Add total director profit to each movie row\n df_filtered = df_filtered.merge(director_profit, on='Director', how='left')\n \n # Select and order columns\n transformed_df = df_filtered[['Director', 'Title', 'Profit', 'Total_Director_Profit']].copy()\n \n # Sort by total director profit (descending) and then by movie profit (descending)\n transformed_df = transformed_df.sort_values(['Total_Director_Profit', 'Profit'], ascending=[False, False])\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Director Profit Title Total_Director_Profit\n0 Steven Spielberg 860067947 Jurassic Park 7169573056\n1 Steven Spielberg 782410554 ET: The Extra-Terrestrial 7169573056\n2 Steven Spielberg 711686679 The Lost World: Jurassic Park 7169573056\n3 Steven Spielberg 601558145 Indiana Jones and the Kingdom of the Crystal Skull 7169573056\n4 Steven Spielberg 459745532 The War of the Worlds 7169573056\n5 Steven Spielberg 458700000 Jaws 7169573056\n6 Steven Spielberg 426171806 Indiana Jones and the Last Crusade 7169573056\n7 Steven Spielberg 416635085 Saving Private Ryan 7169573056\n8 Steven Spielberg 366800358 Raiders of the Lost Ark 7169573056\n9 Steven Spielberg 317700000 Close Encounters of the Third Kind 7169573056\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"break down by movie genre instead\",\n \"chart_type\": \"Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Director\",\n \"y\": \"Profit\",\n \"color\": \"Title\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Show top 20 directors based on total profit, with a breakdown by the major genre of their movies instead of individual movie titles. This will show how each director's profit is distributed across different genres.\",\n \"display_instruction\": \"Show top 20 **directors** by total **profit** with **genre** breakdown\",\n \"output_fields\": [\"Director\", \"Major Genre\", \"Genre_Profit\", \"Total_Director_Profit\"],\n \"chart_encodings\": {\n \"x\": \"Director\",\n \"y\": \"Genre_Profit\",\n \"color\": \"Major Genre\"\n },\n \"reason\": \"The user wants to change the breakdown from individual movies to movie genres. Instead of showing profit by Title, we now aggregate profit by Major Genre for each director. The chart_encodings are updated to use 'Major Genre' for color instead of 'Title', and y-axis now shows 'Genre_Profit' (aggregated by genre) instead of individual movie profit.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director, Profit, or Major Genre values\n df = df.dropna(subset=['Director', 'Profit', 'Major Genre'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Aggregate profit by Director and Major Genre\n genre_breakdown = df_filtered.groupby(['Director', 'Major Genre'])['Profit'].sum().reset_index()\n genre_breakdown.columns = ['Director', 'Major Genre', 'Genre_Profit']\n \n # Add total director profit to each row\n genre_breakdown = genre_breakdown.merge(director_profit, on='Director', how='left')\n \n # Sort by total director profit (descending) and then by genre profit (descending)\n transformed_df = genre_breakdown.sort_values(['Total_Director_Profit', 'Genre_Profit'], ascending=[False, False])\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-770727","sourceTableIds":["movies"],"instruction":"break down by movie genre instead","displayInstruction":"Show top 20 **directors** by total **profit** with **genre** breakdown","chart":{"id":"chart-1760743802354","chartType":"Bar Chart","encodingMap":{"x":{"fieldID":"original--movies--Director"},"y":{"fieldID":"concept-Profit-1760742653681"},"color":{"fieldID":"original--movies--Title"},"opacity":{"channel":"opacity","bin":false},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"table-770727","saved":false,"source":"trigger","unread":false},"resultTableId":"table-89"},"explanation":{"agent":"CodeExplanationAgent","code":"- Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Remove movies with missing **Director**, **Profit**, or **Major Genre** information\n- Aggregate the **Profit** for each **Director** to calculate their **Total_Director_Profit**\n- Identify the **top 20 directors** based on their **Total_Director_Profit**\n- Filter the dataset to include only movies made by these **top 20 directors**\n- Group the filtered movies by **Director** and **Major Genre**, summing the **Profit** for each combination to create **Genre_Profit**\n- Merge the **Total_Director_Profit** back into the genre breakdown data\n- Sort the results first by **Total_Director_Profit** (highest to lowest), then by **Genre_Profit** (highest to lowest) within each director","concepts":[{"explanation":"The financial return of a movie, calculated as \\( \\text{Profit} = \\text{Worldwide\\_Gross} - \\text{Production\\_Budget} \\). This represents the net revenue after accounting for production costs.","field":"Profit"},{"explanation":"The cumulative profit generated by all movies directed by a specific director. This metric aggregates the Profit across all films in their portfolio to measure overall financial success.","field":"Total_Director_Profit"},{"explanation":"The total profit generated by a director within a specific Major Genre. This breaks down a director's Total_Director_Profit by genre category, showing which genres contribute most to their financial success.","field":"Genre_Profit"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., nan, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: 7166365.0, 33619971.0, 140689412.0, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 15000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.5, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., nan, nan, 212985.0, 411088.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director, Profit, or Major Genre values\n df = df.dropna(subset=['Director', 'Profit', 'Major Genre'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Aggregate profit by Director and Major Genre\n genre_breakdown = df_filtered.groupby(['Director', 'Major Genre'])['Profit'].sum().reset_index()\n genre_breakdown.columns = ['Director', 'Major Genre', 'Genre_Profit']\n \n # Add total director profit to each row\n genre_breakdown = genre_breakdown.merge(director_profit, on='Director', how='left')\n \n # Sort by total director profit (descending) and then by genre profit (descending)\n transformed_df = genre_breakdown.sort_values(['Total_Director_Profit', 'Genre_Profit'], ascending=[False, False])\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Remove movies with missing **Director**, **Profit**, or **Major Genre** information\n- Aggregate the **Profit** for each **Director** to calculate their **Total_Director_Profit**\n- Identify the **top 20 directors** based on their **Total_Director_Profit**\n- Filter the dataset to include only movies made by these **top 20 directors**\n- Group the filtered movies by **Director** and **Major Genre**, summing the **Profit** for each combination to create **Genre_Profit**\n- Merge the **Total_Director_Profit** back into the genre breakdown data\n- Sort the results first by **Total_Director_Profit** (highest to lowest), then by **Genre_Profit** (highest to lowest) within each director\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Profit\",\n \"explanation\": \"The financial return of a movie, calculated as \\\\( \\\\text{Profit} = \\\\text{Worldwide\\\\_Gross} - \\\\text{Production\\\\_Budget} \\\\). This represents the net revenue after accounting for production costs.\"\n },\n {\n \"field\": \"Total_Director_Profit\",\n \"explanation\": \"The cumulative profit generated by all movies directed by a specific director. This metric aggregates the Profit across all films in their portfolio to measure overall financial success.\"\n },\n {\n \"field\": \"Genre_Profit\",\n \"explanation\": \"The total profit generated by a director within a specific Major Genre. This breaks down a director's Total_Director_Profit by genre category, showing which genres contribute most to their financial success.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-456490","displayId":"movie-ratings","names":["IMDB Rating","Production Budget","Rotten Tomatoes Rating","Title"],"rows":[{"IMDB Rating":6.1,"Production Budget":8000000,"Rotten Tomatoes Rating":null,"Title":"The Land Girls"},{"IMDB Rating":6.9,"Production Budget":300000,"Rotten Tomatoes Rating":null,"Title":"First Love, Last Rites"},{"IMDB Rating":6.8,"Production Budget":250000,"Rotten Tomatoes Rating":null,"Title":"I Married a Strange Person"},{"IMDB Rating":null,"Production Budget":300000,"Rotten Tomatoes Rating":13,"Title":"Let's Talk About Sex"},{"IMDB Rating":3.4,"Production Budget":1000000,"Rotten Tomatoes Rating":62,"Title":"Slam"},{"IMDB Rating":7.7,"Production Budget":6000,"Rotten Tomatoes Rating":null,"Title":"Following"},{"IMDB Rating":3.8,"Production Budget":1600000,"Rotten Tomatoes Rating":null,"Title":"Foolish"},{"IMDB Rating":5.8,"Production Budget":40000000,"Rotten Tomatoes Rating":25,"Title":"Pirates"},{"IMDB Rating":7,"Production Budget":6000000,"Rotten Tomatoes Rating":86,"Title":"Duel in the Sun"},{"IMDB Rating":7,"Production Budget":1000000,"Rotten Tomatoes Rating":81,"Title":"Tom Jones"},{"IMDB Rating":7.5,"Production Budget":10000000,"Rotten Tomatoes Rating":84,"Title":"Oliver!"},{"IMDB Rating":8.4,"Production Budget":2000000,"Rotten Tomatoes Rating":97,"Title":"To Kill A Mockingbird"},{"IMDB Rating":6.8,"Production Budget":100000,"Rotten Tomatoes Rating":87,"Title":"Hollywood Shuffle"},{"IMDB Rating":7,"Production Budget":5200000,"Rotten Tomatoes Rating":null,"Title":"Wilson"},{"IMDB Rating":6.1,"Production Budget":22000000,"Rotten Tomatoes Rating":null,"Title":"Darling Lili"},{"IMDB Rating":2.5,"Production Budget":13500000,"Rotten Tomatoes Rating":90,"Title":"The Ten Commandments"},{"IMDB Rating":8.9,"Production Budget":340000,"Rotten Tomatoes Rating":null,"Title":"12 Angry Men"},{"IMDB Rating":8.1,"Production Budget":29000000,"Rotten Tomatoes Rating":null,"Title":"Twelve Monkeys"},{"IMDB Rating":7,"Production Budget":4000000,"Rotten Tomatoes Rating":57,"Title":1776},{"IMDB Rating":5.6,"Production Budget":32000000,"Rotten Tomatoes Rating":33,"Title":1941},{"IMDB Rating":6.3,"Production Budget":1900000,"Rotten Tomatoes Rating":null,"Title":"Chacun sa nuit"},{"IMDB Rating":8.4,"Production Budget":10500000,"Rotten Tomatoes Rating":96,"Title":"2001: A Space Odyssey"},{"IMDB Rating":null,"Production Budget":5000000,"Rotten Tomatoes Rating":92,"Title":"20,000 Leagues Under the Sea"},{"IMDB Rating":6.9,"Production Budget":2000000,"Rotten Tomatoes Rating":null,"Title":"24 7: Twenty Four Seven"},{"IMDB Rating":7.1,"Production Budget":500000,"Rotten Tomatoes Rating":77,"Title":"Twin Falls Idaho"},{"IMDB Rating":5.7,"Production 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Can Count on Me"},{"IMDB Rating":5,"Production Budget":60000000,"Rotten Tomatoes Rating":14,"Title":"Year One"},{"IMDB Rating":7,"Production Budget":50000000,"Rotten Tomatoes Rating":43,"Title":"Yes Man"},{"IMDB Rating":6.7,"Production Budget":4000000,"Rotten Tomatoes Rating":78,"Title":"You Kill Me"},{"IMDB Rating":7.6,"Production Budget":45000000,"Rotten Tomatoes Rating":null,"Title":"Yours, Mine and Ours"},{"IMDB Rating":6.2,"Production Budget":65000000,"Rotten Tomatoes Rating":68,"Title":"You've Got Mail"},{"IMDB Rating":6.7,"Production Budget":18000000,"Rotten Tomatoes Rating":null,"Title":"Youth in Revolt"},{"IMDB Rating":4.5,"Production Budget":800000,"Rotten Tomatoes Rating":null,"Title":"The Young Unknowns"},{"IMDB Rating":7.2,"Production Budget":35000000,"Rotten Tomatoes Rating":76,"Title":"The Young Victoria"},{"IMDB Rating":null,"Production Budget":65000000,"Rotten Tomatoes Rating":75,"Title":"Zathura"},{"IMDB Rating":6.8,"Production Budget":5000000,"Rotten Tomatoes Rating":66,"Title":"Zero Effect"},{"IMDB Rating":6.4,"Production Budget":28000000,"Rotten Tomatoes Rating":62,"Title":"Zoolander"},{"IMDB Rating":7.8,"Production Budget":23600000,"Rotten Tomatoes Rating":89,"Title":"Zombieland"},{"IMDB Rating":7,"Production Budget":24000000,"Rotten Tomatoes Rating":65,"Title":"Zack and Miri Make a Porno"},{"IMDB Rating":null,"Production Budget":85000000,"Rotten Tomatoes Rating":89,"Title":"Zodiac"},{"IMDB Rating":3.4,"Production Budget":35000000,"Rotten Tomatoes Rating":3,"Title":"Zoom"},{"IMDB Rating":5.7,"Production Budget":80000000,"Rotten Tomatoes Rating":26,"Title":"The Legend of Zorro"},{"IMDB Rating":6.7,"Production Budget":65000000,"Rotten Tomatoes Rating":82,"Title":"The Mask of Zorro"}],"metadata":{"IMDB Rating":{"type":"number","semanticType":"Number"},"Production Budget":{"type":"number","semanticType":"Number"},"Rotten Tomatoes Rating":{"type":"number","semanticType":"Number"},"Title":{"type":"string","semanticType":"Name"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values in key fields\n transformed_df = df_movies[['Title', 'Production Budget', 'Rotten Tomatoes Rating', 'IMDB Rating']].copy()\n \n # Remove rows where Production Budget is missing\n transformed_df = transformed_df[transformed_df['Production Budget'].notna()]\n \n # Keep rows where at least one rating is available\n transformed_df = transformed_df[\n (transformed_df['Rotten Tomatoes Rating'].notna()) | \n (transformed_df['IMDB Rating'].notna())\n ]\n \n return transformed_df\n","source":["movies"],"dialog":[{"content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: 15442818.0, 17031573.0, 33619971.0, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 5000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.4, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 212985.0, 237477.0, 417703.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\nDo higher production budgets correlate with better ratings?","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"The user wants to analyze the correlation between production budgets and movie ratings to understand if higher budgets lead to better ratings.\",\n \"display_instruction\": \"Show correlation between **Production Budget** and **ratings**\",\n \"recommendation\": \"Create a scatter plot visualization to show the relationship between Production Budget and ratings. Since there are two rating systems (Rotten Tomatoes and IMDB), we'll analyze both and include them as separate dimensions in the output data.\",\n \"output_fields\": [\"Title\", \"Production Budget\", \"Rotten Tomatoes Rating\", \"IMDB Rating\"],\n \"chart_type\": \"point\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Rotten Tomatoes Rating\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values in key fields\n transformed_df = df_movies[['Title', 'Production Budget', 'Rotten Tomatoes Rating', 'IMDB Rating']].copy()\n \n # Remove rows where Production Budget is missing\n transformed_df = transformed_df[transformed_df['Production Budget'].notna()]\n \n # Keep rows where at least one rating is available\n transformed_df = transformed_df[\n (transformed_df['Rotten Tomatoes Rating'].notna()) | \n (transformed_df['IMDB Rating'].notna())\n ]\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"movies","sourceTableIds":["movies"],"instruction":"Do higher production budgets correlate with better ratings?","displayInstruction":"Show correlation between **Production Budget** and **ratings**","chart":{"id":"chart-1760744462348","chartType":"Auto","encodingMap":{},"tableRef":"movies","saved":false,"source":"trigger","unread":true},"resultTableId":"table-456490"},"explanation":{"agent":"CodeExplanationAgent","code":"- Select key fields from the movies dataset: **Title**, **Production Budget**, **Rotten Tomatoes Rating**, and **IMDB Rating**\n- Filter out movies with missing **Production Budget** values to ensure all movies have known budget information\n- Retain only movies that have at least one rating available (either **Rotten Tomatoes Rating** or **IMDB Rating**), ensuring each movie has some form of critical assessment\n- Return a cleaned dataset containing only movies with complete budget information and at least one rating metric","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., nan, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: nan, 853973.0, 13180936.0, ..., nan, 46260220.0, 124058348.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 212985.0, 229156.0, 237477.0, 417703.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values in key fields\n transformed_df = df_movies[['Title', 'Production Budget', 'Rotten Tomatoes Rating', 'IMDB Rating']].copy()\n \n # Remove rows where Production Budget is missing\n transformed_df = transformed_df[transformed_df['Production Budget'].notna()]\n \n # Keep rows where at least one rating is available\n transformed_df = transformed_df[\n (transformed_df['Rotten Tomatoes Rating'].notna()) | \n (transformed_df['IMDB Rating'].notna())\n ]\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Select key fields from the movies dataset: **Title**, **Production Budget**, **Rotten Tomatoes Rating**, and **IMDB Rating**\n- Filter out movies with missing **Production Budget** values to ensure all movies have known budget information\n- Retain only movies that have at least one rating available (either **Rotten Tomatoes Rating** or **IMDB Rating**), ensuring each movie has some form of critical assessment\n- Return a cleaned dataset containing only movies with complete budget information and at least one rating metric\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-37","displayId":"movie-budget-ratings","names":["Number of Movies","Production Budget Bin","Rotten Tomatoes Rating Bin"],"rows":[{"Number of Movies":6,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"0-20"},{"Number of Movies":9,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"21-40"},{"Number of Movies":12,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"41-60"},{"Number of Movies":19,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"61-80"},{"Number of Movies":54,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"81-100"},{"Number of Movies":61,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"0-20"},{"Number of Movies":72,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"21-40"},{"Number of Movies":58,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"41-60"},{"Number of Movies":104,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"61-80"},{"Number of Movies":219,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"81-100"},{"Number of Movies":229,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"0-20"},{"Number of Movies":257,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"21-40"},{"Number of Movies":241,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"41-60"},{"Number of Movies":245,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"61-80"},{"Number of Movies":210,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"81-100"},{"Number of Movies":62,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"0-20"},{"Number of Movies":99,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"21-40"},{"Number of Movies":98,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"41-60"},{"Number of Movies":95,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"61-80"},{"Number of Movies":54,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"81-100"},{"Number of Movies":9,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"0-20"},{"Number of Movies":24,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"21-40"},{"Number of Movies":26,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"41-60"},{"Number of Movies":26,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"61-80"},{"Number of Movies":31,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"81-100"}],"metadata":{"Number of Movies":{"type":"number","semanticType":"Number"},"Production Budget Bin":{"type":"string","semanticType":"Range","levels":["<$1M","$1M-$10M","$10M-$50M","$50M-$100M",">$100M"]},"Rotten Tomatoes Rating Bin":{"type":"string","semanticType":"Range","levels":["0-20","21-40","41-60","61-80","81-100"]}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna())\n ]\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Count movies in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n ).size().reset_index(name='Number of Movies')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n","source":["movies"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: 15442818.0, 17031573.0, 33619971.0, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 5000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.4, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 212985.0, 237477.0, 417703.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\nDo higher production budgets correlate with better ratings?","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"The user wants to analyze the correlation between production budgets and movie ratings to understand if higher budgets lead to better ratings.\",\n \"display_instruction\": \"Show correlation between **Production Budget** and **ratings**\",\n \"recommendation\": \"Create a scatter plot visualization to show the relationship between Production Budget and ratings. Since there are two rating systems (Rotten Tomatoes and IMDB), we'll analyze both and include them as separate dimensions in the output data.\",\n \"output_fields\": [\"Title\", \"Production Budget\", \"Rotten Tomatoes Rating\", \"IMDB Rating\"],\n \"chart_type\": \"point\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Rotten Tomatoes Rating\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values in key fields\n transformed_df = df_movies[['Title', 'Production Budget', 'Rotten Tomatoes Rating', 'IMDB Rating']].copy()\n \n # Remove rows where Production Budget is missing\n transformed_df = transformed_df[transformed_df['Production Budget'].notna()]\n \n # Keep rows where at least one rating is available\n transformed_df = transformed_df[\n (transformed_df['Rotten Tomatoes Rating'].notna()) | \n (transformed_df['IMDB Rating'].notna())\n ]\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n IMDB Rating Production Budget Rotten Tomatoes Rating Title\n0 6.1 8000000 NaN The Land Girls\n1 6.9 300000 NaN First Love, Last Rites\n2 6.8 250000 NaN I Married a Strange Person\n3 NaN 300000 13.0 Let's Talk About Sex\n4 3.4 1000000 62.0 Slam\n5 7.7 6000 NaN Following\n6 3.8 1600000 NaN Foolish\n7 5.8 40000000 25.0 Pirates\n8 7.0 6000000 86.0 Duel in the Sun\n9 7.0 1000000 81.0 Tom Jones\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"bin rt rating and budget into bins to show number of movies in each range\",\n \"chart_type\": \"Scatter Plot\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Rotten Tomatoes Rating\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Bin the Production Budget and Rotten Tomatoes Rating into categorical ranges, then count the number of movies that fall into each combination of budget and rating bins to create a heatmap-style aggregation.\",\n \"display_instruction\": \"Show **number of movies** in each **Production Budget** and **Rotten Tomatoes Rating** bin\",\n \"output_fields\": [\"Production Budget Bin\", \"Rotten Tomatoes Rating Bin\", \"Number of Movies\"],\n \"chart_encodings\": {\n \"x\": \"Production Budget Bin\",\n \"y\": \"Rotten Tomatoes Rating Bin\",\n \"color\": \"Number of Movies\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n },\n \"reason\": \"The user wants to bin both Production Budget and Rotten Tomatoes Rating into ranges and count movies in each combination. This requires creating categorical bins for both dimensions and aggregating counts. A heatmap visualization (using color encoding for count) would be more appropriate than a scatter plot for showing binned data.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna())\n ]\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Count movies in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n ).size().reset_index(name='Number of Movies')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-456490","sourceTableIds":["movies"],"instruction":"bin rt rating and budget into bins to show number of movies in each range","displayInstruction":"Show **number of movies** in each **Production Budget** and **Rotten Tomatoes Rating** bin","chart":{"id":"chart-1760744514854","chartType":"Scatter Plot","encodingMap":{"x":{"fieldID":"original--movies--Production Budget"},"y":{"fieldID":"original--movies--Rotten Tomatoes Rating"},"color":{"channel":"color","bin":false},"size":{"channel":"size","bin":false},"opacity":{"channel":"opacity","bin":false},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"table-456490","saved":false,"source":"trigger","unread":false},"resultTableId":"table-37"},"explanation":{"agent":"CodeExplanationAgent","code":"- Filter the dataset to include only movies with both **Production Budget** and **Rotten Tomatoes Rating** values available\n- Create **budget categories** by grouping Production Budget into five bins: **<$1M**, **$1M-$10M**, **$10M-$50M**, **$50M-$100M**, and **>$100M**\n- Create **rating categories** by grouping Rotten Tomatoes Rating into five bins: **0-20**, **21-40**, **41-60**, **61-80**, and **81-100**\n- **Count** the number of movies that fall into each combination of budget category and rating category\n- Return a summary table showing the distribution of movies across budget and rating bins","concepts":[{"explanation":"Categorical bins created through discretization of continuous variables. Production Budget is divided into 5 ranges based on million-dollar thresholds, while Rotten Tomatoes Rating is divided into 5 equal-width intervals of 20 points each. This binning approach enables analysis of the relationship between production investment levels and critical reception patterns.","field":"Production Budget Bin, Rotten Tomatoes Rating Bin"},{"explanation":"This analysis employs a **two-dimensional frequency distribution** (contingency table approach) to explore the relationship between production budget and critical reception. The transformation creates a discrete joint distribution by binning two continuous variables and counting occurrences in each cell. This approach is useful for identifying patterns such as whether higher-budget films tend to receive better or worse critical ratings. **Alternative modeling approaches** could include: (1) correlation analysis between raw budget and rating values, (2) regression modeling to predict ratings from budget while controlling for other factors, (3) chi-square tests of independence to formally test whether budget and rating categories are associated, or (4) visualization through heatmaps to reveal concentration patterns across the budget-rating space.","field":"Statistical Analysis"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 402111870.0, 441226247.0, 600788188.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: 618454.0, 853973.0, 1877732.0, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 5000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: nan, 1.5, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 57313.0, nan, 73720.0, 212985.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna())\n ]\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Count movies in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n ).size().reset_index(name='Number of Movies')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Filter the dataset to include only movies with both **Production Budget** and **Rotten Tomatoes Rating** values available\n- Create **budget categories** by grouping Production Budget into five bins: **<$1M**, **$1M-$10M**, **$10M-$50M**, **$50M-$100M**, and **>$100M**\n- Create **rating categories** by grouping Rotten Tomatoes Rating into five bins: **0-20**, **21-40**, **41-60**, **61-80**, and **81-100**\n- **Count** the number of movies that fall into each combination of budget category and rating category\n- Return a summary table showing the distribution of movies across budget and rating bins\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Production Budget Bin, Rotten Tomatoes Rating Bin\",\n \"explanation\": \"Categorical bins created through discretization of continuous variables. Production Budget is divided into 5 ranges based on million-dollar thresholds, while Rotten Tomatoes Rating is divided into 5 equal-width intervals of 20 points each. This binning approach enables analysis of the relationship between production investment levels and critical reception patterns.\"\n },\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"This analysis employs a **two-dimensional frequency distribution** (contingency table approach) to explore the relationship between production budget and critical reception. The transformation creates a discrete joint distribution by binning two continuous variables and counting occurrences in each cell. This approach is useful for identifying patterns such as whether higher-budget films tend to receive better or worse critical ratings. **Alternative modeling approaches** could include: (1) correlation analysis between raw budget and rating values, (2) regression modeling to predict ratings from budget while controlling for other factors, (3) chi-square tests of independence to formally test whether budget and rating categories are associated, or (4) visualization through heatmaps to reveal concentration patterns across the budget-rating space.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-54","displayId":"film-profit-analysis","names":["Average Profit","Production Budget Bin","Rotten Tomatoes Rating Bin"],"rows":[{"Average Profit":1265816.3333333333,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"0-20"},{"Average Profit":6494134,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"21-40"},{"Average Profit":7866065.833333333,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"41-60"},{"Average Profit":6749073.2105263155,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"61-80"},{"Average Profit":20121581.403846152,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"81-100"},{"Average Profit":9285520.68852459,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"0-20"},{"Average Profit":17386269.305555556,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"21-40"},{"Average Profit":13840988.827586208,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"41-60"},{"Average Profit":20654143.125,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"61-80"},{"Average Profit":37024663.62962963,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"81-100"},{"Average Profit":11168889.11790393,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"0-20"},{"Average Profit":32529317.311284047,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"21-40"},{"Average Profit":36640084.60165975,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"41-60"},{"Average Profit":49506279.571428575,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"61-80"},{"Average Profit":93010923.90476191,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"81-100"},{"Average Profit":11584648.806451613,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"0-20"},{"Average Profit":59680849.09090909,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"21-40"},{"Average Profit":104106140.86734694,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"41-60"},{"Average Profit":121416300.37894736,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"61-80"},{"Average Profit":226983168.4814815,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"81-100"},{"Average Profit":132877326,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"0-20"},{"Average Profit":148619038.95833334,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"21-40"},{"Average Profit":297717419.34615386,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"41-60"},{"Average Profit":289246367.34615386,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"61-80"},{"Average Profit":515989525.483871,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"81-100"}],"metadata":{"Average Profit":{"type":"number","semanticType":"Number"},"Production Budget Bin":{"type":"string","semanticType":"Range","levels":["<$1M","$1M-$10M","$10M-$50M","$50M-$100M",">$100M"]},"Rotten Tomatoes Rating Bin":{"type":"string","semanticType":"Range","levels":["0-20","21-40","41-60","61-80","81-100"]}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating', 'Worldwide Gross']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna()) &\n (df_filtered['Worldwide Gross'].notna())\n ]\n \n # Calculate profit\n df_filtered['Profit'] = df_filtered['Worldwide Gross'] - df_filtered['Production Budget']\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Calculate average profit in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n )['Profit'].mean().reset_index(name='Average Profit')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n","source":["movies"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: 15442818.0, 17031573.0, 33619971.0, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 5000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.4, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 212985.0, 237477.0, 417703.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\nDo higher production budgets correlate with better ratings?","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"The user wants to analyze the correlation between production budgets and movie ratings to understand if higher budgets lead to better ratings.\",\n \"display_instruction\": \"Show correlation between **Production Budget** and **ratings**\",\n \"recommendation\": \"Create a scatter plot visualization to show the relationship between Production Budget and ratings. Since there are two rating systems (Rotten Tomatoes and IMDB), we'll analyze both and include them as separate dimensions in the output data.\",\n \"output_fields\": [\"Title\", \"Production Budget\", \"Rotten Tomatoes Rating\", \"IMDB Rating\"],\n \"chart_type\": \"point\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Rotten Tomatoes Rating\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values in key fields\n transformed_df = df_movies[['Title', 'Production Budget', 'Rotten Tomatoes Rating', 'IMDB Rating']].copy()\n \n # Remove rows where Production Budget is missing\n transformed_df = transformed_df[transformed_df['Production Budget'].notna()]\n \n # Keep rows where at least one rating is available\n transformed_df = transformed_df[\n (transformed_df['Rotten Tomatoes Rating'].notna()) | \n (transformed_df['IMDB Rating'].notna())\n ]\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n IMDB Rating Production Budget Rotten Tomatoes Rating Title\n0 6.1 8000000 NaN The Land Girls\n1 6.9 300000 NaN First Love, Last Rites\n2 6.8 250000 NaN I Married a Strange Person\n3 NaN 300000 13.0 Let's Talk About Sex\n4 3.4 1000000 62.0 Slam\n5 7.7 6000 NaN Following\n6 3.8 1600000 NaN Foolish\n7 5.8 40000000 25.0 Pirates\n8 7.0 6000000 86.0 Duel in the Sun\n9 7.0 1000000 81.0 Tom Jones\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"bin rt rating and budget into bins to show number of movies in each range\",\n \"chart_type\": \"Scatter Plot\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Rotten Tomatoes Rating\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Bin the Production Budget and Rotten Tomatoes Rating into categorical ranges, then count the number of movies that fall into each combination of budget and rating bins to create a heatmap-style aggregation.\",\n \"display_instruction\": \"Show **number of movies** in each **Production Budget** and **Rotten Tomatoes Rating** bin\",\n \"output_fields\": [\"Production Budget Bin\", \"Rotten Tomatoes Rating Bin\", \"Number of Movies\"],\n \"chart_encodings\": {\n \"x\": \"Production Budget Bin\",\n \"y\": \"Rotten Tomatoes Rating Bin\",\n \"color\": \"Number of Movies\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n },\n \"reason\": \"The user wants to bin both Production Budget and Rotten Tomatoes Rating into ranges and count movies in each combination. This requires creating categorical bins for both dimensions and aggregating counts. A heatmap visualization (using color encoding for count) would be more appropriate than a scatter plot for showing binned data.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna())\n ]\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Count movies in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n ).size().reset_index(name='Number of Movies')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Number of Movies Production Budget Bin Rotten Tomatoes Rating Bin\n0 6 <$1M 0-20\n1 9 <$1M 21-40\n2 12 <$1M 41-60\n3 19 <$1M 61-80\n4 54 <$1M 81-100\n5 61 $1M-$10M 0-20\n6 72 $1M-$10M 21-40\n7 58 $1M-$10M 41-60\n8 104 $1M-$10M 61-80\n9 219 $1M-$10M 81-100\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show profit relation to budget ratings bin\",\n \"chart_type\": \"Heatmap\",\n \"chart_encodings\": {\n \"x\": \"Rotten Tomatoes Rating Bin\",\n \"y\": \"Production Budget Bin\",\n \"color\": \"Number of Movies\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate profit (Worldwide Gross minus Production Budget) for movies, then bin the Production Budget and Rotten Tomatoes Rating into categorical ranges, and compute the average profit for movies in each combination of budget and rating bins.\",\n \"display_instruction\": \"Show **average profit** in each **Production Budget** and **Rotten Tomatoes Rating** bin\",\n \"output_fields\": [\"Production Budget Bin\", \"Rotten Tomatoes Rating Bin\", \"Average Profit\"],\n \"chart_encodings\": {\n \"x\": \"Rotten Tomatoes Rating Bin\",\n \"y\": \"Production Budget Bin\",\n \"color\": \"Average Profit\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n },\n \"reason\": \"The user wants to analyze the relationship between profit and the budget/rating bins. This requires calculating profit (Worldwide Gross - Production Budget) and then aggregating it by the budget and rating bins. The chart encodings are swapped to match the user's preference (x=Rating, y=Budget).\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating', 'Worldwide Gross']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna()) &\n (df_filtered['Worldwide Gross'].notna())\n ]\n \n # Calculate profit\n df_filtered['Profit'] = df_filtered['Worldwide Gross'] - df_filtered['Production Budget']\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Calculate average profit in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n )['Profit'].mean().reset_index(name='Average Profit')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-37","sourceTableIds":["movies"],"instruction":"show profit relation to budget ratings bin","displayInstruction":"Show **average profit** in each **Production Budget** and **Rotten Tomatoes Rating** bin","chart":{"id":"chart-1760744581372","chartType":"Heatmap","encodingMap":{"x":{"fieldID":"concept-Rotten Tomatoes Rating Bin-1760744520448","sortBy":"[\"0-20\",\"21-40\",\"41-60\",\"61-80\",\"81-100\"]"},"y":{"fieldID":"concept-Production Budget Bin-1760744338462","sortBy":"[\"<$1M\",\"$1M-$10M\",\"$10M-$50M\",\"$50M-$100M\",\">$100M\"]"},"color":{"fieldID":"concept-Number of Movies-1760744520448"},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"table-37","saved":false,"source":"trigger","unread":false},"resultTableId":"table-54"},"explanation":{"agent":"CodeExplanationAgent","code":"This code analyzes the relationship between movie **production budgets**, **critical ratings**, and **profitability**:\n\n- Filters the dataset to include only movies with complete data for **Production Budget**, **Rotten Tomatoes Rating**, and **Worldwide Gross**\n- Calculates **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Creates **Production Budget Bins** to categorize movies into five budget ranges: **<$1M**, **$1M-$10M**, **$10M-$50M**, **$50M-$100M**, and **>$100M**\n- Creates **Rotten Tomatoes Rating Bins** to categorize movies into five rating ranges: **0-20**, **21-40**, **41-60**, **61-80**, and **81-100**\n- Groups movies by their **budget bin** and **rating bin** combinations\n- Calculates the **Average Profit** for each budget-rating combination to reveal profitability patterns across different production scales and critical reception levels","concepts":[{"explanation":"The net financial return of a movie, calculated as: \\[ \\text{Profit} = \\text{Worldwide\\_Gross} - \\text{Production\\_Budget} \\] This represents the total revenue minus the initial investment, indicating whether a movie was financially successful.","field":"Profit"},{"explanation":"This analysis uses a **two-dimensional binning approach** to examine how movie profitability varies across production budget levels and critical reception scores. The model segments movies into categorical bins based on **Production Budget** (five budget tiers from under $1M to over $100M) and **Rotten Tomatoes Rating** (five quality tiers from 0-100), then calculates **Average Profit** within each budget-rating combination. This binning strategy helps identify optimal investment strategies by revealing which budget-quality combinations yield the highest returns. Alternative modeling approaches could include: **multiple linear regression** to model profit as a continuous function of budget and rating, **polynomial regression** to capture non-linear relationships, **interaction effect models** to test whether budget impact varies by rating level, or **quantile regression** to analyze profitability patterns at different profit percentiles rather than just averages.","field":"Statistical Analysis"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 673.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: 13180936.0, 33619971.0, 140689412.0, ..., nan, nan, 46260220.0, 83571732.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 237477.0, 417703.0, nan, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating', 'Worldwide Gross']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna()) &\n (df_filtered['Worldwide Gross'].notna())\n ]\n \n # Calculate profit\n df_filtered['Profit'] = df_filtered['Worldwide Gross'] - df_filtered['Production Budget']\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Calculate average profit in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n )['Profit'].mean().reset_index(name='Average Profit')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThis code analyzes the relationship between movie **production budgets**, **critical ratings**, and **profitability**:\n\n- Filters the dataset to include only movies with complete data for **Production Budget**, **Rotten Tomatoes Rating**, and **Worldwide Gross**\n- Calculates **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Creates **Production Budget Bins** to categorize movies into five budget ranges: **<$1M**, **$1M-$10M**, **$10M-$50M**, **$50M-$100M**, and **>$100M**\n- Creates **Rotten Tomatoes Rating Bins** to categorize movies into five rating ranges: **0-20**, **21-40**, **41-60**, **61-80**, and **81-100**\n- Groups movies by their **budget bin** and **rating bin** combinations\n- Calculates the **Average Profit** for each budget-rating combination to reveal profitability patterns across different production scales and critical reception levels\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Profit\",\n \"explanation\": \"The net financial return of a movie, calculated as: \\\\[ \\\\text{Profit} = \\\\text{Worldwide\\\\_Gross} - \\\\text{Production\\\\_Budget} \\\\] This represents the total revenue minus the initial investment, indicating whether a movie was financially successful.\"\n },\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"This analysis uses a **two-dimensional binning approach** to examine how movie profitability varies across production budget levels and critical reception scores. The model segments movies into categorical bins based on **Production Budget** (five budget tiers from under $1M to over $100M) and **Rotten Tomatoes Rating** (five quality tiers from 0-100), then calculates **Average Profit** within each budget-rating combination. This binning strategy helps identify optimal investment strategies by revealing which budget-quality combinations yield the highest returns. Alternative modeling approaches could include: **multiple linear regression** to model profit as a continuous function of budget and rating, **polynomial regression** to capture non-linear relationships, **interaction effect models** to test whether budget impact varies by rating level, or **quantile regression** to analyze profitability patterns at different profit percentiles rather than just averages.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""}],"charts":[{"id":"chart-1760744573683","chartType":"Heatmap","encodingMap":{"x":{"fieldID":"concept-Rotten Tomatoes Rating Bin-1760744520448","sortBy":"[\"0-20\",\"21-40\",\"41-60\",\"61-80\",\"81-100\"]"},"y":{"fieldID":"concept-Production Budget Bin-1760744338462","sortBy":"[\"<$1M\",\"$1M-$10M\",\"$10M-$50M\",\"$50M-$100M\",\">$100M\"]"},"color":{"fieldID":"concept-Average 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The real story emerges with blockbusters: films budgeted over $100M earn an average of $516M profit when they achieve 81-100% on Rotten Tomatoes—a staggering return that dwarfs all other categories.\n\n[IMAGE(chart-1760744573683)]\n\nMid-budget films ($10M-$50M) show steady but modest profits across all rating tiers, rarely exceeding $30M in average returns. The data reveals a clear pattern: critical acclaim matters most when paired with substantial production investment.\n\n**In summary**, the film industry's most profitable strategy combines massive budgets with quality execution—blockbusters that earn top critic scores generate extraordinary returns, while smaller productions face profit ceilings regardless of critical reception.","style":"short note","selectedChartIds":["chart-1760744573683"],"createdAt":1760831325401},{"id":"report-1760831241705-9562","content":"# Top Directors Dominate with Action & Adventure Films\n\nSteven Spielberg leads all directors with over $7 billion in total profit, driven primarily by action ($2.3B) and drama ($2.2B) films. James Cameron follows at $5.1B, focusing almost exclusively on action blockbusters.\n\n[IMAGE(chart-1760743804092)]\n\nAdventure films emerge as the most profitable genre across multiple directors—Chris Columbus, George Lucas, and Peter Jackson each generated $3B+ primarily from adventure franchises. Meanwhile, directors like Michael Bay and Gore Verbinski found success mixing action with adventure content.\n\n**In summary**, the most profitable directors concentrate on action and adventure genres, with Spielberg's genre diversity being the exception among top earners. This suggests blockbuster franchises in these genres offer the most reliable path to commercial success.","style":"short note","selectedChartIds":["chart-1760743804092"],"createdAt":1760831249446},{"id":"report-1760831215537-1231","content":"# Movie Magic: When Big Budgets Pay Off (and When They Don't)\n\nEver wonder if spending more money guarantees box office gold? The data tells a fascinating story about Hollywood's biggest gambles.\n\n[IMAGE(chart-1760742454293)]\n\nLooking at the relationship between production budgets and worldwide gross, there's a clear trend: **bigger budgets *can* lead to bigger returns**, but it's far from guaranteed. The sweet spot appears to be around $200-250M, where blockbusters like *Avatar* ($2.77B worldwide) dominate. However, the chart reveals something crucial—**most films cluster in the lower-left corner**, suggesting that moderate budgets between $25-100M are the industry standard, though they rarely break the billion-dollar barrier.\n\n[IMAGE(chart-1760743347871)]\n\nWhen we examine the **top performers by genre**, the winners become crystal clear. *Avatar* leads the pack with an astounding $2.53B profit, followed by adventure franchises like *Jurassic Park* and *The Lord of the Rings*. Action and adventure films dominate profitability, while genres like comedy, horror, and romantic comedies show more modest—but still impressive—returns in the $300-450M range. Musicals like *The Sound of Music* prove timeless appeal still pays dividends.\n\n**In summary**, while throwing money at a film doesn't guarantee success, the blockbuster strategy works when executed well—particularly for action and adventure franchises. The real question: *Are mid-budget films becoming an endangered species in modern Hollywood?*","style":"social post","selectedChartIds":["chart-1760743347871","chart-1760742454293"],"createdAt":1760831228624}],"currentReport":{"id":"report-1760750575650-2619","content":"# Hollywood's Billion-Dollar Hitmakers\n\n*Avatar* stands alone—earning over $2.5B in profit, dwarfing all competition. Action and Adventure films dominate the most profitable titles, with franchises like *Jurassic Park*, *The Dark Knight*, and *Lord of the Rings* proving blockbuster formulas work.\n\n\"Chart\"\n\nSteven Spielberg leads all directors with $7.2B in total profit across his career, showcasing remarkable consistency with hits spanning decades—from *Jurassic Park* to *E.T.* His nearest competitors trail by billions, underlining his unmatched commercial impact.\n\n\"Chart\"\n\n**In summary**, mega-budget Action and Adventure films generate extraordinary returns when they succeed, and a handful of elite directors—led by Spielberg—have mastered the formula for sustained box office dominance.","style":"short note","selectedChartIds":["chart-1760743347871","chart-1760743768741"],"chartImages":{},"createdAt":1760750584189,"title":"Report - 10/17/2025"},"activeChallenges":[],"agentWorkInProgress":[],"_persist":{"version":-1,"rehydrated":true}} ================================================ FILE: 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pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n","source":["history"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"input_tables\": [...] // string[], describe names of the input tables that will be used in the transformation.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", 'boxplot'. \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"input_tables\", the names of a subset of input tables from [CONTEXT] section that will be used to achieve the user's goal.\n - **IMPORTANT** Note that the Table 1 in [CONTEXT] section is the table the user is currently viewing, it should take precedence if the user refers to insights about the \"current table\".\n - At the same time, leverage table information to determine which tables are relevant to the user's goal and should be used.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", \"boxplot\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - boxplot: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - (boxplot) Box plots: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical (optional for creating grouped boxplots), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the boxplot will be grouped automatically (items with the same x values will be grouped).\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE]. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n# from sklearn import ... # import from sklearn if you need it.\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- decide the function signature based on the number of tables you decided in the previous step \"input_tables\":\n - if you decide there will only be one input table, then function signature should be `def transform_data(df1)`\n - if you decided there will be k input tables, then function signature should be `def transform_data(df_1, df_2, ..., df_k)`.\n - instead of using generic names like df1, df2, ..., try to use intuitive table names for function arguments, for example, if you have input_tables: [\"City\", \"Weather\"]`, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes.\n - **VERY IMPORTANT** the number of arguments in the function signature must be the same as the number of tables provided in \"input_tables\", and the order of arguments must match the order of tables provided in \"input_tables\".\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[GOAL]\n\n1) First, plot the overall **closing price** trends for each **symbol** over **time** on a single line chart to compare their broad trajectories and volatility."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compare overall closing price trends and volatility over time for each stock symbol on a single line chart.\",\n \"display_instruction\": \"Visualize **closing price** trends by **symbol** over **date**\",\n \"recommendation\": \"A line chart with time on the x-axis and closing price on the y-axis, colored by symbol, best shows comparative trajectories and volatility over time. The data only needs column selection, date normalization, and sorting to be visualization-ready.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"close\",\n \"color\": \"symbol\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n```"}],"trigger":{"tableId":"history","sourceTableIds":["history"],"instruction":"1) First, plot the overall **closing price** trends for each **symbol** over **time** on a single line chart to compare their broad trajectories and volatility.","displayInstruction":"Visualize **closing price** trends by **symbol** over **date**","chart":{"id":"chart-1769471233291","chartType":"Auto","encodingMap":{},"tableRef":"history","saved":false,"source":"trigger","unread":true},"resultTableId":"table-233476"},"explanation":{"status":"ok","concepts":[],"code":"- **Keep only the key fields**: selects the columns **`\"date\"`**, **`\"symbol\"`**, and **`\"close\"`** from the input data and **drops all other columns**.\n- **Standardize the date format**: converts **`date`** into a **string** in the **`\"YYYY-MM-DD\"`** format to ensure consistency (no time component, no different date formats).\n- **Order the data for time-series use**: **sorts** the rows first by **`symbol`** and then by **`date`** so that each stock’s closing prices are in **chronological order**, which is important for **line charts** or time-based analysis.\n- **Return the cleaned dataset**: outputs this **simplified and ordered** table as the final transformed data.","dialog":[{"role":"system","content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n"},{"role":"user","content":"[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n\n\n[EXPLANATION]\n"},{"role":"assistant","content":"[CODE EXPLANATION]\n\n- **Keep only the key fields**: selects the columns **`\"date\"`**, **`\"symbol\"`**, and **`\"close\"`** from the input data and **drops all other columns**.\n- **Standardize the date format**: converts **`date`** into a **string** in the **`\"YYYY-MM-DD\"`** format to ensure consistency (no time component, no different date formats).\n- **Order the data for time-series use**: **sorts** the rows first by **`symbol`** and then by **`date`** so that each stock’s closing prices are in **chronological order**, which is important for **line charts** or time-based analysis.\n- **Return the cleaned dataset**: outputs this **simplified and ordered** table as the final transformed data.\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```"}],"agent":"CodeExplanationAgent"}},"anchored":false,"createdBy":"user","attachedMetadata":"","contentHash":"a2a0adc6"},{"id":"table-265247","displayId":"stock-ma","names":["date","symbol","close","value","ma_window"],"rows":[{"date":"2025-07-31","symbol":"AAPL","close":207.13,"value":207.13,"ma_window":20},{"date":"2025-08-01","symbol":"AAPL","close":201.95,"value":204.54,"ma_window":20},{"date":"2025-08-04","symbol":"AAPL","close":202.92,"value":204,"ma_window":20},{"date":"2025-08-05","symbol":"AAPL","close":202.49,"value":203.6225,"ma_window":20},{"date":"2025-08-06","symbol":"AAPL","close":212.8,"value":205.458,"ma_window":20},{"date":"2025-08-07","symbol":"AAPL","close":219.57,"value":207.81,"ma_window":20},{"date":"2025-08-08","symbol":"AAPL","close":228.87,"value":210.8185714286,"ma_window":20},{"date":"2025-08-11","symbol":"AAPL","close":226.96,"value":212.83625,"ma_window":20},{"date":"2025-08-12","symbol":"AAPL","close":229.43,"value":214.68,"ma_window":20},{"date":"2025-08-13","symbol":"AAPL","close":233.1,"value":216.522,"ma_window":20},{"date":"2025-08-14","symbol":"AAPL","close":232.55,"value":217.9790909091,"ma_window":20},{"date":"2025-08-15","symbol":"AAPL","close":231.37,"value":219.095,"ma_window":20},{"date":"2025-08-18","symbol":"AAPL","close":230.67,"value":219.9853846154,"ma_window":20},{"date":"2025-08-19","symbol":"AAPL","close":230.34,"value":220.725,"ma_window":20},{"date":"2025-08-20","symbol":"AAPL","close":225.79,"value":221.0626666667,"ma_window":20},{"date":"2025-08-21","symbol":"AAPL","close":224.68,"value":221.28875,"ma_window":20},{"date":"2025-08-22","symbol":"AAPL","close":227.54,"value":221.6564705882,"ma_window":20},{"date":"2025-08-25","symbol":"AAPL","close":226.94,"value":221.95,"ma_window":20},{"date":"2025-08-26","symbol":"AAPL","close":229.09,"value":222.3257894737,"ma_window":20},{"date":"2025-08-27","symbol":"AAPL","close":230.27,"value":222.723,"ma_window":20},{"date":"2025-08-28","symbol":"AAPL","close":232.33,"value":223.983,"ma_window":20},{"date":"2025-08-29","symbol":"AAPL","close":231.92,"value":225.4815,"ma_window":20},{"date":"2025-09-02","symbol":"AAPL","close":229.5,"value":226.8105,"ma_window":20},{"date":"2025-09-03","symbol":"AAPL","close":238.24,"value":228.598,"ma_window":20},{"date":"2025-09-04","symbol":"AAPL","close":239.55,"value":229.9355,"ma_window":20},{"date":"2025-09-05","symbol":"AAPL","close":239.46,"value":230.93,"ma_window":20},{"date":"2025-09-08","symbol":"AAPL","close":237.65,"value":231.369,"ma_window":20},{"date":"2025-09-09","symbol":"AAPL","close":234.12,"value":231.727,"ma_window":20},{"date":"2025-09-10","symbol":"AAPL","close":226.57,"value":231.584,"ma_window":20},{"date":"2025-09-11","symbol":"AAPL","close":229.81,"value":231.4195,"ma_window":20},{"date":"2025-09-12","symbol":"AAPL","close":233.84,"value":231.484,"ma_window":20},{"date":"2025-09-15","symbol":"AAPL","close":236.47,"value":231.739,"ma_window":20},{"date":"2025-09-16","symbol":"AAPL","close":237.92,"value":232.1015,"ma_window":20},{"date":"2025-09-17","symbol":"AAPL","close":238.76,"value":232.5225,"ma_window":20},{"date":"2025-09-18","symbol":"AAPL","close":237.65,"value":233.1155,"ma_window":20},{"date":"2025-09-19","symbol":"AAPL","close":245.26,"value":234.1445,"ma_window":20},{"date":"2025-09-22","symbol":"AAPL","close":255.83,"value":235.559,"ma_window":20},{"date":"2025-09-23","symbol":"AAPL","close":254.18,"value":236.921,"ma_window":20},{"date":"2025-09-24","symbol":"AAPL","close":252.07,"value":238.07,"ma_window":20},{"date":"2025-09-25","symbol":"AAPL","close":256.62,"value":239.3875,"ma_window":20},{"date":"2025-09-26","symbol":"AAPL","close":255.21,"value":240.5315,"ma_window":20},{"date":"2025-09-29","symbol":"AAPL","close":254.18,"value":241.6445,"ma_window":20},{"date":"2025-09-30","symbol":"AAPL","close":254.38,"value":242.8885,"ma_window":20},{"date":"2025-10-01","symbol":"AAPL","close":255.2,"value":243.7365,"ma_window":20},{"date":"2025-10-02","symbol":"AAPL","close":256.88,"value":244.603,"ma_window":20},{"date":"2025-10-03","symbol":"AAPL","close":257.77,"value":245.5185,"ma_window":20},{"date":"2025-10-06","symbol":"AAPL","close":256.44,"value":246.458,"ma_window":20},{"date":"2025-10-07","symbol":"AAPL","close":256.23,"value":247.5635,"ma_window":20},{"date":"2025-10-08","symbol":"AAPL","close":257.81,"value":249.1255,"ma_window":20},{"date":"2025-10-09","symbol":"AAPL","close":253.79,"value":250.3245,"ma_window":20},{"date":"2025-10-10","symbol":"AAPL","close":245.03,"value":250.884,"ma_window":20},{"date":"2025-10-13","symbol":"AAPL","close":247.42,"value":251.4315,"ma_window":20},{"date":"2025-10-14","symbol":"AAPL","close":247.53,"value":251.912,"ma_window":20},{"date":"2025-10-15","symbol":"AAPL","close":249.1,"value":252.429,"ma_window":20},{"date":"2025-10-16","symbol":"AAPL","close":247.21,"value":252.907,"ma_window":20},{"date":"2025-10-17","symbol":"AAPL","close":252.05,"value":253.2465,"ma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pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy and keep only needed columns if present\n base_cols = [c for c in [\"date\", \"symbol\", \"close\"] if c in df_history.columns]\n df = df_history[base_cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Ensure we have a clean, simple index to avoid alignment issues\n df = df.reset_index(drop=True)\n\n # Compute rolling means using transform to preserve index alignment\n df[\"ma_20\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).mean())\n )\n df[\"ma_60\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=60, min_periods=1).mean())\n )\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map).astype(int)\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n","source":["history"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"input_tables\": [...] // string[], describe names of the input tables that will be used in the transformation.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", 'boxplot'. \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"input_tables\", the names of a subset of input tables from [CONTEXT] section that will be used to achieve the user's goal.\n - **IMPORTANT** Note that the Table 1 in [CONTEXT] section is the table the user is currently viewing, it should take precedence if the user refers to insights about the \"current table\".\n - At the same time, leverage table information to determine which tables are relevant to the user's goal and should be used.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", \"boxplot\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - boxplot: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - (boxplot) Box plots: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical (optional for creating grouped boxplots), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the boxplot will be grouped automatically (items with the same x values will be grouped).\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE]. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n# from sklearn import ... # import from sklearn if you need it.\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- decide the function signature based on the number of tables you decided in the previous step \"input_tables\":\n - if you decide there will only be one input table, then function signature should be `def transform_data(df1)`\n - if you decided there will be k input tables, then function signature should be `def transform_data(df_1, df_2, ..., df_k)`.\n - instead of using generic names like df1, df2, ..., try to use intuitive table names for function arguments, for example, if you have input_tables: [\"City\", \"Weather\"]`, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes.\n - **VERY IMPORTANT** the number of arguments in the function signature must be the same as the number of tables provided in \"input_tables\", and the order of arguments must match the order of tables provided in \"input_tables\".\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[GOAL]\n\n1) First, plot the overall **closing price** trends for each **symbol** over **time** on a single line chart to compare their broad trajectories and volatility."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compare overall closing price trends and volatility over time for each stock symbol on a single line chart.\",\n \"display_instruction\": \"Visualize **closing price** trends by **symbol** over **date**\",\n \"recommendation\": \"A line chart with time on the x-axis and closing price on the y-axis, colored by symbol, best shows comparative trajectories and volatility over time. The data only needs column selection, date normalization, and sorting to be visualization-ready.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"close\",\n \"color\": \"symbol\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date symbol close\n0 2025-07-30 AAPL 208.61\n1 2025-07-31 AAPL 207.13\n2 2025-08-01 AAPL 201.95\n3 2025-08-04 AAPL 202.92\n4 2025-08-05 AAPL 202.49\n5 2025-08-06 AAPL 212.80\n6 2025-08-07 AAPL 219.57\n7 2025-08-08 AAPL 228.87\n8 2025-08-11 AAPL 226.96\n9 2025-08-12 AAPL 229.43\n......\n\nUpdate the code above based on the following instruction:\n\n2) For each symbol, compute 20-day and 60-day rolling averages of close price (columns: date, symbol, value, ma_window with values 20 or 60) and plot them over time alongside the actual close to compare short- vs long-term trends and reversals."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compute 20-day and 60-day rolling averages of closing prices for each symbol in tidy format for plotting alongside actual closes over time.\",\n \"display_instruction\": \"Add **20/60-day moving averages** of **close** by **symbol** over **date**\",\n \"recommendation\": \"To compare short- and long-term trends per symbol, compute 20- and 60-day rolling means of close, reshape them into a long format with one row per date-symbol-window, and keep the original close for plotting alongside the moving averages.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"value\",\n \"color\": \"ma_window\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"symbol\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy\n df = df_history.copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Group by symbol and compute rolling means on close\n group = df.groupby(\"symbol\", group_keys=False)[\"close\"]\n df[\"ma_20\"] = group.rolling(20, min_periods=1).mean()\n df[\"ma_60\"] = group.rolling(60, min_periods=1).mean()\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map)\n\n # Ensure correct column order and types\n df_long[\"ma_window\"] = df_long[\"ma_window\"].astype(int)\n\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\nEmpty DataFrame\nColumns: []\nIndex: []\n......\n\nUpdate the code above based on the following instruction:\n\nWe run into the following problem executing the code, please fix it:\n\nError: TypeError - incompatible index of inserted column with frame index\n\nPlease think step by step, reflect why the error happens and fix the code so that no more errors would occur."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compute 20-day and 60-day rolling averages of closing prices for each symbol in tidy format for plotting alongside actual closes over time.\",\n \"display_instruction\": \"Compute **20/60-day moving averages** of **close** by **symbol** over **date**\",\n \"recommendation\": \"The previous implementation used groupby().rolling() and assigned the result directly to new columns, which can create an index mismatch and raise a TypeError. Using groupby().transform(...) with an inner rolling mean keeps the index aligned with the original DataFrame, avoiding incompatible index issues. After computing the moving averages, reshape to long format for visualization.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"value\",\n \"color\": \"ma_window\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"symbol\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy and keep only needed columns if present\n base_cols = [c for c in [\"date\", \"symbol\", \"close\"] if c in df_history.columns]\n df = df_history[base_cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Ensure we have a clean, simple index to avoid alignment issues\n df = df.reset_index(drop=True)\n\n # Compute rolling means using transform to preserve index alignment\n df[\"ma_20\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).mean())\n )\n df[\"ma_60\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=60, min_periods=1).mean())\n )\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map).astype(int)\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n```"}],"trigger":{"tableId":"table-233476","sourceTableIds":["history"],"instruction":"2) For each symbol, compute 20-day and 60-day rolling averages of close price (columns: date, symbol, value, ma_window with values 20 or 60) and plot them over time alongside the actual close to compare short- vs long-term trends and reversals.","displayInstruction":"Compute **20/60-day moving averages** of **close** by **symbol** over **date**","chart":{"id":"chart-1769471261112","chartType":"Auto","encodingMap":{},"tableRef":"history","saved":false,"source":"trigger","unread":true},"resultTableId":"table-265247"},"explanation":{"status":"ok","concepts":[{"field":"ma_20, ma_60, value","explanation":"These fields represent a rolling (moving) average of the closing price over a fixed number of recent trading days for each stock symbol. For a window of N days (N = 20 or 60), the moving average on a given date is the average of the last N closing prices up to that date. Mathematically, if close_t is the closing price on day t, then the N-day moving average MA_N(t) is: MA_N(t) = (close_t + close_{t-1} + ... + close_{t-N+1}) / N. At the start of the series, when fewer than N days are available, the average is taken over all available past days instead."},{"field":"ma_window","explanation":"This field indicates the length of the moving-average window, in days. A value of 20 means the corresponding 'value' is a 20-day moving average; a value of 60 means it is a 60-day moving average."}],"code":"- Start from the original **history** table but keep only the **`date`**, **`symbol`**, and **`close`** fields to simplify the data.\n- Convert **`date`** into a standardized text format **`YYYY-MM-DD`** so all dates look consistent.\n- **Sort** the data by **`symbol`** and then by **`date`** to ensure the time series for each stock is in the correct order.\n- For each **`symbol`**, **compute**:\n - **`ma_20`**: a **20-day moving average** of the **`close`** price (uses up to the last **20** days; for the first few days it uses all available past days).\n - **`ma_60`**: a **60-day moving average** of the **`close`** price (uses up to the last **60** days; similarly relaxed at the start).\n- **Reshape** the data so that instead of having separate columns **`ma_20`** and **`ma_60`**, each row represents **one moving-average value**:\n - Keep **`date`**, **`symbol`**, and **`close`**.\n - Put the moving-average values into a single column **`value`**.\n - Use a column **`ma_window`** to indicate whether **`value`** is for **20** days or **60** days.\n- **Map** the labels **`ma_20`** and **`ma_60`** into numeric values **20** and **60** in **`ma_window`**, and keep only:\n - **`date`**, **`symbol`**, **`close`**, **`value`** (moving average), **`ma_window`** (window length in days).","dialog":[{"role":"system","content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n"},{"role":"user","content":"[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy and keep only needed columns if present\n base_cols = [c for c in [\"date\", \"symbol\", \"close\"] if c in df_history.columns]\n df = df_history[base_cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Ensure we have a clean, simple index to avoid alignment issues\n df = df.reset_index(drop=True)\n\n # Compute rolling means using transform to preserve index alignment\n df[\"ma_20\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).mean())\n )\n df[\"ma_60\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=60, min_periods=1).mean())\n )\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map).astype(int)\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n\n\n[EXPLANATION]\n"},{"role":"assistant","content":"[CODE EXPLANATION]\n\n- Start from the original **history** table but keep only the **`date`**, **`symbol`**, and **`close`** fields to simplify the data.\n- Convert **`date`** into a standardized text format **`YYYY-MM-DD`** so all dates look consistent.\n- **Sort** the data by **`symbol`** and then by **`date`** to ensure the time series for each stock is in the correct order.\n- For each **`symbol`**, **compute**:\n - **`ma_20`**: a **20-day moving average** of the **`close`** price (uses up to the last **20** days; for the first few days it uses all available past days).\n - **`ma_60`**: a **60-day moving average** of the **`close`** price (uses up to the last **60** days; similarly relaxed at the start).\n- **Reshape** the data so that instead of having separate columns **`ma_20`** and **`ma_60`**, each row represents **one moving-average value**:\n - Keep **`date`**, **`symbol`**, and **`close`**.\n - Put the moving-average values into a single column **`value`**.\n - Use a column **`ma_window`** to indicate whether **`value`** is for **20** days or **60** days.\n- **Map** the labels **`ma_20`** and **`ma_60`** into numeric values **20** and **60** in **`ma_window`**, and keep only:\n - **`date`**, **`symbol`**, **`close`**, **`value`** (moving average), **`ma_window`** (window length in days).\n\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"ma_20, ma_60, value\",\n \"explanation\": \"These fields represent a rolling (moving) average of the closing price over a fixed number of recent trading days for each stock symbol. For a window of N days (N = 20 or 60), the moving average on a given date is the average of the last N closing prices up to that date. Mathematically, if close_t is the closing price on day t, then the N-day moving average MA_N(t) is: MA_N(t) = (close_t + close_{t-1} + ... + close_{t-N+1}) / N. At the start of the series, when fewer than N days are available, the average is taken over all available past days instead.\"\n },\n {\n \"field\": \"ma_window\",\n \"explanation\": \"This field indicates the length of the moving-average window, in days. A value of 20 means the corresponding 'value' is a 20-day moving average; a value of 60 means it is a 60-day moving average.\"\n }\n]\n```"}],"agent":"CodeExplanationAgent"}},"anchored":false,"createdBy":"user","attachedMetadata":"","contentHash":"63c48b66"},{"id":"table-277926","displayId":"stock-metr","names":["date","symbol","metric","value"],"rows":[{"date":"2025-07-31","symbol":"AAPL","metric":"return","value":null},{"date":"2025-08-01","symbol":"AAPL","metric":"return","value":-0.0250084488},{"date":"2025-08-04","symbol":"AAPL","metric":"return","value":0.0048031691},{"date":"2025-08-05","symbol":"AAPL","metric":"return","value":-0.0021190617},{"date":"2025-08-06","symbol":"AAPL","metric":"return","value":0.0509160946},{"date":"2025-08-07","symbol":"AAPL","metric":"return","value":0.0318139098},{"date":"2025-08-08","symbol":"AAPL","metric":"return","value":0.042355513},{"date":"2025-08-11","symbol":"AAPL","metric":"return","value":-0.0083453489},{"date":"2025-08-12","symbol":"AAPL","metric":"return","value":0.010882975},{"date":"2025-08-13","symbol":"AAPL","metric":"return","value":0.0159961644},{"date":"2025-08-14","symbol":"AAPL","metric":"return","value":-0.0023595024},{"date":"2025-08-15","symbol":"AAPL","metric":"return","value":-0.0050741776},{"date":"2025-08-18","symbol":"AAPL","metric":"return","value":-0.0030254571},{"date":"2025-08-19","symbol":"AAPL","metric":"return","value":-0.0014306152},{"date":"2025-08-20","symbol":"AAPL","metric":"return","value":-0.019753408},{"date":"2025-08-21","symbol":"AAPL","metric":"return","value":-0.0049160725},{"date":"2025-08-22","symbol":"AAPL","metric":"return","value":0.0127292149},{"date":"2025-08-25","symbol":"AAPL","metric":"return","value":-0.002636899},{"date":"2025-08-26","symbol":"AAPL","metric":"return","value":0.0094738697},{"date":"2025-08-27","symbol":"AAPL","metric":"return","value":0.0051508141},{"date":"2025-08-28","symbol":"AAPL","metric":"return","value":0.0089460199},{"date":"2025-08-29","symbol":"AAPL","metric":"return","value":-0.0017647312},{"date":"2025-09-02","symbol":"AAPL","metric":"return","value":-0.0104346326},{"date":"2025-09-03","symbol":"AAPL","metric":"return","value":0.0380827887},{"date":"2025-09-04","symbol":"AAPL","metric":"return","value":0.0054986568},{"date":"2025-09-05","symbol":"AAPL","metric":"return","value":-0.0003757044},{"date":"2025-09-08","symbol":"AAPL","metric":"return","value":-0.0075586737},{"date":"2025-09-09","symbol":"AAPL","metric":"return","value":-0.0148537766},{"date":"2025-09-10","symbol":"AAPL","metric":"return","value":-0.0322484196},{"date":"2025-09-11","symbol":"AAPL","metric":"return","value":0.0143002163},{"date":"2025-09-12","symbol":"AAPL","metric":"return","value":0.0175362256},{"date":"2025-09-15","symbol":"AAPL","metric":"return","value":0.0112470065},{"date":"2025-09-16","symbol":"AAPL","metric":"return","value":0.006131856},{"date":"2025-09-17","symbol":"AAPL","metric":"return","value":0.0035305985},{"date":"2025-09-18","symbol":"AAPL","metric":"return","value":-0.0046490199},{"date":"2025-09-19","symbol":"AAPL","metric":"return","value":0.0320218809},{"date":"2025-09-22","symbol":"AAPL","metric":"return","value":0.0430971214},{"date":"2025-09-23","symbol":"AAPL","metric":"return","value":-0.0064495954},{"date":"2025-09-24","symbol":"AAPL","metric":"return","value":-0.0083012039},{"date":"2025-09-25","symbol":"AAPL","metric":"return","value":0.0180505415},{"date":"2025-09-26","symbol":"AAPL","metric":"return","value":-0.0054945055},{"date":"2025-09-29","symbol":"AAPL","metric":"return","value":-0.004035892},{"date":"2025-09-30","symbol":"AAPL","metric":"return","value":0.000786844},{"date":"2025-10-01","symbol":"AAPL","metric":"return","value":0.0032235239},{"date":"2025-10-02","symbol":"AAPL","metric":"return","value":0.0065830721},{"date":"2025-10-03","symbol":"AAPL","metric":"return","value":0.0034646528},{"date":"2025-10-06","symbol":"AAPL","metric":"return","value":-0.0051596384},{"date":"2025-10-07","symbol":"AAPL","metric":"return","value":-0.000818905},{"date":"2025-10-08","symbol":"AAPL","metric":"return","value":0.0061663349},{"date":"2025-10-09","symbol":"AAPL","metric":"return","value":-0.0155928785},{"date":"2025-10-10","symbol":"AAPL","metric":"return","value":-0.0345167264},{"date":"2025-10-13","symbol":"AAPL","metric":"return","value":0.0097539077},{"date":"2025-10-14","symbol":"AAPL","metric":"return","value":0.0004445881},{"date":"2025-10-15","symbol":"AAPL","metric":"return","value":0.0063426655},{"date":"2025-10-16","symbol":"AAPL","metric":"return","value":-0.0075873143},{"date":"2025-10-17","symbol":"AAPL","metric":"return","value":0.019578496},{"date":"2025-10-20","symbol":"AAPL","metric":"return","value":0.0394366197},{"date":"2025-10-21","symbol":"AAPL","metric":"return","value":0.002022978},{"date":"2025-10-22","symbol":"AAPL","metric":"return","value":-0.0164558891},{"date":"2025-10-23","symbol":"AAPL","metric":"return","value":0.0043764524},{"date":"2025-10-24","symbol":"AAPL","metric":"return","value":0.0124937339},{"date":"2025-10-27","symbol":"AAPL","metric":"return","value":0.02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pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Keep only necessary columns\n cols = [c for c in [\"date\", \"symbol\", \"close\", \"volume\"] if c in df_history.columns]\n df = df_history[cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for time-series operations\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n # Compute daily returns as pct_change of close within each symbol\n df[\"return\"] = df.groupby(\"symbol\")[\"close\"].pct_change()\n\n # Compute 20-day rolling volatility (std of returns) within each symbol\n df[\"vol20\"] = (\n df.groupby(\"symbol\")[\"return\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).std())\n )\n\n # Reshape to long format: metrics = return, vol20, volume\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\"],\n value_vars=[\"return\", \"vol20\", \"volume\"],\n var_name=\"metric\",\n value_name=\"value\",\n )\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"metric\", \"value\"]].reset_index(drop=True)\n return transformed_df\n","source":["history"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"input_tables\": [...] // string[], describe names of the input tables that will be used in the transformation.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", 'boxplot'. \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"input_tables\", the names of a subset of input tables from [CONTEXT] section that will be used to achieve the user's goal.\n - **IMPORTANT** Note that the Table 1 in [CONTEXT] section is the table the user is currently viewing, it should take precedence if the user refers to insights about the \"current table\".\n - At the same time, leverage table information to determine which tables are relevant to the user's goal and should be used.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", \"boxplot\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - boxplot: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - (boxplot) Box plots: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical (optional for creating grouped boxplots), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the boxplot will be grouped automatically (items with the same x values will be grouped).\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE]. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n# from sklearn import ... # import from sklearn if you need it.\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- decide the function signature based on the number of tables you decided in the previous step \"input_tables\":\n - if you decide there will only be one input table, then function signature should be `def transform_data(df1)`\n - if you decided there will be k input tables, then function signature should be `def transform_data(df_1, df_2, ..., df_k)`.\n - instead of using generic names like df1, df2, ..., try to use intuitive table names for function arguments, for example, if you have input_tables: [\"City\", \"Weather\"]`, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes.\n - **VERY IMPORTANT** the number of arguments in the function signature must be the same as the number of tables provided in \"input_tables\", and the order of arguments must match the order of tables provided in \"input_tables\".\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[GOAL]\n\n1) First, plot the overall **closing price** trends for each **symbol** over **time** on a single line chart to compare their broad trajectories and volatility."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compare overall closing price trends and volatility over time for each stock symbol on a single line chart.\",\n \"display_instruction\": \"Visualize **closing price** trends by **symbol** over **date**\",\n \"recommendation\": \"A line chart with time on the x-axis and closing price on the y-axis, colored by symbol, best shows comparative trajectories and volatility over time. The data only needs column selection, date normalization, and sorting to be visualization-ready.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"close\",\n \"color\": \"symbol\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date symbol close\n0 2025-07-30 AAPL 208.61\n1 2025-07-31 AAPL 207.13\n2 2025-08-01 AAPL 201.95\n3 2025-08-04 AAPL 202.92\n4 2025-08-05 AAPL 202.49\n5 2025-08-06 AAPL 212.80\n6 2025-08-07 AAPL 219.57\n7 2025-08-08 AAPL 228.87\n8 2025-08-11 AAPL 226.96\n9 2025-08-12 AAPL 229.43\n......\n\nUpdate the code above based on the following instruction:\n\n2) For each symbol, compute 20-day and 60-day rolling averages of close price (columns: date, symbol, value, ma_window with values 20 or 60) and plot them over time alongside the actual close to compare short- vs long-term trends and reversals."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compute 20-day and 60-day rolling averages of closing prices for each symbol in tidy format for plotting alongside actual closes over time.\",\n \"display_instruction\": \"Add **20/60-day moving averages** of **close** by **symbol** over **date**\",\n \"recommendation\": \"To compare short- and long-term trends per symbol, compute 20- and 60-day rolling means of close, reshape them into a long format with one row per date-symbol-window, and keep the original close for plotting alongside the moving averages.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"value\",\n \"color\": \"ma_window\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"symbol\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy\n df = df_history.copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Group by symbol and compute rolling means on close\n group = df.groupby(\"symbol\", group_keys=False)[\"close\"]\n df[\"ma_20\"] = group.rolling(20, min_periods=1).mean()\n df[\"ma_60\"] = group.rolling(60, min_periods=1).mean()\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map)\n\n # Ensure correct column order and types\n df_long[\"ma_window\"] = df_long[\"ma_window\"].astype(int)\n\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\nEmpty DataFrame\nColumns: []\nIndex: []\n......\n\nUpdate the code above based on the following instruction:\n\nWe run into the following problem executing the code, please fix it:\n\nError: TypeError - incompatible index of inserted column with frame index\n\nPlease think step by step, reflect why the error happens and fix the code so that no more errors would occur."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compute 20-day and 60-day rolling averages of closing prices for each symbol in tidy format for plotting alongside actual closes over time.\",\n \"display_instruction\": \"Compute **20/60-day moving averages** of **close** by **symbol** over **date**\",\n \"recommendation\": \"The previous implementation used groupby().rolling() and assigned the result directly to new columns, which can create an index mismatch and raise a TypeError. Using groupby().transform(...) with an inner rolling mean keeps the index aligned with the original DataFrame, avoiding incompatible index issues. After computing the moving averages, reshape to long format for visualization.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"value\",\n \"color\": \"ma_window\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"symbol\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy and keep only needed columns if present\n base_cols = [c for c in [\"date\", \"symbol\", \"close\"] if c in df_history.columns]\n df = df_history[base_cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Ensure we have a clean, simple index to avoid alignment issues\n df = df.reset_index(drop=True)\n\n # Compute rolling means using transform to preserve index alignment\n df[\"ma_20\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).mean())\n )\n df[\"ma_60\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=60, min_periods=1).mean())\n )\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map).astype(int)\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date symbol close value ma_window\n0 2025-07-30 AAPL 208.61 208.610000 20\n1 2025-07-31 AAPL 207.13 207.870000 20\n2 2025-08-01 AAPL 201.95 205.896667 20\n3 2025-08-04 AAPL 202.92 205.152500 20\n4 2025-08-05 AAPL 202.49 204.620000 20\n5 2025-08-06 AAPL 212.80 205.983333 20\n6 2025-08-07 AAPL 219.57 207.924286 20\n7 2025-08-08 AAPL 228.87 210.542500 20\n8 2025-08-11 AAPL 226.96 212.366667 20\n9 2025-08-12 AAPL 229.43 214.073000 20\n......\n\nUpdate the code above based on the following instruction:\n\n3) For each symbol, compute daily returns as pct_change of close, then 20-day rolling volatility as the rolling std of these returns; keep volume. Reshape to long format with columns [date, symbol, metric, value], where metric ∈ {\"return\", \"vol20\", \"volume\"}, and visualize over time (e.g., faceted by symbol with separate lines for each metric) to highlight periods of strong trends with elevated volatility or volume."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compute daily returns and 20-day rolling volatility for each symbol, keep volume, and reshape to a long format for multi-metric time-series visualization.\",\n \"display_instruction\": \"Show **daily return**, **20-day volatility**, and **volume** by **symbol** over **date**\",\n \"recommendation\": \"To highlight periods of strong trends with elevated volatility or volume, compute per-symbol daily returns as pct_change of close and 20-day rolling volatility as the rolling standard deviation of those returns. Keep volume, then reshape into a long table with one metric per row so time-series lines for return, volatility, and volume can be plotted and compared per symbol.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"metric\", \"value\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"value\",\n \"color\": \"metric\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"symbol\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Keep only necessary columns\n cols = [c for c in [\"date\", \"symbol\", \"close\", \"volume\"] if c in df_history.columns]\n df = df_history[cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for time-series operations\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n # Compute daily returns as pct_change of close within each symbol\n df[\"return\"] = df.groupby(\"symbol\")[\"close\"].pct_change()\n\n # Compute 20-day rolling volatility (std of returns) within each symbol\n df[\"vol20\"] = (\n df.groupby(\"symbol\")[\"return\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).std())\n )\n\n # Reshape to long format: metrics = return, vol20, volume\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\"],\n value_vars=[\"return\", \"vol20\", \"volume\"],\n var_name=\"metric\",\n value_name=\"value\",\n )\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"metric\", \"value\"]].reset_index(drop=True)\n return transformed_df\n```"}],"trigger":{"tableId":"table-265247","sourceTableIds":["history"],"instruction":"3) For each symbol, compute daily returns as pct_change of close, then 20-day rolling volatility as the rolling std of these returns; keep volume. Reshape to long format with columns [date, symbol, metric, value], where metric ∈ {\"return\", \"vol20\", \"volume\"}, and visualize over time (e.g., faceted by symbol with separate lines for each metric) to highlight periods of strong trends with elevated volatility or volume.","displayInstruction":"Show **daily return**, **20-day volatility**, and **volume** by **symbol** over **date**","chart":{"id":"chart-1769471280073","chartType":"Auto","encodingMap":{},"tableRef":"history","saved":false,"source":"trigger","unread":true},"resultTableId":"table-277926"},"explanation":{"status":"ok","concepts":[{"field":"return","explanation":"Daily return measures how much the closing price changes in percentage terms from one trading day to the next for the same symbol. For day t, return_t = (Close_t - Close_{t-1}) / Close_{t-1}."},{"field":"vol20","explanation":"20-day rolling volatility is a short-term risk measure that summarizes how much the daily returns have fluctuated over roughly the past month. For each date, vol20 is the standard deviation of the last up-to-20 'return' values for that symbol; higher vol20 indicates more unstable (more volatile) recent price movements."},{"field":"metric, value","explanation":"These two fields reorganize different measures into a single tidy structure: 'metric' tells you which quantity is being reported (return, vol20, or volume), and 'value' stores the corresponding number. This makes it easy to filter and compare different metrics in the same table."},{"field":"Statistical Analysis","explanation":"The transformation introduces two statistical concepts: (1) daily returns, which quantify relative price changes over time, and (2) rolling volatility, which applies a moving standard deviation to recent returns to estimate short-term risk. The model is implicit: for each symbol, returns are treated as a time series, and volatility is computed over a 20-day rolling window. Other possible analyses on this data could include moving averages of price or volume, exponential moving volatility, correlation between different symbols' returns, or time-series models such as ARIMA or GARCH to forecast returns and volatility."}],"code":"- **Selects** only the key fields from the original data: **\"date\"**, **\"symbol\"**, **\"close\"**, and **\"volume\"**.\n- **Standardizes** the **\"date\"** format to **\"YYYY-MM-DD\"** so all dates are consistent.\n- **Sorts** the data by **\"symbol\"** and then by **\"date\"** to put each stock’s prices in correct time order.\n- For each **symbol**, **calculates daily price change**:\n - Adds a new field **\"return\"** = the **percentage change of the closing price** from the previous trading day for that same symbol.\n- For each **symbol**, **measures recent risk/variability**:\n - Adds a new field **\"vol20\"** = the **20‑day rolling volatility**, i.e., the **standard deviation of the last up-to-20 daily returns** (a larger value means the stock has been more volatile recently).\n- **Restructures** the data into a “long” tidy format:\n - Keeps **\"date\"** and **\"symbol\"**.\n - Converts the three measures **\"return\"**, **\"vol20\"**, and **\"volume\"** into rows under a single **\"metric\"** column, with their numbers stored in a **\"value\"** column.\n- **Outputs** a clean table with columns **\"date\"**, **\"symbol\"**, **\"metric\"**, and **\"value\"**, where each row represents **one metric for one stock on one date**.","dialog":[{"role":"system","content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n"},{"role":"user","content":"[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Keep only necessary columns\n cols = [c for c in [\"date\", \"symbol\", \"close\", \"volume\"] if c in df_history.columns]\n df = df_history[cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for time-series operations\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n # Compute daily returns as pct_change of close within each symbol\n df[\"return\"] = df.groupby(\"symbol\")[\"close\"].pct_change()\n\n # Compute 20-day rolling volatility (std of returns) within each symbol\n df[\"vol20\"] = (\n df.groupby(\"symbol\")[\"return\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).std())\n )\n\n # Reshape to long format: metrics = return, vol20, volume\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\"],\n value_vars=[\"return\", \"vol20\", \"volume\"],\n var_name=\"metric\",\n value_name=\"value\",\n )\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"metric\", \"value\"]].reset_index(drop=True)\n return transformed_df\n\n\n[EXPLANATION]\n"},{"role":"assistant","content":"[CODE EXPLANATION]\n\n- **Selects** only the key fields from the original data: **\"date\"**, **\"symbol\"**, **\"close\"**, and **\"volume\"**.\n- **Standardizes** the **\"date\"** format to **\"YYYY-MM-DD\"** so all dates are consistent.\n- **Sorts** the data by **\"symbol\"** and then by **\"date\"** to put each stock’s prices in correct time order.\n- For each **symbol**, **calculates daily price change**:\n - Adds a new field **\"return\"** = the **percentage change of the closing price** from the previous trading day for that same symbol.\n- For each **symbol**, **measures recent risk/variability**:\n - Adds a new field **\"vol20\"** = the **20‑day rolling volatility**, i.e., the **standard deviation of the last up-to-20 daily returns** (a larger value means the stock has been more volatile recently).\n- **Restructures** the data into a “long” tidy format:\n - Keeps **\"date\"** and **\"symbol\"**.\n - Converts the three measures **\"return\"**, **\"vol20\"**, and **\"volume\"** into rows under a single **\"metric\"** column, with their numbers stored in a **\"value\"** column.\n- **Outputs** a clean table with columns **\"date\"**, **\"symbol\"**, **\"metric\"**, and **\"value\"**, where each row represents **one metric for one stock on one date**.\n\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"return\",\n \"explanation\": \"Daily return measures how much the closing price changes in percentage terms from one trading day to the next for the same symbol. For day t, return_t = (Close_t - Close_{t-1}) / Close_{t-1}.\"\n },\n {\n \"field\": \"vol20\",\n \"explanation\": \"20-day rolling volatility is a short-term risk measure that summarizes how much the daily returns have fluctuated over roughly the past month. For each date, vol20 is the standard deviation of the last up-to-20 'return' values for that symbol; higher vol20 indicates more unstable (more volatile) recent price movements.\"\n },\n {\n \"field\": \"metric, value\",\n \"explanation\": \"These two fields reorganize different measures into a single tidy structure: 'metric' tells you which quantity is being reported (return, vol20, or volume), and 'value' stores the corresponding number. This makes it easy to filter and compare different metrics in the same table.\"\n },\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"The transformation introduces two statistical concepts: (1) daily returns, which quantify relative price changes over time, and (2) rolling volatility, which applies a moving standard deviation to recent returns to estimate short-term risk. The model is implicit: for each symbol, returns are treated as a time series, and volatility is computed over a 20-day rolling window. Other possible analyses on this data could include moving averages of price or volume, exponential moving volatility, correlation between different symbols' returns, or time-series models such as ARIMA or GARCH to forecast returns and volatility.\"\n 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pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Filter for Microsoft (MSFT) and Google (GOOGL)\n target_symbols = [\"MSFT\", \"GOOGL\"]\n df = df[df[\"symbol\"].isin(target_symbols)].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n","source":["history"],"dialog":[{"role":"system","content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"input_tables\", the names of a subset of input tables from [CONTEXT] section that will be used to achieve the user's goal.\n - **IMPORTANT** Note that the Table 1 in [CONTEXT] section is the table the user is currently viewing, it should take precedence if the user refers to insights about the \"current table\".\n - At the same time, leverage table information to determine which tables are relevant to the user's goal and should be used.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, latitude, longitude, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - if the user provides latitude and longitude as visual channels, use \"latitude\" and \"longitude\" as visual channels in \"chart_encodings\" as opposed to \"x\" and \"y\".\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\".\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"input_tables\": [...] // string[], describe names of the input tables that will be used in the transformation.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables described in \"input_tables\") and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], only import libraries allowed in the template, do not modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n# from sklearn import ... # import from sklearn if you need it.\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- decide the function signature based on the number of tables you decided in the previous step \"input_tables\":\n - if you decide there will only be one input table, then function signature should be `def transform_data(df1)`\n - if you decided there will be k input tables, then function signature should be `def transform_data(df_1, df_2, ..., df_k)`.\n - instead of using generic names like df1, df2, ..., try to use intuitive table names for function arguments, for example, if you have input_tables: [\"City\", \"Weather\"]`, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes.\n - **VERY IMPORTANT** the number of arguments in the function signature must be the same as the number of tables provided in \"input_tables\", and the order of arguments must match the order of tables provided in \"input_tables\".\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[GOAL]\n\n1) First, plot the overall **closing price** trends for each **symbol** over **time** on a single line chart to compare their broad trajectories and volatility."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compare overall closing price trends and volatility over time for each stock symbol on a single line chart.\",\n \"display_instruction\": \"Visualize **closing price** trends by **symbol** over **date**\",\n \"recommendation\": \"A line chart with time on the x-axis and closing price on the y-axis, colored by symbol, best shows comparative trajectories and volatility over time. The data only needs column selection, date normalization, and sorting to be visualization-ready.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"close\",\n \"color\": \"symbol\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date symbol close\n0 2025-07-30 AAPL 208.61\n1 2025-07-31 AAPL 207.13\n2 2025-08-01 AAPL 201.95\n3 2025-08-04 AAPL 202.92\n4 2025-08-05 AAPL 202.49\n5 2025-08-06 AAPL 212.80\n6 2025-08-07 AAPL 219.57\n7 2025-08-08 AAPL 228.87\n8 2025-08-11 AAPL 226.96\n9 2025-08-12 AAPL 229.43\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"compare microsoft and google\",\n \"chart_type\": \"\",\n \"chart_encodings\": {}\n}"},{"role":"assistant","content":"{\n \"detailed_instruction\": \"Filter the historical price data to only include Microsoft (MSFT) and Google (GOOGL) and prepare it for a line chart that compares their closing price trends over time on the same plot.\",\n \"display_instruction\": \"Compare **closing price** trends of **MSFT** and **GOOGL** over **date**\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\"],\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"close\",\n \"color\": \"symbol\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n },\n \"reason\": \"The follow-up focuses the existing trend comparison on only Microsoft and Google. This requires filtering the original prepared data to those two symbols while keeping the same line chart structure: date on x, close on y, and symbol as the color encoding.\"\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Filter for Microsoft (MSFT) and Google (GOOGL)\n target_symbols = [\"MSFT\", \"GOOGL\"]\n df = df[df[\"symbol\"].isin(target_symbols)].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n```"}],"trigger":{"tableId":"table-233476","sourceTableIds":["history"],"instruction":"compare microsoft and google","displayInstruction":"Compare **closing price** trends of **MSFT** and **GOOGL** over **date**","chart":{"id":"chart-1769471462237","chartType":"Auto","encodingMap":{},"tableRef":"table-233476","saved":false,"source":"trigger","unread":true},"resultTableId":"table-457008"},"explanation":{"status":"ok","concepts":[],"code":"- **Keep only** the **`date`**, **`symbol`**, and **`close`** price columns from the original data, discarding all others.\n- **Filter rows** to include **only** the two companies: **`MSFT` (Microsoft)** and **`GOOGL` (Google)**.\n- **Standardize the date format** by converting **`date`** into a **string** formatted as **`YYYY-MM-DD`** (year-month-day).\n- **Sort the data** by **`symbol`** and then by **`date`** so that each company’s prices are in **chronological order**, making it ready for **line plotting over time**.\n- **Return** this cleaned and filtered dataset as the final result.","dialog":[{"role":"system","content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n"},{"role":"user","content":"[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Filter for Microsoft (MSFT) and Google (GOOGL)\n target_symbols = [\"MSFT\", \"GOOGL\"]\n df = df[df[\"symbol\"].isin(target_symbols)].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n\n\n[EXPLANATION]\n"},{"role":"assistant","content":"[CODE EXPLANATION]\n\n- **Keep only** the **`date`**, **`symbol`**, and **`close`** price columns from the original data, discarding all others.\n- **Filter rows** to include **only** the two companies: **`MSFT` (Microsoft)** and **`GOOGL` (Google)**.\n- **Standardize the date format** by converting **`date`** into a **string** formatted as **`YYYY-MM-DD`** (year-month-day).\n- **Sort the data** by **`symbol`** and then by **`date`** so that each company’s prices are in **chronological order**, making it ready for **line plotting over time**.\n- **Return** this cleaned and filtered dataset as the final result.\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n]\n```"}],"agent":"CodeExplanationAgent"}},"anchored":false,"createdBy":"user","attachedMetadata":"","contentHash":"903aa346"}],"charts":[{"id":"chart-1769471461230","chartType":"Scatter Plot","encodingMap":{"x":{"fieldID":"original--history--date"},"y":{"fieldID":"original--history--close"},"color":{"fieldID":"original--history--symbol"},"size":{"channel":"size","bin":false},"opacity":{"channel":"opacity","bin":false},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"table-457008","saved":false,"source":"user","unread":false},{"id":"chart-1769471279744","chartType":"Line 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formulator","value":"Successfully loaded Stock Prices (Live)"},{"timestamp":1769535028897,"component":"Data Refresh","type":"info","value":"Table \"stock-hist\" data refreshed (738 rows)"},{"timestamp":1769535029034,"component":"Data Refresh","type":"info","value":"Table \"stock-hist\" data refreshed (738 rows)"},{"timestamp":1769535029121,"component":"Data Refresh","type":"info","value":"Derived table \"stock-close\" refreshed (738 rows)"},{"timestamp":1769535041128,"component":"Data Refresh","type":"info","value":"Table \"stock-hist\" data refreshed (738 rows)"},{"timestamp":1769535041339,"component":"Data Refresh","type":"info","value":"Table \"stock-hist\" data refreshed (738 rows)"},{"timestamp":1769535041452,"component":"Data Refresh","type":"info","value":"Derived table \"stock-close\" refreshed (738 rows)"},{"timestamp":1769535048535,"component":"DB manager","type":"success","value":"Deleted 1 unreferenced derived views: view_ushx"}],"displayedMessageIdx":8,"focusedTableId":"table-233476","focusedChartId":"chart-1769471233467","viewMode":"editor","chartSynthesisInProgress":[],"config":{"formulateTimeoutSeconds":60,"maxRepairAttempts":1,"defaultChartWidth":300,"defaultChartHeight":300},"agentActions":[{"actionId":"exploreDataFromNL_1769471236248","tableId":"table-294972","description":"- Compared raw close-price trajectories across six mega-cap stocks.\n- Analyzed 20/60-day moving averages plus returns, volatility, and volume.\n- Summarized phase (quarterly) performance: avg/cumulative returns and volatility by symbol.","status":"completed","hidden":false,"lastUpdate":1769471309407}],"dataCleanBlocks":[],"cleanInProgress":false,"generatedReports":[{"id":"report-1769471498009-9657","content":"# Monitoring Mega-Cap Tech: Price, Trends, and Risk at a Glance\n\nThis live report is built to help you track six large tech stocks (AAPL, AMZN, GOOGL, META, MSFT, NVDA) across price, trends, and risk. Each chart updates as new rows are added to the `history` table.\n\n[IMAGE(chart-1769471233467)]\n\nThe first chart shows daily closing prices over time for all six symbols. Use it to spot broad moves, compare relative price levels, and see when one stock starts to diverge from the group.\n\n[IMAGE(chart-1769471263442)]\n\nThe second chart adds 20‑day and 60‑day moving averages. Watch how actual prices relate to these smoother trend lines: crossovers, sustained gaps, or trend flattening can signal shifting momentum.\n\n[IMAGE(chart-1769471279744)]\n\nThe third set of panels tracks three risk and activity metrics: daily returns, 20‑day return volatility, and trading volume. Look for periods where volatility or volume spikes, or where returns cluster on one side.\n\n[IMAGE(chart-1769471461230)]\n\nThe final chart zooms in on GOOGL and MSFT closing prices, making it easier to compare their day‑to‑day paths without the distraction of other symbols.\n\n**In summary**, use these views together: price levels and trends, plus returns, volatility, and volume, to monitor how each stock is behaving and how relationships between them evolve over time. Possible follow‑ups: add alerts for threshold moves, overlay events (earnings, macro news), or include benchmark indices for context.","style":"live report","selectedChartIds":["chart-1769471279744","chart-1769471233467","chart-1769471461230","chart-1769471263442"],"createdAt":1769471511269}],"_persist":{"version":-1,"rehydrated":true}} ================================================ FILE: public/df_unemployment.json ================================================ 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rate":6.5}],"metadata":{"date":{"type":"date","semanticType":"YearMonth"},"series":{"type":"string","semanticType":"String"},"rate":{"type":"number","semanticType":"Percentage"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date']).dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns for visualization\n transformed_df = df_unemployment[['date', 'series', 'rate']].copy()\n \n return transformed_df\n","source":["unemployment-across-industries"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[GOAL]\n\nCompare **unemployment trends** across all industries from 2000-2010"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to compare unemployment trends across all industries from 2000-2010 to understand how different sectors were affected over time.\",\n \"display_instruction\": \"Compare **unemployment rates** across **industries** over time\",\n \"recommendation\": \"A line chart with time on x-axis and unemployment rate on y-axis, with different colored lines for each industry series, will effectively show the trends and allow comparison of how unemployment evolved across different sectors during this period.\",\n \"output_fields\": [\"date\", \"series\", \"rate\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date']).dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns for visualization\n transformed_df = df_unemployment[['date', 'series', 'rate']].copy()\n \n return transformed_df\n```"}],"trigger":{"tableId":"unemployment-across-industries","sourceTableIds":["unemployment-across-industries"],"instruction":"Compare **unemployment trends** across all industries from 2000-2010","displayInstruction":"Compare **unemployment rates** across **industries** over time","chart":{"id":"chart-1760745546913","chartType":"Auto","encodingMap":{},"tableRef":"unemployment-across-industries","saved":false,"source":"trigger","unread":true},"resultTableId":"table-544555"},"explanation":{"agent":"CodeExplanationAgent","code":"- Convert the **date** field from timestamp format to a standardized string format (`YYYY-MM-DD`) for cleaner visualization\n- Select and retain only three key fields: **date**, **series** (industry name), and **rate** (unemployment rate percentage)\n- Return a simplified dataset focused on tracking unemployment rates across different industries over time","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date']).dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns for visualization\n transformed_df = df_unemployment[['date', 'series', 'rate']].copy()\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Convert the **date** field from timestamp format to a standardized string format (`YYYY-MM-DD`) for cleaner visualization\n- Select and retain only three key fields: **date**, **series** (industry name), and **rate** (unemployment rate percentage)\n- Return a simplified dataset focused on tracking unemployment rates across different industries over time\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-540763","displayId":"unemployment-rate","names":["avg_rate","month","month_name","series"],"rows":[{"avg_rate":13.7636363636,"month":1,"month_name":"Jan","series":"Agriculture"},{"avg_rate":13.6454545455,"month":2,"month_name":"Feb","series":"Agriculture"},{"avg_rate":13.83,"month":3,"month_name":"Mar","series":"Agriculture"},{"avg_rate":9.13,"month":4,"month_name":"Apr","series":"Agriculture"},{"avg_rate":7.1,"month":5,"month_name":"May","series":"Agriculture"},{"avg_rate":6.77,"month":6,"month_name":"Jun","series":"Agriculture"},{"avg_rate":7.01,"month":7,"month_name":"Jul","series":"Agriculture"},{"avg_rate":8.08,"month":8,"month_name":"Aug","series":"Agriculture"},{"avg_rate":7.09,"month":9,"month_name":"Sep","series":"Agriculture"},{"avg_rate":7.49,"month":10,"month_name":"Oct","series":"Agriculture"},{"avg_rate":10.47,"month":11,"month_name":"Nov","series":"Agriculture"},{"avg_rate":12.94,"month":12,"month_name":"Dec","series":"Agriculture"},{"avg_rate":7.8636363636,"month":1,"month_name":"Jan","series":"Business services"},{"avg_rate":7.6454545455,"month":2,"month_name":"Feb","series":"Business services"},{"avg_rate":7.13,"month":3,"month_name":"Mar","series":"Business services"},{"avg_rate":6.27,"month":4,"month_name":"Apr","series":"Business services"},{"avg_rate":6.6,"month":5,"month_name":"May","series":"Business services"},{"avg_rate":6.72,"month":6,"month_name":"Jun","series":"Business services"},{"avg_rate":6.74,"month":7,"month_name":"Jul","series":"Business services"},{"avg_rate":6.57,"month":8,"month_name":"Aug","series":"Business services"},{"avg_rate":6.79,"month":9,"month_name":"Sep","series":"Business services"},{"avg_rate":6.71,"month":10,"month_name":"Oct","series":"Business services"},{"avg_rate":6.75,"month":11,"month_name":"Nov","series":"Business services"},{"avg_rate":7.08,"month":12,"month_name":"Dec","series":"Business services"},{"avg_rate":12.9090909091,"month":1,"month_name":"Jan","series":"Construction"},{"avg_rate":13.6,"month":2,"month_name":"Feb","series":"Construction"},{"avg_rate":11.29,"month":3,"month_name":"Mar","series":"Construction"},{"avg_rate":9.45,"month":4,"month_name":"Apr","series":"Construction"},{"avg_rate":8.12,"month":5,"month_name":"May","series":"Construction"},{"avg_rate":7.43,"month":6,"month_name":"Jun","series":"Construction"},{"avg_rate":7.35,"month":7,"month_name":"Jul","series":"Construction"},{"avg_rate":7.3,"month":8,"month_name":"Aug","series":"Construction"},{"avg_rate":7.56,"month":9,"month_name":"Sep","series":"Construction"},{"avg_rate":7.84,"month":10,"month_name":"Oct","series":"Construction"},{"avg_rate":8.7,"month":11,"month_name":"Nov","series":"Construction"},{"avg_rate":10.8,"month":12,"month_name":"Dec","series":"Construction"},{"avg_rate":3.3636363636,"month":1,"month_name":"Jan","series":"Education and 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and Utilities"},{"avg_rate":5.6181818182,"month":2,"month_name":"Feb","series":"Transportation and Utilities"},{"avg_rate":5.1,"month":3,"month_name":"Mar","series":"Transportation and Utilities"},{"avg_rate":4.79,"month":4,"month_name":"Apr","series":"Transportation and Utilities"},{"avg_rate":4.5,"month":5,"month_name":"May","series":"Transportation and Utilities"},{"avg_rate":4.82,"month":6,"month_name":"Jun","series":"Transportation and Utilities"},{"avg_rate":5.04,"month":7,"month_name":"Jul","series":"Transportation and Utilities"},{"avg_rate":4.58,"month":8,"month_name":"Aug","series":"Transportation and Utilities"},{"avg_rate":4.65,"month":9,"month_name":"Sep","series":"Transportation and Utilities"},{"avg_rate":4.8,"month":10,"month_name":"Oct","series":"Transportation and Utilities"},{"avg_rate":4.58,"month":11,"month_name":"Nov","series":"Transportation and Utilities"},{"avg_rate":4.8,"month":12,"month_name":"Dec","series":"Transportation and Utilities"},{"avg_rate":6.4818181818,"month":1,"month_name":"Jan","series":"Wholesale and Retail Trade"},{"avg_rate":6.3727272727,"month":2,"month_name":"Feb","series":"Wholesale and Retail Trade"},{"avg_rate":5.86,"month":3,"month_name":"Mar","series":"Wholesale and Retail Trade"},{"avg_rate":5.46,"month":4,"month_name":"Apr","series":"Wholesale and Retail Trade"},{"avg_rate":5.49,"month":5,"month_name":"May","series":"Wholesale and Retail Trade"},{"avg_rate":5.84,"month":6,"month_name":"Jun","series":"Wholesale and Retail Trade"},{"avg_rate":5.75,"month":7,"month_name":"Jul","series":"Wholesale and Retail Trade"},{"avg_rate":5.61,"month":8,"month_name":"Aug","series":"Wholesale and Retail Trade"},{"avg_rate":5.63,"month":9,"month_name":"Sep","series":"Wholesale and Retail Trade"},{"avg_rate":5.56,"month":10,"month_name":"Oct","series":"Wholesale and Retail Trade"},{"avg_rate":5.52,"month":11,"month_name":"Nov","series":"Wholesale and Retail Trade"},{"avg_rate":5.49,"month":12,"month_name":"Dec","series":"Wholesale and Retail Trade"}],"metadata":{"avg_rate":{"type":"number","semanticType":"Percentage"},"month":{"type":"number","semanticType":"Month"},"month_name":{"type":"string","semanticType":"Month","levels":["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"]},"series":{"type":"string","semanticType":"String"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series (industry)\n transformed_df = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Add month name for better readability\n month_names = {\n 1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',\n 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'\n }\n transformed_df['month_name'] = transformed_df['month'].map(month_names)\n \n return transformed_df\n","source":["unemployment-across-industries"],"dialog":[{"content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[GOAL]\n\nHow does **seasonal variation** in unemployment differ across **industries**?","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"Analyze how seasonal variation in unemployment differs across industries by examining the cyclical patterns in unemployment rates throughout the year for different industry sectors.\",\n \"display_instruction\": \"Compare seasonal unemployment patterns across **industries**\",\n \"recommendation\": \"To visualize seasonal variation across industries, we should calculate average unemployment rates by month for each industry series. This will reveal the typical seasonal patterns and allow comparison of how different industries experience seasonal fluctuations in unemployment throughout the year.\",\n \"output_fields\": [\"series\", \"month\", \"avg_rate\", \"month_name\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"month\",\n \"y\": \"avg_rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series (industry)\n transformed_df = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Add month name for better readability\n month_names = {\n 1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',\n 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'\n }\n transformed_df['month_name'] = transformed_df['month'].map(month_names)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"unemployment-across-industries","sourceTableIds":["unemployment-across-industries"],"instruction":"How does **seasonal variation** in unemployment differ across **industries**?","displayInstruction":"Compare seasonal unemployment patterns across **industries**","chart":{"id":"chart-1760745541760","chartType":"Auto","encodingMap":{},"tableRef":"unemployment-across-industries","saved":false,"source":"trigger","unread":true},"resultTableId":"table-540763"},"explanation":{"agent":"CodeExplanationAgent","code":"- Group the unemployment data by **series** (industry) and **month**\n- Calculate the **mean** of the **rate** field for each industry-month combination, creating a new field **avg_rate**\n- Map numeric **month** values (1-12) to their abbreviated names (**Jan**, **Feb**, **Mar**, etc.) and store in **month_name** field for improved readability\n- Return the aggregated dataset containing average unemployment rates per industry per month across all years in the data","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series (industry)\n transformed_df = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Add month name for better readability\n month_names = {\n 1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',\n 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'\n }\n transformed_df['month_name'] = transformed_df['month'].map(month_names)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Group the unemployment data by **series** (industry) and **month**\n- Calculate the **mean** of the **rate** field for each industry-month combination, creating a new field **avg_rate**\n- Map numeric **month** values (1-12) to their abbreviated names (**Jan**, **Feb**, **Mar**, etc.) and store in **month_name** field for improved readability\n- Return the aggregated dataset containing average unemployment rates per industry per month across all years in the data\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-552276","displayId":"unemp-by-sector","names":["series","rate_2008","rate_2009","rate_increase"],"rows":[{"series":"Mining and Extraction","rate_2008":3.0833333333,"rate_2009":11.6583333333,"rate_increase":8.575},{"series":"Construction","rate_2008":10.6,"rate_2009":19.05,"rate_increase":8.45},{"series":"Manufacturing","rate_2008":5.7583333333,"rate_2009":12.075,"rate_increase":6.3166666667},{"series":"Agriculture","rate_2008":9.2666666667,"rate_2009":14.3916666667,"rate_increase":5.125},{"series":"Business services","rate_2008":6.5583333333,"rate_2009":10.8,"rate_increase":4.2416666667},{"series":"Information","rate_2008":5.0166666667,"rate_2009":9.225,"rate_increase":4.2083333333},{"series":"Transportation and Utilities","rate_2008":5.1333333333,"rate_2009":8.8833333333,"rate_increase":3.75},{"series":"Wholesale and Retail Trade","rate_2008":5.8416666667,"rate_2009":9.0333333333,"rate_increase":3.1916666667},{"series":"Leisure and hospitality","rate_2008":8.6416666667,"rate_2009":11.6833333333,"rate_increase":3.0416666667},{"series":"Finance","rate_2008":3.95,"rate_2009":6.4,"rate_increase":2.45},{"series":"Other","rate_2008":5.2666666667,"rate_2009":7.5083333333,"rate_increase":2.2416666667},{"series":"Self-employed","rate_2008":3.6416666667,"rate_2009":5.5,"rate_increase":1.8583333333},{"series":"Education and Health","rate_2008":3.4916666667,"rate_2009":5.2666666667,"rate_increase":1.775},{"series":"Government","rate_2008":2.4416666667,"rate_2009":3.6166666667,"rate_increase":1.175}],"metadata":{"series":{"type":"string","semanticType":"String"},"rate_2008":{"type":"number","semanticType":"Percentage"},"rate_2009":{"type":"number","semanticType":"Percentage"},"rate_increase":{"type":"number","semanticType":"Percentage"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter data for years 2008 and 2009\n df_2008_2009 = df_unemployment[df_unemployment['year'].isin([2008, 2009])].copy()\n \n # Calculate average rate for each industry in 2008 and 2009\n df_2008 = df_2008_2009[df_2008_2009['year'] == 2008].groupby('series')['rate'].mean().reset_index()\n df_2008.columns = ['series', 'rate_2008']\n \n df_2009 = df_2008_2009[df_2008_2009['year'] == 2009].groupby('series')['rate'].mean().reset_index()\n df_2009.columns = ['series', 'rate_2009']\n \n # Merge and calculate the increase\n transformed_df = df_2008.merge(df_2009, on='series')\n transformed_df['rate_increase'] = transformed_df['rate_2009'] - transformed_df['rate_2008']\n \n # Sort by rate increase in descending order\n transformed_df = transformed_df.sort_values('rate_increase', ascending=False).reset_index(drop=True)\n \n return transformed_df\n","source":["unemployment-across-industries"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[GOAL]\n\nCompare **unemployment trends** across all industries from 2000-2010"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to compare unemployment trends across all industries from 2000-2010 to understand how different sectors were affected over time.\",\n \"display_instruction\": \"Compare **unemployment rates** across **industries** over time\",\n \"recommendation\": \"A line chart with time on x-axis and unemployment rate on y-axis, with different colored lines for each industry series, will effectively show the trends and allow comparison of how unemployment evolved across different sectors during this period.\",\n \"output_fields\": [\"date\", \"series\", \"rate\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date']).dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns for visualization\n transformed_df = df_unemployment[['date', 'series', 'rate']].copy()\n \n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date series rate\n0 2000-01-01 Government 2.1\n1 2000-02-01 Government 2.0\n2 2000-03-01 Government 1.5\n3 2000-04-01 Government 1.3\n4 2000-05-01 Government 1.9\n5 2000-06-01 Government 3.1\n6 2000-07-01 Government 2.9\n7 2000-08-01 Government 3.1\n8 2000-09-01 Government 2.1\n9 2000-10-01 Government 2.0\n......\n\nUpdate the code above based on the following instruction:\n\nWhich industries showed the **sharpest increases** in unemployment during **2008-2009**?"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to identify which industries experienced the sharpest increases in unemployment rates during the 2008-2009 financial crisis period by calculating the change in unemployment rate for each industry.\",\n \"display_instruction\": \"Identify industries with **sharpest unemployment rate increases** during **2008-2009**\",\n \"recommendation\": \"A bar chart showing the change in unemployment rate from 2008 to 2009 for each industry will clearly visualize which sectors were hit hardest during the financial crisis. The industries should be sorted by the magnitude of increase to make comparison easier.\",\n \"output_fields\": [\"series\", \"rate_2008\", \"rate_2009\", \"rate_increase\"],\n \"chart_type\": \"bar\",\n \"chart_encodings\": {\n \"x\": \"series\",\n \"y\": \"rate_increase\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter data for years 2008 and 2009\n df_2008_2009 = df_unemployment[df_unemployment['year'].isin([2008, 2009])].copy()\n \n # Calculate average rate for each industry in 2008 and 2009\n df_2008 = df_2008_2009[df_2008_2009['year'] == 2008].groupby('series')['rate'].mean().reset_index()\n df_2008.columns = ['series', 'rate_2008']\n \n df_2009 = df_2008_2009[df_2008_2009['year'] == 2009].groupby('series')['rate'].mean().reset_index()\n df_2009.columns = ['series', 'rate_2009']\n \n # Merge and calculate the increase\n transformed_df = df_2008.merge(df_2009, on='series')\n transformed_df['rate_increase'] = transformed_df['rate_2009'] - transformed_df['rate_2008']\n \n # Sort by rate increase in descending order\n transformed_df = transformed_df.sort_values('rate_increase', ascending=False).reset_index(drop=True)\n \n return transformed_df\n```"}],"trigger":{"tableId":"table-544555","sourceTableIds":["unemployment-across-industries"],"instruction":"Which industries showed the **sharpest increases** in unemployment during **2008-2009**?","displayInstruction":"Identify industries with **sharpest unemployment rate increases** during **2008-2009**","chart":{"id":"chart-1760745556114","chartType":"Auto","encodingMap":{},"tableRef":"unemployment-across-industries","saved":false,"source":"trigger","unread":true},"resultTableId":"table-552276"},"explanation":{"agent":"CodeExplanationAgent","code":"- Convert the **date** field to datetime format for proper date handling\n- Filter the dataset to include only records from **2008** and **2009**\n- Calculate the **average unemployment rate** for each **industry (series)** in **2008**\n- Calculate the **average unemployment rate** for each **industry (series)** in **2009**\n- Merge the two yearly averages and compute the **rate increase** from 2008 to 2009\n- Sort industries by **rate_increase** in descending order to identify which industries experienced the largest growth in unemployment","concepts":[{"explanation":"The difference in average unemployment rate between 2009 and 2008 for each industry, calculated as \\( \\text{rate\\_increase} = \\text{rate\\_2009} - \\text{rate\\_2008} \\). This metric indicates how much unemployment worsened (positive values) or improved (negative values) during the financial crisis period.","field":"rate_increase"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter data for years 2008 and 2009\n df_2008_2009 = df_unemployment[df_unemployment['year'].isin([2008, 2009])].copy()\n \n # Calculate average rate for each industry in 2008 and 2009\n df_2008 = df_2008_2009[df_2008_2009['year'] == 2008].groupby('series')['rate'].mean().reset_index()\n df_2008.columns = ['series', 'rate_2008']\n \n df_2009 = df_2008_2009[df_2008_2009['year'] == 2009].groupby('series')['rate'].mean().reset_index()\n df_2009.columns = ['series', 'rate_2009']\n \n # Merge and calculate the increase\n transformed_df = df_2008.merge(df_2009, on='series')\n transformed_df['rate_increase'] = transformed_df['rate_2009'] - transformed_df['rate_2008']\n \n # Sort by rate increase in descending order\n transformed_df = transformed_df.sort_values('rate_increase', ascending=False).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Convert the **date** field to datetime format for proper date handling\n- Filter the dataset to include only records from **2008** and **2009**\n- Calculate the **average unemployment rate** for each **industry (series)** in **2008**\n- Calculate the **average unemployment rate** for each **industry (series)** in **2009**\n- Merge the two yearly averages and compute the **rate increase** from 2008 to 2009\n- Sort industries by **rate_increase** in descending order to identify which industries experienced the largest growth in unemployment\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"rate_increase\",\n \"explanation\": \"The difference in average unemployment rate between 2009 and 2008 for each industry, calculated as \\\\( \\\\text{rate\\\\_increase} = \\\\text{rate\\\\_2009} - \\\\text{rate\\\\_2008} \\\\). This metric indicates how much unemployment worsened (positive values) or improved (negative values) during the financial crisis period.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-570117","displayId":"unemp-rate-ind1","names":["date","series","rate"],"rows":[{"date":"2008-01-01","series":"Construction","rate":11},{"date":"2008-02-01","series":"Construction","rate":11.4},{"date":"2008-03-01","series":"Construction","rate":12},{"date":"2008-04-01","series":"Construction","rate":11.1},{"date":"2008-05-01","series":"Construction","rate":8.6},{"date":"2008-06-01","series":"Construction","rate":8.2},{"date":"2008-07-01","series":"Construction","rate":8},{"date":"2008-08-01","series":"Construction","rate":8.2},{"date":"2008-09-01","series":"Construction","rate":9.9},{"date":"2008-10-01","series":"Construction","rate":10.8},{"date":"2008-11-01","series":"Construction","rate":12.7},{"date":"2008-12-01","series":"Construction","rate":15.3},{"date":"2009-01-01","series":"Construction","rate":18.2},{"date":"2009-02-01","series":"Construction","rate":21.4},{"date":"2009-03-01","series":"Construction","rate":21.1},{"date":"2009-04-01","series":"Construction","rate":18.7},{"date":"2009-05-01","series":"Construction","rate":19.2},{"date":"2009-06-01","series":"Construction","rate":17.4},{"date":"2009-07-01","series":"Construction","rate":18.2},{"date":"2009-08-01","series":"Construction","rate":16.5},{"date":"2009-09-01","series":"Construction","rate":17.1},{"date":"2009-10-01","series":"Construction","rate":18.7},{"date":"2009-11-01","series":"Construction","rate":19.4},{"date":"2009-12-01","series":"Construction","rate":22.7},{"date":"2010-01-01","series":"Construction","rate":24.7},{"date":"2010-02-01","series":"Construction","rate":27.1},{"date":"2008-01-01","series":"Manufacturing","rate":5.1},{"date":"2008-02-01","series":"Manufacturing","rate":5},{"date":"2008-03-01","series":"Manufacturing","rate":5},{"date":"2008-04-01","series":"Manufacturing","rate":4.8},{"date":"2008-05-01","series":"Manufacturing","rate":5.3},{"date":"2008-06-01","series":"Manufacturing","rate":5.2},{"date":"2008-07-01","series":"Manufacturing","rate":5.5},{"date":"2008-08-01","series":"Manufacturing","rate":5.7},{"date":"2008-09-01","series":"Manufacturing","rate":6},{"date":"2008-10-01","series":"Manufacturing","rate":6.2},{"date":"2008-11-01","series":"Manufacturing","rate":7},{"date":"2008-12-01","series":"Manufacturing","rate":8.3},{"date":"2009-01-01","series":"Manufacturing","rate":10.9},{"date":"2009-02-01","series":"Manufacturing","rate":11.5},{"date":"2009-03-01","series":"Manufacturing","rate":12.2},{"date":"2009-04-01","series":"Manufacturing","rate":12.4},{"date":"2009-05-01","series":"Manufacturing","rate":12.6},{"date":"2009-06-01","series":"Manufacturing","rate":12.6},{"date":"2009-07-01","series":"Manufacturing","rate":12.4},{"date":"2009-08-01","series":"Manufacturing","rate":11.8},{"date":"2009-09-01","series":"Manufacturing","rate":11.9},{"date":"2009-10-01","series":"Manufacturing","rate":12.2},{"date":"2009-11-01","series":"Manufacturing","rate":12.5},{"date":"2009-12-01","series":"Manufacturing","rate":11.9},{"date":"2010-01-01","series":"Manufacturing","rate":13},{"date":"2010-02-01","series":"Manufacturing","rate":12.1},{"date":"2008-01-01","series":"Mining and Extraction","rate":4},{"date":"2008-02-01","series":"Mining and Extraction","rate":2.2},{"date":"2008-03-01","series":"Mining and Extraction","rate":3.7},{"date":"2008-04-01","series":"Mining and Extraction","rate":3.6},{"date":"2008-05-01","series":"Mining and Extraction","rate":3.4},{"date":"2008-06-01","series":"Mining and Extraction","rate":3.3},{"date":"2008-07-01","series":"Mining and Extraction","rate":1.5},{"date":"2008-08-01","series":"Mining and Extraction","rate":1.9},{"date":"2008-09-01","series":"Mining and Extraction","rate":2.8},{"date":"2008-10-01","series":"Mining and Extraction","rate":1.7},{"date":"2008-11-01","series":"Mining and Extraction","rate":3.7},{"date":"2008-12-01","series":"Mining and Extraction","rate":5.2},{"date":"2009-01-01","series":"Mining and Extraction","rate":7},{"date":"2009-02-01","series":"Mining and Extraction","rate":7.6},{"date":"2009-03-01","series":"Mining and Extraction","rate":12.6},{"date":"2009-04-01","series":"Mining and Extraction","rate":16.1},{"date":"2009-05-01","series":"Mining and Extraction","rate":13.3},{"date":"2009-06-01","series":"Mining and Extraction","rate":13.6},{"date":"2009-07-01","series":"Mining and Extraction","rate":12.6},{"date":"2009-08-01","series":"Mining and Extraction","rate":11.8},{"date":"2009-09-01","series":"Mining and Extraction","rate":10.7},{"date":"2009-10-01","series":"Mining and Extraction","rate":10.8},{"date":"2009-11-01","series":"Mining and Extraction","rate":12},{"date":"2009-12-01","series":"Mining and Extraction","rate":11.8},{"date":"2010-01-01","series":"Mining and Extraction","rate":9.1},{"date":"2010-02-01","series":"Mining and Extraction","rate":10.7}],"metadata":{"date":{"type":"date","semanticType":"YearMonth"},"series":{"type":"string","semanticType":"String"},"rate":{"type":"number","semanticType":"Percentage"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter for years 2008-2010\n df_filtered = df_unemployment[df_unemployment['year'].isin([2008, 2009, 2010])].copy()\n \n # Filter for top 3 most affected industries\n top_3_industries = ['Mining and Extraction', 'Construction', 'Manufacturing']\n df_filtered = df_filtered[df_filtered['series'].isin(top_3_industries)].copy()\n \n # Convert date to string format for visualization\n df_filtered['date'] = df_filtered['date'].dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns\n transformed_df = df_filtered[['date', 'series', 'rate']].copy()\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values(['series', 'date']).reset_index(drop=True)\n \n return transformed_df\n","source":["unemployment-across-industries"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[GOAL]\n\nCompare **unemployment trends** across all industries from 2000-2010"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to compare unemployment trends across all industries from 2000-2010 to understand how different sectors were affected over time.\",\n \"display_instruction\": \"Compare **unemployment rates** across **industries** over time\",\n \"recommendation\": \"A line chart with time on x-axis and unemployment rate on y-axis, with different colored lines for each industry series, will effectively show the trends and allow comparison of how unemployment evolved across different sectors during this period.\",\n \"output_fields\": [\"date\", \"series\", \"rate\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date']).dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns for visualization\n transformed_df = df_unemployment[['date', 'series', 'rate']].copy()\n \n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date series rate\n0 2000-01-01 Government 2.1\n1 2000-02-01 Government 2.0\n2 2000-03-01 Government 1.5\n3 2000-04-01 Government 1.3\n4 2000-05-01 Government 1.9\n5 2000-06-01 Government 3.1\n6 2000-07-01 Government 2.9\n7 2000-08-01 Government 3.1\n8 2000-09-01 Government 2.1\n9 2000-10-01 Government 2.0\n......\n\nUpdate the code above based on the following instruction:\n\nWhich industries showed the **sharpest increases** in unemployment during **2008-2009**?"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to identify which industries experienced the sharpest increases in unemployment rates during the 2008-2009 financial crisis period by calculating the change in unemployment rate for each industry.\",\n \"display_instruction\": \"Identify industries with **sharpest unemployment rate increases** during **2008-2009**\",\n \"recommendation\": \"A bar chart showing the change in unemployment rate from 2008 to 2009 for each industry will clearly visualize which sectors were hit hardest during the financial crisis. The industries should be sorted by the magnitude of increase to make comparison easier.\",\n \"output_fields\": [\"series\", \"rate_2008\", \"rate_2009\", \"rate_increase\"],\n \"chart_type\": \"bar\",\n \"chart_encodings\": {\n \"x\": \"series\",\n \"y\": \"rate_increase\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter data for years 2008 and 2009\n df_2008_2009 = df_unemployment[df_unemployment['year'].isin([2008, 2009])].copy()\n \n # Calculate average rate for each industry in 2008 and 2009\n df_2008 = df_2008_2009[df_2008_2009['year'] == 2008].groupby('series')['rate'].mean().reset_index()\n df_2008.columns = ['series', 'rate_2008']\n \n df_2009 = df_2008_2009[df_2008_2009['year'] == 2009].groupby('series')['rate'].mean().reset_index()\n df_2009.columns = ['series', 'rate_2009']\n \n # Merge and calculate the increase\n transformed_df = df_2008.merge(df_2009, on='series')\n transformed_df['rate_increase'] = transformed_df['rate_2009'] - transformed_df['rate_2008']\n \n # Sort by rate increase in descending order\n transformed_df = transformed_df.sort_values('rate_increase', ascending=False).reset_index(drop=True)\n \n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n series rate_2008 rate_2009 rate_increase\n0 Mining and Extraction 3.083333 11.658333 8.575000\n1 Construction 10.600000 19.050000 8.450000\n2 Manufacturing 5.758333 12.075000 6.316667\n3 Agriculture 9.266667 14.391667 5.125000\n4 Business services 6.558333 10.800000 4.241667\n5 Information 5.016667 9.225000 4.208333\n6 Transportation and Utilities 5.133333 8.883333 3.750000\n7 Wholesale and Retail Trade 5.841667 9.033333 3.191667\n8 Leisure and hospitality 8.641667 11.683333 3.041667\n9 Finance 3.950000 6.400000 2.450000\n......\n\nUpdate the code above based on the following instruction:\n\nFor the **top 3 most affected industries** (Mining and Extraction, Construction, Manufacturing), compare their **monthly unemployment rate trends** during the **2008-2010 period** to analyze how quickly each industry recovered after the 2009 peak"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to analyze the monthly unemployment rate trends for the top 3 most affected industries (Mining and Extraction, Construction, Manufacturing) during 2008-2010 to understand their recovery patterns after the 2009 peak.\",\n \"display_instruction\": \"Compare **monthly unemployment trends** for **top 3 affected industries** during **2008-2010 recovery period**\",\n \"recommendation\": \"A line chart with time on x-axis and unemployment rate on y-axis, with different colored lines for the three most affected industries, will effectively show how each industry's unemployment rate evolved during and after the crisis, revealing their different recovery trajectories.\",\n \"output_fields\": [\"date\", \"series\", \"rate\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter for years 2008-2010\n df_filtered = df_unemployment[df_unemployment['year'].isin([2008, 2009, 2010])].copy()\n \n # Filter for top 3 most affected industries\n top_3_industries = ['Mining and Extraction', 'Construction', 'Manufacturing']\n df_filtered = df_filtered[df_filtered['series'].isin(top_3_industries)].copy()\n \n # Convert date to string format for visualization\n df_filtered['date'] = df_filtered['date'].dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns\n transformed_df = df_filtered[['date', 'series', 'rate']].copy()\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values(['series', 'date']).reset_index(drop=True)\n \n return transformed_df\n```"}],"trigger":{"tableId":"table-552276","sourceTableIds":["unemployment-across-industries"],"instruction":"For the **top 3 most affected industries** (Mining and Extraction, Construction, Manufacturing), compare their **monthly unemployment rate trends** during the **2008-2010 period** to analyze how quickly each industry recovered after the 2009 peak","displayInstruction":"Compare **monthly unemployment trends** for **top 3 affected industries** during **2008-2010 recovery period**","chart":{"id":"chart-1760745567484","chartType":"Auto","encodingMap":{},"tableRef":"unemployment-across-industries","saved":false,"source":"trigger","unread":true},"resultTableId":"table-570117"},"explanation":{"agent":"CodeExplanationAgent","code":"- Convert the **date** field from string to datetime format, then back to a standardized string format (`YYYY-MM-DD`)\n- **Filter** the data to include only the years **2008**, **2009**, and **2010** (the period around the financial crisis)\n- **Select** the top 3 most affected industries: **Mining and Extraction**, **Construction**, and **Manufacturing**\n- **Extract** only the essential columns: **date**, **series** (industry name), and **rate** (unemployment rate)\n- **Sort** the data by **series** and **date** to ensure proper chronological ordering for time series visualization","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter for years 2008-2010\n df_filtered = df_unemployment[df_unemployment['year'].isin([2008, 2009, 2010])].copy()\n \n # Filter for top 3 most affected industries\n top_3_industries = ['Mining and Extraction', 'Construction', 'Manufacturing']\n df_filtered = df_filtered[df_filtered['series'].isin(top_3_industries)].copy()\n \n # Convert date to string format for visualization\n df_filtered['date'] = df_filtered['date'].dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns\n transformed_df = df_filtered[['date', 'series', 'rate']].copy()\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values(['series', 'date']).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Convert the **date** field from string to datetime format, then back to a standardized string format (`YYYY-MM-DD`)\n- **Filter** the data to include only the years **2008**, **2009**, and **2010** (the period around the financial crisis)\n- **Select** the top 3 most affected industries: **Mining and Extraction**, **Construction**, and **Manufacturing**\n- **Extract** only the essential columns: **date**, **series** (industry name), and **rate** (unemployment rate)\n- **Sort** the data by **series** and **date** to ensure proper chronological ordering for time series visualization\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-53","displayId":"industry-rates","names":["avg_rate","month","series","year"],"rows":[{"avg_rate":10.3,"month":1,"series":"Agriculture","year":"2000"},{"avg_rate":11.5,"month":2,"series":"Agriculture","year":"2000"},{"avg_rate":10.4,"month":3,"series":"Agriculture","year":"2000"},{"avg_rate":8.9,"month":4,"series":"Agriculture","year":"2000"},{"avg_rate":5.1,"month":5,"series":"Agriculture","year":"2000"},{"avg_rate":6.7,"month":6,"series":"Agriculture","year":"2000"},{"avg_rate":5,"month":7,"series":"Agriculture","year":"2000"},{"avg_rate":7,"month":8,"series":"Agriculture","year":"2000"},{"avg_rate":8.2,"month":9,"series":"Agriculture","year":"2000"},{"avg_rate":8,"month":10,"series":"Agriculture","year":"2000"},{"avg_rate":13.3,"month":11,"series":"Agriculture","year":"2000"},{"avg_rate":13.9,"month":12,"series":"Agriculture","year":"2000"},{"avg_rate":13.8,"month":1,"series":"Agriculture","year":"2001"},{"avg_rate":15.1,"month":2,"series":"Agriculture","year":"2001"},{"avg_rate":19.2,"month":3,"series":"Agriculture","year":"2001"},{"avg_rate":10.4,"month":4,"series":"Agriculture","year":"2001"},{"avg_rate":7.7,"month":5,"series":"Agriculture","year":"2001"},{"avg_rate":9.7,"month":6,"series":"Agriculture","year":"2001"},{"avg_rate":7.6,"month":7,"series":"Agriculture","year":"2001"},{"avg_rate":9.3,"month":8,"series":"Agriculture","year":"2001"},{"avg_rate":7.2,"month":9,"series":"Agriculture","year":"2001"},{"avg_rate":8.7,"month":10,"series":"Agriculture","year":"2001"},{"avg_rate":11.6,"month":11,"series":"Agriculture","year":"2001"},{"avg_rate":15.1,"month":12,"series":"Agriculture","year":"2001"},{"avg_rate":14.8,"month":1,"series":"Agriculture","year":"2002"},{"avg_rate":14.8,"month":2,"series":"Agriculture","year":"2002"},{"avg_rate":19.6,"month":3,"series":"Agriculture","year":"2002"},{"avg_rate":10.8,"month":4,"series":"Agriculture","year":"2002"},{"avg_rate":6.8,"month":5,"series":"Agriculture","year":"2002"},{"avg_rate":6.3,"month":6,"series":"Agriculture","year":"2002"},{"avg_rate":7.3,"month":7,"series":"Agriculture","year":"2002"},{"avg_rate":9,"month":8,"series":"Agriculture","year":"2002"},{"avg_rate":6.3,"month":9,"series":"Agriculture","year":"2002"},{"avg_rate":6.6,"month":10,"series":"Agriculture","year":"2002"},{"avg_rate":11.1,"month":11,"series":"Agriculture","year":"2002"},{"avg_rate":9.8,"month":12,"series":"Agriculture","year":"2002"},{"avg_rate":13.2,"month":1,"series":"Agriculture","year":"2003"},{"avg_rate":14.7,"month":2,"series":"Agriculture","year":"2003"},{"avg_rate":12.9,"month":3,"series":"Agriculture","year":"2003"},{"avg_rate":12,"month":4,"series":"Agriculture","year":"2003"},{"avg_rate":10.2,"month":5,"series":"Agriculture","year":"2003"},{"avg_rate":6.9,"month":6,"series":"Agriculture","year":"2003"},{"avg_rate":8.2,"month":7,"series":"Agriculture","year":"2003"},{"avg_rate":10.7,"month":8,"series":"Agriculture","year":"2003"},{"avg_rate":6.2,"month":9,"series":"Agriculture","year":"2003"},{"avg_rate":8.5,"month":10,"series":"Agriculture","year":"2003"},{"avg_rate":10.3,"month":11,"series":"Agriculture","year":"2003"},{"avg_rate":10.9,"month":12,"series":"Agriculture","year":"2003"},{"avg_rate":15.1,"month":1,"series":"Agriculture","year":"2004"},{"avg_rate":14.2,"month":2,"series":"Agriculture","year":"2004"},{"avg_rate":12.7,"month":3,"series":"Agriculture","year":"2004"},{"avg_rate":8.3,"month":4,"series":"Agriculture","year":"2004"},{"avg_rate":7.4,"month":5,"series":"Agriculture","year":"2004"},{"avg_rate":7.6,"month":6,"series":"Agriculture","year":"2004"},{"avg_rate":10,"month":7,"series":"Agriculture","year":"2004"},{"avg_rate":7,"month":8,"series":"Agriculture","year":"2004"},{"avg_rate":6.4,"month":9,"series":"Agriculture","year":"2004"},{"avg_rate":7.7,"month":10,"series":"Agriculture","year":"2004"},{"avg_rate":10.5,"month":11,"series":"Agriculture","year":"2004"},{"avg_rate":14,"month":12,"series":"Agriculture","year":"2004"},{"avg_rate":13.2,"month":1,"series":"Agriculture","year":"2005"},{"avg_rate":9.9,"month":2,"series":"Agriculture","year":"2005"},{"avg_rate":11.8,"month":3,"series":"Agriculture","year":"2005"},{"avg_rate":6.9,"month":4,"series":"Agriculture","year":"2005"},{"avg_rate":5.3,"month":5,"series":"Agriculture","year":"2005"},{"avg_rate":5.2,"month":6,"series":"Agriculture","year":"2005"},{"avg_rate":4.7,"month":7,"series":"Agriculture","year":"2005"},{"avg_rate":7.1,"month":8,"series":"Agriculture","year":"2005"},{"avg_rate":9.5,"month":9,"series":"Agriculture","year":"2005"},{"avg_rate":6.7,"month":10,"series":"Agriculture","year":"2005"},{"avg_rate":9.6,"month":11,"series":"Agriculture","year":"2005"},{"avg_rate":11.1,"month":12,"series":"Agriculture","year":"2005"},{"avg_rate":11.5,"month":1,"series":"Agriculture","year":"2006"},{"avg_rate":11.8,"month":2,"series":"Agriculture","year":"2006"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Extraction","year":"2007"},{"avg_rate":3.2,"month":9,"series":"Mining and Extraction","year":"2007"},{"avg_rate":1.3,"month":10,"series":"Mining and Extraction","year":"2007"},{"avg_rate":2.3,"month":11,"series":"Mining and Extraction","year":"2007"},{"avg_rate":3.4,"month":12,"series":"Mining and Extraction","year":"2007"},{"avg_rate":4,"month":1,"series":"Mining and Extraction","year":"2008"},{"avg_rate":2.2,"month":2,"series":"Mining and Extraction","year":"2008"},{"avg_rate":3.7,"month":3,"series":"Mining and Extraction","year":"2008"},{"avg_rate":3.6,"month":4,"series":"Mining and Extraction","year":"2008"},{"avg_rate":3.4,"month":5,"series":"Mining and Extraction","year":"2008"},{"avg_rate":3.3,"month":6,"series":"Mining and Extraction","year":"2008"},{"avg_rate":1.5,"month":7,"series":"Mining and Extraction","year":"2008"},{"avg_rate":1.9,"month":8,"series":"Mining and Extraction","year":"2008"},{"avg_rate":2.8,"month":9,"series":"Mining and Extraction","year":"2008"},{"avg_rate":1.7,"month":10,"series":"Mining and Extraction","year":"2008"},{"avg_rate":3.7,"month":11,"series":"Mining and Extraction","year":"2008"},{"avg_rate":5.2,"month":12,"series":"Mining and Extraction","year":"2008"},{"avg_rate":7,"month":1,"series":"Mining and Extraction","year":"2009"},{"avg_rate":7.6,"month":2,"series":"Mining and Extraction","year":"2009"},{"avg_rate":12.6,"month":3,"series":"Mining and Extraction","year":"2009"},{"avg_rate":16.1,"month":4,"series":"Mining and Extraction","year":"2009"},{"avg_rate":13.3,"month":5,"series":"Mining and Extraction","year":"2009"},{"avg_rate":13.6,"month":6,"series":"Mining and Extraction","year":"2009"},{"avg_rate":12.6,"month":7,"series":"Mining and Extraction","year":"2009"},{"avg_rate":11.8,"month":8,"series":"Mining and Extraction","year":"2009"},{"avg_rate":10.7,"month":9,"series":"Mining and Extraction","year":"2009"},{"avg_rate":10.8,"month":10,"series":"Mining and Extraction","year":"2009"},{"avg_rate":12,"month":11,"series":"Mining and Extraction","year":"2009"},{"avg_rate":11.8,"month":12,"series":"Mining and Extraction","year":"2009"},{"avg_rate":9.1,"month":1,"series":"Mining and Extraction","year":"2010"},{"avg_rate":10.7,"month":2,"series":"Mining and Extraction","year":"2010"}],"metadata":{"avg_rate":{"type":"number","semanticType":"Number"},"month":{"type":"number","semanticType":"Month"},"series":{"type":"string","semanticType":"String"},"year":{"type":"number","semanticType":"Year"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series\n monthly_avg = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Calculate seasonal variation (standard deviation across months) for each series\n seasonal_variation = monthly_avg.groupby('series')['avg_rate'].std().reset_index()\n seasonal_variation.columns = ['series', 'seasonal_std']\n \n # Get top 3 most seasonal industries\n top_3_seasonal = seasonal_variation.nlargest(3, 'seasonal_std')['series'].tolist()\n \n # Filter original data for top 3 industries and aggregate by year, month, series\n transformed_df = df1[df1['series'].isin(top_3_seasonal)].groupby(\n ['series', 'year', 'month'], as_index=False\n ).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Convert year to string for color encoding\n transformed_df['year'] = transformed_df['year'].astype(str)\n \n return transformed_df\n","source":["unemployment-across-industries"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[GOAL]\n\nHow does **seasonal variation** in unemployment differ across **industries**?","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"Analyze how seasonal variation in unemployment differs across industries by examining the cyclical patterns in unemployment rates throughout the year for different industry sectors.\",\n \"display_instruction\": \"Compare seasonal unemployment patterns across **industries**\",\n \"recommendation\": \"To visualize seasonal variation across industries, we should calculate average unemployment rates by month for each industry series. This will reveal the typical seasonal patterns and allow comparison of how different industries experience seasonal fluctuations in unemployment throughout the year.\",\n \"output_fields\": [\"series\", \"month\", \"avg_rate\", \"month_name\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"month\",\n \"y\": \"avg_rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series (industry)\n transformed_df = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Add month name for better readability\n month_names = {\n 1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',\n 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'\n }\n transformed_df['month_name'] = transformed_df['month'].map(month_names)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n avg_rate month month_name series\n0 13.763636 1 Jan Agriculture\n1 13.645455 2 Feb Agriculture\n2 13.830000 3 Mar Agriculture\n3 9.130000 4 Apr Agriculture\n4 7.100000 5 May Agriculture\n5 6.770000 6 Jun Agriculture\n6 7.010000 7 Jul Agriculture\n7 8.080000 8 Aug Agriculture\n8 7.090000 9 Sep Agriculture\n9 7.490000 10 Oct Agriculture\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show the seasonal trends for top 3 industries that are most seaonal\",\n \"chart_type\": \"Custom Area\",\n \"chart_encodings\": {\n \"x\": \"month\",\n \"y\": \"avg_rate\",\n \"color\": \"year\",\n \"column\": \"series\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Identify the top 3 industries with the highest seasonal variation in unemployment rates, then show their seasonal trends over time with monthly unemployment rates for each year, faceted by industry.\",\n \"display_instruction\": \"Show seasonal trends for top 3 most seasonal **industries** over **years**\",\n \"output_fields\": [\"series\", \"year\", \"month\", \"avg_rate\"],\n \"chart_encodings\": {\n \"x\": \"month\",\n \"y\": \"avg_rate\",\n \"color\": \"year\",\n \"facet\": \"series\"\n },\n \"reason\": \"To identify the most seasonal industries, we need to calculate the seasonal variation (e.g., standard deviation or range of monthly rates) for each industry. Then select the top 3 industries with highest variation. The output should include year-month-series level data to show trends over time, with faceting by the top 3 most seasonal industries.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series\n monthly_avg = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Calculate seasonal variation (standard deviation across months) for each series\n seasonal_variation = monthly_avg.groupby('series')['avg_rate'].std().reset_index()\n seasonal_variation.columns = ['series', 'seasonal_std']\n \n # Get top 3 most seasonal industries\n top_3_seasonal = seasonal_variation.nlargest(3, 'seasonal_std')['series'].tolist()\n \n # Filter original data for top 3 industries and aggregate by year, month, series\n transformed_df = df1[df1['series'].isin(top_3_seasonal)].groupby(\n ['series', 'year', 'month'], as_index=False\n ).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Convert year to string for color encoding\n transformed_df['year'] = transformed_df['year'].astype(str)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-540763","sourceTableIds":["unemployment-across-industries"],"instruction":"show the seasonal trends for top 3 industries that are most seaonal","displayInstruction":"Show seasonal trends for top 3 most seasonal **industries** over **years**","chart":{"id":"chart-1760745840104","chartType":"Custom Area","encodingMap":{"x":{"fieldID":"original--unemployment-across-industries--month"},"y":{"fieldID":"concept-avg_rate-1760745549715"},"x2":{},"y2":{},"color":{"fieldID":"original--unemployment-across-industries--year"},"column":{"fieldID":"original--unemployment-across-industries--series"},"row":{"channel":"row","bin":false}},"tableRef":"table-540763","saved":false,"source":"trigger","unread":false},"resultTableId":"table-53"},"explanation":{"agent":"CodeExplanationAgent","code":"- Calculate the **average unemployment rate** (`avg_rate`) for each combination of **series** (industry) and **month** across all years\n- Compute the **seasonal variation** for each **series** by calculating the standard deviation of `avg_rate` across the 12 months\n- Identify the **top 3 industries** with the highest `seasonal_std` (most seasonal unemployment patterns)\n- Filter the original data to include only these **top 3 most seasonal industries**\n- Aggregate the filtered data by **series**, **year**, and **month**, computing the mean unemployment **rate** as `avg_rate`\n- Convert **year** to string format for categorical visualization purposes","concepts":[{"explanation":"Standard deviation of average monthly unemployment rates across the 12 months for each industry. This metric quantifies the degree of seasonal variation in unemployment - higher values indicate stronger seasonal patterns (e.g., construction might have higher unemployment in winter months), while lower values indicate more stable year-round employment patterns. Mathematically: \\( \\text{seasonal\\_std} = \\sigma(\\text{avg\\_rate}_{\\text{month}=1..12}) \\)","field":"seasonal_std"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series\n monthly_avg = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Calculate seasonal variation (standard deviation across months) for each series\n seasonal_variation = monthly_avg.groupby('series')['avg_rate'].std().reset_index()\n seasonal_variation.columns = ['series', 'seasonal_std']\n \n # Get top 3 most seasonal industries\n top_3_seasonal = seasonal_variation.nlargest(3, 'seasonal_std')['series'].tolist()\n \n # Filter original data for top 3 industries and aggregate by year, month, series\n transformed_df = df1[df1['series'].isin(top_3_seasonal)].groupby(\n ['series', 'year', 'month'], as_index=False\n ).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Convert year to string for color encoding\n transformed_df['year'] = transformed_df['year'].astype(str)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Calculate the **average unemployment rate** (`avg_rate`) for each combination of **series** (industry) and **month** across all years\n- Compute the **seasonal variation** for each **series** by calculating the standard deviation of `avg_rate` across the 12 months\n- Identify the **top 3 industries** with the highest `seasonal_std` (most seasonal unemployment patterns)\n- Filter the original data to include only these **top 3 most seasonal industries**\n- Aggregate the filtered data by **series**, **year**, and **month**, computing the mean unemployment **rate** as `avg_rate`\n- Convert **year** to string format for categorical visualization purposes\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"seasonal_std\",\n \"explanation\": \"Standard deviation of average monthly unemployment rates across the 12 months for each industry. This metric quantifies the degree of seasonal variation in unemployment - higher values indicate stronger seasonal patterns (e.g., construction might have higher unemployment in winter months), while lower values indicate more stable year-round employment patterns. Mathematically: \\\\( \\\\text{seasonal\\\\_std} = \\\\sigma(\\\\text{avg\\\\_rate}_{\\\\text{month}=1..12}) \\\\)\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""}],"charts":[{"id":"chart-1760745839180","chartType":"Line Chart","encodingMap":{"x":{"fieldID":"original--unemployment-across-industries--month"},"y":{"fieldID":"concept-avg_rate-1760745549715"},"color":{"fieldID":"original--unemployment-across-industries--year"},"opacity":{"channel":"opacity","bin":false},"column":{"fieldID":"original--unemployment-across-industries--series"},"row":{"channel":"row","bin":false}},"tableRef":"table-53","saved":false,"source":"user","unread":false},{"id":"chart-1760745566172","chartType":"Line 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formulator","value":"Successfully loaded Unemployment"}],"displayedMessageIdx":0,"focusedTableId":"table-544555","focusedChartId":"chart-1760745539559","viewMode":"report","chartSynthesisInProgress":[],"config":{"formulateTimeoutSeconds":60,"maxRepairAttempts":1,"defaultChartWidth":300,"defaultChartHeight":300},"agentActions":[{"actionId":"exploreDataFromNL_1760745540005","tableId":"table-570117","description":"• All industries showed rising unemployment trends from 2008-2010, with Mining and Extraction (8.6%), Construction (8.5%), and Manufacturing (6.3%) experiencing the sharpest increases during 2008-2009.\n• Construction unemployment doubled from ~11% in early 2008 to peak at ~27% by early 2010, showing continued deterioration with no recovery by Feb 2010.\n• Manufacturing and Mining stabilized around 12-13% by late 2009, while Construction's unemployment continued climbing, indicating sector-specific recovery patterns with Construction facing the most prolonged crisis.","status":"completed","hidden":false,"lastUpdate":1760745581829}],"dataCleanBlocks":[],"cleanInProgress":false,"generatedReports":[{"id":"report-1760831469874-4137","content":"# The 2008-2010 Recession Hit Construction Hardest\n\nDuring the 2008-2010 financial crisis, unemployment surged dramatically across key industries. Construction workers bore the brunt, with unemployment rates skyrocketing from 11% to a staggering 27% by early 2010—nearly tripling in just two years.\n\n[IMAGE(chart-1760745566172)]\n\nManufacturing and Mining sectors also suffered significant job losses, though less severe than Construction. Manufacturing unemployment doubled from 5% to 13%, while Mining spiked from under 4% to 16% before showing signs of recovery by 2010.\n\n**In summary**, the recession's impact was far from uniform: Construction faced catastrophic job losses, suggesting the housing market collapse disproportionately affected building trades. What factors enabled Mining's earlier recovery? How can vulnerable sectors build resilience against future economic shocks?","style":"short note","selectedChartIds":["chart-1760745566172"],"createdAt":1760831476855},{"id":"report-1760831405296-2186","content":"# Seasonal Employment Patterns Reveal Industry Vulnerabilities Across Economic Cycles\n\nThe unemployment landscape across U.S. industries from 2000-2010 reveals striking seasonal patterns and differential impacts during economic downturns. Analysis of 14 industry sectors demonstrates that certain industries face predictable cyclical challenges while others maintain relative stability throughout the year.\n\n[IMAGE(chart-1760745546664)]\n\n**Cross-industry seasonal dynamics** show distinct unemployment patterns throughout the calendar year. Key findings include:\n\n- **Agriculture** experiences the most dramatic seasonal swings, with unemployment rates peaking above 13% in winter months (January-March) and dropping to approximately 7% during summer harvest seasons\n- **Construction** exhibits similar seasonality, with rates near 13% in January declining to 7-8% during peak building months (May-September)\n- **Government and Finance** sectors demonstrate remarkable stability, maintaining unemployment rates between 2-4% year-round, suggesting these sectors are largely insulated from seasonal fluctuations\n\n[IMAGE(chart-1760745839180)]\n\n**Year-over-year trends for the most seasonal industries** reveal how economic conditions compound seasonal effects. The 2008-2009 recession created unprecedented spikes:\n\n- Construction unemployment surged from typical winter peaks of 10-13% to over 25% by early 2010\n- Agriculture showed dramatic year-to-year variation, with 2009-2010 winter unemployment reaching 19-21%, nearly double the levels seen in 2000\n- Mining and Extraction maintained lower overall rates (2-7%) but still exhibited seasonal patterns, with late-year increases across most periods\n\n**In summary**, industries reliant on weather-dependent operations face significant seasonal unemployment challenges, which are dramatically amplified during economic recessions. While sectors like Government and Finance maintain stable employment year-round, Agriculture and Construction workers experience predictable winter unemployment that can more than double during economic downturns. These patterns suggest the need for targeted workforce development and social safety net programs that account for both seasonal and cyclical employment disruptions. Further investigation should examine whether these patterns have evolved post-2010 and assess the effectiveness of countercyclical policies in stabilizing employment in vulnerable sectors.","style":"executive summary","selectedChartIds":["chart-1760745546664","chart-1760745839180"],"createdAt":1760831421331},{"id":"report-1760831364867-9457","content":"# Unemployment Surge Across Industries: The 2008-2009 Economic Crisis Impact\n\nThe decade from 2000 to 2010 witnessed significant volatility in unemployment rates across major industry sectors, with a dramatic escalation during the 2008-2009 financial crisis that fundamentally reshaped the American labor market.\n\n[IMAGE(chart-1760745539559)]\n\nThe longitudinal analysis reveals that while most industries maintained relatively stable unemployment rates between 2000 and 2007—typically fluctuating between 2% and 8%—the landscape changed dramatically beginning in late 2008. **Construction** emerged as the most severely impacted sector, with unemployment rates soaring to approximately **27% by early 2010**. Similarly, **Agriculture** experienced unprecedented spikes reaching above **22%**, demonstrating the crisis's far-reaching effects beyond traditional white-collar sectors. Even traditionally stable sectors like Government and Finance, which historically maintained rates below 3%, saw significant increases, reaching 5-7% during the peak crisis period.\n\n[IMAGE(chart-1760745556213)]\n\nExamining the year-over-year impact between 2008 and 2009 reveals the crisis's differential industry effects:\n\n- **Mining and Extraction** and **Construction** led all sectors with unemployment rate increases exceeding **8.5 percentage points**\n- **Manufacturing** and **Agriculture** followed with increases above **5 percentage points**\n- **Business services** and **Information** sectors experienced moderate increases of approximately **4 percentage points**\n- **Government** showed the smallest increase at just **1.2 percentage points**, demonstrating relative stability during economic turbulence\n\n**In summary**, the 2008-2009 financial crisis created an unprecedented unemployment shock across American industries, with resource extraction, construction, and manufacturing bearing the heaviest burden. The data reveals that sectors tied to physical production and cyclical economic activity experienced the most severe dislocations, while government employment remained comparatively insulated. Key follow-up questions include: What were the long-term recovery trajectories for these industries post-2010? Did the workers displaced from heavily impacted sectors successfully transition to more stable industries? How did policy interventions differentially affect recovery across these sectors?","style":"executive summary","selectedChartIds":["chart-1760745556213","chart-1760745539559"],"createdAt":1760831379076}],"currentReport":{"id":"report-1760750575650-2619","content":"# Hollywood's Billion-Dollar Hitmakers\n\n*Avatar* stands alone—earning over $2.5B in profit, dwarfing all competition. Action and Adventure films dominate the most profitable titles, with franchises like *Jurassic Park*, *The Dark Knight*, and *Lord of the Rings* proving blockbuster formulas work.\n\n\"Chart\"\n\nSteven Spielberg leads all directors with $7.2B in total profit across his career, showcasing remarkable consistency with hits spanning decades—from *Jurassic Park* to *E.T.* His nearest competitors trail by billions, underlining his unmatched commercial impact.\n\n\"Chart\"\n\n**In summary**, mega-budget Action and Adventure films generate extraordinary returns when they succeed, and a handful of elite directors—led by Spielberg—have mastered the formula for sustained box office dominance.","style":"short note","selectedChartIds":["chart-1760743347871","chart-1760743768741"],"chartImages":{},"createdAt":1760750584189,"title":"Report - 10/17/2025"},"activeChallenges":[],"agentWorkInProgress":[],"_persist":{"version":-1,"rehydrated":true}} ================================================ FILE: public/manifest.json ================================================ { "short_name": "Data Formulator", "name": "Data Formulator", "icons": [ { "src": "data-formulator-logo-128.png", "type": "image/png", "sizes": "128x128" } ], "start_url": ".", "display": "standalone", "theme_color": "#000000", "background_color": "#ffffff" } ================================================ FILE: public/robots.txt ================================================ # https://www.robotstxt.org/robotstxt.html User-agent: * Disallow: ================================================ FILE: py-src/data_formulator/__init__.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. def run_app(): """Launch the Data Formulator Flask application.""" # Import app only when actually running to avoid heavy imports at package load from data_formulator.app import run_app as _run_app return _run_app() __all__ = [ "run_app", ] ================================================ FILE: py-src/data_formulator/__main__.py ================================================ from .app import run_app if __name__ == "__main__": run_app() ================================================ FILE: py-src/data_formulator/agent_routes.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. import argparse import random import sys import os import mimetypes import re import traceback mimetypes.add_type('application/javascript', '.js') mimetypes.add_type('application/javascript', '.mjs') import flask from flask import request, jsonify, Blueprint, current_app, Response, stream_with_context import logging import json import html import pandas as pd from data_formulator.agents.agent_data_transform import DataTransformationAgent from data_formulator.agents.agent_data_rec import DataRecAgent from data_formulator.agents.agent_sort_data import SortDataAgent from data_formulator.auth import get_identity_id from data_formulator.code_signing import sign_result, verify_code, MAX_CODE_SIZE from data_formulator.datalake.workspace import Workspace, WorkspaceWithTempData from data_formulator.workspace_factory import get_workspace from data_formulator.agents.agent_data_load import DataLoadAgent from data_formulator.agents.agent_data_clean_stream import DataCleanAgentStream from data_formulator.agents.agent_code_explanation import CodeExplanationAgent from data_formulator.agents.agent_chart_insight import ChartInsightAgent from data_formulator.agents.agent_interactive_explore import InteractiveExploreAgent from data_formulator.agents.agent_report_gen import ReportGenAgent from data_formulator.agents.client_utils import Client from data_formulator.agents.data_agent import DataAgent # Get logger for this module (logging config done in app.py) logger = logging.getLogger(__name__) def get_temp_tables(workspace, input_tables: list[dict]) -> list[dict]: """ Determine which input tables are temp tables (not persisted in the workspace datalake). Args: workspace: The user's workspace instance input_tables: List of table dicts with 'name' and 'rows' keys Returns: List of table dicts that don't exist in the workspace (temp tables) """ existing_tables = set(workspace.list_tables()) return [table for table in input_tables if table.get('name') not in existing_tables] agent_bp = Blueprint('agent', __name__, url_prefix='/api/agent') @agent_bp.errorhandler(Exception) def handle_agent_error(e): """Catch-all error handler to ensure JSON responses instead of HTML error pages.""" logger.error(f"Unhandled error in agent route: {e}") logger.error(traceback.format_exc()) response = flask.jsonify({ "status": "error", "error_message": sanitize_model_error(str(e)), "results": [], "result": [] }) response.headers.add('Access-Control-Allow-Origin', '*') return response, 500 def get_client(model_config): for key in model_config: model_config[key] = model_config[key].strip() client = Client( model_config["endpoint"], model_config["model"], model_config["api_key"] if "api_key" in model_config else None, html.escape(model_config["api_base"]) if "api_base" in model_config else None, model_config["api_version"] if "api_version" in model_config else None) return client @agent_bp.route('/check-available-models', methods=['GET', 'POST']) def check_available_models(): results = [] # Define configurations for different providers providers = ['openai', 'azure', 'anthropic', 'gemini', 'ollama'] for provider in providers: # Skip if provider is not enabled if not os.getenv(f"{provider.upper()}_ENABLED", "").lower() == "true": continue api_key = os.getenv(f"{provider.upper()}_API_KEY", "") api_base = os.getenv(f"{provider.upper()}_API_BASE", "") api_version = os.getenv(f"{provider.upper()}_API_VERSION", "") models = os.getenv(f"{provider.upper()}_MODELS", "") if not (api_key or api_base): continue if not models: continue # Build config for each model for model in models.split(","): model = model.strip() if not model: continue model_config = { "id": f"{provider}-{model}-{api_key}-{api_base}-{api_version}", "endpoint": provider, "model": model, "api_key": api_key, "api_base": api_base, "api_version": api_version } # Retry with backoff — DefaultAzureCredential and other providers # may need a moment to initialize on cold start. max_retries = 3 for attempt in range(max_retries): try: client = get_client(model_config) response = client.get_completion( messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Respond 'I can hear you.' if you can hear me."}, ] ) if "I can hear you." in response.choices[0].message.content: results.append(model_config) break # success or non-matching response — don't retry except Exception as e: if attempt < max_retries - 1: import time wait = 2 ** attempt # 1s, 2s logger.warning(f"Retrying {provider}/{model} in {wait}s (attempt {attempt+1}/{max_retries}): {e}") time.sleep(wait) else: logger.error(f"Error testing {provider} model {model} after {max_retries} attempts: {e}") return json.dumps(results) def sanitize_model_error(error_message: str) -> str: """Sanitize model API error messages before sending to client.""" # HTML escape the message message = html.escape(error_message) # Remove any potential API keys that might be in the error message = re.sub(r'(api[-_]?key|api[-_]?token)[=:]\s*[^\s&]+', r'\1=', message, flags=re.IGNORECASE) # Keep only the essential error info if len(message) > 500: # Truncate very long messages message = message[:500] + "..." return message @agent_bp.route('/test-model', methods=['GET', 'POST']) def test_model(): if request.is_json: logger.info("# test-model request") content = request.get_json() # contains endpoint, key, model, api_base, api_version logger.debug("content------------------------------") logger.debug(content) client = get_client(content['model']) try: response = client.get_completion( messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Respond 'I can hear you.' if you can hear me. Do not say anything other than 'I can hear you.'"}, ] ) logger.debug(f"model: {content['model']}") logger.debug(f"welcome message: {response.choices[0].message.content}") if "I can hear you." in response.choices[0].message.content: result = { "model": content['model'], "status": 'ok', "message": "" } except Exception as e: print(f"Error: {e}") logger.info(f"Error: {e}") result = { "model": content['model'], "status": 'error', "message": sanitize_model_error(str(e)), } else: result = {'status': 'error'} return json.dumps(result) @agent_bp.route('/process-data-on-load', methods=['GET', 'POST']) def process_data_on_load_request(): if request.is_json: logger.info("# process-data-on-load request") content = request.get_json() token = content["token"] input_data = content["input_data"] client = get_client(content['model']) logger.debug(f" model: {content['model']}") try: # Get workspace (needed for both virtual and in-memory tables) identity_id = get_identity_id() workspace = get_workspace(identity_id) # Check if input table is in workspace, if not add as temp data input_tables = [{"name": input_data.get("name"), "rows": input_data.get("rows", [])}] temp_data = get_temp_tables(workspace, input_tables) with WorkspaceWithTempData(workspace, temp_data) as workspace: agent = DataLoadAgent(client=client, workspace=workspace) candidates = agent.run(content["input_data"]) candidates = [c['content'] for c in candidates if c['status'] == 'ok'] response = flask.jsonify({ "status": "ok", "token": token, "result": candidates }) except Exception as e: logger.exception(e) response = flask.jsonify({ "token": token, "status": "error", "result": [] }) else: response = flask.jsonify({ "token": -1, "status": "error", "result": [] }) response.headers.add('Access-Control-Allow-Origin', '*') return response @agent_bp.route('/clean-data-stream', methods=['GET', 'POST']) def clean_data_stream_request(): def generate(): if request.is_json: logger.info("# clean-data-stream request") content = request.get_json() token = content["token"] client = get_client(content['model']) logger.debug(f" model: {content['model']}") agent = DataCleanAgentStream(client=client) try: for chunk in agent.stream(content.get('prompt', ''), content.get('artifacts', []), content.get('dialog', [])): yield chunk except Exception as e: logger.error(e) if 'unable to download html from url' in str(e): error_data = { "token": token, "status": "error", "result": 'this website doesn\'t allow us to download html from url :(' } else: error_data = { "token": token, "status": "error", "result": 'unable to process data clean request' } yield '\n' + json.dumps(error_data) + '\n' else: error_data = { "token": -1, "status": "error", "result": "Invalid request format" } yield '\n' + json.dumps(error_data) + '\n' response = Response( stream_with_context(generate()), mimetype='application/json', headers={ 'Access-Control-Allow-Origin': '*', 'Access-Control-Allow-Methods': 'GET, POST, OPTIONS', 'Access-Control-Allow-Headers': 'Content-Type' } ) return response @agent_bp.route('/sort-data', methods=['GET', 'POST']) def sort_data_request(): if request.is_json: logger.info("# sort-data request") content = request.get_json() token = content["token"] try: client = get_client(content['model']) agent = SortDataAgent(client=client) candidates = agent.run(content['field'], content['items']) candidates = candidates if candidates != None else [] response = flask.jsonify({ "status": "ok", "token": token, "result": candidates }) except Exception as e: logger.error(f"Error in sort-data: {e}") logger.error(traceback.format_exc()) response = flask.jsonify({ "token": token, "status": "error", "result": [], "error_message": sanitize_model_error(str(e)) }) else: response = flask.jsonify({ "token": -1, "status": "error", "result": [] }) response.headers.add('Access-Control-Allow-Origin', '*') return response @agent_bp.route('/derive-data', methods=['GET', 'POST']) def derive_data(): if request.is_json: logger.info("# derive-data request") content = request.get_json() token = content["token"] client = get_client(content['model']) # each table is a dict with {"name": xxx, "rows": [...]} input_tables = content["input_tables"] instruction = content["extra_prompt"] max_repair_attempts = content["max_repair_attempts"] if "max_repair_attempts" in content else 1 agent_coding_rules = content.get("agent_coding_rules", "") current_visualization = content.get("current_visualization", None) expected_visualization = content.get("expected_visualization", None) if "additional_messages" in content: prev_messages = content["additional_messages"] else: prev_messages = [] logger.debug("== input tables ===>") for table in input_tables: logger.debug(f"===> Table: {table['name']} (first 5 rows)") logger.debug(table['rows'][:5]) logger.debug("== user spec ===") logger.debug(instruction) # If user provided chart encodings (via visualization context), use transform mode; otherwise recommendation mode = "transform" if current_visualization or expected_visualization else "recommendation" try: identity_id = get_identity_id() workspace = get_workspace(identity_id) temp_data = get_temp_tables(workspace, input_tables) max_display_rows = current_app.config['CLI_ARGS']['max_display_rows'] with WorkspaceWithTempData(workspace, temp_data) as workspace: if mode == "recommendation": # Use unified Python agent for recommendations agent = DataRecAgent(client=client, workspace=workspace, agent_coding_rules=agent_coding_rules, max_display_rows=max_display_rows) results = agent.run(input_tables, instruction, n=1, prev_messages=prev_messages) else: # Use unified Python agent that generates Python scripts with DuckDB + pandas agent = DataTransformationAgent(client=client, workspace=workspace, agent_coding_rules=agent_coding_rules, max_display_rows=max_display_rows) results = agent.run(input_tables, instruction, prev_messages, current_visualization=current_visualization, expected_visualization=expected_visualization) repair_attempts = 0 while results[0]['status'] == 'error' and repair_attempts < max_repair_attempts: error_message = results[0]['content'] logger.warning(f"[derive-data] Code generation failed (attempt {repair_attempts + 1}/{max_repair_attempts}), mode={mode}. Error: {error_message}") new_instruction = f"We run into the following problem executing the code, please fix it:\n\n{error_message}\n\nPlease think step by step, reflect why the error happens and fix the code so that no more errors would occur." prev_dialog = results[0]['dialog'] if mode == "transform": results = agent.followup(input_tables, prev_dialog, [], new_instruction, n=1) if mode == "recommendation": results = agent.followup(input_tables, prev_dialog, [], new_instruction, n=1) repair_attempts += 1 logger.warning(f"[derive-data] Repair attempt {repair_attempts}/{max_repair_attempts} result: {results[0]['status']}") if repair_attempts > 0: logger.warning(f"[derive-data] Finished repair loop after {repair_attempts} attempt(s). Final status: {results[0]['status']}") # Sign code in each result so the frontend can send it back # for re-execution during data refresh with proof of authenticity. for r in results: sign_result(r) response = flask.jsonify({ "token": token, "status": "ok", "results": results }) except Exception as e: logger.error(f"Error in derive-data: {e}") logger.error(traceback.format_exc()) response = flask.jsonify({ "token": token, "status": "error", "results": [], "error_message": sanitize_model_error(str(e)) }) else: response = flask.jsonify({ "token": "", "status": "error", "results": [] }) response.headers.add('Access-Control-Allow-Origin', '*') return response @agent_bp.route('/data-agent-streaming', methods=['GET', 'POST']) def data_agent_streaming(): """Autonomous data exploration agent endpoint (SWE-agent style). Accepts a user question, runs the DataAgent observe-think-act loop, and streams events back as newline-delimited JSON. To resume after a clarification, the client sends: - trajectory: the trajectory list returned in the clarify event - clarification_response: the user's answer (string) The server appends the answer to the trajectory and continues the loop. """ def generate(): if request.is_json: logger.setLevel(logging.INFO) logger.info("# data-agent-streaming request") content = request.get_json() token = content.get("token", "") input_tables = content["input_tables"] user_question = content.get("user_question", "") max_iterations = content.get("max_iterations", 5) max_repair_attempts = content.get("max_repair_attempts", 1) agent_exploration_rules = content.get("agent_exploration_rules", "") agent_coding_rules = content.get("agent_coding_rules", "") conversation_history = content.get("conversation_history", None) # Stateless resume: client sends back the trajectory + user answer resume_trajectory = content.get("trajectory", None) clarification_response = content.get("clarification_response", None) completed_step_count = content.get("completed_step_count", 0) logger.debug("== input tables ===>") for table in input_tables: logger.debug(f"===> Table: {table['name']} (first 5 rows)") logger.debug(table['rows'][:5]) logger.debug(f"== user question ===> {user_question}") client = get_client(content['model']) identity_id = get_identity_id() if not identity_id: yield json.dumps({ "token": token, "status": "error", "result": {"type": "error", "error_message": "Identity ID required"}, }) + '\n' return workspace = get_workspace(identity_id) temp_data = get_temp_tables(workspace, input_tables) if input_tables else None try: with WorkspaceWithTempData(workspace, temp_data) as ws: agent = DataAgent( client=client, workspace=ws, agent_exploration_rules=agent_exploration_rules, agent_coding_rules=agent_coding_rules, max_iterations=max_iterations, max_repair_attempts=max_repair_attempts, ) # Build trajectory for resume or fresh start trajectory = None if resume_trajectory and clarification_response: # Append the user's clarification to the saved trajectory trajectory = list(resume_trajectory) trajectory.append({ "role": "user", "content": f"[USER CLARIFICATION]\n\n{clarification_response}", }) logger.debug(f"== resuming with clarification ===> {clarification_response}") for event in agent.run( input_tables=input_tables, user_question=user_question, conversation_history=conversation_history, trajectory=trajectory, completed_step_count=completed_step_count, ): yield json.dumps({ "token": token, "status": "ok", "result": event, }) + '\n' # Stop streaming after terminal events if event.get("type") in ("completion", "clarify"): break except Exception as e: logger.error(f"Error in data-agent-streaming: {e}") logger.error(traceback.format_exc()) yield json.dumps({ "token": token, "status": "error", "result": None, "error_message": sanitize_model_error(str(e)), }) + '\n' logger.setLevel(logging.WARNING) else: yield json.dumps({ "token": "", "status": "error", "result": None, "error_message": "Invalid request format", }) + '\n' response = Response( stream_with_context(generate()), mimetype='application/json', headers={ 'Access-Control-Allow-Origin': '*', 'Access-Control-Allow-Methods': 'GET, POST, OPTIONS', 'Access-Control-Allow-Headers': 'Content-Type', } ) return response @agent_bp.route('/refine-data', methods=['GET', 'POST']) def refine_data(): if request.is_json: logger.info("# refine-data request") content = request.get_json() token = content["token"] client = get_client(content['model']) # each table is a dict with {"name": xxx, "rows": [...]} input_tables = content["input_tables"] dialog = content["dialog"] new_instruction = content["new_instruction"] latest_data_sample = content["latest_data_sample"] max_repair_attempts = content.get("max_repair_attempts", 1) agent_coding_rules = content.get("agent_coding_rules", "") current_visualization = content.get("current_visualization", None) expected_visualization = content.get("expected_visualization", None) logger.debug("== input tables ===>") for table in input_tables: logger.debug(f"===> Table: {table['name']} (first 5 rows)") logger.debug(table['rows'][:5]) logger.debug("== user spec ===>") logger.debug(new_instruction) try: identity_id = get_identity_id() workspace = get_workspace(identity_id) temp_data = get_temp_tables(workspace, input_tables) max_display_rows = current_app.config['CLI_ARGS']['max_display_rows'] with WorkspaceWithTempData(workspace, temp_data) as workspace: # Use unified Python agent for followup transformations agent = DataTransformationAgent(client=client, workspace=workspace, agent_coding_rules=agent_coding_rules, max_display_rows=max_display_rows) results = agent.followup(input_tables, dialog, latest_data_sample, new_instruction, n=1, current_visualization=current_visualization, expected_visualization=expected_visualization) repair_attempts = 0 while results[0]['status'] == 'error' and repair_attempts < max_repair_attempts: error_message = results[0]['content'] logger.info(f"[refine-data] Code generation failed (attempt {repair_attempts + 1}/{max_repair_attempts}). Error: {error_message}") new_instruction = f"We run into the following problem executing the code, please fix it:\n\n{error_message}\n\nPlease think step by step, reflect why the error happens and fix the code so that no more errors would occur." prev_dialog = results[0]['dialog'] results = agent.followup(input_tables, prev_dialog, [], new_instruction, n=1) repair_attempts += 1 logger.info(f"[refine-data] Repair attempt {repair_attempts}/{max_repair_attempts} result: {results[0]['status']}") if repair_attempts > 0: logger.info(f"[refine-data] Finished repair loop after {repair_attempts} attempt(s). Final status: {results[0]['status']}") # Sign code in each result for secure refresh later. for r in results: sign_result(r) response = flask.jsonify({ "token": token, "status": "ok", "results": results}) except Exception as e: logger.error(f"Error in refine-data: {e}") logger.error(traceback.format_exc()) response = flask.jsonify({ "token": token, "status": "error", "results": [], "error_message": sanitize_model_error(str(e)) }) else: response = flask.jsonify({ "token": "", "status": "error", "results": []}) response.headers.add('Access-Control-Allow-Origin', '*') return response @agent_bp.route('/code-expl', methods=['GET', 'POST']) def request_code_expl(): if request.is_json: logger.info("# code-expl request") content = request.get_json() client = get_client(content['model']) # each table is a dict with {"name": xxx, "rows": [...]} input_tables = content["input_tables"] code = content["code"] # Get workspace and mount temp data identity_id = get_identity_id() workspace = get_workspace(identity_id) temp_data = get_temp_tables(workspace, input_tables) with WorkspaceWithTempData(workspace, temp_data) as workspace: try: code_expl_agent = CodeExplanationAgent(client=client, workspace=workspace) candidates = code_expl_agent.run(input_tables, code) # Return the first candidate's content as JSON if candidates and len(candidates) > 0: result = candidates[0] if result['status'] == 'ok': return jsonify(result) else: return jsonify(result), 400 else: return jsonify({'error': 'No explanation generated'}), 400 except Exception as e: logger.error(f"Error in code-expl: {e}") logger.error(traceback.format_exc()) return jsonify({'error': sanitize_model_error(str(e))}), 400 else: return jsonify({'error': 'Invalid request format'}), 400 @agent_bp.route('/chart-insight', methods=['GET', 'POST']) def request_chart_insight(): if request.is_json: logger.info("# chart insight request") content = request.get_json() client = get_client(content['model']) chart_image = content.get("chart_image", "") chart_type = content.get("chart_type", "") field_names = content.get("field_names", []) input_tables = content.get("input_tables", []) # Get workspace identity_id = get_identity_id() workspace = get_workspace(identity_id) temp_data = get_temp_tables(workspace, input_tables) with WorkspaceWithTempData(workspace, temp_data) as workspace: try: agent = ChartInsightAgent(client=client, workspace=workspace) candidates = agent.run(chart_image, chart_type, field_names, input_tables) if candidates and len(candidates) > 0: result = candidates[0] if result['status'] == 'ok': return jsonify(result) else: return jsonify(result), 400 else: return jsonify({'error': 'No insight generated'}), 400 except Exception as e: logger.error(f"Error in chart-insight: {e}") logger.error(traceback.format_exc()) return jsonify({'error': sanitize_model_error(str(e))}), 400 else: return jsonify({'error': 'Invalid request format'}), 400 @agent_bp.route('/get-recommendation-questions', methods=['GET', 'POST']) def get_recommendation_questions(): def generate(): if request.is_json: logger.info("# get recommendation questions request") content = request.get_json() token = content.get("token", "") client = get_client(content['model']) input_tables = content.get("input_tables", []) identity_id = get_identity_id() workspace = get_workspace(identity_id) agent_exploration_rules = content.get("agent_exploration_rules", "") mode = content.get("mode", "interactive") start_question = content.get("start_question", None) exploration_thread = content.get("exploration_thread", None) current_chart = content.get("current_chart", None) current_data_sample = content.get("current_data_sample", None) # Collect all tables that need to be in workspace: # both the input tables and any tables from the exploration thread all_tables = list(input_tables) if exploration_thread: all_tables.extend(exploration_thread) temp_data = get_temp_tables(workspace, all_tables) if all_tables else None with WorkspaceWithTempData(workspace, temp_data) as workspace: agent = InteractiveExploreAgent(client=client, workspace=workspace, agent_exploration_rules=agent_exploration_rules) try: for chunk in agent.run(input_tables, start_question, exploration_thread, current_data_sample, current_chart, mode): yield chunk except Exception as e: logger.error(e) error_data = { "content": "unable to process recommendation questions request" } yield 'error: ' + json.dumps(error_data) + '\n' else: error_data = { "content": "Invalid request format" } yield 'error: ' + json.dumps(error_data) + '\n' response = Response( stream_with_context(generate()), mimetype='application/json', headers={ 'Access-Control-Allow-Origin': '*', } ) return response @agent_bp.route('/generate-report-stream', methods=['GET', 'POST']) def generate_report_stream(): def generate(): if request.is_json: logger.info("# generate report stream request") content = request.get_json() token = content.get("token", "") client = get_client(content['model']) input_tables = content.get("input_tables", []) charts = content.get("charts", []) style = content.get("style", "blog post") identity_id = get_identity_id() workspace = get_workspace(identity_id) temp_data = get_temp_tables(workspace, input_tables) if input_tables else None with WorkspaceWithTempData(workspace, temp_data) as workspace: agent = ReportGenAgent(client=client, workspace=workspace) try: for chunk in agent.stream(input_tables, charts, style): yield chunk except Exception as e: logger.error(e) error_data = { "content": "unable to process report generation request" } yield 'error: ' + json.dumps(error_data) + '\n' else: error_data = { "content": "Invalid request format" } yield 'error: ' + json.dumps(error_data) + '\n' response = Response( stream_with_context(generate()), mimetype='application/json', headers={ 'Access-Control-Allow-Origin': '*', } ) return response @agent_bp.route('/refresh-derived-data', methods=['POST']) def refresh_derived_data(): """ Re-run Python transformation code with updated input data to refresh a derived table. Security: The code must have been previously signed by the server (via ``code_signing.sign_result``) when it was first generated by an agent. The frontend must send the original ``code_signature`` back alongside the code. This endpoint verifies the signature before executing, preventing execution of tampered or injected code. This endpoint: 1. Verifies the code signature (HMAC-SHA256) 2. Gets input tables from workspace (extending with temp data if needed) 3. Re-runs the transformation code in workspace context 4. Updates the derived table in workspace if virtual flag is true Request body: - input_tables: list of {name: string, rows: list} objects representing the parent tables - code: the Python transformation code to execute - code_signature: HMAC-SHA256 signature of the code (required) - output_variable: the variable name containing the result DataFrame (required) - output_table_name: the workspace table name to update with results (required if virtual=true) - virtual: boolean flag indicating whether to save result to workspace Returns: - status: 'ok' or 'error' - rows: the resulting rows if successful (limited to max_display_rows) - virtual: {table_name: string, row_count: number} if output was saved to workspace - message: error message if failed """ try: from data_formulator.sandbox import create_sandbox from flask import current_app data = request.get_json() input_tables = data.get('input_tables', []) code = data.get('code', '') code_signature = data.get('code_signature', '') output_variable = data.get('output_variable') output_table_name = data.get('output_table_name') virtual = data.get('virtual', False) if not input_tables: return jsonify({ "status": "error", "message": "No input tables provided" }), 400 if not code: return jsonify({ "status": "error", "message": "No transformation code provided" }), 400 # ---- Security: verify code signature -------------------------------- # The code must have been signed by the server when the agent first # generated it. Reject unsigned or tampered code. if not code_signature: logger.warning("[refresh-derived-data] Rejected request: missing code_signature") return jsonify({ "status": "error", "message": "Missing code_signature — code must be signed by the server" }), 403 if len(code) > MAX_CODE_SIZE: logger.warning(f"[refresh-derived-data] Rejected request: code too large ({len(code)} bytes)") return jsonify({ "status": "error", "message": f"Code exceeds maximum allowed size ({MAX_CODE_SIZE} bytes)" }), 400 if not verify_code(code, code_signature): logger.warning("[refresh-derived-data] Rejected request: invalid code_signature (code may have been tampered with)") return jsonify({ "status": "error", "message": "Invalid code_signature — code may have been tampered with" }), 403 # ---- Input validation ----------------------------------------------- if len(input_tables) > 50: return jsonify({ "status": "error", "message": "Too many input tables (max 50)" }), 400 if not output_variable: return jsonify({ "status": "error", "message": "No output_variable provided" }), 400 # output_variable is interpolated into generated Python code and used # to construct file paths inside the sandbox. Restrict it to valid # Python identifiers to prevent code-injection and path-traversal. if not output_variable.isidentifier(): return jsonify({ "status": "error", "message": "output_variable must be a valid Python identifier" }), 400 if virtual and not output_table_name: return jsonify({ "status": "error", "message": "output_table_name is required when virtual=true" }), 400 # Get workspace and mount temp data for tables not in workspace identity_id = get_identity_id() workspace = get_workspace(identity_id) temp_data = get_temp_tables(workspace, input_tables) # Get settings from app config cli_args = current_app.config.get('CLI_ARGS', {}) max_display_rows = cli_args.get('max_display_rows', 5000) sandbox = create_sandbox(cli_args.get('sandbox', 'local')) with WorkspaceWithTempData(workspace, temp_data) as workspace: # Run the transformation code in the sandbox result = sandbox.run_python_code( code=code, workspace=workspace, output_variable=output_variable, ) if result['status'] == 'ok': result_df = result['content'] row_count = len(result_df) response_data = { "status": "ok", "message": "Successfully refreshed derived data", "row_count": row_count } if virtual: # Virtual table: update workspace and return limited rows for display workspace.write_parquet(result_df, output_table_name) response_data["virtual"] = { "table_name": output_table_name, "row_count": row_count } # Limit rows for response payload since full data is in workspace if row_count > max_display_rows: display_df = result_df.head(max_display_rows) else: display_df = result_df # Remove duplicate columns to avoid orient='records' error display_df = display_df.loc[:, ~display_df.columns.duplicated()] response_data["rows"] = json.loads(display_df.to_json(orient='records', date_format='iso')) else: # Temp table: return full data since there's no workspace storage # Remove duplicate columns to avoid orient='records' error result_df = result_df.loc[:, ~result_df.columns.duplicated()] response_data["rows"] = json.loads(result_df.to_json(orient='records', date_format='iso')) return jsonify(response_data) else: return jsonify({ "status": "error", "message": result.get('content', 'Unknown error during transformation') }), 400 except Exception as e: logger.error(f"Error refreshing derived data: {str(e)}") logger.error(traceback.format_exc()) return jsonify({ "status": "error", "message": str(e) }), 400 ================================================ FILE: py-src/data_formulator/agents/__init__.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. from data_formulator.agents.agent_data_transform import DataTransformationAgent from data_formulator.agents.agent_data_rec import DataRecAgent from data_formulator.agents.agent_data_load import DataLoadAgent from data_formulator.agents.agent_sort_data import SortDataAgent from data_formulator.agents.agent_interactive_explore import InteractiveExploreAgent from data_formulator.agents.agent_chart_insight import ChartInsightAgent __all__ = [ "DataTransformationAgent", "DataRecAgent", "DataLoadAgent", "SortDataAgent", "InteractiveExploreAgent", "ChartInsightAgent", ] ================================================ FILE: py-src/data_formulator/agents/agent_chart_insight.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. from data_formulator.agents.agent_utils import generate_data_summary, extract_json_objects import logging logger = logging.getLogger(__name__) SYSTEM_PROMPT = r'''You are a data analyst helping users understand their visualizations. You are given a chart image along with metadata about the chart type, data fields used, and a summary of the underlying data (including schema, value ranges, and sample rows). Use both the chart image and the data summary to produce: 1. **title**: A short, descriptive title for the chart (5-10 words). It should summarize what the chart is about — the subject, the dimensions compared, and the scope. Do not include the chart type in the title. Write it in title case. 2. **takeaways**: A list of 1-3 key findings or insights from the chart. Each takeaway should be one sentence. Highlight notable patterns, trends, outliers, or comparisons visible in the chart. Be specific — reference actual values, categories, or trends from the data when possible. Respond with a JSON object in exactly this format (no markdown fences): {"title": "...", "takeaways": ["...", "..."]} ''' class ChartInsightAgent(object): def __init__(self, client, workspace=None): self.client = client self.workspace = workspace def run(self, chart_image_base64, chart_type, field_names, input_tables=None, n=1): """ Generate insight for a chart. Args: chart_image_base64: Base64-encoded PNG data URL of the chart chart_type: The type of chart (e.g., "Bar Chart", "Scatter Plot") field_names: List of field names used in the chart encodings input_tables: Optional list of input table dicts for data context n: Number of candidates to generate """ # Build context about the chart context_parts = [f"Chart type: {chart_type}"] context_parts.append(f"Fields used: {', '.join(field_names)}") if input_tables and self.workspace: data_summary = generate_data_summary( input_tables, workspace=self.workspace, include_data_samples=True, row_sample_size=3 ) context_parts.append(f"\nData summary:\n{data_summary}") context = "\n".join(context_parts) # Build the message with image user_content = [ { "type": "text", "text": f"[CHART METADATA]\n{context}\n\n[CHART IMAGE]\nHere is the chart to analyze:" }, { "type": "image_url", "image_url": { "url": f"data:image/png;base64,{chart_image_base64}", "detail": "low" } } ] messages = [ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": user_content} ] logger.debug(f"ChartInsightAgent: analyzing {chart_type} chart with fields {field_names}") logger.info(f"[ChartInsightAgent] run start | chart_type={chart_type}") response = self.client.get_completion(messages=messages) candidates = [] for choice in response.choices: logger.debug("\n=== Chart insight result ===>\n") logger.debug(choice.message.content + "\n") response_content = choice.message.content title = "" takeaways = [] # Parse JSON response json_blocks = extract_json_objects(response_content + "\n") for parsed in json_blocks: title = parsed.get('title', '') takeaways = parsed.get('takeaways', []) if isinstance(takeaways, str): takeaways = [takeaways] if title or takeaways: break if title or takeaways: result = { 'status': 'ok', 'title': title, 'takeaways': takeaways, } else: logger.error(f"unable to parse insight from response: {response_content}") result = { 'status': 'other error', 'content': 'unable to generate chart insight' } result['dialog'] = [*messages, {"role": choice.message.role, "content": choice.message.content}] result['agent'] = 'ChartInsightAgent' candidates.append(result) status = candidates[0].get('status', '?') if candidates else 'empty' logger.info(f"[ChartInsightAgent] run done | status={status}") return candidates ================================================ FILE: py-src/data_formulator/agents/agent_code_explanation.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. import json from data_formulator.agents.agent_utils import generate_data_summary, extract_json_objects, extract_code_from_gpt_response import logging logger = logging.getLogger(__name__) SYSTEM_PROMPT = r'''You are a data scientist to help user explain derived data concepts, so that a non-coder can clearly understand what new fields mean. You are provided with a summary of the input data, and the transformation code. Your goal: 1. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code. - provide a declarative definition that explains the new field, use a mathematical notation if applicable. - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score) - *DO NOT* explain trivial new fields like "Decade" or "Avg_Rating", "US_Sales" that are self-explanatory. - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.) - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using: - Inline math: `\( ... \)` for formulas within text - Block math: `\[ ... \]` for standalone formulas - Examples: `\( \frac{\text{Revenue}}{\text{Cost}} \)` for ratios, `\[ \text{Score} = \text{Rating} \times \text{Worldwide\_Gross} \]` for formulas - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\_` - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly. 2. If there are multiple fields that have the similar computation, you can explain them together in one explanation. - in "field", you can provide a list of fields in format of "field1, field2, ..." - in "explanation", you can provide a single explanation for the computation of the fields. - for example, if you have fields like "Norm_Rating", "Norm_Gross", "Critical_Commercial_Score", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation. 3. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named "Statistical Analysis". - explain how you model the data, which fields are used, how data processing is done, and what models are used. - suggest some other modeling approaches that can be used to analyze the data in the explanation as well. The focus is to explain how new fields are computed, don't generate explanation for low-level actions like "return", "load data" etc. If there are no non-trivial new fields/concepts, return an empty list. Provide the result as a JSON block (start with ```json) in the [CONCEPTS EXPLANATION] section. [CONCEPTS EXPLANATION] ```json [ { "field": "...", "explanation": "..." } ] ``` ''' EXAMPLE = ''' [CONTEXT] Here are our datasets, here are their field summaries and samples: table_0 (movies) fields: Title -- type: object, values: The Phantom, Twilight, Amores Perros, ..., The Producers: The Movie Musical, Pride and Prejudice, Doomsday, The Perez Family US_Gross -- type: float64, values: 0.0, 401.0, 1336.0, ..., 403706375.0, 533345358.0, 600788188.0, 760167650.0 Worldwide_Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0 US_DVD_Sales -- type: float64, values: nan, 140689412.0, nan, ..., nan, nan, 46260220.0, 124058348.0 Production_Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0 Release_Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006 MPAA_Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R Running_Time_min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale Major_Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western Creative_Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn Rotten_Tomatoes_Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan IMDB_Rating -- type: float64, values: 1.5, 2.5, 3.0, ..., nan, nan, nan, nan IMDB_Votes -- type: float64, values: 24578.0, nan, 18.0, ..., 364077.0, 387438.0, 411088.0, 519541.0 [CODE] ```python import pandas as pd import collections import numpy as np def transform_data(df_movies): # Calculate average rating (mean of Rotten Tomatoes and IMDB rating, normalized to 0-10) rt = df_movies['Rotten_Tomatoes_Rating'] / 10.0 # Rotten Tomatoes is out of 100 imdb = df_movies['IMDB_Rating'] avg_rating = pd.concat([rt, imdb], axis=1).mean(axis=1, skipna=True) # Normalize avg_rating norm_rating = (avg_rating - avg_rating.min()) / (avg_rating.max() - avg_rating.min()) # Normalize Worldwide_Gross gross = df_movies['Worldwide_Gross'] norm_gross = (gross - gross.min()) / (gross.max() - gross.min()) # Calculate 'critical-commercial success' score score = norm_rating * norm_gross # Extract decade from Release_Date def extract_decade(date_str): if pd.isnull(date_str): return np.nan try: year = int(str(date_str)[-4:]) return f"{year // 10 * 10}s" except: return np.nan decade = df_movies['Release_Date'].apply(extract_decade) transformed_df = pd.DataFrame({ 'Title': df_movies['Title'], 'Major_Genre': df_movies['Major_Genre'], 'Release_Date': df_movies['Release_Date'], 'Decade': decade, 'Avg_Rating': avg_rating, 'Norm_Rating': norm_rating, 'Worldwide_Gross': gross, 'Norm_Gross': norm_gross, 'Critical_Commercial_Score': score }) return transformed_df ``` [CONCEPTS EXPLANATION] ```json [ { "field": "Norm_Rating, Norm_Gross", "explanation": "Normalized values that scale the original values between 0 and 1 using min-max normalization. Formula: -BSLASH-(-BSLASH-text{Normalized} = -BSLASH-frac{-BSLASH-text{Value} - -BSLASH-text{Min}}{-BSLASH-text{Max} - -BSLASH-text{Min}} -BSLASH-)" }, { "field": "Critical_Commercial_Score", "explanation": "The critical-commercial success score combines **Norm_Rating** and **Norm_Gross** to represent a movie's critical acclaim and commercial performance. Formula: -BSLASH-(-BSLASH-text{Critical-BSLASH-_Commercial-BSLASH-_Score} = -BSLASH-text{Norm-BSLASH-_Rating} -BSLASH-times -BSLASH-text{Norm-BSLASH-_Gross} -BSLASH-)" } ] ''' class CodeExplanationAgent(object): def __init__(self, client, workspace): self.client = client self.workspace = workspace def run(self, input_tables, code, n=1): data_summary = generate_data_summary(input_tables, workspace=self.workspace, include_data_samples=True) user_query = f"[CONTEXT]\n\n{data_summary}\n\n[CODE]\n\nhere is the transformation code: {code}\n\n[EXPLANATION]\n" logger.debug(user_query) logger.info(f"[CodeExplanationAgent] run start") messages = [{"role":"system", "content": SYSTEM_PROMPT}, {"role":"user","content": user_query}] response = self.client.get_completion(messages = messages) candidates = [] for choice in response.choices: logger.debug("\n=== Code explanation result ===>\n") logger.debug(choice.message.content + "\n") # Inline parsing of concepts section response_content = choice.message.content concepts = [] # Find CONCEPTS EXPLANATION section concepts_start = response_content.find('[CONCEPTS EXPLANATION]') if concepts_start != -1: concepts_start += len('[CONCEPTS EXPLANATION]') # Extract JSON from the concepts section concepts_content = response_content[concepts_start:].strip() try: raw_json_blocks = extract_code_from_gpt_response(concepts_content, "json") json_blocks = [json.loads(block) for block in raw_json_blocks] except Exception as e: json_blocks = [] if json_blocks: concepts = json_blocks[0] # Build result if concepts: result = { 'status': 'ok', 'concepts': concepts, } else: # No non-trivial concepts found — that's ok, return empty list result = {'status': 'ok', 'concepts': []} # individual dialog for the agent result['dialog'] = [*messages, {"role": choice.message.role, "content": choice.message.content}] result['agent'] = 'CodeExplanationAgent' candidates.append(result) status = candidates[0].get('status', '?') if candidates else 'empty' logger.info(f"[CodeExplanationAgent] run done | status={status}") return candidates ================================================ FILE: py-src/data_formulator/agents/agent_data_clean_stream.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. from data_formulator.agents.agent_utils import extract_json_objects from data_formulator.agents.web_utils import download_html_content import logging import re import json import traceback logger = logging.getLogger(__name__) def parse_table_sections(text): """Parse [TABLE_START] to [TABLE_END] sections and extract metadata and content.""" tables = [] # Split by [TABLE_START] and process each section sections = text.strip().split('[TABLE_START]') print(sections) for i, section in enumerate(sections[1:], 1): # Skip first empty section # Find the end of this table section if '[TABLE_END]' not in section: continue table_section = section.split('[TABLE_END]')[0] metadata_index = table_section.find('[METADATA]') content_index = table_section.find('[CONTENT]') # Extract metadata between [METADATA] and ``` if metadata_index != -1 and content_index != -1: metadata_block = table_section[metadata_index + len('[METADATA]'):content_index] metadata_json = extract_json_objects(metadata_block)[0] # Extract content between [CONTENT] and end if content_index != -1: content_block = table_section[content_index + len('[CONTENT]'):].strip() # Create table object table = { "name": metadata_json.get('name', 'unknown'), "context": metadata_json.get('context', ''), "content": { "type": metadata_json.get('type', 'csv'), "value": content_block } } tables.append(table) return tables SYSTEM_PROMPT = '''You are a data scientist to help user to generate, extract data from image, or clean a text input into a structured csv table. If there are multiple tables in the raw data, you should extract them all. Each table can either be a csv block or a url (url of an image that you think contains data). - csv block: a string of csv content (if the content is already available from the input) - image url: link to an image that contains data Based on the raw data provided by the user, extract tables: - each extracted table should be wrapped in a section, its metadata is a json object describes its name and type in [METADATA] section. - if the table is a csv block, it should be wrapped in [CONTENT] tags. Do not wrap it in any other tags, just write plain csv content in the [CONTENT]. - if the table is an image url, [CONTENT] should be the url. - when there are multiple tables, generate one table at a time. Output only extract tables, no other text should be included. Format: [TABLE_START] [METADATA] ```json { "name": "...", // suggest a descriptive, meaningful but short name for this dataset, no more than 5 words, if there are duplicate names, add a suffix -1, -2, etc. (e.g., "sales-2024", "us-customer-survey-2024", "seattle-weather-forecast") "type": "csv" | "image_url", "context": "..." // a paragraph summarizing the original source document or image this table was extracted from. Include: what the document is about, its main topic, any relevant background, and what the table represents within that context. This summary will be used by downstream agents to understand the data, so be descriptive and informative. (around 50-100 words) } ``` [CONTENT] ... // the csv block or image url, directly output the content, no other text should be included and don't wrap it in any other tags. [TABLE_END] **Important:** - NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state. - NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited. **Multiple tables:** - if the raw data contains multiple tables, based on the user's instruction to decide which table to extract. - if the user doesn't specify which tables to extract, extract all tables. - if there are multiple tables yet they can be too large, only extract up to 200 rows for each table. **Instructions for creating csv blocks:** * the output should be a structured csv table: - if the raw data is unstructured, structure it into a csv table. If the table is in other formats, transform it into a csv table. - if the raw data contain other informations other than the table (e.g., title, subtitle, footer, summary, etc.), remove surrounding texts that does not belong to the table, so that the table conforms to csv format. - if the raw data contains multiple levels of header, make it a flat table. It's ok to combine multiple levels of headers to form the new header to not lose information. - the csv table should have the same number of cells for each line, according to the header. If there are some rows with missing values, patch them with empty values. - if the header row misses some columns, add their corresponding column names. E.g., when the header doesn't have an index column, but every row has an index value, add the missing column header. * clean up messy column names: - if the column name contains special characters like "*", "?", "#", "." remove them. * csv value format: - if a column is number but some cells has annotations like "*" "?" or brackets, clean them up. - if values of a column is all numbers but has units like ($, %, s), remove the unit in the value cells, convert them to number, note unit in the header of this column. - you don't need to convert format of the cell. **Instructions for creating image url:** - based on the context provided in the prompt and raw input material, decide which url in the raw data may cotain the data we would like to extract (like an image contains structured data). **Instructions for generating synthetic data:** - NEVER generate data that has implicit bias as noted above, if that happens, neutralize the data. - If the user doesn't indicate how many rows to be generated, plan in generating a dataset with 20-30 rows depending on the content. **IMPORTANT:** - when the user provide an image and ask to extract data, you should extract data from the image into a csv block. - get all tables that contain structured data from the raw data, including the csv blocks and image urls. ''' EXAMPLE = ''' Rank NOC Gold Silver Bronze Total 1 South Korea 5 1 1 7 2 France* 0 1 1 2 United States 0 1 1 2 4 China 0 1 0 1 Germany 0 1 0 1 6 Mexico 0 0 1 1 Turkey 0 0 1 1 Totals (7 entries) 5 5 5 15 ''' class DataCleanAgentStream(object): def __init__(self, client): self.client = client def stream(self, prompt, artifacts=[], dialog=[]): """derive a new concept based on the raw input data Args: prompt (str): the prompt to the agent artifacts (list): the artifacts to the agent of format [{"type": "image_url", "content": ...}, {"type": "web_url", "content": ...}, ...] dialog (list): the dialog history Returns: generator: the result of the agent """ content = [] for artifact in artifacts: if artifact['type'] == 'image_url': content.append({ 'type': 'image_url', 'image_url': { "url": artifact['content'], "detail": "high" } }) elif artifact['type'] == 'web_url': try: content.append({ 'type': 'text', 'text': f"[HTML CONTENT]\n\n{download_html_content(artifact['content'])}" }) except Exception as e: raise Exception('unable to download html from url ' + artifact['content']) content.append({ 'type': 'text', 'text': f'''{prompt}''' }) user_prompt = { 'role': 'user', 'content': content } logger.debug(user_prompt) logger.info(f"[DataCleanAgent] run start (streaming)") system_message = { 'role': 'system', 'content': [ {'type': 'text', 'text': SYSTEM_PROMPT}] } messages = [ system_message, *[message for message in dialog if message['role'] != 'system'], user_prompt ] ###### the part that calls open_ai stream = self.client.get_completion(messages = messages, stream=True) accumulated_content = "" for part in stream: if hasattr(part, 'choices') and len(part.choices) > 0: delta = part.choices[0].delta if hasattr(delta, 'content') and delta.content: accumulated_content += delta.content # Stream each character for real-time display as JSON yield delta.content # Parse the final content the same way as the non-streaming version logger.debug("\n=== Python Data Clean Agent Stream ===>\n") logger.debug(accumulated_content + "\n") # Parse table sections from the accumulated content tables = parse_table_sections(accumulated_content) if len(tables) > 0: # Use the same format as non-streaming version - return the parsed data directly result = { 'status': 'ok', 'content': tables, } else: result = {'status': 'other error', 'content': 'unable to extract tables from response'} result['dialog'] = [*messages, {"role": "assistant", "content": accumulated_content}] result['agent'] = 'DataCleanAgentStream' logger.info(f"[DataCleanAgent] run done | status={result.get('status', '?')}") # add a newline to the beginning of the result to separate it from the previous result yield '\n' + json.dumps(result) + '\n' ================================================ FILE: py-src/data_formulator/agents/agent_data_load.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. import json from data_formulator.agents.agent_utils import extract_json_objects, generate_data_summary from data_formulator.agents.semantic_types import ( generate_semantic_types_prompt, ) import logging logger = logging.getLogger(__name__) SYSTEM_PROMPT = '''You are a data scientist to help user infer data types based off the table provided by the user. Given a dataset provided by the user, 1. suggest a descriptive name for the table if the table name is a generic name like table-6, the suggested name should best capture meaning of the table but also very concise. - if the table already have a descriptive name provided in the bracket (...), use it; if the provided name is not descriptive, suggest a new name. - format table name using '-' when it contains multiple words (e.g., "income", "weather-seattle-atlanta") - the suggested table name should be similar to variable names that are very descriptive and concise, no more than 5 words. - the suggested name should best be within 24 characters, be smart with abbreviations (yet still descriptive and follow common practices), when in doubt, use less words but less abbreviation. 2. identify their type and semantic type 3. provide a very short summary of the dataset. Types to consider include: string, number, date ''' + generate_semantic_types_prompt() + ''' Enriched annotation fields (optional — provide when applicable): - "intrinsic_domain": [min, max] — the known scale bounds of the measurement instrument. - Infer from data values and context: e.g., if a "rating" column has values 1-10, the domain is [1, 10]; if it's clearly a 5-star system, use [1, 5]. - For Percentage: [0, 100] if values are whole-number percentages, [0, 1] if fractional. - For Correlation: always [-1, 1]. - Do NOT provide for open-ended measures like Revenue, Count, Quantity, Temperature, etc. - Only provide when the scale bounds are clear from the data or domain knowledge. - "unit": a short unit string for physical/monetary quantities. - Temperature: "°C", "°F", "K" - Physical: "kg", "km", "mph", "m²", "L", etc. - Currency: "USD", "EUR", "¥", etc. - Duration: "ms", "s", "min", "hr" - Only provide when the unit is clear from column name, data values, or context. Sort order: - if the field is string type and is ordinal, provide the natural sort order of the fields here. - examples: English month name, week name, range, etc. - when the natural sort order is alphabetical or there is not natural sort order, there is no need to generate sort_order, examples: - Name, State, City, etc. Create a json object function based off the [DATA] provided. output should be in the format of: ```json { "suggested_table_name": ..., // the name of the table "fields": { "field1": {"type": ..., "semantic_type": ..., "sort_order": [...], "intrinsic_domain": [...], "unit": ...}, // replace field1 field2 with actual field names // only include sort_order if the field is ordinal with inherent order // only include intrinsic_domain if the field has a known bounded scale // only include unit if the unit is clear from context "field2": {"type": ..., "semantic_type": ...}, ... }, "data_summary": ... // a short summary of the data (50-100 words), should capture the key characteristics of the data } ``` ''' EXAMPLES = ''' [DATA] Here are our datasets, here are their field summaries and samples: table_0 (table_0) fields: name -- type: object, values: Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, District of Columbia, Florida, ..., South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin, Wyoming region -- type: object, values: midwest, northeast, other, south, west state_id -- type: int64, values: 1, 2, 4, 5, 6, 8, 9, 10, 11, 12, ..., 47, 48, 49, 50, 51, 53, 54, 55, 56, 72 pct -- type: float64, values: 0.006, 0.008, 0.02, 0.021, 0.022, 0.024, 0.025, 0.026000000000000002, 0.027, 0.028, ..., 0.192, 0.193, 0.194, 0.196, 0.197, 0.199, 0.2, 0.201, 0.213, 0.289 total -- type: int64, values: 222679, 250875, 256563, 268015, 291468, 326086, 337245, 405504, 410347, 449296, ..., 3522934, 3721358, 3815532, 4551497, 4763457, 4945140, 7168502, 7214163, 8965352, 12581722 group -- type: object, values: 10000 to 14999, 100000 to 149999, 15000 to 24999, 150000 to 199999, 200000+, 25000 to 34999, 35000 to 49999, 50000 to 74999, 75000 to 99999, <10000 table_0 (table_0) sample: ``` |name|region|state_id|pct|total|group 0|Alabama|south|1|0.10200000000000001|1837292|<10000 1|Alabama|south|1|0.07200000000000001|1837292|10000 to 14999 2|Alabama|south|1|0.13|1837292|15000 to 24999 3|Alabama|south|1|0.115|1837292|25000 to 34999 4|Alabama|south|1|0.14300000000000002|1837292|35000 to 49999 ...... ``` [OUTPUT] ```json { "suggested_table_name": "income", "fields": { "name": {"type": "string", "semantic_type": "State"}, "region": {"type": "string", "semantic_type": "Region", "sort_order": ["northeast", "midwest", "south", "west", "other"]}, "state_id": {"type": "number", "semantic_type": "ID"}, "pct": {"type": "number", "semantic_type": "Percentage", "intrinsic_domain": [0, 1]}, "total": {"type": "number", "semantic_type": "Count"}, "group": {"type": "string", "semantic_type": "Range", "sort_order": ["<10000", "10000 to 14999", "15000 to 24999", "25000 to 34999", "35000 to 49999", "50000 to 74999", "75000 to 99999", "100000 to 149999", "150000 to 199999", "200000+"]} }, "data_summary": "Income distribution across US states, with percentage and count by income bracket." } ``` [DATA] Here are our datasets, here are their field summaries and samples: table_0 (weather_seattle_atlanta) fields: Date -- type: object, values: 1/1/2020, 1/10/2020, 1/11/2020, ..., 9/6/2020, 9/7/2020, 9/8/2020, 9/9/2020 City -- type: object, values: Atlanta, Seattle Temperature -- type: int64, values: 30, 31, 32, ..., 83, 84, 85, 86 table_0 (weather_seattle_atlanta) sample: ``` |Date|City|Temperature 0|1/1/2020|Seattle|51 1|1/1/2020|Atlanta|45 2|1/2/2020|Seattle|45 3|1/2/2020|Atlanta|47 4|1/3/2020|Seattle|48 ...... ``` [OUTPUT] ```json { "suggested_table_name": "weather", "fields": { "Date": { "type": "string", "semantic_type": "Date" }, "City": { "type": "string", "semantic_type": "City" }, "Temperature": { "type": "number", "semantic_type": "Temperature", "unit": "°F" } }, "data_summary": "Daily temperature data comparing Seattle and Atlanta throughout 2020, recording daily temperature measurements for each city from January to September." } ```''' class DataLoadAgent(object): def __init__(self, client, workspace): self.client = client self.workspace = workspace def run(self, input_data, n=1): # Always use the unified generate_data_summary approach # For virtual tables, workspace will find them; for in-memory tables, it uses rows data_summary = generate_data_summary( [input_data], workspace=self.workspace, include_data_samples=True, field_sample_size=30 ) user_query = f"[DATA]\n\n{data_summary}\n\n[OUTPUT]" logger.debug(user_query) logger.info(f"[DataLoadAgent] run start") messages = [{"role":"system", "content": SYSTEM_PROMPT}, {"role":"user","content": user_query}] response = self.client.get_completion(messages = messages) candidates = [] for choice in response.choices: logger.debug("\n=== Data load result ===>\n") logger.debug(choice.message.content + "\n") json_blocks = extract_json_objects(choice.message.content + "\n") logger.debug(json_blocks) if len(json_blocks) > 0: result = {'status': 'ok', 'content': json_blocks[0]} else: try: json_block = json.loads(choice.message.content + "\n") result = {'status': 'ok', 'content': json_block} except: result = {'status': 'other error', 'content': 'unable to extract VegaLite script from response'} # individual dialog for the agent result['dialog'] = [*messages, {"role": choice.message.role, "content": choice.message.content}] result['agent'] = 'DataLoadAgent' candidates.append(result) status = candidates[0].get('status', '?') if candidates else 'empty' logger.info(f"[DataLoadAgent] run done | status={status}") return candidates ================================================ FILE: py-src/data_formulator/agents/agent_data_rec.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. import json import time from data_formulator.agents.agent_utils import extract_json_objects, extract_code_from_gpt_response, generate_data_summary import traceback import pandas as pd import logging logger = logging.getLogger(__name__) # ============================================================================= # Shared prompt sections (imported by DataTransformationAgent) # ============================================================================= SHARED_ENVIRONMENT = '''**About the execution environment:** - You can use BOTH DuckDB SQL and pandas operations in the same script - The script will run in the workspace data directory (all data files are in the current directory) - Each table in [CONTEXT] has a **file path** (e.g., `student_exam.parquet`, `sales.csv`). Use EXACTLY that path to load data: - `.parquet`: `pd.read_parquet('file.parquet')` or DuckDB `read_parquet('file.parquet')` - `.csv`: `pd.read_csv('file.csv')` or DuckDB `read_csv_auto('file.csv')` - `.json`: `pd.read_json('file.json')` - `.xlsx`/`.xls`: `pd.read_excel('file.xlsx')` - `.txt`: `pd.read_csv('file.txt', sep='\\t')` - **IMPORTANT:** Use the exact filename from the context — do NOT change the file extension or assume all files are parquet. - **Allowed libraries:** pandas, numpy, duckdb, math, datetime, json, statistics, collections, re, sklearn, scipy, random, itertools, functools, operator, time - **Not allowed:** matplotlib, plotly, seaborn, requests, subprocess, os, sys, io, or any other library not listed above. - File system access (open, write) and network access are also forbidden. **When to use DuckDB vs pandas:** - **Prefer plain pandas** for most tasks — it's simpler and more readable. - Only use DuckDB when the dataset is very large and you need efficient SQL aggregations, filtering, joins, or window functions. - You can combine both: DuckDB for initial loading/filtering on large files, then pandas for complex operations. **Code structure:** standalone script (no function wrapper), imports at top, assign final result to a variable (specified in JSON).''' SHARED_SEMANTIC_TYPE_REFERENCE = '''**[SEMANTIC TYPE REFERENCE]** Choose the most specific type that fits. Only annotate fields used in chart encodings. | Category | Types | |---|---| | Temporal | DateTime, Date, Time, Timestamp, Year, Quarter, Month, Week, Day, Hour, YearMonth, YearQuarter, YearWeek, Decade, Duration | | Monetary measures | Amount, Price, Revenue, Cost | | Physical measures | Quantity, Temperature | | Proportion | Percentage | | Signed/diverging | Profit, PercentageChange, Sentiment, Correlation | | Generic measures | Count, Number | | Discrete numeric | Rank, Score, Rating, Index | | Identifier | ID | | Geographic | Latitude, Longitude, Country, State, City, Region, Address, ZipCode | | Entity names | PersonName, Company, Product, Category, Name | | Coded categorical | Status, Type, Boolean, Direction | | Binned ranges | Range, AgeGroup | | Fallback | String, Unknown | Key guidelines: - Use **Revenue/Cost** for summed monetary totals, **Price** for per-unit prices, **Profit** for values that can be negative. - Use **Temperature** (not Quantity) for temperature — it has special diverging behavior. - Use **Year** (not Number) for columns like "year" with values 2020, 2021.''' SHARED_CHART_REFERENCE = '''**[CHART TYPE REFERENCE]** | chart_type | encodings | config | |---|---|---| | Scatter Plot | x, y, color, size, facet | opacity (0.1–1.0) | | Regression | x, y, color, size, facet | regressionMethod ("linear","log","exp","pow","quad","poly"), polyOrder (2–10) | | Bar Chart | x, y, color, facet | — | | Grouped Bar Chart | x, y, group, facet | — | | Line Chart | x, y, color, strokeDash, facet | interpolate ("linear","monotone","step") | | Area Chart | x, y, color, facet | — | | Heatmap | x, y, color, facet | colorScheme ("viridis","blues","reds","oranges","greens","blueorange","redblue") | | Boxplot | x, y, color, facet | — | | Pie Chart | size, color, facet | innerRadius (0–100; 0=pie, >0=donut) | | Lollipop Chart | x, y, color, facet | — | | Waterfall Chart | x, y, color, facet | — | | Candlestick Chart | x, open, high, low, close, facet | — | | World Map | longitude, latitude, color, size | projection ("mercator","equalEarth","naturalEarth1","orthographic"), projectionCenter ([lon,lat]) | | US Map | longitude, latitude, color, size | — (fixed albersUsa) | **Critical chart rules:** - **Scatter Plot**: good default for relationships/correlations. Use config opacity (0.1–1.0) for dense data instead of encoding opacity. - **Regression**: automatically overlays a trend line — do NOT compute regression in Python. Use color to get separate trend lines per group. - **Bar Chart**: x=categorical, y=quantitative (vertical bars). Swap x↔y for horizontal bars. For histograms/distributions, bin the data in the Python step. Same-x rows are auto-stacked. - **Grouped Bar Chart**: use the group channel (not color) for side-by-side bars. - **Line Chart**: use strokeDash to differentiate line styles (e.g. actual vs forecast). - **Pie Chart**: use "size" channel (not "theta") for the wedge values. Avoid when >7–8 categories. - **Lollipop Chart**: like bar but with dot+line — cleaner for ranked comparisons. - **Waterfall Chart**: cumulative gain/loss — each bar starts where the previous ended. - **Candlestick Chart**: OHLC financial data — requires open, high, low, close columns. - **World Map/US Map**: use "longitude"/"latitude" as channel names, not "x"/"y". - **facet**: available for all chart types; use a categorical field with small cardinality. - All fields in "encodings" must also appear in "output_fields". Typically use 2–3 channels (x, y, color/size).''' SHARED_STATISTICAL_ANALYSIS = '''**Statistical analysis guide:** - **Regression**: use chart_type "Regression" — the trend line is automatic, do NOT compute regression values in Python code. Configure method via `{"regressionMethod": "linear"}` (options: "linear", "log", "exp", "pow", "quad", "poly"; for poly add `{"polyOrder": 3}`). - **Forecasting**: compute predicted future values in Python. Use Line Chart with strokeDash to distinguish actual vs forecast, and color for series grouping. - **Clustering**: compute cluster assignments in Python. Output [x, y, cluster_id]. Use Scatter Plot with color → cluster_id.''' SHARED_DUCKDB_NOTES = '''**DuckDB notes:** - Escape single quotes with '' (not \\') - No Unicode escapes (\\u0400); use character ranges directly: [а-яА-Я] - Cast date columns explicitly: `CAST(col AS DATE)`, `CAST(col AS TIMESTAMP)` - For complex datetime operations, load data first then use pandas datetime functions''' # ============================================================================= # DataRecAgent system prompt # ============================================================================= SYSTEM_PROMPT = f'''You are a data scientist who recommends data and visualizations. Given [CONTEXT] (dataset summaries) and [GOAL] (user intent), recommend a transformed dataset and visualization, then write a Python script to produce it. {SHARED_ENVIRONMENT} You will produce two outputs: a JSON spec (```json```) and a Python script (```python```). No extra text. **Step 1: JSON spec** — infer user intent and recommend a visualization. ```json {{{{ "display_instruction": "", // short verb phrase (<12 words) capturing computation intent. Bold **column names** (semantic matches count). For follow-ups, describe only the new part. "input_tables": [...], // table names from [CONTEXT] to use. Table 1 is the currently viewed table — prioritize it. "output_fields": [...], // desired output fields (include intermediate fields) "chart": {{{{ "chart_type": "", // from [CHART TYPE REFERENCE] "encodings": {{{{}}}}, // visual channels → output field names "config": {{{{}}}} // optional styling }}}}, "field_metadata": {{{{ // semantic type for each encoding field "": "Type" // from [SEMANTIC TYPE REFERENCE] }}}}, "output_variable": "" // descriptive snake_case name (e.g. "sales_by_region"), not "result_df" }}}} ``` **Data format rules:** - Output must be tidy (one field per visual channel, like VegaLite/ggplot2). - For multiple similar columns: reshape to long format (only same semantic type in one column). - For derived metrics: compute new fields (correlation, difference, profit, etc.). - Keep encodings to 2–3 channels (x, y, color/size). Add facet only when needed. {SHARED_SEMANTIC_TYPE_REFERENCE} {SHARED_CHART_REFERENCE} {SHARED_STATISTICAL_ANALYSIS} **Step 2: Python script** — transform input data to produce a DataFrame with all "output_fields". Keep it simple and readable. **Datetime handling:** - Year → number. Year-month / year-month-day → string ("2020-01" / "2020-01-01"). - Hour alone → number. Hour:min or h:m:s → string. Never return raw datetime objects. {SHARED_DUCKDB_NOTES}''' class DataRecAgent(object): def __init__(self, client, workspace, system_prompt=None, agent_coding_rules="", max_display_rows=10000): self.client = client self.workspace = workspace self.max_display_rows = max_display_rows # Incorporate agent coding rules into system prompt if provided if system_prompt is not None: self.system_prompt = system_prompt else: base_prompt = SYSTEM_PROMPT if agent_coding_rules and agent_coding_rules.strip(): self.system_prompt = base_prompt + "\n\n[AGENT CODING RULES]\nPlease follow these rules when generating code. Note: if the user instruction conflicts with these rules, you should prioritize user instructions.\n\n" + agent_coding_rules.strip() else: self.system_prompt = base_prompt def process_gpt_response(self, input_tables, messages, response, t_llm=None): """Process GPT response to handle Python code execution""" t_start = time.time() t_exec_total = 0.0 if isinstance(response, Exception): result = {'status': 'other error', 'content': str(response.body)} return [result] candidates = [] for choice in response.choices: logger.debug("\n=== Data recommendation result ===>\n") logger.debug(choice.message.content + "\n") json_blocks = extract_json_objects(choice.message.content + "\n") # Find the first JSON dict (skip any arrays the model may have emitted) refined_goal = None for jb in json_blocks: if isinstance(jb, dict): refined_goal = jb break if refined_goal is None: refined_goal = {'output_fields': [], 'chart': {'chart_type': "", 'encodings': {}, 'config': {}}, 'output_variable': 'result_df'} output_variable = refined_goal.get('output_variable', 'result_df') code_blocks = extract_code_from_gpt_response(choice.message.content + "\n", "python") if len(code_blocks) > 0: code = code_blocks[-1] try: from data_formulator.sandbox import create_sandbox # Get sandbox setting (with fallback for non-Flask contexts like MCP server) try: from flask import current_app sandbox_mode = current_app.config.get('CLI_ARGS', {}).get('sandbox', 'local') except (ImportError, RuntimeError): sandbox_mode = 'local' # Execute the Python script in the appropriate sandbox t_exec_start = time.time() sandbox = create_sandbox(sandbox_mode) execution_result = sandbox.run_python_code( code=code, workspace=self.workspace, output_variable=output_variable, ) t_exec_total += time.time() - t_exec_start if execution_result['status'] == 'ok': full_df = execution_result['content'] row_count = len(full_df) # Generate unique table name for workspace storage output_table_name = self.workspace.get_fresh_name(f"d-{output_variable}") # Write full result to workspace as parquet self.workspace.write_parquet(full_df, output_table_name) # Limit rows for response payload if row_count > self.max_display_rows: query_output = full_df.head(self.max_display_rows) else: query_output = full_df # Remove duplicate columns to avoid orient='records' error query_output = query_output.loc[:, ~query_output.columns.duplicated()] result = { "status": "ok", "code": code, "content": { 'rows': json.loads(query_output.to_json(orient='records')), 'virtual': { 'table_name': output_table_name, 'row_count': row_count } }, } else: # Execution error error_message = execution_result.get('content', execution_result.get('error_message', 'Unknown error')) result = { 'status': 'error', 'code': code, 'content': error_message } except Exception as e: logger.warning('Error occurred during code execution:') error_message = traceback.format_exc() logger.warning(error_message) result = {'status': 'other error', 'code': code, 'content': f"Unexpected error: {error_message}"} else: result = {'status': 'error', 'code': "", 'content': "No code block found in the response. The model is unable to generate code to complete the task."} result['dialog'] = [*messages, {"role": choice.message.role, "content": choice.message.content}] result['agent'] = 'DataRecAgent' result['refined_goal'] = refined_goal candidates.append(result) logger.debug("=== Recommendation Candidates ===>") for candidate in candidates: for key, value in candidate.items(): if key in ['dialog', 'content']: logger.debug(f"##{key}:\n{str(value)[:1000]}...") else: logger.debug(f"## {key}:\n{value}") t_total = time.time() - t_start t_llm_val = t_llm or 0.0 t_misc = t_total - t_exec_total logger.info(f"[DataRecAgent] timing: llm={t_llm_val:.3f}s, exec={t_exec_total:.3f}s, misc={t_misc:.3f}s, total={t_total + t_llm_val:.3f}s") return candidates def run(self, input_tables, description, n=1, prev_messages: list[dict] = []): """ Args: input_tables: list[dict], each dict contains 'name' (table name in workspace) and 'rows' description: str, the description of what the user wants n: int, the number of candidates prev_messages: list[dict], the previous messages """ table_names = [t.get('name', '?') for t in input_tables] logger.info(f"[DataRecAgent] run start | tables={table_names}") # Generate data summary with file references data_summary = generate_data_summary(input_tables, workspace=self.workspace) user_query = f"[CONTEXT]\n\n{data_summary}\n\n[GOAL]\n\n{description}" if len(prev_messages) > 0: user_query = f"The user wants a new recommendation based off the following updated context and goal:\n\n[CONTEXT]\n\n{data_summary}\n\n[GOAL]\n\n{description}" logger.debug(user_query) # Filter out system messages from prev_messages filtered_prev_messages = [msg for msg in prev_messages if msg.get("role") != "system"] messages = [{"role":"system", "content": self.system_prompt}, *filtered_prev_messages, {"role":"user","content": user_query}] t_llm_start = time.time() response = self.client.get_completion(messages = messages) t_llm = time.time() - t_llm_start candidates = self.process_gpt_response(input_tables, messages, response, t_llm=t_llm) status = candidates[0].get('status', '?') if candidates else 'empty' logger.info(f"[DataRecAgent] run done | status={status}") return candidates def followup(self, input_tables, dialog, latest_data_sample, new_instruction: str, n=1): """ Followup recommendation based on previous dialog and new instruction. Args: input_tables: list of input tables dialog: previous conversation history latest_data_sample: sample of the latest transformation result new_instruction: new user instruction for followup n: number of candidates """ logger.debug(f"GOAL: \n\n{new_instruction}") logger.info(f"[DataRecAgent] followup start") # Format sample data sample_data_str = pd.DataFrame(latest_data_sample).head(10).to_string() + '\n......' # Replace the old system prompt with the current one so that # conversations continued from older threads pick up prompt changes. updated_dialog = [{"role": "system", "content": self.system_prompt}, *dialog[1:]] messages = [*updated_dialog, {"role":"user", "content": f"This is the result from the latest transformation:\n\n{sample_data_str}\n\nUpdate the Python script above based on the following instruction:\n\n{new_instruction}"}] t_llm_start = time.time() response = self.client.get_completion(messages = messages) t_llm = time.time() - t_llm_start return self.process_gpt_response(input_tables, messages, response, t_llm=t_llm) ================================================ FILE: py-src/data_formulator/agents/agent_data_transform.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. import json import time from data_formulator.agents.agent_utils import extract_json_objects, extract_code_from_gpt_response from data_formulator.agents.agent_data_rec import ( SHARED_ENVIRONMENT, SHARED_SEMANTIC_TYPE_REFERENCE, SHARED_CHART_REFERENCE, SHARED_STATISTICAL_ANALYSIS, SHARED_DUCKDB_NOTES, ) import pandas as pd import logging logger = logging.getLogger(__name__) SYSTEM_PROMPT = f'''You are a data scientist who transforms data for visualization. Given [CONTEXT] (dataset summaries) and [GOAL] (user intent + chart spec), refine the goal and write a Python script to produce the transformed data. The user's [GOAL] includes a "chart" object (chart_type, encodings, config) describing the desired visualization and a natural language "goal". {SHARED_ENVIRONMENT} You will produce two outputs: a JSON spec (```json```) and a Python script (```python```). No extra text. **Step 1: JSON spec** — refine the user's goal and finalize the chart. Check if the user's "chart" (chart_type + encodings) is sufficient for their "goal": - If encodings are sufficient, copy them. - If encodings are missing fields, add minimal fields needed (aim for ≤3 channels: x, y, color/size). - If encodings can be optimized, reorder for better visualization. - If the user says "use B instead of A" while A is in encodings, update accordingly. - For lat/lon data, use "latitude"/"longitude" as channel names, not "x"/"y". - The user's chart_type may not be in [CHART TYPE REFERENCE] (e.g., "Radar Chart", "Bump Chart"). Preserve it as-is and infer valid encodings from channel names in the input. ```json {{{{ "input_tables": [...], // table names from [CONTEXT]. Table 1 = currently viewed — prioritize it. "detailed_instruction": "", // elaborated user instruction with details "display_instruction": "", // short verb phrase (<12 words) capturing computation intent. Bold **column names** (semantic matches count). For follow-ups, describe only the new part. "output_fields": [...], // desired output fields (include intermediate fields) "chart": {{{{ "chart_type": "", // from [CHART TYPE REFERENCE], or keep the user's chart_type as-is if not listed "encodings": {{{{}}}}, // visual channels → output field names "config": {{{{}}}} // optional styling }}}}, "field_metadata": {{{{ // semantic type for each encoding field "": "Type" // from [SEMANTIC TYPE REFERENCE] }}}}, "output_variable": "", // descriptive snake_case name (e.g. "sales_by_region"), not "result_df" "reason": "" // why this refinement is made }}}} ``` {SHARED_SEMANTIC_TYPE_REFERENCE} {SHARED_CHART_REFERENCE} {SHARED_STATISTICAL_ANALYSIS} **Step 2: Python script** — transform input data to produce a DataFrame with all "output_fields". Keep it simple and readable. **Datetime handling:** - Year → number. Year-month / year-month-day → string ("2020-01" / "2020-01-01"). - Hour alone → number. Hour:min or h:m:s → string. Never return raw datetime objects. {SHARED_DUCKDB_NOTES}''' class DataTransformationAgent(object): def __init__(self, client, workspace, system_prompt=None, agent_coding_rules="", max_display_rows=10000): self.client = client self.workspace = workspace self.max_display_rows = max_display_rows # Incorporate agent coding rules into system prompt if provided if system_prompt is not None: self.system_prompt = system_prompt else: base_prompt = SYSTEM_PROMPT if agent_coding_rules and agent_coding_rules.strip(): self.system_prompt = base_prompt + "\n\n[AGENT CODING RULES]\nPlease follow these rules when generating code. Note: if the user instruction conflicts with these rules, you should prioritize user instructions.\n\n" + agent_coding_rules.strip() else: self.system_prompt = base_prompt def process_gpt_response(self, response, messages, t_llm=None): """Process GPT response to handle Python code execution""" t_start = time.time() t_exec_total = 0.0 if isinstance(response, Exception): result = {'status': 'other error', 'content': str(response.body)} return [result] candidates = [] for choice in response.choices: logger.debug("=== Python script result ===>") logger.debug(choice.message.content + "\n") json_blocks = extract_json_objects(choice.message.content + "\n") # Find the first JSON dict (skip any arrays the model may have emitted) refined_goal = None for jb in json_blocks: if isinstance(jb, dict): refined_goal = jb break if refined_goal is None: refined_goal = {'chart': {'chart_type': '', 'encodings': {}, 'config': {}}, 'instruction': '', 'reason': '', 'output_variable': 'result_df'} output_variable = refined_goal.get('output_variable', 'result_df') code_blocks = extract_code_from_gpt_response(choice.message.content + "\n", "python") if len(code_blocks) > 0: code = code_blocks[-1] try: from data_formulator.sandbox import create_sandbox # Get sandbox setting (with fallback for non-Flask contexts like MCP server) try: from flask import current_app sandbox_mode = current_app.config.get('CLI_ARGS', {}).get('sandbox', 'local') except (ImportError, RuntimeError): sandbox_mode = 'local' # Execute the Python script in the appropriate sandbox t_exec_start = time.time() sandbox = create_sandbox(sandbox_mode) execution_result = sandbox.run_python_code( code=code, workspace=self.workspace, output_variable=output_variable, ) t_exec_total += time.time() - t_exec_start if execution_result['status'] == 'ok': full_df = execution_result['content'] row_count = len(full_df) # Generate unique table name for workspace storage output_table_name = self.workspace.get_fresh_name(f"d-{output_variable}") # Write full result to workspace as parquet self.workspace.write_parquet(full_df, output_table_name) # Limit rows for response payload if row_count > self.max_display_rows: query_output = full_df.head(self.max_display_rows) else: query_output = full_df # Remove duplicate columns to avoid orient='records' error query_output = query_output.loc[:, ~query_output.columns.duplicated()] result = { "status": "ok", "code": code, "content": { 'rows': json.loads(query_output.to_json(orient='records')), 'virtual': { 'table_name': output_table_name, 'row_count': row_count } }, } else: # Execution error result = { 'status': 'error', 'code': code, 'content': execution_result['content'] } except Exception as e: logger.warning('Error occurred during code execution:') logger.warning(f"Error type: {type(e).__name__}, message: {str(e)}") error_message = f"An error occurred during code execution. Error type: {type(e).__name__}, message: {str(e)}" result = {'status': 'error', 'code': code, 'content': error_message} else: result = {'status': 'error', 'code': "", 'content': "No code block found in the response. The model is unable to generate code to complete the task."} result['dialog'] = [*messages, {"role": choice.message.role, "content": choice.message.content}] result['agent'] = 'DataTransformationAgent' result['refined_goal'] = refined_goal candidates.append(result) logger.debug("=== Transform Candidates ===>") for candidate in candidates: for key, value in candidate.items(): if key in ['dialog', 'content']: logger.debug(f"##{key}:\n{str(value)[:1000]}...") else: logger.debug(f"## {key}:\n{value}") t_total = time.time() - t_start t_llm_val = t_llm or 0.0 t_misc = t_total - t_exec_total logger.info(f"[DataTransformAgent] timing: llm={t_llm_val:.3f}s, exec={t_exec_total:.3f}s, misc={t_misc:.3f}s, total={t_total + t_llm_val:.3f}s") return candidates def run(self, input_tables, description, prev_messages: list[dict] = [], n=1, current_visualization=None, expected_visualization=None): """Args: input_tables: list[dict], each dict contains 'name' (table name in workspace) description: str, the description of the data transformation prev_messages: list[dict], the previous messages n: int, the number of candidates current_visualization: dict or None, contains chart_spec and optional chart_image for complete charts expected_visualization: dict or None, contains chart_spec for incomplete charts """ table_names = [t.get('name', '?') for t in input_tables] logger.info(f"[DataTransformAgent] run start | tables={table_names}") # Generate data summary with file references from data_formulator.agents.agent_utils import generate_data_summary data_summary = generate_data_summary(input_tables, workspace=self.workspace) # Build visualization context section vis_section = "" if current_visualization: vis_section = f"\n\n[CURRENT VISUALIZATION] This is the current visualization the user has:\n\n{json.dumps(current_visualization.get('chart_spec', {}), indent=4)}" elif expected_visualization: vis_section = f"\n\n[EXPECTED VISUALIZATION] This is the visualization expected by the user:\n\n{json.dumps(expected_visualization.get('chart_spec', {}), indent=4)}" # Order: context → visualization → goal if len(prev_messages) > 0: user_query = f"The user wants a new transformation based off the following updated context and goal:\n\n[CONTEXT]\n\n{data_summary}{vis_section}\n\n[GOAL]\n\n{description}" else: user_query = f"[CONTEXT]\n\n{data_summary}{vis_section}\n\n[GOAL]\n\n{description}" logger.debug(user_query) # Filter out system messages from prev_messages filtered_prev_messages = [msg for msg in prev_messages if msg.get("role") != "system"] # Build user message content: include chart image if available chart_image = current_visualization.get('chart_image') if current_visualization else None try: if chart_image: user_content = [ {"type": "text", "text": user_query}, {"type": "image_url", "image_url": {"url": chart_image, "detail": "high"}} ] else: user_content = user_query messages = [{"role":"system", "content": self.system_prompt}, *filtered_prev_messages, {"role":"user","content": user_content}] t_llm_start = time.time() response = self.client.get_completion(messages = messages) t_llm = time.time() - t_llm_start except Exception as e: # Fallback to text-only if model doesn't support images logger.warning(f"Image-based completion failed, falling back to text-only: {e}") messages = [{"role":"system", "content": self.system_prompt}, *filtered_prev_messages, {"role":"user","content": user_query}] t_llm_start = time.time() response = self.client.get_completion(messages = messages) t_llm = time.time() - t_llm_start candidates = self.process_gpt_response(response, messages, t_llm=t_llm) status = candidates[0].get('status', '?') if candidates else 'empty' logger.info(f"[DataTransformAgent] run done | status={status}") return candidates def followup(self, input_tables, dialog, latest_data_sample, new_instruction: str, n=1, current_visualization=None, expected_visualization=None): """ Followup transformation based on previous dialog and new instruction. Args: input_tables: list of input tables dialog: previous conversation history latest_data_sample: sample of the latest transformation result new_instruction: new user instruction for followup n: number of candidates current_visualization: dict or None, contains chart_spec and optional chart_image for complete charts expected_visualization: dict or None, contains chart_spec for incomplete charts """ if not new_instruction or not new_instruction.strip(): new_instruction = "Update the transformation based on the updated visualization context." logger.debug(f"GOAL: \n\n{new_instruction}") logger.info(f"[DataTransformAgent] followup start") updated_dialog = [{"role":"system", "content": self.system_prompt}, *dialog[1:]] # Format sample data sample_data_str = pd.DataFrame(latest_data_sample).head(10).to_string() + '\n......' # Build visualization context section vis_section = "" if current_visualization: vis_section = f"\n\n[CURRENT VISUALIZATION] This is the current visualization the user has:\n\n{json.dumps(current_visualization.get('chart_spec', {}), indent=4)}" elif expected_visualization: vis_section = f"\n\n[EXPECTED VISUALIZATION] This is the visualization expected by the user:\n\n{json.dumps(expected_visualization.get('chart_spec', {}), indent=4)}" # Order: data sample → visualization → instruction followup_text = f"This is the result from the latest transformation:\n\n{sample_data_str}{vis_section}\n\nUpdate the Python script above based on the following instruction:\n\n{new_instruction}" logger.debug(followup_text) # Build user message content: include chart image if available chart_image = current_visualization.get('chart_image') if current_visualization else None try: if chart_image: user_content = [ {"type": "text", "text": followup_text}, {"type": "image_url", "image_url": {"url": chart_image, "detail": "high"}} ] else: user_content = followup_text messages = [*updated_dialog, {"role":"user", "content": user_content}] t_llm_start = time.time() response = self.client.get_completion(messages = messages) t_llm = time.time() - t_llm_start except Exception as e: # Fallback to text-only if model doesn't support images logger.warning(f"Image-based completion failed, falling back to text-only: {e}") messages = [*updated_dialog, {"role":"user", "content": followup_text}] t_llm_start = time.time() response = self.client.get_completion(messages = messages) t_llm = time.time() - t_llm_start candidates = self.process_gpt_response(response, messages, t_llm=t_llm) status = candidates[0].get('status', 'unknown') if candidates else 'empty' logger.info(f"[DataTransformAgent] followup done | status={status}") return candidates ================================================ FILE: py-src/data_formulator/agents/agent_interactive_explore.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. import json import logging import pandas as pd from data_formulator.agents.agent_utils import extract_json_objects, generate_data_summary logger = logging.getLogger(__name__) SYSTEM_PROMPT = '''You are a data exploration expert who suggests interesting questions to help users explore their datasets. This prompt contains the following sections: - [DATASETS] section: available datasets the user is working with. - [EXPLORATION THREAD] section (optional): sequence of datasets that have been explored in the order they were created, and what questions are asked to create them. These tables are all created from tables in the [DATASETS] section. - [CURRENT DATA] section (optional): latest data sample the user is viewing, and the visualization they are looking at at the moment. - [START QUESTION] section (optional): start question from previous exploration steps for context Your task is to suggest 4 exploration questions (unless the user explicitly asks for the number of questions), that users can follow to gain insights from their data. When the exploration context is provided, make your suggestion based on the context as well as the original datasets; otherwise leverage the original datasets to suggest questions. Guidelines for question suggestions: 1. Suggest interesting analytical questions that can uncover new insights from the data. 2. Use a diverse language style to display the questions (can be questions, statements etc). 3. If there are multiple datasets in a thread, consider relationships between them. 4. CONCISENESS: the questions should be concise and to the point 5. QUESTION: the question should be a new question based on the exploration thread: - if no exploration thread is provided, start with a high-level overview question that directly visualizes the data to give the user a sense of the data. - either a followup question, or a new question that is related to the exploration thread - if the current data is rich, you can ask a followup question to further explore the dataset; - if the current data is already specialized to answer the previous question, you can ask a new question that is related to the thread but not related to the previous question in the thread, leverage earlier exploration data to ask questions that can expand the exploration horizon - do not repeat questions that have already been explored in the thread - do not suggest questions that are not related to the thread (e.g. questions that are completely unrelated to the exploration direction in the thread) - do not naively follow up if the question is already too low-level when previous iterations have already come into a small subset of the data (suggest new related areas related to the metric / attributes etc) - leverage other datasets in the [DATASETS] section to suggest questions that are related to the exploration thread. 6. DIVERSITY: the questions should be diverse in difficulty (easy / medium / hard) and the four questions should cover different aspects of the data analysis to expand the user's horizon - simple questions should be short -- single sentence exploratory questions - medium questions can be 1-2 sentences exploratory questions - hard questions should introduce some new analysis concept but still make it concise - you should include both types of questions: - questions that deepdive from the provided data to further refine the exploration (zoom-in). - questions that branch out from the provided data to explore new related directions (zoom-out). - if suitable, suggest a question about statistical analysis: forecasting, regression, or clustering. 7. VISUALIZATION: each question should be visualizable with a chart. 8. FORMATTING: for each question, include a 'goal' that concisely summarizes the essence of the question. - The goal should all be a short single sentence (<10 words). - It should capture the key computation and exploration direction of the question (do not omit any information that may lead to ambiguity), but also keep it concise. - Meaning of the 'goal' should be clear that the user won't misunderstand the actual question descibed in 'text'. - include the **bold** keywords for the attributes / metrics that are important to the question, especially when the goal mentions fields / metrics in the original dataset (don't have to be exact match) - include 'difficulty' to indicate the difficulty of the question, it should be one of 'easy', 'medium', 'hard' - include a 'tag' to describe the type of the question. Output should be a list of json objects in the following format, each line should be a json object representing a question, starting with 'data:': Format: data: {"type": "question", "text": ..., "goal": ..., "difficulty": ..., "tag": ...} data: {"type": "question", "text": ..., "goal": ..., "difficulty": ..., "tag": ...} ... // more questions ''' SYSTEM_PROMPT_AGENT = '''You are a data exploration expert to help users explore their datasets. This prompt contains the following sections: - [DATASETS] section: available datasets the user is working with. - [EXPLORATION THREAD] section (optional): sequence of datasets that have been explored in the order they were created, and what questions are asked to create them. These tables are all created from tables in the [DATASETS] section. - [CURRENT DATA] section (optional): latest data sample the user is viewing, and the visualization they are looking at at the moment. - [START QUESTION] section (optional): start question from previous exploration steps for context Given a dataset (or a thread of datasets that have been explored), your task is to suggest 4 exploration questions (unless the user explicitly asks for the number of questions), that users can follow to gain insights from their data. When the exploration context is provided, make your suggestion based on the context as well as the original datasets; otherwise leverage the original datasets to suggest questions. Guidelines for question suggestions: 1. Suggest a list of question_groups of interesting analytical questions that can uncover new insights from the data. 2. Use a diverse language style to display the questions (can be questions, statements etc) 3. If there are multiple datasets in a thread, consider relationships between them 4. CONCISENESS: the questions should be concise and to the point 5. QUESTION GROUP GENERATION: - different questions groups should cover different aspects of the data analysis for user to choose from. - each question_group is a sequence of 'questions' that builds on top of each other to answer the user's goal. - each question group should have a difficulty level (easy / medium / hard), - simple questions should be short -- single sentence exploratory questions - medium questions can be 1-2 sentences exploratory questions - hard questions should introduce some new analysis concept but still make it concise - if suitable, include a group of questions that are related to statistical analysis: forecasting, regression, or clustering. 6. QUESTIONS WITHIN A QUESTION GROUP: - if the user doesn't provide an exploration thread, start with a high-level overview question that directly visualizes the data to give the user a sense of the data. - raise new questions that are related to the user's goal, do not repeat questions that have already been explored in the context provided to you. - if the user provides a start question, suggested questions should be related to the start question. - the questions should progressively dive deeper into the data, building on top of the previous question. - start of the question should provide an overview of the data in the direction going to be explored. - followup questions should refine the previous question, introducing refined analysis to deep dive into the data (e.g., updated computation, filtering, different grouping, etc.) - don't jump too far from the previous question so that readers can understand the flow of the questions. - every question should be answerable with a visualization. 7. FORMATTING: - include "questions" in the question group: - each question group should have 2-4 questions (or as directed by the user). - For each question group, include a 'goal' that summarizes the goal of the question group. - The goal should all be a short single sentence (<12 words). - Meaning of the 'goal' should be clear that the user won't misunderstand the actual question descibed in 'text'. - It should capture the key computation and exploration direction of the question (do not omit any information that may lead to ambiguity), but also keep it concise. - include the **bold** keywords for the attributes / metrics that are important to the question, especially when the goal mentions fields / metrics in the original dataset (don't have to be exact match) - include 'difficulty' to indicate the difficulty of the question, it should be one of 'easy', 'medium', 'hard' Output should be a list of json objects in the following format, each line should be a json object representing a question group, starting with 'data: ': Format: data: {"questions": [...], "goal": ..., "difficulty": ...} data: {"questions": [...], "goal": ..., "difficulty": ...} ... // more question groups ''' class InteractiveExploreAgent(object): def __init__(self, client, workspace, agent_exploration_rules=""): self.client = client self.agent_exploration_rules = agent_exploration_rules self.workspace = workspace # when set (SQL/datalake mode), use parquet tables for summary def run(self, input_tables, start_question=None, exploration_thread=None, current_data_sample=None, current_chart=None, mode='interactive'): """ Suggest exploration questions for a dataset or exploration thread. Args: input_tables: List of dataset objects with name, rows, description start_question: Optional start question from previous exploration steps for context exploration_thread: Optional list of tables from previous exploration steps for context current_data_sample: Optional data sample from previous exploration steps for context (it should be a json object) current_chart: Optional chart object from previous exploration steps for context (it should be an image in data:image/png format) mode: Optional mode of exploration question: 'interactive' or 'agent' Returns: List of candidate results with suggested exploration questions """ # Generate data summary data_summary = generate_data_summary(input_tables, self.workspace) # Build context including exploration thread if available context = f"[DATASETS] These are the datasets the user is working with:\n\n{data_summary}" if exploration_thread: thread_summary = generate_data_summary( [{ 'name': table.get('name', f'Table {i}'), 'rows': table.get('rows', []), 'attached_metadata': table.get('description', ''), } for i, table in enumerate(exploration_thread, 1)], self.workspace, table_name_prefix="Thread Table" ) context += f"\n\n[EXPLORATION THREAD] These are the sequence of tables the user created in this exploration thread, in the order they were created, and what questions are asked to create them:\n\n{thread_summary}" if current_data_sample: context += f"\n\n[CURRENT DATA SAMPLE] This is the current data sample the user is viewing, and the visualization they are looking at at the moment is shown below:\n\n{pd.DataFrame(current_data_sample).head(10).to_string()}" if start_question: context += f"\n\n[START QUESTION]\n\n{start_question}" base_system_prompt = SYSTEM_PROMPT_AGENT if mode == 'agent' else SYSTEM_PROMPT # Incorporate agent exploration rules into system prompt if provided if self.agent_exploration_rules and self.agent_exploration_rules.strip(): system_prompt = base_system_prompt + "\n\n[AGENT EXPLORATION RULES]\n\n" + self.agent_exploration_rules.strip() + "\n\nPlease follow the above agent exploration rules when suggesting questions." else: system_prompt = base_system_prompt logger.debug(f"Interactive explore agent input: {context}") logger.info(f"[InteractiveExploreAgent] run start") try: if current_chart: messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": [ {"type": "text", "text": context}, {"type": "image_url", "image_url": {"url": current_chart, "detail": "high"}} ]} ] else: messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": context} ] # Get completion from client stream = self.client.get_completion(messages=messages, stream=True) except Exception as e: # if the model doesn't accept image, just use the text context messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": context} ] # Get completion from client stream = self.client.get_completion(messages=messages, stream=True) accumulated_content = "" for part in stream: if hasattr(part, 'choices') and len(part.choices) > 0: delta = part.choices[0].delta if hasattr(delta, 'content') and delta.content: accumulated_content += delta.content # Stream each character for real-time display as JSON yield delta.content logger.info(f"[InteractiveExploreAgent] run done") ================================================ FILE: py-src/data_formulator/agents/agent_report_gen.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. import json from data_formulator.agents.agent_utils import extract_json_objects, generate_data_summary import logging logger = logging.getLogger(__name__) SYSTEM_PROMPT = '''You are a journalist to help the user generate a short blog post based off the data and visualization provided by the user. The user will provide you: - the input data summary (the data analysis is based off) - and a list of visualizations (and their corresponding data) that the user wants to include in the report. - the report style they want the report to be written in. Your job is to generate a short blog post based off the data and visualizations provided by the user. It should be a few paragraphs long, and be easy to read. Note: - You should not make any assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state. - If that happens, highlight the the data may include biases, and suggest the user to be careful when interpreting the data. The report should have two components: 1. A short title of the report 2. Description of findings based on the charts and data. - connect findings between different charts into a coherent story, write in a way that is easy to read and understand. - include the image as part of the blog. Use a placeholder [IMAGE(chart_id)] to include the chart that will be replaced later. - for each chart, write a bit about the what is the chart trying to answer and its findings (use its data as supporting evidence) - descriptions should all be concise only show 2-3 most important findings for the chart. 3. conclude the blog with a summary of the findings and follows up questions. Writing style rules: - The report should be easy to read and understand, the total reading time should be 1 minute for the user, use no more than 200 words. - The report should be concise and to the point. - The output should be in markdown format: - title should be in `# ` - the content should just be paragraphs without subsection headers - put image reference [IMAGE(chart_id)] in its own line among the texts at appropriate places (replace the chart_id with the actual chart_id, keep the format of [IMAGE(...)]). - be flexible about using markdown syntax like bullet points, italics, bold, code blocks, tables, etc. to make the report more readable. - the summary should be in a paragraph start with "**In summary**". - Note that the reader won't be able to see sample data or code, and the report should be self-contained (referring to the charts). - The user may provide you a desired writing style, that means the overall language should follow the style (not that the post should still be within 1min reading time). - "blog post": "blog post", -- a blogpost that is published on a blog platform - "social post": "social post", -- a social post that is published on a social media platform (should be shorter than a blog post) - "executive summary": "executive summary", -- a summary of the report for executives, with more formal language and more details, and more bullet points - "live report": "live report", -- a live report is designed for user to monitor the data and visualization live (like a dashboard), thus, don't interpret the visualization as of now since data will change, instead describe what each chart is designed for and hint what the user should look for as data get updated. The report should be lightweight, and respect facts in the data. Do not make up any facts or make judgements about the data. The report should be based off the data and visualizations provided by the user, do not make up any facts or make judgements about the data. Output markdown directly, do not need to include any other text. ''' class ReportGenAgent(object): def __init__(self, client, workspace): self.client = client self.workspace = workspace def get_data_summary(self, input_tables): return generate_data_summary(input_tables, self.workspace) def stream(self, input_tables, charts=[], style="blog post"): """derive a new concept based on the raw input data Args: - input_tables (list): the input tables to the agent - charts (list): the charts to the agent of format [ { "chart_id": ..., // the id of the chart "code": ..., // the code that derived this table "chart_data": { "name": ..., "rows": ... }, "chart_url": ... // base64 encoded image } ] - style (str): the style of the report, can be "blog post" or "social post" or "executive summary" or "live report" Returns: generator: the result of the agent """ data_summary = self.get_data_summary(input_tables) content = [] content.append({ 'type': 'text', 'text': f'''{data_summary}''' }) for chart in charts: chart_data = chart['chart_data'] chart_data_summary = self.get_data_summary([chart_data]) if chart['chart_url']: content += [ { 'type': 'text', 'text': f''' [CHART] - chart_id: {chart['chart_id']} \n\n - data summary:\n\n{chart_data_summary} \n\n - code:\n\n{chart['code']}''' }, { 'type': 'image_url', 'image_url': { "url": chart['chart_url'], "detail": "high" } } ] user_prompt = { 'role': 'user', 'content': content + [{'type': 'text', 'text': 'Now based off the data and visualizations provided by the user, generate a report in markdown. The style of the report should be ' + style + '.'}] } system_message = { 'role': 'system', 'content': [ {'type': 'text', 'text': SYSTEM_PROMPT}] } messages = [ system_message, user_prompt ] ###### the part that calls open_ai stream = self.client.get_completion(messages = messages, stream=True) accumulated_content = "" for part in stream: if hasattr(part, 'choices') and len(part.choices) > 0: delta = part.choices[0].delta if hasattr(delta, 'content') and delta.content: accumulated_content += delta.content # Stream each character for real-time display as JSON yield delta.content ================================================ FILE: py-src/data_formulator/agents/agent_sort_data.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. import json from data_formulator.agents.agent_utils import extract_json_objects import logging logger = logging.getLogger(__name__) SYSTEM_PROMPT = '''You are a data scientist to help user to sort data. The user will provide list of items in the form of a json object, and your goal is to sort the data in its natural order based on your knowledge. Create an output json object with sorted data based off the [INPUT]. For example: [INPUT] { "name": "grades", "values": [">=60","10", "20", "30", "40", "50"] } [OUTPUT] { "name": "grades", "sorted_values": ["10", "20", "30", "40", "50", ">=60"], "reason": "sort scores in ascending order" } [INPUT] { "name": "month", "values": [ "April", "August", "December", "February", "January", "July", "June", "March", "May", "November", "October", "September" ] } [OUTPUT] { "name": "month", "sorted_values": ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"], "reason": "sort months by their natural order" } [INPUT] { "name": "month", "values": ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"] } [OUTPUT] { "name": "month", "sorted_values": ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"], "reason": "the input list is already ordered months naturally" } ''' class SortDataAgent(object): def __init__(self, client): self.client = client def run(self, name, values, n=1): input_obj = { 'name': name, 'value': values } user_query = f"[INPUT]\n\n{json.dumps(input_obj)}\n\n[OUTPUT]" logger.debug(user_query) logger.info(f"[SortDataAgent] run start") messages = [{"role":"system", "content": SYSTEM_PROMPT}, {"role":"user","content": user_query}] ###### the part that calls open_ai response = self.client.get_completion(messages = messages) #log = {'messages': messages, 'response': response.model_dump(mode='json')} candidates = [] for choice in response.choices: logger.debug("\n=== Sort data agent ===>\n") logger.debug(choice.message.content + "\n") json_blocks = extract_json_objects(choice.message.content + "\n") if len(json_blocks) > 0: result = {'status': 'ok', 'content': json_blocks[0]} else: try: json_block = json.loads(choice.message.content + "\n") result = {'status': 'ok', 'content': json_block} except: result = {'status': 'other error', 'content': 'unable to extract VegaLite script from response'} # individual dialog for the agent result['dialog'] = [*messages, {"role": choice.message.role, "content": choice.message.content}] result['agent'] = 'SortDataAgent' candidates.append(result) status = candidates[0].get('status', '?') if candidates else 'empty' logger.info(f"[SortDataAgent] run done | status={status}") return candidates ================================================ FILE: py-src/data_formulator/agents/agent_utils.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. import json import keyword import numpy as np import re def string_to_py_varname(var_str): var_name = re.sub(r'\W|^(?=\d)', '_', var_str) if keyword.iskeyword(var_name): var_name = f"__{var_name}" return var_name def field_name_to_ts_variable_name(field_name): if field_name.strip() == "": return "inp" clean_name = re.sub('[^A-Za-z0-9]+', ' ', field_name) clean_name = re.sub(' +', ' ', clean_name) var_name = ''.join(x for x in clean_name.title() if not x.isspace()) var_name = var_name[0].lower() + var_name[1:] return var_name def infer_ts_datatype(df, name): if name not in df.columns: return "any" dtype = df[name].dtype if dtype == "object": return "string" elif dtype == "int64" or dtype == "float64": return "number" elif dtype == "bool": return "boolean" elif dtype == "datetime64": return "Date" else: return "any" def value_handling_func(val): """process values to make it comparable""" if isinstance(val, (int,)): return val try: val = float(val) val = np.round(val, 5) except: pass if isinstance(val, (list,)): return str(val) return val def table_hash(table): """hash a table, mostly for the purpose of comparison""" if len(table) == 0: return hash(table) schema = sorted(list(table[0].keys())) frozen_table = tuple(sorted([tuple([hash(value_handling_func(r[key])) for key in schema]) for r in table])) return hash(frozen_table) def extract_code_from_gpt_response(code_raw, language): """search for matches and then look for pairs of ```...``` to extract code""" prefix_pos = [m.span()[0] for m in re.compile(f"```{language}").finditer(code_raw)] all_spans = [m.span() for m in re.compile("```").finditer(code_raw)] matches = [] for i in range(len(all_spans) - 1): if all_spans[i][0] in prefix_pos and all_spans[i+1][0] not in prefix_pos: matches.append([all_spans[i][0], all_spans[i+1][1]]) results = [] if len(matches) > 0: match = matches[0] for match in matches: code = code_raw[match[0]: match[1]] code = code[len(f"```{language}"): len(code) - len("```")] results.append(code) return results def find_matching_bracket(text, start_index, bracket_type='curly'): """Find the index of the matching closing bracket for JSON objects or arrays.""" if bracket_type == 'curly': open_bracket, close_bracket = '{', '}' elif bracket_type == 'square': open_bracket, close_bracket = '[', ']' else: raise ValueError("Invalid bracket_type. Use 'curly' or 'square'.") stack = [] for index in range(start_index, len(text)): char = text[index] if char == open_bracket: stack.append(char) elif char == close_bracket: if not stack: return -1 stack.pop() if not stack: return index return -1 def extract_json_objects(text): """Extracts JSON objects and arrays from a text string. Returns a list of parsed JSON objects and arrays. """ json_objects = [] start_index = 0 while True: # Search for the start of a JSON object or array object_start = text.find('{', start_index) array_start = text.find('[', start_index) # Find the earliest JSON structure start if object_start == -1 and array_start == -1: break elif object_start == -1: start_index = array_start bracket_type = 'square' elif array_start == -1: start_index = object_start bracket_type = 'curly' else: start_index = min(object_start, array_start) bracket_type = 'square' if start_index == array_start else 'curly' # Find the matching closing bracket end_index = find_matching_bracket(text, start_index, bracket_type) if end_index == -1: break json_str = text[start_index:end_index + 1] try: json_obj = json.loads(json_str) json_objects.append(json_obj) except ValueError: pass start_index = end_index + 1 return json_objects def get_field_summary(field_name, df, field_sample_size, max_val_chars=100): # Convert lists to strings to make them hashable def make_hashable(val): if val is None: return None if isinstance(val, list): return str(val) return val try: values = sorted([make_hashable(x) for x in list(set([make_hashable(x) for x in df[field_name].values])) if x is not None]) except: values = [make_hashable(x) for x in list(set([make_hashable(x) for x in df[field_name].values])) if x is not None] val_sample = "" sample_size = field_sample_size if len(values) <= sample_size: val_sample = values else: val_sample = values[:int(sample_size / 2)] + ["..."] + values[-(sample_size - int(sample_size / 2)):] def sample_val_cap(val): if len(str(val)) > max_val_chars: s = str(val)[:max_val_chars] + "..." else: s = str(val) if ',' in s: s = f'"{s}"' return s val_str = ', '.join([sample_val_cap(str(s)) for s in val_sample]) return f"{field_name} -- type: {df[field_name].dtype}, values: {val_str}" def generate_data_summary( input_tables, workspace, include_data_samples=True, field_sample_size=7, row_sample_size=5, max_val_chars=140, table_name_prefix="Table" ): """ Generate a natural, well-organized summary of input tables by reading workspace parquet files. All tables (including temp tables) should be in the workspace before calling this function. Use WorkspaceWithTempData context manager to mount temp tables to workspace. Organization approach: - Each table is clearly separated with a header - Information flows logically: Overview → Schema → Examples - Consistent section ordering for better readability - Shows filename for workspace tables Args: input_tables: list of dicts with 'name' key workspace: Workspace instance with all tables mounted (including temp data) include_data_samples: whether to include sample data field_sample_size: number of example values per field row_sample_size: number of sample rows to show max_val_chars: max characters per value table_name_prefix: prefix for table headers Returns: Formatted string summary of all tables """ def assemble_table_summary(table, idx): table_name = table['name'] description = table.get("attached_metadata", "") # Read data into DataFrame (handles parquet, csv, excel, json, etc.) df = workspace.read_data_as_df(table_name) # Get filename for display (LLM uses this to generate read_parquet/read_csv calls) data_file_path = workspace.get_relative_data_file_path(table_name) num_rows = len(df) num_cols = len(df.columns) # Build sections in logical order: Overview → Description → Schema → Examples sections = [] # 1. Table Header with basic stats header = f"## {table_name_prefix} {idx + 1}: {table_name}" if num_rows > 0: header += f" ({num_rows:,} rows × {num_cols} columns)" sections.append(header) sections.append(f"- **file path:** `{data_file_path}`") sections.append("") # Empty line for spacing # 2. Description (if available) - provides context first if description: sections.append(f"### Description\n{description}\n") # 3. Schema/Fields - core structure information fields_summary = '\n'.join([ ' - ' + get_field_summary(fname, df, field_sample_size, max_val_chars) for fname in df.columns ]) sections.append(f"### Schema ({num_cols} fields)\n{fields_summary}\n") # 4. Sample data (if requested) - concrete examples last if include_data_samples and num_rows > 0: sample_df = df.head(row_sample_size) sections.append( f"### Sample Data (first {min(row_sample_size, num_rows)} rows)\n" f"```\n{sample_df.to_string()}\n```\n" ) return '\n'.join(sections) # Build summaries for all tables table_summaries = [assemble_table_summary(table, i) for i, table in enumerate(input_tables)] # Join with visual separators separator = "\n" + "─" * 60 + "\n\n" return separator.join(table_summaries) ================================================ FILE: py-src/data_formulator/agents/agent_utils_sql.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ SQL-related utility functions for agents. These functions are used across multiple agents for DuckDB operations and SQL data summaries. """ import re def sanitize_table_name(table_name: str) -> str: """Sanitize table name to be used in SQL queries""" # Replace spaces with underscores sanitized_name = table_name.replace(" ", "_") sanitized_name = sanitized_name.replace("-", "_") # Allow alphanumeric, underscore, dot, dash, and dollar sign sanitized_name = re.sub(r'[^a-zA-Z0-9_\.$]', '', sanitized_name) return sanitized_name def create_duckdb_conn_with_parquet_views(workspace, input_tables: list[dict]): """ Create an in-memory DuckDB connection with a view for each parquet table in the workspace. Input tables are expected to be parquet-backed tables in the datalake (parquet-to-parquet). Args: workspace: Workspace instance input_tables: list of dicts with 'name' key for the table name Returns: DuckDB connection with views created for all input tables """ import duckdb conn = duckdb.connect(":memory:") for table in input_tables: name = table["name"] view_name = sanitize_table_name(name) path = workspace.get_parquet_path(name) path_escaped = str(path).replace("\\", "\\\\").replace("'", "''") conn.execute(f'CREATE VIEW "{view_name}" AS SELECT * FROM read_parquet(\'{path_escaped}\')') return conn ================================================ FILE: py-src/data_formulator/agents/client_utils.py ================================================ import litellm import openai from azure.identity import DefaultAzureCredential, get_bearer_token_provider class Client(object): """ Returns a LiteLLM client configured for the specified endpoint and model. Supports OpenAI, Azure, Ollama, and other providers via LiteLLM. """ def __init__(self, endpoint, model, api_key=None, api_base=None, api_version=None): self.endpoint = endpoint self.model = model self.params = {} if api_key is not None and api_key != "": self.params["api_key"] = api_key if api_base is not None and api_base != "": self.params["api_base"] = api_base if api_version is not None and api_version != "": self.params["api_version"] = api_version if self.endpoint == "gemini": if model.startswith("gemini/"): self.model = model else: self.model = f"gemini/{model}" elif self.endpoint == "anthropic": if model.startswith("anthropic/"): self.model = model else: self.model = f"anthropic/{model}" elif self.endpoint == "azure": self.params["api_base"] = api_base self.params["api_version"] = api_version if api_version else "2025-04-01-preview" if api_key is None or api_key == "": token_provider = get_bearer_token_provider( DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default" ) self.params["azure_ad_token_provider"] = token_provider self.params["custom_llm_provider"] = "azure" elif self.endpoint == "ollama": self.params["api_base"] = api_base if api_base else "http://localhost:11434" self.params["max_tokens"] = self.params["max_completion_tokens"] if model.startswith("ollama/"): self.model = model else: self.model = f"ollama/{model}" @classmethod def from_config(cls, model_config: dict[str, str]): """ Create a client instance from model configuration. Args: model_config: Dictionary containing endpoint, model, api_key, api_base, api_version Returns: Client instance for making API calls """ # Strip whitespace from all values for key in model_config: if isinstance(model_config[key], str): model_config[key] = model_config[key].strip() return cls( model_config["endpoint"], model_config["model"], model_config.get("api_key"), model_config.get("api_base"), model_config.get("api_version") ) def get_completion(self, messages, stream=False): """ Returns a LiteLLM client configured for the specified endpoint and model. Supports OpenAI, Azure, Ollama, and other providers via LiteLLM. """ # Configure LiteLLM if self.endpoint == "openai": client = openai.OpenAI( base_url=self.params.get("api_base", None), api_key=self.params.get("api_key", ""), timeout=120 ) completion_params = { "model": self.model, "messages": messages, } if self.model.startswith("gpt-5") or self.model.startswith("o1") or self.model.startswith("o3"): completion_params["reasoning_effort"] = "low" return client.chat.completions.create(**completion_params, stream=stream) else: params = self.params.copy() if (self.model.startswith("gpt-5") or self.model.startswith("o1") or self.model.startswith("o3") or self.model.startswith("claude-sonnet-4-5") or self.model.startswith("claude-opus-4")): params["reasoning_effort"] = "low" return litellm.completion( model=self.model, messages=messages, drop_params=True, stream=stream, **params ) def get_response(self, messages: list[dict], tools: list | None = None): """ Returns a response using OpenAI's Response API approach. """ if self.endpoint == "openai": client = openai.OpenAI( base_url=self.params.get("api_base", None), api_key=self.params.get("api_key", ""), timeout=120 ) return client.responses.create( model=self.model, input=messages, tools=tools, **self.params ) else: return litellm.responses( model=self.model, input=messages, tools=tools, drop_params=True, **self.params ) ================================================ FILE: py-src/data_formulator/agents/data_agent.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """Autonomous data exploration agent (SWE-agent style). The agent receives a high-level user question, then enters an observe → think → act loop where it picks one of three actions per turn: visualize – call DataRecAgent to transform data & create a chart clarify – ask the user a clarification question (pauses the loop) present – summarize findings and terminate the loop The full trajectory (system prompt + observations) is maintained as a standard message list and sent to the LLM on every turn so the model has complete context to make decisions. """ import json import logging import time import uuid from typing import Any, Generator from data_formulator.agents.agent_data_rec import DataRecAgent from data_formulator.agents.agent_utils import extract_json_objects, generate_data_summary from data_formulator.agents.client_utils import Client from data_formulator.code_signing import sign_result from data_formulator.workflows.create_vl_plots import ( assemble_vegailte_chart, coerce_field_type, resolve_field_type, spec_to_base64, field_metadata_to_semantic_types, ) import pandas as pd logger = logging.getLogger(__name__) # --------------------------------------------------------------------------- # System prompt # --------------------------------------------------------------------------- SYSTEM_PROMPT = '''\ You are an autonomous data exploration agent. Your goal is to help the user answer their question by creating one or more data visualizations. You operate in a loop: at every turn you MUST output **exactly one action** as a JSON object (nothing else). ## Available actions ### 1. `visualize` Use this when you want to create a visualization. You provide a concise analytical question that will be forwarded to a data-transformation agent which will write code, transform the data, and pick a chart type. ```json {{ "action": "visualize", "thought": "<your reasoning about what to explore next>", "question": "<concise analytical question / instruction for the chart>" }} ``` Guidelines for the question: - It should be self-contained: mention which fields, aggregations, filters or derived metrics to use. - Each question should target ONE chart. - Keep it concise but precise enough so the data transformation agent can execute without ambiguity. ### 2. `clarify` Use this when the user's question is ambiguous, there are multiple reasonable interpretations, or critical information is missing. ```json {{ "action": "clarify", "thought": "<why you need clarification>", "message": "<a polite, concise clarification question for the user>" }} ``` Guidelines: - Only clarify when genuinely necessary; prefer making a reasonable assumption and proceeding. - Ask at most one question per turn. ### 3. `present` Use this when you believe you have sufficiently answered the user's question and can summarize findings. ```json {{ "action": "present", "thought": "<why you are done>", "summary": "<concise bullet-point summary of key findings>" }} ``` Guidelines: - The summary should reference the visualizations you created. - Keep it to 3-5 bullet points, each ≤ 25 words. - Present after at most {max_iterations} visualization steps, even if there is more to explore. ## Decision guidelines - **Start** by understanding the user question and the data. If the question is clear, go ahead and `visualize`. If it is ambiguous, `clarify` first. - **After a visualization** is created, review the result (data sample + chart image) and decide: - `visualize` again if the question is not yet fully answered. - `present` if the findings are sufficient or interesting enough. - `clarify` if the result reveals that the original question needs scoping. - **Never** output two actions in one turn. - **Never** repeat a visualization that already exists in the trajectory. - Always output valid JSON with one of the three action types. {agent_exploration_rules} ''' # --------------------------------------------------------------------------- # Agent # --------------------------------------------------------------------------- class DataAgent: """Autonomous data exploration agent with observe-think-act loop.""" def __init__( self, client: Client, workspace, agent_exploration_rules: str = "", agent_coding_rules: str = "", max_iterations: int = 5, max_repair_attempts: int = 1, ): self.client = client self.workspace = workspace self.agent_exploration_rules = agent_exploration_rules self.agent_coding_rules = agent_coding_rules self.max_iterations = max_iterations self.max_repair_attempts = max_repair_attempts # Sub-agent for data transformation + chart recommendation self.rec_agent = DataRecAgent( client=client, workspace=workspace, agent_coding_rules=agent_coding_rules, ) # ------------------------------------------------------------------ # Public API # ------------------------------------------------------------------ def run( self, input_tables: list[dict[str, Any]], user_question: str, conversation_history: list[dict[str, str]] | None = None, trajectory: list[dict] | None = None, completed_step_count: int = 0, ) -> Generator[dict[str, Any], None, None]: """Run the autonomous exploration loop. Yields event dicts with ``type`` in: ``"action"`` – the agent's chosen action (for logging/UI) ``"result"`` – a visualization result (data + chart) ``"clarify"`` – a clarification question (loop pauses) ``"completion"`` – final summary (loop terminates) ``"error"`` – error information To resume after a ``clarify`` event, call ``run()`` again with the ``trajectory`` returned in the clarify payload (the caller should have appended the user's clarification as a user message). """ if trajectory is None: trajectory = self._build_initial_messages( input_tables, user_question, conversation_history ) completed_steps: list[dict[str, Any]] = [] # Track DataRecAgent dialog for follow-up calls rec_dialog: list[dict] = [] rec_last_data: dict | list = [] iteration = completed_step_count while iteration < self.max_iterations: iteration += 1 # --- THINK: ask the LLM to pick an action ----------------- t_llm_start = time.time() action = self._get_next_action(trajectory) logger.info(f"[DataAgent] timing: iteration {iteration} think llm={time.time() - t_llm_start:.3f}s") if action is None: yield self._error_event(iteration, "Failed to parse agent action from LLM response") break action_type = action.get("action") logger.info(f"[DataAgent] Iteration {iteration}: action={action_type}") # Append the agent's response to the trajectory trajectory.append({ "role": "assistant", "content": json.dumps(action, ensure_ascii=False), }) # --- ACT -------------------------------------------------- if action_type == "clarify": yield { "type": "clarify", "iteration": iteration, "thought": action.get("thought", ""), "message": action.get("message", ""), "trajectory": self._strip_images(trajectory), "completed_step_count": len(completed_steps), } # Loop pauses – caller resumes by calling run() again # with the trajectory + user's clarification appended. return elif action_type == "present": yield { "type": "completion", "iteration": iteration, "status": "success", "content": { "thought": action.get("thought", ""), "summary": action.get("summary", ""), "total_steps": len(completed_steps), }, } return elif action_type == "visualize": question = action.get("question", user_question) # Yield action event so the UI can show what the agent is doing yield { "type": "action", "iteration": iteration, "action": "visualize", "thought": action.get("thought", ""), "question": question, } # Execute the visualize action viz_result = self._execute_visualize( input_tables=input_tables, question=question, prev_dialog=rec_dialog, prev_data=rec_last_data, ) if viz_result["status"] != "ok": # Append error observation and let agent decide error_msg = viz_result.get("error_message", "Unknown error") observation = f"[OBSERVATION – Step {len(completed_steps) + 1} FAILED]\n\nError: {error_msg}" trajectory.append({"role": "user", "content": observation}) yield self._error_event(iteration, error_msg, question=question) continue # Successful visualization transform_result = viz_result["transform_result"] sign_result(transform_result) chart_image = viz_result.get("chart_image") transformed_data = transform_result["content"] code = transform_result.get("code", "") # Update rec agent state for follow-ups rec_dialog = transform_result.get("dialog", []) rec_last_data = transformed_data # Build step record step = { "question": question, "code": code, "data": { "rows": transformed_data["rows"], "name": ( transformed_data["virtual"]["table_name"] if "virtual" in transformed_data else None ), }, "visualization": chart_image, } completed_steps.append(step) # Yield the result to the frontend yield { "type": "result", "iteration": iteration, "status": "success", "content": { "question": question, "result": transform_result, }, } # Append observation to trajectory for the next think step observation_msg = self._format_observation( step_index=len(completed_steps), question=question, code=code, data=transformed_data, chart_image=chart_image, ) trajectory.append(observation_msg) else: # Unrecognised action – let the LLM know trajectory.append({ "role": "user", "content": ( f"[ERROR] Unknown action '{action_type}'. " "Please choose one of: visualize, clarify, present." ), }) yield self._error_event( iteration, f"Unknown action: {action_type}", ) # Exhausted max iterations – force a completion yield yield { "type": "completion", "iteration": iteration, "status": "max_iterations", "content": { "summary": "Reached the maximum number of exploration steps.", "total_steps": len(completed_steps), }, } # ------------------------------------------------------------------ # Message construction # ------------------------------------------------------------------ def _build_system_prompt(self) -> str: rules_block = "" if self.agent_exploration_rules and self.agent_exploration_rules.strip(): rules_block = ( "\n## Additional exploration rules\n\n" + self.agent_exploration_rules.strip() + "\n\nPlease follow the above rules when exploring data." ) return SYSTEM_PROMPT.format( max_iterations=self.max_iterations, agent_exploration_rules=rules_block, ) def _build_initial_messages( self, input_tables: list[dict[str, Any]], user_question: str, conversation_history: list[dict[str, str]] | None = None, ) -> list[dict]: """Build the initial trajectory with system prompt + data context + user question.""" data_summary = generate_data_summary(input_tables, workspace=self.workspace) # Optionally prepend conversation history history_block = "" if conversation_history: lines = [] for msg in conversation_history: role = "User" if msg.get("role") == "user" else "Assistant" lines.append(f"{role}: {msg.get('content', '')}") history_block = ( "[PREVIOUS CONVERSATION FOR REFERENCE]\n" + "\n".join(lines) + "\n\n" ) user_content = ( f"{history_block}" f"[DATASETS]\n\n{data_summary}\n\n" f"[USER QUESTION]\n\n{user_question}" ) return [ {"role": "system", "content": self._build_system_prompt()}, {"role": "user", "content": user_content}, ] # ------------------------------------------------------------------ # LLM interaction # ------------------------------------------------------------------ def _get_next_action(self, trajectory: list[dict]) -> dict | None: """Call the LLM with the current trajectory and parse the action JSON.""" response = self.client.get_completion(messages=trajectory) if isinstance(response, Exception): logger.error(f"[DataAgent] LLM error: {response}") return None if not response.choices: return None content = response.choices[0].message.content or "" logger.debug(f"[DataAgent] Raw LLM response:\n{content}") json_blocks = extract_json_objects(content) if not json_blocks: # Try to salvage – the model might have wrapped in markdown return None return json_blocks[0] # ------------------------------------------------------------------ # Visualize action execution # ------------------------------------------------------------------ def _execute_visualize( self, input_tables: list[dict[str, Any]], question: str, prev_dialog: list[dict], prev_data: dict | list, ) -> dict[str, Any]: """Execute a visualize action via DataRecAgent, with repair retries. Returns a dict with: status: "ok" | "error" transform_result: the DataRecAgent result (when ok) chart_image: base64 chart image or None error_message: str (when error) """ # Decide whether to follow-up or start fresh if prev_dialog: if isinstance(prev_data, dict) and "rows" in prev_data: sample = prev_data["rows"] else: sample = [] results = self.rec_agent.followup( input_tables=input_tables, new_instruction=question, latest_data_sample=sample, dialog=prev_dialog, ) else: results = self.rec_agent.run( input_tables=input_tables, description=question, ) # Repair loop attempt = 0 while results and results[0]["status"] != "ok" and attempt < self.max_repair_attempts: attempt += 1 error_msg = results[0].get("content", "Unknown error") dialog = results[0].get("dialog", []) logger.warning( f"[DataAgent] Repair attempt {attempt}/{self.max_repair_attempts}: {error_msg}" ) repair_instruction = ( f"We ran into the following problem executing the code, please fix it:\n\n" f"{error_msg}\n\n" "Please think step by step, reflect why the error happened and fix the code." ) results = self.rec_agent.followup( input_tables=input_tables, new_instruction=repair_instruction, latest_data_sample=[], dialog=dialog, ) if not results or results[0]["status"] != "ok": return { "status": "error", "error_message": results[0]["content"] if results else "No results from DataRecAgent", } transform_result = results[0] transformed_data = transform_result["content"] # Create chart chart_image = self._create_chart(transformed_data, transform_result.get("refined_goal", {})) return { "status": "ok", "transform_result": transform_result, "chart_image": chart_image, } def _create_chart( self, transformed_data: dict[str, Any], refined_goal: dict[str, Any], ) -> str | None: """Create a chart from transformed data and return a base64 PNG string.""" chart_obj = refined_goal.get("chart", {}) chart_type = chart_obj.get("chart_type", "Bar Chart") chart_encodings = chart_obj.get("encodings", {}) chart_config = chart_obj.get("config", {}) try: df = pd.DataFrame(transformed_data["rows"]) if df.empty: return None encodings = {} for channel, field in chart_encodings.items(): if field and field in df.columns: field_type = resolve_field_type(df[field], field) field_type = coerce_field_type(chart_type, channel, field_type) encodings[channel] = {"field": field, "type": field_type} spec = assemble_vegailte_chart( df, chart_type, encodings, config=chart_config, semantic_types=field_metadata_to_semantic_types(refined_goal.get("field_metadata")), ) return spec_to_base64(spec) if spec else None except Exception as e: logger.error(f"[DataAgent] Chart creation error: {e}") return None # ------------------------------------------------------------------ # Observation formatting # ------------------------------------------------------------------ def _format_observation( self, step_index: int, question: str, code: str, data: dict[str, Any], chart_image: str | None, ) -> dict: """Format a completed step as a user message for the trajectory.""" # Build data summary data_summary = generate_data_summary( [{"name": data.get("virtual", {}).get("table_name", f"step_{step_index}"), "rows": data["rows"]}], workspace=self.workspace, ) text = ( f"[OBSERVATION – Step {step_index}]\n\n" f"**Question**: {question}\n\n" f"**Code**:\n```python\n{code}\n```\n\n" f"**Transformed Data Sample**:\n{data_summary}" ) if chart_image: # Multimodal content with chart image content: list[dict[str, Any]] = [ {"type": "text", "text": text + "\n\n**Visualization**:"}, ] if chart_image.startswith("data:") or chart_image.startswith("http"): content.append({ "type": "image_url", "image_url": {"url": chart_image}, }) return {"role": "user", "content": content} return {"role": "user", "content": text} # ------------------------------------------------------------------ # Helpers # ------------------------------------------------------------------ @staticmethod def _error_event(iteration: int, error_message: str, **extra) -> dict[str, Any]: return { "type": "error", "iteration": iteration, "status": "error", "error_message": error_message, **extra, } @staticmethod def _strip_images(trajectory: list[dict]) -> list[dict]: """Return a copy of the trajectory with image_url blocks removed. This keeps the payload small when sending the trajectory back to the client for stateless resumption. """ stripped: list[dict] = [] for msg in trajectory: content = msg.get("content") if isinstance(content, list): # Multimodal message – keep only text parts text_parts = [p for p in content if p.get("type") == "text"] if text_parts: stripped.append({**msg, "content": text_parts}) else: stripped.append({**msg, "content": "[image removed]"}) else: stripped.append(msg) return stripped ================================================ FILE: py-src/data_formulator/agents/semantic_types.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ ============================================================================= SEMANTIC TYPE SYSTEM (Python mirror of the TypeScript registry) ============================================================================= The **source of truth** for semantic types lives in the TypeScript library: src/lib/agents-chart/core/type-registry.ts This file mirrors the registered types and provides: 1. String constants for every type in the TS TYPE_REGISTRY 2. Classification sets (measures, temporal, categorical, etc.) 3. Prompt generation for the DataLoadAgent LLM call 4. VL-type mapping + name-heuristic inference for create_vl_plots.py 5. Legacy compatibility list When a type is added/removed in the TS registry, update this file to match. ============================================================================= """ from typing import Dict, List, Optional, Set # --------------------------------------------------------------------------- # All Semantic Types (must match TYPE_REGISTRY keys in type-registry.ts) # --------------------------------------------------------------------------- # TEMPORAL — DateTime DATETIME = "DateTime" # Full date and time: "2024-01-15T14:30:00" DATE = "Date" # Date only: "2024-01-15" TIME = "Time" # Time only: "14:30:00" TIMESTAMP = "Timestamp" # Unix timestamp (seconds or milliseconds since epoch) # TEMPORAL — DateGranule YEAR = "Year" # "2024" (as a time unit, not a measure) QUARTER = "Quarter" # "Q1", "Q2", "2024-Q1" MONTH = "Month" # "January", "Jan", 1-12 WEEK = "Week" # "Week 1", 1-52 DAY = "Day" # "Monday", "Mon", 1-31 HOUR = "Hour" # 0-23 YEAR_MONTH = "YearMonth" # "2024-01", "Jan 2024" YEAR_QUARTER = "YearQuarter"# "2024-Q1" YEAR_WEEK = "YearWeek" # "2024-W01" DECADE = "Decade" # "1990s", "2000s" # TEMPORAL — Duration DURATION = "Duration" # Time span: "2 hours", "3 days", milliseconds # MEASURE — Amount AMOUNT = "Amount" # Monetary or general amounts (additive) PRICE = "Price" # Unit price (intensive — avg, not sum) REVENUE = "Revenue" # Total revenue/sales (additive) COST = "Cost" # Expenses/costs (additive) # MEASURE — Physical QUANTITY = "Quantity" # Generic continuous measure (additive) TEMPERATURE = "Temperature" # Degrees — conditional diverging, arbitrary zero # MEASURE — Proportion PERCENTAGE = "Percentage" # 0-100% or 0-1 ratio (intensive, bounded) # MEASURE — SignedMeasure PROFIT = "Profit" # Signed additive, conditional diverging PERCENTAGE_CHANGE = "PercentageChange" # Signed intensive, conditional diverging SENTIMENT = "Sentiment" # Signed intensive, inherent diverging CORRELATION = "Correlation" # Signed intensive, inherent diverging, bounded [-1,1] # MEASURE — GenericMeasure COUNT = "Count" # Discrete count of items (additive) NUMBER = "Number" # Generic number (measure fallback) # DISCRETE RANK = "Rank" # Position in ordered list: 1st, 2nd, 3rd SCORE = "Score" # Rating score: 1-5, 1-10, 0-100 RATING = "Rating" # Star rating, letter grade INDEX = "Index" # Row number, sequence number # IDENTIFIER ID = "ID" # Unique identifier (not for aggregation!) # GEOGRAPHIC — GeoCoordinate LATITUDE = "Latitude" # -90 to 90 LONGITUDE = "Longitude" # -180 to 180 # GEOGRAPHIC — GeoPlace COUNTRY = "Country" # Country name or code STATE = "State" # State/Province CITY = "City" # City name REGION = "Region" # Geographic region ADDRESS = "Address" # Street address ZIP_CODE = "ZipCode" # Postal code # CATEGORICAL — Entity PERSON_NAME = "PersonName" # Full name, first/last name COMPANY = "Company" # Company/Organization name PRODUCT = "Product" # Product name CATEGORY = "Category" # Product/item category NAME = "Name" # Generic named entity (fallback) # CATEGORICAL — Coded STATUS = "Status" # State: "Active", "Pending", "Closed" TYPE = "Type" # Type classification BOOLEAN = "Boolean" # True/False, Yes/No DIRECTION = "Direction" # N, S, E, W — cyclic ordinal # CATEGORICAL — Binned RANGE = "Range" # Numeric range: "10000-20000", "<50", "50+" AGE_GROUP = "AgeGroup" # Age range: "18-24", "25-34" # FALLBACKS STRING = "String" # Generic string (categorical fallback) UNKNOWN = "Unknown" # Cannot determine type # --------------------------------------------------------------------------- # All Semantic Types List (matches TYPE_REGISTRY key order) # --------------------------------------------------------------------------- ALL_SEMANTIC_TYPES: List[str] = [ # Temporal — DateTime DATETIME, DATE, TIME, TIMESTAMP, # Temporal — DateGranule YEAR, QUARTER, MONTH, WEEK, DAY, HOUR, YEAR_MONTH, YEAR_QUARTER, YEAR_WEEK, DECADE, # Temporal — Duration DURATION, # Measure — Amount AMOUNT, PRICE, REVENUE, COST, # Measure — Physical QUANTITY, TEMPERATURE, # Measure — Proportion PERCENTAGE, # Measure — SignedMeasure PROFIT, PERCENTAGE_CHANGE, SENTIMENT, CORRELATION, # Measure — GenericMeasure COUNT, NUMBER, # Discrete RANK, SCORE, RATING, INDEX, # Identifier ID, # Geographic — GeoCoordinate LATITUDE, LONGITUDE, # Geographic — GeoPlace COUNTRY, STATE, CITY, REGION, ADDRESS, ZIP_CODE, # Categorical — Entity PERSON_NAME, COMPANY, PRODUCT, CATEGORY, NAME, # Categorical — Coded STATUS, TYPE, BOOLEAN, DIRECTION, # Categorical — Binned RANGE, AGE_GROUP, # Fallbacks STRING, UNKNOWN, ] # --------------------------------------------------------------------------- # Type Sets for Classification # --------------------------------------------------------------------------- TIMESERIES_X_TYPES: Set[str] = { DATETIME, DATE, TIME, TIMESTAMP, YEAR_MONTH, YEAR_QUARTER, YEAR_WEEK, YEAR, QUARTER, MONTH, WEEK, DAY, HOUR, DECADE, } MEASURE_TYPES: Set[str] = { AMOUNT, PRICE, REVENUE, COST, QUANTITY, TEMPERATURE, PERCENTAGE, PROFIT, PERCENTAGE_CHANGE, SENTIMENT, CORRELATION, COUNT, NUMBER, DURATION, } NON_MEASURE_NUMERIC_TYPES: Set[str] = { RANK, INDEX, SCORE, RATING, YEAR, MONTH, DAY, HOUR, LATITUDE, LONGITUDE, } CATEGORICAL_TYPES: Set[str] = { NAME, PERSON_NAME, COMPANY, PRODUCT, CATEGORY, STATUS, TYPE, BOOLEAN, DIRECTION, COUNTRY, STATE, CITY, REGION, RANGE, AGE_GROUP, STRING, } ORDINAL_TYPES: Set[str] = { YEAR, QUARTER, MONTH, WEEK, DAY, HOUR, DECADE, RANK, SCORE, RATING, RANGE, AGE_GROUP, DIRECTION, } GEO_TYPES: Set[str] = { LATITUDE, LONGITUDE, COUNTRY, STATE, CITY, REGION, ADDRESS, ZIP_CODE, } SIGNED_MEASURE_TYPES: Set[str] = { PROFIT, PERCENTAGE_CHANGE, SENTIMENT, CORRELATION, } # --------------------------------------------------------------------------- # Grouped by Category (for prompt generation) # --------------------------------------------------------------------------- SEMANTIC_TYPE_CATEGORIES: Dict[str, List[str]] = { "Temporal (point-in-time)": [DATETIME, DATE, TIME, TIMESTAMP], "Temporal (granules)": [YEAR, QUARTER, MONTH, WEEK, DAY, HOUR], "Temporal (combined)": [YEAR_MONTH, YEAR_QUARTER, YEAR_WEEK, DECADE], "Temporal (duration)": [DURATION], "Numeric measures (monetary)": [AMOUNT, PRICE, REVENUE, COST], "Numeric measures (physical)": [QUANTITY, TEMPERATURE], "Numeric measures (proportion)": [PERCENTAGE], "Numeric measures (signed/diverging)": [PROFIT, PERCENTAGE_CHANGE, SENTIMENT, CORRELATION], "Numeric measures (generic)": [COUNT, NUMBER], "Numeric discrete": [RANK, INDEX, SCORE, RATING], "Identifier": [ID], "Geographic coordinates": [LATITUDE, LONGITUDE], "Geographic locations": [COUNTRY, STATE, CITY, REGION, ADDRESS, ZIP_CODE], "Entity names": [PERSON_NAME, COMPANY, PRODUCT, CATEGORY, NAME], "Categorical codes": [STATUS, TYPE, BOOLEAN, DIRECTION], "Binned ranges": [RANGE, AGE_GROUP], "Fallback": [STRING, UNKNOWN], } # --------------------------------------------------------------------------- # Helper Functions # --------------------------------------------------------------------------- def is_measure_type(semantic_type: str) -> bool: """Check if a semantic type is a true measure (suitable for quantitative encoding).""" return semantic_type in MEASURE_TYPES def is_timeseries_type(semantic_type: str) -> bool: """Check if a semantic type is suitable for time-series X axis.""" return semantic_type in TIMESERIES_X_TYPES def is_categorical_type(semantic_type: str) -> bool: """Check if a semantic type is categorical (suitable for color/grouping).""" return semantic_type in CATEGORICAL_TYPES def is_ordinal_type(semantic_type: str) -> bool: """Check if a semantic type is ordinal (has inherent order).""" return semantic_type in ORDINAL_TYPES def is_geo_type(semantic_type: str) -> bool: """Check if a semantic type is geographic.""" return semantic_type in GEO_TYPES def is_non_measure_numeric(semantic_type: str) -> bool: """Check if a semantic type is numeric but should not be aggregated.""" return semantic_type in NON_MEASURE_NUMERIC_TYPES def is_signed_measure(semantic_type: str) -> bool: """Check if a semantic type is a signed measure (can go negative).""" return semantic_type in SIGNED_MEASURE_TYPES # --------------------------------------------------------------------------- # Prompt Generation # --------------------------------------------------------------------------- def generate_semantic_types_prompt() -> str: """Generate the semantic types section for the LLM prompt.""" lines = ["Semantic types to consider (grouped by category):"] lines.append("") for category, types in SEMANTIC_TYPE_CATEGORIES.items(): lines.append(f" {category}:") lines.append(f" {', '.join(types)}") lines.append("") lines.append("Guidelines for choosing semantic types:") lines.append("") lines.append("1. TEMPORAL types:") lines.append(" - Use DateTime/Date/Time for full timestamps or dates") lines.append(" - Use Timestamp for Unix timestamps (seconds or milliseconds since epoch)") lines.append(" - Use YearMonth, YearQuarter for combined temporal like '2024-01' or '2024-Q1'") lines.append(" - Use Year, Month, Day for discrete time units (even if stored as numbers)") lines.append(" - Use Duration for time spans (e.g., '2 hours', milliseconds)") lines.append("") lines.append("2. MONETARY MEASURE types:") lines.append(" - Use Amount for generic monetary values") lines.append(" - Use Price for per-unit prices (averaged, not summed)") lines.append(" - Use Revenue/Cost for totals (summed)") lines.append(" - Use Profit for values that can be negative (profit/loss)") lines.append("") lines.append("3. PHYSICAL / GENERIC MEASURE types:") lines.append(" - Use Quantity for generic continuous measures (weight, distance, area, volume, speed, etc.)") lines.append(" - Provide the 'unit' field for physical quantities (e.g., 'kg', 'km', '°C', 'mph')") lines.append(" - Use Temperature for temperature values (has special diverging color behavior)") lines.append(" - Use Count for discrete counts of items") lines.append(" - Use Number only when no more specific measure type applies") lines.append("") lines.append("4. PROPORTION & SIGNED MEASURE types:") lines.append(" - Use Percentage for 0-100% or 0-1 ratios") lines.append(" - Use PercentageChange for growth rates or % change (can be negative)") lines.append(" - Use Sentiment for sentiment scores (inherently diverging around 0)") lines.append(" - Use Correlation for correlation coefficients (bounded -1 to 1)") lines.append("") lines.append("5. NUMERIC DISCRETE types (should NOT be aggregated):") lines.append(" - Use Rank for positions (1st, 2nd, 3rd)") lines.append(" - Use ID for unique identifiers") lines.append(" - Use Score/Rating for evaluation scores (1-5, A-F)") lines.append(" - For Score/Rating, provide 'intrinsic_domain' as [min, max] inferred from the data (e.g., [1, 10] if values range 1-10)") lines.append(" - IMPORTANT: A column named 'year' with values like 2020, 2021 is Year, not Number!") lines.append("") lines.append("6. GEOGRAPHIC types:") lines.append(" - Use Latitude/Longitude for coordinates") lines.append(" - Use Country, State, City for named locations") lines.append("") lines.append("7. CATEGORICAL types:") lines.append(" - Use specific entity types (PersonName, Company, Product) when applicable") lines.append(" - Use Category for classification fields") lines.append(" - Use Status for state/status fields ('Active', 'Pending')") lines.append(" - Use Boolean for true/false, yes/no fields") lines.append(" - Use Direction for compass directions (N, S, E, W)") lines.append("") lines.append("8. RANGE types:") lines.append(" - Use Range for binned numeric values ('10000-20000', '<50', '50+')") lines.append(" - Use AgeGroup specifically for age ranges ('18-24', '25-34')") lines.append("") lines.append("9. FALLBACK types:") lines.append(" - Use String for generic text when no specific type applies") lines.append(" - Do NOT use generic names like 'Value', 'Data', etc. — pick the closest match above") return "\n".join(lines) # For backward compatibility with existing code LEGACY_SEMANTIC_TYPES = [ "Location", "Decade", "Year", "Month", "YearMonth", "Day", "Date", "Time", "DateTime", "TimeRange", "Range", "Duration", "Name", "Percentage", "String", "Number" ] # --------------------------------------------------------------------------- # Semantic Type → Vega-Lite Encoding Type # --------------------------------------------------------------------------- VL_TYPE_MAP: Dict[str, str] = { # Temporal → temporal "DateTime": "temporal", "Date": "temporal", "Time": "temporal", "Timestamp": "temporal", "YearMonth": "temporal", "YearQuarter": "temporal", "YearWeek": "temporal", "Year": "temporal", # Temporal granules → ordinal "Quarter": "ordinal", "Month": "ordinal", "Week": "ordinal", "Day": "ordinal", "Hour": "ordinal", "Decade": "ordinal", # Duration → quantitative "Duration": "quantitative", # Measures → quantitative "Amount": "quantitative", "Price": "quantitative", "Revenue": "quantitative", "Cost": "quantitative", "Quantity": "quantitative", "Temperature": "quantitative", "Percentage": "quantitative", "Profit": "quantitative", "PercentageChange": "quantitative", "Sentiment": "quantitative", "Correlation": "quantitative", "Count": "quantitative", "Number": "quantitative", # Discrete numerics "Rank": "ordinal", "Index": "ordinal", "Score": "quantitative", "Rating": "quantitative", "ID": "nominal", # Geographic coordinates → quantitative "Latitude": "quantitative", "Longitude": "quantitative", # Geographic locations → nominal "Country": "nominal", "State": "nominal", "City": "nominal", "Region": "nominal", "Address": "nominal", "ZipCode": "nominal", # Entity names → nominal "Name": "nominal", "PersonName": "nominal", "Company": "nominal", "Product": "nominal", "Category": "nominal", # Coded → nominal (Direction can be ordinal but defaults nominal for VL) "Status": "nominal", "Type": "nominal", "Boolean": "nominal", "Direction": "nominal", # Ranges → ordinal "Range": "ordinal", "AgeGroup": "ordinal", # Fallbacks "String": "nominal", "Unknown": "nominal", } def get_vl_type(semantic_type: str) -> Optional[str]: """ Get the Vega-Lite encoding type for a semantic type. Returns 'quantitative', 'ordinal', 'nominal', or 'temporal', or None if unknown. """ return VL_TYPE_MAP.get(semantic_type) # --------------------------------------------------------------------------- # Name-based Heuristic Inference # --------------------------------------------------------------------------- # # For derived columns (from agent code) that lack frontend semantic type # metadata, infer a likely VL type from the column name. # # Pattern matching is intentionally conservative — only triggers when # the column name strongly suggests a specific meaning. # --------------------------------------------------------------------------- import re as _re # Patterns that strongly indicate quantitative (measures) _QUANT_PATTERNS: list[_re.Pattern] = [ _re.compile(r'(?:^|_)(avg|mean|average|sum|total|count|num|min|max|median|std|stdev|var|variance)(?:_|$)', _re.I), _re.compile(r'(?:^|_)(revenue|sales|profit|income|cost|expense|price|amount|quantity|volume|weight|distance|speed|temperature|rate|ratio|pct|percent|percentage|growth|change|diff|delta)(?:_|$)', _re.I), _re.compile(r'(?:^|_)(lat|lon|latitude|longitude)(?:_|$)', _re.I), ] # Patterns that indicate temporal _TEMPORAL_PATTERNS: list[_re.Pattern] = [ _re.compile(r'(?:^|_)(date|datetime|timestamp|time|created_at|updated_at|started_at|ended_at)(?:_|$)', _re.I), _re.compile(r'^year$', _re.I), ] # Patterns that indicate ordinal (time granules) _ORDINAL_PATTERNS: list[_re.Pattern] = [ _re.compile(r'^(month|quarter|week|day|hour|decade|year_month|year_quarter)$', _re.I), _re.compile(r'(?:^|_)(rank|ranking|level|tier|grade|priority)(?:_|$)', _re.I), ] # Patterns that indicate nominal (categorical) _NOMINAL_PATTERNS: list[_re.Pattern] = [ _re.compile(r'(?:^|_)(name|label|category|type|status|group|class|kind|tag|code|id|key)(?:_|$)', _re.I), _re.compile(r'(?:^|_)(country|state|city|region|location|department|brand|company|product)(?:_|$)', _re.I), ] def infer_vl_type_from_name(column_name: str) -> Optional[str]: """ Infer a likely Vega-Lite type from a column name using pattern matching. Returns 'quantitative', 'ordinal', 'nominal', 'temporal', or None if no strong signal is found. """ # Check patterns in priority order for pattern in _TEMPORAL_PATTERNS: if pattern.search(column_name): return 'temporal' for pattern in _ORDINAL_PATTERNS: if pattern.search(column_name): return 'ordinal' for pattern in _QUANT_PATTERNS: if pattern.search(column_name): return 'quantitative' for pattern in _NOMINAL_PATTERNS: if pattern.search(column_name): return 'nominal' return None ================================================ FILE: py-src/data_formulator/agents/web_utils.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. import requests from bs4 import BeautifulSoup import logging from urllib.parse import urlparse import tempfile import os import socket import ipaddress logger = logging.getLogger(__name__) def _is_private_ip(ip_str: str) -> bool: """ Check if an IP address is private, internal, or otherwise restricted. Args: ip_str: IP address as a string Returns: bool: True if IP is private/restricted, False if public """ try: ip_obj = ipaddress.ip_address(ip_str) # Check if IP is private, loopback, link-local, multicast, reserved, or unspecified if ( ip_obj.is_private or ip_obj.is_loopback or ip_obj.is_link_local or ip_obj.is_multicast or ip_obj.is_reserved or ip_obj.is_unspecified ): return True # Explicitly block cloud metadata endpoints # AWS/Azure/GCP metadata endpoint if ip_str == "169.254.169.254": return True # AWS IPv6 metadata endpoint if ip_str.startswith("fd00:ec2::"): return True return False except ValueError: # Not a valid IP address return False def _validate_url_for_ssrf(url: str) -> str: """ Validate a URL to prevent SSRF attacks. Performs the following checks: 1. Protocol validation (HTTP/HTTPS only) 2. Private IP blocking Args: url: The URL to validate Returns: str: The validated URL Raises: ValueError: If the URL fails any security checks """ if not url: raise ValueError("URL cannot be empty") # Parse and validate URL format parsed_url = urlparse(url) if not parsed_url.scheme or not parsed_url.netloc: raise ValueError(f"Invalid URL format: {url}") # Protection 1: Only allow HTTP/HTTPS schemes if parsed_url.scheme.lower() not in ("http", "https"): raise ValueError( f"Blocked: Unsupported URL scheme '{parsed_url.scheme}'. " f"Only HTTP and HTTPS are allowed to prevent SSRF attacks." ) hostname = parsed_url.hostname if not hostname: raise ValueError(f"Could not extract hostname from URL: {url}") # Protection 2: Block requests to private/internal IP addresses try: # Resolve all addresses for the hostname (handles both IPv4 and IPv6) addr_info = socket.getaddrinfo(hostname, None) for res in addr_info: addr = res[4][0] # Check if this resolved IP is private/internal if _is_private_ip(addr): raise ValueError( f"Blocked: URL '{url}' resolves to private/internal IP address {addr}. " f"Access to private networks is not allowed to prevent SSRF attacks." ) except socket.gaierror as e: raise ValueError(f"Could not resolve hostname '{hostname}': {str(e)}") from e return url def download_html_content(url: str, timeout: int = 30, headers: dict | None = None) -> str: """ Download HTML content from a given URL with SSRF protection. This function implements comprehensive SSRF protection: 1. Protocol validation (HTTP/HTTPS only) 2. Private IP blocking (before request) 3. Redirect validation (validates all redirect destinations) 4. Timeout limits (prevents slowloris attacks) 5. Logging of all accessed URLs (for security auditing) Args: url (str): The URL to download HTML from timeout (int): Request timeout in seconds (default: 30, max: 60) headers (dict, optional): Custom headers for the request Returns: str: The HTML content as a string Raises: requests.RequestException: If the request fails ValueError: If the URL is invalid or blocked by SSRF protection """ # Protection 5: Log all URL access attempts for security auditing logger.info(f"Attempting to download HTML from URL: {url}") # Protection 1 & 2: Validate URL for SSRF (protocol and IP checks) _validate_url_for_ssrf(url) # Protection 4: Enforce reasonable timeout limits (prevent slowloris) if timeout <= 0: timeout = 30 elif timeout > 60: logger.warning(f"Timeout of {timeout}s exceeds maximum, capping at 60s") timeout = 60 # Set default headers if none provided if headers is None: headers = { 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36', 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8', 'Accept-Language': 'en-US,en;q=0.5', 'Accept-Encoding': 'gzip, deflate', 'Connection': 'keep-alive', 'Upgrade-Insecure-Requests': '1', } try: # Protection 3: Use a session to handle and validate redirects with requests.Session() as session: # Create a custom adapter to hook into redirect handling class SSRFSafeHTTPAdapter(requests.adapters.HTTPAdapter): def send(self, request, **kwargs): # Validate each request (including redirects) try: _validate_url_for_ssrf(request.url) except ValueError as e: # Log the blocked redirect attempt logger.error(f"Blocked redirect to unsafe URL: {request.url} - {str(e)}") raise return super().send(request, **kwargs) # Mount the adapter for both HTTP and HTTPS adapter = SSRFSafeHTTPAdapter() session.mount('http://', adapter) session.mount('https://', adapter) # Make the request (redirects will be validated automatically) response = session.get( url, timeout=timeout, headers=headers, allow_redirects=True # Safe because we validate each redirect ) response.raise_for_status() # Log successful access and any redirects that occurred if response.history: redirect_chain = " -> ".join([r.url for r in response.history] + [response.url]) logger.info(f"Successfully downloaded HTML with redirects: {redirect_chain}") else: logger.info(f"Successfully downloaded HTML from: {url}") # Ensure we're getting HTML content content_type = response.headers.get('content-type', '').lower() if 'text/html' not in content_type and 'application/xhtml' not in content_type: logger.warning(f"Content-Type is {content_type}, but proceeding anyway") return response.text except ValueError as e: # SSRF protection blocked the request logger.error(f"SSRF protection blocked request to {url}: {str(e)}") raise except requests.RequestException as e: # Network or HTTP error logger.error(f"Failed to download HTML from {url}: {str(e)}") raise def html_to_text(html_content: str, remove_scripts: bool = True, remove_styles: bool = True) -> str: """ Convert HTML content to readable text by extracting and cleaning the text content. Args: html_content (str): HTML content as a string remove_scripts (bool): Whether to remove script tags (default: True) remove_styles (bool): Whether to remove style tags (default: True) Returns: str: Clean, readable text content """ if not html_content or not html_content.strip(): return "" try: # Parse HTML with BeautifulSoup soup = BeautifulSoup(html_content, 'html.parser') # Remove script and style elements if requested if remove_scripts: for script in soup(["script", "noscript"]): script.decompose() if remove_styles: for style in soup(["style"]): style.decompose() # Get text content text = soup.get_text() # Clean up the text lines = (line.strip() for line in text.splitlines()) chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) text = ' '.join(chunk for chunk in chunks if chunk) return text except Exception as e: logger.error(f"Failed to convert HTML to text: {str(e)}") # Fallback: return the raw content if parsing fails return html_content def get_html_title(html_content: str) -> str | None: """ Extract the title from HTML content. Args: html_content (str): HTML content as a string Returns: str or None: The title if found, None otherwise """ if not html_content: return None try: soup = BeautifulSoup(html_content, 'html.parser') title_tag = soup.find('title') return title_tag.get_text().strip() if title_tag else None except Exception as e: logger.error(f"Failed to extract title from HTML: {str(e)}") return None def get_html_meta_description(html_content: str) -> str | None: """ Extract the meta description from HTML content. Args: html_content (str): HTML content as a string Returns: str or None: The meta description if found, None otherwise """ if not html_content: return None try: soup = BeautifulSoup(html_content, 'html.parser') meta_desc = soup.find('meta', attrs={'name': 'description'}) if meta_desc: return meta_desc.get('content', '').strip() # Try Open Graph description meta_og_desc = soup.find('meta', attrs={'property': 'og:description'}) if meta_og_desc: return meta_og_desc.get('content', '').strip() return None except Exception as e: logger.error(f"Failed to extract meta description from HTML: {str(e)}") return None ================================================ FILE: py-src/data_formulator/app.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. import argparse import sys import os import mimetypes mimetypes.add_type('application/javascript', '.js') mimetypes.add_type('application/javascript', '.mjs') import flask from flask import Flask, request, send_from_directory from flask import stream_with_context, Response import webbrowser import threading import numpy as np import time import logging import json from pathlib import Path from dotenv import load_dotenv import secrets import base64 APP_ROOT = Path(Path(__file__).parent).absolute() # Create Flask app (lightweight, no heavy imports yet) app = Flask(__name__, static_url_path='', static_folder=os.path.join(APP_ROOT, "dist")) app.secret_key = secrets.token_hex(16) app.json.sort_keys = False app.config['MAX_CONTENT_LENGTH'] = 100 * 1024 * 1024 # 100 MB class CustomJSONEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, np.int64): return int(obj) if isinstance(obj, (bytes, bytearray)): return base64.b64encode(obj).decode('ascii') return super().default(obj) app.json_encoder = CustomJSONEncoder # Load env files early. load_dotenv(os.path.join(APP_ROOT, "..", "..", '.env')) load_dotenv(os.path.join(APP_ROOT, '.env')) # Default config from env (can be overridden by CLI args) app.config['CLI_ARGS'] = { 'sandbox': os.environ.get('SANDBOX', 'local'), 'disable_display_keys': os.environ.get('DISABLE_DISPLAY_KEYS', 'false').lower() == 'true', 'disable_database': os.environ.get('DISABLE_DATABASE', 'false').lower() == 'true', 'disable_file_upload': os.environ.get('DISABLE_FILE_UPLOAD', 'false').lower() == 'true', 'project_front_page': os.environ.get('PROJECT_FRONT_PAGE', 'false').lower() == 'true', 'max_display_rows': int(os.environ.get('MAX_DISPLAY_ROWS', '10000')), 'data_dir': os.environ.get('DATA_FORMULATOR_HOME', None), 'dev': os.environ.get('DEV_MODE', 'false').lower() == 'true', 'workspace_backend': os.environ.get('WORKSPACE_BACKEND', 'local'), 'azure_blob_connection_string': os.environ.get('AZURE_BLOB_CONNECTION_STRING', None), 'azure_blob_account_url': os.environ.get('AZURE_BLOB_ACCOUNT_URL', None), 'azure_blob_container': os.environ.get('AZURE_BLOB_CONTAINER', 'data-formulator'), } # Get logger for this module (logging config moved to run_app function) logger = logging.getLogger(__name__) def configure_logging(): """Configure logging for the Flask application.""" logging.basicConfig( level=logging.ERROR, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', handlers=[logging.StreamHandler(sys.stdout)] ) # Enable INFO for agent modules so timing logs are visible logging.getLogger('data_formulator.agents').setLevel(logging.INFO) # Suppress verbose logging from third-party libraries logging.getLogger('httpx').setLevel(logging.WARNING) logging.getLogger('litellm').setLevel(logging.WARNING) logging.getLogger('openai').setLevel(logging.WARNING) app.logger.handlers = [] for handler in logging.getLogger().handlers: app.logger.addHandler(handler) _blueprints_registered = False def _register_blueprints(): """ Import and register blueprints. This is where heavy imports happen. Called at module level (for gunicorn) and from run_app() (for CLI). Guarded to prevent double registration. """ global _blueprints_registered if _blueprints_registered: return _blueprints_registered = True # Import tables routes (imports database connectors) print(" Loading data connectors...", flush=True) from data_formulator.tables_routes import tables_bp # Import agent routes (imports AI/ML libraries: litellm, sklearn, etc.) print(" Loading AI agents...", flush=True) from data_formulator.agent_routes import agent_bp # Import session routes from data_formulator.session_routes import session_bp # Import demo stream routes from data_formulator.demo_stream_routes import demo_stream_bp, limiter as demo_stream_limiter, start_iss_collector demo_stream_limiter.init_app(app) # Register blueprints app.register_blueprint(tables_bp) app.register_blueprint(agent_bp) app.register_blueprint(session_bp) app.register_blueprint(demo_stream_bp) # Start background ISS position collector start_iss_collector() # Register blueprints at module level so WSGI servers (gunicorn) pick up all routes. # The guard inside _register_blueprints() prevents double registration when run via CLI. _register_blueprints() @app.route('/api/example-datasets') def get_sample_datasets(): from data_formulator.example_datasets_config import EXAMPLE_DATASETS return flask.jsonify(EXAMPLE_DATASETS) @app.route("/", defaults={"path": ""}) def index_alt(path): logger.info(app.static_folder) return send_from_directory(app.static_folder, "index.html") @app.errorhandler(404) def page_not_found(e): logger.info(app.static_folder) return send_from_directory(app.static_folder, "index.html") @app.route('/api/app-config', methods=['GET']) def get_app_config(): """Provide frontend configuration settings from CLI arguments""" args = app.config['CLI_ARGS'] config = { "SANDBOX": args['sandbox'], "DISABLE_DISPLAY_KEYS": args['disable_display_keys'], "DISABLE_DATABASE": args['disable_database'], "DISABLE_FILE_UPLOAD": args['disable_file_upload'], "PROJECT_FRONT_PAGE": args['project_front_page'], "MAX_DISPLAY_ROWS": args['max_display_rows'], "DEV_MODE": args.get('dev', False), "WORKSPACE_BACKEND": args.get('workspace_backend', 'local'), } if not args['disable_database']: workspace_backend = args.get('workspace_backend', 'local') if workspace_backend != 'azure_blob': from data_formulator.datalake.workspace import get_data_formulator_home config["DATA_FORMULATOR_HOME"] = str(get_data_formulator_home()) return flask.jsonify(config) def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description="Data Formulator") parser.add_argument("-p", "--port", type=int, default=5567, help="The port number you want to use") parser.add_argument("--sandbox", type=str, default=os.environ.get('SANDBOX', 'local'), choices=['local', 'docker'], help="Python code execution backend: 'local' (default, isolated subprocess with audit hooks), " "'docker' (maximum isolation, requires Docker)") parser.add_argument("--disable-display-keys", action='store_true', default=False, help="Whether disable displaying keys in the frontend UI, recommended to turn on if you host the app not just for yourself.") parser.add_argument("--disable-database", action='store_true', default=False, help="Disable server-side data persistence. Data loaders and table routes remain available but data is not saved to disk. " "The frontend forces local-only mode (storeOnServer=false) so all table data lives in the browser.") parser.add_argument("--disable-file-upload", action='store_true', default=False, help="Disable file upload functionality. This prevents the app from uploading files to the server.") parser.add_argument("--project-front-page", action='store_true', default=False, help="Project the front page as the main page instead of the app.") parser.add_argument("--max-display-rows", type=int, default=int(os.environ.get('MAX_DISPLAY_ROWS', '10000')), help="Maximum number of rows to send to the frontend for display (default: 10000)") parser.add_argument("--data-dir", type=str, default=None, help="Data Formulator home directory for workspaces and sessions (default: ~/.data_formulator)") parser.add_argument("--dev", action='store_true', default=False, help="Launch the app in development mode (prevents the app from opening the browser automatically)") parser.add_argument("--workspace-backend", type=str, default=os.environ.get('WORKSPACE_BACKEND', 'local'), choices=['local', 'azure_blob'], help="Workspace storage backend: 'local' (default, filesystem) or 'azure_blob' (Azure Blob Storage)") parser.add_argument("--azure-blob-connection-string", type=str, default=os.environ.get('AZURE_BLOB_CONNECTION_STRING'), help="Azure Blob Storage connection string (mutually exclusive with --azure-blob-account-url)") parser.add_argument("--azure-blob-account-url", type=str, default=os.environ.get('AZURE_BLOB_ACCOUNT_URL'), help="Azure Blob Storage account URL for Entra ID auth, e.g. https://<account>.blob.core.windows.net " "(uses DefaultAzureCredential; mutually exclusive with --azure-blob-connection-string)") parser.add_argument("--azure-blob-container", type=str, default=os.environ.get('AZURE_BLOB_CONTAINER', 'data-formulator'), help="Azure Blob Storage container name (default: data-formulator)") return parser.parse_args() def run_app(): print("Starting Data Formulator...", flush=True) configure_logging() args = parse_args() # Override config from CLI args app.config['CLI_ARGS'] = { 'sandbox': args.sandbox, 'disable_display_keys': args.disable_display_keys, 'disable_database': args.disable_database, 'disable_file_upload': args.disable_file_upload, 'project_front_page': args.project_front_page, 'max_display_rows': args.max_display_rows, 'data_dir': args.data_dir, 'dev': args.dev, 'workspace_backend': args.workspace_backend, 'azure_blob_connection_string': args.azure_blob_connection_string, 'azure_blob_account_url': args.azure_blob_account_url, 'azure_blob_container': args.azure_blob_container, } # Register blueprints (this is where heavy imports happen) _register_blueprints() url = "http://localhost:{0}".format(args.port) print(f"Ready! Open {url} in your browser.", flush=True) if not args.dev: threading.Timer(1.5, lambda: webbrowser.open(url, new=2)).start() debug_mode = args.dev app.run(host='0.0.0.0', port=args.port, debug=debug_mode, use_reloader=debug_mode) if __name__ == '__main__': run_app() ================================================ FILE: py-src/data_formulator/auth.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ Authentication and identity management for Data Formulator. This module provides a hybrid identity system that supports both anonymous browser-based users and authenticated users (via Azure App Service or JWT). Security Model: - Anonymous users: Browser UUID from X-Identity-Id header (prefixed with "browser:") - Authenticated users: Verified identity from Azure headers or JWT (prefixed with "user:") - Namespacing ensures authenticated user data cannot be accessed by spoofing headers """ import logging import re from flask import request, current_app logger = logging.getLogger(__name__) # Maximum raw length for identity values (before namespacing). _MAX_IDENTITY_LENGTH = 256 # Allowed characters: word chars, @, dot, dash (covers emails, UUIDs, etc.). _IDENTITY_RE = re.compile(r'^[\w@.\-]+$', re.ASCII) def _validate_identity_value(value: str, source: str) -> str: """Validate and return a trimmed identity value. Raises ``ValueError`` if the value is empty, too long, or contains characters that should never appear in an identity string (e.g. path separators, control characters, shell metacharacters). """ value = value.strip() if not value: raise ValueError(f"Empty identity value from {source}") if len(value) > _MAX_IDENTITY_LENGTH: raise ValueError( f"Identity value from {source} exceeds " f"{_MAX_IDENTITY_LENGTH} characters" ) if not _IDENTITY_RE.match(value): raise ValueError( f"Identity value from {source} contains disallowed characters" ) return value def get_identity_id() -> str: """ Get identity ID with proper security priority: 1. Verified user from Azure App Service auth headers (trusted, set by Azure) 2. Verified user from JWT bearer token (trusted, cryptographically verified) 3. Browser ID from X-Identity-Id header (untrusted, for anonymous users only) The key insight: for anonymous users, we trust X-Identity-Id because there's no security risk (who cares if someone "steals" a random UUID?). For authenticated users, we MUST extract identity from verified sources, not client-provided headers. Identity is namespaced as "user:<id>" or "browser:<id>" to ensure authenticated user data is never accessible via anonymous browser identity spoofing. Returns: str: The namespaced identity ID string (e.g., "user:alice@..." or "browser:550e8400-...") Raises: ValueError: If no identity could be determined """ # Priority 1: Azure App Service Authentication (EasyAuth) # When deployed to Azure with authentication enabled, Azure injects these headers. # These are SET BY AZURE (not the client) after verifying the user's identity. azure_principal_id = request.headers.get('X-MS-CLIENT-PRINCIPAL-ID') if azure_principal_id: validated = _validate_identity_value(azure_principal_id, "Azure principal") logger.debug(f"Using Azure principal ID: {validated[:8]}...") return f"user:{validated}" # Priority 2: JWT Bearer Token (for custom auth implementations) # If you implement your own auth, verify the JWT here and extract user ID. # Example (uncomment and configure when implementing JWT auth): # # auth_header = request.headers.get('Authorization', '') # if auth_header.startswith('Bearer '): # token = auth_header[7:] # try: # import jwt # payload = jwt.decode(token, current_app.config['JWT_SECRET'], algorithms=['HS256']) # user_id = payload.get('sub') or payload.get('user_id') # if user_id: # logger.debug(f"Using JWT user ID: {user_id[:8]}...") # return f"user:{user_id}" # except Exception as e: # logger.warning(f"Invalid JWT token: {e}") # # Fall through to browser identity # Priority 3: Anonymous browser identity (UNTRUSTED - from client header) # SECURITY: We NEVER trust the namespace prefix from X-Identity-Id header. # Even if client sends "user:alice@...", we force "browser:" prefix. # Only verified auth (Azure headers, JWT) can result in "user:" prefix. client_identity = request.headers.get('X-Identity-Id') if client_identity: # Extract the ID part, ignoring any client-provided prefix # e.g., "browser:550e8400-..." → "550e8400-..." # e.g., "user:alice@..." → "alice@..." (but forced to browser: namespace) if ':' in client_identity: # Strip the prefix - we don't trust client-provided namespaces identity_value = client_identity.split(':', 1)[1] else: identity_value = client_identity validated = _validate_identity_value(identity_value, "X-Identity-Id header") # Always use browser: prefix for client-provided identities return f"browser:{validated}" raise ValueError("X-Identity-Id header is required. Please refresh the page.") ================================================ FILE: py-src/data_formulator/code_signing.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """HMAC-based code signing for transformation code. When the agent generates Python transformation code and the server executes it successfully, the server signs the code with a secret key. The signature is returned to the frontend alongside the code. When the frontend later sends the code back for re-execution (e.g. during data refresh), the server verifies the signature before running the code. This prevents a tampered or injected script from being executed by the sandbox. Secret lifecycle ~~~~~~~~~~~~~~~~ - **Dev mode** (``--dev``): uses a fixed, deterministic key so that signatures survive reloader restarts and hot-reloads during development. This is *not* secure for production. - **Production**: derives the key from Flask's ``app.secret_key``. For multi-worker deploys (gunicorn) set the ``SECRET_KEY`` env-var so all workers share the same Flask secret. - **Explicit override**: set ``DF_CODE_SIGNING_SECRET`` env-var — this takes priority over everything (useful for multi-instance deploys behind a load balancer). """ import hashlib import hmac import logging import os logger = logging.getLogger(__name__) # --------------------------------------------------------------------------- # Server-side secret # --------------------------------------------------------------------------- # Explicit env-var takes highest priority (for multi-instance deploys). _EXPLICIT_SECRET: str | None = os.environ.get("DF_CODE_SIGNING_SECRET") or None # Fixed key used in dev mode so reloader restarts don't invalidate # existing signatures. NOT suitable for production. _DEV_SECRET = b"data-formulator-dev-signing-key" def _is_dev_mode() -> bool: """Return True if the server was started with ``--dev``.""" try: from flask import current_app return current_app.config.get("CLI_ARGS", {}).get("dev", False) except (ImportError, RuntimeError): return False def _get_secret() -> bytes: """Return the signing secret. Priority: 1. ``DF_CODE_SIGNING_SECRET`` env-var (set once, works everywhere) 2. Dev mode → fixed deterministic key (survives reloader restarts) 3. Production → derived from Flask ``app.secret_key`` 4. Fallback for tests / non-Flask callers """ if _EXPLICIT_SECRET: return _EXPLICIT_SECRET.encode("utf-8") # In dev mode use a fixed key so the reloader doesn't break sigs. if _is_dev_mode(): return _DEV_SECRET # Production: derive from Flask's secret_key. try: from flask import current_app flask_secret = current_app.secret_key if flask_secret: # Derive a separate key so changing Flask's secret_key for # session purposes doesn't accidentally share material. return hmac.new( b"df-code-signing", str(flask_secret).encode("utf-8"), hashlib.sha256, ).digest() except (ImportError, RuntimeError): # No Flask app context (e.g. unit tests, CLI scripts). pass # Last resort — should only happen in tests. logger.warning( "code_signing: no DF_CODE_SIGNING_SECRET and no Flask app context; " "using fallback secret (signatures won't survive restarts)" ) return _DEV_SECRET # --------------------------------------------------------------------------- # Public API # --------------------------------------------------------------------------- # Maximum code size we are willing to sign / verify (256 KB). MAX_CODE_SIZE = 256 * 1024 def sign_code(code: str) -> str: """Compute an HMAC-SHA256 signature over *code*. Returns the hex-encoded signature string. The signature covers the raw UTF-8 bytes of *code* — whitespace and encoding matter. """ if not code: return "" return hmac.new( _get_secret(), code.encode("utf-8"), hashlib.sha256, ).hexdigest() def verify_code(code: str, signature: str) -> bool: """Return ``True`` if *signature* is a valid HMAC for *code*. Uses constant-time comparison to prevent timing attacks. """ if not code or not signature: return False expected = sign_code(code) return hmac.compare_digest(expected, signature) def sign_result(result: dict) -> dict: """Add ``code_signature`` to an agent result dict (in-place). If the result contains a non-empty ``code`` key, a signature is computed and stored under ``code_signature``. The result dict is returned for convenience. """ code = result.get("code", "") if code: result["code_signature"] = sign_code(code) return result ================================================ FILE: py-src/data_formulator/data_loader/README.md ================================================ ## Data Loader Module This module provides a framework for loading data from various external sources into the **workspace** (parquet files). It follows an abstract base class pattern so all loaders behave consistently. ### Design - **Storage**: Ingested data is written as **parquet** in the workspace. DuckDB is **not** used for storage; it is only the computation engine elsewhere in the application. - **Data flow**: **External source → PyArrow Table → Parquet (workspace)**. - **Format**: Loaders use **PyArrow** as the standard in-memory format for speed and interoperability. Database loaders (PostgreSQL, MySQL, MSSQL) use **connectorx** for Arrow-native reads where applicable. ### Building a New Data Loader The abstract class `ExternalDataLoader` defines the interface. Each concrete implementation (e.g., `MySQLDataLoader`, `S3DataLoader`) handles one data source. To add a new data loader: 1. Create a class that inherits from `ExternalDataLoader`. 2. Implement the required pieces: - **`list_params()`** (static): Connection parameters (names, types, defaults, descriptions). - **`auth_instructions()`** (static): Short instructions for obtaining credentials/setup. - **`__init__(self, params)`**: Validate params and establish or verify connection to the source. No `duck_db_conn`; storage is workspace-only. - **`fetch_data_as_arrow(source_table, size=..., sort_columns=..., sort_order=...)`**: Fetch data from the source and return a `pyarrow.Table`. Only `source_table` (table/collection/file identifier) is supported; raw query strings are not accepted for security and dialect consistency. - **`list_tables(table_filter=None)`**: Return a list of `{"name": ..., "metadata": {...}}` for tables/files the user can select. Metadata typically includes `row_count`, `columns`, and `sample_rows`. 3. Register the new class in the package `__init__.py` so the front-end can discover it. The base class provides **`ingest_to_workspace(workspace, ...)`**, which calls `fetch_data_as_arrow()` and writes the result to the workspace as parquet. You do not implement ingest logic in the loader. The UI uses the same loaders for connection setup, table listing, and ingestion into the workspace. ### Example Implementations - **`AthenaDataLoader`**: AWS Athena (SQL on S3 data lakes) - **`BigQueryDataLoader`**: Google BigQuery - **`KustoDataLoader`**: Azure Data Explorer (Kusto) - **`MySQLDataLoader`**: MySQL (connectorx) - **`PostgreSQLDataLoader`**: PostgreSQL (connectorx) - **`MSSQLDataLoader`**: Microsoft SQL Server (connectorx) - **`S3DataLoader`**: Amazon S3 files (CSV, Parquet, JSON) via PyArrow S3 filesystem - **`AzureBlobDataLoader`**: Azure Blob Storage via PyArrow - **`MongoDBDataLoader`**: MongoDB ### Testing When implementing or changing a loader: - Handle connection and read errors clearly (e.g., raise `ValueError` with a clear message). - Sanitize or validate table/object names where appropriate. - Respect the `size` limit (and optional sort) in `fetch_data_as_arrow`. - Return the same metadata shape from `list_tables()` (e.g., `row_count`, `columns`, `sample_rows`) so the UI behaves consistently. Test via the front-end: configure the loader, list tables, and run an ingest into the workspace; then confirm parquet appears in the workspace and DuckDB (or other engines) can read it for computation. ================================================ FILE: py-src/data_formulator/data_loader/__init__.py ================================================ from data_formulator.data_loader.external_data_loader import ExternalDataLoader from data_formulator.data_loader.mysql_data_loader import MySQLDataLoader from data_formulator.data_loader.mssql_data_loader import MSSQLDataLoader from data_formulator.data_loader.kusto_data_loader import KustoDataLoader from data_formulator.data_loader.s3_data_loader import S3DataLoader from data_formulator.data_loader.azure_blob_data_loader import AzureBlobDataLoader from data_formulator.data_loader.postgresql_data_loader import PostgreSQLDataLoader from data_formulator.data_loader.mongodb_data_loader import MongoDBDataLoader from data_formulator.data_loader.bigquery_data_loader import BigQueryDataLoader from data_formulator.data_loader.athena_data_loader import AthenaDataLoader DATA_LOADERS = { "mysql": MySQLDataLoader, "mssql": MSSQLDataLoader, "kusto": KustoDataLoader, "s3": S3DataLoader, "azure_blob": AzureBlobDataLoader, "postgresql": PostgreSQLDataLoader, "mongodb": MongoDBDataLoader, "bigquery": BigQueryDataLoader, "athena": AthenaDataLoader } __all__ = [ "ExternalDataLoader", "MySQLDataLoader", "MSSQLDataLoader", "KustoDataLoader", "S3DataLoader", "AzureBlobDataLoader", "PostgreSQLDataLoader", "MongoDBDataLoader", "BigQueryDataLoader", "AthenaDataLoader", "DATA_LOADERS"] ================================================ FILE: py-src/data_formulator/data_loader/athena_data_loader.py ================================================ import logging import re import time import pyarrow as pa import pyarrow.csv as pa_csv import boto3 import botocore.exceptions from pyarrow import fs as pa_fs from data_formulator.data_loader.external_data_loader import ExternalDataLoader, sanitize_table_name from typing import Any log = logging.getLogger(__name__) # Valid patterns for Athena identifiers (database.table or just table) ATHENA_TABLE_PATTERN = re.compile(r'^[a-zA-Z_][a-zA-Z0-9_]*(\.[a-zA-Z_][a-zA-Z0-9_]*)?$') ATHENA_COLUMN_PATTERN = re.compile(r'^[a-zA-Z_][a-zA-Z0-9_]*$') S3_URL_PATTERN = re.compile(r'^s3://[a-zA-Z0-9][a-zA-Z0-9.\-_]*[a-zA-Z0-9](/.*)?$') def _validate_athena_table_name(table_name: str) -> None: """Validate that table_name is a safe Athena identifier (database.table format).""" if not table_name: raise ValueError("Table name cannot be empty") if not ATHENA_TABLE_PATTERN.match(table_name): raise ValueError( f"Invalid table name format: '{table_name}'. " "Expected format: 'database.table' or 'table' with alphanumeric characters and underscores only." ) def _validate_column_name(column_name: str) -> None: """Validate that column_name is a safe identifier.""" if not column_name: raise ValueError("Column name cannot be empty") if not ATHENA_COLUMN_PATTERN.match(column_name): raise ValueError( f"Invalid column name: '{column_name}'. " "Only alphanumeric characters and underscores are allowed." ) def _validate_s3_url(url: str) -> None: """Validate that URL is a proper S3 URL.""" if not url: raise ValueError("S3 URL cannot be empty") if not S3_URL_PATTERN.match(url): raise ValueError(f"Invalid S3 URL format: '{url}'. Expected format: 's3://bucket/path'") class AthenaDataLoader(ExternalDataLoader): """AWS Athena data loader implementation. Executes SQL queries on Athena and reads results from S3 via PyArrow. Output location is taken from the workgroup configuration or the output_location param. Use ingest_to_workspace() to store results as parquet in the workspace. """ @staticmethod def list_params() -> list[dict[str, Any]]: params_list = [ {"name": "aws_profile", "type": "string", "required": False, "default": "", "description": "AWS profile name from ~/.aws/credentials (if set, access key and secret are not required)"}, {"name": "aws_access_key_id", "type": "string", "required": False, "default": "", "description": "AWS access key ID (not required if using aws_profile)"}, {"name": "aws_secret_access_key", "type": "string", "required": False, "default": "", "description": "AWS secret access key (not required if using aws_profile)"}, {"name": "aws_session_token", "type": "string", "required": False, "default": "", "description": "AWS session token (required for temporary credentials)"}, {"name": "region_name", "type": "string", "required": True, "default": "us-east-1", "description": "AWS region name"}, {"name": "workgroup", "type": "string", "required": False, "default": "primary", "description": "Athena workgroup name (output location is fetched from workgroup configuration)"}, {"name": "output_location", "type": "string", "required": False, "default": "", "description": "S3 output location for query results (e.g., s3://bucket/path/). If empty, uses workgroup configuration."}, {"name": "database", "type": "string", "required": False, "default": "", "description": "Default database/catalog to use for queries"}, {"name": "query_timeout", "type": "number", "required": False, "default": 300, "description": "Query execution timeout in seconds (default: 300 = 5 minutes)"} ] return params_list @staticmethod def auth_instructions() -> str: return """**Example (profile):** aws_profile: `default` · region_name: `us-east-1` · workgroup: `primary` · database: `my_database` **Example (keys):** aws_access_key_id: `AKIA...` · aws_secret_access_key: `wJalr...` · region_name: `us-east-1` **Option 1 — AWS Profile (recommended):** Set `aws_profile` to a profile name from `~/.aws/credentials`. Set up with `aws configure --profile <name>`. No access key or secret needed. **Option 2 — Explicit Credentials:** Enter `aws_access_key_id` and `aws_secret_access_key` directly. Add `aws_session_token` for temporary credentials. **Required IAM permissions:** `athena:StartQueryExecution`, `athena:GetQueryExecution`, `athena:GetQueryResults`, `athena:GetWorkGroup`, `athena:ListDatabases`, `athena:ListTableMetadata`, plus S3 and Glue permissions on your data/results buckets.""" def __init__(self, params: dict[str, Any]): self.params = params # Extract parameters self.aws_profile = params.get("aws_profile", "") self.aws_access_key_id = params.get("aws_access_key_id", "") self.aws_secret_access_key = params.get("aws_secret_access_key", "") self.aws_session_token = params.get("aws_session_token", "") self.region_name = params.get("region_name", "us-east-1") self.workgroup = params.get("workgroup", "primary") self.output_location_param = params.get("output_location", "") self.database = params.get("database", "") # Normalize and validate query timeout raw_timeout = params.get("query_timeout", 300) default_timeout = 300 if raw_timeout is None or (isinstance(raw_timeout, str) and not raw_timeout.strip()): timeout_value = default_timeout elif isinstance(raw_timeout, int): timeout_value = raw_timeout elif isinstance(raw_timeout, float): timeout_value = int(raw_timeout) elif isinstance(raw_timeout, str): try: # Allow values like "300" or "300.0" timeout_value = int(float(raw_timeout.strip())) except (ValueError, TypeError): raise ValueError( f"Invalid query_timeout value: {raw_timeout!r}. " "Expected a positive number (int or float-compatible string)." ) else: raise ValueError( f"Invalid type for query_timeout: {type(raw_timeout).__name__}. " "Expected int, float, str, or empty." ) if timeout_value <= 0: raise ValueError( f"query_timeout must be a positive integer number of seconds, got {timeout_value!r}." ) self.query_timeout = timeout_value # Initialize boto3 session and Athena client if self.aws_profile: # Use AWS profile from ~/.aws/credentials or ~/.aws/config (including SSO) log.info(f"Using AWS profile: {self.aws_profile}") try: session = boto3.Session(profile_name=self.aws_profile, region_name=self.region_name) self.athena_client = session.client('athena') # Get credentials from profile for PyArrow S3 access credentials = session.get_credentials() if credentials is None: raise ValueError( f"No credentials found for profile '{self.aws_profile}'. " f"If using SSO, run: aws sso login --profile {self.aws_profile}" ) # get_frozen_credentials() can trigger SSO token refresh/validation frozen_credentials = credentials.get_frozen_credentials() self.aws_access_key_id = frozen_credentials.access_key self.aws_secret_access_key = frozen_credentials.secret_key self.aws_session_token = frozen_credentials.token or "" except botocore.exceptions.SSOTokenLoadError as e: raise ValueError( f"SSO session expired or not logged in for profile '{self.aws_profile}'. " f"Please run: aws sso login --profile {self.aws_profile}" ) from e except botocore.exceptions.UnauthorizedSSOTokenError as e: raise ValueError( f"SSO token is invalid or expired for profile '{self.aws_profile}'. " f"Please run: aws sso login --profile {self.aws_profile}" ) from e except botocore.exceptions.TokenRetrievalError as e: raise ValueError( f"Failed to retrieve SSO token for profile '{self.aws_profile}'. " f"Please run: aws sso login --profile {self.aws_profile}" ) from e except botocore.exceptions.NoCredentialsError as e: raise ValueError( f"No credentials found for profile '{self.aws_profile}'. " f"Check your ~/.aws/credentials or ~/.aws/config file. " f"If using SSO, run: aws sso login --profile {self.aws_profile}" ) from e except botocore.exceptions.ProfileNotFound as e: raise ValueError( f"AWS profile '{self.aws_profile}' not found. " f"Check your ~/.aws/credentials or ~/.aws/config file. " f"Available profiles can be listed with: aws configure list-profiles" ) from e except Exception as e: # Catch any other credential-related errors error_msg = str(e).lower() if 'sso' in error_msg or 'token' in error_msg or 'expired' in error_msg: raise ValueError( f"AWS credential error for profile '{self.aws_profile}'. " f"If using SSO, run: aws sso login --profile {self.aws_profile}\n" f"Original error: {e}" ) from e raise else: # Use explicit credentials if not self.aws_access_key_id or not self.aws_secret_access_key: raise ValueError( "Either 'aws_profile' or both 'aws_access_key_id' and 'aws_secret_access_key' must be provided." ) session_kwargs = { 'aws_access_key_id': self.aws_access_key_id, 'aws_secret_access_key': self.aws_secret_access_key, 'region_name': self.region_name } if self.aws_session_token: session_kwargs['aws_session_token'] = self.aws_session_token self.athena_client = boto3.client('athena', **session_kwargs) # Get output location: prefer user-provided, then try workgroup self.output_location = self._get_output_location() # Setup PyArrow S3 filesystem for reading results self.s3_fs = pa_fs.S3FileSystem( access_key=self.aws_access_key_id, secret_key=self.aws_secret_access_key, session_token=self.aws_session_token if self.aws_session_token else None, region=self.region_name ) log.info("Initialized PyArrow S3 filesystem for Athena results") def _get_output_location(self) -> str: """Get the output location for query results. Priority: user-provided output_location > workgroup configuration. """ # If user provided an output location, validate and use it if self.output_location_param: _validate_s3_url(self.output_location_param) # Normalize to ensure trailing slash for directory path output_location = self.output_location_param.rstrip('/') + '/' log.info(f"Using user-provided output location: {output_location}") return output_location # Try to get from workgroup configuration try: response = self.athena_client.get_work_group(WorkGroup=self.workgroup) workgroup = response.get('WorkGroup', {}) workgroup_config = workgroup.get('Configuration', {}) result_config = workgroup_config.get('ResultConfiguration', {}) output_location = result_config.get('OutputLocation', '') if output_location: log.info(f"Using output location from workgroup '{self.workgroup}': {output_location}") return output_location else: log.warning( f"Workgroup '{self.workgroup}' has no output location configured. " "Note: Athena console 'Settings' are client-side only. " "Configure output location in Workgroups → Edit → Query result configuration." ) except botocore.exceptions.ClientError as e: log.error(f"Failed to get workgroup configuration: {e}") raise ValueError( f"No output location available. Either:\n" f"1. Provide 'output_location' parameter (e.g., 's3://your-bucket/athena-results/'), or\n" f"2. Configure an S3 output location in Athena workgroup '{self.workgroup}' settings." ) def _execute_query(self, query: str) -> str: """Execute an Athena query and wait for completion. Returns the S3 path to the query results (CSV file). """ # Start query execution start_params = { 'QueryString': query, 'WorkGroup': self.workgroup, 'ResultConfiguration': { 'OutputLocation': self.output_location } } if self.database: start_params['QueryExecutionContext'] = {'Database': self.database} response = self.athena_client.start_query_execution(**start_params) query_execution_id = response['QueryExecutionId'] log.info(f"Started Athena query execution: {query_execution_id}") # Poll for query completion start_time = time.time() while True: elapsed = time.time() - start_time if elapsed > self.query_timeout: # Try to cancel the query. This is a best-effort operation: failures are logged # but do not prevent raising the timeout error for the caller. try: self.athena_client.stop_query_execution(QueryExecutionId=query_execution_id) except Exception: log.warning( "Failed to cancel Athena query execution %s after timeout", query_execution_id, exc_info=True, ) raise TimeoutError( f"Query execution timed out after {self.query_timeout} seconds. " "Consider increasing the query_timeout parameter." ) response = self.athena_client.get_query_execution(QueryExecutionId=query_execution_id) state = response['QueryExecution']['Status']['State'] if state == 'SUCCEEDED': output_location = response['QueryExecution']['ResultConfiguration']['OutputLocation'] log.info(f"Query completed successfully. Results at: {output_location}") return output_location elif state == 'FAILED': reason = response['QueryExecution']['Status'].get('StateChangeReason', 'Unknown error') raise RuntimeError(f"Athena query failed: {reason}") elif state == 'CANCELLED': raise RuntimeError("Athena query was cancelled") # Wait before polling again (exponential backoff with cap) wait_time = min(2 ** (elapsed // 10), 10) time.sleep(wait_time) def fetch_data_as_arrow( self, source_table: str, size: int = 1000000, sort_columns: list[str] | None = None, sort_order: str = 'asc' ) -> pa.Table: """ Fetch data from Athena as a PyArrow Table. Executes the query on Athena and reads the CSV results from S3 using PyArrow's S3 filesystem. """ if not source_table: raise ValueError("source_table must be provided") _validate_athena_table_name(source_table) base_query = f"SELECT * FROM {source_table}" # Add ORDER BY if sort columns specified order_by_clause = "" if sort_columns and len(sort_columns) > 0: for col in sort_columns: _validate_column_name(col) order_direction = "DESC" if sort_order == 'desc' else "ASC" sanitized_cols = [f'"{col}" {order_direction}' for col in sort_columns] order_by_clause = f" ORDER BY {', '.join(sanitized_cols)}" query = f"{base_query}{order_by_clause} LIMIT {size}" log.info(f"Executing Athena query: {query[:200]}...") # Execute query and get result location result_location = self._execute_query(query) _validate_s3_url(result_location) log.info(f"Reading Athena results from: {result_location}") # Parse S3 URL: s3://bucket/key -> bucket/key s3_path = result_location[5:] if result_location.startswith("s3://") else result_location # Athena outputs CSV files with self.s3_fs.open_input_file(s3_path) as f: arrow_table = pa_csv.read_csv(f) log.info(f"Fetched {arrow_table.num_rows} rows from Athena [Arrow-native]") return arrow_table def list_tables(self, table_filter: str | None = None) -> list[dict[str, Any]]: """List tables from Athena catalog (Glue Data Catalog).""" results = [] try: # List databases databases_response = self.athena_client.list_databases(CatalogName='AwsDataCatalog') databases = databases_response.get('DatabaseList', []) # If a specific database is configured, filter to just that one if self.database: databases = [db for db in databases if db['Name'] == self.database] for db in databases[:10]: # Limit to 10 databases db_name = db['Name'] try: # List tables in this database tables_response = self.athena_client.list_table_metadata( CatalogName='AwsDataCatalog', DatabaseName=db_name, MaxResults=50 ) for table in tables_response.get('TableMetadataList', []): table_name = table['Name'] full_table_name = f"{db_name}.{table_name}" # Apply filter if provided if table_filter and table_filter.lower() not in full_table_name.lower(): continue # Get column information columns = [] for col in table.get('Columns', [])[:20]: # Limit columns columns.append({ 'name': col['Name'], 'type': col.get('Type', 'unknown') }) # Add partition columns for col in table.get('PartitionKeys', []): columns.append({ 'name': col['Name'], 'type': col.get('Type', 'unknown') + ' (partition)' }) results.append({ "name": full_table_name, "metadata": { "row_count": 0, # Athena doesn't provide row counts directly "columns": columns, "sample_rows": [], # Would require query execution "table_type": table.get('TableType', 'EXTERNAL_TABLE') } }) if len(results) >= 100: log.info("Reached 100 table limit, stopping enumeration") return results except botocore.exceptions.ClientError as e: log.warning(f"Error listing tables in database {db_name}: {e}") continue except botocore.exceptions.ClientError as e: log.error(f"Error listing Athena databases: {e}") log.info(f"Returning {len(results)} tables") return results ================================================ FILE: py-src/data_formulator/data_loader/azure_blob_data_loader.py ================================================ import json import logging import pandas as pd import pyarrow as pa import pyarrow.parquet as pq import pyarrow.csv as pa_csv from azure.storage.blob import BlobServiceClient from azure.identity import DefaultAzureCredential from pyarrow import fs as pa_fs from data_formulator.data_loader.external_data_loader import ExternalDataLoader, sanitize_table_name from typing import Any logger = logging.getLogger(__name__) class AzureBlobDataLoader(ExternalDataLoader): @staticmethod def list_params() -> list[dict[str, Any]]: params_list = [ {"name": "account_name", "type": "string", "required": True, "default": "", "description": "Azure storage account name"}, {"name": "container_name", "type": "string", "required": True, "default": "", "description": "Azure blob container name"}, {"name": "connection_string", "type": "string", "required": False, "default": "", "description": "Azure storage connection string (alternative to account_name + credentials)"}, {"name": "credential_chain", "type": "string", "required": False, "default": "cli;managed_identity;env", "description": "Ordered list of Azure credential providers (cli;managed_identity;env)"}, {"name": "account_key", "type": "string", "required": False, "default": "", "description": "Azure storage account key"}, {"name": "sas_token", "type": "string", "required": False, "default": "", "description": "Azure SAS token"}, {"name": "endpoint", "type": "string", "required": False, "default": "blob.core.windows.net", "description": "Azure endpoint override"} ] return params_list @staticmethod def auth_instructions() -> str: return """**Example (conn string):** connection_string: `DefaultEndpointsProtocol=https;AccountName=...` · container_name: `mydata` **Example (account key):** account_name: `mystorageacct` · container_name: `mydata` · account_key: `abc123...` **Option 1 — Connection String (simplest):** Get it from Azure Portal → Storage Account → Access keys. Enter in `connection_string`; `account_name` can be omitted. **Option 2 — Account Key:** From Azure Portal → Storage Account → Access keys. Use `account_name` + `account_key`. **Option 3 — SAS Token (recommended for limited access):** Generate from Azure Portal → Storage Account → Shared access signature. Use `account_name` + `sas_token`. Can be time-limited and permission-scoped. **Option 4 — Azure CLI / Managed Identity (most secure):** Just provide `account_name` + `container_name`. Requires `az login` or Managed Identity. **Supported formats:** CSV, Parquet, JSON, JSONL""" def __init__(self, params: dict[str, Any]): self.params = params # Extract parameters self.account_name = params.get("account_name", "") self.container_name = params.get("container_name", "") self.connection_string = params.get("connection_string", "") self.credential_chain = params.get("credential_chain", "cli;managed_identity;env") self.account_key = params.get("account_key", "") self.sas_token = params.get("sas_token", "") self.endpoint = params.get("endpoint", "blob.core.windows.net") # Setup PyArrow Azure filesystem if self.account_key: self.azure_fs = pa_fs.AzureFileSystem( account_name=self.account_name, account_key=self.account_key ) elif self.connection_string: self.azure_fs = pa_fs.AzureFileSystem.from_connection_string(self.connection_string) else: # Use default credential chain self.azure_fs = pa_fs.AzureFileSystem(account_name=self.account_name) logger.info(f"Initialized PyArrow Azure filesystem for account: {self.account_name}") def _azure_path(self, azure_url: str) -> str: """Convert Azure URL to path for PyArrow (container/blob).""" if azure_url.startswith("az://"): parts = azure_url[5:].split("/", 1) return parts[1] if len(parts) > 1 else azure_url return f"{self.container_name}/{azure_url}" def _read_sample(self, azure_url: str, limit: int) -> pd.DataFrame: """Read sample rows from an Azure blob using PyArrow. Returns a pandas DataFrame.""" azure_path = self._azure_path(azure_url) if azure_url.lower().endswith('.parquet'): table = pq.read_table(azure_path, filesystem=self.azure_fs) elif azure_url.lower().endswith('.csv'): with self.azure_fs.open_input_file(azure_path) as f: table = pa_csv.read_csv(f) elif azure_url.lower().endswith('.json') or azure_url.lower().endswith('.jsonl'): import pyarrow.json as pa_json with self.azure_fs.open_input_file(azure_path) as f: table = pa_json.read_json(f) else: raise ValueError(f"Unsupported file type: {azure_url}") if table.num_rows > limit: table = table.slice(0, limit) return table.to_pandas() def fetch_data_as_arrow( self, source_table: str, size: int = 1000000, sort_columns: list[str] | None = None, sort_order: str = 'asc' ) -> pa.Table: """ Fetch data from Azure Blob as a PyArrow Table. For files (parquet, csv), reads directly using PyArrow's Azure filesystem. """ if not source_table: raise ValueError("source_table (Azure blob URL) must be provided") azure_url = source_table azure_path = self._azure_path(azure_url) logger.info("Reading Azure blob via PyArrow: %s", azure_url) if azure_url.lower().endswith('.parquet'): arrow_table = pq.read_table(azure_path, filesystem=self.azure_fs) elif azure_url.lower().endswith('.csv'): with self.azure_fs.open_input_file(azure_path) as f: arrow_table = pa_csv.read_csv(f) elif azure_url.lower().endswith('.json') or azure_url.lower().endswith('.jsonl'): import pyarrow.json as pa_json with self.azure_fs.open_input_file(azure_path) as f: arrow_table = pa_json.read_json(f) else: raise ValueError(f"Unsupported file type: {azure_url}") # Apply sorting if specified if sort_columns and len(sort_columns) > 0: df = arrow_table.to_pandas() ascending = sort_order != 'desc' df = df.sort_values(by=sort_columns, ascending=ascending) arrow_table = pa.Table.from_pandas(df, preserve_index=False) # Apply size limit if arrow_table.num_rows > size: arrow_table = arrow_table.slice(0, size) logger.info(f"Fetched {arrow_table.num_rows} rows from Azure Blob [Arrow-native]") return arrow_table def list_tables(self, table_filter: str | None = None) -> list[dict[str, Any]]: # Create blob service client based on authentication method if self.connection_string: blob_service_client = BlobServiceClient.from_connection_string(self.connection_string) elif self.account_key: blob_service_client = BlobServiceClient( account_url=f"https://{self.account_name}.{self.endpoint}", credential=self.account_key ) elif self.sas_token: blob_service_client = BlobServiceClient( account_url=f"https://{self.account_name}.{self.endpoint}", credential=self.sas_token ) else: # Use default credential chain from azure.identity import DefaultAzureCredential credential = DefaultAzureCredential() blob_service_client = BlobServiceClient( account_url=f"https://{self.account_name}.{self.endpoint}", credential=credential ) container_client = blob_service_client.get_container_client(self.container_name) # List blobs in the container blob_list = container_client.list_blobs() results = [] for blob in blob_list: blob_name = blob.name # Skip directories and non-data files if blob_name.endswith('/') or not self._is_supported_file(blob_name): continue # Apply table filter if provided if table_filter and table_filter.lower() not in blob_name.lower(): continue # Create Azure blob URL azure_url = f"az://{self.account_name}.{self.endpoint}/{self.container_name}/{blob_name}" try: sample_df = self._read_sample(azure_url, 10) columns = [{ 'name': col, 'type': str(sample_df[col].dtype) } for col in sample_df.columns] sample_rows = json.loads(sample_df.to_json(orient="records")) row_count = self._estimate_row_count(azure_url, blob) table_metadata = { "row_count": row_count, "columns": columns, "sample_rows": sample_rows } results.append({ "name": azure_url, "metadata": table_metadata }) except Exception as e: logger.warning("Error reading %s: %s", azure_url, e) continue return results def _is_supported_file(self, blob_name: str) -> bool: """Check if the file type is supported (PyArrow can read it).""" supported_extensions = ['.csv', '.parquet', '.json', '.jsonl'] return any(blob_name.lower().endswith(ext) for ext in supported_extensions) def _estimate_row_count(self, azure_url: str, blob_properties=None) -> int: """Estimate the number of rows in a file.""" try: file_extension = azure_url.lower().split('.')[-1] if file_extension == 'parquet': try: azure_path = self._azure_path(azure_url) pf = pq.ParquetFile(azure_path, filesystem=self.azure_fs) return pf.metadata.num_rows except Exception as e: logger.debug("Failed to get parquet row count for %s: %s", azure_url, e) return 0 if file_extension in ['csv', 'json', 'jsonl']: return self._estimate_rows_by_sampling(azure_url, blob_properties, file_extension) return 0 except Exception as e: logger.warning("Error estimating row count for %s: %s", azure_url, e) return 0 def _estimate_rows_by_sampling(self, azure_url: str, blob_properties, file_extension: str) -> int: """Estimate row count for text-based files using PyArrow sampling.""" try: file_size_bytes = None if blob_properties and hasattr(blob_properties, 'size'): file_size_bytes = blob_properties.size if file_size_bytes is None: return self._estimate_by_row_sampling(azure_url, file_extension) sample_size = min(10000, max(1000, file_size_bytes // 100)) try: sample_df = self._read_sample(azure_url, sample_size) sample_rows = len(sample_df) if sample_rows == 0: return 0 if sample_rows < sample_size: return sample_rows min_bytes_per_row = 50 if file_extension == 'csv' else 100 estimated_total_rows = int(file_size_bytes / max(file_size_bytes / sample_rows, min_bytes_per_row)) estimated_total_rows = max(sample_rows, min(estimated_total_rows, file_size_bytes // 10)) return estimated_total_rows except Exception as e: logger.debug("Size-based estimation failed for %s: %s", azure_url, e) return self._estimate_by_row_sampling(azure_url, file_extension) except Exception as e: logger.warning("Error in sampling estimation for %s: %s", azure_url, e) return 0 def _estimate_by_row_sampling(self, azure_url: str, file_extension: str) -> int: """Estimate row count by reading a capped sample with PyArrow.""" try: test_limit = 50000 sample_df = self._read_sample(azure_url, test_limit) return len(sample_df) except Exception as e: logger.debug("Row sampling failed for %s: %s", azure_url, e) return 0 ================================================ FILE: py-src/data_formulator/data_loader/bigquery_data_loader.py ================================================ import logging import re from typing import Any import pyarrow as pa from data_formulator.data_loader.external_data_loader import ExternalDataLoader, sanitize_table_name from google.cloud import bigquery from google.oauth2 import service_account log = logging.getLogger(__name__) class BigQueryDataLoader(ExternalDataLoader): """BigQuery data loader implementation""" @staticmethod def list_params() -> list[dict[str, Any]]: return [ {"name": "project_id", "type": "text", "required": True, "description": "Google Cloud Project ID", "default": ""}, {"name": "dataset_id", "type": "text", "required": False, "description": "Dataset ID(s) - leave empty for all, or specify one (e.g., 'billing') or multiple separated by commas (e.g., 'billing,enterprise_collected,ga_api')", "default": ""}, {"name": "credentials_path", "type": "text", "required": False, "description": "Path to service account JSON file (optional)", "default": ""}, {"name": "location", "type": "text", "required": False, "description": "BigQuery location (default: US)", "default": "US"} ] @staticmethod def auth_instructions() -> str: return """**Example:** project_id: `my-gcp-project` · dataset_id: `analytics` · credentials_path: `/path/to/key.json` · location: `US` **Option 1 — Application Default Credentials (recommended):** Install [Google Cloud SDK](https://cloud.google.com/sdk/docs/install), then run `gcloud auth application-default login`. Leave `credentials_path` empty. **Option 2 — Service Account Key File:** Create a service account in Google Cloud Console, download the JSON key, and enter the full path in `credentials_path`. Grant the account **BigQuery Data Viewer** and **BigQuery Job User** roles. **Option 3 — Environment Variable:** Set `GOOGLE_APPLICATION_CREDENTIALS` to your service account JSON file path. Leave `credentials_path` empty.""" def __init__(self, params: dict[str, Any]): self.params = params self.project_id = params.get("project_id") self.dataset_ids = [d.strip() for d in params.get("dataset_id", "").split(",") if d.strip()] self.location = params.get("location", "US") # Initialize BigQuery client if params.get("credentials_path"): credentials = service_account.Credentials.from_service_account_file(params["credentials_path"]) self.client = bigquery.Client( project=self.project_id, credentials=credentials, location=self.location ) else: # Use default credentials (ADC) self.client = bigquery.Client( project=self.project_id, location=self.location ) log.info(f"Successfully connected to BigQuery project: {self.project_id}") def list_tables(self, table_filter: str | None = None) -> list[dict[str, Any]]: """List tables from BigQuery datasets""" results = [] try: log.info(f"Listing BigQuery datasets for project: {self.project_id}") # List datasets with timeout datasets = list(self.client.list_datasets(max_results=50)) log.info(f"Found {len(datasets)} datasets") # Limit to first 10 datasets if no specific dataset is specified if not self.dataset_ids: datasets = datasets[:10] for dataset in datasets: dataset_id = dataset.dataset_id # Skip if we have specific datasets and this isn't one of them if self.dataset_ids and dataset_id not in self.dataset_ids: continue try: log.info(f"Processing dataset: {dataset_id}") # List tables in dataset with limit tables = list(self.client.list_tables(dataset.reference, max_results=20)) for table in tables: full_table_name = f"{self.project_id}.{dataset_id}.{table.table_id}" # Apply filter if provided if table_filter and table_filter.lower() not in table.table_id.lower(): continue # Get basic table info without full schema for performance try: table_ref = self.client.get_table(table.reference) columns = [{"name": field.name, "type": field.field_type} for field in table_ref.schema[:10]] # Limit columns shown results.append({ "name": full_table_name, "metadata": { "row_count": table_ref.num_rows or 0, "columns": columns, "sample_rows": [] # Empty for performance, can be populated later } }) except Exception as e: log.warning(f"Error getting schema for table {full_table_name}: {e}") # Add table without detailed schema results.append({ "name": full_table_name, "metadata": { "row_count": 0, "columns": [], "sample_rows": [] } }) # Limit total results for performance if len(results) >= 100: log.info("Reached 100 table limit, stopping enumeration") return results except Exception as e: log.warning(f"Error accessing dataset {dataset_id}: {e}") continue except Exception as e: log.error(f"Error listing BigQuery tables: {e}") log.info(f"Returning {len(results)} tables") return results def fetch_data_as_arrow( self, source_table: str, size: int = 1000000, sort_columns: list[str] | None = None, sort_order: str = 'asc' ) -> pa.Table: """ Fetch data from BigQuery as a PyArrow Table using native Arrow support. BigQuery's Python client provides .to_arrow() for efficient Arrow-native data transfer, avoiding pandas conversion overhead. """ if not source_table: raise ValueError("source_table must be provided") # Get table schema to handle nested fields table_ref = self.client.get_table(source_table) select_parts = self._build_select_parts(table_ref, source_table) base_query = f"SELECT {', '.join(select_parts)} FROM `{source_table}`" # Add ORDER BY if sort columns specified order_by_clause = "" if sort_columns and len(sort_columns) > 0: order_direction = "DESC" if sort_order == 'desc' else "ASC" sanitized_cols = [f'`{col}` {order_direction}' for col in sort_columns] order_by_clause = f" ORDER BY {', '.join(sanitized_cols)}" query = f"{base_query}{order_by_clause} LIMIT {size}" log.info(f"Executing BigQuery query: {query[:200]}...") # Execute query and get Arrow table directly (no pandas conversion) query_job = self.client.query(query) arrow_table = query_job.to_arrow() log.info(f"Fetched {arrow_table.num_rows} rows from BigQuery [Arrow-native]") return arrow_table def _build_select_parts(self, table_ref, table_name: str) -> list[str]: """Build SELECT parts handling nested BigQuery fields.""" select_parts: list[str] = [] used_aliases: dict[str, str] = {} def build_alias(field_path: str) -> str: alias = field_path.replace('.', '_') alias = re.sub(r'[^0-9a-zA-Z_]', '_', alias) alias = re.sub(r'_+', '_', alias).strip('_') or "col" if not alias[0].isalpha() and alias[0] != '_': alias = f"_{alias}" base_alias = alias counter = 1 while alias in used_aliases: alias = f"{base_alias}_{counter}" counter += 1 used_aliases[alias] = field_path return alias def add_field(field_path: str): alias = build_alias(field_path) select_parts.append(f"`{table_name}`.{field_path} AS `{alias}`") def process_field(field, parent_path: str = ""): current_path = f"{parent_path}.{field.name}" if parent_path else field.name if field.field_type == "RECORD" and field.mode != "REPEATED": for subfield in field.fields: process_field(subfield, current_path) else: add_field(current_path) for field in table_ref.schema: process_field(field) return select_parts if select_parts else ["*"] ================================================ FILE: py-src/data_formulator/data_loader/external_data_loader.py ================================================ from abc import ABC, abstractmethod from typing import Any, TYPE_CHECKING import pandas as pd import pyarrow as pa import re import logging if TYPE_CHECKING: from data_formulator.datalake.workspace import Workspace from data_formulator.datalake.metadata import TableMetadata logger = logging.getLogger(__name__) # Sensitive parameter names that should be excluded from stored metadata SENSITIVE_PARAMS = {'password', 'api_key', 'secret', 'token', 'access_key', 'secret_key'} def sanitize_table_name(name_as: str) -> str: if not name_as: raise ValueError("Table name cannot be empty") # Remove any SQL injection attempts name_as = name_as.replace(";", "").replace("--", "").replace("/*", "").replace("*/", "") # Replace invalid characters with underscores # This includes special characters, spaces, dots, dashes, and other non-alphanumeric chars sanitized = re.sub(r'[^a-zA-Z0-9_]', '_', name_as) # Ensure the name starts with a letter or underscore if not sanitized[0].isalpha() and sanitized[0] != '_': sanitized = '_' + sanitized # Ensure the name is not a SQL keyword sql_keywords = { 'SELECT', 'FROM', 'WHERE', 'GROUP', 'BY', 'ORDER', 'HAVING', 'LIMIT', 'OFFSET', 'JOIN', 'INNER', 'LEFT', 'RIGHT', 'FULL', 'OUTER', 'ON', 'AND', 'OR', 'NOT', 'NULL', 'TRUE', 'FALSE', 'UNION', 'ALL', 'DISTINCT', 'INSERT', 'UPDATE', 'DELETE', 'CREATE', 'DROP', 'TABLE', 'VIEW', 'INDEX', 'ALTER', 'ADD', 'COLUMN', 'PRIMARY', 'KEY', 'FOREIGN', 'REFERENCES', 'CONSTRAINT', 'DEFAULT', 'CHECK', 'UNIQUE', 'CASCADE', 'RESTRICT' } if sanitized.upper() in sql_keywords: sanitized = '_' + sanitized # Ensure the name is not too long (common SQL limit is 63 characters) if len(sanitized) > 63: sanitized = sanitized[:63] return sanitized class ExternalDataLoader(ABC): """ Abstract base class for external data loaders. Data loaders fetch data from external sources (databases, cloud storage, etc.) and store data as parquet files in the workspace. DuckDB is not used for storage; it is only the computation engine elsewhere in the application. Ingest flow: External Source → PyArrow Table → Parquet (workspace). - `fetch_data_as_arrow()`: each loader must implement; fetches data as PyArrow Table. - `ingest_to_workspace()`: fetches via Arrow and writes parquet to the given workspace. """ def get_safe_params(self) -> dict[str, Any]: """ Get connection parameters with sensitive values removed. Returns: Dictionary of parameters safe to store in metadata """ if not hasattr(self, 'params'): return {} return { k: v for k, v in self.params.items() if k.lower() not in SENSITIVE_PARAMS } @abstractmethod def fetch_data_as_arrow( self, source_table: str, size: int = 1000000, sort_columns: list[str] | None = None, sort_order: str = 'asc' ) -> pa.Table: """ Fetch data from the external source as a PyArrow Table. This is the primary method for data fetching. Each loader must implement this method to fetch data directly as Arrow format for optimal performance. Only source_table is supported (no raw query strings) to avoid security and dialect diversity issues across loaders. Args: source_table: Full table name (or table identifier) to fetch from size: Maximum number of rows to fetch sort_columns: Columns to sort by before limiting sort_order: Sort direction ('asc' or 'desc') Returns: PyArrow Table with the fetched data Raises: ValueError: If source_table is not provided NotImplementedError: If the loader doesn't support this method yet """ pass def fetch_data_as_dataframe( self, source_table: str, size: int = 1000000, sort_columns: list[str] | None = None, sort_order: str = 'asc' ) -> pd.DataFrame: """ Fetch data from the external source as a pandas DataFrame. This method converts the Arrow table to pandas. For better performance, prefer using `fetch_data_as_arrow()` directly when possible. Args: source_table: Full table name to fetch from size: Maximum number of rows to fetch sort_columns: Columns to sort by before limiting sort_order: Sort direction ('asc' or 'desc') Returns: pandas DataFrame with the fetched data """ arrow_table = self.fetch_data_as_arrow( source_table=source_table, size=size, sort_columns=sort_columns, sort_order=sort_order, ) return arrow_table.to_pandas() def ingest_to_workspace( self, workspace: "Workspace", table_name: str, source_table: str, size: int = 1000000, sort_columns: list[str] | None = None, sort_order: str = 'asc' ) -> "TableMetadata": """ Fetch data from external source and store as parquet in workspace. Uses PyArrow for efficient data transfer: External Source → Arrow → Parquet. This avoids pandas conversion overhead entirely. Args: workspace: The workspace to store data in table_name: Name for the table in the workspace source_table: Full table name to fetch from size: Maximum number of rows to fetch sort_columns: Columns to sort by before limiting sort_order: Sort direction ('asc' or 'desc') Returns: TableMetadata for the created parquet file """ # Fetch data as Arrow table (efficient, no pandas conversion) arrow_table = self.fetch_data_as_arrow( source_table=source_table, size=size, sort_columns=sort_columns, sort_order=sort_order, ) # Prepare loader metadata loader_metadata = { "loader_type": self.__class__.__name__, "loader_params": self.get_safe_params(), "source_table": source_table, } # Write Arrow table directly to parquet (no pandas conversion) table_metadata = workspace.write_parquet_from_arrow( table=arrow_table, table_name=table_name, loader_metadata=loader_metadata, ) logger.info( f"Ingested {arrow_table.num_rows} rows from {self.__class__.__name__} " f"to workspace as {table_name}.parquet" ) return table_metadata @staticmethod @abstractmethod def list_params() -> list[dict[str, Any]]: """Return list of parameters needed to configure this data loader.""" pass @staticmethod @abstractmethod def auth_instructions() -> str: """Return human-readable authentication instructions.""" pass @abstractmethod def __init__(self, params: dict[str, Any]): """ Initialize the data loader. Args: params: Configuration parameters for the loader (e.g. host, credentials). """ pass @abstractmethod def list_tables(self, table_filter: str | None = None) -> list[dict[str, Any]]: """ List available tables (or files) from the data source. Returns: List of dicts with: name (table/file identifier), metadata (row_count, columns, sample_rows). """ pass ================================================ FILE: py-src/data_formulator/data_loader/kusto_data_loader.py ================================================ import json import logging from typing import Any import pandas as pd import pyarrow as pa from data_formulator.data_loader.external_data_loader import ExternalDataLoader, sanitize_table_name from azure.kusto.data import KustoClient, KustoConnectionStringBuilder from azure.kusto.data.helpers import dataframe_from_result_table logger = logging.getLogger(__name__) class KustoDataLoader(ExternalDataLoader): @staticmethod def list_params() -> list[dict[str, Any]]: params_list = [ {"name": "kusto_cluster", "type": "string", "required": True, "description": "e.g., https://mycluster.region.kusto.windows.net"}, {"name": "kusto_database", "type": "string", "required": True, "description": "database name"}, {"name": "client_id", "type": "string", "required": False, "description": "only for App Key auth"}, {"name": "client_secret", "type": "string", "required": False, "description": "only for App Key auth"}, {"name": "tenant_id", "type": "string", "required": False, "description": "only for App Key auth"} ] return params_list @staticmethod def auth_instructions() -> str: return """**Example (CLI):** kusto_cluster: `https://mycluster.westus.kusto.windows.net` · kusto_database: `mydb` **Example (App Key):** kusto_cluster: `https://mycluster.westus.kusto.windows.net` · kusto_database: `mydb` · client_id: `abc-123...` · client_secret: `xyz...` · tenant_id: `def-456...` **Option 1 — Azure CLI (recommended):** Run `az login` in your terminal. Leave `client_id`, `client_secret`, and `tenant_id` empty. **Option 2 — App Key Authentication:** Register an Azure AD application, generate a client secret, and grant it access to your Kusto cluster (e.g., "AllDatabasesViewer" role via Azure Portal → Kusto cluster → Permissions). Provide `client_id`, `client_secret`, and `tenant_id`.""" def __init__(self, params: dict[str, Any]): self.params = params self.kusto_cluster = params.get("kusto_cluster", None) self.kusto_database = params.get("kusto_database", None) self.client_id = params.get("client_id", None) self.client_secret = params.get("client_secret", None) self.tenant_id = params.get("tenant_id", None) try: if self.client_id and self.client_secret and self.tenant_id: self.client = KustoClient(KustoConnectionStringBuilder.with_aad_application_key_authentication( self.kusto_cluster, self.client_id, self.client_secret, self.tenant_id)) else: cluster_url = KustoConnectionStringBuilder.with_az_cli_authentication(self.kusto_cluster) logger.info(f"Connecting to Kusto cluster: {self.kusto_cluster}") self.client = KustoClient(cluster_url) logger.info("Using Azure CLI authentication for Kusto client.") except Exception as e: logger.error(f"Error creating Kusto client: {e}") raise RuntimeError( f"Error creating Kusto client: {e}. " "Please authenticate with Azure CLI (az login) when starting the app." ) from e def _convert_kusto_datetime_columns(self, df: pd.DataFrame) -> pd.DataFrame: """Convert Kusto datetime columns to proper pandas datetime format""" logger.info(f"Processing DataFrame with columns: {list(df.columns)}") logger.info(f"Column dtypes before conversion: {dict(df.dtypes)}") for col in df.columns: original_dtype = df[col].dtype if df[col].dtype == 'object': # Try to identify datetime columns by checking sample values sample_values = df[col].dropna().head(3) if len(sample_values) > 0: # Check if values look like datetime strings or timestamp numbers first_val = sample_values.iloc[0] # Handle Kusto datetime format (ISO 8601 strings) if isinstance(first_val, str) and ('T' in first_val or '-' in first_val): try: # Try to parse as datetime pd.to_datetime(sample_values.iloc[0]) logger.info(f"Converting column '{col}' from string to datetime") df[col] = pd.to_datetime(df[col], errors='coerce', utc=True).dt.tz_localize(None) except Exception as e: logger.debug(f"Failed to convert column '{col}' as string datetime: {e}") # Handle numeric timestamps (Unix timestamps in various formats) elif isinstance(first_val, (int, float)) and first_val > 1000000000: try: # Try different timestamp formats if first_val > 1e15: # Likely microseconds since epoch logger.info(f"Converting column '{col}' from microseconds timestamp to datetime") df[col] = pd.to_datetime(df[col], unit='us', errors='coerce', utc=True).dt.tz_localize(None) elif first_val > 1e12: # Likely milliseconds since epoch logger.info(f"Converting column '{col}' from milliseconds timestamp to datetime") df[col] = pd.to_datetime(df[col], unit='ms', errors='coerce', utc=True).dt.tz_localize(None) else: # Likely seconds since epoch logger.info(f"Converting column '{col}' from seconds timestamp to datetime") df[col] = pd.to_datetime(df[col], unit='s', errors='coerce', utc=True).dt.tz_localize(None) except Exception as e: logger.debug(f"Failed to convert column '{col}' as numeric timestamp: {e}") # Handle datetime64 columns that might have timezone info elif pd.api.types.is_datetime64_any_dtype(df[col]): # Ensure timezone-aware datetimes are properly handled if hasattr(df[col].dt, 'tz') and df[col].dt.tz is not None: logger.info(f"Converting timezone-aware datetime column '{col}' to UTC") df[col] = df[col].dt.tz_convert('UTC').dt.tz_localize(None) # Log if conversion happened if original_dtype != df[col].dtype: logger.info(f"Column '{col}' converted from {original_dtype} to {df[col].dtype}") logger.info(f"Column dtypes after conversion: {dict(df.dtypes)}") return df def query(self, kql: str) -> pd.DataFrame: logger.info(f"Executing KQL query: {kql} on database {self.kusto_database}") result = self.client.execute(self.kusto_database, kql) logger.info(f"Query executed successfully, returning results.") df = dataframe_from_result_table(result.primary_results[0]) # Convert datetime columns properly df = self._convert_kusto_datetime_columns(df) return df def fetch_data_as_arrow( self, source_table: str, size: int = 1000000, sort_columns: list[str] | None = None, sort_order: str = 'asc' ) -> pa.Table: """ Fetch data from Kusto/Azure Data Explorer as a PyArrow Table. Kusto SDK returns pandas, so we convert to Arrow format. Args: source_table: Kusto table name size: Maximum number of rows to fetch sort_columns: Columns to sort by sort_order: Sort direction """ if not source_table: raise ValueError("source_table must be provided") base_query = f"['{source_table}']" # Add sort if specified (KQL syntax) sort_clause = "" if sort_columns and len(sort_columns) > 0: order_direction = "desc" if sort_order == 'desc' else "asc" sort_cols_with_order = [f"{col} {order_direction}" for col in sort_columns] sort_clause = f" | sort by {', '.join(sort_cols_with_order)}" # Add take limit kql_query = f"{base_query}{sort_clause} | take {size}" logger.info(f"Executing Kusto query: {kql_query[:200]}...") # Execute query df = self.query(kql_query) # Convert to Arrow arrow_table = pa.Table.from_pandas(df, preserve_index=False) logger.info(f"Fetched {arrow_table.num_rows} rows from Kusto") return arrow_table def list_tables(self, table_filter: str | None = None) -> list[dict[str, Any]]: query = ".show tables" tables_df = self.query(query) tables = [] for table in tables_df.to_dict(orient="records"): table_name = table['TableName'] # Apply table filter if provided if table_filter and table_filter.lower() not in table_name.lower(): continue schema_result = self.query(f".show table ['{table_name}'] schema as json").to_dict(orient="records") columns = [{ 'name': r["Name"], 'type': r["Type"] } for r in json.loads(schema_result[0]['Schema'])['OrderedColumns']] row_count_result = self.query(f".show table ['{table_name}'] details").to_dict(orient="records") row_count = row_count_result[0]["TotalRowCount"] sample_query = f"['{table_name}'] | take {5}" sample_df = self.query(sample_query) # Convert sample data to JSON with proper datetime handling sample_result = json.loads(sample_df.to_json(orient="records", date_format='iso')) table_metadata = { "row_count": row_count, "columns": columns, "sample_rows": sample_result } tables.append({ "type": "table", "name": table_name, "metadata": table_metadata }) return tables ================================================ FILE: py-src/data_formulator/data_loader/mongodb_data_loader.py ================================================ import json import logging from datetime import datetime import pandas as pd import pyarrow as pa import pymongo from bson import ObjectId from data_formulator.data_loader.external_data_loader import ExternalDataLoader, sanitize_table_name from typing import Any logger = logging.getLogger(__name__) class MongoDBDataLoader(ExternalDataLoader): @staticmethod def list_params() -> list[dict[str, Any]]: params_list = [ {"name": "host", "type": "string", "required": True, "default": "localhost", "description": "server address"}, {"name": "port", "type": "int", "required": False, "default": 27017, "description": "server port"}, {"name": "username", "type": "string", "required": False, "default": "", "description": "leave blank if no auth"}, {"name": "password", "type": "string", "required": False, "default": "", "description": "leave blank if no auth"}, {"name": "database", "type": "string", "required": True, "default": "", "description": "database name"}, {"name": "collection", "type": "string", "required": False, "default": "", "description": "leave empty to list all collections"}, {"name": "authSource", "type": "string", "required": False, "default": "", "description": "auth database (defaults to target database)"} ] return params_list @staticmethod def auth_instructions() -> str: return """**Example:** host: `localhost` · port: `27017` · database: `mydb` · collection: `users` **Local setup:** Ensure MongoDB is running. Leave username and password blank if authentication is not enabled. **Remote setup:** Get host, port, username, and password from your database administrator. **Troubleshooting:** Test with `mongosh --host <host> --port <port>`""" def __init__(self, params: dict[str, Any]): self.params = params self.host = self.params.get("host", "localhost") self.port = int(self.params.get("port", 27017)) self.username = self.params.get("username", "") self.password = self.params.get("password", "") self.database_name = self.params.get("database", "") self.collection_name = self.params.get("collection", "") auth_source = self.params.get("authSource", "") or self.database_name try: if self.username and self.password: self.mongo_client = pymongo.MongoClient( host=self.host, port=self.port, username=self.username, password=self.password, authSource=auth_source ) else: self.mongo_client = pymongo.MongoClient(host=self.host, port=self.port) self.db = self.mongo_client[self.database_name] self.collection = self.db[self.collection_name] if self.collection_name else None logger.info(f"Successfully connected to MongoDB: {self.host}:{self.port}/{self.database_name}") except Exception as e: logger.error(f"Failed to connect to MongoDB: {e}") raise RuntimeError(f"Failed to connect to MongoDB: {e}") from e def close(self): """Close the MongoDB connection.""" if hasattr(self, 'mongo_client') and self.mongo_client is not None: try: self.mongo_client.close() self.mongo_client = None except Exception as e: logger.warning(f"Failed to close MongoDB connection: {e}") def __enter__(self): """Context manager entry""" return self def __exit__(self, exc_type, exc_val, exc_tb): """Context manager exit - ensures connection is closed""" self.close() return False def __del__(self): """Destructor to ensure connection is closed""" self.close() @staticmethod def _flatten_document(doc: dict[str, Any], parent_key: str = '', sep: str = '_') -> dict[str, Any]: """ Use recursion to flatten nested MongoDB documents """ items = [] for key, value in doc.items(): new_key = f"{parent_key}{sep}{key}" if parent_key else key if isinstance(value, dict): items.extend(MongoDBDataLoader._flatten_document(value, new_key, sep).items()) elif isinstance(value, list): if len(value) == 0: items.append((new_key, None)) else: for idx, item in enumerate(value, start=1): item_key = f"{new_key}{sep}{idx}" if isinstance(item, dict): items.extend(MongoDBDataLoader._flatten_document(item, item_key, sep).items()) else: items.append((item_key, item)) else: items.append((new_key, value)) return dict(items) @staticmethod def _convert_special_types(doc: dict[str, Any]) -> dict[str, Any]: """ Convert MongoDB special types (ObjectId, datetime, etc.) to serializable types """ result = {} for key, value in doc.items(): if isinstance(value, ObjectId): result[key] = str(value) elif isinstance(value, datetime): result[key] = value.isoformat() elif isinstance(value, bytes): result[key] = value.decode('utf-8', errors='ignore') elif isinstance(value, dict): result[key] = MongoDBDataLoader._convert_special_types(value) elif isinstance(value, list): result[key] = [ MongoDBDataLoader._convert_special_types(item) if isinstance(item, dict) else str(item) if isinstance(item, ObjectId) else item.isoformat() if isinstance(item, datetime) else item for item in value ] else: result[key] = value return result def _process_documents(self, documents: list[dict[str, Any]]) -> pd.DataFrame: """ Process MongoDB documents list, flatten and convert to DataFrame """ if not documents: return pd.DataFrame() processed_docs = [] for doc in documents: converted = self._convert_special_types(doc) flattened = self._flatten_document(converted) processed_docs.append(flattened) df = pd.DataFrame(processed_docs) return df def fetch_data_as_arrow( self, source_table: str, size: int = 1000000, sort_columns: list[str] | None = None, sort_order: str = 'asc' ) -> pa.Table: """ Fetch data from MongoDB as a PyArrow Table. MongoDB doesn't have native Arrow support, so we fetch documents, process them, and convert to Arrow format. Args: source_table: Collection name to fetch from size: Maximum number of documents to fetch sort_columns: Columns to sort by sort_order: Sort direction ('asc' or 'desc') """ if not source_table: raise ValueError("source_table (collection name) must be provided") # Get collection collection_name = source_table # Handle full table names like "database.collection" if '.' in collection_name: parts = collection_name.split('.') collection_name = parts[-1] collection = self.db[collection_name] logger.info(f"Fetching from MongoDB collection: {collection_name}") # Build cursor with optional sorting data_cursor = collection.find() if sort_columns and len(sort_columns) > 0: sort_direction = -1 if sort_order == 'desc' else 1 sort_spec = [(col, sort_direction) for col in sort_columns] data_cursor = data_cursor.sort(sort_spec) data_cursor = data_cursor.limit(size) # Fetch and process documents data_list = list(data_cursor) if not data_list: logger.warning(f"No data found in MongoDB collection '{collection_name}'") return pa.table({}) df = self._process_documents(data_list) # Convert to Arrow arrow_table = pa.Table.from_pandas(df, preserve_index=False) logger.info(f"Fetched {arrow_table.num_rows} rows from MongoDB collection '{collection_name}'") return arrow_table def list_tables(self, table_filter: str | None = None) -> list[dict[str, Any]]: """ List all collections """ results = [] # Get specified collection or all collections collection_param = self.params.get("collection", "") if collection_param: collection_names = [collection_param] else: collection_names = self.db.list_collection_names() for collection_name in collection_names: # Apply filter if table_filter and table_filter.lower() not in collection_name.lower(): continue try: full_table_name = f"{collection_name}" collection = self.db[collection_name] # Get row count row_count = collection.count_documents({}) # Get sample data sample_data = list(collection.find().limit(10)) if sample_data: df = self._process_documents(sample_data) # Construct column information columns = [{ 'name': col, 'type': str(df[col].dtype) } for col in df.columns] # Convert sample_data for return sample_rows = json.loads(df.to_json(orient="records")) else: columns = [] sample_rows = [] table_metadata = { "row_count": row_count, "columns": columns, "sample_rows": sample_rows } results.append({ "name": full_table_name, "metadata": table_metadata }) except Exception as e: logger.debug(f"Error listing collection {collection_name}: {e}") continue return results ================================================ FILE: py-src/data_formulator/data_loader/mssql_data_loader.py ================================================ import json import logging from typing import Any import pandas as pd import pyarrow as pa import connectorx as cx from data_formulator.data_loader.external_data_loader import ExternalDataLoader, sanitize_table_name log = logging.getLogger(__name__) class MSSQLDataLoader(ExternalDataLoader): @staticmethod def list_params() -> list[dict[str, Any]]: params_list = [ { "name": "server", "type": "string", "required": True, "default": "localhost", "description": "SQL Server host address or instance name", }, { "name": "database", "type": "string", "required": True, "default": "master", "description": "Database name to connect to", }, { "name": "user", "type": "string", "required": False, "default": "", "description": "Username (leave empty for Windows Authentication)", }, { "name": "password", "type": "string", "required": False, "default": "", "description": "Password (leave empty for Windows Authentication)", }, { "name": "port", "type": "string", "required": False, "default": "1433", "description": "SQL Server port (default: 1433)", }, { "name": "driver", "type": "string", "required": False, "default": "ODBC Driver 17 for SQL Server", "description": "ODBC driver name", }, { "name": "encrypt", "type": "string", "required": False, "default": "yes", "description": "Enable encryption (yes/no)", }, { "name": "trust_server_certificate", "type": "string", "required": False, "default": "no", "description": "Trust server certificate (yes/no)", }, { "name": "connection_timeout", "type": "string", "required": False, "default": "30", "description": "Connection timeout in seconds", }, ] return params_list @staticmethod def auth_instructions() -> str: return """**Example (SQL auth):** server: `localhost` · database: `mydb` · user: `sa` · password: `MyP@ss` · port: `1433` **Example (Windows auth):** server: `localhost\\SQLEXPRESS` · database: `mydb` (leave user/password empty) **Prerequisites (macOS/Linux only):** Install ODBC driver: `brew install unixodbc msodbcsql17` (macOS) or `sudo apt-get install unixodbc-dev msodbcsql17` (Ubuntu/Debian). Windows usually has these pre-installed. **Authentication:** - **Windows Auth:** Leave user/password empty (recommended for local dev) - **SQL Server Auth:** Provide username and password **Troubleshooting:** Ensure SQL Server service is running. Verify TCP/IP is enabled in SQL Server Configuration Manager. Test with `sqlcmd -S <server> -d <database> -U <user> -P <password>`.""" def __init__(self, params: dict[str, Any]): log.info(f"Initializing MSSQL DataLoader with parameters: {params}") self.params = params self.server = params.get("server", "localhost") self.database = params.get("database", "master") self.user = params.get("user", "").strip() self.password = params.get("password", "").strip() self.port = params.get("port", "1433") # Build connection URL for connectorx: mssql://user:password@host:port/database # - Use explicit empty password (user:@host) when user is set but password is blank. # - Use 127.0.0.1 when server is localhost to force IPv4 TCP and avoid IPv6 ::1 connection issues. server_for_url = "127.0.0.1" if (self.server or "").strip().lower() == "localhost" else self.server if self.user: self.connection_url = f"mssql://{self.user}:{self.password}@{server_for_url}:{self.port}/{self.database}?TrustServerCertificate=true" else: self.connection_url = f"mssql://{server_for_url}:{self.port}/{self.database}?TrustServerCertificate=true&IntegratedSecurity=true" try: cx.read_sql(self.connection_url, "SELECT 1", return_type="arrow") log.info(f"Successfully connected to SQL Server: {self.server}/{self.database}") except Exception as e: log.error(f"Failed to connect to SQL Server: {e}") raise ValueError(f"Failed to connect to SQL Server '{self.server}': {e}") from e # SQL Server types that connectorx cannot handle natively _CX_SPATIAL_TYPES = {'geometry', 'geography'} # use .STAsText() _CX_OTHER_UNSUPPORTED = {'hierarchyid', 'xml', 'sql_variant', 'image', 'timestamp'} _CX_UNSUPPORTED_TYPES = _CX_SPATIAL_TYPES | _CX_OTHER_UNSUPPORTED def _safe_select_list(self, schema: str, table_name: str) -> str: """Build a SELECT column list that converts unsupported types to text. Uses .STAsText() for spatial types, CAST(... AS NVARCHAR(MAX)) for others. Returns '*' if no unsupported columns are found.""" try: columns_query = f""" SELECT COLUMN_NAME, DATA_TYPE FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = '{schema}' AND TABLE_NAME = '{table_name}' ORDER BY ORDINAL_POSITION """ cols_df = self._execute_query_raw(columns_query).to_pandas() has_unsupported = any(r['DATA_TYPE'].lower() in self._CX_UNSUPPORTED_TYPES for _, r in cols_df.iterrows()) if not has_unsupported: return "*" parts = [] for _, r in cols_df.iterrows(): col, dtype = r['COLUMN_NAME'], r['DATA_TYPE'].lower() if dtype in self._CX_SPATIAL_TYPES: parts.append(f"[{col}].STAsText() AS [{col}]") elif dtype in self._CX_OTHER_UNSUPPORTED: parts.append(f"CAST([{col}] AS NVARCHAR(MAX)) AS [{col}]") else: parts.append(f"[{col}]") return ', '.join(parts) except Exception: return "*" def _execute_query_raw(self, query: str) -> pa.Table: """Execute a query via connectorx (no error wrapping).""" return cx.read_sql(self.connection_url, query, return_type="arrow") def _execute_query(self, query: str) -> pa.Table: """Execute a query and return results as a PyArrow Table (via connectorx).""" try: return cx.read_sql(self.connection_url, query, return_type="arrow") except Exception as e: log.error(f"Failed to execute query: {e}") raise def fetch_data_as_arrow( self, source_table: str, size: int = 1000000, sort_columns: list[str] | None = None, sort_order: str = 'asc' ) -> pa.Table: """ Fetch data from SQL Server as a PyArrow Table using connectorx. """ if not source_table: raise ValueError("source_table must be provided") # Parse table name if "." in source_table: schema, table = source_table.split(".", 1) else: schema = "dbo" table = source_table col_list = self._safe_select_list(schema.strip('[]'), table.strip('[]')) base_query = f"SELECT {col_list} FROM [{schema}].[{table}]" # Add ORDER BY if sort columns specified order_by_clause = "" if sort_columns and len(sort_columns) > 0: order_direction = "DESC" if sort_order == 'desc' else "ASC" sanitized_cols = [f'[{col}] {order_direction}' for col in sort_columns] order_by_clause = f" ORDER BY {', '.join(sanitized_cols)}" # SQL Server uses TOP instead of LIMIT query = f"SELECT TOP {size} * FROM ({base_query}{order_by_clause}) AS limited" log.info(f"Executing SQL Server query: {query[:200]}...") arrow_table = cx.read_sql(self.connection_url, query, return_type="arrow") log.info(f"Fetched {arrow_table.num_rows} rows from SQL Server [Arrow-native]") return arrow_table def list_tables(self, table_filter: str | None = None) -> list[dict[str, Any]]: """List all tables from SQL Server database.""" try: tables_query = """ SELECT TABLE_SCHEMA, TABLE_NAME, TABLE_TYPE FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_TYPE = 'BASE TABLE' AND TABLE_SCHEMA NOT IN ('sys', 'INFORMATION_SCHEMA') ORDER BY TABLE_SCHEMA, TABLE_NAME """ tables_df = self._execute_query(tables_query).to_pandas() results = [] for _, row in tables_df.iterrows(): schema = row["TABLE_SCHEMA"] table_name = row["TABLE_NAME"] table_type = row.get("TABLE_TYPE", "BASE TABLE") full_table_name = f"{schema}.{table_name}" if table_filter and table_filter.lower() not in full_table_name.lower(): continue try: # Get column information columns_query = f""" SELECT COLUMN_NAME, DATA_TYPE, IS_NULLABLE, COLUMN_DEFAULT, CHARACTER_MAXIMUM_LENGTH, NUMERIC_PRECISION, NUMERIC_SCALE FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = '{schema}' AND TABLE_NAME = '{table_name}' ORDER BY ORDINAL_POSITION """ columns_df = self._execute_query(columns_query).to_pandas() columns = [] for _, col_row in columns_df.iterrows(): col_info = { "name": col_row["COLUMN_NAME"], "type": col_row["DATA_TYPE"], "nullable": col_row["IS_NULLABLE"] == "YES", "default": col_row["COLUMN_DEFAULT"], } # Add length/precision info for relevant types with NaN handling if ( col_row["CHARACTER_MAXIMUM_LENGTH"] is not None and not pd.isna(col_row["CHARACTER_MAXIMUM_LENGTH"]) ): try: col_info["max_length"] = int(col_row["CHARACTER_MAXIMUM_LENGTH"]) except (ValueError, TypeError): pass # Skip if conversion fails if ( col_row["NUMERIC_PRECISION"] is not None and not pd.isna(col_row["NUMERIC_PRECISION"]) ): try: col_info["precision"] = int(col_row["NUMERIC_PRECISION"]) except (ValueError, TypeError): pass # Skip if conversion fails if ( col_row["NUMERIC_SCALE"] is not None and not pd.isna(col_row["NUMERIC_SCALE"]) ): try: col_info["scale"] = int(col_row["NUMERIC_SCALE"]) except (ValueError, TypeError): pass # Skip if conversion fails columns.append(col_info) # Build safe column list (casts unsupported types to NVARCHAR) col_list = self._safe_select_list(schema, table_name) # Get sample data (first 10 rows) sample_rows = [] sample_query = f"SELECT TOP 10 {col_list} FROM [{schema}].[{table_name}]" try: sample_df = self._execute_query(sample_query).to_pandas() sample_df_clean = sample_df.fillna(value=None) sample_rows = json.loads( sample_df_clean.to_json( orient="records", date_format="iso", default_handler=str ) ) except Exception as e: log.warning( f"Failed to sample table {schema}.{table_name}: {e}" ) # Get row count count_query = f"SELECT COUNT(*) as row_count FROM [{schema}].[{table_name}]" count_df = self._execute_query(count_query).to_pandas() # Handle NaN values in row count raw_count = count_df.iloc[0]["row_count"] if pd.isna(raw_count): row_count = 0 log.warning( f"Row count for table {schema}.{table_name} returned NaN, using 0" ) else: try: row_count = int(raw_count) except (ValueError, TypeError): row_count = 0 log.warning( f"Could not convert row count '{raw_count}' to integer for table {schema}.{table_name}, using 0" ) table_metadata = { "row_count": row_count, "columns": columns, "sample_rows": sample_rows, "table_type": table_type, } results.append({"name": full_table_name, "metadata": table_metadata}) except Exception as e: log.warning(f"Failed to get metadata for table {full_table_name}: {e}") # Add table without detailed metadata results.append( { "name": full_table_name, "metadata": { "row_count": 0, "columns": [], "sample_rows": [], "table_type": table_type, }, } ) except Exception as e: log.error(f"Failed to list tables from SQL Server: {e}") results = [] return results ================================================ FILE: py-src/data_formulator/data_loader/mysql_data_loader.py ================================================ import json import logging from typing import Any import pandas as pd import pyarrow as pa import connectorx as cx from data_formulator.data_loader.external_data_loader import ExternalDataLoader logger = logging.getLogger(__name__) class MySQLDataLoader(ExternalDataLoader): @staticmethod def list_params() -> list[dict[str, Any]]: params_list = [ {"name": "user", "type": "string", "required": True, "default": "root", "description": "MySQL username"}, {"name": "password", "type": "string", "required": False, "default": "", "description": "leave blank for no password"}, {"name": "host", "type": "string", "required": True, "default": "localhost", "description": "server address"}, {"name": "port", "type": "int", "required": False, "default": 3306, "description": "server port"}, {"name": "database", "type": "string", "required": True, "default": "mysql", "description": "database name"} ] return params_list @staticmethod def auth_instructions() -> str: return """**Example:** user: `root` · host: `localhost` · port: `3306` · database: `mydb` **Local setup:** Ensure MySQL is running — `brew services list` (macOS) or `systemctl status mysql` (Linux). Leave password blank if none is set. **Remote setup:** Get host, port, username, and password from your database administrator. Ensure the server allows remote connections and your IP is whitelisted. **Troubleshooting:** Test with `mysql -u <user> -p -h <host> -P <port> <database>`""" def __init__(self, params: dict[str, Any]): self.params = params self.host = self.params.get("host", "") self.user = self.params.get("user", "") self.password = self.params.get("password", "") self.database = self.params.get("database", "") if not self.host: raise ValueError("MySQL host is required") if not self.user: raise ValueError("MySQL user is required") if not self.database: raise ValueError("MySQL database is required") port = self.params.get("port", "") if isinstance(port, str): self.port = int(port) if port else 3306 elif not port: self.port = 3306 else: self.port = int(port) # Build connection URL for connectorx # Format: mysql://user:password@host:port/database # - Use explicit empty password (user:@host) so the URL parser sees user vs password correctly. # - Use 127.0.0.1 when host is localhost to force IPv4 TCP and avoid IPv6 ::1 connection issues. host_for_url = "127.0.0.1" if (self.host or "").strip().lower() == "localhost" else self.host if self.password: self.connection_url = f"mysql://{self.user}:{self.password}@{host_for_url}:{self.port}/{self.database}" else: self.connection_url = f"mysql://{self.user}:@{host_for_url}:{self.port}/{self.database}" self._sanitized_url = f"mysql://{self.user}:***@{self.host}:{self.port}/{self.database}" # Test connection try: cx.read_sql(self.connection_url, "SELECT 1", return_type="arrow") except Exception as e: logger.error(f"Failed to connect to MySQL (mysql://{self.user}:***@{self.host}:{self.port}/{self.database}): {e}") raise ValueError(f"Failed to connect to MySQL database '{self.database}' on host '{self.host}': {e}") from e logger.info(f"Successfully connected to MySQL: mysql://{self.user}:***@{self.host}:{self.port}/{self.database}") # MySQL types that connectorx cannot handle natively _CX_GEOMETRY_TYPES = {'geometry', 'point', 'linestring', 'polygon', 'multipoint', 'multilinestring', 'multipolygon', 'geometrycollection'} _CX_OTHER_UNSUPPORTED = {'bit'} _CX_UNSUPPORTED_TYPES = _CX_GEOMETRY_TYPES | _CX_OTHER_UNSUPPORTED def _safe_select_list(self, schema: str, table_name: str) -> str: """Build a SELECT column list that converts unsupported types to text. Uses ST_AsText() for geometry types, CAST(... AS CHAR) for others. Returns '*' if no unsupported columns are found.""" try: columns_query = f""" SELECT COLUMN_NAME, DATA_TYPE FROM information_schema.columns WHERE TABLE_SCHEMA = '{schema}' AND TABLE_NAME = '{table_name}' ORDER BY ORDINAL_POSITION """ cols_arrow = cx.read_sql(self.connection_url, columns_query, return_type="arrow") cols_df = cols_arrow.to_pandas() has_unsupported = any(r['DATA_TYPE'].lower() in self._CX_UNSUPPORTED_TYPES for _, r in cols_df.iterrows()) if not has_unsupported: return "*" parts = [] for _, r in cols_df.iterrows(): col, dtype = r['COLUMN_NAME'], r['DATA_TYPE'].lower() if dtype in self._CX_GEOMETRY_TYPES: parts.append(f"ST_AsText(`{col}`) AS `{col}`") elif dtype in self._CX_OTHER_UNSUPPORTED: parts.append(f"CAST(`{col}` AS CHAR) AS `{col}`") else: parts.append(f"`{col}`") return ', '.join(parts) except Exception: return "*" def fetch_data_as_arrow( self, source_table: str, size: int = 1000000, sort_columns: list[str] | None = None, sort_order: str = 'asc' ) -> pa.Table: """ Fetch data from MySQL as a PyArrow Table using connectorx. connectorx provides extremely fast Arrow-native database access. """ if not source_table: raise ValueError("source_table must be provided") # Handle table names and build safe column list if '.' in source_table: parts = source_table.split('.', 1) col_list = self._safe_select_list(parts[0].strip('`'), parts[1].strip('`')) base_query = f"SELECT {col_list} FROM {source_table}" else: col_list = self._safe_select_list(self.database, source_table.strip('`')) base_query = f"SELECT {col_list} FROM `{source_table}`" # Add ORDER BY if sort columns specified order_by_clause = "" if sort_columns and len(sort_columns) > 0: order_direction = "DESC" if sort_order == 'desc' else "ASC" sanitized_cols = [f'`{col}` {order_direction}' for col in sort_columns] order_by_clause = f" ORDER BY {', '.join(sanitized_cols)}" query = f"{base_query}{order_by_clause} LIMIT {size}" logger.info(f"Executing MySQL query via connectorx: {query[:200]}...") arrow_table = cx.read_sql(self.connection_url, query, return_type="arrow") logger.info(f"Fetched {arrow_table.num_rows} rows from MySQL [Arrow-native]") return arrow_table def list_tables(self, table_filter: str | None = None) -> list[dict[str, Any]]: """List available tables from MySQL database.""" return self._list_tables_connectorx(table_filter) def _list_tables_connectorx(self, table_filter: str | None = None) -> list[dict[str, Any]]: """List tables using connectorx.""" try: tables_query = f""" SELECT TABLE_SCHEMA, TABLE_NAME FROM information_schema.tables WHERE TABLE_SCHEMA = '{self.database}' AND TABLE_TYPE = 'BASE TABLE' """ tables_arrow = cx.read_sql(self.connection_url, tables_query, return_type="arrow") tables_df = tables_arrow.to_pandas() if tables_df.empty: return [] results = [] for _, row in tables_df.iterrows(): schema = row['TABLE_SCHEMA'] table_name = row['TABLE_NAME'] if table_filter and table_filter.lower() not in table_name.lower(): continue full_table_name = f"{schema}.{table_name}" try: # Get column information columns_query = f""" SELECT COLUMN_NAME, DATA_TYPE FROM information_schema.columns WHERE TABLE_SCHEMA = '{schema}' AND TABLE_NAME = '{table_name}' ORDER BY ORDINAL_POSITION """ columns_arrow = cx.read_sql(self.connection_url, columns_query, return_type="arrow") columns_df = columns_arrow.to_pandas() columns = [{ 'name': col_row['COLUMN_NAME'], 'type': col_row['DATA_TYPE'] } for _, col_row in columns_df.iterrows()] # Build safe column list (casts unsupported types to CHAR) col_list = self._safe_select_list(schema, table_name) # Get sample data sample_rows = [] sample_query = f"SELECT {col_list} FROM `{schema}`.`{table_name}` LIMIT 10" try: sample_arrow = cx.read_sql(self.connection_url, sample_query, return_type="arrow") sample_df = sample_arrow.to_pandas() sample_rows = json.loads(sample_df.to_json(orient="records", date_format='iso')) except Exception as sample_err: logger.warning(f"Could not sample {full_table_name}: {sample_err}") # Get row count count_query = f"SELECT COUNT(*) as cnt FROM `{schema}`.`{table_name}`" count_arrow = cx.read_sql(self.connection_url, count_query, return_type="arrow") row_count = int(count_arrow.to_pandas()['cnt'].iloc[0]) table_metadata = { "row_count": row_count, "columns": columns, "sample_rows": sample_rows } results.append({ "name": full_table_name, "metadata": table_metadata }) except Exception as e: logger.warning(f"Error processing table {full_table_name}: {e}") continue return results except Exception as e: logger.error(f"Error listing tables: {e}") return [] ================================================ FILE: py-src/data_formulator/data_loader/postgresql_data_loader.py ================================================ import json import logging from typing import Any import pandas as pd import pyarrow as pa import connectorx as cx from data_formulator.data_loader.external_data_loader import ExternalDataLoader logger = logging.getLogger(__name__) class PostgreSQLDataLoader(ExternalDataLoader): @staticmethod def list_params() -> list[dict[str, Any]]: params_list = [ {"name": "user", "type": "string", "required": True, "default": "postgres", "description": "PostgreSQL username"}, {"name": "password", "type": "string", "required": False, "default": "", "description": "leave blank for no password"}, {"name": "host", "type": "string", "required": True, "default": "localhost", "description": "PostgreSQL host"}, {"name": "port", "type": "string", "required": False, "default": "5432", "description": "PostgreSQL port"}, {"name": "database", "type": "string", "required": True, "default": "postgres", "description": "PostgreSQL database name"} ] return params_list @staticmethod def auth_instructions() -> str: return """**Example:** user: `postgres` · host: `localhost` · port: `5432` · database: `mydb` **Local setup:** Ensure PostgreSQL is running — `brew services list` (macOS) or `systemctl status postgresql` (Linux). Leave password blank if none is set. **Remote setup:** Get host, port, username, and password from your database administrator. The user must have SELECT permissions on the tables you want to access. **Troubleshooting:** Test with `psql -U <user> -h <host> -p <port> -d <database>`""" def __init__(self, params: dict[str, Any]): self.params = params self.host = self.params.get("host", "") self.port = self.params.get("port", "") or "5432" self.user = self.params.get("user", "") self.database = self.params.get("database", "") self.password = self.params.get("password", "") if not self.host: raise ValueError("PostgreSQL host is required") if not self.user: raise ValueError("PostgreSQL user is required") if not self.database: raise ValueError("PostgreSQL database is required") # Build connection URL for connectorx: postgresql://user:password@host:port/database # - Use explicit empty password (user:@host) so the URL parser sees user vs password correctly. # - Use 127.0.0.1 when host is localhost to force IPv4 TCP and avoid IPv6 ::1 connection issues. host_for_url = "127.0.0.1" if (self.host or "").strip().lower() == "localhost" else self.host if self.password: self.connection_url = f"postgresql://{self.user}:{self.password}@{host_for_url}:{self.port}/{self.database}" else: self.connection_url = f"postgresql://{self.user}:@{host_for_url}:{self.port}/{self.database}" try: cx.read_sql(self.connection_url, "SELECT 1", return_type="arrow") except Exception as e: logger.error(f"Failed to connect to PostgreSQL (postgresql://{self.user}:***@{self.host}:{self.port}/{self.database}): {e}") raise ValueError(f"Failed to connect to PostgreSQL database '{self.database}' on host '{self.host}': {e}") from e logger.info(f"Successfully connected to PostgreSQL: postgresql://{self.user}:***@{self.host}:{self.port}/{self.database}") # PostgreSQL types that connectorx cannot handle natively _CX_SPATIAL_TYPES = {'geometry', 'geography'} # PostGIS types → ST_AsText() _CX_OTHER_UNSUPPORTED = {'box', 'circle', 'line', 'lseg', 'path', 'point', 'polygon', 'bit', 'bit varying', 'xml', 'tsvector', 'tsquery'} _CX_UNSUPPORTED_TYPES = _CX_SPATIAL_TYPES | _CX_OTHER_UNSUPPORTED def _safe_select_list(self, schema: str, table_name: str) -> str: """Build a SELECT column list that converts unsupported types to text. Uses ST_AsText() for PostGIS types, ::text for others. Returns '*' if no unsupported columns are found.""" try: columns_query = f""" SELECT column_name, udt_name FROM information_schema.columns WHERE table_schema = '{schema}' AND table_name = '{table_name}' ORDER BY ordinal_position """ cols_arrow = cx.read_sql(self.connection_url, columns_query, return_type="arrow") cols_df = cols_arrow.to_pandas() has_unsupported = any(r['udt_name'].lower() in self._CX_UNSUPPORTED_TYPES for _, r in cols_df.iterrows()) if not has_unsupported: return "*" parts = [] for _, r in cols_df.iterrows(): col, dtype = r['column_name'], r['udt_name'].lower() if dtype in self._CX_SPATIAL_TYPES: parts.append(f'ST_AsText("{col}") AS "{col}"') elif dtype in self._CX_OTHER_UNSUPPORTED: parts.append(f'"{col}"::text AS "{col}"') else: parts.append(f'"{col}"') return ', '.join(parts) except Exception: return "*" def fetch_data_as_arrow( self, source_table: str, size: int = 1000000, sort_columns: list[str] | None = None, sort_order: str = 'asc' ) -> pa.Table: """ Fetch data from PostgreSQL as a PyArrow Table using connectorx. connectorx provides extremely fast Arrow-native data access, typically 2-10x faster than pandas-based approaches. """ if not source_table: raise ValueError("source_table must be provided") # Handle table names like "mypostgresdb.schema.table" -> "schema.table" table_ref = source_table if source_table.startswith("mypostgresdb."): table_ref = source_table[len("mypostgresdb."):] # Build safe column list for the resolved schema.table if '.' in table_ref: s, t = table_ref.split('.', 1) col_list = self._safe_select_list(s.strip('"'), t.strip('"')) else: col_list = self._safe_select_list('public', table_ref.strip('"')) base_query = f"SELECT {col_list} FROM {table_ref}" # Add ORDER BY if sort columns specified order_by_clause = "" if sort_columns and len(sort_columns) > 0: order_direction = "DESC" if sort_order == 'desc' else "ASC" sanitized_cols = [f'"{col}" {order_direction}' for col in sort_columns] order_by_clause = f" ORDER BY {', '.join(sanitized_cols)}" # Build full query with limit query = f"{base_query}{order_by_clause} LIMIT {size}" logger.info(f"Executing PostgreSQL query via connectorx: {query[:200]}...") # Execute with connectorx - returns Arrow table directly arrow_table = cx.read_sql(self.connection_url, query, return_type="arrow") logger.info(f"Fetched {arrow_table.num_rows} rows from PostgreSQL [Arrow-native]") return arrow_table def list_tables(self, table_filter: str | None = None) -> list[dict[str, Any]]: """List available tables from PostgreSQL.""" return self._list_tables_connectorx(table_filter) def _list_tables_connectorx(self, table_filter: str | None = None) -> list[dict[str, Any]]: """List tables using connectorx.""" try: # Query tables from information_schema query = """ SELECT table_schema as schemaname, table_name as tablename FROM information_schema.tables WHERE table_schema NOT IN ('information_schema', 'pg_catalog', 'pg_toast') AND table_schema NOT LIKE '%_intern%' AND table_schema NOT LIKE '%timescaledb%' AND table_name NOT LIKE '%/%' AND table_type = 'BASE TABLE' ORDER BY table_schema, table_name """ tables_arrow = cx.read_sql(self.connection_url, query, return_type="arrow") tables_df = tables_arrow.to_pandas() logger.info(f"Found {len(tables_df)} tables") results = [] for _, row in tables_df.iterrows(): schema = row['schemaname'] table_name = row['tablename'] full_table_name = f"{schema}.{table_name}" # Apply filter if provided if table_filter and table_filter.lower() not in full_table_name.lower(): continue try: # Get column information columns_query = f""" SELECT column_name, data_type FROM information_schema.columns WHERE table_schema = '{schema}' AND table_name = '{table_name}' ORDER BY ordinal_position """ columns_arrow = cx.read_sql(self.connection_url, columns_query, return_type="arrow") columns_df = columns_arrow.to_pandas() columns = [{ 'name': col_row['column_name'], 'type': col_row['data_type'] } for _, col_row in columns_df.iterrows()] # Build safe column list (casts unsupported types to TEXT) col_list = self._safe_select_list(schema, table_name) # Get sample data sample_rows = [] sample_query = f'SELECT {col_list} FROM "{schema}"."{table_name}" LIMIT 10' try: sample_arrow = cx.read_sql(self.connection_url, sample_query, return_type="arrow") sample_df = sample_arrow.to_pandas() sample_rows = json.loads(sample_df.to_json(orient="records")) except Exception as sample_err: logger.warning(f"Could not sample {full_table_name}: {sample_err}") # Get row count count_query = f'SELECT COUNT(*) as cnt FROM "{schema}"."{table_name}"' count_arrow = cx.read_sql(self.connection_url, count_query, return_type="arrow") row_count = count_arrow.to_pandas()['cnt'].iloc[0] table_metadata = { "row_count": int(row_count), "columns": columns, "sample_rows": sample_rows } results.append({ "name": full_table_name, "metadata": table_metadata }) except Exception as e: logger.warning(f"Error processing table {full_table_name}: {e}") continue return results except Exception as e: logger.error(f"Error listing tables: {e}") return [] ================================================ FILE: py-src/data_formulator/data_loader/s3_data_loader.py ================================================ import json import logging from typing import Any import boto3 import pandas as pd import pyarrow as pa import pyarrow.csv as pa_csv import pyarrow.parquet as pq from pyarrow import fs as pa_fs from data_formulator.data_loader.external_data_loader import ExternalDataLoader logger = logging.getLogger(__name__) class S3DataLoader(ExternalDataLoader): @staticmethod def list_params() -> list[dict[str, Any]]: params_list = [ {"name": "aws_access_key_id", "type": "string", "required": True, "default": "", "description": "AWS access key ID"}, {"name": "aws_secret_access_key", "type": "string", "required": True, "default": "", "description": "AWS secret access key"}, {"name": "aws_session_token", "type": "string", "required": False, "default": "", "description": "AWS session token (required for temporary credentials)"}, {"name": "region_name", "type": "string", "required": True, "default": "us-east-1", "description": "AWS region name"}, {"name": "bucket", "type": "string", "required": True, "default": "", "description": "S3 bucket name"} ] return params_list @staticmethod def auth_instructions() -> str: return """**Example:** aws_access_key_id: `AKIA...` · aws_secret_access_key: `wJalr...` · region_name: `us-east-1` · bucket: `my-data-bucket` **Getting credentials:** AWS Console → IAM → Users → Security credentials → Create access key → choose "Application running outside AWS". **Required permissions:** `s3:GetObject` and `s3:ListBucket` on your bucket. **Supported formats:** CSV, Parquet, JSON, JSONL""" def __init__(self, params: dict[str, Any]): self.params = params self.aws_access_key_id = params.get("aws_access_key_id", "") self.aws_secret_access_key = params.get("aws_secret_access_key", "") self.aws_session_token = params.get("aws_session_token", "") self.region_name = params.get("region_name", "us-east-1") self.bucket = params.get("bucket", "") self.s3_fs = pa_fs.S3FileSystem( access_key=self.aws_access_key_id, secret_key=self.aws_secret_access_key, session_token=self.aws_session_token if self.aws_session_token else None, region=self.region_name, ) logger.info(f"Initialized PyArrow S3 filesystem for bucket: {self.bucket}") def fetch_data_as_arrow( self, source_table: str, size: int = 1000000, sort_columns: list[str] | None = None, sort_order: str = 'asc' ) -> pa.Table: """ Fetch data from S3 as a PyArrow Table using PyArrow's native S3 filesystem. For files (parquet, csv), reads directly using PyArrow. """ if not source_table: raise ValueError("source_table (S3 URL) must be provided") s3_url = source_table # Parse S3 URL: s3://bucket/key -> bucket/key for PyArrow if s3_url.startswith("s3://"): s3_path = s3_url[5:] # Remove "s3://" else: s3_path = f"{self.bucket}/{s3_url}" logger.info(f"Reading S3 file via PyArrow: {s3_url}") # Read based on file extension if s3_url.lower().endswith('.parquet'): arrow_table = pq.read_table(s3_path, filesystem=self.s3_fs) elif s3_url.lower().endswith('.csv'): with self.s3_fs.open_input_file(s3_path) as f: arrow_table = pa_csv.read_csv(f) elif s3_url.lower().endswith('.json') or s3_url.lower().endswith('.jsonl'): import pyarrow.json as pa_json with self.s3_fs.open_input_file(s3_path) as f: arrow_table = pa_json.read_json(f) else: raise ValueError(f"Unsupported file type: {s3_url}") # Apply sorting if specified if sort_columns and len(sort_columns) > 0: df = arrow_table.to_pandas() ascending = sort_order != 'desc' df = df.sort_values(by=sort_columns, ascending=ascending) arrow_table = pa.Table.from_pandas(df, preserve_index=False) # Apply size limit if arrow_table.num_rows > size: arrow_table = arrow_table.slice(0, size) logger.info(f"Fetched {arrow_table.num_rows} rows from S3 [Arrow-native]") return arrow_table def list_tables(self, table_filter: str | None = None) -> list[dict[str, Any]]: """List available files from S3 bucket.""" s3_client = boto3.client( 's3', aws_access_key_id=self.aws_access_key_id, aws_secret_access_key=self.aws_secret_access_key, aws_session_token=self.aws_session_token if self.aws_session_token else None, region_name=self.region_name ) response = s3_client.list_objects_v2(Bucket=self.bucket) results = [] if 'Contents' in response: for obj in response['Contents']: key = obj['Key'] if key.endswith('/') or not self._is_supported_file(key): continue if table_filter and table_filter.lower() not in key.lower(): continue s3_url = f"s3://{self.bucket}/{key}" try: sample_table = self._read_sample_arrow(s3_url, 10) sample_df = sample_table.to_pandas() columns = [{ 'name': col, 'type': str(sample_df[col].dtype) } for col in sample_df.columns] sample_rows = json.loads(sample_df.to_json(orient="records")) row_count = self._estimate_row_count(s3_url) table_metadata = { "row_count": row_count, "columns": columns, "sample_rows": sample_rows } results.append({ "name": s3_url, "metadata": table_metadata }) except Exception as e: logger.warning(f"Error reading {s3_url}: {e}") continue return results def _read_sample_arrow(self, s3_url: str, limit: int) -> pa.Table: """Read sample data using PyArrow S3 filesystem.""" s3_path = s3_url[5:] if s3_url.startswith("s3://") else s3_url if s3_url.lower().endswith('.parquet'): table = pq.read_table(s3_path, filesystem=self.s3_fs) elif s3_url.lower().endswith('.csv'): with self.s3_fs.open_input_file(s3_path) as f: table = pa_csv.read_csv(f) elif s3_url.lower().endswith('.json') or s3_url.lower().endswith('.jsonl'): import pyarrow.json as pa_json with self.s3_fs.open_input_file(s3_path) as f: table = pa_json.read_json(f) else: raise ValueError(f"Unsupported file type: {s3_url}") return table.slice(0, limit) if table.num_rows > limit else table def _is_supported_file(self, key: str) -> bool: """Check if the file type is supported (CSV, Parquet, JSON).""" supported_extensions = [".csv", ".parquet", ".json", ".jsonl"] return any(key.lower().endswith(ext) for ext in supported_extensions) def _estimate_row_count(self, s3_url: str) -> int: """Estimate the number of rows in a file.""" try: # For parquet files, use PyArrow metadata for exact count if s3_url.lower().endswith('.parquet'): s3_path = s3_url[5:] if s3_url.startswith("s3://") else s3_url parquet_file = pq.ParquetFile(s3_path, filesystem=self.s3_fs) return parquet_file.metadata.num_rows # For CSV, JSON, and JSONL files, skip row count for efficiency return 0 except Exception as e: logger.warning(f"Error estimating row count for {s3_url}: {e}") return 0 ================================================ FILE: py-src/data_formulator/datalake/__init__.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ Data Lake module for Data Formulator. This module provides a unified data management layer that: - Manages user workspaces with identity-based directories - Stores user-uploaded files as-is (CSV, Excel, TXT, HTML, JSON, PDF) - Stores data from external loaders as parquet via pyarrow - Tracks all data sources in a workspace.yaml metadata file Example usage: from data_formulator.datalake import Workspace, save_uploaded_file, write_parquet # Get or create a workspace for a user workspace = Workspace("user:123") # Save an uploaded file with open("sales.csv", "rb") as f: metadata = save_uploaded_file(workspace, f.read(), "sales.csv") # Write a DataFrame as parquet (typically from data loaders) import pandas as pd df = pd.DataFrame({"id": [1, 2, 3], "name": ["a", "b", "c"]}) metadata = write_parquet(workspace, df, "customers") # List tables in workspace tables = workspace.list_tables() # Read parquet back df = read_parquet(workspace, "customers") """ # Workspace management from data_formulator.datalake.workspace import ( Workspace, WorkspaceWithTempData, get_data_formulator_home, get_default_workspace_root, ) from data_formulator.datalake.azure_blob_workspace import AzureBlobWorkspace from data_formulator.datalake.cached_azure_blob_workspace import CachedAzureBlobWorkspace from data_formulator.datalake.cache_manager import GlobalCacheManager # Metadata types and operations from data_formulator.datalake.metadata import ( TableMetadata, ColumnInfo, WorkspaceMetadata, load_metadata, save_metadata, metadata_exists, METADATA_VERSION, METADATA_FILENAME, ) # File operations (for user uploads) from data_formulator.datalake.file_manager import ( save_uploaded_file, save_uploaded_file_from_path, is_supported_file, get_file_type, get_file_info, SUPPORTED_EXTENSIONS, ) # Parquet utilities (pure helpers, no Workspace dependency) from data_formulator.datalake.parquet_utils import ( sanitize_table_name, DEFAULT_COMPRESSION, ) __all__ = [ # Workspace "Workspace", "WorkspaceWithTempData", "AzureBlobWorkspace", "CachedAzureBlobWorkspace", "GlobalCacheManager", "get_data_formulator_home", "get_default_workspace_root", # Metadata "TableMetadata", "ColumnInfo", "WorkspaceMetadata", "load_metadata", "save_metadata", "metadata_exists", "METADATA_VERSION", "METADATA_FILENAME", # File manager "save_uploaded_file", "save_uploaded_file_from_path", "get_supported_extensions", "is_supported_file", "get_file_type", "get_file_info", "SUPPORTED_EXTENSIONS", # Parquet utilities "sanitize_table_name", "DEFAULT_COMPRESSION", ] ================================================ FILE: py-src/data_formulator/datalake/azure_blob_workspace.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ Azure Blob Storage–backed workspace for the Data Lake. Drop-in replacement for :class:`Workspace` where every file (data files **and** ``workspace.yaml`` metadata) lives as a blob under:: <container>/<datalake_root>/<sanitized_identity_id>/ Requires ``azure-storage-blob`` (``pip install azure-storage-blob``). Usage:: from azure.storage.blob import ContainerClient container = ContainerClient.from_connection_string(conn_str, "my-container") ws = AzureBlobWorkspace("user:42", container, datalake_root="workspaces") """ from __future__ import annotations import io import json import logging import shutil import tempfile import zipfile from contextlib import contextmanager from datetime import datetime, timezone from pathlib import Path from typing import Any, Optional, TYPE_CHECKING import pandas as pd import pyarrow as pa import pyarrow.parquet as pq import yaml from data_formulator.datalake.metadata import ( WorkspaceMetadata, TableMetadata, METADATA_FILENAME, ) from werkzeug.utils import secure_filename from data_formulator.datalake.parquet_utils import ( sanitize_table_name, get_arrow_column_info, compute_arrow_table_hash, get_column_info, compute_dataframe_hash, sanitize_dataframe_for_arrow, DEFAULT_COMPRESSION, ) from data_formulator.datalake.workspace import Workspace if TYPE_CHECKING: from azure.storage.blob import ContainerClient logger = logging.getLogger(__name__) class AzureBlobWorkspace(Workspace): """ Workspace backed by Azure Blob Storage. All files (data + ``workspace.yaml``) are stored as blobs under ``<datalake_root>/<sanitized_identity_id>/`` inside the given container. Inherits from :class:`Workspace` so it is a drop-in replacement everywhere a ``Workspace`` is expected. Methods that only call other (overridden) methods — ``add_table_metadata``, ``get_table_metadata``, ``list_tables``, ``get_fresh_name``, ``get_relative_data_file_path``, ``refresh_parquet*`` — are inherited unchanged. """ # ------------------------------------------------------------------ # Construction # ------------------------------------------------------------------ def __init__( self, identity_id: str, container_client: ContainerClient, datalake_root: str = "", ): """ Args: identity_id: Unique user identifier (e.g. ``"user:123"``). container_client: An ``azure.storage.blob.ContainerClient`` already authenticated and pointing at the target container. datalake_root: Optional path prefix inside the container (e.g. ``"workspaces"``). Leading/trailing slashes are stripped automatically. """ if not identity_id: raise ValueError("identity_id cannot be empty") # --- identity ------------------------------------------------------- self._identity_id = identity_id self._safe_id = self._sanitize_identity_id(identity_id) # --- blob storage ---------------------------------------------------- self._container: ContainerClient = container_client root = datalake_root.strip("/") self._datalake_root = root self._prefix = f"{root}/{self._safe_id}/" if root else f"{self._safe_id}/" # _path / _root are not meaningful for blob storage but some code # (e.g. sandbox) may reference them, so we set them to None rather # than leaving them undefined. self._root = None # type: ignore[assignment] self._path = None # type: ignore[assignment] # --- in-memory metadata cache ---------------------------------------- # Avoids re-downloading workspace.yaml on every method call. # Invalidated automatically by save_metadata() and cleanup(). self._metadata_cache: Optional[WorkspaceMetadata] = None # --- blob data cache ------------------------------------------------- # Caches downloaded blob bytes keyed by filename. Avoids repeated # downloads of the same data file within one request (e.g. # analyze_table calls run_parquet_sql once per column, each of # which would otherwise re-download the entire parquet blob). # Invalidated per-file on upload/delete, cleared on cleanup. self._blob_data_cache: dict[str, bytes] = {} # --- metadata -------------------------------------------------------- if not self._blob_exists(METADATA_FILENAME): self._init_metadata() logger.debug("Initialized AzureBlobWorkspace at %s", self._prefix) # ------------------------------------------------------------------ # Low-level blob helpers # ------------------------------------------------------------------ def _blob_name(self, filename: str) -> str: """Full blob name for *filename* within this workspace.""" return f"{self._prefix}{filename}" def _get_blob(self, filename: str): """Return a ``BlobClient`` for *filename*.""" return self._container.get_blob_client(self._blob_name(filename)) def _blob_exists(self, filename: str) -> bool: from azure.core.exceptions import ResourceNotFoundError try: self._get_blob(filename).get_blob_properties() return True except ResourceNotFoundError: return False def _upload_bytes( self, filename: str, data: bytes | str, *, overwrite: bool = True ) -> int: """Upload *data* to blob. Returns size in bytes.""" raw = data.encode("utf-8") if isinstance(data, str) else data self._get_blob(filename).upload_blob(raw, overwrite=overwrite) # Invalidate cached copy of this file self._blob_data_cache.pop(filename, None) if hasattr(self, "_temp_file_cache") and filename in self._temp_file_cache: self._temp_file_cache.pop(filename).unlink(missing_ok=True) return len(raw) def _download_bytes(self, filename: str) -> bytes: cached = self._blob_data_cache.get(filename) if cached is not None: return cached data = self._get_blob(filename).download_blob().readall() self._blob_data_cache[filename] = data return data def _delete_blob(self, filename: str) -> None: self._get_blob(filename).delete_blob() self._blob_data_cache.pop(filename, None) if hasattr(self, "_temp_file_cache") and filename in self._temp_file_cache: self._temp_file_cache.pop(filename).unlink(missing_ok=True) @contextmanager def _temp_local_copy(self, filename: str): """Yield a local file path containing the blob's data. The file is cached on disk for the lifetime of this workspace instance so that repeated calls (e.g. ``run_parquet_sql`` once per column in ``analyze_table``) don't re-write the temp file. The cache is keyed by filename and cleaned up when the instance is garbage-collected or when :meth:`cleanup` is called. """ if not hasattr(self, "_temp_file_cache"): self._temp_file_cache: dict[str, Path] = {} tmp_path = self._temp_file_cache.get(filename) if tmp_path is None or not tmp_path.exists(): data = self._download_bytes(filename) suffix = Path(filename).suffix tmp = tempfile.NamedTemporaryFile(suffix=suffix, delete=False) tmp.write(data) tmp.close() tmp_path = Path(tmp.name) self._temp_file_cache[filename] = tmp_path yield tmp_path # Don't delete — reused across calls, cleaned up on GC / cleanup() def _cleanup_temp_files(self) -> None: """Remove all cached temp files from disk.""" for path in getattr(self, "_temp_file_cache", {}).values(): path.unlink(missing_ok=True) if hasattr(self, "_temp_file_cache"): self._temp_file_cache.clear() def __del__(self) -> None: self._cleanup_temp_files() # ------------------------------------------------------------------ # Metadata overrides # ------------------------------------------------------------------ def _init_metadata(self) -> None: metadata = WorkspaceMetadata.create_new() self.save_metadata(metadata) logger.info("Initialized new workspace metadata in blob: %s", self._prefix) def get_metadata(self) -> WorkspaceMetadata: if self._metadata_cache is not None: return self._metadata_cache raw = self._download_bytes(METADATA_FILENAME) parsed = yaml.safe_load(raw) if parsed is None: raise ValueError("Metadata blob parsed to None") self._metadata_cache = WorkspaceMetadata.from_dict(parsed) return self._metadata_cache def save_metadata(self, metadata: WorkspaceMetadata) -> None: metadata.updated_at = datetime.now(timezone.utc) content = yaml.dump( metadata.to_dict(), default_flow_style=False, allow_unicode=True, sort_keys=False, ) self._upload_bytes(METADATA_FILENAME, content) # Update the cache with the just-saved metadata self._metadata_cache = metadata def invalidate_metadata_cache(self) -> None: """Force the next get_metadata() to re-read from blob storage.""" self._metadata_cache = None # ------------------------------------------------------------------ # File / table operations # ------------------------------------------------------------------ def get_file_path(self, filename: str) -> str: # type: ignore[override] """Return the full blob name for *filename*. .. note:: The return type is ``str`` (a blob path), **not** a local ``pathlib.Path``. Code that opens the result as a local file will not work — use :meth:`read_data_as_df`, :meth:`download_file`, or :meth:`_temp_local_copy` instead. """ safe_filename = secure_filename(filename) if not safe_filename: raise ValueError(f"Invalid filename: {filename!r}") return self._blob_name(safe_filename) def file_exists(self, filename: str) -> bool: safe_filename = Path(filename).name return self._blob_exists(safe_filename) def delete_table(self, table_name: str) -> bool: metadata = self.get_metadata() table = metadata.get_table(table_name) if table is None: return False if self._blob_exists(table.filename): self._delete_blob(table.filename) metadata.remove_table(table_name) self.save_metadata(metadata) logger.info("Deleted table %s from blob workspace %s", table_name, self._safe_id) return True def cleanup(self) -> None: """Delete **all** blobs under this workspace's prefix.""" for blob in self._container.list_blobs(name_starts_with=self._prefix): self._container.delete_blob(blob.name) self._metadata_cache = None self._blob_data_cache.clear() self._cleanup_temp_files() logger.info("Cleaned up blob workspace %s", self._safe_id) # ------------------------------------------------------------------ # Read # ------------------------------------------------------------------ def read_data_as_df(self, table_name: str) -> pd.DataFrame: meta = self.get_table_metadata(table_name) if meta is None: raise FileNotFoundError(f"Table not found: {table_name}") if not self._blob_exists(meta.filename): raise FileNotFoundError(f"Blob not found: {meta.filename}") buf = io.BytesIO(self._download_bytes(meta.filename)) readers = { "parquet": lambda b: pd.read_parquet(b), "csv": lambda b: pd.read_csv(b), "excel": lambda b: pd.read_excel(b), "json": lambda b: pd.read_json(b), "txt": lambda b: pd.read_csv(b, sep="\t"), } reader = readers.get(meta.file_type) if reader is None: raise ValueError( f"Unsupported file type '{meta.file_type}' for table '{table_name}'. " f"Supported: {', '.join(readers)}." ) return reader(buf) # ------------------------------------------------------------------ # Parquet write # ------------------------------------------------------------------ def write_parquet_from_arrow( self, table: pa.Table, table_name: str, compression: str = DEFAULT_COMPRESSION, loader_metadata: Optional[dict[str, Any]] = None, ) -> TableMetadata: safe_name = sanitize_table_name(table_name) filename = f"{safe_name}.parquet" # Remove old blob if overwriting ws_meta = self.get_metadata() if safe_name in ws_meta.tables: old_fn = ws_meta.tables[safe_name].filename if self._blob_exists(old_fn): self._delete_blob(old_fn) # Serialise to bytes, upload buf = io.BytesIO() pq.write_table(table, buf, compression=compression) blob_bytes = buf.getvalue() self._upload_bytes(filename, blob_bytes) now = datetime.now(timezone.utc) table_metadata = TableMetadata( name=safe_name, source_type="data_loader", filename=filename, file_type="parquet", created_at=now, content_hash=compute_arrow_table_hash(table), file_size=len(blob_bytes), row_count=table.num_rows, columns=get_arrow_column_info(table), last_synced=now, ) if loader_metadata: table_metadata.loader_type = loader_metadata.get("loader_type") table_metadata.loader_params = loader_metadata.get("loader_params") table_metadata.source_table = loader_metadata.get("source_table") table_metadata.source_query = loader_metadata.get("source_query") self.add_table_metadata(table_metadata) logger.info( "Wrote parquet blob %s: %d rows, %d cols (%d bytes) [Arrow]", filename, table.num_rows, table.num_columns, len(blob_bytes), ) return table_metadata def write_parquet( self, df: pd.DataFrame, table_name: str, compression: str = DEFAULT_COMPRESSION, loader_metadata: Optional[dict[str, Any]] = None, ) -> TableMetadata: safe_name = sanitize_table_name(table_name) filename = f"{safe_name}.parquet" ws_meta = self.get_metadata() if safe_name in ws_meta.tables: old_fn = ws_meta.tables[safe_name].filename if self._blob_exists(old_fn): self._delete_blob(old_fn) sanitized_df = sanitize_dataframe_for_arrow(df) arrow_table = pa.Table.from_pandas(sanitized_df) buf = io.BytesIO() pq.write_table(arrow_table, buf, compression=compression) blob_bytes = buf.getvalue() self._upload_bytes(filename, blob_bytes) now = datetime.now(timezone.utc) table_metadata = TableMetadata( name=safe_name, source_type="data_loader", filename=filename, file_type="parquet", created_at=now, content_hash=compute_dataframe_hash(df), file_size=len(blob_bytes), row_count=len(df), columns=get_column_info(df), last_synced=now, ) if loader_metadata: table_metadata.loader_type = loader_metadata.get("loader_type") table_metadata.loader_params = loader_metadata.get("loader_params") table_metadata.source_table = loader_metadata.get("source_table") table_metadata.source_query = loader_metadata.get("source_query") self.add_table_metadata(table_metadata) logger.info( "Wrote parquet blob %s: %d rows, %d cols (%d bytes)", filename, len(df), len(df.columns), len(blob_bytes), ) return table_metadata # ------------------------------------------------------------------ # Parquet read helpers # ------------------------------------------------------------------ def get_parquet_schema(self, table_name: str) -> dict: meta = self.get_table_metadata(table_name) if meta is None: raise FileNotFoundError(f"Table not found: {table_name}") if meta.file_type != "parquet": raise ValueError(f"Table {table_name} is not a parquet file") if not self._blob_exists(meta.filename): raise FileNotFoundError(f"Parquet blob not found: {meta.filename}") with self._temp_local_copy(meta.filename) as tmp_path: pf = pq.ParquetFile(tmp_path) schema = pf.schema_arrow return { "table_name": table_name, "filename": meta.filename, "num_rows": pf.metadata.num_rows, "num_columns": len(schema), "columns": [ {"name": f.name, "type": str(f.type), "nullable": f.nullable} for f in schema ], "created_at": meta.created_at.isoformat(), "last_synced": ( meta.last_synced.isoformat() if meta.last_synced else None ), } def get_parquet_path(self, table_name: str) -> str: # type: ignore[override] """Return the full blob name for the parquet file. .. warning:: Unlike the base class this returns a *blob path* (``str``), **not** a resolved local ``pathlib.Path``. """ meta = self.get_table_metadata(table_name) if meta is None: raise FileNotFoundError(f"Table not found: {table_name}") if meta.file_type != "parquet": raise ValueError(f"Table {table_name} is not a parquet file") if not self._blob_exists(meta.filename): raise FileNotFoundError(f"Parquet blob not found: {meta.filename}") return self._blob_name(meta.filename) def run_parquet_sql(self, table_name: str, sql: str) -> pd.DataFrame: """Run a DuckDB SQL query against a parquet table. Downloads the blob to a temporary local file for the duration of the query, so DuckDB can use its native parquet reader. """ import duckdb meta = self.get_table_metadata(table_name) if meta is None: raise FileNotFoundError(f"Table not found: {table_name}") if meta.file_type != "parquet": raise ValueError(f"Table {table_name} is not a parquet file") if not self._blob_exists(meta.filename): raise FileNotFoundError(f"Parquet blob not found: {meta.filename}") if "{parquet}" not in sql: raise ValueError("SQL must contain {parquet} placeholder") with self._temp_local_copy(meta.filename) as tmp_path: escaped = str(tmp_path).replace("\\", "\\\\").replace("'", "''") full_sql = sql.format(parquet=f"read_parquet('{escaped}')") conn = duckdb.connect(":memory:") try: return conn.execute(full_sql).fetchdf() finally: conn.close() # ------------------------------------------------------------------ # Local directory materialisation (for sandbox execution) # ------------------------------------------------------------------ @contextmanager def local_dir(self): """Download all workspace files to a temporary local directory. Yields the path to the temp directory. The directory and its contents are removed when the context manager exits. """ tmp = tempfile.mkdtemp(prefix="df_blob_ws_") tmp_path = Path(tmp) try: # Download every blob under this workspace's prefix for blob in self._container.list_blobs(name_starts_with=self._prefix): # Relative filename within the workspace rel = blob.name[len(self._prefix):] if not rel or rel == METADATA_FILENAME: continue # skip the metadata file itself local_file = tmp_path / rel local_file.parent.mkdir(parents=True, exist_ok=True) data = self._container.download_blob(blob.name).readall() local_file.write_bytes(data) yield tmp_path finally: shutil.rmtree(tmp, ignore_errors=True) # ------------------------------------------------------------------ # Raw file upload / download (replaces get_file_path + open() pattern) # ------------------------------------------------------------------ def upload_file(self, content: bytes, filename: str) -> None: """Upload raw file content to the workspace as a blob.""" safe_filename = Path(filename).name self._upload_bytes(safe_filename, content) def download_file(self, filename: str) -> bytes: """Download raw file content from the workspace blob.""" safe_filename = Path(filename).name return self._download_bytes(safe_filename) # ------------------------------------------------------------------ # Workspace snapshot (session save / restore) # ------------------------------------------------------------------ def save_workspace_snapshot(self, dst: Path) -> None: """Download all workspace blobs (including metadata) to *dst*.""" for blob in self._container.list_blobs(name_starts_with=self._prefix): rel = blob.name[len(self._prefix):] if not rel: continue dst.mkdir(parents=True, exist_ok=True) local_file = dst / rel local_file.parent.mkdir(parents=True, exist_ok=True) data = self._container.download_blob(blob.name).readall() local_file.write_bytes(data) def restore_workspace_snapshot(self, src: Path) -> None: """Replace all workspace blobs with files from *src* directory.""" self.cleanup() if src.exists(): for f in src.rglob("*"): if f.is_file(): rel = str(f.relative_to(src)) self._upload_bytes(rel, f.read_bytes()) # Ensure metadata exists even if snapshot didn't include it if not self._blob_exists(METADATA_FILENAME): self._init_metadata() # Invalidate caches since metadata and data were replaced from snapshot self._metadata_cache = None self._blob_data_cache.clear() self._cleanup_temp_files() # ------------------------------------------------------------------ # Session management (blob-backed) # ------------------------------------------------------------------ def _session_blob_prefix(self, session_name: str) -> str: """Blob prefix for a named session. Uses a parallel structure to the workspace prefix:: workspace data: <root>/<safe_id>/... sessions: sessions/<safe_id>/<session_name>/... This mirrors the local filesystem layout where workspaces and sessions live side-by-side. """ safe_name = self._sanitize_session_name(session_name) return f"sessions/{self._safe_id}/{safe_name}/" def save_session(self, session_name: str, state: dict) -> str: safe_name = self._sanitize_session_name(session_name) prefix = self._session_blob_prefix(session_name) # Wipe previous save for blob in self._container.list_blobs(name_starts_with=prefix): self._container.delete_blob(blob.name) # 1. Snapshot workspace files into session blobs for blob in self._container.list_blobs(name_starts_with=self._prefix): rel = blob.name[len(self._prefix):] if not rel: continue data = self._container.download_blob(blob.name).readall() self._container.upload_blob( f"{prefix}workspace/{rel}", data, overwrite=True ) # 2. State JSON state_json = json.dumps(state, default=str) self._container.upload_blob( f"{prefix}state.json", state_json.encode("utf-8"), overwrite=True ) saved_at = datetime.now(timezone.utc).isoformat() logger.info(f"Saved session '{safe_name}' for {self._identity_id} (blob)") return saved_at def load_session(self, session_name: str) -> dict | None: prefix = self._session_blob_prefix(session_name) state_blob = f"{prefix}state.json" from azure.core.exceptions import ResourceNotFoundError try: raw = self._container.download_blob(state_blob).readall() except ResourceNotFoundError: return None # Restore workspace: delete current blobs, copy session workspace blobs ws_prefix = f"{prefix}workspace/" ws_blobs = list(self._container.list_blobs(name_starts_with=ws_prefix)) if ws_blobs: self.cleanup() for blob in ws_blobs: rel = blob.name[len(ws_prefix):] if not rel: continue data = self._container.download_blob(blob.name).readall() self._container.upload_blob( self._blob_name(rel), data, overwrite=True ) if not self._blob_exists(METADATA_FILENAME): self._init_metadata() # Invalidate caches since workspace blobs were replaced self._metadata_cache = None self._blob_data_cache.clear() self._cleanup_temp_files() return json.loads(raw) def _sessions_root_prefix(self) -> str: """Blob prefix for all sessions of this user.""" return f"sessions/{self._safe_id}/" def list_sessions(self) -> list[dict]: sessions_prefix = self._sessions_root_prefix() sessions: list[dict] = [] seen: set[str] = set() for blob in self._container.list_blobs(name_starts_with=sessions_prefix): rel = blob.name[len(sessions_prefix):] parts = rel.split("/", 1) if len(parts) < 2: continue sess_name = parts[0] if sess_name in seen: continue # Check it has a state.json if parts[1] == "state.json": seen.add(sess_name) sessions.append({ "name": sess_name, "saved_at": blob.last_modified.isoformat() if blob.last_modified else datetime.now(timezone.utc).isoformat(), }) sessions.sort(key=lambda s: s["saved_at"], reverse=True) return sessions def delete_session(self, session_name: str) -> bool: prefix = self._session_blob_prefix(session_name) blobs = list(self._container.list_blobs(name_starts_with=prefix)) if not blobs: return False for blob in blobs: self._container.delete_blob(blob.name) logger.info(f"Deleted session '{session_name}' for {self._identity_id} (blob)") return True def export_session_zip(self, state: dict) -> io.BytesIO: buf = io.BytesIO() with zipfile.ZipFile(buf, "w", zipfile.ZIP_DEFLATED) as zf: zf.writestr("state.json", json.dumps(state, default=str)) for blob in self._container.list_blobs(name_starts_with=self._prefix): rel = blob.name[len(self._prefix):] if not rel: continue data = self._container.download_blob(blob.name).readall() zf.writestr(f"workspace/{rel}", data) buf.seek(0) return buf def import_session_zip(self, zip_data: io.BytesIO) -> dict: with zipfile.ZipFile(zip_data, "r") as zf: if "state.json" not in zf.namelist(): raise ValueError("Invalid session file: missing state.json") state = json.loads(zf.read("state.json")) workspace_entries = [ n for n in zf.namelist() if n.startswith("workspace/") and not n.endswith("/") ] if workspace_entries: self.cleanup() for entry in workspace_entries: rel = entry[len("workspace/"):] # Guard against zip-slip: secure_filename strips # path separators and ".." components. safe_rel = secure_filename(rel) if not safe_rel: continue # skip entries that sanitise to empty self._upload_bytes(safe_rel, zf.read(entry)) if not self._blob_exists(METADATA_FILENAME): self._init_metadata() # Invalidate caches since workspace blobs were replaced self._metadata_cache = None self._blob_data_cache.clear() self._cleanup_temp_files() return state # ------------------------------------------------------------------ # Representation # ------------------------------------------------------------------ def __repr__(self) -> str: return ( f"AzureBlobWorkspace(identity_id={self._identity_id!r}, " f"prefix={self._prefix!r})" ) ================================================ FILE: py-src/data_formulator/datalake/cache_manager.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ Global cache manager for multi-user deployments. While each :class:`CachedAzureBlobWorkspace` enforces its own per-workspace limit (default 1 GB), this module enforces a **server-wide ceiling** across ALL user caches, preventing the aggregate local cache from consuming all available disk space when many users are active. Architecture ~~~~~~~~~~~~ ``GlobalCacheManager`` is a **thread-safe singleton**. The first call to :meth:`get_instance` configures it (cache root, max bytes, scan interval); subsequent calls return the same object. Cross-user eviction ~~~~~~~~~~~~~~~~~~~ When the global limit is exceeded, files are evicted across *all* user cache directories using **LRU by mtime** (oldest files first, regardless of which user owns them). Protected files (``workspace.yaml``) are never evicted. Eviction targets 80 % of the global max to avoid thrashing. Graceful degradation ~~~~~~~~~~~~~~~~~~~~ When the global cache is full and eviction cannot free enough space, individual workspaces fall through to direct Azure reads. User-initiated writes always succeed locally (correctness), but read-path caching is skipped so the disk isn't filled further. Disk scanning ~~~~~~~~~~~~~ Total disk usage is computed via ``os.walk()`` over the cache root. To avoid excessive I/O on servers with many files, the scan is **debounced** — at most once per ``scan_interval`` seconds (default 10 s). """ from __future__ import annotations import logging import os import threading import time from pathlib import Path from typing import Any from data_formulator.datalake.metadata import METADATA_FILENAME logger = logging.getLogger(__name__) # Default global cache limit: 50 GB _DEFAULT_GLOBAL_MAX_BYTES = 50 * 1024**3 # Default interval between filesystem scans (seconds) _DEFAULT_SCAN_INTERVAL = 10.0 class GlobalCacheManager: """Thread-safe singleton managing total cache disk usage across all users. Usage:: mgr = GlobalCacheManager.get_instance( cache_root=Path("~/.data_formulator/cache"), max_global_bytes=10 * 1024**3, # 10 GB ) # Before caching a downloaded file (optional write): if mgr.try_acquire_space(len(data)): cache_file.write_bytes(data) # After a mandatory write (e.g. user upload): cache_file.write_bytes(data) mgr.notify_write(len(data)) # Monitoring: stats = mgr.get_global_stats() """ _instance: GlobalCacheManager | None = None _init_lock = threading.Lock() # ------------------------------------------------------------------ # Singleton access # ------------------------------------------------------------------ @classmethod def get_instance( cls, cache_root: Path | str | None = None, max_global_bytes: int = _DEFAULT_GLOBAL_MAX_BYTES, scan_interval: float = _DEFAULT_SCAN_INTERVAL, ) -> GlobalCacheManager: """Return the singleton, creating it on first call. Args: cache_root: Root of the local cache tree. Defaults to ``~/.data_formulator/cache``. max_global_bytes: Global ceiling in bytes (default 10 GB). scan_interval: Min seconds between full filesystem scans (default 10). Subsequent calls return the existing singleton — arguments are ignored after the first call. """ if cls._instance is not None: return cls._instance with cls._init_lock: if cls._instance is not None: return cls._instance if cache_root is None: from data_formulator.datalake.workspace import ( get_data_formulator_home, ) cache_root = get_data_formulator_home() / "cache" cls._instance = cls( Path(cache_root), max_global_bytes, scan_interval ) return cls._instance @classmethod def reset_instance(cls) -> None: """Discard the singleton (for testing only).""" with cls._init_lock: cls._instance = None # ------------------------------------------------------------------ # Construction (private — use get_instance) # ------------------------------------------------------------------ def __init__( self, cache_root: Path, max_global_bytes: int, scan_interval: float, ): self._cache_root = cache_root self._max_global_bytes = max_global_bytes self._scan_interval = scan_interval self._lock = threading.Lock() self._last_scan_time: float = 0.0 self._cached_total_bytes: int = 0 logger.info( "GlobalCacheManager: root=%s max=%d MB scan_interval=%.1fs", cache_root, max_global_bytes // (1024 * 1024), scan_interval, ) # ------------------------------------------------------------------ # Properties # ------------------------------------------------------------------ @property def max_global_bytes(self) -> int: return self._max_global_bytes @property def cache_root(self) -> Path: return self._cache_root # ------------------------------------------------------------------ # Disk scanning (debounced) # ------------------------------------------------------------------ def _scan_total_size(self) -> int: """Walk the cache root and sum file sizes. Debounced: returns the cached value if the last scan was less than ``scan_interval`` seconds ago. **Must be called with ``self._lock`` held.** """ now = time.monotonic() if now - self._last_scan_time < self._scan_interval: return self._cached_total_bytes total = 0 try: for dirpath, _dirnames, filenames in os.walk(self._cache_root): for fn in filenames: try: total += os.path.getsize(os.path.join(dirpath, fn)) except OSError: pass except OSError: pass self._cached_total_bytes = total self._last_scan_time = now return total # ------------------------------------------------------------------ # Space management # ------------------------------------------------------------------ def try_acquire_space(self, needed_bytes: int) -> bool: """Try to make room for *needed_bytes* of new cache data. 1. If total + needed is under the limit, return ``True``. 2. Otherwise, run cross-user LRU eviction. 3. If still insufficient, return ``False`` (caller should skip local caching and serve from Azure directly). This is intended for **optional** cache writes (e.g. caching a download). For **mandatory** writes (user uploads), use :meth:`notify_write` after writing instead. """ with self._lock: total = self._scan_total_size() if total + needed_bytes <= self._max_global_bytes: self._cached_total_bytes = total + needed_bytes return True # Try cross-user eviction freed = self._evict_global_unlocked( target_free=needed_bytes + int(self._max_global_bytes * 0.1) ) # Re-scan after eviction if freed > 0: self._last_scan_time = 0.0 # force fresh scan total = self._scan_total_size() if total + needed_bytes <= self._max_global_bytes: self._cached_total_bytes = total + needed_bytes return True return False def notify_write(self, nbytes: int) -> None: """Notify the manager of a mandatory write (e.g. user upload). The write has already happened. This bumps the cached counter and triggers global eviction if the limit is exceeded. """ with self._lock: self._cached_total_bytes += nbytes if self._cached_total_bytes > self._max_global_bytes: self._evict_global_unlocked( target_free=int( self._cached_total_bytes - self._max_global_bytes * 0.8 ) ) def maybe_evict_global(self) -> int: """Run cross-user eviction if total exceeds the global limit. Returns bytes freed. """ with self._lock: total = self._scan_total_size() if total <= self._max_global_bytes: return 0 return self._evict_global_unlocked( target_free=int(total - self._max_global_bytes * 0.8) ) # ------------------------------------------------------------------ # Cross-user LRU eviction (internal) # ------------------------------------------------------------------ def _evict_global_unlocked(self, target_free: int) -> int: """Evict files across all user caches, LRU by mtime. **Must be called with ``self._lock`` held.** Skips: * ``workspace.yaml`` — correctness-critical metadata. * Hidden files (starting with ``.``). Args: target_free: Bytes to free. Returns: Total bytes actually freed. """ if target_free <= 0: return 0 # Collect all candidate files across all user caches candidates: list[tuple[str, float, int]] = [] try: for dirpath, _, filenames in os.walk(self._cache_root): for fn in filenames: if fn == METADATA_FILENAME: continue if fn.startswith("."): continue full = os.path.join(dirpath, fn) try: st = os.stat(full) candidates.append((full, st.st_mtime, st.st_size)) except OSError: pass except OSError: return 0 # Sort by mtime ascending (oldest first = evict first) candidates.sort(key=lambda x: x[1]) freed = 0 evicted = 0 for full_path, _mtime, size in candidates: if freed >= target_free: break try: os.unlink(full_path) freed += size evicted += 1 except OSError: pass if evicted: logger.info( "Global cache eviction: removed %d file(s), freed %.1f MB " "(target was %.1f MB)", evicted, freed / (1024 * 1024), target_free / (1024 * 1024), ) # Invalidate cached scan so next check is accurate self._last_scan_time = 0.0 return freed # ------------------------------------------------------------------ # Monitoring # ------------------------------------------------------------------ def get_global_stats(self) -> dict[str, Any]: """Return global cache statistics for monitoring / debugging.""" with self._lock: total = self._scan_total_size() # Count user-level cache directories (root/datalake_root/user_id/) user_dirs = 0 try: for entry in os.scandir(self._cache_root): if entry.is_dir(): for sub in os.scandir(entry.path): if sub.is_dir(): user_dirs += 1 except OSError: pass return { "cache_root": str(self._cache_root), "total_size_bytes": total, "total_size_mb": round(total / (1024 * 1024), 2), "max_size_mb": round(self._max_global_bytes / (1024 * 1024), 2), "utilization_pct": ( round(total / self._max_global_bytes * 100, 1) if self._max_global_bytes > 0 else 0 ), "user_cache_count": user_dirs, } # ------------------------------------------------------------------ # Representation # ------------------------------------------------------------------ def __repr__(self) -> str: return ( f"GlobalCacheManager(root={self._cache_root!r}, " f"max={self._max_global_bytes // (1024**2)} MB)" ) ================================================ FILE: py-src/data_formulator/datalake/cached_azure_blob_workspace.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ Cached Azure Blob workspace with persistent local file mirror. Wraps :class:`AzureBlobWorkspace` with a **write-through local cache** under ``~/.data_formulator/cache/``. Reads come from the local mirror (filesystem speed), writes go to the local mirror immediately and are uploaded to Azure Blob Storage in a background thread. Key performance improvements over plain ``AzureBlobWorkspace``: * ``read_data_as_df()`` — reads local parquet directly (no blob download) * ``local_dir()`` — yields the cache directory (no temp-dir downloads) * ``run_parquet_sql()`` — runs DuckDB against local parquet (no temp copy) * ``write_parquet()`` — writes local file immediately, Azure upload in bg Cache eviction -------------- An LRU eviction mechanism prevents unbounded disk growth: * **Max size** — configurable, default 1 GB per workspace. * **Trigger** — checked after every write. * **Policy** — evict least-recently-used files (oldest ``mtime``) until total cache size drops to 80 % of the max. * **Protected** — ``workspace.yaml`` and files with pending background uploads are never evicted. Usage:: from azure.storage.blob import ContainerClient from data_formulator.datalake.cached_azure_blob_workspace import ( CachedAzureBlobWorkspace, ) container = ContainerClient.from_connection_string(conn_str, "my-container") ws = CachedAzureBlobWorkspace( "user:42", container, datalake_root="workspaces", max_cache_bytes=2 * 1024**3, # 2 GB cache ) """ from __future__ import annotations import atexit import collections import io import logging import os import shutil import threading import time from concurrent.futures import Future, ThreadPoolExecutor from contextlib import contextmanager from datetime import datetime, timezone from pathlib import Path from typing import Any, Optional, TYPE_CHECKING import pandas as pd import pyarrow as pa import pyarrow.parquet as pq import yaml from data_formulator.datalake.azure_blob_workspace import AzureBlobWorkspace from data_formulator.datalake.cache_manager import GlobalCacheManager from data_formulator.datalake.metadata import ( METADATA_FILENAME, WorkspaceMetadata, ) from data_formulator.datalake.parquet_utils import sanitize_table_name from data_formulator.datalake.workspace import Workspace, get_data_formulator_home if TYPE_CHECKING: from azure.storage.blob import ContainerClient logger = logging.getLogger(__name__) # Default cache size limit per workspace (1 GB). _DEFAULT_MAX_CACHE_BYTES = 1 * 1024 ** 3 class CachedAzureBlobWorkspace(AzureBlobWorkspace): """Azure Blob workspace with a persistent local file cache. Every file written to Azure is also written to a local cache directory. Reads are served from the local cache whenever possible — falling back to Azure only for files that have been evicted or written by another process. Background uploads ~~~~~~~~~~~~~~~~~~ Data-file uploads are submitted to a :class:`ThreadPoolExecutor` so they don't block the request thread. Metadata (``workspace.yaml``) is always uploaded **synchronously** because it is small and correctness-critical. Call :meth:`wait_for_uploads` to block until all pending uploads finish (e.g. before shutdown or tests). Multi-instance safety ~~~~~~~~~~~~~~~~~~~~~~ When the same user is served by multiple server instances (e.g. behind a load balancer), each instance has its own local cache. Stale-cache detection compares the local ``workspace.yaml``'s ``updated_at`` timestamp against Azure on a configurable interval (default: 30 s). If Azure is newer, the local metadata and any changed data files are re-downloaded. Global cache budget ~~~~~~~~~~~~~~~~~~~~ A :class:`GlobalCacheManager` singleton enforces a server-wide ceiling (default 10 GB) across **all** user caches. When the global limit is exceeded, cross-user LRU eviction removes the oldest files server-wide. If eviction cannot free enough space, download-path caching is skipped (graceful degradation) so reads fall through to Azure Blob Storage directly. Thread safety ~~~~~~~~~~~~~ The local cache directory is per-user so there are no cross-user conflicts. Per-file upload locks prevent concurrent background uploads from racing on the same blob. The ``_pending_uploads`` set is protected by a global lock. """ # ------------------------------------------------------------------ # Construction # ------------------------------------------------------------------ def __init__( self, identity_id: str, container_client: "ContainerClient", datalake_root: str = "", *, cache_root: str | Path | None = None, max_cache_bytes: int = _DEFAULT_MAX_CACHE_BYTES, max_global_cache_bytes: int | None = None, bg_upload_workers: int = 2, staleness_check_interval: float = 30.0, ): """ Args: identity_id: Unique user identifier. container_client: Azure ``ContainerClient``. datalake_root: Path prefix inside the blob container. cache_root: Root of the local cache tree. Defaults to ``~/.data_formulator/cache``. max_cache_bytes: Maximum total size of cached files for this workspace before LRU eviction kicks in. max_global_cache_bytes: Server-wide ceiling across **all** user caches (default: 10 GB). ``None`` uses the :class:`GlobalCacheManager` default. bg_upload_workers: Thread pool size for background uploads. staleness_check_interval: Seconds between Azure metadata freshness checks (default: 30). Set to 0 to check on every ``get_metadata()`` call. """ # We must set up the cache directory *before* calling # super().__init__ because the parent's __init__ may call # _upload_bytes (via _init_metadata / save_metadata). safe_id = Workspace._sanitize_identity_id(identity_id) root = datalake_root.strip("/") if cache_root is None: cache_root = get_data_formulator_home() / "cache" base = Path(cache_root) if root: self._cache_dir = base / root / safe_id else: self._cache_dir = base / safe_id self._cache_dir.mkdir(parents=True, exist_ok=True) self._max_cache_bytes = max_cache_bytes # Background upload machinery self._pending_uploads: set[str] = set() self._upload_lock = threading.Lock() # Per-file locks to serialise concurrent uploads to the same blob self._file_locks: dict[str, threading.Lock] = collections.defaultdict( threading.Lock ) self._upload_executor = ThreadPoolExecutor( max_workers=bg_upload_workers, thread_name_prefix="df_cache_upload", ) self._upload_futures: list[Future] = [] # Register atexit hook to flush pending uploads on interpreter exit atexit.register(self._atexit_flush) # Staleness detection for multi-instance deployments self._staleness_check_interval = staleness_check_interval self._last_staleness_check: float = 0.0 # epoch seconds self._local_metadata_updated_at: Optional[datetime] = None # Initialise the global cache manager (singleton) — must be # before super().__init__ because _upload_bytes references it. gcm_kwargs: dict[str, Any] = {"cache_root": base} if max_global_cache_bytes is not None: gcm_kwargs["max_global_bytes"] = max_global_cache_bytes self._global_cache = GlobalCacheManager.get_instance(**gcm_kwargs) # Now safe to call super().__init__ — our _upload_bytes / etc. # overrides are in place and _cache_dir is ready. super().__init__(identity_id, container_client, datalake_root) # Run initial eviction if cache is over-sized (e.g. from prev run) self._maybe_evict() logger.info( "Initialized CachedAzureBlobWorkspace: prefix=%s cache=%s " "max=%d MB global_max=%d MB", self._prefix, self._cache_dir, self._max_cache_bytes // (1024 * 1024), self._global_cache.max_global_bytes // (1024 * 1024), ) # ------------------------------------------------------------------ # Cache path helper # ------------------------------------------------------------------ def _cache_path(self, filename: str) -> Path: """Return the local cache path for *filename*. Raises ``ValueError`` if the resolved path escapes the cache directory (defence-in-depth against path-traversal attacks). """ resolved = (self._cache_dir / filename).resolve() if not resolved.is_relative_to(self._cache_dir.resolve()): raise ValueError( f"Path traversal detected: {filename!r} resolves outside " f"the cache directory" ) return resolved # ------------------------------------------------------------------ # Low-level blob overrides (write-through cache) # ------------------------------------------------------------------ def _upload_bytes( self, filename: str, data: bytes | str, *, overwrite: bool = True, ) -> int: """Write *data* to local cache **and** Azure. Metadata (``workspace.yaml``) is uploaded synchronously. Data files are uploaded in a background thread. """ raw = data.encode("utf-8") if isinstance(data, str) else data # 1. Write to local cache immediately cache_file = self._cache_path(filename) cache_file.parent.mkdir(parents=True, exist_ok=True) cache_file.write_bytes(raw) # 2. Invalidate in-memory caches (inherited from AzureBlobWorkspace) self._blob_data_cache.pop(filename, None) if hasattr(self, "_temp_file_cache") and filename in self._temp_file_cache: self._temp_file_cache.pop(filename).unlink(missing_ok=True) # 3. Upload to Azure if filename == METADATA_FILENAME: # Metadata: synchronous (small, correctness-critical) self._get_blob(filename).upload_blob(raw, overwrite=overwrite) else: # Data files: background upload with per-file lock with self._upload_lock: self._pending_uploads.add(filename) def _bg_upload(fn: str, payload: bytes) -> None: # Per-file lock ensures concurrent writes to the same # blob are serialised — last write always wins. file_lock = self._file_locks[fn] file_lock.acquire() try: self._get_blob(fn).upload_blob(payload, overwrite=True) logger.debug("Background upload complete: %s", fn) except Exception: logger.warning( "Background upload FAILED for %s — data is safe in " "local cache and will be retried on next write.", fn, exc_info=True, ) finally: file_lock.release() with self._upload_lock: self._pending_uploads.discard(fn) fut = self._upload_executor.submit(_bg_upload, filename, raw) self._upload_futures.append(fut) # 4. Evict if needed (per-workspace, then global) self._maybe_evict() # Notify global manager of the mandatory write self._global_cache.notify_write(len(raw)) return len(raw) def _download_bytes(self, filename: str) -> bytes: """Read from local cache first, then Azure, then populate cache.""" # 1. Local cache hit cache_file = self._cache_path(filename) if cache_file.exists(): data = cache_file.read_bytes() # Touch to update mtime for LRU tracking try: os.utime(cache_file, None) except OSError: pass # Also populate in-memory cache for extra speed on hot paths self._blob_data_cache[filename] = data return data # 2. In-memory cache (shouldn't happen if local cache is warm) cached = self._blob_data_cache.get(filename) if cached is not None: # Backfill local cache — only if global budget allows if self._global_cache.try_acquire_space(len(cached)): try: cache_file.parent.mkdir(parents=True, exist_ok=True) cache_file.write_bytes(cached) except OSError: logger.debug("Failed to backfill cache for %s", filename) return cached # 3. Azure download (cache miss — evicted or written by other instance) data = self._get_blob(filename).download_blob().readall() self._blob_data_cache[filename] = data # Persist to local cache only if global budget allows if self._global_cache.try_acquire_space(len(data)): try: cache_file.parent.mkdir(parents=True, exist_ok=True) cache_file.write_bytes(data) except OSError: logger.debug( "Global cache full — serving %s from Azure without " "local caching", filename, ) else: logger.debug( "Global cache full — serving %s from Azure without " "local caching", filename, ) return data def _blob_exists(self, filename: str) -> bool: """Check local cache first, then Azure.""" if self._cache_path(filename).exists(): return True # Fall back to Azure (file may have been evicted) return super()._blob_exists(filename) # ------------------------------------------------------------------ # Staleness detection (multi-instance safety) # ------------------------------------------------------------------ def _check_staleness(self) -> None: """Compare local metadata timestamp with Azure's. If Azure has a newer ``updated_at``, invalidate local metadata and any data files whose table metadata has changed. Called by ``get_metadata()`` at most once per ``staleness_check_interval``. """ now = time.monotonic() if now - self._last_staleness_check < self._staleness_check_interval: return self._last_staleness_check = now try: # Fetch fresh metadata from Azure (bypass all caches) raw = self._get_blob(METADATA_FILENAME).download_blob().readall() parsed = yaml.safe_load(raw) if parsed is None: return remote_meta = WorkspaceMetadata.from_dict(parsed) except Exception: # If Azure is unreachable, use local cache silently logger.debug("Staleness check: Azure unreachable, using local cache") return local_meta = self._metadata_cache if local_meta is None: # No local metadata cached yet — will be loaded fresh anyway return if remote_meta.updated_at <= local_meta.updated_at: return # local is up to date logger.info( "Stale cache detected: local=%s remote=%s — refreshing", local_meta.updated_at.isoformat(), remote_meta.updated_at.isoformat(), ) # Find data files that changed (different hash or new tables) for table_name, remote_table in remote_meta.tables.items(): local_table = local_meta.tables.get(table_name) if ( local_table is None or local_table.content_hash != remote_table.content_hash ): # Invalidate cached file so next read re-downloads self._cache_path(remote_table.filename).unlink(missing_ok=True) self._blob_data_cache.pop(remote_table.filename, None) logger.debug("Invalidated stale cached file: %s", remote_table.filename) # Find tables deleted remotely for table_name in list(local_meta.tables.keys()): if table_name not in remote_meta.tables: old_fn = local_meta.tables[table_name].filename self._cache_path(old_fn).unlink(missing_ok=True) self._blob_data_cache.pop(old_fn, None) # Update local metadata cache file and in-memory cache self._cache_path(METADATA_FILENAME).write_bytes(raw) self._metadata_cache = remote_meta self._blob_data_cache[METADATA_FILENAME] = raw def _delete_blob(self, filename: str) -> None: """Delete from local cache, in-memory caches, and Azure.""" # Local cache self._cache_path(filename).unlink(missing_ok=True) # In-memory caches self._blob_data_cache.pop(filename, None) if hasattr(self, "_temp_file_cache") and filename in self._temp_file_cache: self._temp_file_cache.pop(filename).unlink(missing_ok=True) # Azure try: self._get_blob(filename).delete_blob() except Exception: logger.debug("Azure delete failed for %s (may not exist)", filename) # ------------------------------------------------------------------ # Temp-local-copy override (use cache file directly) # ------------------------------------------------------------------ # ------------------------------------------------------------------ # Metadata override with staleness check # ------------------------------------------------------------------ def get_metadata(self) -> WorkspaceMetadata: """Return workspace metadata, checking Azure for staleness.""" self._check_staleness() return super().get_metadata() # ------------------------------------------------------------------ # Temp-local-copy override (use cache file directly) # ------------------------------------------------------------------ @contextmanager def _temp_local_copy(self, filename: str): """Yield the local cache path directly — no temp file needed.""" cache_file = self._cache_path(filename) if not cache_file.exists(): # Ensure file is in cache self._download_bytes(filename) yield cache_file # ------------------------------------------------------------------ # Read overrides (read directly from local cache files) # ------------------------------------------------------------------ def read_data_as_df(self, table_name: str) -> pd.DataFrame: """Read table from local cache (fast!) with Azure fallback.""" meta = self.get_table_metadata(table_name) if meta is None: raise FileNotFoundError(f"Table not found: {table_name}") cache_file = self._cache_path(meta.filename) # Ensure file is in cache if not cache_file.exists(): # Download from Azure and populate cache self._download_bytes(meta.filename) # Update mtime for LRU try: os.utime(cache_file, None) except OSError: pass # Read directly from local file — fastest path readers = { "parquet": lambda p: pd.read_parquet(p), "csv": lambda p: pd.read_csv(p), "excel": lambda p: pd.read_excel(p), "json": lambda p: pd.read_json(p), "txt": lambda p: pd.read_csv(p, sep="\t"), } reader = readers.get(meta.file_type) if reader is None: raise ValueError( f"Unsupported file type '{meta.file_type}' for table " f"'{table_name}'. Supported: {', '.join(readers)}." ) return reader(cache_file) def run_parquet_sql(self, table_name: str, sql: str) -> pd.DataFrame: """Run DuckDB SQL against local cache file (no temp copy needed).""" import duckdb meta = self.get_table_metadata(table_name) if meta is None: raise FileNotFoundError(f"Table not found: {table_name}") if meta.file_type != "parquet": raise ValueError(f"Table {table_name} is not a parquet file") if "{parquet}" not in sql: raise ValueError("SQL must contain {parquet} placeholder") cache_file = self._cache_path(meta.filename) if not cache_file.exists(): self._download_bytes(meta.filename) # Update mtime for LRU try: os.utime(cache_file, None) except OSError: pass escaped = str(cache_file).replace("\\", "\\\\").replace("'", "''") full_sql = sql.format(parquet=f"read_parquet('{escaped}')") conn = duckdb.connect(":memory:") try: return conn.execute(full_sql).fetchdf() finally: conn.close() # ------------------------------------------------------------------ # local_dir — the biggest win # ------------------------------------------------------------------ @contextmanager def local_dir(self): """Yield the cache directory — no temp dir, no mass downloads. Verifies that all workspace data files are present in the local cache before yielding. Any missing files (evicted or written by another instance) are downloaded on demand. """ self._ensure_all_cached() yield self._cache_dir def _ensure_all_cached(self) -> None: """Download any workspace files not present in local cache.""" for blob in self._container.list_blobs(name_starts_with=self._prefix): rel = blob.name[len(self._prefix) :] if not rel or rel == METADATA_FILENAME: continue cache_file = self._cache_path(rel) if not cache_file.exists(): data = self._container.download_blob(blob.name).readall() cache_file.parent.mkdir(parents=True, exist_ok=True) cache_file.write_bytes(data) logger.debug("local_dir: downloaded missing file %s", rel) # ------------------------------------------------------------------ # Cache eviction (LRU by mtime) # ------------------------------------------------------------------ def _get_cache_size(self) -> int: """Total bytes of all files in the cache directory.""" total = 0 for f in self._cache_dir.iterdir(): if f.is_file(): try: total += f.stat().st_size except OSError: pass return total def _maybe_evict(self) -> None: """Evict LRU files if cache exceeds ``max_cache_bytes``. Evicts down to 80 % of max. Never evicts ``workspace.yaml`` or files with pending background uploads. """ total = self._get_cache_size() if total <= self._max_cache_bytes: return target = int(self._max_cache_bytes * 0.8) # Collect eviction candidates (sorted oldest mtime first) candidates: list[tuple[Path, float, int]] = [] with self._upload_lock: pending = set(self._pending_uploads) for f in self._cache_dir.iterdir(): if not f.is_file(): continue if f.name == METADATA_FILENAME: continue # never evict metadata if f.name in pending: continue # never evict files being uploaded try: st = f.stat() candidates.append((f, st.st_mtime, st.st_size)) except OSError: pass # Sort by mtime ascending (oldest first = evict first) candidates.sort(key=lambda x: x[1]) evicted = 0 freed = 0 for path, mtime, size in candidates: if total <= target: break try: path.unlink(missing_ok=True) total -= size freed += size evicted += 1 except OSError: pass if evicted: logger.info( "Cache eviction: removed %d file(s), freed %.1f MB, " "remaining %.1f / %.1f MB", evicted, freed / (1024 * 1024), total / (1024 * 1024), self._max_cache_bytes / (1024 * 1024), ) # Also run global cross-user eviction if needed self._global_cache.maybe_evict_global() def get_cache_stats(self) -> dict[str, Any]: """Return cache statistics for monitoring / debugging.""" files = [] total_size = 0 for f in self._cache_dir.iterdir(): if f.is_file(): try: st = f.stat() files.append({"name": f.name, "size": st.st_size, "mtime": st.st_mtime}) total_size += st.st_size except OSError: pass with self._upload_lock: pending = list(self._pending_uploads) return { "cache_dir": str(self._cache_dir), "file_count": len(files), "total_size_bytes": total_size, "total_size_mb": round(total_size / (1024 * 1024), 2), "max_size_mb": round(self._max_cache_bytes / (1024 * 1024), 2), "utilization_pct": round(total_size / self._max_cache_bytes * 100, 1) if self._max_cache_bytes > 0 else 0, "pending_uploads": pending, "files": sorted(files, key=lambda f: f["mtime"], reverse=True), } # ------------------------------------------------------------------ # Lifecycle # ------------------------------------------------------------------ def wait_for_uploads(self, timeout: float | None = 30) -> bool: """Block until all pending background uploads complete. Args: timeout: Max seconds to wait. ``None`` = wait forever. Returns: ``True`` if all uploads finished, ``False`` if timeout was hit. """ # Collect outstanding futures futures = [f for f in self._upload_futures if not f.done()] if not futures: return True from concurrent.futures import wait, FIRST_EXCEPTION done, not_done = wait(futures, timeout=timeout) # Clean up completed futures self._upload_futures = [f for f in self._upload_futures if not f.done()] if not_done: logger.warning( "%d background upload(s) did not complete within %.1fs", len(not_done), timeout or 0, ) return False return True def _atexit_flush(self) -> None: """Best-effort flush of pending uploads on interpreter shutdown.""" with self._upload_lock: if not self._pending_uploads: return pending_count = len(self._pending_uploads) logger.info("Flushing %d pending upload(s) on shutdown...", pending_count) self.wait_for_uploads(timeout=30) def cleanup(self) -> None: """Remove local cache immediately, delete Azure blobs in background. The local cache is cleared **synchronously** so the workspace is immediately reusable. Azure blob deletion is submitted to the background thread pool so the caller isn't blocked by many sequential Azure API calls. """ # 1. Wait for any in-flight uploads (so we don't race with them) self.wait_for_uploads(timeout=60) # 2. Clear local caches immediately (non-blocking) if self._cache_dir.exists(): shutil.rmtree(self._cache_dir, ignore_errors=True) self._cache_dir.mkdir(parents=True, exist_ok=True) self._metadata_cache = None self._blob_data_cache.clear() self._cleanup_temp_files() # 3. Delete Azure blobs in background prefix = self._prefix container = self._container def _bg_cleanup() -> None: try: for blob in container.list_blobs(name_starts_with=prefix): try: container.delete_blob(blob.name) except Exception: logger.debug("Failed to delete blob %s", blob.name) logger.info("Background cleanup finished for %s", prefix) except Exception: logger.warning( "Background Azure cleanup failed for %s", prefix, exc_info=True, ) self._upload_executor.submit(_bg_cleanup) logger.info("Cleanup: local cache cleared, Azure deletion queued for %s", self._safe_id) # ------------------------------------------------------------------ # snapshot / session overrides: ensure cache consistency # ------------------------------------------------------------------ def restore_workspace_snapshot(self, src: Path) -> None: """Restore snapshot and repopulate local cache.""" # Clear local cache first if self._cache_dir.exists(): shutil.rmtree(self._cache_dir, ignore_errors=True) self._cache_dir.mkdir(parents=True, exist_ok=True) # Delegate to parent (uploads blobs to Azure) super().restore_workspace_snapshot(src) # Repopulate cache from the source snapshot if src.exists(): for f in src.rglob("*"): if f.is_file(): rel = str(f.relative_to(src)) cache_file = self._cache_path(rel) cache_file.parent.mkdir(parents=True, exist_ok=True) shutil.copy2(f, cache_file) def invalidate_metadata_cache(self) -> None: """Force re-read of metadata from Azure (clears local + in-memory).""" self._cache_path(METADATA_FILENAME).unlink(missing_ok=True) super().invalidate_metadata_cache() # ------------------------------------------------------------------ # Representation # ------------------------------------------------------------------ def __repr__(self) -> str: return ( f"CachedAzureBlobWorkspace(identity_id={self._identity_id!r}, " f"prefix={self._prefix!r}, cache={self._cache_dir!r})" ) ================================================ FILE: py-src/data_formulator/datalake/file_manager.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ File manager for user-uploaded files in the Data Lake. This module handles storing user-uploaded files (CSV, Excel, TXT, HTML, JSON, PDF) as-is in the workspace without conversion. """ import hashlib import logging import os from datetime import datetime, timezone from pathlib import Path from typing import BinaryIO, Union from werkzeug.utils import secure_filename from data_formulator.datalake.metadata import TableMetadata from data_formulator.datalake.workspace import Workspace logger = logging.getLogger(__name__) # Supported file extensions for upload SUPPORTED_EXTENSIONS = { '.csv': 'csv', '.xlsx': 'excel', '.xls': 'excel', '.txt': 'txt', '.html': 'html', '.htm': 'html', '.json': 'json', '.pdf': 'pdf', } def is_supported_file(filename: str) -> bool: ext = Path(filename).suffix.lower() return ext in SUPPORTED_EXTENSIONS def get_file_type(filename: str) -> str | None: """ Get the file type based on extension. Args: filename: Name of the file Returns: File type string (e.g., 'csv', 'excel') or None if unsupported """ ext = Path(filename).suffix.lower() return SUPPORTED_EXTENSIONS.get(ext) def compute_file_hash(content: bytes) -> str: """ Compute MD5 hash of file content. Args: content: File content as bytes Returns: MD5 hash as hex string """ return hashlib.md5(content).hexdigest() def sanitize_table_name(name: str) -> str: """ Sanitize a string to be a valid table name. Args: name: Original name Returns: Sanitized name suitable for use as a table identifier """ # Remove extension if present name = Path(name).stem # Replace invalid characters with underscores sanitized = [] for char in name: if char.isalnum() or char == '_': sanitized.append(char) else: sanitized.append('_') result = ''.join(sanitized) # Ensure it starts with a letter or underscore if result and not (result[0].isalpha() or result[0] == '_'): result = '_' + result # Ensure it's not empty if not result: result = '_unnamed' return result.lower() def generate_unique_filename( workspace: Workspace, desired_filename: str, ) -> str: """ Generate a unique filename if the desired one already exists. Args: workspace: The workspace to check desired_filename: The desired filename Returns: A unique filename (may be the original if it doesn't exist) """ if not workspace.file_exists(desired_filename): return desired_filename # Split filename and extension path = Path(desired_filename) stem = path.stem suffix = path.suffix # Try adding numbers until we find a unique name counter = 1 while True: new_filename = f"{stem}_{counter}{suffix}" if not workspace.file_exists(new_filename): return new_filename counter += 1 if counter > 1000: # Safety limit raise ValueError(f"Could not generate unique filename for {desired_filename}") def save_uploaded_file( workspace: Workspace, file_content: Union[bytes, BinaryIO], filename: str, table_name: str | None = None, overwrite: bool = False, ) -> TableMetadata: """ Save an uploaded file to the workspace. The file is stored as-is without conversion. Metadata is added to track the file in the workspace. Args: workspace: The workspace to save to file_content: File content as bytes or file-like object filename: Original filename (used for extension detection) table_name: Name to use for the table. If None, derived from filename. overwrite: If True, overwrite existing file. If False, generate unique name. Returns: TableMetadata for the saved file Raises: ValueError: If file type is not supported """ # Sanitize filename to prevent path traversal (defence-in-depth) filename = secure_filename(filename) if not filename: raise ValueError("Invalid filename after sanitization") # Validate file type file_type = get_file_type(filename) if file_type is None: raise ValueError( f"Unsupported file type: {filename}. " f"Supported extensions: {', '.join(SUPPORTED_EXTENSIONS.keys())}" ) # Read content if it's a file-like object if hasattr(file_content, 'read'): content = file_content.read() else: content = file_content # Determine the actual filename to use if overwrite: actual_filename = filename else: actual_filename = generate_unique_filename(workspace, filename) # Determine table name if table_name is None: table_name = sanitize_table_name(actual_filename) # Ensure table name is unique in metadata metadata = workspace.get_metadata() if table_name in metadata.tables and not overwrite: # Generate unique table name base_name = table_name counter = 1 while table_name in metadata.tables: table_name = f"{base_name}_{counter}" counter += 1 # Write the file file_path = workspace.get_file_path(actual_filename) with open(file_path, 'wb') as f: f.write(content) # Compute hash and size content_hash = compute_file_hash(content) file_size = len(content) # Create metadata table_metadata = TableMetadata( name=table_name, source_type="upload", filename=actual_filename, file_type=file_type, created_at=datetime.now(timezone.utc), content_hash=content_hash, file_size=file_size, ) # Save metadata workspace.add_table_metadata(table_metadata) logger.info( f"Saved uploaded file {actual_filename} as table {table_name} " f"({file_size} bytes, hash={content_hash[:8]}...)" ) return table_metadata def save_uploaded_file_from_path( workspace: Workspace, source_path: Union[str, Path], table_name: str | None = None, overwrite: bool = False, ) -> TableMetadata: """ Save a file from a local path to the workspace. Args: workspace: The workspace to save to source_path: Path to the source file table_name: Name to use for the table. If None, derived from filename. overwrite: If True, overwrite existing file. Returns: TableMetadata for the saved file """ source_path = Path(source_path) if not source_path.exists(): raise FileNotFoundError(f"Source file not found: {source_path}") with open(source_path, 'rb') as f: content = f.read() return save_uploaded_file( workspace=workspace, file_content=content, filename=source_path.name, table_name=table_name, overwrite=overwrite, ) def get_file_info(workspace: Workspace, table_name: str) -> dict | None: """ Get information about an uploaded file. Args: workspace: The workspace table_name: Name of the table Returns: Dictionary with file information or None if not found """ table_meta = workspace.get_table_metadata(table_name) if table_meta is None: return None file_path = workspace.get_file_path(table_meta.filename) result = { "table_name": table_name, "filename": table_meta.filename, "file_type": table_meta.file_type, "file_size": table_meta.file_size, "content_hash": table_meta.content_hash, "created_at": table_meta.created_at.isoformat(), "exists": file_path.exists(), } if file_path.exists(): stat = file_path.stat() result["current_size"] = stat.st_size result["modified_at"] = datetime.fromtimestamp(stat.st_mtime).isoformat() return result ================================================ FILE: py-src/data_formulator/datalake/metadata.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ Metadata management for the Data Lake workspace. This module defines the schema and operations for workspace.yaml, which tracks all data sources (uploaded files and data loader ingests). """ from dataclasses import dataclass, field, asdict from datetime import datetime, date, timezone from decimal import Decimal from pathlib import Path from typing import Literal, Any import yaml import logging import tempfile import time import os import sys logger = logging.getLogger(__name__) METADATA_VERSION = "1.1" METADATA_FILENAME = "workspace.yaml" LOCK_FILENAME = ".workspace.lock" MAX_LOCK_WAIT_SECONDS = 10 if sys.platform == 'win32': # Windows: use LockFileEx/UnlockFileEx via ctypes for whole-file locking, # semantically equivalent to fcntl.flock on Unix. import ctypes import ctypes.wintypes import msvcrt as _msvcrt # use_last_error=True: ctypes saves GetLastError() per-thread immediately # after each call, avoiding races with other threads. _kernel32 = ctypes.WinDLL('kernel32', use_last_error=True) _LOCKFILE_EXCLUSIVE_LOCK = 0x0002 _LOCKFILE_FAIL_IMMEDIATELY = 0x0001 class _OVERLAPPED(ctypes.Structure): _fields_ = [ ('Internal', ctypes.POINTER(ctypes.c_ulong)), ('InternalHigh', ctypes.POINTER(ctypes.c_ulong)), ('Offset', ctypes.wintypes.DWORD), ('OffsetHigh', ctypes.wintypes.DWORD), ('hEvent', ctypes.wintypes.HANDLE), ] def _lock_file(fd: int) -> None: """Acquire an exclusive, non-blocking lock on the whole file (Windows).""" handle = _msvcrt.get_osfhandle(fd) overlapped = _OVERLAPPED() result = _kernel32.LockFileEx( ctypes.wintypes.HANDLE(handle), ctypes.wintypes.DWORD(_LOCKFILE_EXCLUSIVE_LOCK | _LOCKFILE_FAIL_IMMEDIATELY), ctypes.wintypes.DWORD(0), # reserved ctypes.wintypes.DWORD(0xFFFFFFFF), # bytes to lock (low) ctypes.wintypes.DWORD(0xFFFFFFFF), # bytes to lock (high) ctypes.byref(overlapped), ) if not result: raise ctypes.WinError(ctypes.get_last_error()) def _unlock_file(fd: int) -> None: """Release the whole-file lock (Windows).""" handle = _msvcrt.get_osfhandle(fd) overlapped = _OVERLAPPED() result = _kernel32.UnlockFileEx( ctypes.wintypes.HANDLE(handle), ctypes.wintypes.DWORD(0), # reserved ctypes.wintypes.DWORD(0xFFFFFFFF), ctypes.wintypes.DWORD(0xFFFFFFFF), ctypes.byref(overlapped), ) if not result: raise ctypes.WinError(ctypes.get_last_error()) else: import fcntl as _fcntl def _lock_file(fd: int) -> None: """Acquire an exclusive, non-blocking lock on the whole file (Unix).""" _fcntl.flock(fd, _fcntl.LOCK_EX | _fcntl.LOCK_NB) def _unlock_file(fd: int) -> None: """Release the whole-file lock (Unix).""" _fcntl.flock(fd, _fcntl.LOCK_UN) class WorkspaceLock: """ Context manager for acquiring an exclusive lock on workspace metadata. Prevents race conditions when multiple processes/threads modify metadata concurrently. Uses LockFileEx on Windows and fcntl.flock on Unix — both provide whole-file locking. """ def __init__(self, workspace_path: Path, timeout: float = MAX_LOCK_WAIT_SECONDS): self.lock_file = workspace_path / LOCK_FILENAME self.timeout = timeout self.lock_fd = None def __enter__(self): """Acquire exclusive lock with timeout.""" # Ensure the lock file exists self.lock_file.parent.mkdir(parents=True, exist_ok=True) start_time = time.time() while True: try: # Open lock file (create if doesn't exist) # 'a+' creates the file atomically if missing and allows seek/read self.lock_fd = open(self.lock_file, 'a+') # Try to acquire exclusive whole-file lock (non-blocking) _lock_file(self.lock_fd.fileno()) logger.debug(f"Acquired workspace lock: {self.lock_file}") return self except (IOError, OSError) as e: # Lock is held by another process if self.lock_fd: self.lock_fd.close() self.lock_fd = None elapsed = time.time() - start_time if elapsed >= self.timeout: raise TimeoutError( f"Failed to acquire workspace lock after {self.timeout}s. " f"Another process may be holding it." ) # Wait a bit before retrying time.sleep(0.05) def __exit__(self, exc_type, exc_val, exc_tb): """Release lock.""" if self.lock_fd: try: _unlock_file(self.lock_fd.fileno()) self.lock_fd.close() logger.debug(f"Released workspace lock: {self.lock_file}") except Exception as e: logger.warning(f"Error releasing lock: {e}") finally: self.lock_fd = None def make_json_safe(value: Any) -> Any: """ Convert a value (possibly containing numpy/pandas/pyarrow scalars) into a JSON/YAML-safe primitive structure. """ if value is None or isinstance(value, (bool, int, float, str)): return value if isinstance(value, (datetime, date)): return value.isoformat() if isinstance(value, Decimal): return str(value) if isinstance(value, Path): return str(value) if isinstance(value, dict): return {str(k): make_json_safe(v) for k, v in value.items()} if isinstance(value, (list, tuple)): return [make_json_safe(v) for v in value] # numpy scalars, pandas scalars, etc. item = getattr(value, "item", None) if callable(item): try: return make_json_safe(item()) except Exception: pass return str(value) @dataclass class ColumnInfo: """Information about a single column in a table.""" name: str dtype: str def to_dict(self) -> dict: return {"name": self.name, "dtype": self.dtype} @classmethod def from_dict(cls, data: dict) -> "ColumnInfo": return cls(name=data["name"], dtype=data["dtype"]) @dataclass class TableMetadata: """Metadata for a single table/file in the workspace.""" name: str source_type: Literal["upload", "data_loader"] filename: str file_type: str created_at: datetime content_hash: str | None = None file_size: int | None = None # For data_loader sources: loader_type: str | None = None loader_params: dict | None = None source_table: str | None = None source_query: str | None = None last_synced: datetime | None = None row_count: int | None = None columns: list[ColumnInfo] | None = None def to_dict(self) -> dict: """Convert to dictionary for YAML serialization.""" result = { "source_type": self.source_type, "filename": self.filename, "file_type": self.file_type, "created_at": self.created_at.isoformat(), } if self.content_hash is not None: result["content_hash"] = self.content_hash if self.file_size is not None: result["file_size"] = self.file_size if self.loader_type is not None: result["loader_type"] = self.loader_type if self.loader_params is not None: result["loader_params"] = self.loader_params if self.source_table is not None: result["source_table"] = self.source_table if self.source_query is not None: result["source_query"] = self.source_query if self.last_synced is not None: result["last_synced"] = self.last_synced.isoformat() if self.row_count is not None: result["row_count"] = self.row_count if self.columns is not None: result["columns"] = [col.to_dict() for col in self.columns] return result @classmethod def from_dict(cls, name: str, data: dict) -> "TableMetadata": """Create from dictionary (YAML deserialization).""" columns = None if "columns" in data and data["columns"] is not None: columns = [ColumnInfo.from_dict(col) for col in data["columns"]] created_at = data["created_at"] if isinstance(created_at, str): created_at = datetime.fromisoformat(created_at) last_synced = data.get("last_synced") if isinstance(last_synced, str): last_synced = datetime.fromisoformat(last_synced) return cls( name=name, source_type=data["source_type"], filename=data["filename"], file_type=data["file_type"], created_at=created_at, content_hash=data.get("content_hash"), file_size=data.get("file_size"), loader_type=data.get("loader_type"), loader_params=data.get("loader_params"), source_table=data.get("source_table"), source_query=data.get("source_query"), last_synced=last_synced, row_count=data.get("row_count"), columns=columns, ) @dataclass class WorkspaceMetadata: """Metadata for the entire workspace.""" version: str created_at: datetime updated_at: datetime tables: dict[str, TableMetadata] = field(default_factory=dict) def add_table(self, table: TableMetadata) -> None: """Add or update a table in the metadata.""" self.tables[table.name] = table self.updated_at = datetime.now(timezone.utc) def remove_table(self, name: str) -> bool: """Remove a table from the metadata. Returns True if removed.""" if name in self.tables: del self.tables[name] self.updated_at = datetime.now(timezone.utc) return True return False def get_table(self, name: str) -> TableMetadata | None: """Get metadata for a specific table.""" return self.tables.get(name) def list_tables(self) -> list[str]: """List all table names.""" return list(self.tables.keys()) def to_dict(self) -> dict: """Convert to dictionary for YAML serialization.""" return { "version": self.version, "created_at": self.created_at.isoformat(), "updated_at": self.updated_at.isoformat(), "tables": { name: table.to_dict() for name, table in self.tables.items() }, } @classmethod def from_dict(cls, data: dict) -> "WorkspaceMetadata": """Create from dictionary (YAML deserialization).""" created_at = data["created_at"] if isinstance(created_at, str): created_at = datetime.fromisoformat(created_at) updated_at = data["updated_at"] if isinstance(updated_at, str): updated_at = datetime.fromisoformat(updated_at) tables = {} tables_data = data.get("tables", {}) if tables_data: for name, table_data in tables_data.items(): tables[name] = TableMetadata.from_dict(name, table_data) return cls( version=data["version"], created_at=created_at, updated_at=updated_at, tables=tables, ) @classmethod def create_new(cls) -> "WorkspaceMetadata": """Create a new empty workspace metadata.""" now = datetime.now(timezone.utc) return cls( version=METADATA_VERSION, created_at=now, updated_at=now, tables={}, ) def load_metadata(workspace_path: Path) -> WorkspaceMetadata: """ Load workspace metadata from YAML file with file locking. Args: workspace_path: Path to the workspace directory Returns: WorkspaceMetadata object Raises: FileNotFoundError: If metadata file doesn't exist ValueError: If metadata file is invalid """ metadata_file = workspace_path / METADATA_FILENAME if not metadata_file.exists(): raise FileNotFoundError(f"Metadata file not found: {metadata_file}") # Acquire shared lock for reading with WorkspaceLock(workspace_path): try: with open(metadata_file, "r", encoding="utf-8") as f: content = f.read() # Check for empty file (can happen during concurrent writes) if not content or not content.strip(): raise ValueError( f"Empty metadata file - this may indicate a concurrent write conflict. " f"File: {metadata_file}" ) data = yaml.safe_load(content) if data is None: raise ValueError("Metadata file parsed to None") return WorkspaceMetadata.from_dict(data) except yaml.YAMLError as e: raise ValueError(f"Invalid YAML in metadata file: {e}") def save_metadata(workspace_path: Path, metadata: WorkspaceMetadata) -> None: """ Save workspace metadata to YAML file with atomic writes and file locking. Uses atomic write (write to temp file + rename) to prevent partial writes and file locking to prevent concurrent modifications. Args: workspace_path: Path to the workspace directory metadata: WorkspaceMetadata object to save """ metadata_file = workspace_path / METADATA_FILENAME # Update the updated_at timestamp metadata.updated_at = datetime.now(timezone.utc) # Ensure directory exists workspace_path.mkdir(parents=True, exist_ok=True) # Acquire exclusive lock for writing with WorkspaceLock(workspace_path): # Write to temporary file first (atomic operation) temp_fd, temp_path = tempfile.mkstemp( dir=workspace_path, prefix=".workspace_", suffix=".yaml.tmp", text=True ) try: with os.fdopen(temp_fd, "w", encoding="utf-8") as f: yaml.dump( metadata.to_dict(), f, default_flow_style=False, allow_unicode=True, sort_keys=False, ) # Atomic rename - replaces old file os.replace(temp_path, metadata_file) logger.debug(f"Saved metadata to {metadata_file}") except Exception as e: # Clean up temp file on error try: os.unlink(temp_path) except: pass raise e def metadata_exists(workspace_path: Path) -> bool: """Check if workspace metadata file exists.""" return (workspace_path / METADATA_FILENAME).exists() ================================================ FILE: py-src/data_formulator/datalake/parquet_utils.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ Parquet utility functions for the Data Lake. Pure helper functions for parquet I/O, hashing, column introspection, and name sanitisation. These utilities have **no dependency on Workspace** and are consumed by Workspace methods that handle metadata bookkeeping. """ import hashlib import logging from typing import Any import pandas as pd import pyarrow as pa import pyarrow.parquet as pq from werkzeug.utils import secure_filename from data_formulator.datalake.metadata import ColumnInfo, make_json_safe logger = logging.getLogger(__name__) # --------------------------------------------------------------------------- # Constants # --------------------------------------------------------------------------- # Default compression for parquet files DEFAULT_COMPRESSION = "snappy" # Default number of rows to persist in metadata for preview DEFAULT_METADATA_SAMPLE_ROWS = 50 # --------------------------------------------------------------------------- # Name helpers # --------------------------------------------------------------------------- def sanitize_table_name(name: str) -> str: """ Sanitize a string to be a valid table/file name. Uses ``werkzeug.utils.secure_filename`` as the first pass to strip path separators, leading dots, and other dangerous components (this is the sanitiser recognised by CodeQL / static-analysis tools). Additional rules are then applied to guarantee the result is a valid, lowercase, Python-identifier-style name. Args: name: Original name Returns: Sanitized name """ # First pass: werkzeug's secure_filename neutralises path-traversal # components ("../", leading dots, etc.) and keeps only ASCII # alphanumerics plus ".", "_", and "-". name = secure_filename(name) # Second pass: replace any remaining chars that are not alphanumeric # or underscore (e.g. dots and hyphens kept by secure_filename). sanitized = [] for char in name: if char.isalnum() or char == '_': sanitized.append(char) else: sanitized.append('_') result = ''.join(sanitized) # Ensure it starts with a letter or underscore if result and not (result[0].isalpha() or result[0] == '_'): result = '_' + result # Ensure it's not empty if not result: result = '_unnamed' return result.lower() # --------------------------------------------------------------------------- # Arrow / DataFrame introspection # --------------------------------------------------------------------------- def get_sample_rows_from_arrow( table: pa.Table, limit: int = DEFAULT_METADATA_SAMPLE_ROWS ) -> list[dict[str, Any]]: """Get a small sample of rows from an Arrow table as JSON/YAML-safe records.""" if table.num_rows <= 0 or limit <= 0: return [] sample = table.slice(0, min(limit, table.num_rows)) return make_json_safe(sample.to_pylist()) def get_arrow_column_info(table: pa.Table) -> list[ColumnInfo]: """Extract column information from a PyArrow Table.""" return [ColumnInfo(name=field.name, dtype=str(field.type)) for field in table.schema] def get_column_info(df: pd.DataFrame) -> list[ColumnInfo]: """Extract column information from a pandas DataFrame.""" return [ColumnInfo(name=str(col), dtype=str(df[col].dtype)) for col in df.columns] # --------------------------------------------------------------------------- # Hashing # --------------------------------------------------------------------------- def compute_arrow_table_hash(table: pa.Table, sample_rows: int = 100) -> str: """ Compute an MD5 hash representing the Arrow Table content. Uses row count, column names, and sampled rows for efficiency. """ hash_parts = [ f"rows:{table.num_rows}", f"cols:{','.join(table.column_names)}", ] if table.num_rows > 0: if table.num_rows <= sample_rows: sample = table else: n = sample_rows // 3 indices = ( list(range(n)) + list(range(table.num_rows // 4, table.num_rows // 4 + n)) + list(range(table.num_rows - n, table.num_rows)) ) sample = table.take(indices) hash_parts.append(f"data:{sample.to_string()}") content = '|'.join(hash_parts) return hashlib.md5(content.encode()).hexdigest() def sanitize_dataframe_for_arrow(df: pd.DataFrame) -> pd.DataFrame: """ Sanitize a DataFrame for conversion to PyArrow Table. Handles common issues that cause ArrowTypeError: - Mixed types in object columns (e.g., strings and integers) - Columns with all nulls that have ambiguous type For object dtype columns, converts all non-null values to strings to ensure consistent typing. Returns: A copy of the DataFrame with sanitized columns. """ df = df.copy() for col in df.columns: # Handle object dtype columns (potential mixed types) if df[col].dtype == 'object': # Convert all non-null values to string # This handles mixed int/string columns safely df[col] = df[col].apply( lambda x: str(x) if pd.notna(x) and x is not None else None ) return df def compute_dataframe_hash(df: pd.DataFrame, sample_rows: int = 100) -> str: """ Compute an MD5 hash representing the DataFrame content. Uses row count, column names, and sampled rows for efficiency. """ hash_parts = [ f"rows:{len(df)}", f"cols:{','.join(df.columns.tolist())}", ] if len(df) > 0: if len(df) <= sample_rows: sample = df else: n = sample_rows // 3 first = df.head(n) last = df.tail(n) middle = df.iloc[len(df) // 4 : len(df) * 3 // 4].sample( min(n, len(df) // 2), random_state=42 ) sample = pd.concat([first, middle, last]) hash_parts.append(f"data:{sample.to_string()}") content = '|'.join(hash_parts) return hashlib.md5(content.encode()).hexdigest() ================================================ FILE: py-src/data_formulator/datalake/workspace.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ Workspace management for the Data Lake. Each user has a workspace directory identified by their identity_id. The workspace contains all their data files (uploaded and ingested) plus a workspace.yaml metadata file. """ import io import json import os import re import shutil import logging import tempfile import time import zipfile from contextlib import contextmanager from datetime import datetime, timezone from pathlib import Path from typing import Any, Optional import pandas as pd import pyarrow as pa import pyarrow.parquet as pq from data_formulator.datalake.metadata import ( WorkspaceMetadata, TableMetadata, load_metadata, save_metadata, metadata_exists, ) from data_formulator.datalake.parquet_utils import ( sanitize_table_name, get_arrow_column_info, compute_arrow_table_hash, get_column_info, compute_dataframe_hash, sanitize_dataframe_for_arrow, DEFAULT_COMPRESSION, ) from werkzeug.utils import secure_filename logger = logging.getLogger(__name__) def get_data_formulator_home() -> Path: """ Get the Data Formulator home directory. Resolution order: 1. Flask app.config['CLI_ARGS']['data_dir'] (set via --data-dir CLI flag) 2. DATA_FORMULATOR_HOME environment variable 3. Default: ~/.data_formulator """ # Try Flask app config first (set by --data-dir CLI arg) try: from flask import current_app data_dir = current_app.config.get('CLI_ARGS', {}).get('data_dir') if data_dir: return Path(data_dir) except (RuntimeError, ImportError): pass env_home = os.getenv("DATA_FORMULATOR_HOME") if env_home: return Path(env_home) return Path.home() / ".data_formulator" def get_default_workspace_root() -> Path: """ Get the default workspace root directory. Returns DATA_FORMULATOR_HOME / "workspaces". """ return get_data_formulator_home() / "workspaces" def cleanup_stale_temp_files(workspace_path: Path, max_age_hours: int = 24) -> int: """ Remove stale temporary files from workspace directory. This handles crash recovery by cleaning up temp files (.temp_*.parquet) that were not properly deleted due to server crashes or unexpected shutdowns. Args: workspace_path: Path to the workspace directory max_age_hours: Remove temp files older than this many hours (default: 24) Returns: Number of files cleaned up """ if not workspace_path.exists(): return 0 cleaned_count = 0 current_time = time.time() max_age_seconds = max_age_hours * 3600 try: for file_path in workspace_path.glob('.temp_*.parquet'): try: # Check file age using modification time file_age = current_time - file_path.stat().st_mtime if file_age > max_age_seconds: file_path.unlink(missing_ok=True) cleaned_count += 1 logger.info( f"Cleaned up stale temp file: {file_path.name} " f"(age: {file_age / 3600:.1f} hours)" ) except Exception as e: logger.warning(f"Failed to clean temp file {file_path}: {e}") except Exception as e: logger.warning(f"Error during temp file cleanup in {workspace_path}: {e}") if cleaned_count > 0: logger.info(f"Cleaned up {cleaned_count} stale temp file(s) from {workspace_path}") return cleaned_count class Workspace: """ Manages a user's workspace directory in the Data Lake. The workspace contains: - workspace.yaml: Metadata file tracking all data sources - Data files: User uploaded files (CSV, Excel, etc.) and parquet files from data loaders All files are stored in a single flat directory per user. """ def __init__(self, identity_id: str, root_dir: Optional[str | Path] = None): """ Initialize a workspace for a user. Args: identity_id: Unique identifier for the user (e.g., "user:123" or "browser:abc") root_dir: Root directory for all workspaces. If None, uses default. """ if not identity_id: raise ValueError("identity_id cannot be empty") # Sanitize identity_id for filesystem safety self._identity_id = identity_id self._safe_id = self._sanitize_identity_id(identity_id) # Determine root directory if root_dir is None: self._root = get_default_workspace_root() else: self._root = Path(root_dir) # Workspace path is root / sanitized_identity_id self._path = self._root / self._safe_id # Verify the constructed path hasn't escaped the root directory resolved = self._path.resolve() root_resolved = self._root.resolve() if not str(resolved).startswith(str(root_resolved) + os.sep) and resolved != root_resolved: raise ValueError( f"Path traversal detected: workspace path escapes root directory" ) if resolved == root_resolved: raise ValueError( "identity_id must not resolve to the workspace root itself" ) # Ensure workspace directory exists self._path.mkdir(parents=True, exist_ok=True) # Initialize metadata if it doesn't exist if not metadata_exists(self._path): self._init_metadata() # --- in-memory metadata cache ---------------------------------------- # Avoids re-reading and re-parsing workspace.yaml on every method call. # Invalidated automatically by save_metadata() and cleanup(). self._metadata_cache: Optional[WorkspaceMetadata] = None # Clean up any stale temp files from previous crashes (older than 24 hours) # This is safe because active temp files are always created fresh and # cleaned up within minutes of their creation cleanup_stale_temp_files(self._path, max_age_hours=24) logger.debug(f"Initialized workspace at {self._path}") @staticmethod def _sanitize_identity_id(identity_id: str) -> str: """ Sanitize identity_id for use as a directory name. Uses ``secure_filename`` to produce a safe single-component name. Raises ``ValueError`` if the result is empty or too long. """ if len(identity_id) > 256: raise ValueError("identity_id too long") result = secure_filename(identity_id) if not result: raise ValueError("identity_id sanitized to empty string") return result def _init_metadata(self) -> None: """Initialize a new workspace with empty metadata.""" metadata = WorkspaceMetadata.create_new() save_metadata(self._path, metadata) logger.info(f"Initialized new workspace metadata at {self._path}") def get_file_path(self, filename: str) -> Path: """ Get the full path for a file in the workspace. Args: filename: Name of the file Returns: Full path to the file """ # secure_filename strips path separators and ".." components. safe_filename = secure_filename(filename) if not safe_filename: raise ValueError(f"Invalid filename: {filename!r}") return self._path / safe_filename def file_exists(self, filename: str) -> bool: """ Check if a file exists in the workspace. Args: filename: Name of the file Returns: True if file exists, False otherwise """ return self.get_file_path(filename).exists() def delete_table(self, table_name: str) -> bool: """ Delete a table by name (removes both file and metadata). Args: table_name: Name of the table to delete Returns: True if table was deleted, False if it didn't exist """ metadata = self.get_metadata() table = metadata.get_table(table_name) if table is None: return False # Delete the file file_path = self.get_file_path(table.filename) if file_path.exists(): file_path.unlink() # Remove from metadata metadata.remove_table(table_name) self.save_metadata(metadata) logger.info(f"Deleted table {table_name} from workspace {self._safe_id}") return True def get_metadata(self) -> WorkspaceMetadata: if self._metadata_cache is not None: return self._metadata_cache self._metadata_cache = load_metadata(self._path) return self._metadata_cache def save_metadata(self, metadata: WorkspaceMetadata) -> None: save_metadata(self._path, metadata) # Update the cache with the just-saved metadata self._metadata_cache = metadata def invalidate_metadata_cache(self) -> None: """Force the next get_metadata() to re-read from disk.""" self._metadata_cache = None def add_table_metadata(self, table: TableMetadata) -> None: metadata = self.get_metadata() metadata.add_table(table) self.save_metadata(metadata) def get_table_metadata(self, table_name: str) -> Optional[TableMetadata]: """Look up table metadata, falling back to sanitized name.""" ws_metadata = self.get_metadata() result = ws_metadata.get_table(table_name) if result is None: result = ws_metadata.get_table(sanitize_table_name(table_name)) return result def list_tables(self) -> list[str]: metadata = self.get_metadata() return metadata.list_tables() def get_fresh_name(self, name: str) -> str: """ Generate a unique table name that doesn't conflict with existing tables. Sanitizes the input name, then checks if it already exists in the workspace. If it does, appends an incrementing numeric suffix (_2, _3, ...) until a unique name is found. Args: name: Desired table name (will be sanitized) Returns: A sanitized, unique table name safe for use in write_parquet etc. """ base = sanitize_table_name(name) existing = set(self.list_tables()) if base not in existing: return base # Try incrementing suffixes counter = 2 while f"{base}_{counter}" in existing: counter += 1 return f"{base}_{counter}" def cleanup(self) -> None: """ Remove the entire workspace directory. """ if self._path.exists(): shutil.rmtree(self._path) logger.info(f"Cleaned up workspace {self._safe_id}") self._metadata_cache = None def get_relative_data_file_path(self, table_name: str) -> str: """ Get the filename for a table, suitable for use in generated code. Returns just the filename (e.g. "sales_data.parquet"). The sandbox ensures the script runs with the workspace as its working directory, so ``pd.read_parquet("sales_data.parquet")`` works directly. Args: table_name: Name of the table in the workspace Returns: Filename string that can be used in read_parquet() / read_csv() etc. Raises: FileNotFoundError: If the table doesn't exist """ metadata = self.get_table_metadata(table_name) if metadata is None: raise FileNotFoundError(f"Table not found: {table_name}") return metadata.filename def read_data_as_df(self, table_name: str) -> pd.DataFrame: """ Read a table from the workspace as a pandas DataFrame. Automatically selects the appropriate reader based on the file's type (stored in metadata). Supports parquet, csv, excel, json, and txt. Falls back to sanitized table name if the original name is not found. Args: table_name: Name of the table in the workspace Returns: pandas DataFrame with the table data Raises: FileNotFoundError: If the table or file doesn't exist ValueError: If the file type is not supported for DataFrame reading """ metadata = self.get_table_metadata(table_name) if metadata is None: raise FileNotFoundError(f"Table not found: {table_name}") file_path = self.get_file_path(metadata.filename) if not file_path.exists(): raise FileNotFoundError(f"File not found: {file_path}") file_type = metadata.file_type if file_type == "parquet": return pd.read_parquet(file_path) elif file_type == "csv": return pd.read_csv(file_path) elif file_type == "excel": return pd.read_excel(file_path) elif file_type == "json": return pd.read_json(file_path) elif file_type == "txt": return pd.read_csv(file_path, sep="\t") else: raise ValueError( f"Unsupported file type '{file_type}' for table '{table_name}'. " f"Supported types: parquet, csv, excel, json, txt." ) # ------------------------------------------------------------------ # Parquet management # ------------------------------------------------------------------ def write_parquet_from_arrow( self, table: pa.Table, table_name: str, compression: str = DEFAULT_COMPRESSION, loader_metadata: Optional[dict[str, Any]] = None, ) -> TableMetadata: """ Write a PyArrow Table directly to parquet. This is the preferred path because it avoids pandas conversion. """ safe_name = sanitize_table_name(table_name) filename = f"{safe_name}.parquet" # Overwrite existing file if present metadata = self.get_metadata() if safe_name in metadata.tables: old_file = self.get_file_path(metadata.tables[safe_name].filename) if old_file.exists(): old_file.unlink() file_path = self.get_file_path(filename) pq.write_table(table, file_path, compression=compression) now = datetime.now(timezone.utc) table_metadata = TableMetadata( name=safe_name, source_type="data_loader", filename=filename, file_type="parquet", created_at=now, content_hash=compute_arrow_table_hash(table), file_size=file_path.stat().st_size, row_count=table.num_rows, columns=get_arrow_column_info(table), last_synced=now, ) if loader_metadata: table_metadata.loader_type = loader_metadata.get('loader_type') table_metadata.loader_params = loader_metadata.get('loader_params') table_metadata.source_table = loader_metadata.get('source_table') table_metadata.source_query = loader_metadata.get('source_query') self.add_table_metadata(table_metadata) logger.info( f"Wrote parquet {filename}: {table.num_rows} rows, " f"{table.num_columns} cols ({table_metadata.file_size} bytes) [Arrow]" ) return table_metadata def write_parquet( self, df: pd.DataFrame, table_name: str, compression: str = DEFAULT_COMPRESSION, loader_metadata: Optional[dict[str, Any]] = None, ) -> TableMetadata: """Write a pandas DataFrame to parquet.""" safe_name = sanitize_table_name(table_name) filename = f"{safe_name}.parquet" metadata = self.get_metadata() if safe_name in metadata.tables: old_file = self.get_file_path(metadata.tables[safe_name].filename) if old_file.exists(): old_file.unlink() file_path = self.get_file_path(filename) # Sanitize DataFrame to handle mixed types in object columns sanitized_df = sanitize_dataframe_for_arrow(df) arrow_table = pa.Table.from_pandas(sanitized_df) pq.write_table(arrow_table, file_path, compression=compression) now = datetime.now(timezone.utc) table_metadata = TableMetadata( name=safe_name, source_type="data_loader", filename=filename, file_type="parquet", created_at=now, content_hash=compute_dataframe_hash(df), file_size=file_path.stat().st_size, row_count=len(df), columns=get_column_info(df), last_synced=now, ) if loader_metadata: table_metadata.loader_type = loader_metadata.get('loader_type') table_metadata.loader_params = loader_metadata.get('loader_params') table_metadata.source_table = loader_metadata.get('source_table') table_metadata.source_query = loader_metadata.get('source_query') self.add_table_metadata(table_metadata) logger.info( f"Wrote parquet {filename}: {len(df)} rows, " f"{len(df.columns)} cols ({table_metadata.file_size} bytes)" ) return table_metadata def get_parquet_schema(self, table_name: str) -> dict: """Get schema information for a parquet table without reading all data.""" meta = self.get_table_metadata(table_name) if meta is None: raise FileNotFoundError(f"Table not found: {table_name}") if meta.file_type != "parquet": raise ValueError(f"Table {table_name} is not a parquet file") path = self.get_file_path(meta.filename) if not path.exists(): raise FileNotFoundError(f"Parquet file not found: {path}") pf = pq.ParquetFile(path) schema = pf.schema_arrow return { "table_name": table_name, "filename": meta.filename, "num_rows": pf.metadata.num_rows, "num_columns": len(schema), "columns": [ {"name": f.name, "type": str(f.type), "nullable": f.nullable} for f in schema ], "created_at": meta.created_at.isoformat(), "last_synced": meta.last_synced.isoformat() if meta.last_synced else None, } def get_parquet_path(self, table_name: str) -> Path: """Return the resolved filesystem path of the parquet file for *table_name*.""" meta = self.get_table_metadata(table_name) if meta is None: raise FileNotFoundError(f"Table not found: {table_name}") if meta.file_type != "parquet": raise ValueError(f"Table {table_name} is not a parquet file") path = self.get_file_path(meta.filename) if not path.exists(): raise FileNotFoundError(f"Parquet file not found: {path}") return path.resolve() def run_parquet_sql(self, table_name: str, sql: str) -> pd.DataFrame: """ Run a DuckDB SQL query against a parquet table. The *sql* string must contain a ``{parquet}`` placeholder which will be replaced with ``read_parquet('<path>')``. Example: ``SELECT * FROM {parquet} AS t LIMIT 10`` This gives efficient column-pruned / row-group-skipped reads on large parquet files without loading the full table into memory. """ import duckdb path = self.get_parquet_path(table_name) path_escaped = str(path).replace("\\", "\\\\").replace("'", "''") if "{parquet}" not in sql: raise ValueError("SQL must contain {parquet} placeholder") full_sql = sql.format(parquet=f"read_parquet('{path_escaped}')") conn = duckdb.connect(":memory:") try: return conn.execute(full_sql).fetchdf() finally: conn.close() def refresh_parquet_from_arrow( self, table_name: str, table: pa.Table, compression: str = DEFAULT_COMPRESSION, ) -> tuple[TableMetadata, bool]: """ Refresh a parquet table with new Arrow data. Returns ``(new_metadata, data_changed)``. """ old_meta = self.get_table_metadata(table_name) if old_meta is None: raise FileNotFoundError(f"Table not found: {table_name}") new_hash = compute_arrow_table_hash(table) if old_meta.content_hash == new_hash: old_meta.last_synced = datetime.now(timezone.utc) self.add_table_metadata(old_meta) logger.info(f"Table {table_name} unchanged (hash: {new_hash[:8]}…)") return old_meta, False loader_metadata = { 'loader_type': old_meta.loader_type, 'loader_params': old_meta.loader_params, 'source_table': old_meta.source_table, 'source_query': old_meta.source_query, } new_meta = self.write_parquet_from_arrow( table=table, table_name=table_name, compression=compression, loader_metadata=loader_metadata, ) logger.info(f"Refreshed {table_name}: {old_meta.row_count} → {new_meta.row_count} rows") return new_meta, True def refresh_parquet( self, table_name: str, df: pd.DataFrame, compression: str = DEFAULT_COMPRESSION, ) -> tuple[TableMetadata, bool]: """Refresh a parquet table with new DataFrame data.""" return self.refresh_parquet_from_arrow( table_name, pa.Table.from_pandas(df), compression ) @contextmanager def local_dir(self): """Context manager yielding a local directory containing workspace files. For local workspaces this simply yields ``self._path``. Subclasses (e.g. Azure Blob) override this to download files to a temporary directory that is cleaned up on exit. Usage:: with workspace.local_dir() as wd: subprocess.run(["python", "script.py"], cwd=wd) """ yield self._path def save_workspace_snapshot(self, dst: Path) -> None: """Copy all workspace files (including metadata) to *dst* directory. Used by session save / export to capture the full workspace state. """ if self._path.exists() and any(self._path.iterdir()): dst.mkdir(parents=True, exist_ok=True) shutil.copytree(self._path, dst, dirs_exist_ok=True) def restore_workspace_snapshot(self, src: Path) -> None: """Replace all workspace files with the contents of *src* directory. Used by session load / import to restore a previously saved workspace. """ if self._path.exists(): shutil.rmtree(self._path) self._path.mkdir(parents=True, exist_ok=True) if src.exists(): shutil.copytree(src, self._path, dirs_exist_ok=True) # ------------------------------------------------------------------ # Session management # ------------------------------------------------------------------ @staticmethod def _sanitize_session_name(name: str) -> str: """Produce a storage-safe session name.""" return secure_filename(name) or 'unnamed' def _get_sessions_root(self) -> Path: """Return the per-user sessions directory on local filesystem.""" p = get_data_formulator_home() / "sessions" / self._safe_id p.mkdir(parents=True, exist_ok=True) return p def _session_dir(self, session_name: str) -> Path: return self._get_sessions_root() / self._sanitize_session_name(session_name) def save_session(self, session_name: str, state: dict) -> str: """Save a session (state + workspace snapshot). Returns the ISO timestamp of the save. """ safe_name = self._sanitize_session_name(session_name) sess_dir = self._session_dir(session_name) # Wipe previous save with the same name if sess_dir.exists(): shutil.rmtree(sess_dir) sess_dir.mkdir(parents=True) # 1. Workspace snapshot self.save_workspace_snapshot(sess_dir / "workspace") # 2. State JSON (sess_dir / "state.json").write_text( json.dumps(state, default=str), encoding="utf-8" ) saved_at = datetime.now(timezone.utc).isoformat() logger.info(f"Saved session '{safe_name}' for {self._identity_id}") return saved_at def load_session(self, session_name: str) -> dict | None: """Load a session. Restores workspace and returns the state dict. Returns ``None`` if the session does not exist. """ sess_dir = self._session_dir(session_name) state_file = sess_dir / "state.json" if not state_file.exists(): return None # 1. Restore workspace ws_saved = sess_dir / "workspace" if ws_saved.exists(): self.restore_workspace_snapshot(ws_saved) # 2. Return state return json.loads(state_file.read_text(encoding="utf-8")) def list_sessions(self) -> list[dict]: """List saved sessions (newest first).""" root = self._get_sessions_root() sessions: list[dict] = [] if root.exists(): for child in sorted(root.iterdir()): if not child.is_dir(): continue state_file = child / "state.json" if not state_file.exists(): continue stat = state_file.stat() sessions.append({ "name": child.name, "saved_at": datetime.fromtimestamp( stat.st_mtime, tz=timezone.utc ).isoformat(), }) sessions.sort(key=lambda s: s["saved_at"], reverse=True) return sessions def delete_session(self, session_name: str) -> bool: """Delete a saved session. Returns True if it existed.""" sess_dir = self._session_dir(session_name) if not sess_dir.exists(): return False shutil.rmtree(sess_dir) logger.info(f"Deleted session '{session_name}' for {self._identity_id}") return True def export_session_zip(self, state: dict) -> io.BytesIO: """Export current state + workspace as a .dfsession zip.""" buf = io.BytesIO() with zipfile.ZipFile(buf, "w", zipfile.ZIP_DEFLATED) as zf: zf.writestr("state.json", json.dumps(state, default=str)) with tempfile.TemporaryDirectory(prefix="df_session_export_") as tmp_dir: ws_snap = Path(tmp_dir) / "workspace" self.save_workspace_snapshot(ws_snap) if ws_snap.exists(): for ws_file in ws_snap.rglob("*"): if ws_file.is_file(): arcname = "workspace/" + str(ws_file.relative_to(ws_snap)) zf.write(ws_file, arcname) buf.seek(0) return buf def import_session_zip(self, zip_data: io.BytesIO) -> dict: """Import a .dfsession zip. Restores workspace, returns state dict. Raises ``ValueError`` on invalid zip / missing state.json. """ with zipfile.ZipFile(zip_data, "r") as zf: if "state.json" not in zf.namelist(): raise ValueError("Invalid session file: missing state.json") state = json.loads(zf.read("state.json")) workspace_entries = [ n for n in zf.namelist() if n.startswith("workspace/") and not n.endswith("/") ] if workspace_entries: with tempfile.TemporaryDirectory(prefix="df_session_import_") as tmp_dir: ws_tmp = Path(tmp_dir) / "workspace" ws_tmp.mkdir(parents=True, exist_ok=True) for entry in workspace_entries: rel = entry[len("workspace/"):] # Guard against zip-slip: secure_filename strips # path separators and ".." components, and is # recognised by CodeQL as a path-injection sanitiser. safe_rel = secure_filename(rel) if not safe_rel: continue # skip entries that sanitise to empty dest = ws_tmp / safe_rel dest.parent.mkdir(parents=True, exist_ok=True) dest.write_bytes(zf.read(entry)) self.restore_workspace_snapshot(ws_tmp) return state def __repr__(self) -> str: return f"Workspace(identity_id={self._identity_id!r}, path={self._path!r})" class WorkspaceWithTempData: """ A Workspace wrapper with an in-memory metadata overlay for temporary tables. Delegates **all** attribute and method access to the wrapped workspace via ``__getattr__``, ensuring the correct backend-specific implementations (e.g. ``AzureBlobWorkspace.read_data_as_df``, ``local_dir``, etc.) are always used. Does **not** inherit from :class:`Workspace` — this is intentional so that Python's MRO cannot short-circuit to base-class implementations and skip subclass overrides on the wrapped workspace. Used as a context manager: .. code-block:: python with WorkspaceWithTempData(workspace, temp_data) as ws: ws.read_data_as_df("my_temp_table") # works On enter: - Writes each temp table into the workspace via the workspace's own ``write_parquet`` method (works for local *and* blob backends). - Overrides ``get_table_metadata`` / ``list_tables`` to resolve temp tables from a fast in-memory dict, without touching ``workspace.yaml``. On exit: - Deletes the temp parquet files via the workspace's own ``delete_table`` / file deletion methods. The original ``Workspace`` is never mutated (metadata is overlaid). Nesting is supported: inner overlays delegate to outer overlays. """ def __init__(self, workspace: Workspace, temp_data: Optional[list[dict[str, Any]]] = None): # Do NOT call Workspace.__init__ — we delegate everything to _base. # We also do NOT copy __dict__; instead __getattr__ proxies to _base. object.__setattr__(self, '_base', workspace) object.__setattr__(self, '_temp_data', temp_data if temp_data else None) object.__setattr__(self, '_temp_table_names', []) object.__setattr__(self, '_overlay', {}) # ---- proxy everything else to the real workspace ----------------------- def __getattr__(self, name: str) -> Any: """Delegate attribute access to the wrapped workspace.""" return getattr(self._base, name) # ---- metadata overrides ------------------------------------------------ def get_table_metadata(self, table_name: str) -> Optional[TableMetadata]: result = self._overlay.get(table_name) if result is None: result = self._overlay.get(sanitize_table_name(table_name)) if result is not None: return result return self._base.get_table_metadata(table_name) def list_tables(self) -> list[str]: names = self._base.list_tables() for name in self._overlay: if name not in names: names.append(name) return names def read_data_as_df(self, table_name: str) -> pd.DataFrame: """Read a table — temp overlay first, then delegate to base.""" safe = sanitize_table_name(table_name) if table_name in self._overlay or safe in self._overlay: # Temp table was written via base workspace; delegate read to it return self._base.read_data_as_df(safe) return self._base.read_data_as_df(table_name) # ---- context manager --------------------------------------------------- def __enter__(self) -> "WorkspaceWithTempData": if not self._temp_data: return self for item in self._temp_data: base_name = item.get("name", "table") safe_name = sanitize_table_name(base_name) rows = item.get("rows", []) df = pd.DataFrame(rows) if rows else pd.DataFrame() # Write through the base workspace (handles local *and* blob) self._base.write_parquet(df, safe_name) # Build an overlay entry so get_table_metadata resolves without # touching the real workspace.yaml. meta = self._base.get_table_metadata(safe_name) if meta is not None: self._overlay[safe_name] = meta self._temp_table_names.append(safe_name) logger.debug( f"Mounted temp table '{safe_name}' " f"({len(df)} rows, in-memory metadata)" ) return self def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None: for name in self._temp_table_names: try: self._base.delete_table(name) except Exception as e: logger.warning(f"Failed to remove temp table {name}: {e}") self._temp_table_names.clear() self._overlay.clear() ================================================ FILE: py-src/data_formulator/demo_stream_routes.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ Demo data REST APIs for streaming/refresh demos. Design Philosophy: - Each endpoint returns a COMPLETE dataset (not just a single row) - Datasets are meaningful on their own for analysis/visualization - When refreshed, datasets change over time: * New rows may be added (accumulating data) * Existing values may update (latest readings) - This allows tracking trends, changes, and patterns over time Example Use Cases: - Stock prices: "Last 30 days" grows daily with new data - ISS position: Track trajectory over last N minutes - Earthquakes: All quakes since a start date accumulates Rate Limiting: - External API routes are rate-limited to prevent abuse - Limits are set per IP address using Flask-Limiter """ import random import logging import requests import io import csv import math from datetime import datetime, timedelta from flask import Blueprint, Response, request, jsonify from typing import Any from collections import deque import threading logger = logging.getLogger(__name__) demo_stream_bp = Blueprint('demo_stream', __name__, url_prefix='/api/demo-stream') # ============================================================================ # Rate Limiting Configuration # ============================================================================ # Uses Flask-Limiter with deferred app initialization to avoid circular imports. # The limiter is created here and init_app() is called from app.py. from flask_limiter import Limiter from flask_limiter.util import get_remote_address # Create limiter without app - will be initialized via init_app() in app.py limiter = Limiter( key_func=get_remote_address, default_limits=[], # No default limits - only apply to specific routes storage_uri="memory://", strategy="fixed-window", ) # Rate limit strings for different endpoint types # These are applied to routes that call external APIs # ISS API (open-notify.org) - generous limits ISS_RATE_LIMIT = "30 per minute" # USGS Earthquake API - allow reasonable queries EARTHQUAKE_RATE_LIMIT = "20 per minute" # Weather APIs (Open-Meteo, NWS) - moderate limits WEATHER_RATE_LIMIT = "20 per minute" # yfinance API - more restrictive due to Yahoo's limits YFINANCE_RATE_LIMIT = "10 per minute" # Simulated/mock data - no external calls, more generous MOCK_RATE_LIMIT = "60 per minute" # Try to import yfinance import yfinance as yf # ============================================================================ # Helper Functions # ============================================================================ def make_csv_response(rows: list, filename: str = "data.csv") -> Response: """Convert list of dicts to CSV text response""" if not rows: return Response("", mimetype='text/csv') output = io.StringIO() writer = csv.DictWriter(output, fieldnames=rows[0].keys()) writer.writeheader() writer.writerows(rows) return Response( output.getvalue(), mimetype='text/csv', headers={'Access-Control-Allow-Origin': '*'} ) # ============================================================================ # ISS Location Tracking - Real-time trajectory # Returns accumulated position history that grows over time # Background thread automatically collects positions every 5 seconds # ============================================================================ # Thread-safe storage for ISS position history _iss_track_lock = threading.Lock() _iss_track_history: deque = deque(maxlen=10000) # Keep last 10000 positions _iss_last_fetch: datetime | None = None _iss_collector_started = False def _fetch_iss_position() -> dict[str, Any] | None: """Fetch current ISS position from API""" try: response = requests.get("http://api.open-notify.org/iss-now.json", timeout=10) response.raise_for_status() data = response.json() position = data.get("iss_position", {}) return { "timestamp": datetime.utcfromtimestamp(data.get("timestamp", 0)).isoformat() + "Z", "latitude": float(position.get("latitude", 0)), "longitude": float(position.get("longitude", 0)), } except Exception as e: logger.warning(f"Failed to fetch ISS position: {e}") return None def _iss_collector_loop(): """Background thread that continuously collects ISS positions every 5 seconds.""" global _iss_last_fetch logger.info("ISS position collector started") while True: try: position = _fetch_iss_position() if position: now = datetime.utcnow() position["fetched_at"] = now.isoformat() + "Z" with _iss_track_lock: _iss_track_history.append(position) _iss_last_fetch = now except Exception as e: logger.warning(f"ISS collector error: {e}") threading.Event().wait(10) # Sleep 10 seconds between fetches def start_iss_collector(): """Start the background ISS position collector if not already running.""" global _iss_collector_started if _iss_collector_started: return _iss_collector_started = True t = threading.Thread(target=_iss_collector_loop, daemon=True) t.start() @demo_stream_bp.route('/iss', methods=['GET']) @limiter.limit(ISS_RATE_LIMIT) def get_iss(): """ ISS position trajectory over time. Positions are collected automatically in the background every 5 seconds. Query params: - minutes: How many minutes of history to return (default: 1440, max: 1440) - limit: Max number of points to return (default: 10000, max: 10000) The ISS completes one orbit in ~90 minutes, so 30 min shows ~1/3 of orbit. """ # Ensure the collector is running start_iss_collector() minutes = min(1440, max(1, int(request.args.get('minutes', 1440)))) limit = min(10000, max(1000, int(request.args.get('limit', 10000)))) now = datetime.utcnow() cutoff = now - timedelta(minutes=minutes) with _iss_track_lock: # Filter to requested time window and limit rows = [] for pos in _iss_track_history: try: pos_time = datetime.fromisoformat(pos["timestamp"].replace("Z", "+00:00")).replace(tzinfo=None) if pos_time >= cutoff: rows.append(pos) except: continue # Limit and sort by time rows = sorted(rows, key=lambda x: x["timestamp"])[-limit:] # If we have no data yet (just started), do a one-time fetch if not rows: position = _fetch_iss_position() if position: position["fetched_at"] = now.isoformat() + "Z" rows = [position] return make_csv_response(rows) @demo_stream_bp.route('/iss/reset', methods=['POST']) def reset_iss(): """Clear all accumulated ISS position history and start fresh.""" with _iss_track_lock: _iss_track_history.clear() return jsonify({"status": "ok", "message": "ISS position history cleared"}) # ============================================================================ # USGS Earthquakes - Accumulating dataset of seismic events # Data naturally grows over time as new earthquakes occur # Recommended refresh: 60 seconds # ============================================================================ @demo_stream_bp.route('/earthquakes', methods=['GET']) @limiter.limit(EARTHQUAKE_RATE_LIMIT) def get_earthquakes(): """ Earthquakes from USGS. Dataset grows as new quakes occur. Query params: - timeframe: 'hour', 'day', 'week', 'month' (default: 'day') - min_magnitude: Minimum magnitude filter (default: 0) - max_magnitude: Maximum magnitude filter (optional) - since: ISO date string - only return quakes after this time - limit: Maximum number of results (default: 20000, max: 20000) - use_query_api: 'true' to use query API for more data (default: 'false' for quick summary) Use case: - Set timeframe='week' to see a week of earthquake data - Each refresh may show new earthquakes that occurred - Use min_magnitude to filter for significant quakes (e.g., 4.0+) - Set use_query_api=true and limit=10000 to get large datasets Recommended refresh: 60 seconds """ timeframe = request.args.get('timeframe', 'day') min_magnitude = float(request.args.get('min_magnitude', 0)) max_magnitude = request.args.get('max_magnitude') since_str = request.args.get('since') limit = min(20000, max(1, int(request.args.get('limit', 20000)))) use_query_api = request.args.get('use_query_api', 'false').lower() == 'true' fetched_at = datetime.utcnow().isoformat() + "Z" rows = [] # Parse 'since' filter if provided since_timestamp = None if since_str: try: since_dt = datetime.fromisoformat(since_str.replace("Z", "+00:00")).replace(tzinfo=None) since_timestamp = since_dt.timestamp() * 1000 # USGS uses milliseconds except: pass try: if use_query_api or limit > 1000: # Use USGS Query API for larger datasets now = datetime.utcnow() timeframe_deltas = { "hour": timedelta(hours=1), "day": timedelta(days=1), "week": timedelta(weeks=1), "month": timedelta(days=30) } start_time = now - timeframe_deltas.get(timeframe, timedelta(days=1)) params = { "format": "geojson", "starttime": start_time.strftime("%Y-%m-%dT%H:%M:%S"), "endtime": now.strftime("%Y-%m-%dT%H:%M:%S"), "minmagnitude": min_magnitude, "limit": limit, "orderby": "time" # Most recent first } if max_magnitude: params["maxmagnitude"] = float(max_magnitude) url = "https://earthquake.usgs.gov/fdsnws/event/1/query" response = requests.get(url, params=params, timeout=60) response.raise_for_status() data = response.json() else: # Use summary feeds for quick queries feeds = {"hour": "all_hour", "day": "all_day", "week": "all_week", "month": "all_month"} feed = feeds.get(timeframe, "all_day") url = f"https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/{feed}.geojson" response = requests.get(url, timeout=30) response.raise_for_status() data = response.json() for feature in data.get("features", []): props = feature.get("properties", {}) coords = feature.get("geometry", {}).get("coordinates", [0, 0, 0]) # Filter by magnitude (additional client-side filter for summary feeds) mag = props.get("mag") if mag is not None: if mag < min_magnitude: continue if max_magnitude and mag > float(max_magnitude): continue # Filter by 'since' time quake_time = props.get("time", 0) if since_timestamp and quake_time <= since_timestamp: continue rows.append({ "id": feature.get("id"), "time": datetime.utcfromtimestamp(quake_time / 1000).isoformat() + "Z", "latitude": coords[1] if len(coords) > 1 else None, "longitude": coords[0] if len(coords) > 0 else None, "depth_km": coords[2] if len(coords) > 2 else None, "magnitude": mag, "place": props.get("place"), "type": props.get("type", "earthquake"), "status": props.get("status"), "felt": props.get("felt"), # Number of people who reported feeling it "cdi": props.get("cdi"), # Maximum reported intensity "mmi": props.get("mmi"), # Maximum estimated instrumental intensity "tsunami": props.get("tsunami", 0), # Tsunami warning (0 or 1) "sig": props.get("sig"), # Significance (0-1000) "net": props.get("net"), # Network that reported the event "code": props.get("code"), # Event code "url": props.get("url"), # USGS detail page URL "fetched_at": fetched_at }) # Limit results if using summary feed if not use_query_api and len(rows) >= limit: break # Sort by time, most recent first rows.sort(key=lambda x: x["time"], reverse=True) # Apply limit (in case summary feed returned more than requested) if len(rows) > limit: rows = rows[:limit] return make_csv_response(rows) except Exception as e: logger.warning(f"Failed to fetch earthquakes: {e}") return Response(f"error,{str(e)}", mimetype='text/csv'), 500 # ============================================================================ # Current Weather (Open-Meteo) - Updates every 15 minutes # Recommended refresh: 300 seconds # ============================================================================ WEATHER_CITIES = [ {"name": "Seattle", "lat": 47.6062, "lon": -122.3321, "state": "WA"}, {"name": "New York", "lat": 40.7128, "lon": -74.0060, "state": "NY"}, {"name": "Los Angeles", "lat": 34.0522, "lon": -118.2437, "state": "CA"}, {"name": "Chicago", "lat": 41.8781, "lon": -87.6298, "state": "IL"}, {"name": "Miami", "lat": 25.7617, "lon": -80.1918, "state": "FL"}, {"name": "Denver", "lat": 39.7392, "lon": -104.9903, "state": "CO"}, {"name": "Boston", "lat": 42.3601, "lon": -71.0589, "state": "MA"}, {"name": "Phoenix", "lat": 33.4484, "lon": -112.0740, "state": "AZ"}, {"name": "Atlanta", "lat": 33.7490, "lon": -84.3880, "state": "GA"}, {"name": "Dallas", "lat": 32.7767, "lon": -96.7970, "state": "TX"}, {"name": "Houston", "lat": 29.7604, "lon": -95.3698, "state": "TX"}, {"name": "Portland", "lat": 45.5152, "lon": -122.6784, "state": "OR"}, {"name": "San Francisco", "lat": 37.7749, "lon": -122.4194, "state": "CA"}, {"name": "Las Vegas", "lat": 36.1699, "lon": -115.1398, "state": "NV"}, {"name": "Minneapolis", "lat": 44.9778, "lon": -93.2650, "state": "MN"}, {"name": "Detroit", "lat": 42.3314, "lon": -83.0458, "state": "MI"}, {"name": "Philadelphia", "lat": 39.9526, "lon": -75.1652, "state": "PA"}, {"name": "Washington", "lat": 38.9072, "lon": -77.0369, "state": "DC"}, {"name": "Nashville", "lat": 36.1627, "lon": -86.7816, "state": "TN"}, {"name": "New Orleans", "lat": 29.9511, "lon": -90.0715, "state": "LA"}, ] @demo_stream_bp.route('/weather', methods=['GET']) @limiter.limit(WEATHER_RATE_LIMIT) def get_weather(): """ Current weather for major US cities. Updates every 15 minutes. Query params: - cities: Comma-separated list of city names (default: all cities) Example: cities=Seattle,New York,Los Angeles - fields: Comma-separated list of fields to include (default: all) Available: temperature,humidity,wind,precipitation,pressure,cloud_cover Recommended refresh: 300 seconds """ fetched_at = datetime.utcnow().isoformat() + "Z" rows = [] # Parse city filter cities_param = request.args.get('cities', '').strip() if cities_param: city_names = [c.strip() for c in cities_param.split(',')] cities_to_fetch = [c for c in WEATHER_CITIES if c["name"] in city_names] if not cities_to_fetch: # If no matches, use all cities cities_to_fetch = WEATHER_CITIES else: cities_to_fetch = WEATHER_CITIES # Parse fields filter fields_param = request.args.get('fields', '').strip() include_all = not fields_param include_temp = include_all or 'temperature' in fields_param include_humidity = include_all or 'humidity' in fields_param include_wind = include_all or 'wind' in fields_param include_precip = include_all or 'precipitation' in fields_param include_pressure = include_all or 'pressure' in fields_param include_cloud = include_all or 'cloud_cover' in fields_param for city in cities_to_fetch: try: # Build current weather parameters current_fields = [] if include_temp: current_fields.append("temperature_2m") if include_humidity: current_fields.append("relative_humidity_2m") if include_wind: current_fields.extend(["wind_speed_10m", "wind_direction_10m"]) if include_precip: current_fields.append("precipitation") if include_pressure: current_fields.append("surface_pressure") if include_cloud: current_fields.append("cloud_cover") if not current_fields: current_fields = ["temperature_2m", "relative_humidity_2m", "wind_speed_10m", "precipitation"] params = { "latitude": city["lat"], "longitude": city["lon"], "current": ",".join(current_fields), "timezone": "auto" } response = requests.get("https://api.open-meteo.com/v1/forecast", params=params, timeout=10) response.raise_for_status() data = response.json() current = data.get("current", {}) row = { "city": city["name"], "state": city.get("state", ""), "latitude": city["lat"], "longitude": city["lon"], "fetched_at": fetched_at } if include_temp: row["temperature_c"] = current.get("temperature_2m") if include_humidity: row["humidity_percent"] = current.get("relative_humidity_2m") if include_wind: row["wind_speed_kmh"] = current.get("wind_speed_10m") row["wind_direction_deg"] = current.get("wind_direction_10m") if include_precip: row["precipitation_mm"] = current.get("precipitation") if include_pressure: row["pressure_hpa"] = current.get("surface_pressure") if include_cloud: row["cloud_cover_percent"] = current.get("cloud_cover") rows.append(row) except Exception as e: logger.warning(f"Failed to fetch weather for {city['name']}: {e}") return make_csv_response(rows) # ============================================================================ # Weather History (Open-Meteo) - Past days of hourly data # Dataset grows as new hours pass # ============================================================================ @demo_stream_bp.route('/weather/history', methods=['GET']) @limiter.limit(WEATHER_RATE_LIMIT) def get_weather_history(): """ Hourly weather history for one or more locations. Dataset grows with each hour. Query params: - city: City name (default: Seattle) - one of the WEATHER_CITIES Can also be comma-separated list: city=Seattle,New York,Los Angeles - cities: Alternative parameter name for comma-separated list of city names - days: Number of past days to include (default: 7, max: 92 for archive, 14 for forecast) - use_archive: 'true' to use historical archive API (for data older than 5 days, default: 'auto') Use case: - Track temperature/weather trends over the past week - Each hour, a new row appears in the dataset - Great for visualizing weather patterns - Use days=30 with use_archive=true for month-long analysis - Compare weather history across multiple cities Recommended refresh: 3600 seconds (hourly) """ # Support both 'city' and 'cities' parameters for backward compatibility city_param = request.args.get('city', '').strip() cities_param = request.args.get('cities', '').strip() # Use cities if provided, otherwise fall back to city cities_input = cities_param if cities_param else city_param days = min(92, max(1, int(request.args.get('days', 7)))) use_archive_param = request.args.get('use_archive', 'auto').lower() # Parse city filter - support both single city and comma-separated list if cities_input: city_names = [c.strip() for c in cities_input.split(',')] cities_to_fetch = [c for c in WEATHER_CITIES if c["name"] in city_names] if not cities_to_fetch: # If no matches, default to Seattle cities_to_fetch = [WEATHER_CITIES[0]] else: # Default to Seattle if no cities specified cities_to_fetch = [WEATHER_CITIES[0]] fetched_at = datetime.utcnow().isoformat() + "Z" rows = [] end_date = datetime.utcnow() start_date = end_date - timedelta(days=days) # Determine which API to use # Archive API has 5-day delay, so use forecast API for recent data (< 6 days) # unless explicitly requested use_archive = False if use_archive_param == 'true': use_archive = True elif use_archive_param == 'false': use_archive = False else: # 'auto' # Use archive for data older than 6 days use_archive = days > 6 # Weather code descriptions (WMO Weather interpretation codes) weather_desc = { 0: "Clear", 1: "Mainly Clear", 2: "Partly Cloudy", 3: "Overcast", 45: "Foggy", 48: "Depositing Rime Fog", 51: "Light Drizzle", 53: "Moderate Drizzle", 55: "Dense Drizzle", 61: "Slight Rain", 63: "Moderate Rain", 65: "Heavy Rain", 71: "Slight Snow", 73: "Moderate Snow", 75: "Heavy Snow", 80: "Slight Showers", 81: "Moderate Showers", 82: "Violent Showers", 85: "Slight Snow Showers", 86: "Heavy Snow Showers", 95: "Thunderstorm", 96: "Thunderstorm with Hail", 99: "Thunderstorm with Heavy Hail" } for city in cities_to_fetch: try: params = { "latitude": city["lat"], "longitude": city["lon"], "hourly": "temperature_2m,relative_humidity_2m,wind_speed_10m,precipitation,weather_code,pressure_msl", "timezone": "auto", "start_date": start_date.strftime("%Y-%m-%d"), "end_date": end_date.strftime("%Y-%m-%d") } # Use archive API for historical data, forecast API for recent data if use_archive: api_url = "https://api.open-meteo.com/v1/archive" else: api_url = "https://api.open-meteo.com/v1/forecast" response = requests.get(api_url, params=params, timeout=30) response.raise_for_status() data = response.json() hourly = data.get("hourly", {}) times = hourly.get("time", []) temps = hourly.get("temperature_2m", []) humidity = hourly.get("relative_humidity_2m", []) wind = hourly.get("wind_speed_10m", []) precip = hourly.get("precipitation", []) weather_codes = hourly.get("weather_code", []) pressure = hourly.get("pressure_msl", []) for i, time_str in enumerate(times): code = weather_codes[i] if i < len(weather_codes) else 0 # Parse timestamp - handle both formats if "T" in time_str: timestamp_str = time_str else: timestamp_str = time_str + "T00:00:00Z" rows.append({ "city": city["name"], "state": city.get("state", ""), "timestamp": timestamp_str, "temperature_c": round(temps[i], 1) if i < len(temps) and temps[i] is not None else None, "humidity_percent": humidity[i] if i < len(humidity) and humidity[i] is not None else None, "wind_speed_kmh": round(wind[i], 1) if i < len(wind) and wind[i] is not None else None, "precipitation_mm": round(precip[i], 2) if i < len(precip) and precip[i] is not None else None, "pressure_hpa": round(pressure[i], 1) if i < len(pressure) and pressure[i] is not None else None, "weather_code": code, "weather": weather_desc.get(code, "Unknown"), "fetched_at": fetched_at }) except Exception as e: logger.warning(f"Failed to fetch weather history for {city['name']}: {e}") # Sort by city, then timestamp rows.sort(key=lambda x: (x["city"], x["timestamp"])) return make_csv_response(rows) # ============================================================================ # Weather Forecast (Open-Meteo) - Multi-day forecasts for multiple cities # Recommended refresh: 3600 seconds (1 hour) # ============================================================================ @demo_stream_bp.route('/weather/forecast', methods=['GET']) @limiter.limit(WEATHER_RATE_LIMIT) def get_weather_forecast(): """ Multi-day weather forecast for US cities. Updates every few hours. Query params: - cities: Comma-separated list of city names (default: all cities) Example: cities=Seattle,New York,Los Angeles - days: Number of forecast days (default: 7, max: 16) - hourly: 'true' to get hourly forecast data (default: 'false' for daily) Use case: - Compare forecasts across multiple cities - Track upcoming weather patterns - Plan based on forecasted conditions - Use hourly=true for detailed hourly forecasts Recommended refresh: 3600 seconds (1 hour) """ cities_param = request.args.get('cities', '').strip() days = min(16, max(1, int(request.args.get('days', 7)))) hourly_mode = request.args.get('hourly', 'false').lower() == 'true' # Parse city filter if cities_param: city_names = [c.strip() for c in cities_param.split(',')] cities_to_fetch = [c for c in WEATHER_CITIES if c["name"] in city_names] if not cities_to_fetch: cities_to_fetch = WEATHER_CITIES else: cities_to_fetch = WEATHER_CITIES fetched_at = datetime.utcnow().isoformat() + "Z" rows = [] for city in cities_to_fetch: try: if hourly_mode: # Hourly forecast params = { "latitude": city["lat"], "longitude": city["lon"], "hourly": "temperature_2m,relative_humidity_2m,wind_speed_10m,precipitation,weather_code,pressure_msl", "forecast_days": days, "timezone": "auto" } response = requests.get("https://api.open-meteo.com/v1/forecast", params=params, timeout=30) response.raise_for_status() data = response.json() hourly = data.get("hourly", {}) times = hourly.get("time", []) temps = hourly.get("temperature_2m", []) humidity = hourly.get("relative_humidity_2m", []) wind = hourly.get("wind_speed_10m", []) precip = hourly.get("precipitation", []) weather_codes = hourly.get("weather_code", []) pressure = hourly.get("pressure_msl", []) weather_desc = { 0: "Clear", 1: "Mainly Clear", 2: "Partly Cloudy", 3: "Overcast", 45: "Foggy", 48: "Depositing Rime Fog", 51: "Light Drizzle", 53: "Moderate Drizzle", 55: "Dense Drizzle", 61: "Slight Rain", 63: "Moderate Rain", 65: "Heavy Rain", 71: "Slight Snow", 73: "Moderate Snow", 75: "Heavy Snow", 80: "Slight Showers", 81: "Moderate Showers", 82: "Violent Showers", 85: "Slight Snow Showers", 86: "Heavy Snow Showers", 95: "Thunderstorm", 96: "Thunderstorm with Hail", 99: "Thunderstorm with Heavy Hail" } for i, time_str in enumerate(times): code = weather_codes[i] if i < len(weather_codes) else 0 rows.append({ "city": city["name"], "state": city.get("state", ""), "timestamp": time_str, "temperature_c": round(temps[i], 1) if i < len(temps) and temps[i] is not None else None, "humidity_percent": humidity[i] if i < len(humidity) and humidity[i] is not None else None, "wind_speed_kmh": round(wind[i], 1) if i < len(wind) and wind[i] is not None else None, "precipitation_mm": round(precip[i], 2) if i < len(precip) and precip[i] is not None else None, "pressure_hpa": round(pressure[i], 1) if i < len(pressure) and pressure[i] is not None else None, "weather_code": code, "weather": weather_desc.get(code, "Unknown"), "fetched_at": fetched_at }) else: # Daily forecast params = { "latitude": city["lat"], "longitude": city["lon"], "daily": "temperature_2m_max,temperature_2m_min,precipitation_sum,wind_speed_10m_max,weather_code", "forecast_days": days, "timezone": "auto" } response = requests.get("https://api.open-meteo.com/v1/forecast", params=params, timeout=30) response.raise_for_status() data = response.json() daily = data.get("daily", {}) times = daily.get("time", []) temp_max = daily.get("temperature_2m_max", []) temp_min = daily.get("temperature_2m_min", []) precip = daily.get("precipitation_sum", []) wind = daily.get("wind_speed_10m_max", []) weather_codes = daily.get("weather_code", []) weather_desc = { 0: "Clear", 1: "Mainly Clear", 2: "Partly Cloudy", 3: "Overcast", 45: "Foggy", 48: "Depositing Rime Fog", 51: "Light Drizzle", 53: "Moderate Drizzle", 55: "Dense Drizzle", 61: "Slight Rain", 63: "Moderate Rain", 65: "Heavy Rain", 71: "Slight Snow", 73: "Moderate Snow", 75: "Heavy Snow", 80: "Slight Showers", 81: "Moderate Showers", 82: "Violent Showers", 85: "Slight Snow Showers", 86: "Heavy Snow Showers", 95: "Thunderstorm", 96: "Thunderstorm with Hail", 99: "Thunderstorm with Heavy Hail" } for i, time_str in enumerate(times): code = weather_codes[i] if i < len(weather_codes) else 0 rows.append({ "city": city["name"], "state": city.get("state", ""), "date": time_str, "temperature_max_c": round(temp_max[i], 1) if i < len(temp_max) and temp_max[i] is not None else None, "temperature_min_c": round(temp_min[i], 1) if i < len(temp_min) and temp_min[i] is not None else None, "precipitation_mm": round(precip[i], 2) if i < len(precip) and precip[i] is not None else None, "wind_speed_max_kmh": round(wind[i], 1) if i < len(wind) and wind[i] is not None else None, "weather_code": code, "weather": weather_desc.get(code, "Unknown"), "fetched_at": fetched_at }) except Exception as e: logger.warning(f"Failed to fetch forecast for {city['name']}: {e}") return make_csv_response(rows) # ============================================================================ # Today's Weather Comparison - Current weather across multiple cities # Recommended refresh: 300 seconds (5 minutes) # ============================================================================ @demo_stream_bp.route('/weather/today', methods=['GET']) @limiter.limit(WEATHER_RATE_LIMIT) def get_weather_today(): """ Today's current weather for multiple US cities - perfect for comparison. Query params: - cities: Comma-separated list of city names (default: all cities) Example: cities=Seattle,New York,Los Angeles,Miami - limit: Maximum number of cities to return (default: 20, max: 50) Use case: - Compare current weather conditions across US cities - Visualize temperature, humidity, wind patterns geographically - Great for maps, bar charts, and comparison visualizations - Perfect for "weather dashboard" style analysis Recommended refresh: 300 seconds (5 minutes) """ cities_param = request.args.get('cities', '').strip() limit = min(50, max(1, int(request.args.get('limit', 20)))) # Parse city filter if cities_param: city_names = [c.strip() for c in cities_param.split(',')] cities_to_fetch = [c for c in WEATHER_CITIES if c["name"] in city_names][:limit] if not cities_to_fetch: cities_to_fetch = WEATHER_CITIES[:limit] else: cities_to_fetch = WEATHER_CITIES[:limit] fetched_at = datetime.utcnow().isoformat() + "Z" rows = [] for city in cities_to_fetch: try: params = { "latitude": city["lat"], "longitude": city["lon"], "current": "temperature_2m,relative_humidity_2m,wind_speed_10m,wind_direction_10m,precipitation,pressure_msl,cloud_cover,weather_code", "timezone": "auto" } response = requests.get("https://api.open-meteo.com/v1/forecast", params=params, timeout=10) response.raise_for_status() data = response.json() current = data.get("current", {}) weather_desc = { 0: "Clear", 1: "Mainly Clear", 2: "Partly Cloudy", 3: "Overcast", 45: "Foggy", 48: "Depositing Rime Fog", 51: "Light Drizzle", 53: "Moderate Drizzle", 55: "Dense Drizzle", 61: "Slight Rain", 63: "Moderate Rain", 65: "Heavy Rain", 71: "Slight Snow", 73: "Moderate Snow", 75: "Heavy Snow", 80: "Slight Showers", 81: "Moderate Showers", 82: "Violent Showers", 85: "Slight Snow Showers", 86: "Heavy Snow Showers", 95: "Thunderstorm", 96: "Thunderstorm with Hail", 99: "Thunderstorm with Heavy Hail" } weather_code = current.get("weather_code", 0) rows.append({ "city": city["name"], "state": city.get("state", ""), "latitude": city["lat"], "longitude": city["lon"], "temperature_c": round(current.get("temperature_2m"), 1) if current.get("temperature_2m") is not None else None, "temperature_f": round(current.get("temperature_2m") * 9/5 + 32, 1) if current.get("temperature_2m") is not None else None, "humidity_percent": current.get("relative_humidity_2m"), "wind_speed_kmh": round(current.get("wind_speed_10m"), 1) if current.get("wind_speed_10m") is not None else None, "wind_direction_deg": current.get("wind_direction_10m"), "precipitation_mm": round(current.get("precipitation"), 2) if current.get("precipitation") is not None else None, "pressure_hpa": round(current.get("pressure_msl"), 1) if current.get("pressure_msl") is not None else None, "cloud_cover_percent": current.get("cloud_cover"), "weather_code": weather_code, "weather": weather_desc.get(weather_code, "Unknown"), "fetched_at": fetched_at }) except Exception as e: logger.warning(f"Failed to fetch weather for {city['name']}: {e}") return make_csv_response(rows) # ============================================================================ # yfinance - Stock/Financial Data via Yahoo Finance # Three pre-baked APIs: history (daily), recent (intraday), financials (metrics) # ============================================================================ DEFAULT_SYMBOLS = ["AAPL", "MSFT", "GOOGL", "AMZN", "META", "NVDA", "TSLA"] # S&P 100 Companies (as of 2024) SP100_SYMBOLS = [ # Technology "AAPL", "MSFT", "GOOGL", "GOOG", "META", "NVDA", "AVGO", "ORCL", "CRM", "CSCO", "ACN", "ADBE", "AMD", "INTC", "IBM", "QCOM", "TXN", # Consumer/Retail "AMZN", "TSLA", "HD", "MCD", "NKE", "SBUX", "TGT", "LOW", "COST", # Financial Services "JPM", "V", "MA", "BAC", "WFC", "GS", "MS", "BLK", "SPGI", "AXP", "C", "BK", # Healthcare "UNH", "JNJ", "LLY", "PFE", "ABBV", "MRK", "TMO", "ABT", "DHR", "BMY", "AMGN", "GILD", "MDT", "CVS", # Energy "XOM", "CVX", "COP", "SLB", # Industrials "CAT", "GE", "HON", "UNP", "UPS", "RTX", "BA", "LMT", "DE", "MMM", "EMR", # Consumer Staples "PG", "KO", "PEP", "WMT", "PM", "MO", "CL", "MDLZ", # Communications "DIS", "CMCSA", "NFLX", "T", "VZ", "TMUS", # Utilities & Real Estate "NEE", "DUK", "SO", # Materials "LIN", "DOW", # Other Major Companies "BRK-B", "PYPL", "NOW", "INTU", "AMAT", "ADI", "MU", "LRCX", "KLAC", "SCHW", "CB", "PNC", "USB", "TFC", "ISRG", "VRTX", "REGN", "ZTS", "SYK", "ELV", "F", "GM", "ADP", "FIS", "ITW", "GD", "NOC", "EXC", "AEP", "D", "SRE", "PEG" ] # Helper functions for yfinance endpoints def _yf_is_valid(val): """Check if value is valid (not NaN/None)""" try: return val is not None and not (isinstance(val, float) and math.isnan(val)) except: return False def _yf_format_timestamp(date_obj): """Convert pandas Timestamp or datetime to string""" try: if hasattr(date_obj, 'tz_convert'): date_utc = date_obj.tz_convert('UTC') elif hasattr(date_obj, 'tz_localize') and date_obj.tz is not None: date_utc = date_obj.tz_convert('UTC') else: date_utc = date_obj if hasattr(date_utc, 'to_pydatetime'): date_utc = date_utc.to_pydatetime() elif hasattr(date_utc, 'timestamp'): date_utc = datetime.fromtimestamp(date_utc.timestamp()) if isinstance(date_utc, datetime): return date_utc.strftime("%Y-%m-%d %H:%M:%S") else: return str(date_utc) except: return str(date_obj) @demo_stream_bp.route('/yfinance/history', methods=['GET']) @limiter.limit(YFINANCE_RATE_LIMIT) def get_yfinance_history(): """ 6-month daily stock price history via yfinance. Returns daily OHLCV data for the last 6 months. Query params: - symbols: comma-separated stock symbols (default: AAPL,MSFT,GOOGL,AMZN,META,NVDA,TSLA) Example: /api/demo-stream/yfinance/history?symbols=AAPL,MSFT,GOOGL Recommended refresh: 3600 seconds (1 hour) """ symbols_param = request.args.get('symbols', ','.join(DEFAULT_SYMBOLS)) symbols = [s.strip().upper() for s in symbols_param.split(',') if s.strip()][:10] now = datetime.utcnow() start_date = now - timedelta(days=180) # 6 months fetched_at = now.isoformat() + "Z" rows = [] for symbol in symbols: try: ticker = yf.Ticker(symbol) hist = ticker.history(start=start_date.strftime("%Y-%m-%d"), end=now.strftime("%Y-%m-%d")) for date, row in hist.iterrows(): try: date_str = date.strftime("%Y-%m-%d") if hasattr(date, 'strftime') else str(date) except: date_str = str(date) rows.append({ "symbol": symbol, "date": date_str, "open": round(row["Open"], 2) if _yf_is_valid(row["Open"]) else None, "high": round(row["High"], 2) if _yf_is_valid(row["High"]) else None, "low": round(row["Low"], 2) if _yf_is_valid(row["Low"]) else None, "close": round(row["Close"], 2) if _yf_is_valid(row["Close"]) else None, "volume": int(row["Volume"]) if _yf_is_valid(row["Volume"]) else None, "fetched_at": fetched_at }) except Exception as e: logger.warning(f"Failed to fetch history for {symbol}: {e}") # Sort by symbol, then date rows.sort(key=lambda x: (x["symbol"], x["date"])) return make_csv_response(rows) @demo_stream_bp.route('/yfinance/recent', methods=['GET']) @limiter.limit(YFINANCE_RATE_LIMIT) def get_yfinance_recent(): """ Recent intraday stock prices (15-minute intervals) via yfinance. Returns 15-minute interval data for the last 5 trading days. yfinance typically provides intraday data for the last 5-7 days only. Query params: - symbols: comma-separated stock symbols (default: AAPL,MSFT,GOOGL,AMZN,META,NVDA,TSLA) Example: /api/demo-stream/yfinance/recent?symbols=AAPL,MSFT,GOOGL Recommended refresh: 300 seconds (5 minutes) during market hours """ symbols_param = request.args.get('symbols', ','.join(DEFAULT_SYMBOLS)) symbols = [s.strip().upper() for s in symbols_param.split(',') if s.strip()][:10] now = datetime.utcnow() fetched_at = now.isoformat() + "Z" rows = [] for symbol in symbols: try: ticker = yf.Ticker(symbol) # Get 5 days of 15-minute interval data hist = ticker.history(interval='15m', period='5d') if not hist.empty: for date, row in hist.iterrows(): timestamp_str = _yf_format_timestamp(date) rows.append({ "symbol": symbol, "timestamp": timestamp_str, "date": timestamp_str.split()[0] if ' ' in timestamp_str else str(date), "open": round(row["Open"], 2) if _yf_is_valid(row["Open"]) else None, "high": round(row["High"], 2) if _yf_is_valid(row["High"]) else None, "low": round(row["Low"], 2) if _yf_is_valid(row["Low"]) else None, "close": round(row["Close"], 2) if _yf_is_valid(row["Close"]) else None, "volume": int(row["Volume"]) if _yf_is_valid(row["Volume"]) else None, "fetched_at": fetched_at }) except Exception as e: logger.warning(f"Failed to fetch recent data for {symbol}: {e}") # Sort by symbol, then timestamp rows.sort(key=lambda x: (x["symbol"], x["timestamp"])) return make_csv_response(rows) @demo_stream_bp.route('/yfinance/financials', methods=['GET']) @limiter.limit(YFINANCE_RATE_LIMIT) def get_yfinance_financials(): """ Key financial metrics snapshot via yfinance for S&P 100 companies. Returns current financial data for each stock including market cap, P/E ratio, EPS, dividend yield, 52-week range, and more. Query params: - symbols: comma-separated stock symbols (default: all S&P 100 companies) Example: /api/demo-stream/yfinance/financials /api/demo-stream/yfinance/financials?symbols=AAPL,MSFT,GOOGL,AMZN Recommended refresh: 3600 seconds (1 hour) """ symbols_param = request.args.get('symbols') if symbols_param: symbols = [s.strip().upper() for s in symbols_param.split(',') if s.strip()] else: symbols = SP100_SYMBOLS now = datetime.utcnow() fetched_at = now.isoformat() + "Z" rows = [] for symbol in symbols: try: ticker = yf.Ticker(symbol) info = ticker.info # Extract key financial metrics rows.append({ "symbol": symbol, "name": info.get('shortName') or info.get('longName') or symbol, "sector": info.get('sector') or 'N/A', "industry": info.get('industry') or 'N/A', "current_price": round(info.get('currentPrice') or info.get('regularMarketPrice') or 0, 2), "previous_close": round(info.get('previousClose') or 0, 2), "market_cap": info.get('marketCap') or 0, "pe_ratio": round(info.get('trailingPE') or 0, 2) if info.get('trailingPE') else None, "forward_pe": round(info.get('forwardPE') or 0, 2) if info.get('forwardPE') else None, "eps": round(info.get('trailingEps') or 0, 2) if info.get('trailingEps') else None, "dividend_yield": round((info.get('dividendYield') or 0) * 100, 2) if info.get('dividendYield') else 0, "week_52_high": round(info.get('fiftyTwoWeekHigh') or 0, 2), "week_52_low": round(info.get('fiftyTwoWeekLow') or 0, 2), "avg_volume": info.get('averageVolume') or 0, "beta": round(info.get('beta') or 0, 2) if info.get('beta') else None, "profit_margin": round((info.get('profitMargins') or 0) * 100, 2) if info.get('profitMargins') else None, "revenue": info.get('totalRevenue') or 0, "fetched_at": fetched_at }) except Exception as e: logger.warning(f"Failed to fetch financials for {symbol}: {e}") # Sort by symbol rows.sort(key=lambda x: x["symbol"]) return make_csv_response(rows) # ============================================================================ # Mock Live Sales Feed - Accumulating dataset with rolling 1000 records # Similar to ISS tracking - data accumulates in memory # Recommended refresh: 1-5 seconds # ============================================================================ # Thread-safe storage for sales transaction history _sales_lock = threading.Lock() _sales_history: deque = deque(maxlen=1000) # Keep last 1000 transactions _sales_last_update: datetime | None = None # Products with realistic pricing and popularity _SALES_PRODUCTS = [ {"name": "Wireless Headphones", "category": "Electronics", "base_price": 79.99, "popularity": 0.15}, {"name": "Smart Watch", "category": "Electronics", "base_price": 199.99, "popularity": 0.10}, {"name": "Running Shoes", "category": "Sports", "base_price": 129.99, "popularity": 0.12}, {"name": "Yoga Mat", "category": "Sports", "base_price": 34.99, "popularity": 0.08}, {"name": "Coffee Maker", "category": "Home", "base_price": 89.99, "popularity": 0.09}, {"name": "Desk Lamp", "category": "Home", "base_price": 45.99, "popularity": 0.07}, {"name": "Backpack", "category": "Accessories", "base_price": 59.99, "popularity": 0.11}, {"name": "Water Bottle", "category": "Accessories", "base_price": 24.99, "popularity": 0.13}, {"name": "Bluetooth Speaker", "category": "Electronics", "base_price": 49.99, "popularity": 0.08}, {"name": "Fitness Tracker", "category": "Electronics", "base_price": 69.99, "popularity": 0.07}, ] _SALES_REGIONS = ["North America", "Europe", "Asia Pacific", "Latin America"] _SALES_REGION_WEIGHTS = [0.45, 0.30, 0.18, 0.07] _SALES_CHANNELS = ["Web", "Mobile App", "In-Store", "Partner"] _SALES_CHANNEL_WEIGHTS = [0.40, 0.35, 0.15, 0.10] def _generate_sale_transaction(timestamp: datetime) -> dict[str, Any]: """Generate a single sale transaction""" product = random.choices(_SALES_PRODUCTS, weights=[p["popularity"] for p in _SALES_PRODUCTS])[0] region = random.choices(_SALES_REGIONS, weights=_SALES_REGION_WEIGHTS)[0] channel = random.choices(_SALES_CHANNELS, weights=_SALES_CHANNEL_WEIGHTS)[0] quantity = random.choices([1, 2, 3, 4, 5], weights=[0.5, 0.25, 0.15, 0.07, 0.03])[0] discount = random.choices([0, 5, 10, 15, 20], weights=[0.6, 0.15, 0.12, 0.08, 0.05])[0] unit_price = round(product["base_price"] * (1 - discount / 100), 2) total = round(unit_price * quantity, 2) return { "transaction_id": f"TX{int(timestamp.timestamp() * 1000) % 100000000:08d}", "timestamp": timestamp.isoformat() + "Z", "product": product["name"], "category": product["category"], "quantity": quantity, "unit_price": unit_price, "discount_pct": discount, "total": total, "region": region, "channel": channel, } @demo_stream_bp.route('/live-sales', methods=['GET']) @limiter.limit(MOCK_RATE_LIMIT) def get_live_sales(): """ Simulated live sales feed with accumulating transaction history. Data accumulates in memory and maintains a rolling record of the last 1000 transactions. Each refresh may add new transactions and returns the complete accumulated dataset. Query params: - limit: Maximum number of records to return (default: 1000, max: 1000) Recommended refresh: 1-5 seconds """ global _sales_last_update now = datetime.utcnow() limit = min(1000, max(1, int(request.args.get('limit', 1000)))) # Generate new transactions if enough time has passed (at least 1 second) with _sales_lock: should_update = _sales_last_update is None or (now - _sales_last_update).total_seconds() >= 1 if should_update: # If no data exists yet, generate initial batch of transactions if len(_sales_history) == 0: # Generate 5-10 initial transactions num_initial = random.randint(5, 10) for _ in range(num_initial): tx_time = now - timedelta(seconds=random.randint(0, 60)) transaction = _generate_sale_transaction(tx_time) transaction["fetched_at"] = now.isoformat() + "Z" _sales_history.append(transaction) else: # Generate 1-3 new transactions per update num_new_transactions = random.randint(1, 3) for _ in range(num_new_transactions): # Spread transactions over the last second tx_time = now - timedelta(milliseconds=random.randint(0, 1000)) transaction = _generate_sale_transaction(tx_time) transaction["fetched_at"] = now.isoformat() + "Z" _sales_history.append(transaction) _sales_last_update = now # Return all accumulated records (up to limit) rows = list(_sales_history)[-limit:] # Sort by timestamp descending (most recent first) rows.sort(key=lambda x: x["timestamp"], reverse=True) return make_csv_response(rows) # ============================================================================ # API Info Endpoint # ============================================================================ @demo_stream_bp.route('/info', methods=['GET']) def get_info(): """List all available demo data endpoints with their parameters""" return jsonify({ "name": "Demo Data REST APIs", "description": "Each endpoint returns CSV text with complete datasets that change over time. " "Import URL in frontend, set auto-refresh to watch data evolve.", "design_philosophy": [ "Each endpoint returns a COMPLETE dataset (not just one row)", "Datasets are meaningful for analysis and visualization", "When refreshed, new data may appear (accumulating) or values may update", "Use date parameters to track data from a specific point in time" ], "demo_examples": [ # Stock Market Data - History, Intraday, and Financials { "id": "stocks-history", "url": "/api/demo-stream/yfinance/history?symbols=AAPL,MSFT,GOOGL,AMZN,META,NVDA", "name": "📈 Yahoo Finance: 6-Month Stock Price History - Tech Companies (updates daily)", "refresh_seconds": 86400, }, { "id": "stocks-intraday", "url": "/api/demo-stream/yfinance/recent?symbols=AAPL,MSFT,GOOGL,AMZN,META,NVDA,TSLA", "name": "📈 Yahoo Finance: Recent Intraday Stock Prices - Tech Companies (updates every 15 min)", "refresh_seconds": 900, }, { "id": "stocks-financials", "url": "/api/demo-stream/yfinance/financials", "name": "📈 Yahoo Finance: S&P 100 Key Financial Metrics (updates daily)", "refresh_seconds": 86400, }, # ISS Tracking Variations { "id": "iss-trajectory-recent", "url": "/api/demo-stream/iss", "name": "🛰️ Open Notify: International Space Station Real-time Positions (updates every 30 sec)", "refresh_seconds": 30, "reset_url": "/api/demo-stream/iss/reset", }, # Earthquake Data Variations { "id": "earthquakes-significant-week", "url": "/api/demo-stream/earthquakes?timeframe=week&min_magnitude=4", "name": "🌍 USGS: Significant Earthquakes Worldwide - Last Week (updates every minute)", "refresh_seconds": 60, }, # Weather Data Variations { "id": "weather-today-all-cities", "url": "/api/demo-stream/weather/today", "name": "🌤️ Open Meteo: Today's Weather - 20 Major US Cities (updates daily)", "refresh_seconds": 86400, }, { "id": "weather-forecast-hourly", "url": "/api/demo-stream/weather/forecast?days=3&hourly=true", "name": "🌤️ Open Meteo: 3-Day Hourly Weather Forecast - US Cities (updates hourly)", "refresh_seconds": 3600, }, # Live Sales & E-commerce Variations { "id": "live-sales-feed", "url": "/api/demo-stream/live-sales", "name": "💰 Simulated: Live E-commerce Sales Feed (updates every 5 seconds)", "refresh_seconds": 5, } ], "usage": "Click any example to load it, or enter a custom URL. Set auto-refresh to watch data change over time." }) ================================================ FILE: py-src/data_formulator/example_datasets_config.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ Sample datasets configuration for Data Formulator. """ EXAMPLE_DATASETS = [ { 'source': 'vegadatasets', 'name': 'Gapminder', 'description': 'A simplified dataset of global development indicators tracking population, and life expectancy across countries over time.', 'tables': [ { "format": 'json', "url": 'https://raw.githubusercontent.com/vega/vega-datasets/refs/heads/main/data/gapminder.json', "sample": [{"year": 1955, "country": "Afghanistan", "cluster": 0, "pop": 7971931, "life_expect": 43.88, "fertility": 7.42}, {"year": 1960, "country": "Afghanistan", "cluster": 0, "pop": 8622466, "life_expect": 45.03, "fertility": 7.38}, {"year": 1965, "country": "Afghanistan", "cluster": 0, "pop": 9565147, "life_expect": 46.13, "fertility": 7.35}, {"year": 1970, "country": "Afghanistan", "cluster": 0, "pop": 10752971, "life_expect": 47.08, "fertility": 7.4}, {"year": 1975, "country": "Afghanistan", "cluster": 0, "pop": 12157386, "life_expect": 47.55, "fertility": 7.54}, {"year": 1980, "country": "Afghanistan", "cluster": 0, "pop": 12486631, "life_expect": 43.68, "fertility": 7.59}, {"year": 1985, "country": "Afghanistan", "cluster": 0, "pop": 10512221, "life_expect": 42.03, "fertility": 7.52}, {"year": 1990, "country": "Afghanistan", "cluster": 0, "pop": 10694796, "life_expect": 53.83, "fertility": 7.57}, {"year": 1995, "country": "Afghanistan", "cluster": 0, "pop": 16418912, "life_expect": 54.33, "fertility": 7.71}, {"year": 2000, "country": "Afghanistan", "cluster": 0, "pop": 19542982, "life_expect": 54.73, "fertility": 7.53}, {"year": 2005, "country": "Afghanistan", "cluster": 0, "pop": 24411191, "life_expect": 57.63, "fertility": 6.91}, {"year": 1955, "country": "Argentina", "cluster": 3, "pop": 18700686, "life_expect": 64.51, "fertility": 3.14}, {"year": 1960, "country": "Argentina", "cluster": 3, "pop": 20349744, "life_expect": 65.26, "fertility": 3.08}, {"year": 1965, "country": "Argentina", "cluster": 3, "pop": 22053661, "life_expect": 66.13, "fertility": 3.06}, {"year": 1970, "country": "Argentina", "cluster": 3, "pop": 23842803, "life_expect": 66.13, "fertility": 3.09}, {"year": 1975, "country": "Argentina", "cluster": 3, "pop": 25875558, "life_expect": 68.03, "fertility": 3.3}, {"year": 1980, "country": "Argentina", "cluster": 3, "pop": 28024803, "life_expect": 70.23, "fertility": 3.3}, {"year": 1985, "country": "Argentina", "cluster": 3, "pop": 30287112, "life_expect": 71.73, "fertility": 3.1}, {"year": 1990, "country": "Argentina", "cluster": 3, "pop": 32637657, "life_expect": 72.47, "fertility": 3.03}, {"year": 1995, "country": "Argentina", "cluster": 3, "pop": 34946110, "life_expect": 73.44, "fertility": 2.86}, {"year": 2000, "country": "Argentina", "cluster": 3, "pop": 37070774, "life_expect": 74.22, "fertility": 2.59}] } ] }, { 'source': 'vegadatasets', 'name': 'US Income', 'description': 'US income distribution data showing how household incomes are spread across different brackets and states.', 'tables': [ { "format": 'json', "url": 'https://raw.githubusercontent.com/vega/vega-datasets/refs/heads/main/data/income.json', "sample": [{"name":"Alabama","region":"south","id":1,"pct":0.102,"total":1837292,"group":"<10000"},{"name":"Alabama","region":"south","id":1,"pct":0.072,"total":1837292,"group":"10000 to 14999"},{"name":"Alabama","region":"south","id":1,"pct":0.13,"total":1837292,"group":"15000 to 24999"},{"name":"Alabama","region":"south","id":1,"pct":0.115,"total":1837292,"group":"25000 to 34999"},{"name":"Alabama","region":"south","id":1,"pct":0.143,"total":1837292,"group":"35000 to 49999"},{"name":"Alabama","region":"south","id":1,"pct":0.108,"total":1837292,"group":"75000 to 99999"}] } ] }, { 'source': 'vegadatasets', 'name': 'Disasters', 'description': 'Historical records of natural disasters worldwide, including fatalities, types, and locations.', 'tables': [ { "format": 'csv', "url": 'https://raw.githubusercontent.com/vega/vega-datasets/refs/heads/main/data/disasters.csv', "sample": '''Entity,Year,Deaths All natural disasters,1900,1267360 All natural disasters,1901,200018 All natural disasters,1902,46037 All natural disasters,1903,6506 All natural disasters,1905,22758 All natural disasters,1906,42970 All natural disasters,1907,1325641 All natural disasters,1908,75033 All natural disasters,1909,1511524 All natural disasters,1910,148233 All natural disasters,1911,102408 All natural disasters,1912,52093 All natural disasters,1913,882 All natural disasters,1914,289''' } ] }, { 'source': 'vegadatasets', 'name': 'Movies', 'description': 'Box office performance, budgets, and ratings for films across different genres and time periods.', 'tables': [ { "format": 'json', "url": 'https://raw.githubusercontent.com/vega/vega-datasets/refs/heads/main/data/movies.json', "sample": [ {"Title": "The Land Girls", "US Gross": 146083, "Worldwide Gross": 146083, "US DVD Sales": None, "Production Budget": 8000000, "Release Date": "Jun 12 1998", "MPAA Rating": "R", "Running Time min": None, "Distributor": "Gramercy", "Source": None, "Major Genre": None, "Creative Type": None, "Director": None, "Rotten Tomatoes Rating": None, "IMDB Rating": 6.1, "IMDB Votes": 1071}, {"Title": "First Love, Last Rites", "US Gross": 10876, "Worldwide Gross": 10876, "US DVD Sales": None, "Production Budget": 300000, "Release Date": "Aug 07 1998", "MPAA Rating": "R", "Running Time min": None, "Distributor": "Strand", "Source": None, "Major Genre": "Drama", "Creative Type": None, "Director": None, "Rotten Tomatoes Rating": None, "IMDB Rating": 6.9, "IMDB Votes": 207}, {"Title": "I Married a Strange Person", "US Gross": 203134, "Worldwide Gross": 203134, "US DVD Sales": None, "Production Budget": 250000, "Release Date": "Aug 28 1998", "MPAA Rating": None, "Running Time min": None, "Distributor": "Lionsgate", "Source": None, "Major Genre": "Comedy", "Creative Type": None, "Director": None, "Rotten Tomatoes Rating": None, "IMDB Rating": 6.8, "IMDB Votes": 865}, {"Title": "Let's Talk About Sex", "US Gross": 373615, "Worldwide Gross": 373615, "US DVD Sales": None, "Production Budget": 300000, "Release Date": "Sep 11 1998", "MPAA Rating": None, "Running Time min": None, "Distributor": "Fine Line", "Source": None, "Major Genre": "Comedy", "Creative Type": None, "Director": None, "Rotten Tomatoes Rating": 13, "IMDB Rating": None, "IMDB Votes": None}, {"Title": "Slam", "US Gross": 1009819, "Worldwide Gross": 1087521, "US DVD Sales": None, "Production Budget": 1000000, "Release Date": "Oct 09 1998", "MPAA Rating": "R", "Running Time min": None, "Distributor": "Trimark", "Source": "Original Screenplay", "Major Genre": "Drama", "Creative Type": "Contemporary Fiction", "Director": None, "Rotten Tomatoes Rating": 62, "IMDB Rating": 3.4, "IMDB Votes": 165}, {"Title": "Mississippi Mermaid", "US Gross": 24551, "Worldwide Gross": 2624551, "US DVD Sales": None, "Production Budget": 1600000, "Release Date": "Jan 15 1999", "MPAA Rating": None, "Running Time min": None, "Distributor": "MGM", "Source": None, "Major Genre": None, "Creative Type": None, "Director": None, "Rotten Tomatoes Rating": None, "IMDB Rating": None, "IMDB Votes": None}, {"Title": "Following", "US Gross": 44705, "Worldwide Gross": 44705, "US DVD Sales": None, "Production Budget": 6000, "Release Date": "Apr 04 1999", "MPAA Rating": "R", "Running Time min": None, "Distributor": "Zeitgeist", "Source": None, "Major Genre": None, "Creative Type": None, "Director": "Christopher Nolan", "Rotten Tomatoes Rating": None, "IMDB Rating": 7.7, "IMDB Votes": 15133}, {"Title": "Foolish", "US Gross": 6026908, "Worldwide Gross": 6026908, "US DVD Sales": None, "Production Budget": 1600000, "Release Date": "Apr 09 1999", "MPAA Rating": "R", "Running Time min": None, "Distributor": "Artisan", "Source": "Original Screenplay", "Major Genre": "Comedy", "Creative Type": "Contemporary Fiction", "Director": None, "Rotten Tomatoes Rating": None, "IMDB Rating": 3.8, "IMDB Votes": 353}, {"Title": "Pirates", "US Gross": 1641825, "Worldwide Gross": 6341825, "US DVD Sales": None, "Production Budget": 40000000, "Release Date": "Jul 01 1986", "MPAA Rating": "R", "Running Time min": None, "Distributor": None, "Source": None, "Major Genre": None, "Creative Type": None, "Director": "Roman Polanski", "Rotten Tomatoes Rating": 25, "IMDB Rating": 5.8, "IMDB Votes": 3275}, {"Title": "Duel in the Sun", "US Gross": 20400000, "Worldwide Gross": 20400000, "US DVD Sales": None, "Production Budget": 6000000, "Release Date": "Dec 31 2046", "MPAA Rating": None, "Running Time min": None, "Distributor": None, "Source": None, "Major Genre": None, "Creative Type": None, "Director": None, "Rotten Tomatoes Rating": 86, "IMDB Rating": 7, "IMDB Votes": 2906}, {"Title": "Tom Jones", "US Gross": 37600000, "Worldwide Gross": 37600000, "US DVD Sales": None, "Production Budget": 1000000, "Release Date": "Oct 07 1963", "MPAA Rating": None, "Running Time min": None, "Distributor": None, "Source": None, "Major Genre": None, "Creative Type": None, "Director": None, "Rotten Tomatoes Rating": 81, "IMDB Rating": 7, "IMDB Votes": 4035} ] } ] }, { 'source': 'vegadatasets', 'name': 'Unemployment', 'description': 'Unemployment rates across different economic sectors and industries over time.', 'tables': [ { "format": 'json', "url": 'https://raw.githubusercontent.com/vega/vega-datasets/refs/heads/main/data/unemployment-across-industries.json', "sample": [{"series":"Government","year":2000,"month":1,"count":430,"rate":2.1,"date":"2000-01-01T08:00:00.000Z"},{"series":"Government","year":2000,"month":2,"count":409,"rate":2,"date":"2000-02-01T08:00:00.000Z"},{"series":"Government","year":2000,"month":3,"count":311,"rate":1.5,"date":"2000-03-01T08:00:00.000Z"},{"series":"Government","year":2000,"month":4,"count":269,"rate":1.3,"date":"2000-04-01T08:00:00.000Z"},{"series":"Government","year":2000,"month":5,"count":370,"rate":1.9,"date":"2000-05-01T07:00:00.000Z"},{"series":"Government","year":2000,"month":6,"count":603,"rate":3.1,"date":"2000-06-01T07:00:00.000Z"},{"series":"Government","year":2000,"month":7,"count":545,"rate":2.9,"date":"2000-07-01T07:00:00.000Z"},{"series":"Government","year":2000,"month":8,"count":583,"rate":3.1,"date":"2000-08-01T07:00:00.000Z"},{"series":"Government","year":2000,"month":9,"count":408,"rate":2.1,"date":"2000-09-01T07:00:00.000Z"},{"series":"Government","year":2000,"month":10,"count":391,"rate":2,"date":"2000-10-01T07:00:00.000Z"},{"series":"Government","year":2000,"month":11,"count":384,"rate":1.9,"date":"2000-11-01T08:00:00.000Z"},{"series":"Government","year":2000,"month":12,"count":365,"rate":1.8,"date":"2000-12-01T08:00:00.000Z"},{"series":"Government","year":2001,"month":1,"count":463,"rate":2.3,"date":"2001-01-01T08:00:00.000Z"},{"series":"Government","year":2001,"month":2,"count":298,"rate":1.5,"date":"2001-02-01T08:00:00.000Z"},{"series":"Government","year":2001,"month":3,"count":355,"rate":1.8,"date":"2001-03-01T08:00:00.000Z"},{"series":"Government","year":2001,"month":4,"count":369,"rate":1.9,"date":"2001-04-01T08:00:00.000Z"},{"series":"Government","year":2001,"month":5,"count":361,"rate":1.8,"date":"2001-05-01T07:00:00.000Z"},{"series":"Government","year":2001,"month":6,"count":525,"rate":2.7,"date":"2001-06-01T07:00:00.000Z"},{"series":"Government","year":2001,"month":7,"count":548,"rate":2.8,"date":"2001-07-01T07:00:00.000Z"},{"series":"Government","year":2001,"month":8,"count":540,"rate":2.8,"date":"2001-08-01T07:00:00.000Z"}] } ] }, { 'source': 'tidytuesday', 'name': 'College Majors', 'description': 'A dataset of college majors and their related fields', 'tables': [ { "format": 'csv', "url": 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2018/2018-10-16/recent-grads.csv', "sample": '''Rank,Major_code,Major,Total,Men,Women,Major_category,ShareWomen,Sample_size,Employed,Full_time,Part_time,Full_time_year_round,Unemployed,Unemployment_rate,Median,P25th,P75th,College_jobs,Non_college_jobs,Low_wage_jobs 1,2419,PETROLEUM ENGINEERING,2339,2057,282,Engineering,0.120564344,36,1976,1849,270,1207,37,0.018380527,110000,95000,125000,1534,364,193 2,2416,MINING AND MINERAL ENGINEERING,756,679,77,Engineering,0.101851852,7,640,556,170,388,85,0.117241379,75000,55000,90000,350,257,50 3,2415,METALLURGICAL ENGINEERING,856,725,131,Engineering,0.153037383,3,648,558,133,340,16,0.024096386,73000,50000,105000,456,176,0 4,2417,NAVAL ARCHITECTURE AND MARINE ENGINEERING,1258,1123,135,Engineering,0.107313196,16,758,1069,150,692,40,0.050125313,70000,43000,80000,529,102,0 5,2405,CHEMICAL ENGINEERING,32260,21239,11021,Engineering,0.341630502,289,25694,23170,5180,16697,1672,0.061097712,65000,50000,75000,18314,4440,972 6,2418,NUCLEAR ENGINEERING,2573,2200,373,Engineering,0.144966965,17,1857,2038,264,1449,400,0.177226407,65000,50000,102000,1142,657,244 7,6202,ACTUARIAL SCIENCE,3777,2110,1667,Business,0.441355573,51,2912,2924,296,2482,308,0.095652174,62000,53000,72000,1768,314,259 8,5001,ASTRONOMY AND ASTROPHYSICS,1792,832,960,Physical Sciences,0.535714286,10,1526,1085,553,827,33,0.021167415,62000,31500,109000,972,500,220 9,2414,MECHANICAL ENGINEERING,91227,80320,10907,Engineering,0.119558903,1029,76442,71298,13101,54639,4650,0.057342278,60000,48000,70000,52844,16384,3253 10,2408,ELECTRICAL ENGINEERING,81527,65511,16016,Engineering,0.196450256,631,61928,55450,12695,41413,3895,0.059173845,60000,45000,72000,45829,10874,3170 11,2407,COMPUTER ENGINEERING,41542,33258,8284,Engineering,0.199412643,399,32506,30315,5146,23621,2275,0.065409275,60000,45000,75000,23694,5721,980''' } ] },{ 'source': 'bls.gov', 'name': 'Consumer Price Index', 'description': 'Average price of consumer goods and services in the United States', 'tables': [ { "format": 'csv', "url": 'https://gist.githubusercontent.com/Chenglong-MS/75827bc7daac7ba407863f499c494b37/raw/95af6fd0cc978932af9f6cacc7cd3c0c01d4dffa/average-price-data.csv', "sample": '''Month,"Bananas per lb.","Oranges Navel per lb.","Bread white pan per lb.","Tomatoes field grown per lb.","Chicken fresh whole per lb.",Electricity per KWH,"Eggs grade A large per doz.","Gasoline unleaded regular per gallon","Ground chuck 100% beef per lb.",Utility (piped) gas per therm,"Milk fresh whole fortified per gal." 2005-08-01,0.487,,1.06,1.416,1.042,0.105,1.166,2.506,2.502,1.189,3.136 2005-09-01,0.485,1.363,1.052,1.429,1.056,0.106,1.279,2.927,2.535,1.324,3.133 2005-10-01,0.491,1.388,1.043,1.547,1.062,0.102,1.264,2.785,2.564,1.512,3.171 2005-11-01,0.48,1.172,1.055,1.574,1.059,0.102,1.279,2.343,2.568,1.548,3.211 2005-12-01,0.482,0.885,1.046,1.848,1.061,0.102,1.35,2.186,2.606,1.498,3.241 2006-01-01,0.49,0.837,1.046,2.162,1.062,0.108,1.449,2.315,2.607,1.531,3.197 2006-02-01,0.508,0.915,1.029,1.91,1.045,0.108,1.328,2.31,2.556,1.402,3.224 2006-03-01,0.508,0.888,1.04,1.649,1.047,0.109,1.302,2.401,2.568,1.335,3.161 2006-04-01,0.508,0.876,1.072,1.573,1.054,0.109,1.283,2.757,2.599,1.278,3.123 2006-05-01,0.514,0.99,1.086,1.543,1.034,0.11,1.206,2.947,2.508,1.263,3.066 2006-06-01,0.511,1.119,1.074,1.457,1.055,0.118,1.242,2.917,2.543,1.21,3.001''', } ] }, { 'source': 'tidytuesday', 'name': 'Weekly Gas Price', 'description': 'Weekly gas prices in US for different grades and formulations', 'tables': [ { "format": 'csv', "url": 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2025/2025-07-01/weekly_gas_prices.csv', "sample": '''date,fuel,grade,formulation,price 1990-08-20,gasoline,regular,all,1.191 1990-08-27,gasoline,regular,all,1.245 1990-08-27,gasoline,regular,conventional,1.245 1990-09-03,gasoline,regular,all,1.242 1990-09-03,gasoline,regular,conventional,1.242 1990-09-10,gasoline,regular,all,1.252 1990-09-10,gasoline,regular,conventional,1.252 1990-09-17,gasoline,regular,all,1.266 1990-09-17,gasoline,regular,conventional,1.266 1990-09-24,gasoline,regular,all,1.272 1990-09-24,gasoline,regular,conventional,1.272 1990-10-01,gasoline,regular,all,1.321 1990-10-01,gasoline,regular,conventional,1.321 1990-10-08,gasoline,regular,all,1.333 1990-10-08,gasoline,regular,conventional,1.333 1990-10-15,gasoline,regular,all,1.339 1990-10-15,gasoline,regular,conventional,1.339''' } ] }, { 'source': 'tidytuesday', 'name': 'Netflix', 'description': 'What movies and shows are we watching on Netflix?', 'tables': [ { "format": 'csv', "url": 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2025/2025-07-29/movies.csv', "sample": '''source,report,title,available_globally,release_date,hours_viewed,runtime,views 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun,2025Jan-Jun,Back in Action,Yes,2025-01-17,313000000,1H 54M 0S,164700000 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun,2025Jan-Jun,STRAW,Yes,2025-06-06,185200000,1H 48M 0S,102900000 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun,2025Jan-Jun,The Life List,Yes,2025-03-28,198900000,2H 5M 0S,95500000 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun,2025Jan-Jun,Exterritorial,Yes,2025-04-30,159000000,1H 49M 0S,87500000 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun,2025Jan-Jun,Havoc,Yes,2025-04-25,154900000,1H 47M 0S,86900000 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun,2025Jan-Jun,The Secret Life of Pets 2,No,NA,106800000,1H 26M 0S,74500000 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun,2025Jan-Jun,The Electric State,Yes,2025-03-14,158200000,2H 8M 0S,74200000 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun,2025Jan-Jun,Counterattack // Contraataque,Yes,2025-02-28,101000000,1H 25M 0S,71300000 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun,2025Jan-Jun,Ad Vitam,Yes,2025-01-10,114000000,1H 38M 0S,69800000''', }, { "format": 'csv', "url": 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2025/2025-07-29/shows.csv', "sample": '''source,report,title,available_globally,release_date,hours_viewed,runtime,views 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun,2025Jan-Jun,Adolescence: Limited Series,Yes,2025-03-13,555100000,3H 50M 0S,144800000 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun,2025Jan-Jun,Squid Game: Season 2 // 오징어 게임: 시즌 2,Yes,2024-12-26,840300000,7H 10M 0S,117300000 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun,2025Jan-Jun,Squid Game: Season 3 // 오징어 게임: 시즌 3,Yes,2025-06-27,438600000,6H 8M 0S,71500000 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun,2025Jan-Jun,Zero Day: Limited Series,Yes,2025-02-20,315800000,5H 9M 0S,61300000 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun,2025Jan-Jun,Missing You: Limited Series,Yes,2025-01-01,218600000,3H 46M 0S,58000000 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun,2025Jan-Jun,American Murder: Gabby Petito: Season 1,Yes,2025-02-17,120600000,2H 9M 0S,56100000 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun,2025Jan-Jun,Ms. Rachel: Season 1,Yes,NA,162100000,3H 2M 0S,53400000 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun,2025Jan-Jun,Sirens: Limited Series,Yes,2025-05-22,252300000,4H 44M 0S,53300000''' } ], }, { 'source': 'tidytuesday', 'name': 'Billboard Hot 100', 'description': 'Data about every song to ever top the Billboard Hot 100 between August 4, 1958 and January 11, 2025. It was compiled by Chris Dalla Riva as he wrote the book Uncharted Territory: What Numbers Tell Us about the Biggest Hit Songs and Ourselves.', 'tables': [ { "format": 'csv', 'url': 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2025/2025-08-26/billboard.csv', 'sample': '''song,artist,date,weeks_at_number_one,non_consecutive,rating_1,rating_2,rating_3,overall_rating,divisiveness,label,parent_label,cdr_genre,cdr_style,discogs_genre,discogs_style,artist_structure,featured_artists,multiple_lead_vocalists,group_named_after_non_lead_singer,talent_contestant,posthumous,artist_place_of_origin,front_person_age,artist_male,artist_white,artist_black,songwriters,songwriters_w_o_interpolation_sample_credits,songwriter_male,songwriter_white,artist_is_a_songwriter,artist_is_only_songwriter,producers,producer_male,producer_white,artist_is_a_producer,artist_is_only_producer,songwriter_is_a_producer,time_signature,keys,simplified_key,bpm,energy,danceability,happiness,loudness_d_b,acousticness,vocally_based,bass_based,guitar_based,piano_keyboard_based,orchestral_strings,horns_winds,accordion,banjo,bongos,clarinet,cowbell,falsetto_vocal,flute_piccolo,handclaps_snaps,harmonica,human_whistling,kazoo,mandolin,pedal_lap_steel,ocarina,saxophone,sitar,trumpet,ukulele,violin,sound_effects,song_structure,rap_verse_in_a_non_rap_song,length_sec,instrumental,instrumental_length_sec,intro_length_sec,vocal_introduction,free_time_vocal_introduction,fade_out,live,cover,sample,interpolation,inspired_by_a_different_song,lyrics,lyrical_topic,lyrical_narrative,spoken_word,explicit,foreign_language,written_for_a_play,featured_in_a_then_contemporary_play,written_for_a_film,featured_in_a_then_contemporary_film,written_for_a_t_v_show,featured_in_a_then_contemporary_t_v_show,associated_with_dance,topped_the_charts_by_multiple_artist,double_a_side,eurovision_entry,u_s_artwork Poor Little Fool,Ricky Nelson,1958-08-04T00:00:00Z,2,0,4,5,3,4,1.3333333333333333,Imperial,Imperial,Pop;Rock,Acoustic Rock,Rock,Rock & Roll,1,NA,0,0,NA,0,United States,18,1,1,0,Sharon Sheeley,Sharon Sheeley,0,1,0,0,Jimmie Haskell;Ozzie Nelson;Ricky Nelson,1,1,1,0,0,4/4,C,C,155,33,54,80,-12,67,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,A2,0,154,0,12,12,0,0,0,0,0,0,0,0,I used to play around with hearts.That hastened at my call.But when I met that little girl.I knew that I would fall.Poor little fool oh yeah.I was a fool uh huh.Oh oh poor little fool.I was a fool oh yeah.She played around and teased me.With her carefree devil eyes.She'd hold me close and kiss me.But her heart was full of lies.Poor little fool oh yeah.I was a fool uh huh.Oh oh poor little fool.I was a fool oh yeah.She told me how she cared for me.And that we'd never part.And so for the very first time.I gave away my heart.Poor little fool oh yeah.I was a fool uh huh.Oh oh poor little fool.I was a fool oh yeah.The next day she was gone.And I knew she'd lied to me.She left me with a broken heart.And won her victory.Poor little fool oh yeah.I was a fool uh huh.Oh oh poor little fool.I was a fool oh yeah.Well I'd played this game with other hearts.But I never thought I'd see.The day that someone else would play.Love's foolish game with me.Poor little fool oh yeah.I was a fool uh huh.Oh oh poor little fool.I was a fool oh yeah.Poor little fool oh yeah.I was a fool uh huh.Oh oh poor little fool.Poor little fool.,Lost Love,0,0,0,0,0,NA,0,NA,0,NA,0,0,NA,0,Cannot Locate Nel Blu Dipinto Di Blu,Domenico Modugno,1958-08-18T00:00:00Z,5,1,7,7,5,6.333333333333333,1.3333333333333333,Decca,Decca,Pop,Vocal,"Pop;Folk, World, & Country",Vocal;Canzone Napoletana;Ballad,1,NA,0,0,NA,0,Italy,30,1,1,0,Franco Migliacci;Domenico Modugno;Mitchell Parish,Franco Migliacci;Domenico Modugno;Mitchell Parish,1,1,1,0,Unknown,NA,NA,NA,NA,0,Free;6/8;4/4,Bb,Bb,130,6,55,48,-17,98,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,E1,0,219,0,11,40,1,1,0,0,0,0,0,0,NA,Flying;Dreaming,0,0,0,1,0,NA,0,NA,0,NA,0,0,NA,1,Cannot Locate Little Star,The Elegants,1958-08-25T00:00:00Z,1,0,5,6,6,5.666666666666667,0.6666666666666666,Apt,ABC,Rock,Rock & Roll,Rock,Rock & Roll;Doo Wop,0,NA,0,0,NA,0,United States,17,1,1,0,Vito Picone;Arthur Venosa,Vito Picone;Arthur Venosa,1,1,1,1,Unknown,NA,NA,NA,NA,0,Free;4/4,A,A,73,40,41,70,-13,87,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,D3,0,163,0,0,10,1,1,0,0,0,0,1,1,Where are you little star.Where are you.Twinkle twinkle little star.How I wonder where you are.Wish I may wish I might.Make this wish come true tonight.Searched all over for a love.You're the one I'm thinking of.Twinkle twinkle little star.How I wonder where you are.High above the clouds somewhere.Send me down a love to share.Oh there you are.High above.Oh oh God.Send me a love.Oh there you are.Lighting up the sky.I need a love.Oh me oh me oh my.Twinkle twinkle little star.How I wonder where you are.Wish I may wish I might.Make this wish come true tonight.There you are little star.,Longing for Love,0,0,0,0,0,NA,0,NA,0,NA,0,0,NA,0,Cannot Locate It's All in the Game,Tommy Edwards,1958-09-29T00:00:00Z,6,0,3,3,7,4.333333333333333,2.6666666666666665,MGM,MGM,Pop,Vocal,Rock;Pop,Ballad;Doo Wop,1,NA,0,0,NA,0,United States,35,1,0,1,Carl Sigman;Charles G. Dawes,Carl Sigman;Charles G. Dawes,1,1,0,0,Harry Myerson,1,1,0,0,0,3/4,Eb,Eb,71,15,33,61,-18,4,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,D1,0,156,0,0,3,0,0,0,0,0,0,1,1,Many a tear have to fall.But it's all in the game.All in the wonderful game.That we know as love.You have words with him.And your future's looking dim.But these things.Your hearts can rise above.Once in a while he will call.But it's all in the game.Soon he'll be there at your side.With a sweet bouquet.And he'll kiss your lips.And caress your waiting fingertips.And your hearts will fly.Away.Soon he'll be there at your side.With a sweet bouquet.Then he'll kiss your lips.And caress your waiting fingertips.And your hearts will fly.Away.,Love,0,0,0,0,0,NA,0,NA,0,NA,0,0,NA,0,Cannot Locate It's Only Make Believe,Conway Twitty,1958-11-10T00:00:00Z,2,1,7,8,9,8,1.3333333333333333,MGM,MGM,Pop,Vocal,Rock,Rock & Roll;Pop Rock,1,NA,0,0,NA,0,United States,25,1,1,0,Jack Nance;Conway Twitty,Jack Nance;Conway Twitty,1,1,1,0,Jim Vienneau,1,1,0,0,0,Free;6/8,B,B,127,43,44,36,-10,86,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,A3,0,134,0,0,24,1,1,0,0,0,0,0,0,People see us everywhere.They think you really care.But myself I can't deceive.I know it's only make believe.My one and only prayer.Is that someday you'll care.My hopes my dreams come true.My one and only you.No one will ever know.How much I love you so.My only prayer will be.Someday you'll care for me.But it's only make believe.My hopes my dreams come true.My life I'd give for you.My heart a wedding ring.My all my everything.My heart I can't control.You rule my very soul.My only prayer will be.Someday you'll care for me.But it's only make believe.My one and only prayer.Is that someday you'll care.My hopes my dreams come true.My one and only you.No one will ever know.How much I love you so.My prayers my hopes my schemes.You are my every dream.But it's only make believe.,Lost Love,0,0,0,0,0,NA,0,NA,0,NA,0,0,NA,0,Cannot Locate Tom Dooley,The Kingston Trio,1958-11-17T00:00:00Z,1,0,5,5,2,4,2,Capitol,EMI,Folk/Country,Folk,"Folk, World, & Country",Folk,0,NA,0,0,NA,0,United States,24.333333333333332,1,1,0,Alan Lomax;Frank Warner,Alan Lomax;Frank Warner,1,1,0,0,Voyle Gilmore,1,1,0,0,0,4/4,E,E,126,14,63,52,-15,83,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,C2,0,185,0,7,31,1,0,0,0,1,0,0,1,Throughout history there have been many songs written about the eternal triangle. This next one tells the story of Mister Grayson a beautiful woman and a condemned man named Tom Dooley. When the sun rises tomorrow Tom Dooley must hang.Hang down your head Tom Dooley.Hang down your head and cry.Hang down your head Tom Dooley.Poor boy you're bound to die.I met her on the mountain.There I took her life.Met her on the mountain.Stabbed her with my knife.Hang down your head Tom Dooley.Hang down your head and cry.Hang down your head Tom Dooley.Poor boy you're bound to die.This time tomorrow.Reckon where I'll be.Hadn't a been for Grayson.I'd a been in Tennessee.Well now boy.Hang down your head Tom Dooley.Hang down your head and cry.Hang down your head Tom Dooley.Poor boy you're bound to die.Hang down your head and try Tom Dooley.Hang down your head and cry.Hang down your head and try Tom Dooley.Poor boy you're bound to die.This time tomorrow.Reckon where I'll be.Down in some lonesome valley.Hanging from a white oak tree.Hang down your head Tom Dooley.Hang down your head and cry.Hang down your head Tom Dooley.Poor boy you're bound to die.Well now boy.Hang down your head Tom Dooley.Hang down your head and cry.Hang down your head Tom Dooley.Poor boy you're bound to die.Poor boy you're bound to die.Poor boy you're bound to die.Poor boy you're bound to die.,Murder;Death,1,1,0,0,0,NA,0,NA,0,NA,0,0,NA,0,Cannot Locate To Know Him is to Love Him,The Teddy Bears,1958-12-01T00:00:00Z,3,0,8,8,8,8,0,Dore,Dore,Pop,Vocal,Pop,Vocal;Ballad,0,NA,0,0,NA,0,United States,18,2,1,0,Phil Spector,Phil Spector,1,1,1,1,Phil Spector,1,1,1,1,1,12/8,D;F,Multiple Keys,175,20,32,35,-16,89,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,D1,0,142,0,8,8,0,0,1,0,0,0,0,0,To know know know him.Is to love love love him.Just to see him smile.Makes my life worthwhile.To know know know him.Is to love love love him.And I do.And I do and I.And I do and I.And I do and I.And I do and I.I'd be good to him.I'd bring love to him.Everyone says there'll come a day.When I'll walk along side of him.Yes yes to know him.Is to love love love him.And I do.And I do and I.And I do and I.And I do and I.And I do and I.Why can't he see.How blind can he be.Someday he will see that he.Was meant for me.Oh oh yes.To know know know him.Is to love love love him.Just to see him smile.Makes my life worthwhile.To know know know him.Is to love love love him.And I do.And I do and I.And I do and I.And I do and I.And I do and I.,Longing for Love,0,0,0,0,0,NA,0,NA,0,NA,0,0,NA,0,Cannot Locate The Chipmunk Song,The Chipmunks,1958-12-22T00:00:00Z,4,0,1,5,2,2.6666666666666665,2.6666666666666665,Liberty,Liberty,Pop,Novelty;Holiday,Pop;Children's,Novelty,0,NA,0,0,NA,0,United States,39,1,0,0,Ross Bagdasarian Sr.,Ross Bagdasarian Sr.,1,0,1,1,Ross Bagdasarian Sr.,1,0,1,1,1,3/4,Ab;Bb&%,Ab,153,37,61,78,-12,71,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,A2,0,141,0,28,19,0,0,1,0,0,0,0,0,Alright you Chipmunks Ready to sing your song.I'd say we are.Yeah Lets sing it now.Okay Simon.Okay.Okay Theodore.Okay.Okay Alvin Alvin Alvin.Okay.Christmas Christmas time is near.Time for toys and time for cheer.We've been good but we can't last.Hurry Christmas hurry fast.Want a plane that loops the loop.Me I want a Hula-Hoop.We can hardly stand the wait.Please Christmas don't be late.Ok Fellas Get ready.That was very good Simon.Naturally.Very Good Theodore.He He He He.Uh Alvin You were a little flat.Watch it Alvin Alvin Alvin.Okay.Want a plane that loops the loop.I still want a Hula-Hoop.We can hardly stand the wait.Please Christmas don't be late.We can hardly stand the wait.Please Christmas don't be late.,Christmas,0,0,0,0,0,NA,0,NA,0,NA,0,0,NA,0,Cannot Locate Smoke Gets in Your Eyes,The Platters,1959-01-19T00:00:00Z,3,0,9,9,8,8.666666666666666,0.6666666666666666,Mercury,Mercury,Pop,Vocal,Funk/Soul,Rhythm & Blues,0,NA,0,0,NA,0,United States,30,2,0,1,Otto Harbach;Jerome Kern,Otto Harbach;Jerome Kern,1,1,0,0,Buck Ram,1,1,0,0,0,4/4,Eb;B,Multiple Keys,113,27,32,26,-11,93,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,D1,0,158,0,16,8,0,0,0,0,1,0,0,1,They asked me how I knew.My true love was true.I of course replied.Something here inside.Cannot be denied.They said someday you'll find.All who love are blind.When your heart's on fire.You must realize.Smoke gets in your eyes.So I chaffed them and I gaily laughed.To think they could doubt my love.And yet today my love has flown away.I am without my love.Now laughing friends deride.Tears I cannot hide.So I smile and say.When a lovely flame dies.Smoke gets in your eyes.Smoke gets in your eyes.,Lost Love,0,0,0,0,1,NA,0,NA,0,NA,0,0,NA,0,Cannot Locate Stagger Lee,Lloyd Price,1959-02-02T00:00:00Z,4,0,6,6,9,7,2,ABC-Paramount,ABC,Rock,Rhythm & Blues,Rock,Rock & Roll,1,NA,0,0,NA,0,United States,25,1,0,1,Lloyd Price;Harold Logan,Lloyd Price;Harold Logan,1,0,1,0,Don Costa,1,1,0,0,0,Free;4/4,Eb,Eb,71,62,36,79,-8,74,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,A4,0,145,0,32,13,1,1,1,0,1,0,0,1,The night was clear.And the moon was yellow.And the leaves came tumbling down.I was standing on the corner.When I heard my bulldog bark.He was barking at the two men.Who were gambling in the dark.It was Stagger Lee and Billy.Two men who gambled late.Stagger Lee threw seven.Billy swore that he threw eight.Stagger Lee told Billy.I can't let you go with that.You have won all my money.And my brand new Stetson hat.Stagger Lee started off.Going down that railroad track.He said I can't get you Billy.But don't be here when I come back.Go on Stagger Lee.Stagger Lee went home.And he got his forty-four.Said I'm going to the bar room.Just to pay that debt I owe.Stagger Lee went to the bar room.And he stood across the bar room door.Said Now nobody move.And he pulled his forty-four.Stagger Lee cried Billy.Oh please don't take my life.I got three little children.And a very sickly wife.Stagger Lee shot Billy.Oh he shot that poor boy so bad.Till the bullet came through Billy.And it broke the bartender's glass.Now look out Stagg come on.,Murder;Death,1,0,0,0,0,NA,0,NA,0,NA,0,0,NA,0,Cannot Locate Venus,Frankie Avalon,1959-03-09T00:00:00Z,5,0,3,2,2,2.3333333333333335,0.6666666666666666,Chancellor,ABC,Pop,Vocal,Pop,Vocal,1,NA,0,0,NA,0,United States,18,1,1,0,Ed Marshall,Ed Marshall,1,1,0,0,Peter DeAngelis;Bob Marcucci,1,1,0,0,0,4/4,Bb,Bb,115,48,56,75,-10,73,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,D1,0,142,0,23,16,0,0,0,0,0,0,0,0,Venus.Venus.Venus if you will.Please send a little girl for me to thrill.A girl who wants my kisses and my arms.A girl with all the charms of you.Venus make her fair.A lovely girl with sunlight in her hair.And take the brightest stars up in the skies.And place them in her eyes for me.Venus goddess of love that you are.Surely the things I ask.Can't be too great a task.Venus if you do.I promise that I always will be true.I'll give her all the love I have to give.As long as we both shall live.Venus goddess of love that you are.Surely the things I ask.Can't be too great a task.Venus if you do.I promise that I always will be true.I'll give her all the love I have to give.As long as we both shall live.Venus.Venus.Make my wish come true.,Longing for Love,0,0,0,0,0,NA,0,NA,0,NA,0,0,NA,0,Artist Photograph''' } ] }, { 'source': 'tidytuesday', 'name': 'Patient Risk Profiles', 'description': "This dataset contains 100 simulated patient's medical history features and the predicted 1-year risk of 14 outcomes based on each patient's medical history features. The predictions used real logistic regression models developed on a large real world healthcare dataset.", 'tables': [ { "format": 'csv', 'url': 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2023/2023-10-24/patient_risk_profiles.csv', 'sample': '''personId,age group: 10 - 14,age group: 15 - 19,age group: 20 - 24,age group: 65 - 69,age group: 40 - 44,age group: 45 - 49,age group: 55 - 59,age group: 85 - 89,age group: 75 - 79,age group: 5 - 9,age group: 25 - 29,age group: 0 - 4,age group: 70 - 74,age group: 50 - 54,age group: 60 - 64,age group: 35 - 39,age group: 30 - 34,age group: 80 - 84,age group: 90 - 94,Sex = FEMALE,Sex = MALE,Acetaminophen exposures in prior year,Occurrence of Alcoholism in prior year,Anemia in prior year,Angina events in prior year,ANTIEPILEPTICS in prior year,Occurrence of Anxiety in prior year,Osteoarthritis in prior year,Aspirin exposures in prior year,Occurrence of Asthma in prior year,"Atrial Fibrillation, incident in prior year",HORMONAL CONTRACEPTIVES in prior year,Any cancer (excl. prostate cancer and benign cancer) in prior year,Acute Kidney Injury (AKI) in prior year,Chronic kidney disease or end stage renal disease in prior year,Heart failure in prior year,Chronic obstructive pulmonary disease (COPD) in prior year,Coronary artery disease (CAD) in prior year,"Major depressive disorder, with NO occurrence of certain psychiatric disorder in prior year",Type 1 diabetes and no prior specific non-T1DM diabetes in prior year,"Type 2 Diabetes Mellitus (DM), with no type 1 or secondary DM in prior year",Deep Vein Thrombosis (DVT) in prior year,Dyspnea in prior year,Edema in prior year,Gastroesophageal reflux disease in prior year,Acute gastrointestinal (GI) bleeding in prior year,Heart valve disorder in prior year,Chronic hepatitis in prior year,Hyperlipidemia in prior year,Hypertension in prior year,Hypothyroidism in prior year,Inflammatory Bowel Disease in prior year,Low back pain in prior year,Occurrence of neuropathy in prior year,Obesity in prior year,Opioids in prior year,Osteoporosis in prior year,Peripheral vascular disease in prior year,Pneumonia in prior year,Psychotic disorder in prior year,Acute Respiratory failure in prior year,Rheumatoid Arthritis in prior year,Seizure in prior year,Sepsis in prior year,Skin ulcer in prior year,Sleep apnea in prior year,Smoking in prior year,STEROIDS in prior year,Hemorrhagic stroke in an inpatient setting in prior year,Non-hemorrhagic Stroke in an inpatient setting in prior year,Urinary tract infectious disease in prior year,Antibiotics Carbapenems in prior year,Antibiotics Aminoglycosides in prior year,Antibiotics Cephalosporins in prior year,Antibiotics Fluoroquinolones in prior year,Antibiotics Glycopeptides and lipoglycopeptides in prior year,Antibiotics Macrolides in prior year,Antibiotics Monobactams in prior year,Antibiotics Oxazolidinones in prior year,Antibiotics Penicillins in prior year,Antibiotics Polypeptides in prior year,Antibiotics Rifamycins in prior year,Antibiotics Sulfonamides in prior year,Antibiotics Streptogramins in prior year,Antibiotics Tetracyclines in prior year,predicted risk of Pulmonary Embolism,"predicted risk of Sudden Hearing Loss, No congenital anomaly or middle or inner ear conditions",predicted risk of Restless Leg Syndrome,"predicted risk of Sudden Vision Loss, with no eye pathology causes",predicted risk of Muscle weakness or injury,predicted risk of Ankylosing Spondylitis,predicted risk of Autoimmune hepatitis,predicted risk of Multiple Sclerosis,"predicted risk of Acute pancreatitis, with No chronic or hereditary or common causes of pancreatitis",predicted risk of Ulcerative colitis,predicted risk of Migraine,predicted risk of Dementia,predicted risk of Treatment resistant depression (TRD),"predicted risk of Parkinson's disease, inpatient or with 2nd diagnosis" 1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,1,0,1,0,1,0,0,0,0,0,0,0,6.99575517946153E-06,0.00118799448452361,0.00113521200511238,0.000111508301767788,0.0188317440572134,7.58027636940557E-05,7.96570781215902E-05,0.000468549568476105,0.00012436069695809,0.000230991182800591,0.00654426768381862,7.30568202867115E-05,0.000393828939554244,4.04339639325133E-05 2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1,0,0,0,0,0,1,0,1,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0.00441422061324454,0.0358259270965809,0.00628615304402316,0.00160728545057666,0.179579353683346,0.000501552796659848,7.88137732912271E-06,0.000421360646091043,0.000816636102151383,0.00156719415098486,0.0243635943152779,0.283879214688442,0.0138886132877911,0.0195526139594774 3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0.00246179423902851,0.00352409954417798,0.00123963239201273,0.000145891713126901,0.0223178022257278,0.000461715423994332,2.28874258509341E-05,0.000527096330768595,0.000416669691599939,0.00127466041950367,0.00542984527203618,0.00128155335881044,0.00101738679514043,0.000113499343433664 4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,1,0,0,0,1,1,1,0.00267000861643238,0.00247336321987715,0.000441054921283152,0.000152773704749624,0.0220617756570553,0.000481510930093349,3.43010599599209E-05,0.000910960176825296,0.000235986486697154,0.00638625829979481,0.006636578736903,0.000706627519596284,0.00226615949859508,4.10984109899065E-05 5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,0,1,0,0,0,0,0,0,1,1,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,1,0,1,0,0,1,0,0,0,0,0,0,1,0,0.0206818512182543,0.0117893418676085,0.00427664772229725,0.000325865473878066,0.0633105390455467,0.0041153809531189,8.3473321779432E-05,0.00185928919073779,0.000699811142238753,0.0620649084463635,0.00468263807806029,0.0163716009191329,5.64951086173088E-05,0.00331195228607955 6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0.00455665231961284,0.0215984207477867,0.00178776442479255,0.000466726635059575,0.0389368132012184,0.000666036384000457,2.15545946607044E-05,0.000127202886974176,0.000858813148174222,0.0010155590042039,0.0100388811465988,0.0457689464750165,0.000659150042438028,0.00336552536596351 7,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,1,0,0,0,0,1,0,0,1,0,1,1,1,0,1,0,0,1,0,1,1,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0.0186826613299857,0.0187010037764903,0.00212554337046552,0.000214068587596829,0.134144553239361,0.00108141814357676,0.00139509039790527,0.000744241842425037,0.000916803656046385,0.00637231340606943,0.0326189204574906,0.331324640121089,0.000314660993270718,0.00498660937569948 ''' } ] }, { 'source': 'tidytuesday', 'name': 'Life Expectancy', 'description': 'Across the world, people are living longer. In 1900, the average life expectancy of a newborn was 32 years. By 2021 this had more than doubled to 71 years. But where, when, how, and why has this dramatic change occurred? To understand it, we can look at data on life expectancy worldwide.', 'tables': [ { "format": 'csv', "url": 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2023/2023-12-05/life_expectancy.csv', 'sample': '''Entity,Code,Year,LifeExpectancy Afghanistan,AFG,1950,27.7275 Afghanistan,AFG,1951,27.9634 Afghanistan,AFG,1952,28.4456 Afghanistan,AFG,1953,28.9304 Afghanistan,AFG,1954,29.2258 Afghanistan,AFG,1955,29.9206 Afghanistan,AFG,1956,30.4078 Afghanistan,AFG,1957,30.9458 ''' }, { "format": 'csv', "url": 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2023/2023-12-05/life_expectancy_different_ages.csv', 'sample': '''Entity,Code,Year,LifeExpectancy0,LifeExpectancy10,LifeExpectancy25,LifeExpectancy45,LifeExpectancy65,LifeExpectancy80 Afghanistan,AFG,1950,27.7275,49.1459,54.4422,63.4225,73.4901,83.7259 Afghanistan,AFG,1951,27.9634,49.2941,54.5644,63.500603,73.5289,83.7448 Afghanistan,AFG,1952,28.4456,49.5822,54.7998,63.6476,73.6018,83.7796 Afghanistan,AFG,1953,28.9304,49.8634,55.028603,63.788902,73.6706,83.8118 Afghanistan,AFG,1954,29.2258,49.9306,55.1165,63.8481,73.7041,83.8334 Afghanistan,AFG,1955,29.9206,50.4315,55.4902,64.0732,73.8087,83.876 Afghanistan,AFG,1956,30.4078,50.7036,55.7131,64.2102,73.8755,83.9073 Afghanistan,AFG,1957,30.9458,51.0233,55.9721,64.3705,73.9542,83.9434 Afghanistan,AFG,1958,31.5066,51.3565,56.244698,64.5401,74.0384,83.9826''' }, { "format": 'csv', 'url': 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2023/2023-12-05/life_expectancy_female_male.csv', 'sample': '''Entity,Code,Year,LifeExpectancyDiffFM Afghanistan,AFG,1950,1.2619 Afghanistan,AFG,1951,1.2706013 Afghanistan,AFG,1952,1.2882996 Afghanistan,AFG,1953,1.3066006 Afghanistan,AFG,1954,1.2765007 Afghanistan,AFG,1955,1.3688011 Afghanistan,AFG,1956,1.4055996 Afghanistan,AFG,1957,1.4146996 Afghanistan,AFG,1958,1.3987999 Afghanistan,AFG,1959,1.3992996 Afghanistan,AFG,1960,1.4146004''' } ] }, { 'source': 'tidytuesday', 'name': 'PhDs Awarded', 'description': 'The data comes from the NSF - where there are at least 72 different datasets if you wanted to approach the data from a different angle.', 'tables': [ { "format": 'csv', "url": 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2019/2019-02-19/phd_by_field.csv', 'sample': '''broad_field,major_field,field,year,n_phds Life sciences,Agricultural sciences and natural resources,Agricultural economics,2008,111 Life sciences,Agricultural sciences and natural resources,Agricultural and horticultural plant breeding,2008,28 Life sciences,Agricultural sciences and natural resources,Agricultural animal breeding,2008,3 Life sciences,Agricultural sciences and natural resources,Agronomy and crop science,2008,68 Life sciences,Agricultural sciences and natural resources,Animal nutrition,2008,41 Life sciences,Agricultural sciences and natural resources,"Animal science, poultry or avian",2008,18 Life sciences,Agricultural sciences and natural resources,"Animal sciences, other",2008,77 Life sciences,Agricultural sciences and natural resources,Environmental science,2008,182 Life sciences,Agricultural sciences and natural resources,Fishing and fisheries sciences and management,2008,52 Life sciences,Agricultural sciences and natural resources,Food science,2008,96 Life sciences,Agricultural sciences and natural resources,"Food science and technology, other",2008,41 Life sciences,Agricultural sciences and natural resources,Forest sciences and biology,2008,32 Life sciences,Agricultural sciences and natural resources,"Forest management, forest resources management",2008,44 Life sciences,Agricultural sciences and natural resources,"Forestry, other",2008,17 Life sciences,Agricultural sciences and natural resources,Horticulture science,2008,50 Life sciences,Agricultural sciences and natural resources,Natural resource and environmental policy,2008,NA ''' } ] }, { 'source': 'tidytuesday', 'name': 'Nuclear Explosions', 'description': 'This dataset is from Stockholm International Peace Research Institute, by way of data is plural with credit to Jesus Castagnetto for sharing the dataset.', 'tables': [ { "format": 'csv', "url": 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2019/2019-08-20/nuclear_explosions.csv', 'sample': '''date_long,year,id_no,country,region,source,latitude,longitude,magnitude_body,magnitude_surface,depth,yield_lower,yield_upper,purpose,name,type 19450716,1945,45001,USA,ALAMOGORDO,DOE,32.54,-105.57,0,0,-0.1,21,21,WR,TRINITY,TOWER 19450805,1945,45002,USA,HIROSHIMA,DOE,34.23,132.27,0,0,-0.6,15,15,COMBAT,LITTLEBOY,AIRDROP 19450809,1945,45003,USA,NAGASAKI,DOE,32.45,129.52,0,0,-0.6,21,21,COMBAT,FATMAN,AIRDROP 19460630,1946,46001,USA,BIKINI,DOE,11.35,165.2,0,0,-0.2,21,21,WE,ABLE,AIRDROP 19460724,1946,46002,USA,BIKINI,DOE,11.35,165.2,0,0,0.03,21,21,WE,BAKER,UW 19480414,1948,48001,USA,ENEWETAK,DOE,11.3,162.15,0,0,-0.08,37,37,WR,X-RAY,TOWER 19480430,1948,48002,USA,ENEWETAK,DOE,11.3,162.15,0,0,-0.08,49,49,WR,YOKE,TOWER 19480514,1948,48003,USA,ENEWETAK,DOE,11.3,162.15,0,0,-0.08,18,18,WR,ZEBRA,TOWER 19490829,1949,49001,USSR,SEMI KAZAKH,DOE,48,76,0,0,0,22,22,WR,NA,SURFACE 19510127,1951,51001,USA,NTS,DOE,37,-116,0,0,-0.35,1,1,WR,ABLE,AIRDROP 19510128,1951,51002,USA,NTS,DOE,37,-116,0,0,-0.35,8,8,WR,BAKER,AIRDROP 19510201,1951,51003,USA,NTS,DOE,37,-116,0,0,-0.35,1,1,WR,EASY,AIRDROP 19510202,1951,51004,USA,NTS,DOE,37,-116,0,0,-0.4,8,8,WR,BAKER2,AIRDROP 19510206,1951,51005,USA,NTS,DOE,37,-116,0,0,-0.5,22,22,WR,FOX,AIRDROP 19510407,1951,51006,USA,ENEWETAK,DOE,11.3,162.15,0,0,-0.1,81,81,WR,DOG,TOWER 19510420,1951,51007,USA,ENEWETAK,DOE,11.3,162.15,0,0,-0.1,47,47,WR,EASY,TOWER 19510508,1951,51008,USA,ENEWETAK,DOE,11.3,162.15,0,0,-0.07,225,225,WR,GEORGE,TOWER 19510524,1951,51009,USA,ENEWETAK,DOE,11.3,162.15,0,0,-0.07,45.5,45.5,WR,ITEM,TOWER''' } ] }, { 'source': 'tidytuesday', 'name': 'Nobel Laureate', 'description': '"The Nobel Prize is a set of annual international awards bestowed in several categories by Swedish and Norwegian institutions in recognition of academic, cultural, or scientific advances." - Wikipedia.', 'tables': [ { "format": 'csv', "url": 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2019/2019-05-14/nobel_winners.csv', 'sample': '''prize_year,category,prize,motivation,prize_share,laureate_id,laureate_type,full_name,birth_date,birth_city,birth_country,gender,organization_name,organization_city,organization_country,death_date,death_city,death_country 1901,Chemistry,The Nobel Prize in Chemistry 1901,"""in recognition of the extraordinary services he has rendered by the discovery of the laws of chemical dynamics and osmotic pressure in solutions""",1/1,160,Individual,Jacobus Henricus van 't Hoff,1852-08-30,Rotterdam,Netherlands,Male,Berlin University,Berlin,Germany,1911-03-01,Berlin,Germany 1901,Literature,The Nobel Prize in Literature 1901,"""in special recognition of his poetic composition, which gives evidence of lofty idealism, artistic perfection and a rare combination of the qualities of both heart and intellect""",1/1,569,Individual,Sully Prudhomme,1839-03-16,Paris,France,Male,NA,NA,NA,1907-09-07,Châtenay,France 1901,Medicine,The Nobel Prize in Physiology or Medicine 1901,"""for his work on serum therapy, especially its application against diphtheria, by which he has opened a new road in the domain of medical science and thereby placed in the hands of the physician a victorious weapon against illness and deaths""",1/1,293,Individual,Emil Adolf von Behring,1854-03-15,Hansdorf (Lawice),Prussia (Poland),Male,Marburg University,Marburg,Germany,1917-03-31,Marburg,Germany 1901,Peace,The Nobel Peace Prize 1901,NA,1/2,462,Individual,Jean Henry Dunant,1828-05-08,Geneva,Switzerland,Male,NA,NA,NA,1910-10-30,Heiden,Switzerland 1901,Peace,The Nobel Peace Prize 1901,NA,1/2,463,Individual,Frédéric Passy,1822-05-20,Paris,France,Male,NA,NA,NA,1912-06-12,Paris,France 1901,Physics,The Nobel Prize in Physics 1901,"""in recognition of the extraordinary services he has rendered by the discovery of the remarkable rays subsequently named after him""",1/1,1,Individual,Wilhelm Conrad Röntgen,1845-03-27,Lennep (Remscheid),Prussia (Germany),Male,Munich University,Munich,Germany,1923-02-10,Munich,Germany 1902,Chemistry,The Nobel Prize in Chemistry 1902,"""in recognition of the extraordinary services he has rendered by his work on sugar and purine syntheses""",1/1,161,Individual,Hermann Emil Fischer,1852-10-09,Euskirchen,Prussia (Germany),Male,Berlin University,Berlin,Germany,1919-07-15,Berlin,Germany 1902,Literature,The Nobel Prize in Literature 1902,"""the greatest living master of the art of historical writing, with special reference to his monumental work, <I>A history of Rome</I>""",1/1,571,Individual,Christian Matthias Theodor Mommsen,1817-11-30,Garding,Schleswig (Germany),Male,NA,NA,NA,1903-11-01,Charlottenburg,Germany 1902,Medicine,The Nobel Prize in Physiology or Medicine 1902,"""for his work on malaria, by which he has shown how it enters the organism and thereby has laid the foundation for successful research on this disease and methods of combating it""",1/1,294,Individual,Ronald Ross,1857-05-13,Almora,India,Male,University College,Liverpool,United Kingdom,1932-09-16,Putney Heath,United Kingdom 1902,Peace,The Nobel Peace Prize 1902,NA,1/2,464,Individual,Élie Ducommun,1833-02-19,Geneva,Switzerland,Male,NA,NA,NA,1906-12-07,Bern,Switzerland 1902,Peace,The Nobel Peace Prize 1902,NA,1/2,465,Individual,Charles Albert Gobat,1843-05-21,Tramelan,Switzerland,Male,NA,NA,NA,1914-03-16,Bern,Switzerland 1902,Physics,The Nobel Prize in Physics 1902,"""in recognition of the extraordinary service they rendered by their researches into the influence of magnetism upon radiation phenomena""",1/2,2,Individual,Hendrik Antoon Lorentz,1853-07-18,Arnhem,Netherlands,Male,Leiden University,Leiden,Netherlands,1928-02-04,NA,Netherlands''' } ] }, { 'source': 'tidytuesday', 'name': 'Space launches', 'description': 'Taken from Economist GitHub (https://github.com/rfordatascience/tidytuesday/tree/main/data/2018/2018-08-21). These are the data behind the "space launches" article, The space race is dominated by new contenders. Principal data came from the Jonathan McDowell\'s JSR Launch Vehicle Database.', 'tables': [ { "format": 'csv', "url": 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2019/2019-01-15/agencies.csv', 'sample': '''agency,count,ucode,state_code,type,class,tstart,tstop,short_name,name,location,longitude,latitude,error,parent,short_english_name,english_name,unicode_name,agency_type RVSN,1528,RVSN,SU,O/LA,D,1960,1991 Dec,RVSN,Rakentiye Voiska Strategicheskogo Naznacheniye,Mosvka?,-,-,-,-,-,Strategic Rocket Forces,Ракетные войска стратегического назначения,state UNKS,904,GUKOS,SU,O/LA,D,1986 Apr 24,1991,UNKS,Upravleniye Nachalnika Kosmicheskikh Sredstv,Moskva,-,-,-,MO,-,-,Управление начальника космических средств МО СССР,state NASA,469,NASA,US,O/LA/LV/PL/S,C,1958 Oct 1,-,NASA,National Aeronautics and Space Administration,"Washington, D.C.",-,-,-,-,-,-,National Aeronautics and Space Administration,state USAF,388,USAF,US,O/LA/S,D,1947 Sep 18,-,USAF,United States Air Force,"Washington, DC-Pentagon",-,-,-,-,-,-,United States Air Force,state AE,258,AE,F,O/LA,B,1980 Mar 26,*,Arianespace,"Arianespace, Inc.","Paris-Evry, France",-,-,-,-,Arianespace,-,"Arianespace, Inc.",private AFSC,247,AFSC,US,LA,D,1961 Apr 1,1992 Jul 1,AFSC,"US Air Force Systems Command, Los Angeles AFS","El Segundo, California",-,-,-,USAF,-,-,"US Air Force Systems Command, Los Angeles AFS",state VKSR,200,GUKOS,RU,O/LA,D,1997 Jul,2001 Jun 1,VKS RVSN,"Voenno-Kosmicheskiye Sili (Military Space Forces), RVSN","Mosvka-Solnechnogorsk, Rossiya",-,-,-,RVSN,-,Russian Military Space Forces,Военно-космические силы РВСН,state CALT,181,CALT,CN,LA/LV/PL/E,C,1957 Nov 11,-,CALT,Zhongguo yunzaihuojian jishu yanjiu yuan,Beijing-Nanyuan,-,-,-,CASC,CALT,Chinese Academy of Launch Vehicle Technology (CASC 1st Acad),中国运载火箭技术研究院,state FKA,128,MOM,RU,O/LA,C,2004,2016 Jan 1,Roskosmos,Federal'noe kosmicheskoe agentstvo Rossii (Roskosmos),Moskva,-,-,-,-,-,Roskosmos,Федеральное космическое агентство (Роскосмос),state SAST,105,SBA,CN,O/LA/LV/PL,B,1993,-,SAST,Shanghai hangtian jishu yanjiuyuan,Shanghai-Minghan,-,-,-,CASC,-,Shanghai Academy of Space Technology (CASC 8th Acad),上海航天技术研究院,state ILSK,97,ILSK,RU,LA,B,1995,-,ILS-K,"International Launch Services, Khrunichev",Moskva,-,-,-,KHRU,-,-,"International Launch Services, Khrunichev",private KVR,78,GUKOS,RU,O/LA,D,2001 Jun 1,2011 Dec 1,KVR,Kosmichesikiye voyska Rossii,Moskva-Solnechnogorsk,-,-,-,-,-,Russian Space Forces,Космические войска России,state ULAL,70,ULAL,US,LA,B,2006 Dec,-,ULA/LMA,United Launch Alliance/Lockheed Martin Astronautics,"Denver-Centennial, Colorado",-,-,-,LM,-,-,United Launch Alliance/Lockheed Martin Astronautics,private KHRU,67,KHRU,RU,O/LA/LV/PL/S,B,1994,1998,Khrunichev,"GKNPTs im. M.V. Khrunichev, Zavod Khrunichev",Moskva-Fili,-,-,-,-,-,Khrunichev State Research and Production Center,ГКНПЦ им. М.В.Хруничева,state SPX,65,SPX,US,O/LA/LV/PL/S,B,2007 Aug,-,SpaceX,SpaceX,"Hawthorne, California",-,-,-,-,-,-,SpaceX,startup''' }, { "format": 'csv', "url": 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2019/2019-01-15/launches.csv', 'sample': '''tag,JD,launch_date,launch_year,type,variant,mission,agency,state_code,category,agency_type 1967-065,2439671.38,1967-06-29,1967,Thor Burner 2,,Secor Type II S/N 10,US,US,O,state 1967-080,2439725.7,1967-08-23,1967,Thor Burner 2,,DAPP 3419,US,US,O,state 1967-096,2439774.83,1967-10-11,1967,Thor Burner 2,,DAPP 4417,US,US,O,state 1968-042,2439999.69,1968-05-23,1968,Thor Burner 2,,DAPP 5420,US,US,O,state 1968-092,2440152.69,1968-10-23,1968,Thor Burner 2,,DAPP 6422,US,US,O,state 1969-062,2440425.69,1969-07-23,1969,Thor Burner 2,,DAPP 7421,US,US,O,state 1970-012,2440628.86,1970-02-11,1970,Thor Burner 2,,DAPP Block 5A F-1,US,US,O,state 1970-070,2440832.86,1970-09-03,1970,Thor Burner 2,,DAPP Block 5A F-2,US,US,O,state 1971-012,2440999.66,1971-02-17,1971,Thor Burner 2,,DAPP Block 5A F-3,US,US,O,state 1971-054,2441111.08,1971-06-08,1971,Thor Burner 2,,P70-1,US,US,O,state 1971-087,2441238.83,1971-10-14,1971,Thor Burner 2A,,DMSP Block 5B F-1 (SV-2),US,US,O,state 1972-018,2441400.87,1972-03-24,1972,Thor Burner 2A,,DMSP Block 5B F-2 (SV-1),US,US,O,state 1972-089,2441630.64,1972-11-09,1972,Thor Burner 2A,,DMSP Block 5B F-3,US,US,O,state 1973-054,2441911.7,1973-08-17,1973,Thor Burner 2A,,DMSP Block 5B F-4,US,US,O,state 1974-015,2442122.83,1974-03-16,1974,Thor Burner 2A,,DMSP Block 5B F-5,US,US,O,state 1974-063,2442268.64,1974-08-09,1974,Thor Burner 2A,,DMSP Block 5C F-1,US,US,O,state''' } ] }, ] ================================================ FILE: py-src/data_formulator/sandbox/__init__.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. from .local_sandbox import LocalSandbox from .docker_sandbox import DockerSandbox # Valid values for the --sandbox CLI option / SANDBOX env var. SANDBOX_OPTIONS = ("local", "docker") def create_sandbox(sandbox: str = "local") -> LocalSandbox | DockerSandbox: """Instantiate a sandbox from a config string. Parameters ---------- sandbox : str ``"local"`` (default) or ``"docker"``. """ if sandbox == "docker": return DockerSandbox() return LocalSandbox() ================================================ FILE: py-src/data_formulator/sandbox/base.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ Abstract base class for code-execution sandboxes. Every sandbox backend must subclass :class:`Sandbox` and implement :meth:`run_python_code`. The return contract is a dict with: * ``{'status': 'ok', 'content': <pandas.DataFrame>}`` on success * ``{'status': 'error', 'content': '<error message>'}`` on failure """ from abc import ABC, abstractmethod import pandas as pd class Sandbox(ABC): """Base class for sandbox execution backends.""" @abstractmethod def run_python_code( self, code: str, workspace, output_variable: str, ) -> dict: """Execute a Python script and return the resulting DataFrame. The script runs with the workspace directory as its working directory (read-only). Scripts can therefore read files directly via e.g. ``pd.read_csv("file.csv")``. Parameters ---------- code : str Python source code to execute. The script is expected to populate a variable named *output_variable* with a :class:`pandas.DataFrame`. workspace A :class:`~data_formulator.datalake.workspace.Workspace` instance. The sandbox uses ``workspace.local_dir()`` to set up the execution environment. output_variable : str Name of the variable in *code* that holds the result. Returns ------- dict ``{'status': 'ok', 'content': DataFrame}`` on success, or ``{'status': 'error', 'content': str}`` on failure. """ ... ================================================ FILE: py-src/data_formulator/sandbox/docker_sandbox.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ Docker-based sandbox for executing Python code in an isolated container. The workspace directory is mounted **read-only** as the container's working directory so user scripts can read data files via e.g. ``pd.read_csv("file.csv")`` but cannot tamper with the host filesystem. The output DataFrame is serialised to Parquet and read back via a bind-mounted output directory. """ import logging import os import shutil import subprocess import tempfile import textwrap import pandas as pd from werkzeug.utils import secure_filename from .base import Sandbox logger = logging.getLogger(__name__) # --------------------------------------------------------------------------- # Configuration # --------------------------------------------------------------------------- DEFAULT_DOCKER_IMAGE = os.environ.get( "DOCKER_SANDBOX_IMAGE", "data-formulator-sandbox" ) DEFAULT_TIMEOUT = int(os.environ.get("DOCKER_SANDBOX_TIMEOUT", "120")) class DockerSandbox(Sandbox): """Execute Python code inside a Docker container. The workspace directory is bind-mounted **read-only** as the container's working directory. Scripts read files directly via e.g. ``pd.read_csv("file.csv")``. Parameters ---------- docker_image : str Docker image tag to use. The default ``data-formulator-sandbox`` is built from ``Dockerfile.sandbox`` and has all required packages pre-installed. Override with ``DOCKER_SANDBOX_IMAGE`` env-var. timeout : int Wall-clock timeout in seconds (default: 120 or ``DOCKER_SANDBOX_TIMEOUT``). """ def __init__( self, docker_image: str = DEFAULT_DOCKER_IMAGE, timeout: int = DEFAULT_TIMEOUT, ): self.docker_image = docker_image self.timeout = timeout # ------------------------------------------------------------------ # Public interface # ------------------------------------------------------------------ def run_python_code( self, code: str, workspace, output_variable: str, ) -> dict: """Execute *code* in a Docker container and return the result DataFrame. The wrapper script runs the user code, then serialises ``output_variable`` to Parquet so the host can read it back. Returns ------- dict ``{'status': 'ok', 'content': DataFrame}`` on success, or ``{'status': 'error', 'content': str}`` on failure. """ # Use local_dir() to materialise workspace files locally # (no-op for local workspaces, downloads blobs for Azure). with workspace.local_dir() as local_path: workspace_path = str(local_path) tmpdir = tempfile.mkdtemp(prefix="df_docker_") output_dir = os.path.join(tmpdir, "outputs") os.makedirs(output_dir, exist_ok=True) # ---- build wrapper script ----------------------------------------- output_parquet = f"/sandbox/outputs/{output_variable}.parquet" wrapper_script = textwrap.dedent("""\ import warnings warnings.filterwarnings('ignore') # --- user code --- {user_code} # --- serialise output --- import pandas as _pd _out = {output_variable} if not isinstance(_out, _pd.DataFrame): raise TypeError( '{output_variable} is not a DataFrame ' f'(type: {{type(_out).__name__}})' ) _out.to_parquet('{output_parquet}', index=False) print("__DOCKER_SANDBOX_OK__") """).format( user_code=code, output_variable=output_variable, output_parquet=output_parquet, ) script_path = os.path.join(tmpdir, "run.py") with open(script_path, "w") as f: f.write(wrapper_script) # ---- assemble docker command -------------------------------------- docker_cmd: list[str] = [ "docker", "run", "--rm", "--memory", "512m", "--cpus", "1", "--pids-limit", "256", ] abs_ws = os.path.abspath(workspace_path) docker_cmd += ["-v", f"{abs_ws}:/sandbox/workdir:ro"] docker_cmd += ["-v", f"{output_dir}:/sandbox/outputs:rw"] docker_cmd += ["-v", f"{script_path}:/sandbox/run.py:ro"] docker_cmd += ["-w", "/sandbox/workdir"] docker_cmd += [self.docker_image] docker_cmd += ["python", "/sandbox/run.py"] # ---- execute ------------------------------------------------------ try: proc = subprocess.run( docker_cmd, capture_output=True, timeout=self.timeout, text=True, ) except subprocess.TimeoutExpired: self._cleanup(tmpdir) return { "status": "error", "content": f"Docker sandbox execution timed out after {self.timeout}s", } except FileNotFoundError: self._cleanup(tmpdir) return { "status": "error", "content": ( "Docker is not installed or not on the PATH. " "Install Docker to use the docker sandbox." ), } except Exception as exc: self._cleanup(tmpdir) return { "status": "error", "content": f"Failed to start Docker container: {exc}", } stdout = proc.stdout or "" stderr = proc.stderr or "" if proc.returncode != 0 or "__DOCKER_SANDBOX_OK__" not in stdout: self._cleanup(tmpdir) err_detail = stderr.strip() or stdout.strip() or "Unknown error" return { "status": "error", "content": f"Docker sandbox execution failed:\n{err_detail}", } # ---- read back output --------------------------------------------- # Defensive: ensure the filename stays inside output_dir even if # output_variable somehow contains path separators. safe_name = secure_filename(output_variable) if not safe_name: self._cleanup(tmpdir) return { "status": "error", "content": "Invalid output_variable", } parquet_out = os.path.join(output_dir, f"{safe_name}.parquet") if not os.path.exists(parquet_out): self._cleanup(tmpdir) return { "status": "error", "content": f'Output variable "{output_variable}" was not produced', } try: output_df = pd.read_parquet(parquet_out) except Exception as exc: self._cleanup(tmpdir) return { "status": "error", "content": f"Failed to read output parquet: {exc}", } self._cleanup(tmpdir) return {"status": "ok", "content": output_df} # ------------------------------------------------------------------ # Internal helpers # ------------------------------------------------------------------ @staticmethod def _cleanup(tmpdir: str) -> None: """Best-effort recursive removal of *tmpdir*.""" try: shutil.rmtree(tmpdir, ignore_errors=True) except Exception: pass ================================================ FILE: py-src/data_formulator/sandbox/local_sandbox.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """Local sandbox -- executes Python code in a persistent warm subprocess. The script runs with the workspace directory as its working directory so user scripts access files via e.g. ``pd.read_csv("sample.csv")``. """ import atexit import logging import os import threading import warnings from multiprocessing import Pipe, Process from sys import addaudithook import pandas as pd from .base import Sandbox logger = logging.getLogger(__name__) # --------------------------------------------------------------------------- # Persistent warm worker # --------------------------------------------------------------------------- def _warm_worker_loop(conn): """Long-lived child process that pre-imports heavy libraries then waits for code to execute. Protocol (over *conn*): Host -> worker: (code: str, allowed_objects: dict) Worker -> host: {"status": "ok", "allowed_objects": {...}} or {"status": "error", "error_message": "..."} Host -> worker: None -> terminate """ warnings.filterwarnings("ignore") # Install audit hooks once -- they persist for the process lifetime. def block_mischief(event, arg): if type(event) != str: raise RuntimeError("bad audit event") # Block file writes (only allow reading) if event == "open" and type(arg[1]) == str and arg[1] not in ("r", "rb"): raise IOError("file write forbidden") # Block dangerous filesystem / process operations _blocked_prefixes = ("subprocess", "shutil", "winreg", "webbrowser") if event.split(".")[0] in _blocked_prefixes: raise IOError("potentially dangerous, filesystem-accessing functions forbidden") # Block network access — code should only transform data, not # make outbound connections (prevents data exfiltration). if event in ("socket.connect", "socket.bind", "socket.sendto", "socket.sendmsg", "socket.getaddrinfo"): raise IOError("network access forbidden in sandbox") # Block ctypes / dynamic library loading (could bypass audit hooks) if event in ("ctypes.dlopen", "ctypes.dlsym", "ctypes.set_errno"): raise IOError("ctypes access forbidden in sandbox") # Block import of dangerous modules if event == "import" and type(arg[0]) == str: _blocked_modules = ("subprocess", "shutil", "socket", "http", "urllib", "requests", "ctypes", "multiprocessing", "signal", "resource") mod_name = arg[0].split(".")[0] if mod_name in _blocked_modules: raise ImportError(f"import of '{mod_name}' is forbidden in sandbox") addaudithook(block_mischief) del block_mischief # Pre-import heavy libraries so first call is fast. # These stay in sys.modules for the lifetime of the worker. try: import numpy # noqa: F401 import pandas # noqa: F401 import duckdb # noqa: F401 except ImportError: pass while True: try: msg = conn.recv() except (EOFError, OSError): break if msg is None: break code, allowed_objects = msg namespace = {**allowed_objects} try: # Security: code is HMAC-SHA256 signed by the server when first generated # by AI agents (see code_signing.py). The /refresh-derived-data endpoint # verifies the signature before forwarding code here, ensuring only # server-originated code is executed. Additional audit hooks above block # file writes, network access, subprocess spawning, and dangerous imports. exec(code, namespace) # nosec # codeql[py/code-injection] except Exception as err: conn.send({"status": "error", "error_message": f"Error: {type(err).__name__} - {err}"}) continue conn.send({"status": "ok", "allowed_objects": {k: namespace[k] for k in allowed_objects}}) conn.close() class _WarmWorkerPool: """Pool of persistent child processes with pre-imported libraries. Workers are forked once and reuse the same process for multiple calls, avoiding the ~600ms pandas/numpy import overhead each time. A simple LIFO stack ensures thread-safe checkout/return. """ def __init__(self, size: int = 2): self._size = size self._lock = threading.Lock() self._available: list[tuple[Process, object]] = [] self._all: list[tuple[Process, object]] = [] self._closed = False atexit.register(self.shutdown) def _spawn(self) -> tuple[Process, object]: parent_conn, child_conn = Pipe() p = Process(target=_warm_worker_loop, args=(child_conn,), daemon=True) p.start() return p, parent_conn def acquire(self) -> tuple[Process, object]: """Get a warm worker (process, conn). Spawns one if needed.""" with self._lock: while self._available: proc, conn = self._available.pop() if proc.is_alive(): return proc, conn # Dead worker -- discard and try next # No available workers -- spawn a new one (up to pool size is advisory) pair = self._spawn() self._all.append(pair) return pair def release(self, proc: Process, conn) -> None: """Return a worker to the pool for reuse.""" with self._lock: if not self._closed and proc.is_alive(): self._available.append((proc, conn)) def discard(self, proc: Process, conn) -> None: """Discard a broken worker (don't put it back).""" try: conn.send(None) except Exception: pass try: proc.terminate() except Exception: pass def shutdown(self) -> None: with self._lock: self._closed = True all_workers = list(self._all) self._available.clear() self._all.clear() for proc, conn in all_workers: try: conn.send(None) except Exception: pass try: proc.join(timeout=2) except Exception: pass if proc.is_alive(): proc.terminate() # Module-level pool -- shared by all LocalSandbox instances using subprocess mode. _worker_pool = _WarmWorkerPool(size=2) class LocalSandbox(Sandbox): """Execute Python code in a persistent warm subprocess. Uses a pool of pre-warmed child processes with pandas/numpy/duckdb already imported, giving ~1 ms execution overhead per call. Audit hooks in the child block file writes and dangerous operations. """ # ------------------------------------------------------------------ # Public interface # ------------------------------------------------------------------ def run_python_code( self, code: str, workspace, output_variable: str, ) -> dict: """Execute *code* and return the result DataFrame. The script runs with the workspace directory as its working directory so scripts access data files via e.g. ``pd.read_csv("sample.csv")``. Returns ------- dict ``{'status': 'ok', 'content': DataFrame}`` on success, or ``{'status': 'error', 'content': str}`` on failure. """ with workspace.local_dir() as local_path: workspace_path = os.path.abspath(str(local_path)) # Prepend a chdir so the script runs inside the workspace directory. ws_escaped = workspace_path.replace("\\", "\\\\").replace("'", "\\'") chdir_preamble = f"import os as _sandbox_os; _sandbox_os.chdir('{ws_escaped}')\n" code_with_chdir = chdir_preamble + code try: allowed_objects = {output_variable: None} result = self._run_in_warm_subprocess(code_with_chdir, allowed_objects) if result["status"] == "ok": output_df = result["allowed_objects"][output_variable] if not isinstance(output_df, pd.DataFrame): return { "status": "error", "content": ( f'Output variable "{output_variable}" is not a ' f"DataFrame (type: {type(output_df).__name__})" ), } return {"status": "ok", "content": output_df} else: return result except Exception as e: return { "status": "error", "content": f"Error during execution setup: {type(e).__name__} - {e}", } # ------------------------------------------------------------------ # Warm subprocess execution (persistent worker pool) # ------------------------------------------------------------------ # Maximum wall-clock time for a single code execution (seconds). EXECUTION_TIMEOUT = int(os.environ.get("DF_SANDBOX_TIMEOUT", "120")) @staticmethod def _run_in_warm_subprocess(code, allowed_objects): """Send code to a warm worker from the pool, return the result.""" proc, conn = _worker_pool.acquire() try: conn.send((code, {**allowed_objects})) # Enforce a wall-clock timeout to prevent runaway code if conn.poll(timeout=LocalSandbox.EXECUTION_TIMEOUT): result = conn.recv() else: # Timed out — kill and discard the worker _worker_pool.discard(proc, conn) return { "status": "error", "error_message": ( f"Code execution timed out after " f"{LocalSandbox.EXECUTION_TIMEOUT}s" ), } _worker_pool.release(proc, conn) return result except Exception as e: _worker_pool.discard(proc, conn) return {"status": "error", "error_message": f"Error: worker communication failed - {e}"} ================================================ FILE: py-src/data_formulator/sandbox/not_a_sandbox.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """Unsandboxed main-process executor -- for benchmarking only. This runs user code directly in the main process with no isolation. It is NOT exposed as a CLI option and should only be used to measure the raw execution overhead baseline in benchmarks. """ import os import warnings import pandas as pd from .base import Sandbox class NotASandbox(Sandbox): """Execute Python code directly in the main process (no isolation). For benchmarking only -- measures raw exec() overhead without any subprocess or container overhead. No security restrictions are applied. """ def run_python_code( self, code: str, workspace, output_variable: str, ) -> dict: with workspace.local_dir() as local_path: workspace_path = os.path.abspath(str(local_path)) original_cwd = os.getcwd() try: os.chdir(workspace_path) warnings.filterwarnings("ignore") namespace = {output_variable: None} # Security: this sandbox has NO isolation and is for benchmarking only. # In production, use LocalSandbox or DockerSandbox. Code executed here # is HMAC-SHA256 signed at generation time (see code_signing.py) and # verified by the /refresh-derived-data endpoint before reaching exec(). exec(code, namespace) # nosec # codeql[py/code-injection] output_df = namespace[output_variable] if not isinstance(output_df, pd.DataFrame): return { "status": "error", "content": ( f'Output variable "{output_variable}" is not a ' f"DataFrame (type: {type(output_df).__name__})" ), } return {"status": "ok", "content": output_df} except Exception as e: return { "status": "error", "content": f"Error: {type(e).__name__} - {e}", } finally: os.chdir(original_cwd) ================================================ FILE: py-src/data_formulator/session_routes.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ Session save / load routes. Delegates all storage operations to the workspace object, which handles both local filesystem and Azure Blob backends transparently. """ import io import json import logging from datetime import datetime from flask import Blueprint, request, jsonify, send_file, current_app from data_formulator.auth import get_identity_id from data_formulator.workspace_factory import get_workspace logger = logging.getLogger(__name__) def _disk_persistence_enabled() -> bool: """Return True unless --disable-database was passed (no disk persistence).""" try: return not current_app.config.get('CLI_ARGS', {}).get('disable_database', False) except RuntimeError: return True session_bp = Blueprint("sessions", __name__, url_prefix="/api/sessions") # Fields that must never be persisted (contain secrets / ephemeral info) _SENSITIVE_FIELDS = frozenset([ "models", "selectedModelId", "testedModels", "dataLoaderConnectParams", "identity", "agentRules", "serverConfig", ]) def _strip_sensitive(state: dict) -> dict: """Return a copy of *state* with sensitive / ephemeral fields removed.""" return {k: v for k, v in state.items() if k not in _SENSITIVE_FIELDS} # --------------------------------------------------------------------------- # Routes # --------------------------------------------------------------------------- @session_bp.route("/save", methods=["POST"]) def save_session(): if not _disk_persistence_enabled(): return jsonify(status="error", message="Session save is disabled (no disk persistence)"), 403 data = request.get_json(force=True) name: str = data.get("name", "").strip() state: dict = data.get("state") if not name: return jsonify(status="error", message="Session name is required"), 400 if state is None: return jsonify(status="error", message="State payload is required"), 400 identity_id = get_identity_id() ws = get_workspace(identity_id) clean_state = _strip_sensitive(state) saved_at = ws.save_session(name, clean_state) return jsonify(status="ok", name=name, saved_at=saved_at) @session_bp.route("/list", methods=["GET"]) def list_sessions(): if not _disk_persistence_enabled(): return jsonify(status="ok", sessions=[]) identity_id = get_identity_id() ws = get_workspace(identity_id) return jsonify(status="ok", sessions=ws.list_sessions()) @session_bp.route("/load", methods=["POST"]) def load_session(): if not _disk_persistence_enabled(): return jsonify(status="error", message="Session load is disabled (no disk persistence)"), 403 data = request.get_json(force=True) name: str = data.get("name", "").strip() if not name: return jsonify(status="error", message="Session name is required"), 400 identity_id = get_identity_id() ws = get_workspace(identity_id) state = ws.load_session(name) if state is None: return jsonify(status="error", message=f"Session '{name}' not found"), 404 return jsonify(status="ok", name=name, state=state) @session_bp.route("/delete", methods=["POST"]) def delete_session(): if not _disk_persistence_enabled(): return jsonify(status="error", message="Session delete is disabled (no disk persistence)"), 403 data = request.get_json(force=True) name: str = data.get("name", "").strip() if not name: return jsonify(status="error", message="Session name is required"), 400 identity_id = get_identity_id() ws = get_workspace(identity_id) if not ws.delete_session(name): return jsonify(status="error", message=f"Session '{name}' not found"), 404 return jsonify(status="ok", name=name) @session_bp.route("/export", methods=["POST"]) def export_session(): data = request.get_json(force=True) state: dict = data.get("state") if state is None: return jsonify(status="error", message="State payload is required"), 400 identity_id = get_identity_id() ws = get_workspace(identity_id) clean_state = _strip_sensitive(state) buf = ws.export_session_zip(clean_state) filename = f"df_session_{datetime.now().strftime('%Y%m%d_%H%M%S')}.dfsession" return send_file(buf, mimetype="application/zip", as_attachment=True, download_name=filename) @session_bp.route("/import", methods=["POST"]) def import_session(): if "file" not in request.files: return jsonify(status="error", message="No file uploaded"), 400 file = request.files["file"] try: identity_id = get_identity_id() ws = get_workspace(identity_id) state = ws.import_session_zip(io.BytesIO(file.read())) return jsonify(status="ok", state=state) except ValueError as e: return jsonify(status="error", message=str(e)), 400 except Exception as e: logger.error(f"Error importing session: {e}") return jsonify(status="error", message=str(e)), 400 ================================================ FILE: py-src/data_formulator/tables_routes.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. import logging import sys import os import mimetypes mimetypes.add_type('application/javascript', '.js') mimetypes.add_type('application/javascript', '.mjs') import json import traceback from flask import request, jsonify, Blueprint, Response import pandas as pd from pathlib import Path from werkzeug.utils import secure_filename from data_formulator.data_loader import DATA_LOADERS from data_formulator.auth import get_identity_id from data_formulator.datalake.workspace import Workspace from data_formulator.workspace_factory import get_workspace as _create_workspace from data_formulator.datalake.parquet_utils import sanitize_table_name as parquet_sanitize_table_name from data_formulator.datalake.file_manager import save_uploaded_file, is_supported_file from data_formulator.datalake.metadata import TableMetadata as DatalakeTableMetadata import re # Get logger for this module (logging config done in app.py) logger = logging.getLogger(__name__) import os tables_bp = Blueprint('tables', __name__, url_prefix='/api/tables') def _get_workspace(): """Get workspace for the current identity.""" return _create_workspace(get_identity_id()) # Row-count threshold above which we use DuckDB for parquet tables # (avoids loading the entire file into memory via pandas). _LARGE_TABLE_THRESHOLD = 100_000 def _should_use_duckdb(workspace, table_name: str) -> bool: """Return True if the table is a large parquet file that benefits from DuckDB. Small parquet tables are faster to handle with pandas (avoids DuckDB connection overhead and repeated YAML reads). """ meta = workspace.get_table_metadata(table_name) if meta is None or meta.file_type != "parquet": return False row_count = meta.row_count or 0 return row_count > _LARGE_TABLE_THRESHOLD def _quote_duckdb(col: str) -> str: """Quote identifier for DuckDB (double quotes, escape internal quotes).""" return '"' + str(col).replace('"', '""') + '"' def _dedup_dataframe_columns(df: pd.DataFrame) -> pd.DataFrame: """Remove duplicate columns from a DataFrame, keeping the first occurrence.""" if df.columns.duplicated().any(): return df.loc[:, ~df.columns.duplicated()] return df def _dedup_list(items: list) -> list: """Remove duplicates from a list while preserving order.""" return list(dict.fromkeys(items)) def _build_parquet_sample_sql( columns: list[str], aggregate_fields_and_functions: list, select_fields: list, method: str, order_by_fields: list, sample_size: int, ) -> tuple[str, str]: """ Build DuckDB SQL for sampling (and optional aggregation) over parquet. Returns (main_sql, count_sql) where each contains {parquet} placeholder. """ valid_agg = [(f, fn) for (f, fn) in aggregate_fields_and_functions if f is None or f in columns] valid_select = _dedup_list([f for f in select_fields if f in columns]) valid_order = [f for f in order_by_fields if f in columns] if valid_agg: select_parts = [] for field, function in valid_agg: fn = function.lower() if field is None and fn == "count": select_parts.append("COUNT(*) AS _count") elif field in columns: q = _quote_duckdb(field) if fn == "count": select_parts.append(f"COUNT({q}) AS _count") elif fn in ("avg", "average", "mean"): select_parts.append(f"AVG({q}) AS {_quote_duckdb(field + '_' + function)}") elif fn == "sum": select_parts.append(f"SUM({q}) AS {_quote_duckdb(field + '_sum')}") elif fn == "min": select_parts.append(f"MIN({q}) AS {_quote_duckdb(field + '_min')}") elif fn == "max": select_parts.append(f"MAX({q}) AS {_quote_duckdb(field + '_max')}") for f in valid_select: select_parts.append(f"t.{_quote_duckdb(f)}") group_cols = valid_select group_by = f" GROUP BY {', '.join('t.' + _quote_duckdb(c) for c in group_cols)}" if group_cols else "" inner = f"SELECT {', '.join(select_parts)} FROM {{parquet}} AS t{group_by}" count_sql = f"SELECT COUNT(*) FROM ({inner}) AS sub" if method == "random": order_by = " ORDER BY RANDOM()" elif method == "head" and valid_order: order_by = " ORDER BY " + ", ".join(f"sub.{_quote_duckdb(c)} ASC" for c in valid_order) elif method == "bottom" and valid_order: order_by = " ORDER BY " + ", ".join(f"sub.{_quote_duckdb(c)} DESC" for c in valid_order) else: order_by = "" main_sql = f"SELECT * FROM ({inner}) AS sub{order_by} LIMIT {sample_size}" return main_sql, count_sql count_sql = "SELECT COUNT(*) FROM {parquet} AS t" if method == "random": order_by = " ORDER BY RANDOM()" elif method == "head" and valid_order: order_by = " ORDER BY " + ", ".join(f"t.{_quote_duckdb(c)} ASC" for c in valid_order) elif method == "bottom" and valid_order: order_by = " ORDER BY " + ", ".join(f"t.{_quote_duckdb(c)} DESC" for c in valid_order) else: order_by = "" if valid_select: select_list = ", ".join(f"t.{_quote_duckdb(c)}" for c in valid_select) main_sql = f"SELECT {select_list} FROM {{parquet}} AS t{order_by} LIMIT {sample_size}" else: main_sql = f"SELECT * FROM {{parquet}} AS t{order_by} LIMIT {sample_size}" return main_sql, count_sql def _table_metadata_to_source_metadata(meta: DatalakeTableMetadata) -> dict | None: """Convert workspace TableMetadata to API source_metadata dict (for refresh).""" if meta.loader_type is None and meta.loader_params is None: return None return { "table_name": meta.name, "data_loader_type": meta.loader_type or "", "data_loader_params": meta.loader_params or {}, "source_table_name": meta.source_table, "source_query": meta.source_query, "last_refreshed": meta.last_synced.isoformat() if meta.last_synced else None, "content_hash": meta.content_hash, } @tables_bp.route('/open-workspace', methods=['POST']) def open_workspace(): """Open the Data Formulator home directory in the system file manager.""" from flask import current_app from data_formulator.datalake.workspace import get_data_formulator_home import subprocess, platform if current_app.config.get('CLI_ARGS', {}).get('disable_database', False): return jsonify(status="error", message="Workspace access is disabled"), 403 try: home_path = str(get_data_formulator_home()) # Ensure directory exists Path(home_path).mkdir(parents=True, exist_ok=True) system = platform.system() if system == "Darwin": subprocess.Popen(["open", home_path]) elif system == "Windows": subprocess.Popen(["explorer", home_path]) else: subprocess.Popen(["xdg-open", home_path]) return jsonify(status="ok", path=home_path) except Exception as e: logger.error(f"Failed to open workspace: {e}") return jsonify(status="error", message=str(e)), 500 @tables_bp.route('/list-tables', methods=['GET']) def list_tables(): """List all tables in the current workspace (datalake).""" try: workspace = _get_workspace() result = [] for table_name in workspace.list_tables(): try: meta = workspace.get_table_metadata(table_name) if meta is None: continue columns = [{"name": c.name, "type": c.dtype} for c in (meta.columns or [])] if not columns and meta.file_type == "parquet": try: schema_info = workspace.get_parquet_schema(table_name) columns = [{"name": c["name"], "type": c["type"]} for c in schema_info.get("columns", [])] except Exception: pass row_count = meta.row_count if row_count is None and meta.file_type == "parquet": try: schema_info = workspace.get_parquet_schema(table_name) row_count = schema_info.get("num_rows", 0) or 0 except Exception: row_count = 0 if row_count is None: row_count = 0 sample_rows = [] if row_count > 0: try: if _should_use_duckdb(workspace, table_name): df = workspace.run_parquet_sql(table_name, "SELECT * FROM {parquet} AS t LIMIT 1000") else: df = workspace.read_data_as_df(table_name) df = df.head(1000) df = _dedup_dataframe_columns(df) sample_rows = json.loads(df.to_json(orient='records', date_format='iso')) except Exception: pass source_metadata = _table_metadata_to_source_metadata(meta) result.append({ "name": table_name, "columns": columns, "row_count": row_count, "sample_rows": sample_rows, "view_source": None, "source_metadata": source_metadata, }) except Exception as e: logger.error(f"Error getting table metadata for {table_name}: {str(e)}") continue return jsonify({"status": "success", "tables": result}) except Exception as e: safe_msg, status_code = sanitize_db_error_message(e) return jsonify({"status": "error", "message": safe_msg}), status_code def _apply_aggregation_and_sample( df: pd.DataFrame, aggregate_fields_and_functions: list, select_fields: list, method: str, order_by_fields: list, sample_size: int, ) -> tuple[pd.DataFrame, int]: """ Apply aggregation (optional), then sample with ordering. Returns (sampled_df, total_row_count_after_aggregation). """ columns = list(df.columns) valid_agg = [ (f, fn) for (f, fn) in aggregate_fields_and_functions if f is None or f in columns ] valid_select = _dedup_list([f for f in select_fields if f in columns]) valid_order = [f for f in order_by_fields if f in columns] if valid_agg: group_cols = valid_select agg_spec = {} for field, function in valid_agg: fn = function.lower() if field is None and fn == "count": agg_spec["_count"] = ("__size__", "size") elif field in columns: if fn == "count": agg_spec["_count"] = (field, "count") elif fn in ("avg", "average", "mean"): agg_spec[f"{field}_{function}"] = (field, "mean") elif fn == "sum": agg_spec[f"{field}_sum"] = (field, "sum") elif fn == "min": agg_spec[f"{field}_min"] = (field, "min") elif fn == "max": agg_spec[f"{field}_max"] = (field, "max") if "_count" in agg_spec and agg_spec["_count"] == ("__size__", "size"): df = df.assign(__size__=1) agg_spec["_count"] = ("__size__", "count") if group_cols: df_agg = df.groupby(group_cols, dropna=False).agg(**{k: (c, f) for k, (c, f) in agg_spec.items()}).reset_index() else: df_agg = pd.DataFrame([{k: df[c].agg(f) for k, (c, f) in agg_spec.items()}]) total_row_count = len(df_agg) work = df_agg else: total_row_count = len(df) work = df[valid_select].copy() if valid_select else df.copy() if method == "random": work = work.sample(n=min(sample_size, len(work)), random_state=None) elif method == "head": work = work.sort_values(by=valid_order, ascending=True).head(sample_size) if valid_order else work.head(sample_size) elif method == "bottom": work = work.sort_values(by=valid_order, ascending=False).head(sample_size) if valid_order else work.tail(sample_size).iloc[::-1].reset_index(drop=True) else: work = work.head(sample_size) return work, total_row_count @tables_bp.route('/sample-table', methods=['POST']) def sample_table(): """Sample a table from the workspace. Uses DuckDB for parquet (no full load).""" try: data = request.get_json() table_id = data.get('table') sample_size = data.get('size', 1000) aggregate_fields_and_functions = data.get('aggregate_fields_and_functions', []) select_fields = data.get('select_fields', []) method = data.get('method', 'random') order_by_fields = data.get('order_by_fields', []) workspace = _get_workspace() if _should_use_duckdb(workspace, table_id): schema_info = workspace.get_parquet_schema(table_id) columns = [c["name"] for c in schema_info.get("columns", [])] main_sql, count_sql = _build_parquet_sample_sql( columns, aggregate_fields_and_functions, select_fields, method, order_by_fields, sample_size, ) total_row_count = int(workspace.run_parquet_sql(table_id, count_sql).iloc[0, 0]) result_df = workspace.run_parquet_sql(table_id, main_sql) else: df = workspace.read_data_as_df(table_id) result_df, total_row_count = _apply_aggregation_and_sample( df, aggregate_fields_and_functions, select_fields, method, order_by_fields, sample_size, ) result_df = _dedup_dataframe_columns(result_df) rows_json = json.loads(result_df.to_json(orient='records', date_format='iso')) return jsonify({ "status": "success", "rows": rows_json, "total_row_count": total_row_count, }) except Exception as e: logger.error(f"Error sampling table: {str(e)}") safe_msg, status_code = sanitize_db_error_message(e) return jsonify({"status": "error", "message": safe_msg}), status_code @tables_bp.route('/get-table', methods=['GET']) def get_table_data(): """Get data from a specific table in the workspace. Uses DuckDB for parquet (LIMIT/OFFSET only).""" try: table_name = request.args.get('table_name') page = int(request.args.get('page', 1)) page_size = int(request.args.get('page_size', 100)) offset = (page - 1) * page_size if not table_name: return jsonify({"status": "error", "message": "Table name is required"}), 400 workspace = _get_workspace() if _should_use_duckdb(workspace, table_name): count_df = workspace.run_parquet_sql(table_name, "SELECT COUNT(*) FROM {parquet} AS t") total_rows = int(count_df.iloc[0, 0]) page_df = workspace.run_parquet_sql( table_name, f"SELECT * FROM {{parquet}} AS t LIMIT {page_size} OFFSET {offset}", ) page_df = _dedup_dataframe_columns(page_df) columns = list(page_df.columns) rows = json.loads(page_df.to_json(orient='records', date_format='iso')) else: df = workspace.read_data_as_df(table_name) df = _dedup_dataframe_columns(df) total_rows = len(df) columns = list(df.columns) page_df = df.iloc[offset : offset + page_size] rows = json.loads(page_df.to_json(orient='records', date_format='iso')) return jsonify({ "status": "success", "table_name": table_name, "columns": columns, "rows": rows, "total_rows": total_rows, "page": page, "page_size": page_size, }) except Exception as e: logger.error(f"Error getting table data: {str(e)}") safe_msg, status_code = sanitize_db_error_message(e) return jsonify({"status": "error", "message": safe_msg}), status_code @tables_bp.route('/create-table', methods=['POST']) def create_table(): """Create a new table from uploaded file or raw data in the workspace.""" try: has_file = 'file' in request.files has_raw_data = 'raw_data' in request.files or 'raw_data' in request.form if not has_file and not has_raw_data: return jsonify({"status": "error", "message": "No file or raw data provided"}), 400 table_name = request.form.get('table_name') if not table_name: return jsonify({"status": "error", "message": "No table name provided"}), 400 workspace = _get_workspace() base_name = parquet_sanitize_table_name(table_name) sanitized_table_name = base_name counter = 1 while sanitized_table_name in workspace.list_tables(): sanitized_table_name = f"{base_name}_{counter}" counter += 1 if has_file: file = request.files['file'] if not file.filename or not is_supported_file(file.filename): return jsonify({"status": "error", "message": "Unsupported file format"}), 400 safe_name = secure_filename(file.filename) if not safe_name: return jsonify({"status": "error", "message": "Invalid filename"}), 400 meta = save_uploaded_file( workspace, file.stream, safe_name, table_name=sanitized_table_name, overwrite=False, ) sanitized_table_name = meta.name row_count = meta.row_count columns = [c.name for c in (meta.columns or [])] if row_count is None or not columns: df = workspace.read_data_as_df(sanitized_table_name) row_count = len(df) columns = list(df.columns) else: # raw_data can come as a file upload (Blob) or as a form field if 'raw_data' in request.files: raw_data = request.files['raw_data'].read().decode('utf-8') else: raw_data = request.form.get('raw_data') try: df = pd.DataFrame(json.loads(raw_data)) except Exception as e: return jsonify({"status": "error", "message": f"Invalid JSON data: {str(e)}, it must be a list of dictionaries"}), 400 workspace.write_parquet(df, sanitized_table_name) row_count = len(df) columns = list(df.columns) return jsonify({ "status": "success", "table_name": sanitized_table_name, "row_count": row_count, "columns": columns, "original_name": base_name, "is_renamed": base_name != sanitized_table_name, }) except Exception as e: logger.error(f"Error creating table: {str(e)}") safe_msg, status_code = sanitize_db_error_message(e) return jsonify({"status": "error", "message": safe_msg}), status_code @tables_bp.route('/sync-table-data', methods=['POST']) def sync_table_data(): """Update an existing workspace table's parquet with new row data. Used when the frontend has fresher data than the workspace (e.g., from stream refresh) and needs to sync it so sandbox code reads the latest data. """ try: data = request.get_json() table_name = data.get('table_name') rows = data.get('rows') if not table_name: return jsonify({"status": "error", "message": "table_name is required"}), 400 if rows is None: return jsonify({"status": "error", "message": "rows is required"}), 400 workspace = _get_workspace() if table_name not in workspace.list_tables(): return jsonify({"status": "error", "message": f"Table '{table_name}' not found in workspace"}), 404 df = pd.DataFrame(rows) if rows else pd.DataFrame() workspace.write_parquet(df, table_name) return jsonify({ "status": "success", "table_name": table_name, "row_count": len(df), }) except Exception as e: logger.error(f"Error syncing table data: {str(e)}") safe_msg, status_code = sanitize_db_error_message(e) return jsonify({"status": "error", "message": safe_msg}), status_code @tables_bp.route('/delete-table', methods=['POST']) def drop_table(): """Drop a table from the workspace.""" try: data = request.get_json() table_name = data.get('table_name') if not table_name: return jsonify({"status": "error", "message": "No table name provided"}), 400 workspace = _get_workspace() if not workspace.delete_table(table_name): return jsonify({"status": "error", "message": f"Table '{table_name}' does not exist"}), 404 return jsonify({"status": "success", "message": f"Table {table_name} dropped"}) except Exception as e: logger.error(f"Error dropping table: {str(e)}") safe_msg, status_code = sanitize_db_error_message(e) return jsonify({"status": "error", "message": safe_msg}), status_code @tables_bp.route('/upload-db-file', methods=['POST']) def upload_db_file(): """No longer used: storage is workspace/datalake, not DuckDB. Kept for API compatibility.""" return jsonify({ "status": "error", "message": "Database file upload is no longer supported. Data is stored in the workspace; use create-table with a file or data loaders to add data.", }), 410 @tables_bp.route('/download-db-file', methods=['GET']) def download_db_file(): """No longer used: storage is workspace/datalake. Kept for API compatibility.""" return jsonify({ "status": "error", "message": "Database file download is no longer supported. Data lives in the workspace.", }), 410 @tables_bp.route('/reset-db-file', methods=['POST']) def reset_db_file(): """Reset the workspace for the current session (removes all tables and files).""" try: workspace = _get_workspace() workspace.cleanup() return jsonify({"status": "success", "message": "Workspace reset successfully"}) except Exception as e: logger.error(f"Error resetting workspace: {str(e)}") safe_msg, status_code = sanitize_db_error_message(e) return jsonify({"status": "error", "message": safe_msg}), status_code def _is_numeric_duckdb_type(col_type: str) -> bool: """Return True if DuckDB/parquet type is numeric for min/max/avg.""" t = (col_type or "").upper() return any( t.startswith(k) for k in ("INT", "BIGINT", "SMALLINT", "TINYINT", "DOUBLE", "FLOAT", "REAL", "DECIMAL", "NUMERIC") ) @tables_bp.route('/analyze', methods=['POST']) def analyze_table(): """Get basic statistics about a table in the workspace. Uses DuckDB for parquet (no full load).""" try: data = request.get_json() table_name = data.get('table_name') if not table_name: return jsonify({"status": "error", "message": "No table name provided"}), 400 workspace = _get_workspace() if _should_use_duckdb(workspace, table_name): schema_info = workspace.get_parquet_schema(table_name) col_infos = schema_info.get("columns", []) stats = [] for col_info in col_infos: col_name = col_info["name"] col_type = col_info.get("type", "") q = _quote_duckdb(col_name) if _is_numeric_duckdb_type(col_type): sql = ( f"SELECT COUNT(*) AS count, COUNT(DISTINCT t.{q}) AS unique_count, " f"COUNT(*) - COUNT(t.{q}) AS null_count, " f"MIN(t.{q}) AS min_val, MAX(t.{q}) AS max_val, AVG(t.{q}) AS avg_val " f"FROM {{parquet}} AS t" ) df = workspace.run_parquet_sql(table_name, sql) row = df.iloc[0] stats_dict = { "count": int(row["count"]), "unique_count": int(row["unique_count"]), "null_count": int(row["null_count"]), "min": float(row["min_val"]) if row["min_val"] is not None else None, "max": float(row["max_val"]) if row["max_val"] is not None else None, "avg": float(row["avg_val"]) if row["avg_val"] is not None else None, } else: sql = ( f"SELECT COUNT(*) AS count, COUNT(DISTINCT t.{q}) AS unique_count, " f"COUNT(*) - COUNT(t.{q}) AS null_count FROM {{parquet}} AS t" ) df = workspace.run_parquet_sql(table_name, sql) row = df.iloc[0] stats_dict = { "count": int(row["count"]), "unique_count": int(row["unique_count"]), "null_count": int(row["null_count"]), } stats.append({"column": col_name, "type": col_type, "statistics": stats_dict}) else: df = workspace.read_data_as_df(table_name) stats = [] for col_name in df.columns: s = df[col_name] col_type = str(s.dtype) stats_dict = { "count": int(s.count()), "unique_count": int(s.nunique()), "null_count": int(s.isna().sum()), } if pd.api.types.is_numeric_dtype(s): stats_dict["min"] = float(s.min()) if s.notna().any() else None stats_dict["max"] = float(s.max()) if s.notna().any() else None stats_dict["avg"] = float(s.mean()) if s.notna().any() else None stats.append({"column": col_name, "type": col_type, "statistics": stats_dict}) return jsonify({"status": "success", "table_name": table_name, "statistics": stats}) except Exception as e: logger.error(f"Error analyzing table: {str(e)}") safe_msg, status_code = sanitize_db_error_message(e) return jsonify({"status": "error", "message": safe_msg}), status_code def sanitize_table_name(table_name: str) -> str: """Sanitize a table name for use in the workspace.""" return parquet_sanitize_table_name(table_name) def sanitize_db_error_message(error: Exception) -> tuple[str, int]: """ Sanitize error messages before sending to client. Returns a tuple of (sanitized_message, status_code) """ # Convert error to string error_msg = str(error) # Define patterns for known safe errors safe_error_patterns = { # Database table errors r"Table.*does not exist": (error_msg, 404), r"Table.*already exists": (error_msg, 409), # Query errors r"syntax error": (error_msg, 400), r"Catalog Error": (error_msg, 404), r"Binder Error": (error_msg, 400), r"Invalid input syntax": (error_msg, 400), # File errors r"No such file": (error_msg, 404), r"Permission denied": ("Access denied", 403), # Data loader errors r"Entity ID": (error_msg, 500), r"identity": ("Identity not found, please refresh the page", 500), } # Check if error matches any safe pattern for pattern, (safe_msg, status_code) in safe_error_patterns.items(): if re.search(pattern, error_msg, re.IGNORECASE): return safe_msg, status_code # Log the full error for debugging logger.error(f"Unexpected error occurred: {error_msg}") # Return a generic error message for unknown errors return f"An unexpected error occurred: {error_msg}", 500 @tables_bp.route('/data-loader/list-data-loaders', methods=['GET']) def data_loader_list_data_loaders(): """List all available data loaders""" try: return jsonify({ "status": "success", "data_loaders": { name: { "params": data_loader.list_params(), "auth_instructions": data_loader.auth_instructions() } for name, data_loader in DATA_LOADERS.items() } }) except Exception as e: logger.error(f"Error listing data loaders: {str(e)}") safe_msg, status_code = sanitize_db_error_message(e) return jsonify({ "status": "error", "message": safe_msg }), status_code @tables_bp.route('/data-loader/list-tables', methods=['POST']) def data_loader_list_tables(): """List tables from a data loader (no workspace needed).""" try: data = request.get_json() data_loader_type = data.get('data_loader_type') data_loader_params = data.get('data_loader_params') table_filter = data.get('table_filter', None) if data_loader_type not in DATA_LOADERS: return jsonify({"status": "error", "message": f"Invalid data loader type. Must be one of: {', '.join(DATA_LOADERS.keys())}"}), 400 data_loader = DATA_LOADERS[data_loader_type](data_loader_params) if hasattr(data_loader, 'list_tables') and 'table_filter' in data_loader.list_tables.__code__.co_varnames: tables = data_loader.list_tables(table_filter=table_filter) else: tables = data_loader.list_tables() return jsonify({"status": "success", "tables": tables}) except Exception as e: logger.error(f"Error listing tables from data loader: {str(e)}") safe_msg, status_code = sanitize_db_error_message(e) return jsonify({"status": "error", "message": safe_msg}), status_code @tables_bp.route('/data-loader/ingest-data', methods=['POST']) def data_loader_ingest_data(): """Ingest data from a data loader into the workspace as parquet.""" try: data = request.get_json() data_loader_type = data.get('data_loader_type') data_loader_params = data.get('data_loader_params') table_name = data.get('table_name') import_options = data.get('import_options', {}) or {} row_limit = import_options.get('row_limit', 1000000) sort_columns = import_options.get('sort_columns') sort_order = import_options.get('sort_order', 'asc') if data_loader_type not in DATA_LOADERS: return jsonify({"status": "error", "message": f"Invalid data loader type. Must be one of: {', '.join(DATA_LOADERS.keys())}"}), 400 workspace = _get_workspace() data_loader = DATA_LOADERS[data_loader_type](data_loader_params) safe_name = parquet_sanitize_table_name(table_name.split('.')[-1] if '.' in table_name else table_name) meta = data_loader.ingest_to_workspace( workspace, safe_name, source_table=table_name, size=row_limit, sort_columns=sort_columns, sort_order=sort_order, ) return jsonify({ "status": "success", "message": "Successfully ingested data from data loader", "table_name": meta.name, }) except Exception as e: logger.error(f"Error ingesting data from data loader: {str(e)}") safe_msg, status_code = sanitize_db_error_message(e) return jsonify({"status": "error", "message": safe_msg}), status_code @tables_bp.route('/data-loader/view-query-sample', methods=['POST']) def data_loader_view_query_sample(): """View a sample of data from a query (fetches from external source, no workspace).""" try: data = request.get_json() data_loader_type = data.get('data_loader_type') data_loader_params = data.get('data_loader_params') query = data.get('query') if data_loader_type not in DATA_LOADERS: return jsonify({"status": "error", "message": f"Invalid data loader type. Must be one of: {', '.join(DATA_LOADERS.keys())}"}), 400 data_loader = DATA_LOADERS[data_loader_type](data_loader_params) if hasattr(data_loader, 'view_query_sample') and callable(getattr(data_loader, 'view_query_sample')): sample = data_loader.view_query_sample(query) else: return jsonify({ "status": "error", "message": "Query sample is only supported for loaders that implement view_query_sample. Use a source table to fetch data.", }), 400 return jsonify({"status": "success", "sample": sample, "message": "Successfully retrieved query sample"}) except Exception as e: logger.error(f"Error viewing query sample: {str(e)}") safe_msg, status_code = sanitize_db_error_message(e) return jsonify({"status": "error", "sample": [], "message": safe_msg}), status_code @tables_bp.route('/data-loader/fetch-data', methods=['POST']) def data_loader_fetch_data(): """Fetch data from an external data loader and return as JSON rows WITHOUT saving to workspace. This is used when storeOnServer=false (local-only / incognito mode). The data is returned directly to the frontend without being persisted as parquet. """ try: data = request.get_json() data_loader_type = data.get('data_loader_type') data_loader_params = data.get('data_loader_params') table_name = data.get('table_name') row_limit = data.get('row_limit', 10000) sort_columns = data.get('sort_columns') sort_order = data.get('sort_order', 'asc') if not data_loader_type or not table_name: return jsonify({"status": "error", "message": "data_loader_type and table_name are required"}), 400 if data_loader_type not in DATA_LOADERS: return jsonify({"status": "error", "message": f"Invalid data loader type. Must be one of: {', '.join(DATA_LOADERS.keys())}"}), 400 data_loader = DATA_LOADERS[data_loader_type](data_loader_params) # Fetch data as DataFrame (not Arrow, since we need JSON output not parquet) df = data_loader.fetch_data_as_dataframe( source_table=table_name, size=row_limit, sort_columns=sort_columns, sort_order=sort_order, ) total_row_count = len(df) # Apply row limit if len(df) > row_limit: df = df.head(row_limit) df = _dedup_dataframe_columns(df) rows = json.loads(df.to_json(orient='records', date_format='iso')) columns = [{"name": col, "type": str(df[col].dtype)} for col in df.columns] return jsonify({ "status": "success", "rows": rows, "columns": columns, "total_row_count": total_row_count, "row_limit_applied": row_limit, }) except Exception as e: logger.error(f"Error fetching data from data loader: {str(e)}") logger.error(traceback.format_exc()) safe_msg, status_code = sanitize_db_error_message(e) return jsonify({"status": "error", "message": safe_msg}), status_code @tables_bp.route('/data-loader/ingest-data-from-query', methods=['POST']) def data_loader_ingest_data_from_query(): """Ingest data from a query into the workspace as parquet.""" return jsonify({ "status": "error", "message": "Ingestion from custom query is not supported. Please select a source table to ingest.", }), 400 @tables_bp.route('/data-loader/refresh-table', methods=['POST']) def data_loader_refresh_table(): """Refresh a table by re-fetching from its source and updating parquet in the workspace.""" try: data = request.get_json() table_name = data.get('table_name') updated_params = data.get('data_loader_params', {}) if not table_name: return jsonify({"status": "error", "message": "table_name is required"}), 400 workspace = _get_workspace() meta = workspace.get_table_metadata(table_name) if meta is None: return jsonify({"status": "error", "message": f"No table '{table_name}' found. Cannot refresh."}), 400 if not meta.loader_type: return jsonify({"status": "error", "message": f"No source metadata for table '{table_name}'. Cannot refresh."}), 400 old_content_hash = meta.content_hash data_loader_type = meta.loader_type data_loader_params = {**(meta.loader_params or {}), **updated_params} if data_loader_type not in DATA_LOADERS: return jsonify({"status": "error", "message": f"Unknown data loader type: {data_loader_type}"}), 400 data_loader = DATA_LOADERS[data_loader_type](data_loader_params) if meta.source_table: arrow_table = data_loader.fetch_data_as_arrow(source_table=meta.source_table) else: return jsonify({ "status": "error", "message": "Refresh is not supported for tables ingested from a query. Only table-based sources can be refreshed.", }), 400 new_meta, data_changed = workspace.refresh_parquet_from_arrow(table_name, arrow_table) return jsonify({ "status": "success", "message": f"Successfully refreshed table '{table_name}'", "row_count": new_meta.row_count, "content_hash": new_meta.content_hash, "data_changed": data_changed, }) except Exception as e: logger.error(f"Error refreshing table: {str(e)}") logger.error(traceback.format_exc()) safe_msg, status_code = sanitize_db_error_message(e) return jsonify({"status": "error", "message": safe_msg}), status_code @tables_bp.route('/data-loader/get-table-metadata', methods=['POST']) def data_loader_get_table_metadata(): """Get source metadata for a specific table from workspace.""" try: data = request.get_json() table_name = data.get('table_name') if not table_name: return jsonify({"status": "error", "message": "table_name is required"}), 400 workspace = _get_workspace() meta = workspace.get_table_metadata(table_name) metadata = _table_metadata_to_source_metadata(meta) if meta else None return jsonify({ "status": "success", "metadata": metadata, "message": f"No metadata found for table '{table_name}'" if metadata is None else None, }) except Exception as e: logger.error(f"Error getting table metadata: {str(e)}") safe_msg, status_code = sanitize_db_error_message(e) return jsonify({"status": "error", "message": safe_msg}), status_code @tables_bp.route('/data-loader/list-table-metadata', methods=['GET']) def data_loader_list_table_metadata(): """Get source metadata for all tables in the workspace.""" try: workspace = _get_workspace() metadata_list = [] for name in workspace.list_tables(): meta = workspace.get_table_metadata(name) m = _table_metadata_to_source_metadata(meta) if meta else None if m: metadata_list.append(m) return jsonify({"status": "success", "metadata": metadata_list}) except Exception as e: logger.error(f"Error listing table metadata: {str(e)}") safe_msg, status_code = sanitize_db_error_message(e) return jsonify({"status": "error", "message": safe_msg}), status_code ================================================ FILE: py-src/data_formulator/workflows/__init__.py ================================================ ================================================ FILE: py-src/data_formulator/workflows/chart_semantics.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ ============================================================================= CHART SEMANTICS — Lightweight type resolution for VL spec assembly ============================================================================= Provides semantic-aware type resolution for create_vl_plots.py: - Type registry (maps semantic types → VL encoding types) - VL type resolution (nominal / ordinal / temporal / quantitative) - Ordinal sort order (months, days, quarters) This is intentionally minimal. The TS agents-chart library is the canonical source of truth for formatting, color schemes, tick constraints, domain constraints, and other visual refinements. The Python side focuses on getting the structural type decisions right (which directly affect chart shape), and leaves cosmetic details to defaults or the front-end. ============================================================================= """ from __future__ import annotations import re import math from dataclasses import dataclass from datetime import datetime, date from typing import Any, Dict, List, Optional, Tuple # --------------------------------------------------------------------------- # §1 Type Registry (mirrors type-registry.ts) # --------------------------------------------------------------------------- @dataclass(frozen=True) class TypeRegistryEntry: t0: str # Top-level family t1: str # Mid-level category vis_encodings: tuple # Primary VL types, e.g. ('quantitative',) agg_role: str # additive | intensive | signed-additive | dimension | identifier domain_shape: str # open | bounded | fixed | cyclic _UNKNOWN = TypeRegistryEntry('Categorical', 'Entity', ('nominal',), 'dimension', 'open') TYPE_REGISTRY: Dict[str, TypeRegistryEntry] = { # --- Temporal: DateTime --- 'DateTime': TypeRegistryEntry('Temporal', 'DateTime', ('temporal',), 'dimension', 'open'), 'Date': TypeRegistryEntry('Temporal', 'DateTime', ('temporal',), 'dimension', 'open'), 'Time': TypeRegistryEntry('Temporal', 'DateTime', ('temporal',), 'dimension', 'open'), 'Timestamp': TypeRegistryEntry('Temporal', 'DateTime', ('temporal',), 'dimension', 'open'), # --- Temporal: DateGranule --- 'Year': TypeRegistryEntry('Temporal', 'DateGranule', ('temporal', 'ordinal'), 'dimension', 'open'), 'Quarter': TypeRegistryEntry('Temporal', 'DateGranule', ('ordinal',), 'dimension', 'cyclic'), 'Month': TypeRegistryEntry('Temporal', 'DateGranule', ('ordinal',), 'dimension', 'cyclic'), 'Week': TypeRegistryEntry('Temporal', 'DateGranule', ('ordinal',), 'dimension', 'cyclic'), 'Day': TypeRegistryEntry('Temporal', 'DateGranule', ('ordinal',), 'dimension', 'cyclic'), 'Hour': TypeRegistryEntry('Temporal', 'DateGranule', ('ordinal',), 'dimension', 'cyclic'), 'YearMonth': TypeRegistryEntry('Temporal', 'DateGranule', ('temporal', 'ordinal'), 'dimension', 'open'), 'YearQuarter': TypeRegistryEntry('Temporal', 'DateGranule', ('temporal', 'ordinal'), 'dimension', 'open'), 'YearWeek': TypeRegistryEntry('Temporal', 'DateGranule', ('temporal', 'ordinal'), 'dimension', 'open'), 'Decade': TypeRegistryEntry('Temporal', 'DateGranule', ('temporal', 'ordinal'), 'dimension', 'open'), # --- Temporal: Duration --- 'Duration': TypeRegistryEntry('Temporal', 'Duration', ('quantitative',), 'additive', 'open'), # --- Measure: Amount --- 'Amount': TypeRegistryEntry('Measure', 'Amount', ('quantitative',), 'additive', 'open'), 'Price': TypeRegistryEntry('Measure', 'Amount', ('quantitative',), 'intensive', 'open'), 'Revenue': TypeRegistryEntry('Measure', 'Amount', ('quantitative',), 'additive', 'open'), 'Cost': TypeRegistryEntry('Measure', 'Amount', ('quantitative',), 'additive', 'open'), 'Currency': TypeRegistryEntry('Measure', 'Amount', ('quantitative',), 'additive', 'open'), 'GDP': TypeRegistryEntry('Measure', 'Amount', ('quantitative',), 'additive', 'open'), # --- Measure: Physical --- 'Quantity': TypeRegistryEntry('Measure', 'Physical', ('quantitative',), 'additive', 'open'), 'Temperature': TypeRegistryEntry('Measure', 'Physical', ('quantitative',), 'intensive', 'open'), # --- Measure: Proportion --- 'Percentage': TypeRegistryEntry('Measure', 'Proportion', ('quantitative',), 'intensive', 'bounded'), # --- Measure: SignedMeasure --- 'Profit': TypeRegistryEntry('Measure', 'SignedMeasure', ('quantitative',), 'signed-additive', 'open'), 'PercentageChange': TypeRegistryEntry('Measure', 'SignedMeasure', ('quantitative',), 'intensive', 'open'), 'Sentiment': TypeRegistryEntry('Measure', 'SignedMeasure', ('quantitative',), 'intensive', 'open'), 'Correlation': TypeRegistryEntry('Measure', 'SignedMeasure', ('quantitative',), 'intensive', 'bounded'), # --- Measure: GenericMeasure --- 'Count': TypeRegistryEntry('Measure', 'GenericMeasure', ('quantitative',), 'additive', 'open'), 'Number': TypeRegistryEntry('Measure', 'GenericMeasure', ('quantitative',), 'additive', 'open'), # --- Discrete --- 'Rank': TypeRegistryEntry('Discrete', 'Rank', ('ordinal',), 'dimension', 'open'), 'Score': TypeRegistryEntry('Discrete', 'Score', ('quantitative', 'ordinal'), 'intensive', 'bounded'), 'Rating': TypeRegistryEntry('Discrete', 'Score', ('quantitative', 'ordinal'), 'intensive', 'bounded'), 'Index': TypeRegistryEntry('Discrete', 'Index', ('ordinal',), 'dimension', 'open'), 'ID': TypeRegistryEntry('Identifier', 'ID', ('nominal',), 'identifier', 'open'), # --- Geographic --- 'Country': TypeRegistryEntry('Categorical', 'GeoPlace', ('nominal',), 'dimension', 'open'), 'State': TypeRegistryEntry('Categorical', 'GeoPlace', ('nominal',), 'dimension', 'open'), 'City': TypeRegistryEntry('Categorical', 'GeoPlace', ('nominal',), 'dimension', 'open'), 'Region': TypeRegistryEntry('Categorical', 'GeoPlace', ('nominal',), 'dimension', 'open'), 'Latitude': TypeRegistryEntry('Measure', 'GeoCoord', ('geographic', 'quantitative'), 'dimension', 'fixed'), 'Longitude': TypeRegistryEntry('Measure', 'GeoCoord', ('geographic', 'quantitative'), 'dimension', 'fixed'), # --- Categorical --- 'Category': TypeRegistryEntry('Categorical', 'Entity', ('nominal',), 'dimension', 'open'), 'String': TypeRegistryEntry('Categorical', 'Entity', ('nominal',), 'dimension', 'open'), 'Boolean': TypeRegistryEntry('Categorical', 'Status', ('nominal',), 'dimension', 'open'), 'Status': TypeRegistryEntry('Categorical', 'Status', ('nominal',), 'dimension', 'open'), 'Gender': TypeRegistryEntry('Categorical', 'Demographic', ('nominal',), 'dimension', 'open'), 'AgeGroup': TypeRegistryEntry('Categorical', 'Range', ('ordinal',), 'dimension', 'open'), 'Range': TypeRegistryEntry('Categorical', 'Range', ('ordinal',), 'dimension', 'open'), 'Direction': TypeRegistryEntry('Categorical', 'Entity', ('nominal',), 'dimension', 'cyclic'), # --- Identifiers --- 'Name': TypeRegistryEntry('Identifier', 'Name', ('nominal',), 'identifier', 'open'), 'Label': TypeRegistryEntry('Identifier', 'Name', ('nominal',), 'identifier', 'open'), 'Code': TypeRegistryEntry('Identifier', 'Code', ('nominal',), 'identifier', 'open'), 'Population': TypeRegistryEntry('Measure', 'Amount', ('quantitative',), 'additive', 'open'), } def get_registry_entry(semantic_type: str) -> TypeRegistryEntry: """Look up a type in the registry. Falls back to UNKNOWN.""" return TYPE_REGISTRY.get(semantic_type, _UNKNOWN) def is_registered(semantic_type: str) -> bool: return semantic_type in TYPE_REGISTRY # --------------------------------------------------------------------------- # §2 Channel Semantics (minimal) # --------------------------------------------------------------------------- @dataclass class ChannelSemantics: """Resolved semantic decisions for a single channel.""" field: str = '' semantic_type: str = '' vl_type: str = 'nominal' # quantitative | nominal | ordinal | temporal ordinal_sort_order: list[str] | None = None # --------------------------------------------------------------------------- # §3 VL Type Resolution # --------------------------------------------------------------------------- def resolve_vl_type(semantic_type: str, values: List[Any]) -> str: """ Determine the best VL encoding type for a field. Uses semantic type first, then disambiguates with data. """ if not semantic_type or not is_registered(semantic_type): return _infer_vl_type_from_data(values) entry = get_registry_entry(semantic_type) candidates = entry.vis_encodings if len(candidates) == 1: return candidates[0] if candidates[0] != 'geographic' else 'quantitative' # Disambiguate quantitative vs ordinal if 'quantitative' in candidates and 'ordinal' in candidates: distinct = len(set(v for v in values if v is not None)) # Guard: if values contain non-integer floats, they are continuous # and should be quantitative regardless of cardinality. # E.g. Rating values like 1.2, 3.7, 4.1 are clearly continuous. nums = [v for v in values if isinstance(v, (int, float))] if nums: has_fractions = any(v % 1 != 0 for v in nums) if has_fractions: return 'quantitative' return 'ordinal' if distinct <= 12 else 'quantitative' # Disambiguate temporal vs ordinal if 'temporal' in candidates and 'ordinal' in candidates: non_null = [v for v in values if v is not None] if non_null and all(isinstance(v, (int, float)) for v in non_null): # Pure numeric — check if they look like 4-digit years. # When semantic type is Year/Decade and values are 4-digit # integers in a plausible year range (1000–2999), use temporal # for larger sets so VL gets a continuous time axis. # convert_temporal_data will convert int → str("1980") before # the data reaches VL, avoiding the unix-ms misinterpretation. distinct = len(set(non_null)) if _looks_like_year_integers(non_null): return 'ordinal' if distinct <= 6 else 'temporal' # Not year-like integers → ordinal (safe default) return 'ordinal' if distinct <= 20 else 'quantitative' # Date strings or datetime objects → temporal distinct = len(set(v for v in values if v is not None)) return 'ordinal' if distinct <= 6 else 'temporal' # geographic + quantitative if 'geographic' in candidates and 'quantitative' in candidates: return 'quantitative' return candidates[0] def _looks_like_year_integers(values: List[Any]) -> bool: """Check if a list of numeric values look like 4-digit year integers. Returns True when >=80% of non-null values are integers in the plausible year range 1000-2999. This is used to decide whether integer Year/Decade data should get a temporal axis (with string conversion) rather than being treated as plain quantitative numbers. """ nums = [v for v in values if isinstance(v, (int, float)) and not isinstance(v, bool)] if not nums: return False year_count = sum( 1 for v in nums if v == int(v) and 1000 <= int(v) <= 2999 ) return year_count >= len(nums) * 0.8 def _infer_vl_type_from_data(values: List[Any]) -> str: """Infer VL type purely from data values.""" non_null = [v for v in values if v is not None] if not non_null: return 'nominal' if all(isinstance(v, bool) for v in non_null): return 'nominal' # Check for native datetime/date objects first if all(isinstance(v, (datetime, date)) for v in non_null): return 'temporal' # Check for pandas Timestamp objects try: import pandas as pd if all(isinstance(v, pd.Timestamp) for v in non_null): return 'temporal' except ImportError: pass if all(isinstance(v, (int, float)) for v in non_null): # Pure numeric — check for likely timestamps if all(_is_likely_timestamp(v) for v in non_null[:20]): return 'temporal' return 'quantitative' # String values — check if they look like dates str_vals = [v for v in non_null[:30] if isinstance(v, str)] if str_vals and len(str_vals) >= len(non_null[:30]) * 0.8: date_count = sum(1 for s in str_vals if _looks_like_date(s)) if date_count >= len(str_vals) * 0.7: return 'temporal' return 'nominal' # --------------------------------------------------------------------------- # Timestamp detection (mirrors TS isLikelyTimestamp) # --------------------------------------------------------------------------- _MAX_TIMESTAMP_SEC = 4102444800 # ~2099-12-31 in epoch seconds _MAX_TIMESTAMP_MS = 4102444800000 # ~2099-12-31 in epoch milliseconds def _is_likely_timestamp(val: Any) -> bool: """Check if a numeric value is likely a unix timestamp (s or ms).""" if not isinstance(val, (int, float)): return False if isinstance(val, bool): return False if math.isnan(val) or math.isinf(val): return False if val >= 1e9 and val <= _MAX_TIMESTAMP_SEC: return True if val > _MAX_TIMESTAMP_SEC and val <= _MAX_TIMESTAMP_MS: return True return False def _timestamp_to_ms(val: float) -> float: """Convert seconds-epoch to ms-epoch if needed.""" return val * 1000 if val <= _MAX_TIMESTAMP_SEC else val # --------------------------------------------------------------------------- # Date string detection (mirrors TS looksLikeDateString, much more robust) # --------------------------------------------------------------------------- # Pre-compiled patterns for efficiency _DATE_PATTERNS = [ # ISO 8601: 2020-01-15, 2020-01-15T12:00:00Z re.compile(r'^\d{4}-\d{2}-\d{2}'), # Slash-separated: 2020/01/15 or 01/15/2020 or 15/01/2020 re.compile(r'^\d{1,4}[/]\d{1,2}[/]\d{1,4}$'), # Month name variants: Jan 2020, January 2020, Jan-2020, 2020-Jan re.compile(r'^(jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)\w*[\s,.-]+\d', re.I), re.compile(r'^\d{1,2}[\s,.-]+(jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)', re.I), re.compile(r'^\d{4}[\s,.-]+(jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)', re.I), # Quarter formats: Q1 2020, 2020-Q1, 2020 Q1 re.compile(r'^Q[1-4][\s,-]+\d{4}$', re.I), re.compile(r'^\d{4}[\s,-]*Q[1-4]$', re.I), # Dot-separated: 15.01.2020 or 2020.01.15 re.compile(r'^\d{1,2}\.\d{1,2}\.\d{2,4}$'), re.compile(r'^\d{4}\.\d{1,2}\.\d{1,2}$'), # Dash-separated with short month: 15-Jan-2020, 2020-01-15 re.compile(r'^\d{1,2}-\w+-\d{2,4}$'), # Year-month: 2020-01, 2020/01 re.compile(r'^\d{4}[-/]\d{1,2}$'), # Month-year: Jan-2020 re.compile(r'^(jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)\w*[-/]\d{4}$', re.I), # Compact: 20200115 re.compile(r'^\d{8}$'), ] def _looks_like_date(s: str) -> bool: """Check if a string looks like a date/datetime value. This is a lightweight heuristic used for type inference. Does NOT match bare 4-digit years ("2020") — those should be handled via semantic type (Year/Decade) to avoid false positives with generic integer IDs. """ t = s.strip() if not t: return False return any(p.search(t) for p in _DATE_PATTERNS) # --------------------------------------------------------------------------- # Robust date parsing (try multiple strategies) # --------------------------------------------------------------------------- def _try_parse_date(val: Any) -> Optional[datetime]: """Try to parse a value as a datetime. Returns None on failure.""" if isinstance(val, datetime): return val if isinstance(val, date) and not isinstance(val, datetime): return datetime(val.year, val.month, val.day) try: import pandas as pd if isinstance(val, pd.Timestamp): return val.to_pydatetime() except ImportError: pass if isinstance(val, (int, float)): if _is_likely_timestamp(val): ms = _timestamp_to_ms(val) return datetime.utcfromtimestamp(ms / 1000) return None if isinstance(val, str): t = val.strip() if not t: return None # Fast-path ISO for fmt in ( '%Y-%m-%d', '%Y-%m-%dT%H:%M:%S', '%Y-%m-%dT%H:%M:%SZ', '%Y-%m-%dT%H:%M:%S.%f', '%Y-%m-%dT%H:%M:%S.%fZ', '%m/%d/%Y', '%d/%m/%Y', '%Y/%m/%d', '%b %d, %Y', '%B %d, %Y', '%d %b %Y', '%d %B %Y', '%b %Y', '%B %Y', '%Y-%m', '%Y/%m', ): try: return datetime.strptime(t, fmt) except ValueError: continue # Fallback: pandas parser (very flexible) try: import pandas as pd return pd.to_datetime(t).to_pydatetime() except Exception: pass return None # --------------------------------------------------------------------------- # §4 Ordinal Sort Order (months, days, quarters) # --------------------------------------------------------------------------- _MONTH_FULL = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'] _MONTH_ABBR = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'] _MONTH_NUM = [str(i) for i in range(1, 13)] _DOW_FULL = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'] _DOW_ABBR = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'] _DOW_FULL_SUN = ['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'] _DOW_ABBR_SUN = ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat'] _QUARTER = ['Q1', 'Q2', 'Q3', 'Q4'] _COMPASS_8 = ['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW'] _COMPASS_4 = ['N', 'E', 'S', 'W'] _ORDINAL_SEQUENCES: Dict[str, List[Tuple[List[str], bool]]] = { 'Month': [(_MONTH_FULL, True), (_MONTH_ABBR, True), (_MONTH_NUM, False)], 'Day': [(_DOW_FULL, True), (_DOW_ABBR, True), (_DOW_FULL_SUN, True), (_DOW_ABBR_SUN, True)], 'Quarter': [(_QUARTER, True)], 'Direction': [(_COMPASS_8, True), (_COMPASS_4, True)], } def infer_ordinal_sort_order(semantic_type: str, values: List[Any]) -> list[str] | None: """ Detect canonical ordinal sort order for months, days, quarters, etc. Returns sorted unique values in canonical order, or None. """ # Try by explicit semantic type sequences = _ORDINAL_SEQUENCES.get(semantic_type) if sequences: result = _match_sequence(values, sequences) if result: return result # Auto-detect for generic types if not semantic_type or semantic_type in ('Category', 'String', 'Unknown'): for seqs in _ORDINAL_SEQUENCES.values(): result = _match_sequence(values, seqs) if result: return result return None def _match_sequence( values: List[Any], sequences: List[Tuple[List[str], bool]], ) -> list[str] | None: unique_vals = list(dict.fromkeys(str(v) for v in values if v is not None)) if not unique_vals: return None for labels, case_insensitive in sequences: lookup = {(l.lower() if case_insensitive else l): i for i, l in enumerate(labels)} matched = [] unmatched = [] for val in unique_vals: key = val.lower() if case_insensitive else val idx = lookup.get(key) if idx is not None: matched.append((val, idx)) else: unmatched.append(val) if len(matched) >= len(unique_vals) * 0.6 and len(matched) >= 2: matched.sort(key=lambda x: x[1]) return [m[0] for m in matched] + unmatched return None # --------------------------------------------------------------------------- # §5 Temporal Data Conversion (mirrors TS convertTemporalData) # --------------------------------------------------------------------------- def _expand_to_full_year(val: str) -> str: """Expand 2-digit year to 4-digit: '98' → '1998', '07' → '2007'.""" t = val.strip() if re.fullmatch(r'\d{2}', t): n = int(t) return str(2000 + n if n <= 49 else 1900 + n) return val def convert_temporal_data( data: List[Dict[str, Any]], semantic_types: Dict[str, Any], all_values: Dict[str, List[Any]] | None = None, ) -> List[Dict[str, Any]]: """ Convert temporal field values to canonical string representations so that Vega-Lite can parse them correctly. Mirrors the TS ``convertTemporalData`` function. This handles: - Year/Decade integers → string ("2015" not 2015, avoids VL unix-ms interpretation) - Unix timestamps → ISO datetime strings - datetime/date objects → ISO strings - pd.Timestamp objects → ISO strings - 2-digit year strings → 4-digit - Any other temporal values → str() Parameters: - data: list of row dicts (will be cloned) - semantic_types: field_name → semantic type string or annotation dict - all_values: optional precomputed {field: [values]} for type inference Returns: new list of row dicts with temporal fields converted to strings. """ if not data: return data import copy keys = list(data[0].keys()) temporal_keys = [] for k in keys: sem_type = _extract_sem_type(semantic_types.get(k)) # Check semantic type if sem_type and is_registered(sem_type): entry = get_registry_entry(sem_type) if 'temporal' in entry.vis_encodings: temporal_keys.append(k) continue # Check data values vals = all_values[k] if all_values and k in all_values else [r.get(k) for r in data[:50]] inferred = _infer_vl_type_from_data(vals) if inferred == 'temporal': temporal_keys.append(k) if not temporal_keys: return data result = copy.deepcopy(data) for row in result: for k in temporal_keys: val = row.get(k) if val is None: continue sem_type = _extract_sem_type(semantic_types.get(k)) if isinstance(val, (int, float)) and not isinstance(val, bool): if sem_type in ('Year', 'Decade'): # Year/Decade: always convert int to string representation row[k] = str(int(val)) elif _is_likely_timestamp(val): ms = _timestamp_to_ms(val) row[k] = datetime.utcfromtimestamp(ms / 1000).isoformat() + 'Z' elif _looks_like_year_integers([val]): # 4-digit year-like integer without explicit Year semantic type # → convert to string so VL doesn't treat it as unix-ms row[k] = str(int(val)) else: row[k] = str(val) elif isinstance(val, datetime): row[k] = val.isoformat() elif isinstance(val, date) and not isinstance(val, datetime): row[k] = val.isoformat() else: try: import pandas as pd if isinstance(val, pd.Timestamp): row[k] = val.isoformat() continue except ImportError: pass # String handling if isinstance(val, str): if sem_type in ('Year', 'Decade'): row[k] = _expand_to_full_year(val) else: row[k] = str(val) else: row[k] = str(val) return result def _extract_sem_type(annotation: Any) -> str: """Extract the semantic type string from an annotation (str or dict).""" if isinstance(annotation, str): return annotation if isinstance(annotation, dict): return annotation.get('type', annotation.get('semantic_type', '')) return '' # --------------------------------------------------------------------------- # §6 Full Channel Resolution # --------------------------------------------------------------------------- def resolve_channel_semantics( field_name: str, semantic_type: str, channel: str, mark_type: str, values: List[Any], unit: str | None = None, intrinsic_domain: tuple[float, float] | None = None, ) -> ChannelSemantics: """ Resolve semantic decisions for one (field, channel) pair. This is the main entry point used by create_vl_plots.py. Focuses on the critical structural decision: VL type + ordinal sort. Formatting, domains, ticks, zero-baseline, color schemes, etc. are left to VL defaults (or the front-end TS library). """ vl_type = resolve_vl_type(semantic_type, values) cs = ChannelSemantics( field=field_name, semantic_type=semantic_type, vl_type=vl_type, ) # Ordinal sort order (months, days, quarters — affects axis label order) if vl_type in ('ordinal', 'nominal'): cs.ordinal_sort_order = infer_ordinal_sort_order(semantic_type, values) return cs ================================================ FILE: py-src/data_formulator/workflows/create_vl_plots.py ================================================ import pandas as pd import numpy as np from typing import Any import vl_convert as vlc import base64 import logging from data_formulator.agents.semantic_types import infer_vl_type_from_name from data_formulator.workflows.chart_semantics import ( resolve_channel_semantics, resolve_vl_type, is_registered, ChannelSemantics, convert_temporal_data, _looks_like_date, _looks_like_year_integers, ) logger = logging.getLogger(__name__) def field_metadata_to_semantic_types( field_metadata: dict[str, Any] | None, ) -> dict[str, Any] | None: """Convert agent ``field_metadata`` to the ``semantic_types`` dict expected by :func:`assemble_vegailte_chart`. ``field_metadata`` comes from the LLM's ``refined_goal.field_metadata`` and has the shape:: { "Revenue": {"semantic_type": "Revenue", "unit": "USD"}, "Month": {"semantic_type": "Month"}, } The returned dict maps field names to either a plain string (the semantic type name) or a dict with ``type``, ``unit``, and ``intrinsic_domain`` keys — exactly what ``assemble_vegailte_chart`` already accepts. """ if not field_metadata: return None result: dict[str, Any] = {} for field_name, meta in field_metadata.items(): if isinstance(meta, str): result[field_name] = meta continue if not isinstance(meta, dict): continue sem_type = meta.get("semantic_type") if not sem_type: continue extras = {} if "unit" in meta and meta["unit"]: extras["unit"] = meta["unit"] if "intrinsic_domain" in meta and meta["intrinsic_domain"]: extras["intrinsic_domain"] = meta["intrinsic_domain"] if extras: result[field_name] = {"type": sem_type, **extras} else: result[field_name] = sem_type return result if result else None def resolve_field_type( series: pd.Series, field_name: str | None = None, ) -> str: """ Resolve the Vega-Lite type for a field. Priority: 1. Column-name heuristic (catches derived columns like avg_revenue, year, etc.) 2. Pandas dtype detection (fallback) Parameters: - series: the pandas Series for the field - field_name: column name (used for name heuristics) Returns one of: 'quantitative', 'nominal', 'ordinal', 'temporal' """ # 1. Try column-name heuristic (useful for derived columns) if field_name: inferred = infer_vl_type_from_name(field_name) if inferred: return inferred # 2. Fall back to pandas-based detection return detect_field_type(series) def detect_field_type(series: pd.Series) -> str: """ Detect the appropriate Vega-Lite field type for a pandas Series. Returns one of: 'quantitative', 'nominal', 'ordinal', 'temporal' """ if pd.api.types.is_datetime64_any_dtype(series): return 'temporal' if pd.api.types.is_bool_dtype(series): return 'nominal' if pd.api.types.is_numeric_dtype(series): # Check if values look like 4-digit years (1000-2999). # Year integers should be ordinal (discrete labels), not quantitative # (which causes VL to format them with SI prefixes like ".024"). non_null_vals = series.dropna().tolist() if non_null_vals and _looks_like_year_integers(non_null_vals): return 'ordinal' # Check if it looks like a discrete categorical variable unique_count = series.nunique() total_count = len(series) if unique_count <= 20 and unique_count / total_count < 0.5: return 'ordinal' return 'quantitative' # String or object type — check for date strings non_null = series.dropna().head(30).tolist() if non_null: str_vals = [v for v in non_null if isinstance(v, str)] if str_vals and len(str_vals) >= len(non_null) * 0.8: date_count = sum(1 for s in str_vals if _looks_like_date(s)) if date_count >= len(str_vals) * 0.7: return 'temporal' unique_count = series.nunique() if unique_count <= 50: return 'nominal' return 'nominal' # Chart Templates CHART_TEMPLATES = [ { "chart": "Scatter Plot", "mark": "circle", "channels": ["x", "y", "color", "opacity", "size", "shape", "column", "row"] }, { "chart": "Line Chart", "mark": "line", "channels": ["x", "y", "color", "strokeDash", "opacity", "detail", "column", "row"] }, { "chart": "Dotted Line Chart", "mark": {"type": "line", "point": True}, "channels": ["x", "y", "color", "detail", "column", "row"] }, { "chart": "Bar Chart", "mark": "bar", "channels": ["x", "y", "color", "opacity", "column", "row"] }, { "chart": "Grouped Bar Chart", "mark": "bar", "channels": ["x", "y", "color", "opacity", "column", "row"] }, { "chart": "Stacked Bar Chart", "mark": "bar", "channels": ["x", "y", "color", "column", "row"] }, { "chart": "Heatmap", "mark": "rect", "channels": ["x", "y", "color", "column", "row"] }, { "chart": "Area Chart", "mark": "area", "channels": ["x", "y", "color", "column", "row"] }, { "chart": "Streamgraph", "mark": "area", "channels": ["x", "y", "color", "column", "row"] }, { "chart": "Boxplot", "mark": "boxplot", "channels": ["x", "y", "color", "opacity", "column", "row"] }, { "chart": "Histogram", "mark": "bar", "channels": ["x", "color", "column", "row"] }, { "chart": "Pie Chart", "mark": "arc", "channels": ["theta", "color", "column", "row"] }, { "chart": "Lollipop Chart", "mark": "rule", # layered: rule + circle "channels": ["x", "y", "color", "column", "row"] }, { "chart": "Strip Plot", "mark": {"type": "circle", "opacity": 0.7}, "channels": ["x", "y", "color", "size", "column", "row"] }, { "chart": "Density Plot", "mark": "area", "channels": ["x", "color", "column", "row"] }, { "chart": "Bump Chart", "mark": {"type": "line", "point": True, "interpolate": "monotone", "strokeWidth": 2}, "channels": ["x", "y", "color", "detail", "column", "row"] }, { "chart": "Regression", "mark": "circle", # layered: circle + regression line "channels": ["x", "y", "size", "color", "column", "row"] }, { "chart": "Ranged Dot Plot", "mark": "point", # layered: line + point "channels": ["x", "y", "color"] }, { "chart": "Candlestick Chart", "mark": "rule", # layered: rule + bar "channels": ["x", "open", "high", "low", "close", "column", "row"] }, { "chart": "Waterfall Chart", "mark": "bar", "channels": ["x", "y", "color", "column", "row"] }, { "chart": "Radar Chart", "mark": "line", # computed polar projection "channels": ["x", "y", "color", "column", "row"] }, { "chart": "Rose Chart", "mark": {"type": "arc", "stroke": "white", "padAngle": 0.02}, "channels": ["x", "y", "color", "column", "row"] }, { "chart": "Pyramid Chart", "mark": "bar", # hconcat of 2 panels "channels": ["x", "y", "color"] }, { "chart": "World Map", "mark": "circle", "channels": ["longitude", "latitude", "color", "size", "opacity"] }, { "chart": "US Map", "mark": "circle", "channels": ["longitude", "latitude", "color", "size"] } ] # Chart-type-aware expected types per (chart_type, channel). # When detect_field_type disagrees with what the chart semantics require, # the value here wins. None means "keep detected". _CHANNEL_TYPE_OVERRIDES: dict[str, dict[str, str]] = { "Histogram": {"x": "quantitative"}, "Heatmap": {"x": "nominal", "y": "nominal"}, "Boxplot": {"x": "nominal", "y": "quantitative"}, "Pie Chart": {"theta": "quantitative", "color": "nominal"}, "World Map": {"longitude": "quantitative", "latitude": "quantitative"}, "US Map": {"longitude": "quantitative", "latitude": "quantitative"}, "Candlestick Chart": {"x": "ordinal"}, "Density Plot": {"x": "quantitative"}, "Waterfall Chart": {"x": "ordinal", "y": "quantitative"}, "Pyramid Chart": {"y": "nominal", "x": "quantitative"}, "Strip Plot": {}, # detected dynamically "Rose Chart": {"x": "nominal", "y": "quantitative"}, } # Chart types where temporal fields on position channels should be ordinal # (discrete bars/cells rather than a continuous time axis). # For these charts, coerce_field_type downgrades "temporal" → "ordinal" on x/y. _BAR_LIKE_CHARTS = {"Bar Chart", "Grouped Bar Chart", "Stacked Bar Chart", "Lollipop Chart", "Waterfall Chart"} def coerce_field_type(chart_type: str, channel: str, detected_type: str) -> str: """ Return the Vega-Lite type that should actually be used for this (chart_type, channel) combination. If no override is needed the originally detected type is returned unchanged. """ overrides = _CHANNEL_TYPE_OVERRIDES.get(chart_type, {}) forced = overrides.get(channel) if forced: return forced # Bar-like charts: temporal on x/y should become ordinal (discrete bars) if chart_type in _BAR_LIKE_CHARTS and channel in ("x", "y") and detected_type == "temporal": return "ordinal" return detected_type def get_chart_template(chart_type: str) -> dict | None: """Find a chart template by its full name (e.g. "Scatter Plot").""" for template in CHART_TEMPLATES: if template["chart"] == chart_type: return template return None def create_chart_spec(df: pd.DataFrame, fields: list[str], chart_type: str) -> dict[str, dict[str, str]]: """ Assign fields to appropriate visualization channels based on their data types and chart type. """ encodings = fields_to_encodings(df, chart_type, fields) return assemble_vegailte_chart(df, chart_type, encodings) def fields_to_encodings(df, chart_type: str, fields: list[str]) -> dict[str, dict[str, str]]: """ Assign fields to appropriate visualization channels based on their data types and chart type. Parameters: - df: pandas DataFrame containing the data - chart_type: string matching one of the chart types in CHART_TEMPLATES (e.g. "Scatter Plot") - fields: list of column names to assign to channels Returns: - dict: mapping of channel names to encoding objects with "field" and "type" properties ("nominal", "quantitative", "temporal") """ if not fields: return {} # Get available channels for this chart type template = get_chart_template(chart_type) if not template: return {} available_channels = template["channels"] # Analyze field types and properties field_info = [] for field in fields: if field not in df.columns: continue field_type = detect_field_type(df[field]) cardinality = df[field].nunique() field_info.append({ "field": field, "type": field_type, "cardinality": cardinality, "is_low_cardinality": cardinality <= 20, "is_very_low_cardinality": cardinality <= 10 }) if not field_info: return {} # Sort fields by priority for assignment def field_priority(field_info_item): # Prioritize temporal fields for time-based charts if chart_type in ["Line Chart", "Area Chart"] and field_info_item["type"] == "temporal": return 0 # Prioritize quantitative fields elif field_info_item["type"] == "quantitative": return 1 # Then categorical with reasonable cardinality elif field_info_item["is_low_cardinality"]: return 2 # Finally high cardinality categoricals else: return 3 field_info.sort(key=field_priority) encodings = {} used_fields = set() def get_field_info_by_name(field_name: str): """Helper to get field info by field name""" return next((f for f in field_info if f["field"] == field_name), None) def add_encoding(channel: str, field_name: str): """Add encoding with type conversion if necessary""" field_info_item = get_field_info_by_name(field_name) if not field_info_item: return original_type = field_info_item["type"] encoding = {"field": field_name} # Determine if type conversion is needed needs_conversion = False target_type = original_type # Heatmap axes should be categorical if chart_type == "Heatmap" and channel in ["x", "y"]: if original_type in ["quantitative", "temporal"]: needs_conversion = True target_type = "nominal" # Bar chart x-axis should be categorical for grouping elif chart_type == "Bar Chart" and channel == "x": if original_type == "quantitative": needs_conversion = True target_type = "nominal" # Color channel in non-heatmap charts should be categorical for discrete colors elif channel == "color" and chart_type != "Heatmap": if original_type == "quantitative" and field_info_item["is_low_cardinality"]: needs_conversion = True target_type = "nominal" # Faceting channels should be categorical elif channel in ["column", "row"]: if original_type in ["quantitative", "temporal"]: needs_conversion = True target_type = "nominal" # Shape channel should always be categorical elif channel == "shape": if original_type in ["quantitative", "temporal"]: needs_conversion = True target_type = "nominal" # Add type to encoding only if conversion is necessary if needs_conversion: encoding["type"] = target_type encodings[channel] = encoding used_fields.add(field_name) # Define channel assignment rules by chart type def assign_primary_channels(): """Assign the main x and y channels""" if "x" in available_channels and "y" in available_channels: if chart_type == "Bar Chart": # Bar chart: x = categorical, y = quantitative categorical_field = next((f for f in field_info if f["field"] not in used_fields and f["type"] in ["nominal", "ordinal"]), None) any_available_field = next((f for f in field_info if f["field"] not in used_fields and f["type"] in ["quantitative", "temporal"]), None) if categorical_field: add_encoding("x", categorical_field["field"]) elif any_available_field: add_encoding("x", any_available_field["field"]) quantitative_field = next((f for f in field_info if f["field"] not in used_fields and f["type"] == "quantitative"), None) if quantitative_field: add_encoding("y", quantitative_field["field"]) elif chart_type in ["Line Chart", "Area Chart"]: # Line/Area chart: x = temporal/ordinal, y = quantitative temporal_field = next((f for f in field_info if f["field"] not in used_fields and f["type"] in ["temporal", "ordinal"]), None) # Fallback: use first available field of any type any_available_field = next((f for f in field_info if f["field"] not in used_fields), None) if temporal_field: add_encoding("x", temporal_field["field"]) elif any_available_field: add_encoding("x", any_available_field["field"]) # Re-evaluate quantitative field after x-axis assignment quantitative_field = next((f for f in field_info if f["field"] not in used_fields and f["type"] == "quantitative"), None) if quantitative_field: add_encoding("y", quantitative_field["field"]) elif chart_type == "Scatter Plot": # Point charts: flexible for scatter plots, bubble charts, etc. # Prefer quantitative fields but accept any field type available_fields = [f for f in field_info if f["field"] not in used_fields] # Try to get quantitative fields first for traditional scatter plot quant_fields = [f for f in available_fields if f["type"] == "quantitative"] if len(quant_fields) >= 2: # Traditional scatter plot with two quantitative axes add_encoding("x", quant_fields[0]["field"]) add_encoding("y", quant_fields[1]["field"]) elif len(available_fields) >= 2: # Bubble chart or categorical point plot - use any available fields add_encoding("x", available_fields[0]["field"]) add_encoding("y", available_fields[1]["field"]) elif len(available_fields) == 1: # Single field - use for y-axis, x can be index or categorical add_encoding("y", available_fields[0]["field"]) elif chart_type == "Heatmap": # Heatmap: x = categorical, y = categorical, color = quantitative categorical_fields = [f for f in field_info if f["field"] not in used_fields and f["type"] in ["nominal", "ordinal"]] # Assign x and y to categorical fields if len(categorical_fields) >= 2: add_encoding("x", categorical_fields[0]["field"]) add_encoding("y", categorical_fields[1]["field"]) elif len(categorical_fields) >= 1: # Use available categorical field for one axis add_encoding("x", categorical_fields[0]["field"]) # Try to find another field for y-axis other_field = next((f for f in field_info if f["field"] not in used_fields), None) if other_field: add_encoding("y", other_field["field"]) else: # Fallback: use any available fields available_fields = [f for f in field_info if f["field"] not in used_fields] if len(available_fields) >= 2: add_encoding("x", available_fields[0]["field"]) add_encoding("y", available_fields[1]["field"]) elif chart_type == "Boxplot": # Box plot: x = categorical, y = quantitative categorical_field = next((f for f in field_info if f["field"] not in used_fields and f["type"] in ["nominal", "ordinal"]), None) quantitative_field = next((f for f in field_info if f["field"] not in used_fields and f["type"] == "quantitative"), None) if categorical_field: add_encoding("x", categorical_field["field"]) if quantitative_field: add_encoding("y", quantitative_field["field"]) else: # Default: assign first two available fields available_fields = [f for f in field_info if f["field"] not in used_fields] if len(available_fields) >= 1: add_encoding("x", available_fields[0]["field"]) if len(available_fields) >= 2: add_encoding("y", available_fields[1]["field"]) def assign_aesthetic_channels(): """Assign color, size, shape channels""" remaining_fields = [f for f in field_info if f["field"] not in used_fields] # Special handling for heatmap color channel if chart_type == "Heatmap" and "color" in available_channels and remaining_fields: # For heatmaps, color should be quantitative for intensity color_field = next((f for f in remaining_fields if f["type"] == "quantitative"), None) if not color_field: # Fallback to any remaining field color_field = remaining_fields[0] add_encoding("color", color_field["field"]) remaining_fields = [f for f in remaining_fields if f["field"] != color_field["field"]] # Color for other chart types: prefer low cardinality categorical fields elif "color" in available_channels and remaining_fields: color_field = next((f for f in remaining_fields if f["is_low_cardinality"] and f["type"] in ["nominal", "ordinal"]), None) if not color_field: color_field = remaining_fields[0] add_encoding("color", color_field["field"]) remaining_fields = [f for f in remaining_fields if f["field"] != color_field["field"]] # Size: prefer quantitative fields (great for bubble charts) if "size" in available_channels and remaining_fields: size_field = next((f for f in remaining_fields if f["type"] == "quantitative"), None) if size_field: add_encoding("size", size_field["field"]) remaining_fields = [f for f in remaining_fields if f["field"] != size_field["field"]] # Shape: prefer very low cardinality categorical fields if "shape" in available_channels and remaining_fields: shape_field = next((f for f in remaining_fields if f["is_very_low_cardinality"] and f["type"] in ["nominal", "ordinal"]), None) if shape_field: add_encoding("shape", shape_field["field"]) remaining_fields = [f for f in remaining_fields if f["field"] != shape_field["field"]] def assign_faceting_channels(): """Assign column and row channels for faceting""" remaining_fields = [f for f in field_info if f["field"] not in used_fields] # Column: prefer low cardinality fields if "column" in available_channels and remaining_fields: col_field = next((f for f in remaining_fields if f["is_low_cardinality"]), None) if col_field: add_encoding("column", col_field["field"]) remaining_fields = [f for f in remaining_fields if f["field"] != col_field["field"]] # Row: prefer very low cardinality fields (fewer rows than columns) if "row" in available_channels and remaining_fields: row_field = next((f for f in remaining_fields if f["is_very_low_cardinality"]), None) if row_field: add_encoding("row", row_field["field"]) # Execute assignment strategy assign_primary_channels() assign_aesthetic_channels() assign_faceting_channels() return encodings def assemble_vegailte_chart( df: pd.DataFrame, chart_type: str, encodings: dict[str, dict[str, str]], max_nominal_values: int = 68, config: dict | None = None, semantic_types: dict[str, Any] | None = None, ) -> dict: """ Assemble a Vega-Lite chart specification from a dataframe, chart type, and encodings. Parameters: - df: pandas DataFrame containing the data - chart_type: string matching one of the chart types in CHART_TEMPLATES - encodings: dict mapping channel names to encoding objects with "field" property Examples: - Simple: {"x": {"field": "field1"}, "y": {"field": "field2"}} - With aggregation: {"x": {"field": "category"}, "y": {"field": "sales", "aggregate": "mean"}} - max_nominal_values: maximum number of values for nominal axes before truncating - config: optional chart configuration (binCount, innerRadius, colorScheme, etc.) - semantic_types: optional dict mapping field names to semantic type strings or annotation dicts {"type": "Revenue", "unit": "USD", "intrinsic_domain": [0, 100]} Returns: - dict: Vega-Lite specification """ semantic_types = semantic_types or {} # Find the chart template template = get_chart_template(chart_type) if not template: raise ValueError(f"Chart type '{chart_type}' not found in templates") # Build initial spec — some chart types need special structure spec = _build_initial_spec(chart_type, template, df, encodings, config) # Remove duplicate columns before converting to records if df.columns.duplicated().any(): df = df.loc[:, ~df.columns.duplicated()] # Add data to the spec (inline data from dataframe) table_data = df.to_dict('records') # Resolve mark type for semantic decisions mark_type = template["mark"] if isinstance(mark_type, dict): mark_type = mark_type.get("type", "point") # Chart types that self-manage their encodings in _post_process_chart. # For these, we still resolve field types but skip adding to spec encoding # (the _post_process function distributes them from the raw encodings dict). _SELF_MANAGED_CHARTS = {"Radar Chart", "Pyramid Chart", "Waterfall Chart"} # Density: x/y are pre-set by template; only color goes through normal path _DENSITY_SKIP_CHANNELS = {"x", "y"} if chart_type == "Density Plot" else set() # Apply encodings with semantic-aware handling for channel, encoding_input in encodings.items(): # Parse encoding input (always a dict with "field" property) field_name = encoding_input.get("field") aggregate_func = encoding_input.get("aggregate") if not field_name or field_name not in df.columns: continue encoding_obj = {} cs: ChannelSemantics | None = None # channel semantics (when available) # Special scale configuration for radius if channel == "radius": encoding_obj["scale"] = {"type": "sqrt", "zero": True} # Handle aggregation if aggregate_func == "count": encoding_obj["field"] = "*" encoding_obj["aggregate"] = "count" encoding_obj["title"] = "Count" encoding_obj["type"] = "quantitative" elif aggregate_func: encoding_obj["field"] = field_name encoding_obj["aggregate"] = aggregate_func encoding_obj["type"] = "quantitative" else: # --- Resolve field type and semantics --- field_values = df[field_name].dropna().tolist() sem_annotation = semantic_types.get(field_name) # Extract semantic type string and optional metadata sem_type = '' sem_unit = None sem_domain = None if isinstance(sem_annotation, str): sem_type = sem_annotation elif isinstance(sem_annotation, dict): sem_type = sem_annotation.get('type', sem_annotation.get('semantic_type', '')) sem_unit = sem_annotation.get('unit') raw_domain = sem_annotation.get('intrinsic_domain') if isinstance(raw_domain, (list, tuple)) and len(raw_domain) == 2: sem_domain = (raw_domain[0], raw_domain[1]) # Use semantic-aware type resolution when available, else fall back if sem_type and is_registered(sem_type): cs = resolve_channel_semantics( field_name, sem_type, channel, mark_type, field_values, unit=sem_unit, intrinsic_domain=sem_domain, ) encoding_obj["field"] = field_name encoding_obj["type"] = cs.vl_type else: field_type = resolve_field_type(df[field_name], field_name) encoding_obj["field"] = field_name encoding_obj["type"] = field_type # Special handling for year/date fields if pd.api.types.is_datetime64_any_dtype(df[field_name]): if channel in ['color', 'size', 'column', 'row']: encoding_obj["type"] = "nominal" else: encoding_obj["type"] = "temporal" # Chart-type-aware type coercion (e.g. histogram x must be quantitative, # bar x must be nominal, heatmap x/y must be nominal, etc.) encoding_obj["type"] = coerce_field_type(chart_type, channel, encoding_obj["type"]) # ── Apply semantic enhancements when available ───────────────────── if cs: _apply_semantic_encoding(encoding_obj, cs, channel, chart_type, config) else: # Legacy fallback: basic color handling if encoding_obj.get("type") == "nominal" and channel == "color": unique_values = df[field_name].unique() if len(unique_values) >= 16: encoding_obj["scale"] = {"scheme": "tableau20"} encoding_obj["legend"] = {"symbolSize": 12, "labelFontSize": 8} # For map charts, encodings go into the second layer if chart_type in ("World Map", "US Map"): spec["layer"][1]["encoding"][channel] = encoding_obj elif chart_type in _SELF_MANAGED_CHARTS: pass # post_process will handle distribution from raw encodings elif channel in _DENSITY_SKIP_CHANNELS: pass # density template pre-sets x/y else: # Add encoding to spec spec.setdefault("encoding", {})[channel] = encoding_obj # Special handling for histogram: ensure x has bin:true and y has count if chart_type == "Histogram": if "x" in spec["encoding"]: spec["encoding"]["x"]["bin"] = True if "y" not in spec["encoding"]: spec["encoding"]["y"] = {"aggregate": "count"} # Special handling for pie: mark is 'arc' if chart_type == "Pie Chart": spec["mark"] = "arc" # Special handling for group_bar: add xOffset using the same field as color if chart_type == "Grouped Bar Chart" and "color" in spec.get("encoding", {}): color_encoding = spec["encoding"]["color"] spec["encoding"]["xOffset"] = { "field": color_encoding["field"], "type": color_encoding.get("type", "nominal") } # Special handling for stacked_bar: set stack mode on quant axis if chart_type == "Stacked Bar Chart": stack_mode = (config or {}).get("stackMode", "stacked") for axis in ("y", "x"): enc = spec.get("encoding", {}).get(axis) if enc and enc.get("type") == "quantitative": if stack_mode == "normalize": enc["stack"] = "normalize" elif stack_mode == "center": enc["stack"] = "center" elif stack_mode == "layered": enc["stack"] = None # default "stacked" uses VL default (true) break # Post-process special chart types (layered specs handled by _build_initial_spec) _post_process_chart(spec, chart_type, df, encodings, config) # Apply config options if config: _apply_chart_config(spec, chart_type, config) # Handle agent "facet" channel → map to "column" so the existing column→facet logic picks it up enc_target = spec["layer"][1]["encoding"] if chart_type in ("World Map", "US Map") else spec.get("encoding", {}) if "facet" in enc_target and "column" not in enc_target: enc_target["column"] = enc_target.pop("facet") # Handle faceting (column without row becomes facet) if "encoding" in spec: if "column" in spec["encoding"] and "row" not in spec["encoding"]: spec["encoding"]["facet"] = spec["encoding"]["column"] spec["encoding"]["facet"]["columns"] = 6 del spec["encoding"]["column"] # Handle nominal axes with many entries spec_encoding = spec.get("encoding", {}) for channel in ['x', 'y', 'column', 'row']: if channel in spec_encoding: encoding = spec_encoding[channel] if encoding.get("type") == "nominal": field_name = encoding["field"] unique_values = df[field_name].unique() if len(unique_values) > max_nominal_values: values_to_keep = _get_top_values( df, field_name, unique_values, channel, spec, max_nominal_values ) # Filter the data table_data = [ row for row in table_data if row[field_name] in values_to_keep ] # Add placeholder for omitted values omitted_count = len(unique_values) - len(values_to_keep) placeholder = f"...{omitted_count} items omitted" # Update domain encoding["scale"] = encoding.get("scale", {}) encoding["scale"]["domain"] = list(values_to_keep) + [placeholder] # Style the placeholder encoding["axis"] = encoding.get("axis", {}) encoding["axis"]["labelColor"] = { "condition": { "test": f"datum.label == '{placeholder}'", "value": "#999999" }, "value": "#000000" } # Apply spec quality improvements (null handling, tooltips, sizing, etc.) table_data = _apply_spec_quality(spec, table_data, df, chart_type) # Convert temporal fields to strings for Vega-Lite. # Uses the robust convert_temporal_data which handles datetime objects, # timestamps, year integers, date strings, etc. table_data = convert_temporal_data(table_data, semantic_types) # Post-encoding temporal guard: any field resolved as type="temporal" # must have its integer values converted to strings so VL doesn't # interpret e.g. 2024 as 2024ms-since-epoch. convert_temporal_data # above handles fields with known semantic types, but fields resolved # as temporal purely by name heuristic (e.g. column named "year") # may still have raw integer values. _temporal_fields = set() for _enc in spec.get("encoding", {}).values(): if isinstance(_enc, dict) and _enc.get("type") == "temporal": _f = _enc.get("field") if _f: _temporal_fields.add(_f) if _temporal_fields and table_data: for row in table_data: for _f in _temporal_fields: v = row.get(_f) if isinstance(v, (int, float)) and not isinstance(v, bool): row[_f] = str(int(v)) if v == int(v) else str(v) # Charts that manage their own data (waterfall, radar, pyramid) set # spec["data"] in _post_process_chart — don't overwrite it. if "data" not in spec: spec["data"] = {"values": table_data} return spec # --------------------------------------------------------------------------- # _build_initial_spec — construct the initial VL spec skeleton for each type # --------------------------------------------------------------------------- def _build_initial_spec( chart_type: str, template: dict, df: pd.DataFrame, encodings: dict, config: dict | None, ) -> dict: """Build the initial VL spec skeleton before encoding channels are applied.""" if chart_type == "Histogram": return { "mark": "bar", "encoding": { "x": {"bin": True}, "y": {"aggregate": "count"}, }, } if chart_type in ("World Map", "US Map"): projection_type = "albersUsa" if chart_type == "US Map" else "equalEarth" topo_url = ( "https://vega.github.io/vega-lite/data/us-10m.json" if chart_type == "US Map" else "https://vega.github.io/vega-lite/data/world-110m.json" ) topo_feature = "states" if chart_type == "US Map" else "countries" return { "width": 500 if chart_type == "US Map" else 600, "height": 300 if chart_type == "US Map" else 350, "layer": [ { "data": {"url": topo_url, "format": {"type": "topojson", "feature": topo_feature}}, "projection": {"type": projection_type}, "mark": {"type": "geoshape", "fill": "lightgray", "stroke": "white"}, }, { "projection": {"type": projection_type}, "mark": "circle", "encoding": {}, }, ], } if chart_type == "Lollipop Chart": return { "encoding": {}, "layer": [ {"mark": {"type": "rule", "strokeWidth": 1.5}, "encoding": {}}, {"mark": {"type": "circle", "size": int((config or {}).get("dotSize", 80))}, "encoding": {}}, ], } if chart_type == "Regression": reg_transform: dict = {"regression": "__y__", "on": "__x__"} method = (config or {}).get("regressionMethod", "linear") if method and method != "linear": reg_transform["method"] = method if method == "poly": reg_transform["order"] = int((config or {}).get("polyOrder", 3)) return { "layer": [ {"mark": "circle", "encoding": {}}, { "mark": {"type": "line", "color": "red"}, "transform": [reg_transform], "encoding": {}, }, ], "encoding": {}, } if chart_type == "Ranged Dot Plot": return { "encoding": {}, "layer": [ {"mark": "line", "encoding": {"detail": {}}}, {"mark": {"type": "point", "filled": True}, "encoding": {"color": {}}}, ], } if chart_type == "Candlestick Chart": return { "encoding": {}, "layer": [ {"mark": "rule", "encoding": {}}, {"mark": {"type": "bar", "size": 14}, "encoding": {}}, ], } if chart_type == "Waterfall Chart": # Will be rebuilt into layered spec in _post_process_chart return {"mark": "bar", "encoding": {}} if chart_type == "Density Plot": return { "mark": "area", "transform": [{"density": "__field__"}], "encoding": { "x": {"field": "value", "type": "quantitative"}, "y": {"field": "density", "type": "quantitative"}, }, } if chart_type == "Radar Chart": # Radar is entirely computed; placeholder spec return {"mark": "point", "encoding": {}} if chart_type == "Pyramid Chart": return { "spacing": 0, "resolve": {"scale": {"y": "shared"}}, "hconcat": [ { "mark": "bar", "encoding": { "y": {}, "x": {"scale": {"reverse": True}, "stack": None}, "opacity": {"value": 0.9}, "color": {"value": "#4e79a7"}, }, }, { "mark": "bar", "encoding": { "y": {"axis": None}, "x": {"stack": None}, "opacity": {"value": 0.9}, "color": {"value": "#e15759"}, }, }, ], "config": {"view": {"stroke": None}, "axis": {"grid": False}}, } if chart_type == "Streamgraph": return {"mark": "area", "encoding": {}} if chart_type == "Rose Chart": mark = {"type": "arc", "stroke": "white", "padAngle": 0.02} return {"mark": mark, "encoding": {}} # Default: use template mark mark = template["mark"] return {"mark": mark, "encoding": {}} # --------------------------------------------------------------------------- # _post_process_chart — chart-type-specific post-processing # --------------------------------------------------------------------------- def _post_process_chart( spec: dict, chart_type: str, df: pd.DataFrame, encodings: dict, config: dict | None, ) -> None: """Apply chart-type-specific post-processing after encodings are set.""" if chart_type == "Lollipop Chart": _post_process_lollipop(spec, df, encodings) elif chart_type == "Regression": _post_process_regression(spec, encodings) elif chart_type == "Ranged Dot Plot": _post_process_ranged_dot(spec, encodings) elif chart_type == "Candlestick Chart": _post_process_candlestick(spec, df, encodings, config) elif chart_type == "Waterfall Chart": _post_process_waterfall(spec, df, encodings, config) elif chart_type == "Density Plot": _post_process_density(spec, encodings, config) elif chart_type == "Radar Chart": _post_process_radar(spec, df, encodings, config) elif chart_type == "Pyramid Chart": _post_process_pyramid(spec, df, encodings) elif chart_type == "Streamgraph": _post_process_streamgraph(spec, encodings, config) elif chart_type == "Bump Chart": _post_process_bump(spec, encodings) elif chart_type == "Strip Plot": _post_process_strip(spec, df, encodings, config) elif chart_type == "Rose Chart": _post_process_rose(spec, df, encodings, config) def _post_process_lollipop(spec: dict, df: pd.DataFrame, encodings: dict) -> None: """Lollipop: rule from 0 + circle at value. Both layers share positional encodings.""" enc = spec.get("encoding", {}) layer_rule = spec["layer"][0] layer_circle = spec["layer"][1] for ch in ("x", "y"): if ch in enc: layer_rule["encoding"][ch] = dict(enc[ch]) layer_circle["encoding"][ch] = dict(enc[ch]) # Anchor rule from 0 on the quantitative axis for axis in ("y", "x"): if enc.get(axis, {}).get("type") == "quantitative": layer_rule["encoding"][f"{axis}2"] = {"datum": 0} break # Color → circle layer only if "color" in enc: layer_circle["encoding"]["color"] = enc.pop("color") # Facets stay at top-level for ch in list(enc.keys()): if ch not in ("x", "y"): pass # keep in top encoding for faceting def _post_process_regression(spec: dict, encodings: dict) -> None: """Regression: scatter layer + regression trend line.""" scatter = spec["layer"][0] regression = spec["layer"][1] top_enc = spec.get("encoding", {}) x_enc = top_enc.get("x") y_enc = top_enc.get("y") if x_enc: scatter["encoding"]["x"] = dict(x_enc) regression["encoding"]["x"] = dict(x_enc) regression["transform"][0]["on"] = x_enc.get("field", "__x__") if y_enc: scatter["encoding"]["y"] = dict(y_enc) regression["encoding"]["y"] = dict(y_enc) regression["transform"][0]["regression"] = y_enc.get("field", "__y__") if "color" in top_enc: color_enc = top_enc.pop("color") scatter["encoding"]["color"] = color_enc # Group regression by color field so each class gets its own trend line color_field = color_enc.get("field") if color_field: regression["transform"][0]["groupby"] = [color_field] regression["encoding"]["color"] = dict(color_enc) # Remove hardcoded red so Vega-Lite uses the shared color scale regression["mark"] = {"type": "line"} if "size" in top_enc: scatter["encoding"]["size"] = top_enc.pop("size") # Facets for ch in ("column", "row"): if ch in top_enc: pass # keep at top level def _post_process_ranged_dot(spec: dict, encodings: dict) -> None: """Ranged dot plot: line + point layers; detail links line segments.""" enc = spec.get("encoding", {}) line_layer = spec["layer"][0] point_layer = spec["layer"][1] if "color" in enc: point_layer["encoding"]["color"] = enc.pop("color") # Copy nominal axis into detail encoding for line layer if enc.get("y", {}).get("type") == "nominal": import copy line_layer["encoding"]["detail"] = copy.deepcopy(enc["y"]) elif enc.get("x", {}).get("type") == "nominal": import copy line_layer["encoding"]["detail"] = copy.deepcopy(enc["x"]) def _post_process_candlestick( spec: dict, df: pd.DataFrame, encodings: dict, config: dict | None ) -> None: """Candlestick: rule (wick) + bar (body) with conditional coloring.""" enc = spec.get("encoding", {}) rule = spec["layer"][0] bar = spec["layer"][1] # x shared if "x" in enc: x_enc = dict(enc["x"]) if x_enc.get("type") in ("nominal", "ordinal"): x_enc["sort"] = None spec["encoding"]["x"] = x_enc # Shared y-axis y_axis = {"type": "quantitative", "scale": {"zero": False}, "axis": {"title": None}} spec["title"] = {"text": "Price", "anchor": "start", "fontSize": 11, "fontWeight": "normal", "color": "#666"} # Rule: y = low, y2 = high open_f = encodings.get("open", {}).get("field") high_f = encodings.get("high", {}).get("field") low_f = encodings.get("low", {}).get("field") close_f = encodings.get("close", {}).get("field") if low_f and high_f: rule["encoding"]["y"] = {"field": low_f, **y_axis} rule["encoding"]["y2"] = {"field": high_f} if open_f and close_f: bar["encoding"]["y"] = {"field": open_f, **y_axis} bar["encoding"]["y2"] = {"field": close_f} # Conditional color: green up, red down bar["encoding"]["color"] = { "condition": { "test": f"datum['{open_f}'] < datum['{close_f}']", "value": "#06982d", }, "value": "#ae1325", } # Adaptive bar size x_field = enc.get("x", {}).get("field") if x_field and x_field in df.columns: card = df[x_field].nunique() plot_w = 400 bar_size = max(2, min(20, round(plot_w * 0.6 / max(card, 1)))) bar["mark"]["size"] = bar_size # Remove OHLC channels from top-level encoding (not VL channels) for ch in ("open", "high", "low", "close"): enc.pop(ch, None) def _post_process_waterfall( spec: dict, df: pd.DataFrame, encodings: dict, config: dict | None ) -> None: """Waterfall: cumulative bar chart with positive/negative coloring.""" x_field = encodings.get("x", {}).get("field") y_field = encodings.get("y", {}).get("field") if not x_field or not y_field or x_field not in df.columns or y_field not in df.columns: return # Build cumulative waterfall data wf_data = [] cumsum = 0 rows = df[[x_field, y_field]].to_dict("records") total_rows = len(rows) for i, row in enumerate(rows): val = row[y_field] if isinstance(row[y_field], (int, float)) else 0 prev = cumsum if i == 0 or i == total_rows - 1: wf_type = "total" cumsum = val prev = 0 else: wf_type = "increase" if val >= 0 else "decrease" cumsum += val wf_data.append({ x_field: row[x_field], "__wf_prev_sum": prev, "__wf_sum": cumsum, "__wf_color": wf_type, }) corner_radius = int((config or {}).get("cornerRadius", 0)) mark_obj: dict = {"type": "bar"} if corner_radius: mark_obj["cornerRadius"] = corner_radius spec.clear() spec.update({ "data": {"values": wf_data}, "encoding": { "x": {"field": x_field, "type": "ordinal", "sort": None, "axis": {"labelAngle": -45}}, }, "layer": [ { "mark": mark_obj, "encoding": { "y": {"field": "__wf_prev_sum", "type": "quantitative", "title": y_field}, "y2": {"field": "__wf_sum"}, "color": { "field": "__wf_color", "type": "nominal", "scale": { "domain": ["total", "increase", "decrease"], "range": ["#f7e0b6", "#93c4aa", "#f78a64"], }, "legend": {"title": "Type"}, }, }, } ], }) def _post_process_density(spec: dict, encodings: dict, config: dict | None) -> None: """Density plot: kernel density transform.""" x_enc = encodings.get("x", {}) x_field = x_enc.get("field") if x_field: spec["transform"][0]["density"] = x_field spec["encoding"]["x"]["title"] = x_field color_enc = encodings.get("color", {}) if color_enc.get("field"): spec["transform"][0]["groupby"] = [color_enc["field"]] spec["encoding"]["color"] = { "field": color_enc["field"], "type": color_enc.get("type", "nominal"), } bandwidth = (config or {}).get("bandwidth") if bandwidth and float(bandwidth) > 0: spec["transform"][0]["bandwidth"] = float(bandwidth) # Remove x/y from top-level encoding (already set by template) # Don't re-add them from the encoding loop def _post_process_radar( spec: dict, df: pd.DataFrame, encodings: dict, config: dict | None ) -> None: """Radar chart: entirely client-computed polar projection.""" x_enc = encodings.get("x", {}) y_enc = encodings.get("y", {}) color_enc = encodings.get("color", {}) axis_field = x_enc.get("field") value_field = y_enc.get("field") group_field = color_enc.get("field") if not axis_field or not value_field: return if axis_field not in df.columns or value_field not in df.columns: return axes = df[axis_field].unique().tolist() n_axes = len(axes) if n_axes < 3: return groups = df[group_field].unique().tolist() if group_field and group_field in df.columns else ["__all__"] # Compute per-axis max for normalization axis_max = {} for ax in axes: vals = df[df[axis_field] == ax][value_field].dropna() mx = float(vals.max()) if len(vals) > 0 else 1 axis_max[ax] = mx if mx > 0 else 1 # Build polar coordinates import math as _math polygon_data = [] point_data = [] for grp in groups: subset = df if grp == "__all__" else df[df[group_field] == grp] for i, ax in enumerate(axes): angle = 2 * _math.pi * i / n_axes vals = subset[subset[axis_field] == ax][value_field].dropna() mean_val = float(vals.mean()) if len(vals) > 0 else 0 norm = mean_val / axis_max[ax] px = norm * _math.sin(angle) py = -norm * _math.cos(angle) rec = {"__x": px, "__y": py, "__group": grp, "__angle": angle, "__axis": ax, "__value": mean_val} polygon_data.append(rec) point_data.append(rec) # Close polygon if n_axes > 0: first = polygon_data[-(n_axes)] polygon_data.append({**first}) # Spoke data spoke_data = [] for i, ax in enumerate(axes): angle = 2 * _math.pi * i / n_axes spoke_data.append({"__x": 0, "__y": 0, "__x2": _math.sin(angle), "__y2": -_math.cos(angle)}) # Ring data (concentric polygons at 0.25, 0.5, 0.75, 1.0) ring_data = [] for level in (0.25, 0.5, 0.75, 1.0): for i in range(n_axes + 1): angle = 2 * _math.pi * (i % n_axes) / n_axes ring_data.append({ "__x": level * _math.sin(angle), "__y": -level * _math.cos(angle), "__level": level, "__order": i, }) # Label data label_data = [] for i, ax in enumerate(axes): angle = 2 * _math.pi * i / n_axes r = 1.12 label_data.append({ "__x": r * _math.sin(angle), "__y": -r * _math.cos(angle), "__label": f"{ax}\n({axis_max[ax]:.0f})", "__align": "center" if abs(_math.sin(angle)) < 0.01 else ("left" if _math.sin(angle) > 0 else "right"), }) filled = (config or {}).get("filled", True) fill_opacity = float((config or {}).get("fillOpacity", 0.15)) stroke_width = float((config or {}).get("strokeWidth", 1.5)) scale_xy = {"domain": [-1.18, 1.18]} layers = [ # Spokes { "data": {"values": spoke_data}, "mark": {"type": "rule", "color": "#ddd"}, "encoding": { "x": {"field": "__x", "type": "quantitative", "scale": scale_xy, "axis": None}, "y": {"field": "__y", "type": "quantitative", "scale": scale_xy, "axis": None}, "x2": {"field": "__x2"}, "y2": {"field": "__y2"}, }, }, # Rings { "data": {"values": ring_data}, "mark": {"type": "line", "color": "#eee", "strokeWidth": 0.5}, "encoding": { "x": {"field": "__x", "type": "quantitative", "scale": scale_xy, "axis": None}, "y": {"field": "__y", "type": "quantitative", "scale": scale_xy, "axis": None}, "detail": {"field": "__level", "type": "nominal"}, "order": {"field": "__order", "type": "quantitative"}, }, }, # Labels { "data": {"values": label_data}, "mark": {"type": "text", "fontSize": 11}, "encoding": { "x": {"field": "__x", "type": "quantitative", "scale": scale_xy, "axis": None}, "y": {"field": "__y", "type": "quantitative", "scale": scale_xy, "axis": None}, "text": {"field": "__label", "type": "nominal"}, "align": {"field": "__align", "type": "nominal"}, }, }, # Data polygon { "data": {"values": polygon_data}, "mark": { "type": "line", "interpolate": "linear-closed", "strokeWidth": stroke_width, **({"filled": True, "fillOpacity": fill_opacity} if filled else {}), }, "encoding": { "x": {"field": "__x", "type": "quantitative", "scale": scale_xy, "axis": None}, "y": {"field": "__y", "type": "quantitative", "scale": scale_xy, "axis": None}, "color": {"field": "__group", "type": "nominal"}, "order": {"field": "__angle", "type": "quantitative"}, }, }, # Data points { "data": {"values": point_data}, "mark": {"type": "point", "filled": True, "size": 25}, "encoding": { "x": {"field": "__x", "type": "quantitative", "scale": scale_xy, "axis": None}, "y": {"field": "__y", "type": "quantitative", "scale": scale_xy, "axis": None}, "color": {"field": "__group", "type": "nominal"}, }, }, ] size = min(400, 400) spec.clear() spec.update({ "width": size, "height": size, "layer": layers, "config": {"view": {"stroke": None}}, }) def _post_process_pyramid(spec: dict, df: pd.DataFrame, encodings: dict) -> None: """Pyramid: hconcat of two mirrored bar panels split by color field.""" if "hconcat" not in spec: return x_enc = encodings.get("x", {}) y_enc = encodings.get("y", {}) color_enc = encodings.get("color", {}) y_field = y_enc.get("field") or x_enc.get("field") x_field = x_enc.get("field") if y_enc.get("field") else y_enc.get("field") group_field = color_enc.get("field") if not y_field or not x_field or not group_field: return if y_field not in df.columns or x_field not in df.columns or group_field not in df.columns: return groups = df[group_field].unique().tolist() left_group = groups[0] if len(groups) > 0 else None right_group = groups[1] if len(groups) > 1 else None table_data = df.to_dict("records") left_panel = spec["hconcat"][0] right_panel = spec["hconcat"][1] # y is the categorical axis (shared) left_panel["encoding"]["y"] = {"field": y_field, "type": "nominal", "sort": None} right_panel["encoding"]["y"] = {"field": y_field, "type": "nominal", "axis": None, "sort": None} # x is the quantitative axis x_max = float(df[x_field].max()) if pd.api.types.is_numeric_dtype(df[x_field]) else 100 left_panel["encoding"]["x"] = { "field": x_field, "type": "quantitative", "scale": {"reverse": True, "domain": [0, x_max]}, "stack": None, } right_panel["encoding"]["x"] = { "field": x_field, "type": "quantitative", "scale": {"domain": [0, x_max]}, "stack": None, } # Filter each panel if left_group is not None: left_panel["transform"] = [{"filter": {"field": group_field, "equal": left_group}}] left_panel["encoding"]["color"] = {"value": "#4e79a7"} if right_group is not None: right_panel["transform"] = [{"filter": {"field": group_field, "equal": right_group}}] right_panel["encoding"]["color"] = {"value": "#e15759"} spec["data"] = {"values": table_data} def _post_process_streamgraph(spec: dict, encodings: dict, config: dict | None) -> None: """Streamgraph: stacked area with center baseline.""" enc = spec.get("encoding", {}) # Force center stack on the quantitative axis for axis in ("y", "x"): if enc.get(axis, {}).get("type") == "quantitative": enc[axis]["stack"] = "center" enc[axis]["axis"] = None break interpolate = (config or {}).get("interpolate") if interpolate: spec["mark"] = {"type": "area", "interpolate": interpolate} def _post_process_bump(spec: dict, encodings: dict) -> None: """Bump chart: reversed y-axis so rank 1 is at top.""" enc = spec.get("encoding", {}) # Detect rank axis (defaults to y) y_enc = enc.get("y", {}) if y_enc: y_enc.setdefault("scale", {})["reverse"] = True def _post_process_strip( spec: dict, df: pd.DataFrame, encodings: dict, config: dict | None ) -> None: """Strip plot: jittered points along a categorical axis.""" enc = spec.get("encoding", {}) cfg = config or {} step_width = int(cfg.get("stepWidth", 20)) # Detect categorical axis cat_axis = None cont_axis = None for axis in ("x", "y"): if enc.get(axis, {}).get("type") in ("nominal", "ordinal"): cat_axis = axis cont_axis = "y" if axis == "x" else "x" break if cat_axis is None: return # Set step-based width/height dim = "width" if cat_axis == "x" else "height" spec[dim] = {"step": step_width} # Auto point size cat_field = enc[cat_axis].get("field") if cat_field and cat_field in df.columns: max_group = int(df.groupby(cat_field).size().max()) cont_dim = spec.get("height" if cat_axis == "x" else "width", 300) if isinstance(cont_dim, dict): cont_len = 300 else: cont_len = cont_dim ideal_size = max(5, min(100, int(0.35 * step_width * cont_len / max(max_group, 1)))) point_size = int(cfg.get("pointSize", 0)) or ideal_size else: point_size = int(cfg.get("pointSize", 0)) or 30 mark = spec.get("mark", {}) if isinstance(mark, dict): mark["size"] = point_size else: spec["mark"] = {"type": "circle", "opacity": 0.7, "size": point_size} # Jitter transform jitter_width = step_width * 0.7 spec.setdefault("transform", []).append({ "calculate": f"{-jitter_width/2} + random() * {jitter_width}", "as": "__jitter", }) offset_ch = "xOffset" if cat_axis == "x" else "yOffset" enc[offset_ch] = { "field": "__jitter", "type": "quantitative", "axis": None, "scale": {"domain": [-step_width / 2, step_width / 2]}, } def _post_process_rose( spec: dict, df: pd.DataFrame, encodings: dict, config: dict | None ) -> None: """Rose (Nightingale) chart: polar bar using arc mark.""" enc = spec.get("encoding", {}) cfg = config or {} x_enc = enc.pop("x", None) y_enc = enc.pop("y", None) # x → theta (the angular/category axis) if x_enc: theta_enc = {"field": x_enc["field"], "type": "nominal", "stack": True} if x_enc.get("sort"): theta_enc["sort"] = x_enc["sort"] enc["theta"] = theta_enc # y → radius if y_enc: radius_field = y_enc["field"] enc["radius"] = {"field": radius_field, "type": "quantitative", "scale": {"type": "sqrt"}} else: enc["radius"] = {"aggregate": "count", "type": "quantitative", "scale": {"type": "sqrt"}} # color: use the explicit color field or fall back to x field if "color" not in enc and x_enc: enc["color"] = {"field": x_enc["field"], "type": "nominal"} # Config inner_radius = int(cfg.get("innerRadius", 0)) pad_angle = float(cfg.get("padAngle", 0.02)) mark = spec.get("mark", {}) if isinstance(mark, dict): if inner_radius: mark["innerRadius"] = inner_radius mark["padAngle"] = pad_angle else: spec["mark"] = {"type": "arc", "stroke": "white", "padAngle": pad_angle} if inner_radius: spec["mark"]["innerRadius"] = inner_radius # Square aspect ratio spec["width"] = 300 spec["height"] = 300 def _apply_semantic_encoding( encoding_obj: dict, cs: ChannelSemantics, channel: str, chart_type: str, config: dict | None, ) -> None: """ Apply semantic-aware enhancements to a VL encoding object. Kept intentionally minimal — only ordinal sort order (months, days). Formatting, domains, ticks, zero-baseline, etc. are left to VL defaults or handled by the front-end TS library. """ vl_type = encoding_obj.get("type", "nominal") # --- Ordinal sort order (months, days, quarters) --- if cs.ordinal_sort_order and vl_type in ('ordinal', 'nominal'): encoding_obj["sort"] = cs.ordinal_sort_order # --- Basic color scheme fallback --- if vl_type == 'nominal' and channel == 'color': encoding_obj.setdefault("scale", {}).setdefault("scheme", "tableau10") def _apply_spec_quality( spec: dict, table_data: list[dict], df: pd.DataFrame, chart_type: str, ) -> list[dict]: """ Post-processing quality improvements for VL specs. Mirrors the TS ``vlApplyLayoutToSpec`` logic in instantiate-spec.ts. Returns (potentially filtered) table_data. """ encoding = spec.get("encoding", {}) config = spec.setdefault("config", {}) # ── 1. Filter nulls for line / area y-field (prevents broken lines) ─── # Default VL behaviour leaves gaps at null points. Filtering them # out from the data connects the line across missing values. if chart_type in ('Line Chart', 'Area Chart'): y_field = encoding.get("y", {}).get("field") if y_field: table_data = [ row for row in table_data if row.get(y_field) is not None and not (isinstance(row.get(y_field), float) and pd.isna(row[y_field])) ] # ── 2. Tooltips ─────────────────────────────────────────────────────── config.setdefault("mark", {})["tooltip"] = True # ── 3. Canvas sizing defaults ───────────────────────────────────────── view = config.setdefault("view", {}) view.setdefault("continuousWidth", 400) view.setdefault("continuousHeight", 300) # ── 4. Axis label limits (prevent long labels from overflowing) ────── ax_x = config.setdefault("axisX", {}) ax_x.setdefault("labelLimit", 120) ax_y = config.setdefault("axisY", {}) ax_y.setdefault("labelLimit", 120) # ── 5. Step-based sizing for wide discrete axes ─────────────────────── for axis_ch, dim_key in [("x", "width"), ("y", "height")]: enc = encoding.get(axis_ch, {}) if ( enc.get("type") in ("nominal", "ordinal") and enc.get("field") and dim_key not in spec ): field = enc["field"] if field in df.columns: n_unique = df[field].nunique() if n_unique > 12: step = max(12, min(30, 600 // n_unique)) spec[dim_key] = {"step": step} # ── 6. X-axis label rotation for crowded labels ────────────────────── x_enc = encoding.get("x", {}) if x_enc.get("type") in ("nominal", "ordinal") and x_enc.get("field"): field = x_enc["field"] if field in df.columns: n_unique = df[field].nunique() vals = df[field].dropna().astype(str) max_label_len = int(vals.str.len().max()) if len(vals) > 0 else 0 if n_unique > 8 or (n_unique > 4 and max_label_len > 10): if "labelAngle" not in ax_x: ax_x["labelAngle"] = -45 ax_x["labelAlign"] = "right" ax_x["labelBaseline"] = "top" # ── 7. Facet header limits ──────────────────────────────────────────── for facet_ch in ("facet", "column", "row"): enc = encoding.get(facet_ch) if enc and enc.get("field") and enc["field"] in df.columns: n_facets = df[enc["field"]].nunique() if n_facets > 6: config.setdefault("header", {}).update({ "labelLimit": 120, "labelFontSize": 9, }) break # ── 8. Faceted chart lighter axis titles ────────────────────────────── has_row = "row" in encoding has_col = "column" in encoding or "facet" in encoding if has_row or has_col: light_title = { "titleFontWeight": "normal", "titleFontSize": 11, "titleColor": "#666", } for ax_key in ("axisX", "axisY"): ax_cfg = config.setdefault(ax_key, {}) for k, v in light_title.items(): ax_cfg.setdefault(k, v) # ── 9. Dual-legend repositioning ────────────────────────────────────── # When two+ channels produce legends (e.g. color + size), move the # categorical legend to the bottom to free plot area width. legend_channels = [ ch for ch in ("color", "size", "shape", "opacity") if ch in encoding and encoding[ch].get("field") ] if len(legend_channels) >= 2: cat_chs = [ ch for ch in legend_channels if encoding[ch].get("type") in ("nominal", "ordinal") ] quant_chs = [ ch for ch in legend_channels if encoding[ch].get("type") in ("quantitative", "temporal") ] if cat_chs and quant_chs: for ch in cat_chs: legend = encoding[ch].setdefault("legend", {}) legend["orient"] = "bottom" legend["direction"] = "horizontal" return table_data def _apply_chart_config(spec: dict, chart_type: str, config: dict): """Apply optional config overrides to a Vega-Lite spec.""" if not config: return def _ensure_mark_obj(s): """Convert string mark to object so we can add properties.""" if isinstance(s.get("mark"), str): s["mark"] = {"type": s["mark"]} if chart_type == "Histogram": bin_count = config.get("binCount") if bin_count and "encoding" in spec and "x" in spec["encoding"]: spec["encoding"]["x"]["bin"] = {"maxbins": int(bin_count)} elif chart_type == "Pie Chart": inner_radius = config.get("innerRadius") if inner_radius is not None: _ensure_mark_obj(spec) spec["mark"]["innerRadius"] = int(inner_radius) elif chart_type == "Heatmap": color_scheme = config.get("colorScheme") if color_scheme and "encoding" in spec and "color" in spec["encoding"]: spec["encoding"]["color"].setdefault("scale", {})["scheme"] = color_scheme elif chart_type == "Scatter Plot": opacity = config.get("opacity") if opacity is not None: _ensure_mark_obj(spec) spec["mark"]["opacity"] = float(opacity) elif chart_type in ("Bar Chart", "Grouped Bar Chart", "Stacked Bar Chart"): corner_radius = config.get("cornerRadius") if corner_radius is not None: _ensure_mark_obj(spec) spec["mark"]["cornerRadius"] = int(corner_radius) elif chart_type in ("Line Chart", "Dotted Line Chart"): interpolate = config.get("interpolate") if interpolate: _ensure_mark_obj(spec) spec["mark"]["interpolate"] = interpolate elif chart_type == "Area Chart": interpolate = config.get("interpolate") opacity = config.get("opacity") stack_mode = config.get("stackMode") _ensure_mark_obj(spec) if interpolate: spec["mark"]["interpolate"] = interpolate if opacity is not None: spec["mark"]["opacity"] = float(opacity) if stack_mode and "encoding" in spec: for axis in ("y", "x"): enc = spec["encoding"].get(axis) if enc and enc.get("type") == "quantitative": if stack_mode == "normalize": enc["stack"] = "normalize" elif stack_mode == "center": enc["stack"] = "center" elif stack_mode == "layered": enc["stack"] = None break elif chart_type == "World Map": projection = config.get("projection") projection_center = config.get("projectionCenter") if projection and "layer" in spec: for layer in spec["layer"]: if "projection" in layer: layer["projection"]["type"] = projection if projection_center and "layer" in spec: # projectionCenter [lon, lat] → rotate [-lon, -lat, 0] lon, lat = projection_center for layer in spec["layer"]: if "projection" in layer: layer["projection"]["rotate"] = [-lon, -lat, 0] def _get_top_values(df: pd.DataFrame, field_name: str, unique_values: list, channel: str, spec: dict, max_values: int) -> list: """ Get top values for nominal fields with many entries. """ if channel in ['x', 'y']: # Find opposite quantitative channel for sorting opposite_channel = 'y' if channel == 'x' else 'x' opposite_encoding = spec["encoding"].get(opposite_channel) if (opposite_encoding and opposite_encoding.get("type") == "quantitative"): # Sort by quantitative field sum quant_field = opposite_encoding["field"] value_sums = [] for val in unique_values: subset = df[df[field_name] == val] total = subset[quant_field].sum() if quant_field in df.columns else 0 value_sums.append((val, total)) # Sort by sum descending and take top values value_sums.sort(key=lambda x: x[1], reverse=True) return [val for val, _ in value_sums[:max_values]] elif channel == 'row': # Limit rows more strictly return unique_values[:20] # Default: just take first N values return unique_values[:max_values] def vl_spec_to_png(spec: dict, output_path: str | None = None, scale: float = 1.0) -> bytes: """ Convert a Vega-Lite specification to a PNG image. Parameters: - spec: Vega-Lite specification dictionary - output_path: Optional path to save the PNG file - scale: Scale factor for higher resolution (default 1.0) Returns: - bytes: PNG image data Requires: pip install vl-convert-python """ # Convert directly to PNG bytes using vl-convert with higher scale for better fidelity png_data = vlc.vegalite_to_png(spec, scale=scale, ppi=150) # Save to file if path provided if output_path: with open(output_path, 'wb') as f: f.write(png_data) print(f"Chart saved to {output_path}") return png_data def spec_to_base64(spec: dict, scale: float = 1.0) -> str: """ Convert a Vega-Lite specification to a base64 encoded PNG string. Parameters: - spec: Vega-Lite specification dictionary - width: Optional width in pixels (defaults to spec width or 400) - height: Optional height in pixels (defaults to spec height or 300) - scale: Scale factor for higher resolution (default 2.0 for 2x resolution) Returns: - str: Base64 encoded PNG data (data:image/png;base64,...) Requires: pip install vl-convert-python """ # Get PNG bytes with higher fidelity png_data = vl_spec_to_png(spec, scale=scale) # Convert to base64 base64_string = base64.b64encode(png_data).decode('utf-8') return f"data:image/png;base64,{base64_string}" ================================================ FILE: py-src/data_formulator/workspace_factory.py ================================================ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ Flask-aware workspace factory. Reads the workspace backend configuration from Flask's ``current_app.config`` (populated by CLI args / env vars in ``app.py``) and returns the appropriate :class:`Workspace` subclass. This keeps the data-layer modules (``datalake.workspace``, ``datalake.azure_blob_workspace``) free of any Flask dependency. """ import os import logging from data_formulator.datalake.workspace import Workspace logger = logging.getLogger(__name__) def _build_azure_container_client(cfg: dict): """ Create an Azure ``ContainerClient`` using the best available credential. Resolution order: 1. **Connection string** (``AZURE_BLOB_CONNECTION_STRING``) — shared-key access, convenient for local development and testing. 2. **Account URL** (``AZURE_BLOB_ACCOUNT_URL``) + ``DefaultAzureCredential`` — uses Entra ID (Managed Identity on Azure, ``az login`` locally, workload identity on Kubernetes). No secrets required in production. At least one of the two must be set. """ from azure.storage.blob import ContainerClient conn_str = cfg.get( "azure_blob_connection_string", os.getenv("AZURE_BLOB_CONNECTION_STRING"), ) account_url = cfg.get( "azure_blob_account_url", os.getenv("AZURE_BLOB_ACCOUNT_URL"), ) container_name = cfg.get( "azure_blob_container", os.getenv("AZURE_BLOB_CONTAINER", "data-formulator"), ) if conn_str: # Option 1: connection string (shared key / SAS) return ContainerClient.from_connection_string(conn_str, container_name) if account_url: # Option 2: Entra ID via DefaultAzureCredential from azure.identity import DefaultAzureCredential credential = DefaultAzureCredential() return ContainerClient(account_url, container_name, credential=credential) raise ValueError( "Azure Blob workspace requires either a connection string or an " "account URL. Set --azure-blob-connection-string / " "AZURE_BLOB_CONNECTION_STRING, or --azure-blob-account-url / " "AZURE_BLOB_ACCOUNT_URL." ) def get_workspace(identity_id: str) -> Workspace: """ Return a :class:`Workspace` (or subclass) for *identity_id*. The backend is selected via the running Flask app's ``CLI_ARGS`` config, which mirrors CLI flags and environment variables set in ``app.py``: ================================= ================================ ================== CLI flag Env var Default ================================= ================================ ================== ``--workspace-backend`` ``WORKSPACE_BACKEND`` ``local`` ``--azure-blob-connection-string````AZURE_BLOB_CONNECTION_STRING`` (none) ``--azure-blob-account-url`` ``AZURE_BLOB_ACCOUNT_URL`` (none) ``--azure-blob-container`` ``AZURE_BLOB_CONTAINER`` ``data-formulator`` ================================= ================================ ================== ``datalake_root`` reuses ``--data-dir`` / ``DATA_FORMULATOR_HOME`` (defaulting to ``"workspaces"``). """ from flask import current_app cfg = current_app.config.get("CLI_ARGS", {}) backend = cfg.get( "workspace_backend", os.getenv("WORKSPACE_BACKEND", "local") ) if backend == "azure_blob": from data_formulator.datalake.cached_azure_blob_workspace import ( CachedAzureBlobWorkspace, ) client = _build_azure_container_client(cfg) root = ( cfg.get("data_dir") or os.getenv("DATA_FORMULATOR_HOME") or "workspaces" ) # Cache configuration from env vars / CLI args max_cache_mb = int( cfg.get("cache_max_mb", os.getenv("DF_CACHE_MAX_MB", "1024")) ) max_global_cache_mb = int( cfg.get( "global_cache_max_mb", os.getenv("DF_GLOBAL_CACHE_MAX_MB", "10240"), ) ) return CachedAzureBlobWorkspace( identity_id, client, datalake_root=root, max_cache_bytes=max_cache_mb * 1024 * 1024, max_global_cache_bytes=max_global_cache_mb * 1024 * 1024, ) # Default: local filesystem workspace return Workspace(identity_id) ================================================ FILE: pyproject.toml ================================================ [build-system] requires = [ "setuptools >= 75.0" ] build-backend = "setuptools.build_meta" [project] name = "data_formulator" version = "0.7.0a1" requires-python = ">=3.11" authors = [ {name = "Chenglong Wang", email = "chenglong.wang@microsoft.com"}, {name = "Dan Marshall", email = "danmar@microsoft.com"}, ] readme = "README.md" license = "MIT" description = "Data Formulator is research protoype data visualization tool powered by AI." keywords = ["data visualization", "LLM", "AI"] classifiers = [ "Development Status :: 4 - Beta", "Programming Language :: Python" ] dependencies = [ "jupyter", "pandas", "flask", "flask-cors", "flask-limiter", "openai", "python-dotenv", "vega_datasets", "litellm", "duckdb", "numpy", "vl-convert-python", "backoff", "beautifulsoup4", "scikit-learn", "azure-identity", "azure-kusto-data", "azure-keyvault-secrets", "azure-storage-blob", "google-cloud-bigquery", "google-auth", "db-dtypes", "boto3", "pymysql", "pyodbc", "pymongo", "yfinance", "connectorx>=0.4.5", "pyarrow>=23.0.0", ] [project.urls] Homepage = "https://github.com/microsoft/data-formulator" Repository = "https://github.com/microsoft/data-formulator.git" "Bug Tracker" = "https://github.com/microsoft/data-formulator/issues" [tool.setuptools] package-dir = {"" = "py-src"} include-package-data = true [project.scripts] data_formulator = "data_formulator:run_app" [tool.uv] dev-dependencies = [ "build", ] ================================================ FILE: requirements.txt ================================================ # Minimal top-level dependencies for deployment. # Generated from pyproject.toml [project.dependencies]. # Use this instead of the full uv-exported requirements.txt for faster # App Service deployments (pip resolves transitive deps automatically). jupyter pandas flask flask-cors flask-limiter openai python-dotenv vega_datasets litellm duckdb numpy vl-convert-python backoff beautifulsoup4 scikit-learn azure-identity azure-kusto-data azure-keyvault-secrets azure-storage-blob google-cloud-bigquery google-auth db-dtypes boto3 pymysql pyodbc pymongo yfinance connectorx>=0.4.5 pyarrow>=23.0.0 ================================================ FILE: src/app/App.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React, { FC, useCallback, useEffect, useState } from 'react'; import '../scss/App.scss'; import { useDispatch, useSelector } from "react-redux"; import { DataFormulatorState, dfActions, dfSelectors, fetchAvailableModels, } from './dfSlice' import { getBrowserId } from './identity'; import { red, purple, blue, brown, yellow, orange, } from '@mui/material/colors'; import { palettes, defaultPaletteKey, paletteKeys, bgAlpha } from './tokens'; import _ from 'lodash'; import { Button, Tooltip, Typography, Box, Toolbar, Divider, DialogTitle, Dialog, DialogContent, Link, DialogContentText, DialogActions, ToggleButtonGroup, ToggleButton, Menu, MenuItem, TextField, SvgIcon, IconButton, Select, FormControl, InputLabel, ListItemIcon, ListItemText, CircularProgress, LinearProgress, } from '@mui/material'; import MuiAppBar from '@mui/material/AppBar'; import { alpha, createTheme, styled, ThemeProvider } from '@mui/material/styles'; import PowerSettingsNewIcon from '@mui/icons-material/PowerSettingsNew'; import ClearIcon from '@mui/icons-material/Clear'; import { DataFormulatorFC } from '../views/DataFormulator'; import GridViewIcon from '@mui/icons-material/GridView'; import ViewSidebarIcon from '@mui/icons-material/ViewSidebar'; import SettingsIcon from '@mui/icons-material/Settings'; import { createBrowserRouter, RouterProvider, } from "react-router-dom"; import { About } from '../views/About'; import ChartGallery from '../views/ChartGallery'; import { MessageSnackbar } from '../views/MessageSnackbar'; import { ChartRenderService } from '../views/ChartRenderService'; import { DictTable } from '../components/ComponentType'; import { AppDispatch } from './store'; import dfLogo from '../assets/df-logo.png'; import { ModelSelectionButton } from '../views/ModelSelectionDialog'; import KeyboardArrowDownIcon from '@mui/icons-material/KeyboardArrowDown'; import UploadFileIcon from '@mui/icons-material/UploadFile'; import DownloadIcon from '@mui/icons-material/Download'; import SaveIcon from '@mui/icons-material/Save'; import FolderOpenIcon from '@mui/icons-material/FolderOpen'; import RefreshIcon from '@mui/icons-material/Refresh'; import { getUrls, fetchWithIdentity } from './utils'; import { persistor } from './store'; import { UnifiedDataUploadDialog } from '../views/UnifiedDataUploadDialog'; import ChatIcon from '@mui/icons-material/Chat'; import ArticleIcon from '@mui/icons-material/Article'; import EditIcon from '@mui/icons-material/Edit'; import ArrowBackIcon from '@mui/icons-material/ArrowBack'; import GitHubIcon from '@mui/icons-material/GitHub'; import UploadIcon from '@mui/icons-material/Upload'; import InfoOutlinedIcon from '@mui/icons-material/InfoOutlined'; import YouTubeIcon from '@mui/icons-material/YouTube'; import PublicIcon from '@mui/icons-material/Public'; // Discord Icon Component const DiscordIcon: FC<{ sx?: any }> = ({ sx }) => ( <SvgIcon sx={sx} viewBox="0 0 24 24"> <path d="M20.317 4.37a19.791 19.791 0 0 0-4.885-1.515a.074.074 0 0 0-.079.037c-.21.375-.444.864-.608 1.25a18.27 18.27 0 0 0-5.487 0a12.64 12.64 0 0 0-.617-1.25a.077.077 0 0 0-.079-.037A19.736 19.736 0 0 0 3.677 4.37a.07.07 0 0 0-.032.027C.533 9.046-.32 13.58.099 18.057a.082.082 0 0 0 .031.057a19.9 19.9 0 0 0 5.993 3.03a.078.078 0 0 0 .084-.028a14.09 14.09 0 0 0 1.226-1.994a.076.076 0 0 0-.041-.106a13.107 13.107 0 0 1-1.872-.892a.077.077 0 0 1-.008-.128a10.2 10.2 0 0 0 .372-.292a.074.074 0 0 1 .077-.01c3.928 1.793 8.18 1.793 12.062 0a.074.074 0 0 1 .078.01c.12.098.246.198.373.292a.077.077 0 0 1-.006.127a12.299 12.299 0 0 1-1.873.892a.077.077 0 0 0-.041.107c.36.698.772 1.362 1.225 1.993a.076.076 0 0 0 .084.028a19.839 19.839 0 0 0 6.002-3.03a.077.077 0 0 0 .032-.054c.5-5.177-.838-9.674-3.549-13.66a.061.061 0 0 0-.031-.03zM8.02 15.33c-1.183 0-2.157-1.085-2.157-2.419c0-1.333.956-2.419 2.157-2.419c1.21 0 2.176 1.096 2.157 2.42c0 1.333-.956 2.418-2.157 2.418zm7.975 0c-1.183 0-2.157-1.085-2.157-2.419c0-1.333.955-2.419 2.157-2.419c1.21 0 2.176 1.096 2.157 2.42c0 1.333-.946 2.418-2.157 2.418z" fill="currentColor"/> </SvgIcon> ); const AppBar = styled(MuiAppBar)(({ theme }) => ({ color: 'black', backgroundColor: "transparent", //borderBottom: "1px solid #C3C3C3", boxShadow: "none", transition: theme.transitions.create(['margin', 'width'], { easing: theme.transitions.easing.sharp, duration: theme.transitions.duration.leavingScreen, }), })); declare module '@mui/material/styles' { interface PaletteColor { bgcolor?: string; textColor?: string; } interface SimplePaletteColorOptions { bgcolor?: string; textColor?: string; } interface Palette { derived: Palette['primary']; custom: Palette['primary']; } interface PaletteOptions { derived: PaletteOptions['primary']; custom: PaletteOptions['primary']; } } export const toolName = "Data Formulator" export interface AppFCProps { } // Extract menu components into separate components to prevent full app re-renders const TableMenu: React.FC = () => { const [dialogOpen, setDialogOpen] = useState<boolean>(false); return ( <> <Button variant="text" onClick={() => setDialogOpen(true)} sx={{ textTransform: 'none' }} > Data </Button> {/* Unified Data Upload Dialog */} <UnifiedDataUploadDialog open={dialogOpen} onClose={() => setDialogOpen(false)} initialTab="menu" /> </> ); }; const SaveSessionDialog: React.FC<{open: boolean, onClose: () => void}> = ({open, onClose}) => { const [sessionName, setSessionName] = useState(''); const [saving, setSaving] = useState(false); const dispatch = useDispatch(); const tables = useSelector((state: DataFormulatorState) => state.tables); const fullState = useSelector((state: DataFormulatorState) => { const excludedFields = new Set([ 'models', 'selectedModelId', 'testedModels', 'dataLoaderConnectParams', 'identity', 'agentRules', 'serverConfig', ]); const stateToSerialize: any = {}; for (const [key, value] of Object.entries(state)) { if (!excludedFields.has(key)) { stateToSerialize[key] = value; } } return stateToSerialize; }); const handleSave = async () => { if (!sessionName.trim()) return; setSaving(true); try { const res = await fetchWithIdentity(getUrls().SESSION_SAVE, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ name: sessionName.trim(), state: fullState }), }); const data = await res.json(); if (data.status === 'ok') { dispatch(dfActions.addMessages({ timestamp: Date.now(), component: "Session", type: "success", value: `Session "${sessionName}" saved` })); onClose(); } else { dispatch(dfActions.addMessages({ timestamp: Date.now(), component: "Session", type: "error", value: data.message || 'Save failed' })); } } catch (e) { dispatch(dfActions.addMessages({ timestamp: Date.now(), component: "Session", type: "error", value: 'Failed to save session' })); } setSaving(false); }; return ( <Dialog open={open} onClose={onClose} maxWidth="xs" fullWidth> <DialogTitle>Save Session</DialogTitle> <DialogContent> <TextField autoFocus fullWidth margin="dense" label="Session name" value={sessionName} onChange={(e) => setSessionName(e.target.value)} onKeyDown={(e) => { if (e.key === 'Enter') handleSave(); }} helperText={`${tables.length} table(s) will be saved`} /> </DialogContent> <DialogActions> <Button onClick={onClose}>Cancel</Button> <Button onClick={handleSave} disabled={!sessionName.trim() || saving}> {saving ? 'Saving...' : 'Save'} </Button> </DialogActions> </Dialog> ); }; const LoadSessionDialog: React.FC<{open: boolean, onClose: () => void}> = ({open, onClose}) => { const [sessions, setSessions] = useState<{name: string, saved_at: string}[]>([]); const [loading, setLoading] = useState(false); const [listLoading, setListLoading] = useState(false); const [confirmDelete, setConfirmDelete] = useState<string | null>(null); const dispatch = useDispatch(); const fetchSessions = useCallback(async () => { setListLoading(true); try { const res = await fetchWithIdentity(getUrls().SESSION_LIST); const data = await res.json(); if (data.status === 'ok') setSessions(data.sessions); } catch (e) { /* ignore */ } setListLoading(false); }, []); useEffect(() => { if (!open) return; fetchSessions(); }, [open, fetchSessions]); const handleLoad = async (name: string) => { setLoading(true); dispatch(dfActions.setSessionLoading({ loading: true, label: `Loading session "${name}"...` })); onClose(); try { const res = await fetchWithIdentity(getUrls().SESSION_LOAD, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ name }), }); const data = await res.json(); if (data.status === 'ok') { dispatch(dfActions.loadState(data.state)); dispatch(dfActions.addMessages({ timestamp: Date.now(), component: "Session", type: "success", value: `Session "${name}" loaded` })); } else { dispatch(dfActions.addMessages({ timestamp: Date.now(), component: "Session", type: "error", value: data.message || 'Load failed' })); } } catch (e) { dispatch(dfActions.addMessages({ timestamp: Date.now(), component: "Session", type: "error", value: 'Failed to load session' })); } setLoading(false); dispatch(dfActions.setSessionLoading({ loading: false })); }; const handleDelete = async (name: string) => { try { const res = await fetchWithIdentity(getUrls().SESSION_DELETE, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ name }), }); const data = await res.json(); if (data.status === 'ok') { setSessions(prev => prev.filter(s => s.name !== name)); dispatch(dfActions.addMessages({ timestamp: Date.now(), component: "Session", type: "success", value: `Session "${name}" deleted` })); } } catch (e) { /* ignore */ } setConfirmDelete(null); }; return ( <Dialog open={open} onClose={onClose} maxWidth="xs" fullWidth> <DialogTitle sx={{ display: 'flex', alignItems: 'center', justifyContent: 'space-between' }}> Load Session <Tooltip title="Refresh session list"> <IconButton size="small" onClick={fetchSessions} disabled={listLoading}> {listLoading ? <CircularProgress size={18} /> : <RefreshIcon fontSize="small" />} </IconButton> </Tooltip> </DialogTitle> <DialogContent sx={{ px: 1 }}> {listLoading && sessions.length === 0 ? ( <Box sx={{ display: 'flex', flexDirection: 'column', alignItems: 'center', py: 4, gap: 1.5 }}> <CircularProgress size={28} /> <Typography variant="body2" color="text.secondary">Loading sessions...</Typography> </Box> ) : sessions.length === 0 ? ( <DialogContentText sx={{ px: 1 }}>No saved sessions found.</DialogContentText> ) : ( sessions.map(s => ( <Box key={s.name} sx={{ display: 'flex', alignItems: 'center', justifyContent: 'space-between', px: 1.5, py: 1, mx: 0, my: 0.5, borderRadius: 1, cursor: 'pointer', '&:hover': { backgroundColor: 'action.hover' }, transition: 'background-color 0.15s', }} onClick={() => handleLoad(s.name)} > <Box sx={{ flex: 1, minWidth: 0 }}> <Typography variant="body2" fontWeight="bold" noWrap>{s.name}</Typography> <Typography variant="caption" color="text.secondary"> {new Date(s.saved_at).toLocaleString()} </Typography> </Box> {confirmDelete === s.name ? ( <Box sx={{ display: 'flex', alignItems: 'center', gap: 0.5 }} onClick={e => e.stopPropagation()}> <Button size="small" color="error" sx={{ minWidth: 0, fontSize: 11, textTransform: 'none' }} onClick={() => handleDelete(s.name)}>delete</Button> <Button size="small" sx={{ minWidth: 0, fontSize: 11, textTransform: 'none' }} onClick={() => setConfirmDelete(null)}>cancel</Button> </Box> ) : ( <Tooltip title="Delete session"> <IconButton size="small" onClick={(e) => { e.stopPropagation(); setConfirmDelete(s.name); }}> <ClearIcon fontSize="small" /> </IconButton> </Tooltip> )} </Box> )) )} </DialogContent> <DialogActions> <Button onClick={onClose}>Close</Button> </DialogActions> </Dialog> ); }; const SessionMenu: React.FC = () => { const [anchorEl, setAnchorEl] = useState<null | HTMLElement>(null); const [saveDialogOpen, setSaveDialogOpen] = useState(false); const [loadDialogOpen, setLoadDialogOpen] = useState(false); const [recentSessions, setRecentSessions] = useState<{name: string, saved_at: string}[]>([]); const [exporting, setExporting] = useState(false); const importRef = React.useRef<HTMLInputElement>(null); const open = Boolean(anchorEl); const dispatch = useDispatch(); const serverConfig = useSelector((state: DataFormulatorState) => state.serverConfig); const diskPersistenceDisabled = serverConfig.DISABLE_DATABASE; const fullState = useSelector((state: DataFormulatorState) => { const excludedFields = new Set([ 'models', 'selectedModelId', 'testedModels', 'dataLoaderConnectParams', 'identity', 'agentRules', 'serverConfig', ]); const obj: any = {}; for (const [key, value] of Object.entries(state)) { if (!excludedFields.has(key)) obj[key] = value; } return obj; }); // Fetch recent sessions when the menu opens useEffect(() => { if (!open || diskPersistenceDisabled) return; (async () => { try { const res = await fetchWithIdentity(getUrls().SESSION_LIST); const data = await res.json(); if (data.status === 'ok') setRecentSessions(data.sessions.slice(0, 3)); } catch (e) { /* ignore */ } })(); }, [open]); const closeMenu = () => setAnchorEl(null); const handleLoadSession = async (name: string) => { closeMenu(); dispatch(dfActions.setSessionLoading({ loading: true, label: `Loading session "${name}"...` })); try { const res = await fetchWithIdentity(getUrls().SESSION_LOAD, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ name }), }); const data = await res.json(); if (data.status === 'ok') { dispatch(dfActions.loadState(data.state)); dispatch(dfActions.addMessages({ timestamp: Date.now(), component: "Session", type: "success", value: `Session "${name}" loaded` })); } else { dispatch(dfActions.addMessages({ timestamp: Date.now(), component: "Session", type: "error", value: data.message || 'Load failed' })); } } catch (e) { dispatch(dfActions.addMessages({ timestamp: Date.now(), component: "Session", type: "error", value: 'Failed to load session' })); } dispatch(dfActions.setSessionLoading({ loading: false })); }; const handleExport = async () => { closeMenu(); setExporting(true); try { const res = await fetchWithIdentity(getUrls().SESSION_EXPORT, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ state: fullState }), }); if (!res.ok) throw new Error('Export failed'); const blob = await res.blob(); const disposition = res.headers.get('content-disposition'); const match = disposition?.match(/filename="?(.+?)"?$/); const filename = match?.[1] || 'session.dfsession'; const a = document.createElement('a'); a.href = URL.createObjectURL(blob); a.download = filename; a.click(); URL.revokeObjectURL(a.href); dispatch(dfActions.addMessages({ timestamp: Date.now(), component: "Session", type: "success", value: "Session exported" })); } catch (e) { dispatch(dfActions.addMessages({ timestamp: Date.now(), component: "Session", type: "error", value: 'Failed to export session' })); } setExporting(false); }; const handleImport = async (event: React.ChangeEvent<HTMLInputElement>) => { const file = event.target.files?.[0]; if (!file) return; closeMenu(); dispatch(dfActions.setSessionLoading({ loading: true, label: `Importing session from ${file.name}...` })); try { const formData = new FormData(); formData.append('file', file); const res = await fetchWithIdentity(getUrls().SESSION_IMPORT, { method: 'POST', body: formData, }); const data = await res.json(); if (data.status === 'ok') { dispatch(dfActions.loadState(data.state)); dispatch(dfActions.addMessages({ timestamp: Date.now(), component: "Session", type: "success", value: `Session imported from ${file.name}` })); } else { dispatch(dfActions.addMessages({ timestamp: Date.now(), component: "Session", type: "error", value: data.message || 'Import failed' })); } } catch (e) { dispatch(dfActions.addMessages({ timestamp: Date.now(), component: "Session", type: "error", value: 'Failed to import session' })); } dispatch(dfActions.setSessionLoading({ loading: false })); if (importRef.current) importRef.current.value = ''; }; return ( <> <Button variant="text" onClick={(e) => setAnchorEl(e.currentTarget)} endIcon={<KeyboardArrowDownIcon />} sx={{ textTransform: 'none' }} > Session </Button> <Menu anchorEl={anchorEl} open={open} onClose={closeMenu} slotProps={{ paper: { sx: { minWidth: 200 } } }} > <Tooltip title={diskPersistenceDisabled ? "Install locally to use this feature" : ""} placement="right"> <span> <MenuItem disabled={diskPersistenceDisabled} onClick={() => { setSaveDialogOpen(true); closeMenu(); }} sx={{ fontSize: '12px', display: 'flex', alignItems: 'center', gap: 1 }}> <SaveIcon sx={{ fontSize: 16, color: 'text.secondary' }} /> Save session </MenuItem> </span> </Tooltip> <Tooltip title={diskPersistenceDisabled ? "Install locally to use this feature" : ""} placement="right"> <span> <MenuItem disabled={diskPersistenceDisabled} onClick={() => { setLoadDialogOpen(true); closeMenu(); }} sx={{ fontSize: '12px', display: 'flex', alignItems: 'center', gap: 1 }}> <FolderOpenIcon sx={{ fontSize: 16, color: 'text.secondary' }} /> Open session... </MenuItem> </span> </Tooltip> {!diskPersistenceDisabled && recentSessions.length > 0 && [ <Divider key="div-recent" />, <Typography key="label-recent" variant="caption" sx={{ px: 2, py: 0.5, color: 'text.secondary', display: 'block', fontSize: 10 }}> Quick resume </Typography>, ...recentSessions.map(s => ( <MenuItem key={s.name} onClick={() => handleLoadSession(s.name)} sx={{ pl: 4, py: 0.25, minHeight: 0, fontSize: '12px', display: 'flex', alignItems: 'center', justifyContent: 'space-between', gap: 2 }}> <Typography noWrap sx={{ fontSize: 12 }}>{s.name}</Typography> <Typography noWrap sx={{ fontSize: 10, color: 'text.secondary', flexShrink: 0 }}> {new Date(s.saved_at).toLocaleDateString()} </Typography> </MenuItem> )), ]} <Divider /> <MenuItem onClick={handleExport} disabled={exporting} sx={{ fontSize: '12px', display: 'flex', alignItems: 'center', gap: 1 }}> <DownloadIcon sx={{ fontSize: 16, color: 'text.secondary' }} /> {exporting ? 'Exporting...' : 'Export to file'} </MenuItem> <MenuItem onClick={() => importRef.current?.click()} sx={{ fontSize: '12px', display: 'flex', alignItems: 'center', gap: 1 }}> <UploadFileIcon sx={{ fontSize: 16, color: 'text.secondary' }} /> Import from file <input type="file" hidden accept=".dfsession,.zip" ref={importRef} onChange={handleImport} /> </MenuItem> </Menu> <SaveSessionDialog open={saveDialogOpen} onClose={() => setSaveDialogOpen(false)} /> <LoadSessionDialog open={loadDialogOpen} onClose={() => setLoadDialogOpen(false)} /> </> ); }; const ResetDialog: React.FC = () => { const [open, setOpen] = useState(false); const [exiting, setExiting] = useState(false); const dispatch = useDispatch(); const handleExit = async () => { setExiting(true); // Clear workspace on server first try { await fetchWithIdentity(getUrls().RESET_DB_FILE, { method: 'POST' }); } catch (e) { console.warn('Failed to reset server workspace:', e); } dispatch(dfActions.resetState()); // Flush the reset state to IndexedDB so the persisted // state matches (preserves models, config, agentRules). await persistor.flush(); window.location.reload(); }; return ( <> <Button variant="text" sx={{textTransform: 'none'}} onClick={() => setOpen(true)} endIcon={<PowerSettingsNewIcon />} > Exit </Button> <Dialog onClose={exiting ? undefined : () => setOpen(false)} open={open} sx={{ '& .MuiDialog-paper': { position: 'relative', overflow: 'hidden' } }}> <DialogTitle sx={{ display: "flex", alignItems: "center" }}>Exit Session?</DialogTitle> <DialogContent> <DialogContentText> All unsaved content (data, charts, reports) will be lost. Make sure to save your session before exiting. </DialogContentText> </DialogContent> <DialogActions> <Button disabled={exiting} onClick={handleExit} endIcon={<PowerSettingsNewIcon />} > Exit session </Button> <Button onClick={() => setOpen(false)} disabled={exiting}>cancel</Button> </DialogActions> {/* Cleaning overlay on top of dialog */} {exiting && ( <Box sx={{ position: 'absolute', top: 0, left: 0, right: 0, bottom: 0, backgroundColor: 'rgba(255, 255, 255, 0.92)', backdropFilter: 'blur(4px)', display: 'flex', flexDirection: 'column', alignItems: 'center', justifyContent: 'center', gap: 2, zIndex: 1, borderRadius: 'inherit', }}> <Typography sx={{ fontSize: 36, animation: 'sweepBroom 1.2s ease-in-out infinite', '@keyframes sweepBroom': { '0%, 100%': { transform: 'rotate(-15deg) translateX(0px)', }, '25%': { transform: 'rotate(-5deg) translateX(8px)', }, '50%': { transform: 'rotate(-15deg) translateX(0px)', }, '75%': { transform: 'rotate(-25deg) translateX(-8px)', }, }, transformOrigin: 'top center', }}> 🧹 </Typography> <Typography variant="body1" color="text.secondary" sx={{ fontWeight: 500 }}> Cleaning workspace... </Typography> <LinearProgress sx={{ width: 200, mt: 1, borderRadius: 1 }} /> </Box> )} </Dialog> </> ); }; const ConfigDialog: React.FC = () => { const [open, setOpen] = useState(false); const dispatch = useDispatch(); const config = useSelector((state: DataFormulatorState) => state.config); const [formulateTimeoutSeconds, setFormulateTimeoutSeconds] = useState(config.formulateTimeoutSeconds ?? 30); const [defaultChartWidth, setDefaultChartWidth] = useState(config.defaultChartWidth ?? 300); const [defaultChartHeight, setDefaultChartHeight] = useState(config.defaultChartHeight ?? 300); const [maxStretchFactor, setMaxStretchFactor] = useState(config.maxStretchFactor ?? 2.0); const [frontendRowLimit, setFrontendRowLimit] = useState(config.frontendRowLimit ?? 50000); const [paletteKey, setPaletteKey] = useState( (config.paletteKey && palettes[config.paletteKey]) ? config.paletteKey : defaultPaletteKey ); // Add check for changes const hasChanges = formulateTimeoutSeconds !== config.formulateTimeoutSeconds || defaultChartWidth !== config.defaultChartWidth || defaultChartHeight !== config.defaultChartHeight || maxStretchFactor !== config.maxStretchFactor || frontendRowLimit !== config.frontendRowLimit || paletteKey !== ((config.paletteKey && palettes[config.paletteKey]) ? config.paletteKey : defaultPaletteKey); return ( <> <Button variant="text" sx={{textTransform: 'none'}} onClick={() => setOpen(true)} startIcon={<SettingsIcon />}> Settings </Button> <Dialog onClose={() => setOpen(false)} open={open}> <DialogTitle>Settings</DialogTitle> <DialogContent> <Box sx={{ display: 'flex', flexDirection: 'column', gap: 3, maxWidth: 400 }}> <Divider><Typography variant="caption">Frontend</Typography></Divider> <FormControl fullWidth size="small"> <InputLabel id="palette-select-label" sx={{ fontSize: 13 }}>Color Theme</InputLabel> <Select labelId="palette-select-label" value={paletteKey} label="Color Theme" onChange={(e) => setPaletteKey(e.target.value)} sx={{ fontSize: 13 }} renderValue={(key) => { const p = palettes[key]; return ( <Box sx={{ display: 'flex', alignItems: 'center', gap: 1 }}> <Box sx={{ width: 12, height: 12, borderRadius: '50%', backgroundColor: p.primary.main, flexShrink: 0 }} /> <Box sx={{ width: 12, height: 12, borderRadius: '50%', backgroundColor: p.custom.main, flexShrink: 0 }} /> <Typography sx={{ fontSize: 13 }}>{p.name}</Typography> </Box> ); }} > {paletteKeys.map(key => { const p = palettes[key]; return ( <MenuItem key={key} value={key} sx={{ py: 0.5 }}> <Box sx={{ display: 'flex', alignItems: 'center', gap: 1, mr: 1.5 }}> <Box sx={{ width: 14, height: 14, borderRadius: '50%', backgroundColor: p.primary.main, border: '1px solid rgba(0,0,0,0.1)' }} /> <Box sx={{ width: 14, height: 14, borderRadius: '50%', backgroundColor: p.custom.main, border: '1px solid rgba(0,0,0,0.1)' }} /> </Box> <ListItemText primary={p.name} slotProps={{ primary: { sx: { fontSize: 13 } } }} /> </MenuItem> ); })} </Select> </FormControl> <Box sx={{ display: 'flex', alignItems: 'center', gap: 2 }}> <Box sx={{ flex: 1 }}> <TextField label="default chart width" type="number" variant="outlined" value={defaultChartWidth} onChange={(e) => { const value = parseInt(e.target.value); setDefaultChartWidth(value); }} fullWidth slotProps={{ input: { inputProps: { min: 100, max: 1000 } } }} error={defaultChartWidth < 100 || defaultChartWidth > 1000} helperText={defaultChartWidth < 100 || defaultChartWidth > 1000 ? "Value must be between 100 and 1000 pixels" : ""} /> </Box> <Typography variant="caption" color="text.secondary" sx={{ mt: 1, display: 'block' }}> <ClearIcon fontSize="small" /> </Typography> <Box sx={{ flex: 1 }}> <TextField label="default chart height" type="number" variant="outlined" value={defaultChartHeight} onChange={(e) => { const value = parseInt(e.target.value); setDefaultChartHeight(value); }} fullWidth slotProps={{ input: { inputProps: { min: 100, max: 1000 } } }} error={defaultChartHeight < 100 || defaultChartHeight > 1000} helperText={defaultChartHeight < 100 || defaultChartHeight > 1000 ? "Value must be between 100 and 1000 pixels" : ""} /> </Box> </Box> <Box sx={{ display: 'flex', alignItems: 'center', gap: 2 }}> <Box sx={{ flex: 1 }}> <TextField label="local-only row limit" type="number" variant="outlined" value={frontendRowLimit} onChange={(e) => { const value = parseInt(e.target.value); setFrontendRowLimit(value); }} fullWidth slotProps={{ input: { inputProps: { min: 100, max: 1000000 } } }} error={frontendRowLimit < 100 || frontendRowLimit > 1000000} helperText={frontendRowLimit < 100 || frontendRowLimit > 1000000 ? "Value must be between 100 and 1,000,000 rows" : ""} /> <Typography variant="caption" color="text.secondary" sx={{ mt: 1, display: 'block' }}> Maximum number of rows kept when loading data locally (not stored on server). </Typography> </Box> </Box> <Box sx={{ display: 'flex', alignItems: 'center', gap: 2 }}> <Box sx={{ flex: 1 }}> <TextField label="max chart stretch factor" type="number" variant="outlined" value={maxStretchFactor} onChange={(e) => { const value = parseFloat(e.target.value); setMaxStretchFactor(value); }} fullWidth slotProps={{ input: { inputProps: { min: 1, max: 5, step: 0.1 } } }} error={isNaN(maxStretchFactor) || maxStretchFactor < 1 || maxStretchFactor > 5} helperText={isNaN(maxStretchFactor) || maxStretchFactor < 1 || maxStretchFactor > 5 ? "Value must be between 1.0 and 5.0" : ""} /> <Typography variant="caption" color="text.secondary" sx={{ mt: 1, display: 'block' }}> How much charts can grow beyond the base size (1.0 = no stretch, 2.0 = up to 2×). </Typography> </Box> </Box> <Divider><Typography variant="caption">Backend</Typography></Divider> <Box sx={{ display: 'flex', alignItems: 'center', gap: 2 }}> <Box sx={{ flex: 1 }}> <TextField label="formulate timeout (seconds)" type="number" variant="outlined" value={formulateTimeoutSeconds} onChange={(e) => { const value = parseInt(e.target.value); setFormulateTimeoutSeconds(value); }} inputProps={{ min: 0, max: 3600, }} error={formulateTimeoutSeconds <= 0 || formulateTimeoutSeconds > 3600} helperText={formulateTimeoutSeconds <= 0 || formulateTimeoutSeconds > 3600 ? "Value must be between 1 and 3600 seconds" : ""} fullWidth /> <Typography variant="caption" color="text.secondary" sx={{ mt: 1, display: 'block' }}> Maximum time allowed for the formulation process before timing out. </Typography> </Box> </Box> </Box> </DialogContent> <DialogActions sx={{'.MuiButton-root': {textTransform: 'none'}}}> <Button sx={{marginRight: 'auto'}} onClick={() => { setFormulateTimeoutSeconds(30); setDefaultChartWidth(300); setDefaultChartHeight(300); setMaxStretchFactor(2.0); setFrontendRowLimit(50000); setPaletteKey(defaultPaletteKey); }}>Reset to default</Button> <Button onClick={() => setOpen(false)}>Cancel</Button> <Button variant={hasChanges ? "contained" : "text"} disabled={!hasChanges || isNaN(formulateTimeoutSeconds) || formulateTimeoutSeconds <= 0 || formulateTimeoutSeconds > 3600 || isNaN(defaultChartWidth) || defaultChartWidth <= 0 || defaultChartWidth > 1000 || isNaN(defaultChartHeight) || defaultChartHeight <= 0 || defaultChartHeight > 1000 || isNaN(maxStretchFactor) || maxStretchFactor < 1 || maxStretchFactor > 5 || isNaN(frontendRowLimit) || frontendRowLimit < 100 || frontendRowLimit > 1000000} onClick={() => { dispatch(dfActions.setConfig({formulateTimeoutSeconds, defaultChartWidth, defaultChartHeight, maxStretchFactor, frontendRowLimit, paletteKey})); setOpen(false); }} > Apply </Button> </DialogActions> </Dialog> </> ); } export const AppFC: FC<AppFCProps> = function AppFC(appProps) { const dispatch = useDispatch<AppDispatch>(); const viewMode = useSelector((state: DataFormulatorState) => state.viewMode); const tables = useSelector((state: DataFormulatorState) => state.tables); const generatedReports = useSelector((state: DataFormulatorState) => state.generatedReports); const focusedId = useSelector((state: DataFormulatorState) => state.focusedId); const serverConfig = useSelector((state: DataFormulatorState) => state.serverConfig); const rawPaletteKey = useSelector((state: DataFormulatorState) => state.config.paletteKey); const activePaletteKey = (rawPaletteKey && palettes[rawPaletteKey]) ? rawPaletteKey : defaultPaletteKey; useEffect(() => { fetchWithIdentity(getUrls().APP_CONFIG) .then(response => response.json()) .then(data => { dispatch(dfActions.setServerConfig(data)); }); }, []); // User authentication state const [userInfo, setUserInfo] = useState<{ name: string, userId: string } | undefined>(undefined); const [authChecked, setAuthChecked] = useState(false); // Check for authenticated user first useEffect(() => { fetch('/.auth/me') .then(function (response) { return response.json(); }) .then(function (result) { if (Array.isArray(result) && result.length > 0) { let authInfo = result[0]; let userInfo = { name: authInfo['user_claims'].find((item: any) => item.typ == 'name')?.val || '', userId: authInfo['user_id'] } setUserInfo(userInfo); } }).catch(err => { // User is not logged in, will use browser identity }).finally(() => { setAuthChecked(true); }); }, []); // Initialize identity after auth check completes // No server round-trip needed - identity is determined client-side: // Priority: user identity (if logged in) > browser identity (localStorage-based, shared across tabs) useEffect(() => { if (authChecked) { if (userInfo?.userId) { // User is logged in - use their user ID dispatch(dfActions.setIdentity({ type: 'user', id: userInfo.userId })); } else { // Not logged in - use browser ID (from localStorage, shared across tabs) dispatch(dfActions.setIdentity({ type: 'browser', id: getBrowserId() })); } } }, [authChecked, userInfo?.userId]); useEffect(() => { document.title = toolName; dispatch(fetchAvailableModels()); }, []); let theme = createTheme({ typography: { fontFamily: [ "Arial", "Roboto", "Helvetica Neue", "sans-serif" ].join(",") }, // Default Material UI palette // Active palette from user config — selectable via Settings dialog // Available: material, fluent, vivid, jewel, electric, tealCoral, copilot palette: (() => { const p = palettes[activePaletteKey]; const bg = (entry: { main: string; bgcolor?: string }) => entry.bgcolor ?? alpha(entry.main, bgAlpha); const tc = (entry: { main: string; textColor?: string }) => entry.textColor ?? entry.main; return { primary: { main: p.primary.main, bgcolor: bg(p.primary), textColor: tc(p.primary) }, secondary: { main: p.secondary.main, bgcolor: bg(p.secondary), textColor: tc(p.secondary) }, derived: { main: p.derived.main, bgcolor: bg(p.derived), textColor: tc(p.derived) }, custom: { main: p.custom.main, bgcolor: bg(p.custom), textColor: tc(p.custom) }, warning: { main: p.warning.main }, }; })(), components: { MuiButton: { styleOverrides: { text: ({ ownerState, theme: t }) => { const c = ownerState.color; if (c && c !== 'inherit' && c !== 'error' && c !== 'info' && c !== 'success' && c in t.palette) { const p = (t.palette as any)[c]; if (p?.textColor) return { color: p.textColor }; } return {}; }, outlined: ({ ownerState, theme: t }) => { const c = ownerState.color; if (c && c !== 'inherit' && c !== 'error' && c !== 'info' && c !== 'success' && c in t.palette) { const p = (t.palette as any)[c]; if (p?.textColor) return { color: p.textColor, borderColor: alpha(p.textColor, 0.5) }; } return {}; }, }, }, MuiIconButton: { styleOverrides: { root: ({ ownerState, theme: t }) => { const c = ownerState.color; if (c && c !== 'inherit' && c !== 'default' && c !== 'error' && c !== 'info' && c !== 'success' && c in t.palette) { const p = (t.palette as any)[c]; if (p?.textColor) return { color: p.textColor }; } return {}; }, }, }, MuiLink: { styleOverrides: { root: ({ ownerState, theme: t }) => { const c = ownerState.color as string | undefined; if (c && c !== 'inherit' && c in t.palette) { const p = (t.palette as any)[c]; if (p?.textColor) return { color: p.textColor }; } return {}; }, }, }, }, }); // Check if we're on the about page const isAboutPage = window.location.pathname === '/about'; const isGalleryPage = window.location.pathname === '/gallery'; const isAppPage = !isAboutPage && !isGalleryPage; let appBar = [ <AppBar position="static" key="app-bar-main" > <Toolbar variant="dense" sx={{height: 40, minHeight: 36, position: 'relative'}}> <Button sx={{ display: "flex", flexDirection: "row", textTransform: "none", alignItems: 'stretch', backgroundColor: 'transparent', "&:hover": { backgroundColor: "transparent" } }} color="inherit"> <Box component="img" sx={{ height: 20, mr: 0.5 }} alt="" src={dfLogo} /> <Typography noWrap component="h1" sx={{ fontWeight: 300, display: { xs: 'none', sm: 'block' }, letterSpacing: '0.03em' }}> {toolName} </Typography> </Button> <Box sx={{ ml: 2, height: '28px', my: 'auto', display: 'flex', }} > <Button component="a" href="/about" sx={{ textDecoration: 'none', textTransform: 'none', fontSize: '13px', fontWeight: 400, border: 'none', borderRadius: 0, px: 1.5, py: 0.5, minWidth: 'auto', color: isAboutPage ? 'text.primary' : 'text.secondary', backgroundColor: isAboutPage ? 'rgba(0, 0, 0, 0.08)' : 'transparent', '&:hover': { color: 'text.primary', backgroundColor: isAboutPage ? 'rgba(0, 0, 0, 0.08)' : 'rgba(0, 0, 0, 0.04)', }, }} > About </Button> <Button component="a" href="/app" sx={{ textDecoration: 'none', textTransform: 'none', fontSize: '13px', fontWeight: 400, border: 'none', borderRadius: 0, px: 1.5, py: 0.5, minWidth: 'auto', color: isAppPage ? 'text.primary' : 'text.secondary', backgroundColor: isAppPage ? 'rgba(0, 0, 0, 0.08)' : 'transparent', '&:hover': { color: 'text.primary', backgroundColor: isAppPage ? 'rgba(0, 0, 0, 0.08)' : 'rgba(0, 0, 0, 0.04)', }, }} > App </Button> <Button component="a" href="/gallery" sx={{ textDecoration: 'none', textTransform: 'none', fontSize: '13px', fontWeight: 400, border: 'none', borderRadius: 0, px: 1.5, py: 0.5, minWidth: 'auto', color: isGalleryPage ? 'text.primary' : 'text.secondary', backgroundColor: isGalleryPage ? 'rgba(0, 0, 0, 0.08)' : 'transparent', '&:hover': { color: 'text.primary', backgroundColor: isGalleryPage ? 'rgba(0, 0, 0, 0.08)' : 'rgba(0, 0, 0, 0.04)', }, }} > Gallery </Button> </Box> {tables.length === 0 && ( <Typography noWrap sx={{ position: 'absolute', left: '50%', transform: 'translateX(-50%)', fontWeight: 500, fontSize: '0.65rem', color: 'text.disabled', letterSpacing: '0.15em', textTransform: 'uppercase' }}> Microsoft Research </Typography> )} {isAppPage && ( <Box sx={{ display: 'flex', ml: 'auto', fontSize: 14 }}> {focusedId !== undefined && <React.Fragment><ToggleButtonGroup value={viewMode} exclusive onChange={(_, newMode) => { if (newMode !== null) { dispatch(dfActions.setViewMode(newMode)); } }} sx={{ mr: 2, height: '28px', my: 'auto', '& .MuiToggleButton-root': { textTransform: 'none', fontWeight: 500, border: 'none', '&:hover': { backgroundColor: 'rgba(0, 0, 0, 0.04)', color: 'text.primary', }, }, }} > <ToggleButton value="editor"> <Box sx={{ display: 'flex', alignItems: 'center', gap: 1 }}> <Box component="span">Explore</Box> </Box> </ToggleButton> <ToggleButton value="report"> <Box sx={{ display: 'flex', alignItems: 'center', gap: 1 }}> <Box component="span"> {generatedReports.length > 0 ? `Reports (${generatedReports.length})` : 'Reports'} </Box> </Box> </ToggleButton> </ToggleButtonGroup> <ConfigDialog /> <Divider orientation="vertical" variant="middle" flexItem /></React.Fragment>} <ModelSelectionButton /> <Divider orientation="vertical" variant="middle" flexItem /> <Typography fontSize="inherit" sx={{ display: 'flex', alignItems: 'center', gap: 1 }}> <TableMenu /> </Typography> <Typography fontSize="inherit" sx={{ display: 'flex', alignItems: 'center', gap: 1 }}> <SessionMenu /> </Typography> {tables.length > 0 && <ResetDialog />} </Box> )} {isAboutPage && ( <Box sx={{ ml: 'auto', display: 'flex', gap: 0.5 }}> <Tooltip title="Watch Video"> <IconButton component="a" href="https://youtu.be/3ndlwt0Wi3c" target="_blank" rel="noopener noreferrer" aria-label="Watch Video" sx={{ color: 'inherit', '&:hover': { backgroundColor: 'rgba(0, 0, 0, 0.04)' } }} > <YouTubeIcon fontSize="small" /> </IconButton> </Tooltip> <Tooltip title="View on GitHub"> <IconButton component="a" href="https://github.com/microsoft/data-formulator" target="_blank" rel="noopener noreferrer" aria-label="View on GitHub" sx={{ color: 'inherit', '&:hover': { backgroundColor: 'rgba(0, 0, 0, 0.04)' } }} > <GitHubIcon fontSize="small" /> </IconButton> </Tooltip> <Tooltip title="Pip Install"> <IconButton component="a" href="https://pypi.org/project/data-formulator/" target="_blank" rel="noopener noreferrer" aria-label="Pip Install" sx={{ color: 'inherit', '&:hover': { backgroundColor: 'rgba(0, 0, 0, 0.04)' } }} > <Box component="img" src="/pip-logo.svg" sx={{ width: 20, height: 20 }} alt="pip logo" /> </IconButton> </Tooltip> <Tooltip title="Join Discord"> <IconButton component="a" href="https://discord.gg/mYCZMQKYZb" target="_blank" rel="noopener noreferrer" aria-label="Join Discord" sx={{ color: 'inherit', '&:hover': { backgroundColor: 'rgba(0, 0, 0, 0.04)' } }} > <DiscordIcon sx={{ fontSize: 20 }} /> </IconButton> </Tooltip> </Box> )} {isAppPage && ( <Tooltip title="View on GitHub"> <Button component="a" href="https://github.com/microsoft/data-formulator" target="_blank" rel="noopener noreferrer" sx={{ minWidth: 'auto', color: 'inherit', '&:hover': { backgroundColor: 'rgba(0, 0, 0, 0.04)' } }} > <GitHubIcon fontSize="medium" /> </Button> </Tooltip> )} </Toolbar> </AppBar> ]; let router = createBrowserRouter([ { path: "/about", element: <About />, }, { path: "/gallery", element: <ChartGallery />, }, { path: "/", element: <DataFormulatorFC />, }, { path: "*", element: <DataFormulatorFC />, errorElement: <Box sx={{ width: "100%", height: "100%", display: "flex" }}> <Typography color="gray" sx={{ margin: "150px auto" }}>An error has occurred, please <Link href="/">refresh the session</Link>. If the problem still exists, click close session.</Typography> </Box> } ]); let app = <Box sx={{ position: 'absolute', backgroundColor: 'rgba(255, 255, 255, 0.3)', top: 0, left: 0, right: 0, bottom: 0, overflow: 'auto', '& > *': { minWidth: '1000px', minHeight: '600px' }, }}> <Box sx={{ display: 'flex', flexDirection: 'column', height: '100%', width: '100%', overflow: 'hidden' }}> {appBar} <Box sx={{ flex: 1, minHeight: 0, overflow: 'hidden', '& > div': { height: '100%' } }}> <RouterProvider router={router} /> </Box> <MessageSnackbar /> <ChartRenderService /> </Box> </Box>; return ( <ThemeProvider theme={theme}> {app} </ThemeProvider> ); } function stringAvatar(name: string) { let displayName = "" try { let nameSplit = name.split(' ') displayName = `${nameSplit[0][0]}${nameSplit.length > 1 ? nameSplit[nameSplit.length - 1][0] : ''}` } catch { displayName = name ? name[0] : "?"; } return { sx: { bgcolor: "cornflowerblue", width: 36, height: 36, margin: "auto", fontSize: "1rem" }, children: displayName, }; } ================================================ FILE: src/app/chartCache.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Module-level chart render cache. * * Stores pre-rendered SVG and PNG thumbnail data for each chart, * keyed by chart ID. A specKey is stored alongside to detect * when the chart's inputs have changed and re-rendering is needed. * * This is deliberately NOT stored in Redux to avoid: * - Bloating serializable state with large SVG/PNG strings * - Breaking Redux DevTools with huge payloads * - Persisting stale render artifacts across sessions */ export interface ChartCacheEntry { svg: string; // Full-size SVG string (for VisualizationView) thumbnailDataUrl: string; // PNG data URL (for DataThread thumbnails) specKey: string; // Deterministic key of the inputs that produced this render } const cache = new Map<string, ChartCacheEntry>(); /** Get a cached entry for a chart. Returns undefined if not cached. */ export function getCachedChart(chartId: string): ChartCacheEntry | undefined { return cache.get(chartId); } /** Store a rendered chart in the cache. */ export function setCachedChart(chartId: string, entry: ChartCacheEntry): void { cache.set(chartId, entry); } /** Remove a chart from the cache (e.g., on deletion). */ export function invalidateChart(chartId: string): void { cache.delete(chartId); } /** Clear the entire cache. */ export function clearCache(): void { cache.clear(); } /** * Get a higher-resolution PNG data URL from the cached SVG. * Renders the SVG onto a canvas at specified dimensions and * exports as a PNG data URL suitable for sending to a vision model. * Falls back to the thumbnail if SVG rendering fails. */ export async function getChartPngDataUrl( chartId: string, width: number = 400, height: number = 400, ): Promise<string | undefined> { const entry = cache.get(chartId); if (!entry) return undefined; try { const svgBlob = new Blob([entry.svg], { type: 'image/svg+xml;charset=utf-8' }); const url = URL.createObjectURL(svgBlob); const img = new Image(); await new Promise<void>((resolve, reject) => { img.onload = () => resolve(); img.onerror = reject; img.src = url; }); const canvas = document.createElement('canvas'); canvas.width = width * 2; // 2x for clarity canvas.height = height * 2; const ctx = canvas.getContext('2d')!; ctx.fillStyle = 'white'; ctx.fillRect(0, 0, canvas.width, canvas.height); ctx.drawImage(img, 0, 0, canvas.width, canvas.height); URL.revokeObjectURL(url); return canvas.toDataURL('image/png'); } catch (err) { console.warn('getChartPngDataUrl: SVG render failed, falling back to thumbnail', err); return entry.thumbnailDataUrl || undefined; } } /** * Compute a deterministic cache key from chart rendering inputs. * This is used to detect when a chart needs re-rendering. * We intentionally use JSON.stringify for simplicity — it's fast enough * for the data sizes involved (~KB range). */ export function computeCacheKey( chartType: string, encodingMap: any, config: any, tableRowCount: number, tableContentHash: string | undefined, tableId: string, tableMetadata?: any, ): string { return JSON.stringify({ chartType, encodingMap, config: config || {}, tableRowCount, tableContentHash: tableContentHash || tableId, tableMetadata: tableMetadata || {}, }); } ================================================ FILE: src/app/chartRecommendation.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Agent response resolution — maps AI agent chart recommendations to * concrete Chart objects. * * The encoding recommendation engine has moved to the agents-chart library. * Use `vlRecommendEncodings` / `ecRecommendEncodings` / etc. directly. */ import { Channel, Chart, DictTable, FieldItem } from '../components/ComponentType'; import { generateFreshChart } from './dfSlice'; import { vlGetTemplateDef } from '../lib/agents-chart'; /** Map from agent short names to display chart type names. */ const AGENT_CHART_TYPE_MAP: Record<string, string> = { scatter: 'Scatter Plot', regression: 'Regression', bar: 'Bar Chart', grouped_bar: 'Grouped Bar Chart', histogram: 'Histogram', line: 'Line Chart', area: 'Area Chart', heatmap: 'Heatmap', boxplot: 'Boxplot', pie: 'Pie Chart', lollipop: 'Lollipop Chart', waterfall: 'Waterfall Chart', candlestick: 'Candlestick Chart', world_map: 'World Map', us_map: 'US Map', // Legacy aliases (backward compat with older agent responses) point: 'Scatter Plot', group_bar: 'Grouped Bar Chart', worldmap: 'World Map', usmap: 'US Map', }; /** * Resolve an AI agent's chart recommendation into a concrete Chart object. * The agent returns a `refinedGoal` with `chart.chart_type` which may be * a short name (e.g. "scatter"), a full template name (e.g. "Radar Chart"), * or a user-chosen chart type passed through from the UI. */ export const resolveRecommendedChart = (refinedGoal: any, allFields: FieldItem[], table: DictTable): Chart => { const chartObj = refinedGoal['chart'] || {}; const rawChartType = chartObj['chart_type']; const chartEncodings = chartObj['encodings']; if (chartEncodings == undefined || rawChartType == undefined) { let newChart = generateFreshChart(table.id, 'Scatter Plot') as Chart; const basicEncodings: { [key: string]: string } = table.names.length > 1 ? { x: table.names[0], y: table.names[1] } : {}; newChart = resolveChartFields(newChart, allFields, basicEncodings, table); return newChart; } // Resolve chart type: try short-name map first, then check if it's already a valid template name const chartType = AGENT_CHART_TYPE_MAP[rawChartType] || (vlGetTemplateDef(rawChartType) ? rawChartType : undefined) || 'Scatter Plot'; let newChart = generateFreshChart(table.id, chartType) as Chart; newChart = resolveChartFields(newChart, allFields, chartEncodings, table); // Apply chart config properties from agent recommendation if (chartObj['config'] && typeof chartObj['config'] === 'object') { newChart.config = { ...chartObj['config'] }; } return newChart; }; /** * Populate a chart's encodingMap from a plain { channel: fieldName } object. */ export const resolveChartFields = ( chart: Chart, allFields: FieldItem[], chartEncodings: { [key: string]: string }, table: DictTable, ): Chart => { // Get the keys that should be present after this update const newEncodingKeys = new Set(Object.keys(chartEncodings).map(key => key === 'facet' ? 'column' : key)); // Remove encodings that are no longer in chartEncodings for (const key of Object.keys(chart.encodingMap)) { if (!newEncodingKeys.has(key) && chart.encodingMap[key as Channel]?.fieldID != undefined) { chart.encodingMap[key as Channel] = {}; } } // Add/update encodings from chartEncodings for (let [key, value] of Object.entries(chartEncodings)) { if (key === 'facet') { key = 'column'; } const field = allFields.find(c => c.name === value); if (field) { chart.encodingMap[key as Channel] = { fieldID: field.id }; } } return chart; }; ================================================ FILE: src/app/dfSlice.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { createAsyncThunk, createSlice, PayloadAction, createSelector } from '@reduxjs/toolkit' import { Channel, Chart, ChartTemplate, DataCleanBlock, DataSourceConfig, EncodingItem, EncodingMap, FieldItem, Trigger, computeInsightKey, ChartInsight } from '../components/ComponentType' import { enableMapSet } from 'immer'; import { DictTable } from "../components/ComponentType"; import { Message } from '../views/MessageSnackbar'; import { getChartTemplate, getChartChannels } from "../components/ChartTemplates" import { vlAdaptChart, vlRecommendEncodings } from '../lib/agents-chart'; import { getDataTable } from '../views/VisualizationView'; import { getTriggers, getUrls, computeContentHash, fetchWithIdentity } from './utils'; import { getChartPngDataUrl } from './chartCache'; import { Type } from '../data/types'; import { createTableFromFromObjectArray, inferTypeFromValueArray } from '../data/utils'; import { Identity, IdentityType, getBrowserId } from './identity'; enableMapSet(); // Redux Persist will handle persistence automatically with enableMapSet() export const generateFreshChart = (tableRef: string, chartType: string, source: "user" | "trigger" = "user") : Chart => { return { id: `chart-${Date.now()- Math.floor(Math.random() * 10000)}`, chartType: chartType, encodingMap: Object.assign({}, ...getChartChannels(chartType).map((channel) => ({ [channel]: { channel: channel, bin: false } }))), tableRef: tableRef, saved: false, source: source, } } export interface SSEMessage { type: "heartbeat" | "notification" | "action"; text: string; data?: Record<string, any>; timestamp: number; } // Add interface for app configuration export interface ServerConfig { DISABLE_DISPLAY_KEYS: boolean; DISABLE_DATABASE: boolean; DISABLE_FILE_UPLOAD: boolean; PROJECT_FRONT_PAGE: boolean; MAX_DISPLAY_ROWS: number; DATA_FORMULATOR_HOME?: string; DEV_MODE: boolean; WORKSPACE_BACKEND: string; // 'local' | 'azure_blob' } export interface ModelConfig { id: string; // unique identifier for the model / client combination endpoint: string; model: string; api_key?: string; api_base?: string; api_version?: string; } export type FocusedId = | { type: 'table'; tableId: string } | { type: 'chart'; chartId: string } | undefined; export interface ClientConfig { formulateTimeoutSeconds: number; defaultChartWidth: number; defaultChartHeight: number; maxStretchFactor: number; // max per-axis stretch multiplier for chart sizing (default 2.0) frontendRowLimit: number; // max rows to keep in browser when loading locally (non-virtual) paletteKey: string; // active color palette key from tokens.ts } export interface GeneratedReport { id: string; content: string; style: string; selectedChartIds: string[]; createdAt: number; } export interface DataFormulatorState { agentRules: { coding: string; exploration: string; }; // Identity management: user identity (if logged in) or browser identity (localStorage-based) // Always initialized with browser identity, updated to user identity if logged in identity: Identity; models: ModelConfig[]; selectedModelId: string | undefined; testedModels: {id: string, status: 'ok' | 'error' | 'testing' | 'unknown', message: string}[]; tables : DictTable[]; charts: Chart[]; conceptShelfItems: FieldItem[]; // controls logs and message index messages: Message[]; displayedMessageIdx: number; focusedDataCleanBlockId: {blockId: string, itemId: number} | undefined; focusedId: FocusedId; viewMode: 'editor' | 'report'; chartSynthesisInProgress: string[]; chartInsightInProgress: string[]; serverConfig: ServerConfig; config: ClientConfig; dataLoaderConnectParams: Record<string, Record<string, string>>; // {table_name: {param_name: param_value}} // which table is the agent working on agentActions: { actionId: string, originTableId: string, // the table the user was focused on when they triggered this action sequence description: string, status: 'running' | 'completed' | 'warning' | 'failed', lastUpdate: number, // the time the action is last updated hidden: boolean, // whether the action is hidden messages: { content: string, role: 'user' | 'thinking' | 'action' | 'completion' | 'error' | 'clarify', observeTableId?: string, resultTableId?: string, timestamp: number }[], // accumulated messages from this action pendingClarification?: { trajectory: any[], completedStepCount: number, lastCreatedTableId: string | null } | null, // stored when agent asks for clarification, cleared on resume/cancel }[]; // Data cleaning dialog state dataCleanBlocks: DataCleanBlock[]; cleanInProgress: boolean; // Generated reports state generatedReports: GeneratedReport[]; // Session loading overlay sessionLoading: boolean; sessionLoadingLabel: string; } // Define the initial state using that type const initialState: DataFormulatorState = { agentRules: { coding: "", exploration: "", }, identity: { type: 'browser', id: getBrowserId() }, models: [], selectedModelId: undefined, testedModels: [], tables: [], charts: [], conceptShelfItems: [], messages: [], displayedMessageIdx: -1, focusedDataCleanBlockId: undefined, focusedId: undefined, viewMode: 'editor', chartSynthesisInProgress: [], chartInsightInProgress: [], serverConfig: { DISABLE_DISPLAY_KEYS: false, DISABLE_DATABASE: false, // will be overridden by /api/app-config DISABLE_FILE_UPLOAD: false, PROJECT_FRONT_PAGE: false, MAX_DISPLAY_ROWS: 10000, DEV_MODE: false, WORKSPACE_BACKEND: 'local', }, config: { formulateTimeoutSeconds: 60, defaultChartWidth: 400, defaultChartHeight: 300, maxStretchFactor: 2.0, frontendRowLimit: 50000, paletteKey: 'fluent', }, dataLoaderConnectParams: {}, agentActions: [], dataCleanBlocks: [], cleanInProgress: false, generatedReports: [], sessionLoading: false, sessionLoadingLabel: '', } let getUnrefedDerivedTableIds = (state: DataFormulatorState) => { // find tables directly referred by charts let allCharts = dfSelectors.getAllCharts(state); let chartRefedTables = allCharts.map(chart => getDataTable(chart, state.tables, allCharts, state.conceptShelfItems)) .filter(t => t != undefined).map(t => t.id); let tableWithDescendants = state.tables.filter(table => state.tables.some(t => t.derive?.trigger.tableId == table.id)).map(t => t.id); return state.tables.filter(table => table.derive && !tableWithDescendants.includes(table.id) && !chartRefedTables.includes(table.id)).map(t => t.id); } /** * Fire-and-forget cleanup of virtual tables from the workspace backend. * Called when derived tables are removed from the frontend state. */ export function cleanupVirtualTablesFromWorkspace(tables: DictTable[]) { for (const table of tables) { if (table.virtual?.tableId) { fetchWithIdentity(getUrls().DELETE_TABLE, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ table_name: table.virtual.tableId }), }).catch(err => { console.warn(`Failed to clean up virtual table ${table.virtual?.tableId}:`, err); }); } } } // Helper function to auto-populate latitude/longitude encodings for map charts let deleteChartsRoutine = (state: DataFormulatorState, chartIds: string[]) => { let charts = state.charts.filter(c => !chartIds.includes(c.id)); let currentFocusedChartId = state.focusedId?.type === 'chart' ? state.focusedId.chartId : undefined; if (currentFocusedChartId && chartIds.includes(currentFocusedChartId)) { let leafCharts = charts; if (leafCharts.length > 0) { state.focusedId = { type: 'chart', chartId: leafCharts[0].id }; } else { state.focusedId = undefined; } } state.chartSynthesisInProgress = state.chartSynthesisInProgress.filter(s => !chartIds.includes(s)); // update focusedChart and activeThreadChart state.charts = charts; let unrefedDerivedTableIds = getUnrefedDerivedTableIds(state); let tableIdsToDelete = state.tables.filter(t => !t.anchored && unrefedDerivedTableIds.includes(t.id)).map(t => t.id); // Clean up virtual tables from workspace before removing from state let tablesToDelete = state.tables.filter(t => tableIdsToDelete.includes(t.id)); cleanupVirtualTablesFromWorkspace(tablesToDelete); state.tables = state.tables.filter(t => !tableIdsToDelete.includes(t.id)); } export const fetchFieldSemanticType = createAsyncThunk( "dataFormulatorSlice/fetchFieldSemanticType", async (table: DictTable, { getState }) => { console.log(">>> call agent to infer semantic types <<<") let state = getState() as DataFormulatorState; let message = { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ token: Date.now(), input_data: {name: table.id, rows: table.rows, virtual: table.virtual ? true : false}, model: dfSelectors.getActiveModel(state) }), }; // timeout the request after 20 seconds const controller = new AbortController() const timeoutId = setTimeout(() => controller.abort(), 20000) let response = await fetchWithIdentity(getUrls().SERVER_PROCESS_DATA_ON_LOAD, {...message, signal: controller.signal }) return response.json(); } ); export const fetchCodeExpl = createAsyncThunk( "dataFormulatorSlice/fetchCodeExpl", async (derivedTable: DictTable, { getState }) => { console.log(">>> call agent to obtain code explanations <<<") let state = getState() as DataFormulatorState; let message = { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ token: Date.now(), input_tables: derivedTable.derive?.source .map(tId => state.tables.find(t => t.id == tId) as DictTable) .map(t => ({ name: t.id, rows: t.rows, attached_metadata: t.attachedMetadata })), code: derivedTable.derive?.code, model: dfSelectors.getActiveModel(state) }), }; // timeout the request after 20 seconds const controller = new AbortController() const timeoutId = setTimeout(() => controller.abort(), 20000) let response = await fetchWithIdentity(getUrls().CODE_EXPL_URL, {...message, signal: controller.signal }) return response.json(); } ); export const fetchChartInsight = createAsyncThunk( "dataFormulatorSlice/fetchChartInsight", async (args: { chartId: string; tableId: string }, { getState }) => { console.log(">>> call agent to generate chart insight <<<"); let state = getState() as DataFormulatorState; let chart = dfSelectors.getAllCharts(state).find(c => c.id === args.chartId); if (!chart) throw new Error(`Chart not found: ${args.chartId}`); // Get high-res PNG from the rendered chart let chartImage = await getChartPngDataUrl(args.chartId); if (!chartImage) throw new Error(`No rendered chart image for: ${args.chartId}`); // Strip the data:image/png;base64, prefix for the backend const base64Prefix = 'data:image/png;base64,'; if (chartImage.startsWith(base64Prefix)) { chartImage = chartImage.substring(base64Prefix.length); } // Collect field names from the encoding map let fieldNames = Object.values(chart.encodingMap) .map(enc => enc.fieldID) .filter((id): id is string => !!id) .map(id => { let field = state.conceptShelfItems.find(f => f.id === id); return field?.name || id; }); // Collect input table info (include source tables for derived tables) let table = state.tables.find(t => t.id === args.tableId); let tableIds = table?.derive?.source ? [...table.derive.source, table.id] : [table?.id].filter(Boolean); let inputTables = [...new Set(tableIds)] .map(tId => state.tables.find(t => t.id === tId)) .filter((t): t is DictTable => !!t) .map(t => ({ name: t.id, rows: t.rows, attached_metadata: t.attachedMetadata, })); let message = { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ token: Date.now(), chart_image: chartImage, chart_type: chart.chartType, field_names: fieldNames, input_tables: inputTables, model: dfSelectors.getActiveModel(state), }), }; const controller = new AbortController(); const timeoutId = setTimeout(() => controller.abort(), 30000); let response = await fetchWithIdentity(getUrls().CHART_INSIGHT_URL, { ...message, signal: controller.signal }); clearTimeout(timeoutId); let result = await response.json(); return { ...result, chartId: args.chartId, insightKey: computeInsightKey(chart) }; } ); export const fetchAvailableModels = createAsyncThunk( "dataFormulatorSlice/fetchAvailableModels", async () => { console.log(">>> call agent to fetch available models <<<") let message = { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ token: Date.now(), }), }; // timeout the request after 20 seconds const controller = new AbortController() const timeoutId = setTimeout(() => controller.abort(), 20000) let response = await fetchWithIdentity(getUrls().CHECK_AVAILABLE_MODELS, {...message, signal: controller.signal }) return response.json(); } ); // No server round-trip needed - identity is determined client-side: // - User ID from auth provider (if logged in) // - Browser ID from localStorage (shared across all tabs) export const dataFormulatorSlice = createSlice({ name: 'dataFormulatorSlice', initialState: initialState, reducers: { resetState: (state) => { //state.table = undefined; // Preserve: models, selectedModelId, testedModels, agentRules, // config, dataLoaderConnectParams, identity state.tables = []; state.charts = []; state.conceptShelfItems = []; state.messages = []; state.displayedMessageIdx = -1; state.focusedDataCleanBlockId = undefined; state.focusedId = undefined; state.viewMode = 'editor'; state.chartSynthesisInProgress = []; state.serverConfig = initialState.serverConfig; state.dataCleanBlocks = []; state.cleanInProgress = false; state.agentActions = []; state.generatedReports = []; // Redux Persist will handle persistence automatically }, setSessionLoading: (state, action: PayloadAction<{loading: boolean, label?: string}>) => { state.sessionLoading = action.payload.loading; state.sessionLoadingLabel = action.payload.label || ''; }, loadState: (state, action: PayloadAction<any>) => { const saved = action.payload; // Return a brand-new state object so Immer skips // recursive proxy / freeze on potentially huge table rows. return { // Preserve local-only / sensitive fields from current state identity: state.identity, agentRules: state.agentRules || initialState.agentRules, models: state.models || [], selectedModelId: state.selectedModelId || undefined, testedModels: state.testedModels || [], dataLoaderConnectParams: state.dataLoaderConnectParams || {}, serverConfig: initialState.serverConfig, // Restore from saved payload tables: saved.tables || [], charts: saved.charts || [], conceptShelfItems: saved.conceptShelfItems || [], focusedDataCleanBlockId: saved.focusedDataCleanBlockId || undefined, // Migrate from old focusedTableId/focusedChartId to new focusedId focusedId: saved.focusedId || ( saved.focusedChartId ? { type: 'chart' as const, chartId: saved.focusedChartId } : saved.focusedTableId ? { type: 'table' as const, tableId: saved.focusedTableId } : undefined ), config: { ...initialState.config, ...(saved.config || {}) }, dataCleanBlocks: saved.dataCleanBlocks || [], agentActions: saved.agentActions || [], generatedReports: saved.generatedReports || [], // Reset transient fields messages: [], displayedMessageIdx: -1, viewMode: saved.viewMode || 'editor', chartSynthesisInProgress: [], chartInsightInProgress: [], cleanInProgress: false, sessionLoading: false, sessionLoadingLabel: '', }; }, updateAgentWorkInProgress: (state, action: PayloadAction<{actionId: string, originTableId?: string, description: string, status: 'running' | 'completed' | 'warning' | 'failed', hidden: boolean, message?: { content: string, role: 'user' | 'thinking' | 'action' | 'completion' | 'error' | 'clarify', observeTableId?: string, resultTableId?: string }, pendingClarification?: { trajectory: any[], completedStepCount: number, lastCreatedTableId: string | null } | null }>) => { const now = Date.now(); if (state.agentActions.some(a => a.actionId == action.payload.actionId)) { state.agentActions = state.agentActions.map(a => { if (a.actionId != action.payload.actionId) return a; const updated = {...a, ...action.payload, lastUpdate: now}; if (action.payload.message) { updated.messages = [...(a.messages || []), { ...action.payload.message, timestamp: now }]; } return updated; }); } else { const messages = action.payload.message ? [{ ...action.payload.message, timestamp: now }] : []; state.agentActions = [...state.agentActions, {...action.payload, originTableId: action.payload.originTableId || "", lastUpdate: now, hidden: action.payload.hidden, messages}]; } }, deleteAgentWorkInProgress: (state, action: PayloadAction<string>) => { state.agentActions = state.agentActions.filter(a => a.actionId != action.payload); }, setServerConfig: (state, action: PayloadAction<ServerConfig>) => { state.serverConfig = action.payload; }, setConfig: (state, action: PayloadAction<ClientConfig>) => { state.config = action.payload; }, setViewMode: (state, action: PayloadAction<'editor' | 'report'>) => { state.viewMode = action.payload; }, setAgentRules: (state, action: PayloadAction<{coding: string, exploration: string}>) => { state.agentRules = action.payload; }, selectModel: (state, action: PayloadAction<string | undefined>) => { state.selectedModelId = action.payload; }, addModel: (state, action: PayloadAction<ModelConfig>) => { state.models = [...state.models, action.payload]; }, removeModel: (state, action: PayloadAction<string>) => { state.models = state.models.filter(model => model.id != action.payload); if (state.selectedModelId == action.payload) { state.selectedModelId = undefined; } }, updateModelStatus: (state, action: PayloadAction<{id: string, status: 'ok' | 'error' | 'testing' | 'unknown', message: string}>) => { let id = action.payload.id; let status = action.payload.status; let message = action.payload.message; state.testedModels = [ ...state.testedModels.filter(t => t.id != id), {id: id, status, message} ]; }, addTableToStore: (state, action: PayloadAction<DictTable>) => { let table = action.payload; // Compute content hash if not already set if (!table.contentHash) { table = { ...table, contentHash: computeContentHash(table.rows, table.names) }; } state.tables = [...state.tables, table]; state.charts = [...state.charts]; state.conceptShelfItems = [...state.conceptShelfItems, ...getDataFieldItems(table)]; state.focusedId = { type: 'table', tableId: table.id }; }, deleteTable: (state, action: PayloadAction<string>) => { let tableId = action.payload; // Find the table to delete let tableToDelete = state.tables.find(t => t.id == tableId); if (!tableToDelete) return; // Clean up virtual table from workspace before removing from state cleanupVirtualTablesFromWorkspace([tableToDelete]); // Find direct children: tables derived from or triggered by this table let directChildren = state.tables.filter(t => t.derive?.trigger.tableId === tableId || t.derive?.source.includes(tableId) ); // If the deleted table is derived and has children, re-parent their triggers if (directChildren.length > 0 && tableToDelete.derive) { const parentTriggerId = tableToDelete.derive.trigger.tableId; state.tables = state.tables.map(t => { if (!t.derive) return t; // Only update trigger.tableId — derive.source stays as-is // since each table has its own specific data sources if (t.derive.trigger.tableId !== tableId) return t; return { ...t, derive: { ...t.derive, trigger: { ...t.derive.trigger, tableId: parentTriggerId, } } }; }); } // Remove the table state.tables = state.tables.filter(t => t.id != tableId); // Clean up concept shelf items referencing this table state.conceptShelfItems = state.conceptShelfItems.filter(f => !(f.tableRef == tableId)); // Delete charts that refer to this table let chartIdsToDelete = state.charts.filter(c => c.tableRef == tableId).map(c => c.id); deleteChartsRoutine(state, chartIdsToDelete); // Clean up agent actions: remove non-running actions whose originTableId is the deleted table // AND whose resultTableIds all point to tables that no longer exist const survivingTableIds = new Set(state.tables.map(t => t.id)); state.agentActions = state.agentActions.filter(a => { if (a.status === 'running') return true; // never remove running actions if (a.originTableId !== tableId) return true; // not related to deleted table // Keep if any resultTableId still exists in surviving tables const resultTableIds = a.messages ?.filter(m => m.resultTableId) .map(m => m.resultTableId!) ?? []; return resultTableIds.some(id => survivingTableIds.has(id)); }); // If the deleted table was focused, reset focus if (state.focusedId?.type === 'table' && state.focusedId.tableId === tableId) { state.focusedId = state.tables.length > 0 ? { type: 'table', tableId: state.tables[0].id } : undefined; } }, updateTableAnchored: (state, action: PayloadAction<{tableId: string, anchored: boolean}>) => { let tableId = action.payload.tableId; let anchored = action.payload.anchored; state.tables = state.tables.map(t => t.id == tableId ? {...t, anchored} : t); }, updateTableDisplayId: (state, action: PayloadAction<{tableId: string, displayId: string}>) => { let tableId = action.payload.tableId; let displayId = action.payload.displayId; state.tables = state.tables.map(t => t.id == tableId ? {...t, displayId} : t); }, updateTableAttachedMetadata: (state, action: PayloadAction<{tableId: string, attachedMetadata: string}>) => { let tableId = action.payload.tableId; let attachedMetadata = action.payload.attachedMetadata; state.tables = state.tables.map(t => t.id == tableId ? {...t, attachedMetadata} : t); }, updateTableRows: (state, action: PayloadAction<{tableId: string, rows: any[], contentHash?: string}>) => { // Update the rows of a table while preserving all other table properties // This is used for refreshing data in original (non-derived) tables let tableId = action.payload.tableId; let newRows = action.payload.rows; let providedContentHash = action.payload.contentHash; state.tables = state.tables.map(t => { if (t.id == tableId) { // Update metadata type inference based on new data let newMetadata = { ...t.metadata }; for (let name of t.names) { if (newRows.length > 0 && name in newRows[0]) { newMetadata[name] = { ...newMetadata[name], type: inferTypeFromValueArray(newRows.map(r => r[name])), }; } } // Update lastRefreshed timestamp if source exists const updatedSource = t.source ? { ...t.source, lastRefreshed: Date.now() } : undefined; // Use provided content hash (from backend for virtual/DB tables) or compute locally // For virtual tables, backend hash reflects full table; for stream tables, compute from actual rows const newContentHash = providedContentHash || computeContentHash(newRows, t.names); return { ...t, rows: newRows, metadata: newMetadata, source: updatedSource, contentHash: newContentHash }; } return t; }); }, updateMultipleTableRows: (state, action: PayloadAction<{tableId: string, rows: any[], contentHash?: string}[]>) => { // Batch-update rows for multiple tables in a single state mutation. // This avoids N separate dispatches (each creating a new state.tables reference) // when refreshing derived tables after a source table changes. const updates = new Map(action.payload.map(u => [u.tableId, u])); state.tables = state.tables.map(t => { const update = updates.get(t.id); if (!update) return t; const newRows = update.rows; const providedContentHash = update.contentHash; let newMetadata = { ...t.metadata }; for (let name of t.names) { if (newRows.length > 0 && name in newRows[0]) { newMetadata[name] = { ...newMetadata[name], type: inferTypeFromValueArray(newRows.map(r => r[name])), }; } } const updatedSource = t.source ? { ...t.source, lastRefreshed: Date.now() } : undefined; const newContentHash = providedContentHash || computeContentHash(newRows, t.names); return { ...t, rows: newRows, metadata: newMetadata, source: updatedSource, contentHash: newContentHash }; }); }, updateTableSource: (state, action: PayloadAction<{tableId: string, source: DataSourceConfig}>) => { // Update the source configuration of a table let tableId = action.payload.tableId; let source = action.payload.source; state.tables = state.tables.map(t => t.id == tableId ? {...t, source} : t); }, updateTableSourceRefreshSettings: (state, action: PayloadAction<{tableId: string, autoRefresh: boolean, refreshIntervalSeconds?: number}>) => { // Update just the refresh settings of a table's source let tableId = action.payload.tableId; let autoRefresh = action.payload.autoRefresh; let refreshIntervalSeconds = action.payload.refreshIntervalSeconds; state.tables = state.tables.map(t => { if (t.id == tableId && t.source) { return { ...t, source: { ...t.source, autoRefresh, ...(refreshIntervalSeconds !== undefined ? { refreshIntervalSeconds } : {}) } }; } return t; }); }, extendTableWithNewFields: (state, action: PayloadAction<{tableId: string, columnName: string, values: any[], previousName: string | undefined, parentIDs: string[]}>) => { // extend the existing extTable with new columns from the new table let newValues = action.payload.values; let tableId = action.payload.tableId; let columnName = action.payload.columnName; let previousName = action.payload.previousName; let parentIDs = action.payload.parentIDs; // Find the first parent's column name let lastParentField = state.conceptShelfItems.find(f => f.id === parentIDs[parentIDs.length - 1]); let lastParentName = lastParentField?.name; let table = state.tables.find(t => t.id == tableId) as DictTable; let newNames = []; if (previousName && table.names.indexOf(previousName) != -1) { let replacePosition = table.names.indexOf(previousName); newNames[replacePosition] = columnName; } else { let insertPosition = lastParentName ? table.names.indexOf(lastParentName) : table.names.length - 1; newNames = table.names.slice(0, insertPosition + 1).concat(columnName).concat(table.names.slice(insertPosition + 1)); } let newMetadata = structuredClone(table.metadata); for (let name of newNames) { newMetadata[name] = {type: inferTypeFromValueArray(newValues), semanticType: "", levels: []}; } // Create new rows with the column positioned after the first parent let newRows = table.rows.map((row, i) => { let newRow: {[key: string]: any} = {}; for (let key of Object.keys(row)) { newRow[key] = row[key]; if (key === lastParentName) { newRow[columnName] = newValues[i]; } } if (!lastParentName) { newRow[columnName] = newValues[i]; } if (previousName) { delete newRow[previousName]; } return newRow; }); table.names = newNames; table.metadata = newMetadata; table.rows = newRows; }, removeDerivedField: (state, action: PayloadAction<{tableId: string, fieldId: string}>) => { let tableId = action.payload.tableId; let fieldId = action.payload.fieldId; let table = state.tables.find(t => t.id == tableId) as DictTable; let fieldName = state.conceptShelfItems.find(f => f.id == fieldId)?.name as string; let fieldIndex = table.names.indexOf(fieldName); if (fieldIndex != -1) { table.names = table.names.slice(0, fieldIndex).concat(table.names.slice(fieldIndex + 1)); delete table.metadata[fieldName]; table.rows = table.rows.map(r => { delete r[fieldName]; return r; }); } }, createNewChart: (state, action: PayloadAction<{chartType: string, tableId: string}>) => { let chartType = action.payload.chartType; let tableId = action.payload.tableId || state.tables[0].id; let freshChart = generateFreshChart(tableId, chartType, "user") as Chart; // Auto-populate encodings based on table metadata let table = state.tables.find(t => t.id === tableId); if (table) { const semanticTypes: Record<string, string> = {}; for (const [fn, meta] of Object.entries(table.metadata)) { if (meta?.semanticType) semanticTypes[fn] = meta.semanticType; } const suggested = vlRecommendEncodings(chartType, table.rows, semanticTypes); for (const [channel, fieldName] of Object.entries(suggested)) { if (freshChart.encodingMap[channel as Channel]?.fieldID == undefined) { const fieldItem = state.conceptShelfItems.find(f => f.name === fieldName && table!.names.includes(f.name)); if (fieldItem) freshChart.encodingMap[channel as Channel] = { fieldID: fieldItem.id }; } } } state.charts = [ freshChart , ...state.charts]; state.focusedId = { type: 'chart', chartId: freshChart.id }; }, addChart: (state, action: PayloadAction<Chart>) => { let chart = action.payload; state.charts = [chart, ...state.charts]; }, addAndFocusChart: (state, action: PayloadAction<Chart>) => { let chart = action.payload; state.charts = [chart, ...state.charts]; state.focusedId = { type: 'chart', chartId: chart.id }; }, duplicateChart: (state, action: PayloadAction<string>) => { let chartId = action.payload; let chartCopy = JSON.parse(JSON.stringify(state.charts.find(chart => chart.id == chartId) as Chart)) as Chart; chartCopy = { ...chartCopy, saved: false } chartCopy.id = `chart-${Date.now()- Math.floor(Math.random() * 10000)}`; state.charts.push(chartCopy); state.focusedId = { type: 'chart', chartId: chartCopy.id }; }, saveUnsaveChart: (state, action: PayloadAction<string>) => { let chartId = action.payload; state.charts = state.charts.map(chart => { if (chart.id == chartId) { return { ...chart, saved: !chart.saved }; } else { return chart; } }) }, deleteChartById: (state, action: PayloadAction<string>) => { let chartId = action.payload; deleteChartsRoutine(state, [chartId]); }, updateChartType: (state, action: PayloadAction<{chartId: string, chartType: string}>) => { let chartId = action.payload.chartId; let chartType = action.payload.chartType; let chart = dfSelectors.getAllCharts(state).find(c => c.id == chartId); if (chart) { const template = getChartTemplate(chartType) as ChartTemplate; const sourceType = chart.chartType; // Get data table + semantic types for recommendation-based adaptation let allCharts = dfSelectors.getAllCharts(state); let table = getDataTable(chart, state.tables, allCharts, state.conceptShelfItems); const semanticTypes: Record<string, string> = {}; if (table) { for (const [fn, meta] of Object.entries(table.metadata)) { if (meta?.semanticType) semanticTypes[fn] = meta.semanticType; } } // Extract current encodings as field names const filledEncodings: Record<string, string> = {}; for (const [ch, enc] of Object.entries(chart.encodingMap)) { if (enc.fieldID != null) { const field = state.conceptShelfItems.find(f => f.id === enc.fieldID); if (field) filledEncodings[ch] = field.name; } } // Adapt encodings: re-recommends with preference for existing fields let adapted = vlAdaptChart(sourceType, chartType, filledEncodings, table?.rows, semanticTypes); // Fallback: if adaptation returned nothing but we had fields, // keep fields in channels that exist in both source and target if (Object.keys(adapted).length === 0 && Object.keys(filledEncodings).length > 0) { const targetChannelSet = new Set(template.channels); for (const [ch, fieldName] of Object.entries(filledEncodings)) { if (targetChannelSet.has(ch)) { adapted[ch] = fieldName; } } } // Build new encoding map from adapted field names const newEncodingMap = Object.assign( {}, ...template.channels.map((ch: string) => ({ [ch]: {} as EncodingItem })), ) as EncodingMap; for (const [ch, fieldName] of Object.entries(adapted)) { const field = state.conceptShelfItems.find(f => f.name === fieldName); if (field) newEncodingMap[ch as Channel] = { fieldID: field.id }; } chart = { ...chart, chartType, encodingMap: newEncodingMap }; // Fill any remaining empty channels via full recommendation if (table) { const suggested = vlRecommendEncodings(chartType, table.rows, semanticTypes); for (const [channel, fieldName] of Object.entries(suggested)) { if (chart.encodingMap[channel as Channel]?.fieldID == undefined) { const fieldItem = state.conceptShelfItems.find(f => f.name === fieldName && table!.names.includes(f.name)); if (fieldItem) chart.encodingMap[channel as Channel] = { fieldID: fieldItem.id }; } } } dfSelectors.replaceChart(state, chart); } }, updateTableRef: (state, action: PayloadAction<{chartId: string, tableRef: string}>) => { let chartId = action.payload.chartId; let tableRef = action.payload.tableRef; state.charts = state.charts.map(chart => { if (chart.id == chartId) { return { ...chart, tableRef } } else { return chart } }) }, updateChartConfig: (state, action: PayloadAction<{chartId: string, key: string, value: any}>) => { let chartId = action.payload.chartId; let key = action.payload.key; let value = action.payload.value; let chart = dfSelectors.getAllCharts(state).find(c => c.id == chartId); if (chart) { if (!chart.config) { chart.config = {}; } if (value === undefined) { delete chart.config[key]; } else { chart.config[key] = value; } } }, updateChartThumbnail: (state, action: PayloadAction<{chartId: string, thumbnail: string}>) => { let chart = dfSelectors.getAllCharts(state).find(c => c.id == action.payload.chartId); if (chart) { chart.thumbnail = action.payload.thumbnail; } }, updateChartInsight: (state, action: PayloadAction<{chartId: string, insight: ChartInsight}>) => { let chart = dfSelectors.getAllCharts(state).find(c => c.id == action.payload.chartId); if (chart) { chart.insight = action.payload.insight; } }, updateChartEncoding: (state, action: PayloadAction<{chartId: string, channel: Channel, encoding: EncodingItem}>) => { let chartId = action.payload.chartId; let channel = action.payload.channel; let encoding = action.payload.encoding; let chart = dfSelectors.getAllCharts(state).find(c => c.id == chartId); if (chart) { chart.encodingMap[channel] = encoding; } }, updateChartEncodingProp: (state, action: PayloadAction<{chartId: string, channel: Channel, prop: string, value: any}>) => { let chartId = action.payload.chartId; let channel = action.payload.channel; let prop = action.payload.prop; let value = action.payload.value; let chart = dfSelectors.getAllCharts(state).find(c => c.id == chartId); let table = state.tables.find(t => t.id == chart?.tableRef) as DictTable; if (chart) { //TODO: check this, finding reference and directly update?? let encoding = chart.encodingMap[channel]; if (prop == 'fieldID') { encoding.fieldID = value; // automatcially fetch the auto-sort order from the field let field = state.conceptShelfItems.find(f => f.id == value); if (table && field && table.metadata[field.name] && table.metadata[field.name].levels && table.metadata[field.name].levels.length > 0) { encoding.sortBy = JSON.stringify(table.metadata[field.name].levels); } } else if (prop == 'aggregate') { encoding.aggregate = value; } else if (prop == "sortOrder") { encoding.sortOrder = value == "auto" ? undefined : value; } else if (prop == "sortBy") { encoding.sortBy = value == "auto" ? undefined : value; } else if (prop == "scheme") { encoding.scheme = value; } else if (prop == "dtype") { encoding.dtype = value; } } }, swapChartEncoding: (state, action: PayloadAction<{chartId: string, channel1: Channel, channel2: Channel}>) => { let chartId = action.payload.chartId; let channel1 = action.payload.channel1; let channel2 = action.payload.channel2; let chart = dfSelectors.getAllCharts(state).find(c => c.id == chartId); if (chart) { let enc1 = chart.encodingMap[channel1]; let enc2 = chart.encodingMap[channel2]; chart.encodingMap[channel1] = { fieldID: enc2.fieldID, aggregate: enc2.aggregate, sortBy: enc2.sortBy, sortOrder: enc2.sortOrder }; chart.encodingMap[channel2] = { fieldID: enc1.fieldID, aggregate: enc1.aggregate, sortBy: enc1.sortBy, sortOrder: enc1.sortOrder }; } }, addConceptItems: (state, action: PayloadAction<FieldItem[]>) => { state.conceptShelfItems = [...action.payload, ...state.conceptShelfItems]; }, updateConceptItems: (state, action: PayloadAction<FieldItem>) => { let concept = action.payload; let conceptShelfItems = [...state.conceptShelfItems]; let index = conceptShelfItems.findIndex(field => field.id === concept.id); if (index != -1) { conceptShelfItems[index] = concept; } else { conceptShelfItems = [concept, ...conceptShelfItems]; } state.conceptShelfItems = conceptShelfItems; }, deleteConceptItemByID: (state, action: PayloadAction<string>) => { let conceptID = action.payload; let allCharts = dfSelectors.getAllCharts(state); // remove concepts from encoding maps if (allCharts.some(chart => chart.saved && Object.entries(chart.encodingMap).some(([channel, encoding]) => encoding.fieldID && conceptID == encoding.fieldID))) { console.log("cannot delete!") } else { state.conceptShelfItems = state.conceptShelfItems.filter(f => f.id != conceptID); for (let chart of allCharts) { for (let [channel, encoding] of Object.entries(chart.encodingMap)) { if (encoding.fieldID && conceptID == encoding.fieldID) { // clear the encoding chart.encodingMap[channel as Channel] = { } } } } } }, batchDeleteConceptItemByID: (state, action: PayloadAction<string[]>) => { let allCharts = dfSelectors.getAllCharts(state); for (let conceptID of action.payload) { // remove concepts from encoding maps if (allCharts.some(chart => chart.saved && Object.entries(chart.encodingMap).some(([channel, encoding]) => encoding.fieldID && conceptID == encoding.fieldID))) { console.log("cannot delete!") } else { state.conceptShelfItems = state.conceptShelfItems.filter(field => field.id != conceptID); for (let chart of allCharts) { for (let [channel, encoding] of Object.entries(chart.encodingMap)) { if (encoding.fieldID && conceptID == encoding.fieldID) { // clear the encoding chart.encodingMap[channel as Channel] = { } } } } } } }, insertDerivedTables: (state, action: PayloadAction<DictTable>) => { state.tables = [...state.tables, action.payload]; }, overrideDerivedTables: (state, action: PayloadAction<DictTable>) => { let table = action.payload; // Clean up old virtual table from workspace since it's being replaced let oldTable = state.tables.find(t => t.id == table.id); if (oldTable) { cleanupVirtualTablesFromWorkspace([oldTable]); } state.tables = [...state.tables.filter(t => t.id != table.id), table]; }, deleteDerivedTableById: (state, action: PayloadAction<string>) => { // delete a synthesis output based on index let tableId = action.payload; // Clean up virtual table from workspace before removing from state let tableToDelete = state.tables.find(t => t.derive && t.id == tableId); if (tableToDelete) { cleanupVirtualTablesFromWorkspace([tableToDelete]); } state.tables = state.tables.filter(t => !(t.derive && t.id == tableId)); }, clearUnReferencedTables: (state) => { // remove all tables that are not referred let allCharts = dfSelectors.getAllCharts(state); let referredTableId = allCharts.map(chart => getDataTable(chart, state.tables, allCharts, state.conceptShelfItems)) .filter(t => t != undefined).map(t => t.id); let tablesToRemove = state.tables.filter(t => t.derive && !referredTableId.some(tableId => tableId == t.id)); // Clean up virtual tables from workspace cleanupVirtualTablesFromWorkspace(tablesToRemove); state.tables = state.tables.filter(t => !tablesToRemove.some(tr => tr.id == t.id)); }, clearUnReferencedCustomConcepts: (state) => { let fieldNamesFromTables = state.tables.map(t => t.names).flat(); let fieldIdsReferredByCharts = dfSelectors.getAllCharts(state).map(c => Object.values(c.encodingMap).map(enc => enc.fieldID).filter(fid => fid != undefined) as string[]).flat(); state.conceptShelfItems = state.conceptShelfItems.filter(field => !(field.source == "custom" && !(fieldNamesFromTables.includes(field.name) || fieldIdsReferredByCharts.includes(field.id)))) }, addMessages: (state, action: PayloadAction<Message>) => { state.messages = [...state.messages, action.payload]; }, setDisplayedMessageIndex: (state, action: PayloadAction<number>) => { state.displayedMessageIdx = action.payload }, setFocused: (state, action: PayloadAction<FocusedId>) => { state.focusedId = action.payload; if (action.payload?.type === 'chart' && state.viewMode == 'report') { state.viewMode = 'editor'; } }, setFocusedDataCleanBlockId: (state, action: PayloadAction<{blockId: string, itemId: number} | undefined>) => { state.focusedDataCleanBlockId = action.payload; }, changeChartRunningStatus: (state, action: PayloadAction<{chartId: string, status: boolean}>) => { if (action.payload.status) { state.chartSynthesisInProgress = [...new Set([...state.chartSynthesisInProgress, action.payload.chartId])] } else { state.chartSynthesisInProgress = state.chartSynthesisInProgress.filter(s => s != action.payload.chartId); } }, setIdentity: (state, action: PayloadAction<Identity>) => { state.identity = action.payload; }, updateDataLoaderConnectParams: (state, action: PayloadAction<{dataLoaderType: string, params: Record<string, string>}>) => { let dataLoaderType = action.payload.dataLoaderType; let params = action.payload.params; state.dataLoaderConnectParams[dataLoaderType] = params; }, updateDataLoaderConnectParam: (state, action: PayloadAction<{dataLoaderType: string, paramName: string, paramValue: string}>) => { let dataLoaderType = action.payload.dataLoaderType; if (!state.dataLoaderConnectParams[dataLoaderType]) { state.dataLoaderConnectParams[dataLoaderType] = {}; } let paramName = action.payload.paramName; let paramValue = action.payload.paramValue; state.dataLoaderConnectParams[dataLoaderType][paramName] = paramValue; }, deleteDataLoaderConnectParams: (state, action: PayloadAction<string>) => { let dataLoaderType = action.payload; delete state.dataLoaderConnectParams[dataLoaderType]; }, clearMessages: (state) => { state.messages = []; }, // Data cleaning dialog actions addDataCleanBlock: (state, action: PayloadAction<DataCleanBlock>) => { state.dataCleanBlocks = [...state.dataCleanBlocks, action.payload]; }, removeDataCleanBlocks: (state, action: PayloadAction<{blockIds: string[]}>) => { state.dataCleanBlocks = state.dataCleanBlocks.filter(block => !action.payload.blockIds.includes(block.id)); }, resetDataCleanBlocks: (state) => { state.dataCleanBlocks = []; }, updateLastDataCleanBlock: (state, action: PayloadAction<Partial<DataCleanBlock>>) => { if (state.dataCleanBlocks.length > 0) { const lastIndex = state.dataCleanBlocks.length - 1; state.dataCleanBlocks[lastIndex] = { ...state.dataCleanBlocks[lastIndex], ...action.payload }; } }, setCleanInProgress: (state, action: PayloadAction<boolean>) => { state.cleanInProgress = action.payload; }, // Generated reports actions saveGeneratedReport: (state, action: PayloadAction<GeneratedReport>) => { const report = action.payload; // Check if report with same ID already exists and update it, otherwise add new const existingIndex = state.generatedReports.findIndex(r => r.id === report.id); if (existingIndex >= 0) { state.generatedReports[existingIndex] = report; } else { state.generatedReports.unshift(report); // Add to beginning of array } // Redux Persist will handle persistence automatically }, deleteGeneratedReport: (state, action: PayloadAction<string>) => { const reportId = action.payload; state.generatedReports = state.generatedReports.filter(r => r.id !== reportId); // Redux Persist will handle persistence automatically }, clearGeneratedReports: (state) => { state.generatedReports = []; // Redux Persist will handle persistence automatically } }, extraReducers: (builder) => { builder .addCase(fetchFieldSemanticType.fulfilled, (state, action) => { let data = action.payload; let tableId = action.meta.arg.id; let table = state.tables.find(t => t.id == tableId) as DictTable; if (data["status"] == "ok" && data["result"].length > 0) { let typeMap = data['result'][0]['fields']; for (let name of table.names) { table.metadata[name] = { type: typeMap[name]['type'] as Type, semanticType: typeMap[name]['semantic_type'], levels: typeMap[name]['sort_order'] || undefined, intrinsicDomain: typeMap[name]['intrinsic_domain'] || undefined, unit: typeMap[name]['unit'] || undefined, }; } if (data["result"][0]["suggested_table_name"]) { // avoid duplicate display ids let existingDisplayIds = state.tables.filter(t => t.id != tableId).map(t => t.displayId); let suffix = ""; let displayId = `${data["result"][0]["suggested_table_name"] as string}${suffix}`; let suffixId = 1; while (existingDisplayIds.includes(displayId)) { displayId = `${data["result"][0]["suggested_table_name"] as string}${suffixId}`; suffixId++; suffix = `-${suffixId}`; } table.displayId = displayId; } // Store data summary as attached metadata if not already set (e.g., from extraction context) let dataSummary = data["result"][0]["data_summary"] || data["result"][0]["data summary"]; if (dataSummary && !table.attachedMetadata) { table.attachedMetadata = dataSummary; } } }) .addCase(fetchAvailableModels.fulfilled, (state, action) => { let defaultModels = action.payload; state.models = [ ...defaultModels, ...state.models.filter(e => !defaultModels.some((m: ModelConfig) => m.endpoint === e.endpoint && m.model === e.model && m.api_base === e.api_base && m.api_version === e.api_version )) ]; state.testedModels = [ ...defaultModels.map((m: ModelConfig) => {return {id: m.id, status: 'ok'}}) , ...state.testedModels.filter(t => !defaultModels.map((m: ModelConfig) => m.id).includes(t.id)) ] if (defaultModels.length > 0 && state.selectedModelId == undefined) { state.selectedModelId = defaultModels[0].id; } // console.log("load model complete"); // console.log("state.models", state.models); }) .addCase(fetchCodeExpl.fulfilled, (state, action) => { let codeExplResponse = action.payload; let derivedTableId = action.meta.arg.id; let derivedTable = state.tables.find(t => t.id == derivedTableId) if (derivedTable?.derive) { // The response is now an object with code and concepts derivedTable.derive.explanation = codeExplResponse; } console.log("fetched codeExpl"); console.log(action.payload); }) .addCase(fetchChartInsight.pending, (state, action) => { let chartId = action.meta.arg.chartId; if (!state.chartInsightInProgress.includes(chartId)) { state.chartInsightInProgress.push(chartId); } }) .addCase(fetchChartInsight.fulfilled, (state, action) => { let { chartId, insightKey, title, takeaways } = action.payload; let chart = dfSelectors.getAllCharts(state).find(c => c.id === chartId); if (chart && (title || (takeaways && takeaways.length > 0))) { chart.insight = { title, takeaways: takeaways || [], key: insightKey }; } state.chartInsightInProgress = state.chartInsightInProgress.filter(id => id !== chartId); console.log("fetched chart insight", action.payload); }) .addCase(fetchChartInsight.rejected, (state, action) => { let chartId = action.meta.arg.chartId; state.chartInsightInProgress = state.chartInsightInProgress.filter(id => id !== chartId); console.error("chart insight failed", action.error); }) }, }) // ─── Memoized granular selectors ───────────────────────────────────────────── // These avoid re-renders in components that don't care about row data changes. /** Returns a stable array of table IDs. Only changes when tables are added/removed/reordered. */ export const selectTableIds = createSelector( [(state: DataFormulatorState) => state.tables], (tables) => tables.map(t => t.id), { memoizeOptions: { resultEqualityCheck: (prev: string[], next: string[]) => { if (prev.length !== next.length) return false; for (let i = 0; i < prev.length; i++) { if (prev[i] !== next[i]) return false; } return true; } } } ); /** * Returns a stable "refresh config" fingerprint for auto-refresh timer management. * Only changes when a table's autoRefresh/refreshIntervalSeconds/source.type changes, * or when tables are added/removed — NOT when rows are updated. */ export const selectRefreshConfigs = createSelector( [(state: DataFormulatorState) => state.tables], (tables) => tables.map(t => ({ id: t.id, autoRefresh: t.source?.autoRefresh ?? false, refreshIntervalSeconds: t.source?.refreshIntervalSeconds, sourceType: t.source?.type, canRefresh: t.source?.canRefresh ?? false, url: t.source?.url, hasVirtual: !!t.virtual?.tableId, hasDerive: !!t.derive, })), { memoizeOptions: { resultEqualityCheck: (prev: any[], next: any[]) => { if (prev.length !== next.length) return false; for (let i = 0; i < prev.length; i++) { const a = prev[i], b = next[i]; if (a.id !== b.id || a.autoRefresh !== b.autoRefresh || a.refreshIntervalSeconds !== b.refreshIntervalSeconds || a.sourceType !== b.sourceType || a.canRefresh !== b.canRefresh || a.url !== b.url || a.hasVirtual !== b.hasVirtual || a.hasDerive !== b.hasDerive) return false; } return true; } } } ); /** * Extracts trigger charts from tables. Uses a stable serialization key so the * output array only changes when trigger charts are actually added/removed/modified * — not when table rows change. */ const selectTriggerCharts = createSelector( [(state: DataFormulatorState) => state.tables], (tables) => { return tables .filter(t => t.derive?.trigger?.chart) .map(t => t.derive?.trigger?.chart) as Chart[]; }, { memoizeOptions: { // Use a result equality check so row-only changes don't produce a new array // if trigger charts themselves haven't changed. resultEqualityCheck: (prev: Chart[], next: Chart[]) => { if (prev.length !== next.length) return false; for (let i = 0; i < prev.length; i++) { if (prev[i] !== next[i]) return false; } return true; } } } ); export const dfSelectors = { getActiveModel: (state: DataFormulatorState) : ModelConfig => { return state.models.find(m => m.id == state.selectedModelId) || state.models[0]; }, getEffectiveTableId: (state: DataFormulatorState): string | undefined => { if (!state.focusedId) return undefined; if (state.focusedId.type === 'table') return state.focusedId.tableId; // type === 'chart': derive table from the chart's tableRef let allCharts = dfSelectors.getAllCharts(state); let chart = allCharts.find(c => c.id === (state.focusedId as { type: 'chart'; chartId: string }).chartId); return chart?.tableRef; }, getFocusedChartId: (state: DataFormulatorState): string | undefined => { return state.focusedId?.type === 'chart' ? state.focusedId.chartId : undefined; }, getActiveBaseTableIds: (state: DataFormulatorState) => { let effectiveTableId = dfSelectors.getEffectiveTableId(state); let tables = state.tables; let focusedTable = tables.find(t => t.id == effectiveTableId); let sourceTables = focusedTable?.derive?.source || [focusedTable?.id]; return sourceTables; }, // Memoized chart selector that combines both sources. // Decoupled from row-data changes via selectTriggerCharts. getAllCharts: createSelector( [(state: DataFormulatorState) => state.charts, selectTriggerCharts], (userCharts, triggerCharts) => { return [...userCharts, ...triggerCharts]; } ), replaceChart: (state: DataFormulatorState, chart: Chart) => { if (state.charts.find(c => c.id == chart.id)) { // chart is from charts state.charts = state.charts.map(c => c.id == chart.id ? chart : c); } else { // chart is from tables let table = state.tables.find(t => t.derive?.trigger?.chart?.id == chart.id) as DictTable; if (table.derive?.trigger) { table.derive = { ...table.derive, trigger: { ...table.derive?.trigger, chart: chart } }; } } }, // Generated reports selectors getAllGeneratedReports: (state: DataFormulatorState) => state.generatedReports, getReportById: (state: DataFormulatorState, reportId: string) => state.generatedReports.find(r => r.id === reportId), } // derived field: extra all field items from the table export const getDataFieldItems = (baseTable: DictTable): FieldItem[] => { let dataFieldItems = baseTable.names.map((name) => { const id = `original--${baseTable.id}--${name}`; return { id, name, source: "original", tableRef: baseTable.id } as FieldItem; }) || []; return dataFieldItems; } // Action creators are generated for each case reducer function export const dfActions = dataFormulatorSlice.actions; export const dataFormulatorReducer = dataFormulatorSlice.reducer; ================================================ FILE: src/app/identity.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Identity management for Data Formulator * * This module provides a layered identity system: * 1. User identity (if logged in via auth provider) - highest priority * 2. Browser identity (localStorage-based UUID) - fallback for anonymous users * * Browser identity is shared across all tabs of the same origin via localStorage, * ensuring consistent state across tabs without requiring login. */ const BROWSER_ID_KEY = 'df_browser_id'; export type IdentityType = 'user' | 'browser'; export interface Identity { type: IdentityType; id: string; } export interface UserInfo { name: string; userId: string; } /** * Generates a UUID v4 * Uses crypto.randomUUID if available, falls back to manual generation */ function generateUUID(): string { if (typeof crypto !== 'undefined' && crypto.randomUUID) { return crypto.randomUUID(); } // Fallback for older browsers return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, (c) => { const r = Math.random() * 16 | 0; const v = c === 'x' ? r : (r & 0x3 | 0x8); return v.toString(16); }); } /** * Gets or creates a persistent browser identity stored in localStorage. * This ID is shared across all tabs of the same origin. * * @returns The browser identity UUID */ export function getBrowserId(): string { try { let browserId = localStorage.getItem(BROWSER_ID_KEY); if (!browserId) { browserId = generateUUID(); localStorage.setItem(BROWSER_ID_KEY, browserId); } return browserId; } catch (e) { // localStorage might be unavailable (e.g., private browsing in some browsers) // Generate a session-only ID as fallback console.warn('localStorage unavailable, using session-only browser ID'); return generateUUID(); } } /** * Clears the browser identity from localStorage. * Useful for testing or when user wants to reset their identity. */ export function clearBrowserId(): void { try { localStorage.removeItem(BROWSER_ID_KEY); } catch (e) { console.warn('Failed to clear browser ID from localStorage'); } } /** * Resolves the current identity based on available authentication. * Priority: User identity (if logged in) > Browser identity * * @param userInfo - Optional user info if user is authenticated * @returns The resolved identity */ export function resolveIdentity(userInfo?: UserInfo | null): Identity { if (userInfo?.userId) { return { type: 'user', id: userInfo.userId }; } return { type: 'browser', id: getBrowserId() }; } /** * Creates the identity key used for state storage on the backend. * Format: "{type}:{id}" (e.g., "user:alice@example.com" or "browser:550e8400-...") * * @param identity - The identity object * @returns A string key suitable for state storage */ export function getIdentityKey(identity: Identity): string { return `${identity.type}:${identity.id}`; } ================================================ FILE: src/app/store.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { configureStore } from '@reduxjs/toolkit' import { dataFormulatorReducer } from './dfSlice'; import { persistReducer, persistStore } from 'redux-persist' import localforage from 'localforage'; export type AppDispatch = typeof store.dispatch const persistConfig = { key: 'root', //storage, storage: localforage, blacklist: ['serverConfig'], // Always fetch fresh from /api/app-config } const persistedReducer = persistReducer(persistConfig, dataFormulatorReducer) let store = configureStore({ reducer: persistedReducer, middleware: (getDefaultMiddleware) => getDefaultMiddleware({ serializableCheck: false, }), }) export const persistor = persistStore(store); export default store; // Export store instance for use in utilities export { store }; ================================================ FILE: src/app/tableThunks.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Unified table loading thunk: loadTable * * All data loaders (file, paste, URL, example, database, extract) should use * this thunk to load tables into the application. It handles: * - Optionally storing data on the server (workspace) via API calls * - Applying row limits when data stays local (storeOnServer = false) * - Building DictTable with appropriate virtual/source fields * - Adding the table to Redux state + fetching semantic types */ import { createAsyncThunk } from '@reduxjs/toolkit'; import { DataSourceConfig, DictTable } from '../components/ComponentType'; import { Type } from '../data/types'; import { inferTypeFromValueArray } from '../data/utils'; import { fetchWithIdentity, getUrls, computeContentHash } from './utils'; import { DataFormulatorState, dfActions, fetchFieldSemanticType } from './dfSlice'; export interface LoadTablePayload { // The table data (already parsed into rows/names/metadata on the frontend) table: DictTable; // Whether to store on the server workspace (true) or keep local-only (false) storeOnServer: boolean; // For file uploads to server: the raw File object file?: File; // For database sources loaded via external data loader: dataLoaderType?: string; dataLoaderParams?: Record<string, string>; sourceTableName?: string; importOptions?: { rowLimit?: number; sortColumns?: string[]; sortOrder?: 'asc' | 'desc'; }; } export interface LoadTableResult { table: DictTable; truncated?: boolean; // whether rows were truncated due to frontendRowLimit originalRowCount?: number; // original count before truncation duplicate?: boolean; // whether the table was already loaded (skipped) } /** * Unified thunk to load a table from any source. * * Routes: * - storeOnServer=true + file/paste/url/example/extract: POST to /api/tables/create-table * - storeOnServer=true + database: use existing /api/tables/data-loader/ingest-data * (caller should have already ingested; table comes from workspace list) * - storeOnServer=false + database: call /api/tables/data-loader/fetch-data (new endpoint) * - storeOnServer=false + other: keep data local, apply frontendRowLimit * * In all cases: adds table to Redux state + fetches semantic types */ export const loadTable = createAsyncThunk< LoadTableResult, LoadTablePayload, { state: DataFormulatorState } >( 'dataFormulator/loadTable', async (payload, { dispatch, getState }) => { const { table, storeOnServer, file, dataLoaderType, dataLoaderParams, sourceTableName, importOptions } = payload; const state = getState(); const frontendRowLimit = state.config?.frontendRowLimit ?? 50000; const existingTables = state.tables; // === DUPLICATE CHECK === // Check if a table with the same id already exists locally const existingById = existingTables.find(t => t.id === table.id); if (existingById) { // Table with same id already loaded — just focus it dispatch(dfActions.setFocused({ type: 'table', tableId: existingById.id })); return { table: existingById, duplicate: true }; } // Check by content hash — avoid loading identical data under a different name const incomingHash = table.contentHash || computeContentHash(table.rows, table.names); const existingByContent = existingTables.find(t => { if (!t.contentHash) return false; return t.contentHash === incomingHash; }); if (existingByContent) { dispatch(dfActions.setFocused({ type: 'table', tableId: existingByContent.id })); dispatch(dfActions.addMessages({ timestamp: Date.now(), type: 'warning', component: 'data loader', value: `This data is identical to the already-loaded table "${existingByContent.displayId}". Skipped duplicate load.`, })); return { table: existingByContent, duplicate: true }; } // For workspace / virtual tables loaded from the DB manager, also check if the // same workspace table (by virtual tableId) is already in the frontend. if (table.virtual) { const existingByVirtual = existingTables.find(t => t.virtual?.tableId === table.virtual?.tableId); if (existingByVirtual) { dispatch(dfActions.setFocused({ type: 'table', tableId: existingByVirtual.id })); return { table: existingByVirtual, duplicate: true }; } } let finalTable: DictTable = { ...table }; let truncated = false; let originalRowCount = 0; const sourceType = table.source?.type; if (storeOnServer) { // === STORE ON SERVER PATH === if (sourceType === 'database' && dataLoaderType && sourceTableName) { // Database source: ingest to workspace via data loader try { const response = await fetchWithIdentity(getUrls().DATA_LOADER_INGEST_DATA, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ data_loader_type: dataLoaderType, data_loader_params: dataLoaderParams, table_name: sourceTableName, import_options: importOptions || {}, }), }); const data = await response.json(); if (data.status === 'success') { // Now fetch the table info from workspace to get sample rows const listResp = await fetchWithIdentity(getUrls().LIST_TABLES, { method: 'GET' }); const listData = await listResp.json(); if (listData.status === 'success') { const wsTable = listData.tables.find((t: any) => t.name === data.table_name); if (wsTable) { finalTable = buildDictTableFromWorkspace(wsTable, table.source); } } } else { throw new Error(data.message || 'Failed to ingest data'); } } catch (err) { console.error('Failed to ingest database table to workspace:', err); throw err; } } else if (file) { // File upload to workspace try { const formData = new FormData(); formData.append('file', file); formData.append('table_name', table.id); const response = await fetchWithIdentity(getUrls().CREATE_TABLE, { method: 'POST', body: formData, }); const data = await response.json(); if (data.status === 'success') { // Fetch back from workspace to get proper virtual info const listResp = await fetchWithIdentity(getUrls().LIST_TABLES, { method: 'GET' }); const listData = await listResp.json(); if (listData.status === 'success') { const wsTable = listData.tables.find((t: any) => t.name === data.table_name); if (wsTable) { finalTable = buildDictTableFromWorkspace(wsTable, table.source); } } } else { throw new Error(data.message || 'Failed to upload file'); } } catch (err) { console.error('Failed to upload file to workspace:', err); throw err; } } else if (table.virtual) { // Table already exists in workspace (e.g., loaded from DB table manager) finalTable = { ...table }; } else { // Other sources (paste/url/example/extract): upload raw data to workspace try { const formData = new FormData(); const jsonBlob = new Blob([JSON.stringify(table.rows)], { type: 'application/json' }); formData.append('raw_data', jsonBlob, 'data.json'); formData.append('table_name', table.id); const response = await fetchWithIdentity(getUrls().CREATE_TABLE, { method: 'POST', body: formData, }); const data = await response.json(); if (data.status === 'success') { // Set virtual info from the response — virtual indicates server storage finalTable = { ...table, virtual: { tableId: data.table_name, rowCount: data.row_count, }, id: data.table_name, // use the sanitized name from server displayId: table.displayId || data.table_name, }; } else { throw new Error(data.message || 'Failed to save data to workspace'); } } catch (err) { console.error('Failed to save data to workspace:', err); throw err; } } } else { // === LOCAL ONLY PATH (storeOnServer = false) === if (sourceType === 'database' && dataLoaderType && dataLoaderParams && sourceTableName) { // Database source: fetch data without saving to workspace try { const response = await fetchWithIdentity(getUrls().DATA_LOADER_FETCH_DATA, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ data_loader_type: dataLoaderType, data_loader_params: dataLoaderParams, table_name: sourceTableName, row_limit: frontendRowLimit, sort_columns: importOptions?.sortColumns, sort_order: importOptions?.sortOrder, }), }); const data = await response.json(); if (data.status === 'success') { const rows = data.rows; const names = rows.length > 0 ? Object.keys(rows[0]) : []; const totalCount: number = data.total_row_count ?? rows.length; originalRowCount = totalCount; truncated = rows.length < totalCount; finalTable = { ...table, id: table.id, displayId: table.displayId || table.id, names, rows, metadata: names.reduce((acc: Record<string, any>, name: string) => ({ ...acc, [name]: { type: inferTypeFromValueArray(rows.map((r: any) => r[name])), semanticType: "", levels: [] } }), {}), // No virtual field = local-only (not stored on server) anchored: true, }; } else { throw new Error(data.message || 'Failed to fetch data from external source'); } } catch (err) { console.error('Failed to fetch data from external source:', err); throw err; } } else { // Other sources: apply row limit originalRowCount = table.rows.length; if (table.rows.length > frontendRowLimit) { truncated = true; finalTable = { ...table, rows: table.rows.slice(0, frontendRowLimit), }; } else { finalTable = { ...table }; } } } // Dispatch the table into Redux state dispatch(dfActions.addTableToStore(finalTable)); dispatch(fetchFieldSemanticType(finalTable)); // Notify user about truncation if (truncated && originalRowCount) { const diskDisabled = state.serverConfig?.DISABLE_DATABASE; const workspaceBackend = state.serverConfig?.WORKSPACE_BACKEND; const storageLabel = workspaceBackend === 'azure_blob' ? 'Azure' : 'Disk'; const baseMsg = `Table "${finalTable.displayId || finalTable.id}" was truncated from ${originalRowCount.toLocaleString()} to ${frontendRowLimit.toLocaleString()} rows (browser limit).`; const installHint = diskDisabled ? ` To load the full dataset, install Data Formulator locally and use disk storage.` : ` To load the full dataset, switch to "${storageLabel}" storage mode.`; dispatch(dfActions.addMessages({ timestamp: Date.now(), type: 'warning', component: 'data loader', value: baseMsg + installHint, })); } return { table: finalTable, truncated, originalRowCount, duplicate: false }; } ); /** * Helper: Build a DictTable from a workspace table listing (as returned by /api/tables/list-tables). */ function buildDictTableFromWorkspace( wsTable: any, source: DataSourceConfig | undefined, ): DictTable { const convertSqlTypeToAppType = (sqlType: string): Type => { sqlType = sqlType.toUpperCase(); if (sqlType.includes('INT') || sqlType === 'BIGINT' || sqlType === 'SMALLINT' || sqlType === 'TINYINT') { return Type.Integer; } else if (sqlType.includes('FLOAT') || sqlType.includes('DOUBLE') || sqlType.includes('DECIMAL') || sqlType.includes('NUMERIC') || sqlType.includes('REAL')) { return Type.Number; } else if (sqlType.includes('BOOL')) { return Type.Boolean; } else if (sqlType.includes('DATE') || sqlType.includes('TIME') || sqlType.includes('TIMESTAMP')) { return Type.Date; } else { return Type.String; } }; // Build source config for database tables const sourceMeta = wsTable.source_metadata; const sourceConfig: DataSourceConfig = source || { type: 'database', databaseTable: wsTable.name, canRefresh: sourceMeta != null, lastRefreshed: Date.now(), }; return { id: wsTable.name, displayId: wsTable.name, names: wsTable.columns.map((col: any) => col.name), metadata: wsTable.columns.reduce((acc: Record<string, any>, col: any) => ({ ...acc, [col.name]: { type: convertSqlTypeToAppType(col.type), semanticType: "", levels: [] } }), {}), rows: wsTable.sample_rows, virtual: { tableId: wsTable.name, rowCount: wsTable.row_count, }, anchored: true, createdBy: 'user', attachedMetadata: '', source: sourceConfig, }; } /** * Check if any ancestor table of a given table is local-only (no virtual field). * A table without `virtual` has all its data in the browser only, not on the server. * Used by derivation code to decide whether derived results should also stay local. */ export function hasLocalOnlyAncestor(tableId: string, tables: DictTable[]): boolean { const visited = new Set<string>(); const check = (id: string): boolean => { if (visited.has(id)) return false; visited.add(id); const t = tables.find(tbl => tbl.id === id); if (!t) return false; // virtual field indicates data is stored on server; absence means local-only if (!t.virtual && !t.derive) return true; if (t.derive?.source) { for (const sourceId of t.derive.source) { if (check(sourceId)) return true; } } return false; }; return check(tableId); } ================================================ FILE: src/app/tokens.ts ================================================ // ════════════════════════════════════════════════════════════════════════ // Design tokens — single source of truth for visual constants. // // Usage: // import { borderColor, shadow, transition, radius, ComponentBorderStyle } from '../app/tokens'; // sx={{ ...ComponentBorderStyle, borderRadius: radius.md, transition: transition.fast }} // sx={{ borderBottom: `1px solid ${borderColor.divider}`, boxShadow: shadow.sm }} // ════════════════════════════════════════════════════════════════════════ import type { SxProps } from '@mui/material'; // ── Border colors ────────────────────────────────────────────────────── export const borderColor = { /** 0.12 — section dividers, table borders, tab underlines, sidebar edges * DataLoadingChat, ExplComponents, RefreshDataDialog, ReportView tables, * TableSelectionView, DataLoadingThread, DBTableManager */ divider: 'rgba(0, 0, 0, 0.12)', /** 0.10 — inner components: cards, chips, inputs, thumbnails * DataThreadCards table card, EncodingShelfCard tab divider */ component: 'rgba(0, 0, 0, 0.10)', /** 0.12 — outer containers: panels, dialogs, popovers, drop zones * DataThread popups, UnifiedDataUploadDialog, AgentRulesDialog */ view: 'rgba(0, 0, 0, 0.12)', } as const; // ── Composite border styles (spread into sx) ─────────────────────────── /** Section divider border — tabs, sidebars, table wrappers */ export const DividerBorderStyle: SxProps = { border: `1px solid ${borderColor.divider}` }; /** Inner component border — cards, chips, input fields */ export const ComponentBorderStyle: SxProps = { border: `1px solid ${borderColor.component}` }; /** Outer container border — panels, dialogs, popovers */ export const ViewBorderStyle: SxProps = { border: `1px solid ${borderColor.view}` }; // ── Box shadows ──────────────────────────────────────────────────────── export const shadow = { /** Resting cards, chips * ExplComponents, DataLoadingThread, DataThreadCards */ sm: '0 1px 2px rgba(0,0,0,0.05)', /** Hovered cards, table headers * DataThreadCards, SelectableDataGrid, DataLoadingThread hover */ md: '0 2px 4px rgba(0,0,0,0.08)', /** Expanded items, hovered panels * ExplComponents hover, ReportView compose toolbar */ lg: '0 2px 8px rgba(0,0,0,0.12)', /** Floating overlays, dialogs, snackbars * ReportView hover, DataFormulator overlay, MessageSnackbar */ xl: '0 4px 12px rgba(0,0,0,0.10)', } as const; // ── Transitions ──────────────────────────────────────────────────────── export const transition = { /** Hover highlights, tab toggles, icon reactions * DataThread, EncodingShelfCard, DataThreadCards, ChartRecBox */ fast: 'all 0.1s linear', /** Panel animations, hover effects, expand/collapse * ReportView, RefreshDataDialog, UnifiedDataUploadDialog, SelectableDataGrid */ normal: 'all 0.2s ease', /** Drawer slides, focus rings, snackbar entrances * MessageSnackbar, DataLoadingChat, AgentRulesDialog */ slow: 'all 0.3s ease', } as const; // ── Border radius ────────────────────────────────────────────────────── // Values are MUI spacing units (1 unit = 4px via theme.spacing) export const radius = { /** Cards, chips, inputs, code blocks * UnifiedDataUploadDialog, AgentRulesDialog, ChatDialog, ExplComponents */ sm: 1, /** Floating panels, dialogs, chat cards, table containers * DataThread popups, ChatDialog, About, DataLoadingChat, TableSelectionView */ md: 2, /** Status indicators, model icons * ModelSelectionDialog status badges */ lg: 3, /** Fully rounded pill shape — FABs, floating overlays * DataFormulator chart overlay */ pill: '16px', } as const; // ── Color palettes ───────────────────────────────────────────────────── // Each palette defines five color roles: primary, secondary, derived, custom, warning. // `main` is the prominent color; `bgcolor` is a light tint for backgrounds. export interface AppPaletteEntry { main: string; bgcolor?: string; textColor?: string; } export interface AppPalette { name: string; primary: AppPaletteEntry; secondary: AppPaletteEntry; derived: AppPaletteEntry; custom: AppPaletteEntry; warning: AppPaletteEntry; } export const palettes: Record<string, AppPalette> = { /** Default Material UI inspired palette * Primary/Custom: split-complementary pair (blue 215° ↔ orange 25°) */ material: { name: 'Material', primary: { main: '#1565c0' }, // H215 S83 L42 — blue secondary: { main: '#7b1fa2' }, // purple derived: { main: '#f9a825' }, // yellow custom: { main: '#c75b1e' }, // H25 S83 L45 — warm orange (matched S/L) warning: { main: '#bf5600' }, }, /** Microsoft Fluent UI palette * Primary/Custom: split-complementary (blue 205° ↔ orange 18°) */ fluent: { name: 'Fluent UI', primary: { main: '#0078d4' }, // H205 S100 L42 — Fluent blue secondary: { main: '#8764b8' }, // Fluent purple derived: { main: '#ffb900' }, // Fluent gold custom: { main: '#c85a17' }, // H18 S85 L44 — warm burnt orange (matched L) warning: { main: '#a4262c' }, // Fluent red }, /** Vivid Spectrum — high saturation, modern * Primary/Custom: split-complementary (blue 224° ↔ amber 38°) */ vivid: { name: 'Vivid', primary: { main: '#2563eb' }, // H224 S84 L54 secondary: { main: '#7c3aed' }, custom: { main: '#d97218' }, // H38 S84 L47 — vivid amber derived: { main: '#ea580c' }, warning: { main: '#dc2626' }, }, /** Deep Jewel — rich, saturated jewel tones * Primary/Custom: split-complementary (blue 224° ↔ copper 24°) */ jewel: { name: 'Jewel', primary: { main: '#1d4ed8' }, // H224 S80 L48 secondary: { main: '#6d28d9' }, derived: { main: '#d97706' }, custom: { main: '#b85a1a' }, // H24 S80 L41 — deep copper warning: { main: '#b91c1c' }, }, /** Electric Modern — punchy, high contrast * Primary/Custom: split-complementary (blue 220° ↔ tangerine 28°) */ electric: { name: 'Electric', primary: { main: '#0066ff' }, // H220 S100 L50 secondary: { main: '#8b5cf6' }, derived: { main: '#eab308' }, custom: { main: '#e07020' }, // H28 S78 L50 — electric tangerine warning: { main: '#ef4444' }, }, /** Coastal — teal & coral, distinctive, less corporate * Primary/Custom: split-complementary (teal 190° ↔ coral 350°) */ coastal: { name: 'Coastal', primary: { main: '#0891b2' }, // H190 S93 L37 custom: { main: '#c03050' }, // H350 S75 L47 — coral rose derived: { main: '#ca8a04' }, secondary: { main: '#7c3aed' }, warning: { main: '#dc2626' }, }, /** Microsoft Copilot — inspired by the Copilot gradient spectrum * Primary/Custom: analogous pair from the gradient (blue → purple) */ copilot: { name: 'Copilot', primary: { main: '#0f6cbd', textColor: '#0e5ea3' }, // Fluent 2 blue — darker for text secondary: { main: '#0e9a6c', textColor: '#087a55' }, // Copilot green — darker for text derived: { main: '#d946ef', textColor: '#a020c0' }, // Copilot magenta — darker for text custom: { main: '#7160e8', textColor: '#5a48c8' }, // Copilot purple — darker for text warning: { main: '#d13438' }, // Fluent 2 red }, /** Evergreen — Shopify Polaris green, proven in data-heavy merchant dashboards * Primary/Custom: complement (green 162° ↔ terracotta 345°) */ evergreen: { name: 'Evergreen', primary: { main: '#008060' }, // Shopify Polaris green — WCAG AA on white secondary: { main: '#5c6ac4' }, // Polaris indigo derived: { main: '#b98900' }, // dark gold custom: { main: '#b4456e' }, // H345 S50 L49 — muted rose warning: { main: '#d72c0d' }, // Polaris critical red }, /** Orchid — Figma purple, designed as accent-on-white in professional design tools * Primary/Custom: complement (purple 270° ↔ olive 90°) */ orchid: { name: 'Orchid', primary: { main: '#9747ff', bgcolor: '#f3ecff', textColor: '#7030d4' }, // Figma purple — darkened for text secondary: { main: '#1264a3' }, // strong blue derived: { main: '#e0a526', textColor: '#a87b10' }, // warm gold — darkened for text custom: { main: '#538a3a', bgcolor: '#ecf4e8', textColor: '#3d6e28' }, // leaf green — darkened for text warning: { main: '#cc3333' }, }, /** Mono — minimalist black & white, ink-on-paper * Achromatic palette with pure grayscale tones */ mono: { name: 'Mono', primary: { main: '#1a1a1a', bgcolor: '#f0f0f0', textColor: '#1a1a1a' }, // near-black (same as main) secondary: { main: '#555555', bgcolor: '#f2f2f2', textColor: '#3a3a3a' }, // darkened for readability derived: { main: '#777777', bgcolor: '#f5f5f5', textColor: '#4a4a4a' }, // darkened for readability custom: { main: '#444444', bgcolor: '#f0f0f0', textColor: '#333333' }, // darkened for readability warning: { main: '#8b2020' }, // muted dark red }, } as const; /** Default palette key (used when no user preference is stored). */ export const defaultPaletteKey: keyof typeof palettes = 'fluent'; /** List of palette keys in display order */ export const paletteKeys = Object.keys(palettes) as (keyof typeof palettes)[]; /** Background tint opacity — applied to `main` to produce `bgcolor`. */ export const bgAlpha = 0.1; ================================================ FILE: src/app/useDataRefresh.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { useEffect, useRef, useCallback } from 'react'; import { useDispatch, useSelector } from 'react-redux'; import { DataFormulatorState, dfActions, selectRefreshConfigs } from './dfSlice'; import { AppDispatch } from './store'; import { DictTable } from '../components/ComponentType'; import { createTableFromText } from '../data/utils'; import { fetchWithIdentity, getUrls, computeContentHash } from './utils'; interface RefreshResult { tableId: string; success: boolean; message: string; newRows?: any[]; contentHash?: string; // Hash from backend for database sources } /** * Custom hook that manages automatic data refresh for tables with streaming or database sources. * It sets up intervals for each table that has auto-refresh enabled. * * Performance: timers are driven by `selectRefreshConfigs` (which is stable when * only row data changes) instead of the full `state.tables` array. A ref keeps * track of the latest tables snapshot so callbacks always have fresh data without * causing the effect to re-run. */ export function useDataRefresh() { const dispatch = useDispatch<AppDispatch>(); const tables = useSelector((state: DataFormulatorState) => state.tables); const refreshConfigs = useSelector(selectRefreshConfigs); const timeoutRefs = useRef<Map<string, NodeJS.Timeout>>(new Map()); const refreshInProgressRef = useRef<Map<string, boolean>>(new Map()); const isActiveRef = useRef<Map<string, boolean>>(new Map()); const initializedTablesRef = useRef<Set<string>>(new Set()); // Keep a ref to the latest tables so callbacks can read fresh data // without adding `tables` to useEffect/useCallback deps. const tablesRef = useRef(tables); tablesRef.current = tables; /** Read latest table from store (avoids stale closure issues). */ const getLatestTable = useCallback((tableId: string): DictTable | undefined => { return tablesRef.current.find(t => t.id === tableId); }, []); /** * Fetches fresh data from a streaming URL */ const fetchStreamData = useCallback(async (table: DictTable): Promise<RefreshResult> => { const source = table.source; if (!source?.url) { return { tableId: table.id, success: false, message: 'No URL configured' }; } try { const response = await fetch(source.url); if (!response.ok) { throw new Error(`HTTP ${response.status}: ${response.statusText}`); } const content = await response.text(); let newRows: any[] = []; try { const jsonContent = JSON.parse(content); if (Array.isArray(jsonContent)) { newRows = jsonContent; } else { throw new Error('JSON content must be an array'); } } catch { // Try parsing as CSV/TSV const tempTable = createTableFromText('temp', content); if (tempTable) { newRows = tempTable.rows; } else { throw new Error('Unable to parse response as JSON or CSV/TSV'); } } return { tableId: table.id, success: true, message: 'Data refreshed successfully', newRows }; } catch (error) { return { tableId: table.id, success: false, message: error instanceof Error ? error.message : 'Unknown error' }; } }, []); /** * Refreshes a virtual table from its original database source. * Backend stores connection info and knows how to refresh - frontend just triggers it. * Backend returns data_changed flag - if false, skip resampling to avoid unnecessary work. * DuckDB views that depend on this table will auto-recalculate only if data changed. */ const refreshDatabaseTable = useCallback(async (table: DictTable): Promise<RefreshResult> => { if (!table.virtual?.tableId) { return { tableId: table.id, success: false, message: 'Not a virtual table' }; } const tableName = table.virtual.tableId; try { // Tell backend to refresh the table - it has the connection info stored console.log(`[DataRefresh] Requesting backend to refresh "${tableName}" from external source...`); const refreshResponse = await fetchWithIdentity(getUrls().DATA_LOADER_REFRESH_TABLE, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ table_name: tableName }) }); const refreshData = await refreshResponse.json(); if (refreshData.status !== 'success') { // Backend doesn't have connection info for this table console.log(`[DataRefresh] Cannot refresh "${tableName}": ${refreshData.message}`); return { tableId: table.id, success: false, message: refreshData.message || 'No connection info stored for this table' }; } console.log(`[DataRefresh] Backend refreshed "${tableName}" (${refreshData.row_count} rows, data_changed=${refreshData.data_changed}, hash=${refreshData.content_hash?.slice(0, 8)})`); // If data hasn't changed, skip resampling - no need to update frontend if (!refreshData.data_changed) { console.log(`[DataRefresh] Data unchanged for "${tableName}", skipping resample`); return { tableId: table.id, success: true, message: `Data unchanged (${refreshData.row_count} rows)`, // Don't include newRows - signals no update needed }; } // Data changed - get a fresh sample for the frontend const sampleResponse = await fetchWithIdentity(getUrls().SAMPLE_TABLE, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ table: tableName, size: Math.min(table.rows.length, 10000) }) }); const sampleData = await sampleResponse.json(); if (sampleData.status === 'success') { return { tableId: table.id, success: true, message: `Refreshed from source (${refreshData.row_count} rows)`, newRows: sampleData.rows, contentHash: refreshData.content_hash }; } else { return { tableId: table.id, success: false, message: sampleData.error || 'Failed to sample refreshed table' }; } } catch (error) { return { tableId: table.id, success: false, message: error instanceof Error ? error.message : 'Unknown error' }; } }, [dispatch]); /** * Refresh a single table's data */ const refreshTable = useCallback(async (table: DictTable): Promise<RefreshResult> => { const source = table.source; // Stream source - frontend has URL if (source?.type === 'stream' && source.url) { return fetchStreamData(table); } // Database source - backend has connection info if (table.virtual && source?.type === 'database' && source.canRefresh) { return refreshDatabaseTable(table); } return { tableId: table.id, success: false, message: 'Table does not support refresh' }; }, [fetchStreamData, refreshDatabaseTable]); /** * Refresh table and update state, then refresh derived tables */ const performRefresh = useCallback(async (table: DictTable) => { // Prevent overlapping refreshes for the same table if (refreshInProgressRef.current.get(table.id)) { console.log(`[DataRefresh] Refresh already in progress for "${table.id}", skipping...`); return; } refreshInProgressRef.current.set(table.id, true); try { const result = await refreshTable(table); if (result.success) { // Check if we have new rows to process if (result.newRows) { // For stream sources: compute hash locally and compare // For database sources: backend already determined data changed const newContentHash = result.contentHash || computeContentHash(result.newRows, table.names); const oldContentHash = table.contentHash; const dataChanged = oldContentHash !== newContentHash; if (dataChanged) { console.log(`[DataRefresh] Table "${table.id}" data changed (hash: ${oldContentHash?.slice(0, 8) || 'none'} -> ${newContentHash.slice(0, 8)}), updating...`); // For stream sources with virtual tables, sync the new data to workspace // so that sandbox code (derived table refresh) reads fresh data from parquet. // Database sources don't need this — their backend refresh already updates workspace. if (table.source?.type === 'stream' && table.virtual?.tableId) { try { await fetchWithIdentity(getUrls().SYNC_TABLE_DATA, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ table_name: table.virtual.tableId, rows: result.newRows }) }); console.log(`[DataRefresh] Synced stream data for "${table.virtual.tableId}" to workspace`); } catch (syncError) { console.warn(`[DataRefresh] Failed to sync stream data to workspace:`, syncError); } } // Update the table rows - this will trigger useDerivedTableRefresh // Pass contentHash from backend for virtual/DB tables so it reflects full table state dispatch(dfActions.updateTableRows({ tableId: table.id, rows: result.newRows, contentHash: result.contentHash // Use backend hash for DB tables })); // Notify about the refresh dispatch(dfActions.addMessages({ timestamp: Date.now(), component: 'Data Refresh', type: 'info', value: `Table "${table.displayId || table.id}" data refreshed (${result.newRows.length} rows)` })); } else { console.log(`[DataRefresh] Table "${table.id}" data unchanged (hash: ${newContentHash.slice(0, 8)})`); } } else { // Success but no newRows means database refresh detected no data change // No need to update frontend state or trigger derived table refresh console.log(`[DataRefresh] Table "${table.id}" refresh complete - data unchanged (backend check)`); } } else { console.warn(`[DataRefresh] Failed to refresh "${table.id}": ${result.message}`); dispatch(dfActions.addMessages({ timestamp: Date.now(), component: 'Data Refresh', type: 'warning', value: `Failed to refresh "${table.displayId || table.id}": ${result.message}` })); } } finally { refreshInProgressRef.current.set(table.id, false); } }, [dispatch, refreshTable]); /** * Schedule the next refresh for a table after the current one completes. * Uses recursive setTimeout pattern to ensure updates wait until complete. */ const scheduleNextRefresh = useCallback((tableId: string) => { // Look up the current table state to get latest source config const table = getLatestTable(tableId); if (!table) { isActiveRef.current.set(tableId, false); return; } const source = table.source; if (!source?.autoRefresh || !source.refreshIntervalSeconds || source.refreshIntervalSeconds <= 0) { isActiveRef.current.set(tableId, false); return; } if (!isActiveRef.current.get(tableId)) { return; } const intervalMs = source.refreshIntervalSeconds * 1000; const timeout = setTimeout(async () => { if (!isActiveRef.current.get(tableId)) { return; } // Read latest table from ref (not stale closure) const currentTable = getLatestTable(tableId); if (!currentTable) { isActiveRef.current.set(tableId, false); return; } await performRefresh(currentTable); scheduleNextRefresh(tableId); }, intervalMs); timeoutRefs.current.set(tableId, timeout); }, [getLatestTable, performRefresh]); /** * Set up refresh intervals for tables with auto-refresh enabled. * * This effect depends on `refreshConfigs` (a memoized projection of tables * that only contains refresh-relevant fields) — so it does NOT re-run when * only table rows change. Timer churn is eliminated. */ useEffect(() => { // Clear all existing timeouts timeoutRefs.current.forEach((timeout) => clearTimeout(timeout)); timeoutRefs.current.clear(); isActiveRef.current.clear(); // Set up new refresh schedules refreshConfigs.forEach((config) => { // Skip derived tables — they are refreshed by useDerivedTableRefresh // when their source tables change, not by polling an external source. const shouldAutoRefresh = config.autoRefresh && config.refreshIntervalSeconds && config.refreshIntervalSeconds > 0 && !config.hasDerive; if (shouldAutoRefresh) { const intervalMs = config.refreshIntervalSeconds! * 1000; const isNewTable = !initializedTablesRef.current.has(config.id); console.log(`[DataRefresh] Setting up auto-refresh for "${config.id}" every ${config.refreshIntervalSeconds}s (new=${isNewTable})`); isActiveRef.current.set(config.id, true); initializedTablesRef.current.add(config.id); if (isNewTable) { console.log(`[DataRefresh] Triggering immediate first refresh for newly loaded table "${config.id}"`); (async () => { if (!isActiveRef.current.get(config.id)) { return; } const table = getLatestTable(config.id); if (table) { await performRefresh(table); } scheduleNextRefresh(config.id); })(); } else { const initialTimeout = setTimeout(async () => { if (!isActiveRef.current.get(config.id)) { return; } const table = getLatestTable(config.id); if (table) { await performRefresh(table); } scheduleNextRefresh(config.id); }, intervalMs); timeoutRefs.current.set(config.id, initialTimeout); } } }); // Clean up tables that no longer exist from the initialized set const currentTableIds = new Set(refreshConfigs.map(c => c.id)); initializedTablesRef.current.forEach(tableId => { if (!currentTableIds.has(tableId)) { initializedTablesRef.current.delete(tableId); } }); return () => { timeoutRefs.current.forEach((timeout) => clearTimeout(timeout)); timeoutRefs.current.clear(); isActiveRef.current.clear(); }; }, [refreshConfigs, performRefresh, scheduleNextRefresh, getLatestTable]); /** * Manual refresh function that can be called from components */ const manualRefresh = useCallback(async (tableId: string) => { const table = getLatestTable(tableId); if (table) { await performRefresh(table); } }, [getLatestTable, performRefresh]); /** * Get refresh info for a table */ const getRefreshInfo = useCallback((tableId: string) => { const table = getLatestTable(tableId); if (!table) return null; const source = table.source; const canRefresh = (source?.type === 'stream' && !!source.url) || (source?.type === 'database' && source.canRefresh === true); return { canRefresh, autoRefreshEnabled: source?.autoRefresh ?? false, refreshIntervalSeconds: source?.refreshIntervalSeconds, lastRefreshed: source?.lastRefreshed, sourceType: source?.type }; }, [getLatestTable]); return { manualRefresh, getRefreshInfo }; } /** * Hook to handle refreshing derived tables when their source tables change. * This should be used in conjunction with useDataRefresh. * * Key performance optimisation: all derived table refreshes triggered by a * single source-table change are fetched in parallel and then dispatched as * a **single** `updateMultipleTableRows` action — collapsing N state updates * (and N full-app re-renders) into one. */ export function useDerivedTableRefresh() { const dispatch = useDispatch<AppDispatch>(); const tables = useSelector((state: DataFormulatorState) => state.tables); const prevTableRowsRef = useRef<Map<string, string>>(new Map()); const refreshInProgressRef = useRef<boolean>(false); /** * Refresh a SQL view (virtual table) by re-sampling from DuckDB. * Returns the result rather than dispatching directly. */ const refreshSqlView = useCallback(async (derivedTable: DictTable): Promise<{tableId: string, rows: any[]} | null> => { if (!derivedTable.virtual?.tableId) return null; const tableName = derivedTable.virtual.tableId; console.log(`[DerivedRefresh] Re-sampling SQL view "${tableName}" (DuckDB auto-updated)...`); try { const response = await fetchWithIdentity(getUrls().SAMPLE_TABLE, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ table: tableName, size: Math.max(derivedTable.rows.length, 1000) }) }); const data = await response.json(); if (data.status === 'success' && data.rows) { console.log(`[DerivedRefresh] Successfully re-sampled SQL view "${tableName}" (${data.rows.length} rows)`); return { tableId: derivedTable.id, rows: data.rows }; } return null; } catch (error) { console.error(`[DerivedRefresh] Error re-sampling SQL view ${tableName}:`, error); return null; } }, []); /** * Refresh a single derived table. Returns the result for batching. */ const refreshOneDerivedTable = useCallback(async ( derivedTable: DictTable, allTables: DictTable[] ): Promise<{tableId: string, rows: any[]} | null> => { if (!derivedTable.derive) return null; try { // SQL views — DuckDB auto-updates; just re-sample if (derivedTable.virtual?.tableId && !derivedTable.derive?.code) { console.log(`[DerivedRefresh] Table "${derivedTable.id}" is an SQL view - DuckDB auto-updates it`); return await refreshSqlView(derivedTable); } // Python-derived tables — re-run the transformation code const { source: sourceTableIds, code, codeSignature } = derivedTable.derive; // Security: refuse to send code without a valid server signature if (!codeSignature) { console.warn(`[DerivedRefresh] Table "${derivedTable.id}" has no code signature — skipping refresh (code may predate signing)`); dispatch(dfActions.addMessages({ timestamp: Date.now(), component: 'Data Refresh', type: 'warning', value: `Cannot refresh "${derivedTable.displayId || derivedTable.id}": missing code signature. Re-derive the table to obtain a signed version.` })); return null; } console.log(`[DerivedRefresh] Looking for source tables: ${sourceTableIds.join(', ')}`); const inputTables: {name: string, rows: any[]}[] = []; for (const sourceId of sourceTableIds) { const sourceTable = allTables.find(t => t.id === sourceId); if (!sourceTable) { console.warn(`[DerivedRefresh] Source table not found: ${sourceId}`); continue; } const tableName = sourceTable.virtual?.tableId || sourceTable.id.replace(/\.[^/.]+$/, ""); inputTables.push({ name: tableName, rows: sourceTable.rows }); } if (inputTables.length !== sourceTableIds.length) { console.error(`[DerivedRefresh] Missing source tables for: ${derivedTable.id} (got ${inputTables.length}/${sourceTableIds.length})`); return null; } console.log(`[DerivedRefresh] Calling server to refresh "${derivedTable.id}" with ${inputTables.length} input tables, code length: ${code.length}`); const requestBody: any = { input_tables: inputTables, code: code, code_signature: codeSignature, // HMAC proof that server generated this code output_variable: derivedTable.derive?.outputVariable || 'result_df', virtual: !!derivedTable.virtual?.tableId, output_table_name: derivedTable.virtual?.tableId }; const response = await fetchWithIdentity(getUrls().REFRESH_DERIVED_DATA, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(requestBody) }); const data = await response.json(); if (data.status === 'ok' && data.rows) { console.log(`[DerivedRefresh] Successfully refreshed "${derivedTable.id}" with ${data.rows.length} rows`); return { tableId: derivedTable.id, rows: data.rows }; } else { console.error(`[DerivedRefresh] Failed to refresh "${derivedTable.id}": ${data.message}`); dispatch(dfActions.addMessages({ timestamp: Date.now(), component: 'Data Refresh', type: 'warning', value: `Failed to refresh "${derivedTable.displayId || derivedTable.id}": ${data.message}` })); return null; } } catch (error) { console.error(`[DerivedRefresh] Error refreshing derived table ${derivedTable.id}:`, error); dispatch(dfActions.addMessages({ timestamp: Date.now(), component: 'Data Refresh', type: 'error', value: `Error refreshing "${derivedTable.displayId || derivedTable.id}": ${error instanceof Error ? error.message : 'Unknown error'}` })); return null; } }, [dispatch, refreshSqlView]); /** * Check for table changes and refresh dependent derived tables. * All refreshes are fetched in parallel, then committed as a single batch dispatch. */ useEffect(() => { console.log(`[DerivedRefresh] useEffect triggered, ${tables.length} tables in state`); // Build a map of content hashes for source tables only (non-derived tables) const currentHashMap = new Map<string, string>(); tables.forEach(table => { if (!table.derive) { const hash = table.contentHash || computeContentHash(table.rows, table.names); currentHashMap.set(table.id, hash); } }); // Check which source tables have changed const changedTableIds: string[] = []; currentHashMap.forEach((hash, tableId) => { const prevHash = prevTableRowsRef.current.get(tableId); if (prevHash && prevHash !== hash) { changedTableIds.push(tableId); console.log(`[DerivedRefresh] Detected change in source table "${tableId}" (hash: ${prevHash.slice(0, 8)} -> ${hash.slice(0, 8)})`); } }); if (prevTableRowsRef.current.size === 0 && tables.length > 0) { const sourceTableCount = tables.filter(t => !t.derive).length; console.log(`[DerivedRefresh] First run, initializing content hashes for ${sourceTableCount} source tables`); } // If any source tables changed, find and refresh all dependent derived tables in parallel if (changedTableIds.length > 0 && !refreshInProgressRef.current) { console.log(`[DerivedRefresh] Source tables changed: ${changedTableIds.join(', ')}`); const directlyDependentTables: DictTable[] = []; tables.forEach(table => { if (table.derive) { const dependsOnChanged = table.derive.source.some( sourceId => changedTableIds.includes(sourceId) ); console.log(`[DerivedRefresh] Checking derived table "${table.id}": dependsOnChanged=${dependsOnChanged}, sources=[${table.derive.source.join(', ')}]`); if (dependsOnChanged) { directlyDependentTables.push(table); } } }); if (directlyDependentTables.length > 0) { console.log(`[DerivedRefresh] Will refresh ${directlyDependentTables.length} directly dependent tables in parallel: ${directlyDependentTables.map(t => t.id).join(', ')}`); refreshInProgressRef.current = true; // Fire all refreshes in parallel, then batch the results into ONE dispatch Promise.all( directlyDependentTables.map(dt => refreshOneDerivedTable(dt, tables)) ).then(results => { const successfulUpdates = results.filter((r): r is {tableId: string, rows: any[]} => r !== null); if (successfulUpdates.length > 0) { // Single dispatch for ALL derived table updates dispatch(dfActions.updateMultipleTableRows(successfulUpdates)); // One summary message instead of N individual messages const names = successfulUpdates.map(u => { const t = tables.find(t => t.id === u.tableId); return `"${t?.displayId || u.tableId}" (${u.rows.length} rows)`; }); dispatch(dfActions.addMessages({ timestamp: Date.now(), component: 'Data Refresh', type: 'info', value: `Refreshed ${successfulUpdates.length} derived table(s): ${names.join(', ')}` })); } }).finally(() => { refreshInProgressRef.current = false; }); } else { console.log(`[DerivedRefresh] No derived tables need refreshing`); } } // Update the previous hashes reference (only source tables) prevTableRowsRef.current = currentHashMap; }, [tables, refreshOneDerivedTable, dispatch]); } ================================================ FILE: src/app/useFormulateData.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { useSelector, useDispatch } from 'react-redux'; import { DataFormulatorState, dfActions, dfSelectors, fetchCodeExpl, fetchChartInsight, fetchFieldSemanticType } from './dfSlice'; import { AppDispatch } from './store'; import { Chart, FieldItem, Trigger, createDictTable, DictTable } from '../components/ComponentType'; import { getUrls, getTriggers, fetchWithIdentity } from './utils'; export type IdeaItem = { text: string; goal: string; difficulty: 'easy' | 'medium' | 'hard'; tag?: string; }; export interface StreamIdeasOptions { actionTableIds: string[]; currentTable: DictTable; onIdeas: (ideas: IdeaItem[]) => void; onThinkingBuffer: (buffer: string) => void; onLoadingChange: (loading: boolean) => void; /** Chart image (PNG data URL) for current visualization context */ currentChartImage?: string | null; /** Sample rows from the current table */ currentDataSample?: any[]; /** Optional start question for idea generation */ startQuestion?: string; /** If true, only blocks with type==="question" are included (default: false) */ filterByType?: boolean; } export interface FormulateDataOptions { instruction: string; mode: 'formulate' | 'ideate'; actionTableIds: string[]; currentTable: DictTable; overrideTableId?: string; currentVisualization?: any; expectedVisualization?: any; /** The chart spec to embed in the trigger for the derived table */ triggerChart: Chart; /** * Component-specific chart creation callback. * Called with the candidate table, refined goal, and resolved concepts. * Should dispatch chart creation actions and return the focused chart ID (or undefined). */ createChart: (params: { candidateTable: DictTable; refinedGoal: any; currentConcepts: FieldItem[]; }) => string | undefined; /** Called before the request is made */ onStarted?: () => void; /** Called on successful formulation */ onSuccess?: (params: { displayInstruction: string; candidateTable: DictTable; focusedChartId?: string }) => void; /** Called on error */ onError?: (error: any) => void; /** Called after the request completes (success or error) */ onFinally?: () => void; } function generateTableId(tables: DictTable[]): string { let tableSuffix = Number.parseInt((Date.now() - Math.floor(Math.random() * 10000)).toString().slice(-6)); let tableId = `table-${tableSuffix}`; while (tables.find(t => t.id === tableId) !== undefined) { tableSuffix += 1; tableId = `table-${tableSuffix}`; } return tableId; } /** * Shared hook for data formulation and idea streaming. * Used by both EncodingShelfCard (chart-aware formulation) and ChartRecBox (NL-driven formulation). */ export function useFormulateData() { const dispatch = useDispatch<AppDispatch>(); const tables = useSelector((state: DataFormulatorState) => state.tables); const config = useSelector((state: DataFormulatorState) => state.config); const agentRules = useSelector((state: DataFormulatorState) => state.agentRules); const conceptShelfItems = useSelector((state: DataFormulatorState) => state.conceptShelfItems); const activeModel = useSelector(dfSelectors.getActiveModel); /** * Build an exploration thread from the current table's derivation chain. */ function buildExplorationThread(currentTable: DictTable): any[] { if (!currentTable.derive || currentTable.anchored) return []; const triggers = getTriggers(currentTable, tables); return triggers.map(trigger => ({ name: trigger.resultTableId, rows: tables.find(t2 => t2.id === trigger.resultTableId)?.rows, description: `Derive from ${tables.find(t2 => t2.id === trigger.resultTableId)?.derive?.source} with instruction: ${trigger.instruction}`, })); } /** * Stream ideas/recommendations from the exploration agent via SSE. */ async function streamIdeas(options: StreamIdeasOptions): Promise<void> { const { actionTableIds, currentTable, onIdeas, onThinkingBuffer, onLoadingChange, currentChartImage, currentDataSample, startQuestion, filterByType = false, } = options; onLoadingChange(true); onThinkingBuffer(""); onIdeas([]); try { const explorationThread = buildExplorationThread(currentTable); const actionTables = actionTableIds.map(id => tables.find(t => t.id === id) as DictTable); const messageBody = JSON.stringify({ token: String(Date.now()), model: activeModel, mode: 'interactive', input_tables: actionTables.map(t => ({ name: t.virtual?.tableId || t.id.replace(/\.[^/.]+$/, ""), rows: t.rows, attached_metadata: t.attachedMetadata, })), exploration_thread: explorationThread, agent_exploration_rules: agentRules.exploration, ...(currentDataSample ? { current_data_sample: currentDataSample } : {}), ...(currentChartImage ? { current_chart: currentChartImage } : {}), ...(startQuestion ? { start_question: startQuestion } : {}), }); const engine = getUrls().GET_RECOMMENDATION_QUESTIONS; const controller = new AbortController(); const timeoutId = setTimeout(() => controller.abort(), config.formulateTimeoutSeconds * 1000); const response = await fetchWithIdentity(engine, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: messageBody, signal: controller.signal, }); clearTimeout(timeoutId); if (!response.ok) { throw new Error(`HTTP error! status: ${response.status}`); } const reader = response.body?.getReader(); if (!reader) { throw new Error('No response body reader available'); } const decoder = new TextDecoder(); let lines: string[] = []; let buffer = ''; const updateState = (currentLines: string[]) => { const dataBlocks = currentLines .map(line => { try { return JSON.parse(line.trim()); } catch (e) { return null; } }) .filter(block => block != null); const questions = (filterByType ? dataBlocks.filter((block: any) => block.type === "question") : dataBlocks) .map((block: any) => ({ text: block.text, goal: block.goal, difficulty: block.difficulty, tag: block.tag, })); onIdeas(questions); }; try { while (true) { const { done, value } = await reader.read(); if (done) break; buffer += decoder.decode(value, { stream: true }); const newLines = buffer.split('data: ').filter(line => line.trim() !== ""); buffer = newLines.pop() || ''; if (newLines.length > 0) { lines.push(...newLines); updateState(lines); } onThinkingBuffer(buffer.replace(/^data: /, "")); } } finally { reader.releaseLock(); } lines.push(buffer); updateState(lines); if (lines.length === 0) { throw new Error('No valid results returned from agent'); } } catch (error) { dispatch(dfActions.addMessages({ "timestamp": Date.now(), "type": "error", "component": "chart builder", "value": "Failed to get ideas from the exploration agent. Please try again.", "detail": error instanceof Error ? error.message : 'Unknown error', })); } finally { onLoadingChange(false); } } /** * Formulate data: send instruction to derive/refine endpoint and process the result. * Handles request building, dialog continuation, table/concept creation, and error handling. * Chart creation is delegated to the caller via the createChart callback. */ function formulateData(options: FormulateDataOptions): void { const { instruction, mode, actionTableIds, currentTable, overrideTableId, currentVisualization, expectedVisualization, triggerChart, createChart, onStarted, onSuccess, onError, onFinally, } = options; if (actionTableIds.length === 0) return; onStarted?.(); const actionTables = actionTableIds.map(id => tables.find(t => t.id === id) as DictTable); const token = String(Date.now()); // Build input_tables payload (shared across all request variants) const inputTablesPayload = actionTables.map(t => ({ name: t.virtual?.tableId || t.id.replace(/\.[^/.]+$/, ""), rows: t.rows, attached_metadata: t.attachedMetadata, })); // Build base request body let messageBody: any = { token, mode, input_tables: inputTablesPayload, extra_prompt: instruction, model: activeModel, agent_coding_rules: agentRules.coding, ...(currentVisualization ? { current_visualization: currentVisualization } : {}), ...(expectedVisualization ? { expected_visualization: expectedVisualization } : {}), }; let engine = getUrls().DERIVE_DATA; // Handle dialog continuation / refinement if (currentTable.derive?.dialog && !currentTable.anchored) { const sourceTableIds = currentTable.derive.source; const tableIdsChanged = !sourceTableIds.every((id: string) => actionTableIds.includes(id)) || !actionTableIds.every(id => sourceTableIds.includes(id)); if (mode === 'ideate' || tableIdsChanged) { // Start fresh with prior dialog as additional context messageBody.additional_messages = currentTable.derive.dialog; engine = getUrls().DERIVE_DATA; } else { // Refine: continue existing dialog messageBody = { token, mode, input_tables: inputTablesPayload, dialog: currentTable.derive.dialog, latest_data_sample: currentTable.rows.slice(0, 10), new_instruction: instruction, model: activeModel, agent_coding_rules: agentRules.coding, ...(currentVisualization ? { current_visualization: currentVisualization } : {}), ...(expectedVisualization ? { expected_visualization: expectedVisualization } : {}), }; engine = getUrls().REFINE_DATA; } } const controller = new AbortController(); const timeoutId = setTimeout(() => controller.abort(), config.formulateTimeoutSeconds * 1000); fetchWithIdentity(engine, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(messageBody), signal: controller.signal, }) .then((response: Response) => { if (!response.ok) { return response.text().then(text => { try { const errorData = JSON.parse(text); throw new Error(errorData.error_message || errorData.error || `Server error (${response.status})`); } catch (parseError) { if (parseError instanceof SyntaxError) { throw new Error(`Server error (${response.status}): The server returned an unexpected response.`); } throw parseError; } }); } return response.json(); }) .then((data) => { if (data.status === "error" && data.error_message) { dispatch(dfActions.addMessages({ "timestamp": Date.now(), "component": "chart builder", "type": "error", "value": `Data formulation failed: ${data.error_message}`, })); return; } if (!data.results || data.results.length === 0) { dispatch(dfActions.addMessages({ "timestamp": Date.now(), "component": "chart builder", "type": "error", "value": "No result is returned from the data formulation agent. Please try again.", })); return; } if (data["token"] !== token) return; const candidates = data["results"].filter((item: any) => item["status"] === "ok"); if (candidates.length === 0) { dispatch(dfActions.addMessages({ "timestamp": Date.now(), "type": "error", "component": "chart builder", "value": "Data formulation failed, please try again.", "code": data.results[0].code, "detail": data.results[0].content, })); return; } // Process the best candidate const candidate = candidates[0]; const code = candidate["code"]; const codeSignature = candidate["code_signature"]; // HMAC signature from server const rows = candidate["content"]["rows"]; const dialog = candidate["dialog"]; const refinedGoal = candidate['refined_goal']; const displayInstruction = refinedGoal["display_instruction"]; // Determine table ID let candidateTableId: string; if (overrideTableId) { candidateTableId = overrideTableId; } else if (candidate["content"]["virtual"]) { candidateTableId = candidate["content"]["virtual"]["table_name"]; } else { candidateTableId = generateTableId(tables); } // Create trigger const trigger: Trigger = { tableId: currentTable.id, instruction, displayInstruction, chart: triggerChart, resultTableId: candidateTableId, }; // Create candidate table with derive info const candidateTable = createDictTable(candidateTableId, rows, { code, codeSignature, outputVariable: refinedGoal['output_variable'] || 'result_df', source: actionTableIds, dialog, trigger, }); if (candidate["content"]["virtual"]) { candidateTable.virtual = { tableId: candidate["content"]["virtual"]["table_name"], rowCount: candidate["content"]["virtual"]["row_count"], }; } // Bootstrap metadata from agent field_metadata (temporary until fetchFieldSemanticType completes) const fieldMetadata = refinedGoal['field_metadata']; if (fieldMetadata && typeof fieldMetadata === 'object') { for (const [fieldName, meta] of Object.entries(fieldMetadata)) { if (!candidateTable.metadata[fieldName]) continue; if (typeof meta === 'string') { // Plain string format: { "field": "SemanticType" } candidateTable.metadata[fieldName].semanticType = meta; } else if (typeof meta === 'object' && meta !== null) { // Dict format: { "field": { "semantic_type": "...", "unit": "...", ... } } const m = meta as Record<string, any>; if (m['semantic_type']) { candidateTable.metadata[fieldName].semanticType = m['semantic_type']; } if (m['unit']) { candidateTable.metadata[fieldName].unit = m['unit']; } if (m['intrinsic_domain']) { candidateTable.metadata[fieldName].intrinsicDomain = m['intrinsic_domain']; } } } } // Insert or override table if (overrideTableId) { dispatch(dfActions.overrideDerivedTables(candidateTable)); } else { dispatch(dfActions.insertDerivedTables(candidateTable)); } // Add missing concepts const names = candidateTable.names; const missingNames = names.filter((name: string) => !conceptShelfItems.some(field => field.name === name)); const conceptsToAdd = missingNames.map((name: string) => ({ id: `concept-${name}-${Date.now()}`, name, source: "custom", tableRef: "custom", } as FieldItem)); dispatch(dfActions.addConceptItems(conceptsToAdd)); dispatch(fetchFieldSemanticType(candidateTable)); dispatch(fetchCodeExpl(candidateTable)); // Compute current concepts for chart creation const currentConcepts = [...conceptShelfItems.filter(c => names.includes(c.name)), ...conceptsToAdd]; // Delegate chart creation to the caller const focusedChartId = createChart({ candidateTable, refinedGoal, currentConcepts }); // Auto-generate chart insight after rendering if (focusedChartId) { const chartIdForInsight = focusedChartId; setTimeout(() => { dispatch(fetchChartInsight({ chartId: chartIdForInsight, tableId: candidateTable.id }) as any); }, 1500); } onSuccess?.({ displayInstruction, candidateTable, focusedChartId }); }) .catch((error) => { if (error.name === 'AbortError') { dispatch(dfActions.addMessages({ "timestamp": Date.now(), "component": "chart builder", "type": "error", "value": `Data formulation timed out after ${config.formulateTimeoutSeconds} seconds. Consider breaking down the task, using a different model or prompt, or increasing the timeout limit.`, "detail": "Request exceeded timeout limit", })); } else { console.error(error); dispatch(dfActions.addMessages({ "timestamp": Date.now(), "component": "chart builder", "type": "error", "value": "Data formulation failed, please try again.", "detail": error.message, })); } onError?.(error); }) .finally(() => { clearTimeout(timeoutId); onFinally?.(); }); } return { streamIdeas, formulateData }; } ================================================ FILE: src/app/utils.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import _, { } from "lodash"; import { useEffect, useRef } from "react"; import ts from "typescript"; import { Channel, Chart, EncodingItem, EncodingMap, FieldItem, Trigger } from "../components/ComponentType"; import { DictTable } from "../components/ComponentType"; import { Type } from "../data/types"; import * as d3 from 'd3'; import { assembleVegaLite, type ChartEncoding, type AssembleOptions } from "../lib/agents-chart"; export function getUrls() { return { APP_CONFIG: `/api/app-config`, AUTH_INFO_PREFIX: `/api/.auth/`, EXAMPLE_DATASETS: `/api/example-datasets`, // these functions involves ai agents CHECK_AVAILABLE_MODELS: `/api/agent/check-available-models`, TEST_MODEL: `/api/agent/test-model`, SORT_DATA_URL: `/api/agent/sort-data`, CLEAN_DATA_URL: `/api/agent/clean-data-stream`, CODE_EXPL_URL: `/api/agent/code-expl`, CHART_INSIGHT_URL: `/api/agent/chart-insight`, SERVER_PROCESS_DATA_ON_LOAD: `/api/agent/process-data-on-load`, DERIVE_DATA: `/api/agent/derive-data`, REFINE_DATA: `/api/agent/refine-data`, DATA_AGENT_STREAMING: `/api/agent/data-agent-streaming`, // these functions involves database UPLOAD_DB_FILE: `/api/tables/upload-db-file`, DOWNLOAD_DB_FILE: `/api/tables/download-db-file`, RESET_DB_FILE: `/api/tables/reset-db-file`, LIST_TABLES: `/api/tables/list-tables`, TABLE_DATA: `/api/tables/get-table`, CREATE_TABLE: `/api/tables/create-table`, DELETE_TABLE: `/api/tables/delete-table`, GET_COLUMN_STATS: `/api/tables/analyze`, SAMPLE_TABLE: `/api/tables/sample-table`, SYNC_TABLE_DATA: `/api/tables/sync-table-data`, EXPORT_TABLE_CSV: `/api/tables/export-table-csv`, DATA_LOADER_LIST_DATA_LOADERS: `/api/tables/data-loader/list-data-loaders`, DATA_LOADER_LIST_TABLES: `/api/tables/data-loader/list-tables`, DATA_LOADER_INGEST_DATA: `/api/tables/data-loader/ingest-data`, DATA_LOADER_VIEW_QUERY_SAMPLE: `/api/tables/data-loader/view-query-sample`, DATA_LOADER_INGEST_DATA_FROM_QUERY: `/api/tables/data-loader/ingest-data-from-query`, DATA_LOADER_REFRESH_TABLE: `/api/tables/data-loader/refresh-table`, DATA_LOADER_FETCH_DATA: `/api/tables/data-loader/fetch-data`, DATA_LOADER_GET_TABLE_METADATA: `/api/tables/data-loader/get-table-metadata`, DATA_LOADER_LIST_TABLE_METADATA: `/api/tables/data-loader/list-table-metadata`, GET_RECOMMENDATION_QUESTIONS: `/api/agent/get-recommendation-questions`, GENERATE_REPORT_STREAM: `/api/agent/generate-report-stream`, // Refresh data endpoint REFRESH_DERIVED_DATA: `/api/agent/refresh-derived-data`, // Session management SESSION_SAVE: `/api/sessions/save`, SESSION_LIST: `/api/sessions/list`, SESSION_LOAD: `/api/sessions/load`, SESSION_DELETE: `/api/sessions/delete`, SESSION_EXPORT: `/api/sessions/export`, SESSION_IMPORT: `/api/sessions/import`, // Workspace OPEN_WORKSPACE: `/api/tables/open-workspace`, }; } /** * Get the current namespaced identity from the Redux store, or fall back to browser ID. * Returns identity in "type:id" format (e.g., "user:alice@example.com" or "browser:550e8400-...") * * This namespaced format ensures the backend can distinguish between authenticated users * and anonymous browser sessions, preventing identity spoofing attacks. */ async function getCurrentNamespacedIdentity(): Promise<string> { try { const { store } = await import('./store'); const state = store.getState(); if (state.identity?.id && state.identity?.type) { return `${state.identity.type}:${state.identity.id}`; } } catch (e) { // Store not available } // Fall back to browser ID from localStorage const { getBrowserId } = await import('./identity'); return `browser:${getBrowserId()}`; } /** * Get auth token if available (for future JWT auth support). * Currently returns null - implement when adding custom auth. */ function getAuthToken(): string | null { // Future: retrieve JWT from localStorage or auth context // Example: return localStorage.getItem('auth_token'); return null; } /** * Enhanced fetch wrapper that automatically adds identity and auth headers for API requests. * * Security model: * - X-Identity-Id: Namespaced identity ("type:id" format) for all requests * - Authorization: Bearer token (when implementing custom JWT auth) * * The backend prioritizes verified auth (Azure headers, JWT) over X-Identity-Id. * For anonymous users, X-Identity-Id is used with "browser:" namespace prefix. * * Use this instead of native fetch() for all /api/ calls. * * @param url - The URL to fetch * @param options - Fetch options (same as native fetch) * @returns Promise<Response> */ export async function fetchWithIdentity( url: string | URL, options: RequestInit = {} ): Promise<Response> { const urlString = typeof url === 'string' ? url : url.toString(); // Add identity and auth headers for all API requests if (urlString.startsWith('/api/')) { const headers = new Headers(options.headers); // Always send namespaced identity (fallback for backend) const namespacedIdentity = await getCurrentNamespacedIdentity(); headers.set('X-Identity-Id', namespacedIdentity); // Send auth token if available (for custom JWT auth) const authToken = getAuthToken(); if (authToken) { headers.set('Authorization', `Bearer ${authToken}`); } options = { ...options, headers }; } return fetch(url, options); } import * as vm from 'vm-browserify'; export function usePrevious<T>(value: T): T | undefined { const ref = useRef<T>(); useEffect(() => { ref.current = value; }); return ref.current; } /** * Simple hash function (djb2 algorithm) for creating content fingerprints * @param str - The string to hash * @returns A hexadecimal hash string */ function djb2Hash(str: string): string { let hash = 5381; for (let i = 0; i < str.length; i++) { hash = ((hash << 5) + hash) + str.charCodeAt(i); hash = hash & hash; // Convert to 32-bit integer } return (hash >>> 0).toString(16); // Convert to unsigned and then to hex } /** * Computes a content hash for table data to detect changes. * Uses a sampling strategy for efficiency with large datasets: * - Always includes column names and row count * - Samples first 50, last 50, and 50 evenly distributed rows from the middle * - This catches most data changes while remaining efficient * * @param rows - The table rows to hash * @param names - Optional column names (for additional fingerprinting) * @returns A hash string representing the content */ export function computeContentHash(rows: any[], names?: string[]): string { const parts: string[] = []; // Include column names if provided if (names && names.length > 0) { parts.push(`cols:${names.join(',')}`); } // Include row count const rowCount = rows.length; parts.push(`count:${rowCount}`); if (rowCount === 0) { return djb2Hash(parts.join('|')); } // Get column names from first row if not provided const columnNames = names || Object.keys(rows[0] || {}); parts.push(`fields:${columnNames.join(',')}`); // Sampling strategy for efficiency const sampleSize = 50; const samplesToInclude: number[] = []; // Always include first N rows for (let i = 0; i < Math.min(sampleSize, rowCount); i++) { samplesToInclude.push(i); } // Include evenly distributed rows from the middle if (rowCount > sampleSize * 2) { const step = Math.floor((rowCount - sampleSize * 2) / sampleSize); if (step > 0) { for (let i = sampleSize; i < rowCount - sampleSize; i += step) { if (samplesToInclude.length < sampleSize * 2) { samplesToInclude.push(i); } } } } // Always include last N rows for (let i = Math.max(rowCount - sampleSize, sampleSize); i < rowCount; i++) { if (!samplesToInclude.includes(i)) { samplesToInclude.push(i); } } // Sort indices for consistent ordering samplesToInclude.sort((a, b) => a - b); // Build content string from sampled rows const rowStrings: string[] = []; for (const idx of samplesToInclude) { const row = rows[idx]; if (row) { // Create a deterministic string representation of the row const rowValues = columnNames.map(col => { const val = row[col]; if (val === null) return 'null'; if (val === undefined) return 'undefined'; if (typeof val === 'object') return JSON.stringify(val); return String(val); }); rowStrings.push(`${idx}:${rowValues.join(',')}`); } } parts.push(`rows:${rowStrings.join(';')}`); return djb2Hash(parts.join('|')); } export function runCodeOnInputListsInVM( code: string, inputTupleList: any[][], mode: "faster" | "safer") : [any[], any][] { // inputList is a list of testInputs, each item can be an arg or a list of args (if the function takes more than one argument) "use strict"; let ioPairs : [any[], any][] = inputTupleList.map(args => [args, undefined]); if (mode == "safer") { try { // slightly safer? if (code != "") { let jsCode = ts.transpile(code); //target = eval(jsCode)(s); //console.log(`let func = ${code}; func(arg)`) let context = { inputTupleList : inputTupleList, outputs: inputTupleList.map(args => undefined) }; //console.log(`let func = ${jsCode}; let outputs = inputList.map(arg => func(arg));`); vm.runInNewContext(`let func = ${jsCode}; outputs = inputTupleList.map(args => func(...args));`, context); ioPairs = inputTupleList.map((args, i) => [args, context.outputs[i]]); return ioPairs; } } catch(err) { console.warn(err); } } else if (mode == "faster") { try { if (code != "") { let jsCode = ts.transpile(code); let func = eval(jsCode); ioPairs = inputTupleList.map(args => { let target = undefined; try { // copy args to ensure correctness of mapping target = func(...structuredClone(args)) } catch (err) { console.warn(`execution err ${err}`) } return [args, target] }); return ioPairs; } } catch (err) { console.warn(err); } } return ioPairs; } export function extractFieldsFromEncodingMap(encodingMap: EncodingMap, allFields: FieldItem[]) { let aggregateFields: [string | undefined, string][] = [] let groupByFields: string[] = [] for (const [channel, encoding] of Object.entries(encodingMap)) { const field = encoding.fieldID ? _.find(allFields, (f) => f.id === encoding.fieldID) : undefined; if (encoding.aggregate) { aggregateFields.push([field?.name, encoding.aggregate]); } else { if (field) { groupByFields.push(field.name); } } } return { aggregateFields, groupByFields }; } export function prepVisTable(table: any[], allFields: FieldItem[], encodingMap: EncodingMap) { let { aggregateFields, groupByFields } = extractFieldsFromEncodingMap(encodingMap, allFields); let processedTable = [...table]; let result = processedTable; if (aggregateFields.length > 0) { // Step 2: Group by and aggregate let grouped = []; if (groupByFields.length > 0) { grouped = d3.flatGroup(processedTable, ...groupByFields.map(field => (d: any) => d[field])); } else { grouped = [["_default", processedTable]]; } result = grouped.map(row => { // Last element is the array of grouped items, rest are group values const groupValues = row.slice(0, -1); const group = row[row.length - 1]; return { // Add group by fields ...Object.fromEntries(groupByFields.map((field, i) => [field, groupValues[i]])), // Add aggregations ...(aggregateFields.some(([_, type]) => type === 'count') ? { _count: group.length } : {}), ...Object.fromEntries( aggregateFields .filter(([fieldName, aggType]) => aggType !== 'count' && fieldName) .map(([fieldName, aggType]) => { const values = group.map((r: any) => r[fieldName!]); const suffix = `_${aggType}`; const aggFunc = { 'sum': d3.sum, 'max': d3.max, 'min': d3.min, 'mean': d3.mean, 'median': d3.median, 'average': d3.mean, 'mode': d3.mode }[aggType] as (values: any[]) => number | undefined; return [fieldName + suffix, aggFunc ? aggFunc(values) : undefined]; }) ) }; }); } return result; } export const assembleVegaChart = ( chartType: string, encodingMap: { [key in Channel]: EncodingItem; }, conceptShelfItems: FieldItem[], workingTable: any[], tableMetadata: {[key: string]: {type: Type, semanticType: string, levels: any[], intrinsicDomain?: [number, number], unit?: string}}, baseChartWidth: number = 100, baseChartHeight: number = 80, addTooltips: boolean = false, chartProperties?: Record<string, any>, scaleFactor: number = 1, maxStretchFactor?: number, assembleOptions?: AssembleOptions, semanticAnnotationOverrides?: Record<string, any>, ) => { // Convert app-level EncodingMap (fieldID-based) to library-level encodings (field-name-based) const encodings: Record<string, ChartEncoding> = {}; for (const [channel, encoding] of Object.entries(encodingMap)) { const field = encoding.fieldID ? _.find(conceptShelfItems, (f) => f.id === encoding.fieldID) : undefined; encodings[channel] = { field: field?.name, type: encoding.dtype, aggregate: encoding.aggregate, sortOrder: encoding.sortOrder, sortBy: encoding.sortBy, scheme: encoding.scheme, }; } // Extract semantic types from table metadata // Build SemanticAnnotation objects when enriched metadata (intrinsicDomain, unit) is available const semanticTypes: Record<string, string | any> = {}; for (const [fieldName, meta] of Object.entries(tableMetadata)) { if (meta.semanticType) { if (meta.intrinsicDomain || meta.unit) { // Build enriched annotation object const annotation: any = { semanticType: meta.semanticType }; if (meta.intrinsicDomain) annotation.intrinsicDomain = meta.intrinsicDomain; if (meta.unit) annotation.unit = meta.unit; if (meta.levels && meta.levels.length > 0) annotation.sortOrder = meta.levels; semanticTypes[fieldName] = annotation; } else { semanticTypes[fieldName] = meta.semanticType; } } } // Merge enriched annotations (e.g., intrinsicDomain, unit) when provided if (semanticAnnotationOverrides) { for (const [fieldName, annotation] of Object.entries(semanticAnnotationOverrides)) { semanticTypes[fieldName] = annotation; } } // Hack: pie-like radial charts grow too large because the circumference // pressure model + VL's auto-radius both amplify the canvas size. // Apply two dampening levers: // 1. Shrink the base canvas so VL's arc radius starts smaller // 2. Cap maxStretch more aggressively so pressure growth is limited const PIE_LIKE_TYPES = new Set([ 'Pie Chart', 'Rose Chart', 'Sunburst Chart', 'Radar Chart', 'Gauge Chart', ]); const isPieLike = PIE_LIKE_TYPES.has(chartType); // Lever 1: reduce base canvas for pie-like charts (0.75× → smaller pie) const canvasShrink = isPieLike ? 0.75 : 1; const effectiveW = Math.round(baseChartWidth * scaleFactor * canvasShrink); const effectiveH = Math.round(baseChartHeight * scaleFactor * canvasShrink); // Lever 2: tighter stretch cap for pie-like charts let effectiveMaxStretch = maxStretchFactor; if (effectiveMaxStretch != null && isPieLike) { // Compress toward 1: e.g. 2.0 → 1.3, 3.0 → 1.6, 5.0 → 2.2 effectiveMaxStretch = 1 + (effectiveMaxStretch - 1) * 0.3; } return assembleVegaLite({ data: { values: workingTable }, semantic_types: semanticTypes, chart_spec: { chartType, encodings, canvasSize: { width: effectiveW, height: effectiveH }, chartProperties, }, options: { addTooltips, ...(effectiveMaxStretch != null ? { maxStretch: effectiveMaxStretch } : {}), ...assembleOptions, }, }); } // resolveRecommendedChart & resolveChartFields remain in app layer (need generateFreshChart, Chart) export { resolveRecommendedChart, resolveChartFields } from './chartRecommendation'; export let getTriggers = (leafTable: DictTable, tables: DictTable[]) => { // recursively find triggers that ends in leafTable (if the leaf table is anchored, we will find till the previous table is anchored) let triggers : Trigger[] = []; let t = leafTable; while(true) { // this is when we find an original table if (t.derive == undefined) { break; } // this is when we find an anchored table (which is not the leaf table) if (t !== leafTable && t.anchored) { break; } let trigger = t.derive.trigger as Trigger; triggers = [trigger, ...triggers]; let parentTable = tables.find(x => x.id == trigger.tableId); if (parentTable) { t = parentTable; } else { break; } } return triggers; } /** * Returns a hash code from a string * @param {String} str The string to hash. * @return {Number} A 32bit integer * @see http://werxltd.com/wp/2010/05/13/javascript-implementation-of-javas-string-hashcode-method/ */ export function hashCode(str: string) { let hash = 0; for (let i = 0, len = str.length; i < len; i++) { let chr = str.charCodeAt(i); hash = (hash << 5) - hash + chr; hash |= 0; // Convert to 32bit integer } return hash; } ================================================ FILE: src/assets/icon-design.md ================================================ # Chart Icon Design Guide Design conventions for chart template icons in `src/assets/`. ## Color Palette | Token | Hex | Usage | |-------|-----|-------| | **Light Blue** | `#76c6ff` | Primary fill — dots, bars, areas (series A) | | **Dark Blue** | `#4a8acb` | Primary stroke / line (series A) | | **Light Gray** | `#B8B8B8` | Secondary fill — dots, bars, areas (series B) | | **Dark Gray** | `#7a7a7a` | Secondary stroke / line (series B) | | **Axis / Outline** | `#363636` | Axes, mark outlines, stems | | **Pastel Red** | `#e8868f` | Bearish / negative (candlestick) | | **Pastel Green** | `#6dc48d` | Bullish / positive (candlestick) | All colors are muted/pastel to match the overall UI tone. Avoid saturated primaries. ## Mark Style Rules ### Filled marks (bars, circles, areas) - **Fill** with light color (`#76c6ff` or `#B8B8B8`) - **Outline** with `#363636`, stroke-width `2`–`3` - This gives marks a clean, defined edge consistent with the designer samples ### Lines (line charts, bump charts) - **Stroke** with dark color (`#4a8acb` for blue series, `#7a7a7a` for gray series) - **Stroke-width**: `4`–`5`, `stroke-linecap="round"`, `stroke-linejoin="round"` - Dots on lines: fill with the corresponding light color, outline `#363636` stroke-width `2` ### Line thickness reference | Element | stroke-width | |---------|-------------| | Axes | `4` | | Data lines (line/bump/area outline) | `4`–`5` | | Mark outlines (bars, dots, rects) | `2`–`3` | | Stems / rules (lollipop) | `4` | | Grid / spokes (radar) | `0.6`–`0.8` | ### Two-series convention Charts that show a color encoding use **two series**: - Series A: light blue fill / dark blue stroke - Series B: light gray fill / dark gray stroke For charts needing **four series** (e.g. bump chart), alternate: - Series 1: `#76c6ff` fill / `#4a8acb` stroke - Series 2: `#B8B8B8` fill / `#7a7a7a` stroke - Series 3: `#5ba8e0` fill / `#3d7ebf` stroke - Series 4: `#d0d0d0` fill / `#a8a8a8` stroke ## Format - **New icons**: SVG (`chart-icon-*.svg`), viewBox `0 0 256 256` - **Legacy icons**: minified PNG (`chart-icon-*-min.png`) - Prefer SVG for new additions — scales cleanly at any size ## Structure (SVG) - Axes drawn last so they layer on top - Y-axis: `x1="48" y1="28" x2="48" y2="220"`, stroke `#363636`, width 4, round cap - X-axis: `x1="44" y1="216" x2="240" y2="216"`, stroke `#363636`, width 4, round cap - Data region: roughly `x ∈ [60, 236]`, `y ∈ [40, 216]` ## Icon Inventory | File | Chart Type | Format | |------|-----------|--------| | `chart-icon-scatter-min.png` | Scatter Plot | PNG | | `chart-icon-linear-regression-min.png` | Linear Regression | PNG | | `chart-icon-dot-plot-horizontal-min.png` | Ranged Dot Plot | PNG | | `chart-icon-box-plot-min.png` | Boxplot | PNG | | `chart-icon-bubble-min.png` | Bubble Chart | PNG | | `chart-icon-column-min.png` | Bar Chart | PNG | | `chart-icon-column-grouped-min.png` | Grouped Bar Chart | PNG | | `chart-icon-column-stacked-min.png` | Stacked Bar Chart | PNG | | `chart-icon-histogram-min.png` | Histogram | PNG | | `chart-icon-heat-map-min.png` | Heatmap | PNG | | `chart-icon-pyramid.svg` | Pyramid Chart | SVG | | `chart-icon-line-min.png` | Line Chart | PNG | | `chart-icon-dotted-line-min.png` | Dotted Line Chart | PNG | | `chart-icon-area.svg` | Area Chart | SVG | | `chart-icon-streamgraph.svg` | Streamgraph | SVG | | `chart-icon-lollipop.svg` | Lollipop Chart | SVG | | `chart-icon-density.svg` | Density Plot | SVG | | `chart-icon-candlestick.svg` | Candlestick Chart | SVG | | `chart-icon-waterfall.svg` | Waterfall Chart | SVG | | `chart-icon-strip-plot.svg` | Strip Plot | SVG | | `chart-icon-radar.svg` | Radar Chart | SVG | | `chart-icon-bump.svg` | Bump Chart | SVG | | `chart-icon-pie-min.png` | Pie Chart | PNG | | `chart-icon-us-map-min.png` | US Map | PNG | | `chart-icon-world-map-min.png` | World Map | PNG | | `chart-icon-custom-point-min.png` | Custom Point | PNG | | `chart-icon-custom-line-min.png` | Custom Line | PNG | | `chart-icon-custom-bar-min.png` | Custom Bar | PNG | | `chart-icon-custom-rect-min.png` | Custom Rect | PNG | | `chart-icon-custom-area-min.png` | Custom Area | PNG | | `chart-icon-dot-plot-vertical-min.png` | *(unused — was Lollipop)* | PNG | | `chart-icon-table-min.png` | Table | PNG | ================================================ FILE: src/components/ChartTemplates.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Chart Templates with UI icons. * * This module wraps the reusable agents-chart template definitions * with React icon components for display in the Data Formulator UI. * The pure template logic (mark, encoding paths, post-processors) lives * in src/lib/agents-chart/templates/. */ import { ChartTemplate } from "./ComponentType"; import { vlTemplateDefs, vlGetTemplateChannels, } from "../lib/agents-chart"; import InsightsIcon from '@mui/icons-material/Insights'; import React from "react"; // Import all chart icons statically so they are included in the build import chartIconScatter from '../assets/chart-icon-scatter-min.png'; import chartIconLinearRegression from '../assets/chart-icon-linear-regression-min.png'; import chartIconDotPlotHorizontal from '../assets/chart-icon-dot-plot-horizontal-min.png'; import chartIconBoxPlot from '../assets/chart-icon-box-plot-min.png'; import chartIconColumn from '../assets/chart-icon-column-min.png'; import chartIconColumnGrouped from '../assets/chart-icon-column-grouped-min.png'; import chartIconColumnStacked from '../assets/chart-icon-column-stacked-min.png'; import chartIconHistogram from '../assets/chart-icon-histogram-min.png'; import chartIconHeatMap from '../assets/chart-icon-heat-map-min.png'; import chartIconLine from '../assets/chart-icon-line-min.png'; import chartIconDottedLine from '../assets/chart-icon-dotted-line-min.png'; import chartIconCustomPoint from '../assets/chart-icon-custom-point-min.png'; import chartIconCustomLine from '../assets/chart-icon-custom-line-min.png'; import chartIconCustomBar from '../assets/chart-icon-custom-bar-min.png'; import chartIconCustomRect from '../assets/chart-icon-custom-rect-min.png'; import chartIconCustomArea from '../assets/chart-icon-custom-area-min.png'; import chartIconArea from '../assets/chart-icon-area.svg'; import chartIconStreamgraph from '../assets/chart-icon-streamgraph.svg'; import chartIconDensity from '../assets/chart-icon-density.svg'; import chartIconLollipop from '../assets/chart-icon-lollipop.svg'; import chartIconPie from '../assets/chart-icon-pie-min.png'; import chartIconUSMap from '../assets/chart-icon-us-map-min.png'; import chartIconPyramid from '../assets/chart-icon-pyramid.svg'; import chartIconWorldMap from '../assets/chart-icon-world-map-min.png'; import chartIconDotPlotVertical from '../assets/chart-icon-dot-plot-vertical-min.png'; import chartIconCandlestick from '../assets/chart-icon-candlestick.svg'; import chartIconWaterfall from '../assets/chart-icon-waterfall.svg'; import chartIconStripPlot from '../assets/chart-icon-strip-plot.svg'; import chartIconRadar from '../assets/chart-icon-radar.svg'; import chartIconBump from '../assets/chart-icon-bump.svg'; import chartIconRose from '../assets/chart-icon-rose.svg'; // Chart Icon Component using static imports const ChartIcon: React.FC<{ src: string; alt?: string }> = ({ src, alt = "" }) => { return <img src={src} alt={alt} style={{ width: '100%', height: '100%', objectFit: 'contain' }} />; }; // --------------------------------------------------------------------------- // Icon mapping: chart name → React icon element // --------------------------------------------------------------------------- const CHART_ICONS: Record<string, any> = { "Auto": <InsightsIcon color="primary" />, "Scatter Plot": <ChartIcon src={chartIconScatter} />, "Regression": <ChartIcon src={chartIconLinearRegression} />, "Ranged Dot Plot": <ChartIcon src={chartIconDotPlotHorizontal} />, "Boxplot": <ChartIcon src={chartIconBoxPlot} />, "Bar Chart": <ChartIcon src={chartIconColumn} />, "Pyramid Chart": <ChartIcon src={chartIconPyramid} />, "Grouped Bar Chart": <ChartIcon src={chartIconColumnGrouped} />, "Stacked Bar Chart": <ChartIcon src={chartIconColumnStacked} />, "Histogram": <ChartIcon src={chartIconHistogram} />, "Heatmap": <ChartIcon src={chartIconHeatMap} />, "US Map": <ChartIcon src={chartIconUSMap} />, "World Map": <ChartIcon src={chartIconWorldMap} />, "Pie Chart": <ChartIcon src={chartIconPie} />, "Rose Chart": <ChartIcon src={chartIconRose} />, "Line Chart": <ChartIcon src={chartIconLine} />, "Dotted Line Chart": <ChartIcon src={chartIconDottedLine} />, "Bump Chart": <ChartIcon src={chartIconBump} />, "Area Chart": <ChartIcon src={chartIconArea} />, "Streamgraph": <ChartIcon src={chartIconStreamgraph} />, "Lollipop Chart": <ChartIcon src={chartIconLollipop} />, "Density Plot": <ChartIcon src={chartIconDensity} />, "Candlestick Chart": <ChartIcon src={chartIconCandlestick} />, "Waterfall Chart": <ChartIcon src={chartIconWaterfall} />, "Strip Plot": <ChartIcon src={chartIconStripPlot} />, "Radar Chart": <ChartIcon src={chartIconRadar} />, "Custom Point": <ChartIcon src={chartIconCustomPoint} />, "Custom Line": <ChartIcon src={chartIconCustomLine} />, "Custom Bar": <ChartIcon src={chartIconCustomBar} />, "Custom Rect": <ChartIcon src={chartIconCustomRect} />, "Custom Area": <ChartIcon src={chartIconCustomArea} />, }; // --------------------------------------------------------------------------- // Build CHART_TEMPLATES by adding icons to library template defs // --------------------------------------------------------------------------- function addIcons(defs: { chart: string }[]): ChartTemplate[] { return defs.map(def => ({ ...def, icon: CHART_ICONS[def.chart] || <InsightsIcon />, })) as ChartTemplate[]; } export const CHART_TEMPLATES: { [key: string]: ChartTemplate[] } = Object.fromEntries( Object.entries(vlTemplateDefs).map(([category, defs]) => [ category, addIcons(defs), ]) ); // --------------------------------------------------------------------------- // Re-exports for backward compatibility // --------------------------------------------------------------------------- // Re-export constants and utilities from the chart engine library export { channels, channelGroups, } from '../lib/agents-chart'; export function getChartTemplate(chartType: string): ChartTemplate | undefined { return Object.values(CHART_TEMPLATES).flat().find(t => t.chart === chartType); } export const getChartChannels = (chartType: string): string[] => { return vlGetTemplateChannels(chartType); } ================================================ FILE: src/components/ComponentType.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { Type } from '../data/types'; import { channels, type ChartTemplateDef } from '../lib/agents-chart'; import { inferTypeFromValueArray } from '../data/utils'; export type FieldSource = "custom" | "original"; export interface FieldItem { id: string; name: string; source: FieldSource; tableRef: string; // which table it belongs to } export const duplicateField = (field: FieldItem) => { return { id: field.id, name: field.name, source: field.source, tableRef: field.tableRef, } as FieldItem; } export interface Trigger { tableId: string, // on which table this action is triggered chart?: Chart, // what's the intented chart from the user when running formulation instruction: string, displayInstruction: string, // the short instruction that will be displayed to the user resultTableId: string, } // Define data cleaning message types export type DataCleanTableOutput = { name: string; context: string; content: { type: 'csv' | 'image_url' | 'web_url'; value: string; incomplete?: boolean; }; }; export interface DataCleanBlock { id: string; // the id of the item items: DataCleanTableOutput[]; // the items that are cleaned in this block derive: { sourceId: string | undefined; // the source of the block that leads to this block prompt: string; artifacts: {type: 'image_url' | 'web_url', value: string}[]; // images sent along with the prompt } // For output messages dialogItem?: any; // Store the dialog item from the model response } // Data source types for tracking where data originated export type DataSourceType = 'paste' | 'file' | 'url' | 'stream' | 'database' | 'example' | 'extract'; // Configuration for data source refresh behavior // Note: For database sources, connection details are stored in DuckDB backend, // not in the frontend. Frontend only manages refresh timing/toggle. export interface DataSourceConfig { type: DataSourceType; // For URL/stream sources - the URL to fetch data from url?: string; // Refresh interval in seconds (used for streams and database auto-refresh) refreshIntervalSeconds?: number; // For database sources - the DuckDB table name (backend knows how to refresh it) databaseTable?: string; // Whether auto-refresh is enabled (frontend controls this for all source types) autoRefresh?: boolean; // Last refresh timestamp lastRefreshed?: number; // Original file name (for file uploads) fileName?: string; // Whether this table can be refreshed (backend has connection info) canRefresh?: boolean; } export interface DictTable { id: string; // name/id of the table displayId: string; // display id of the table names: string[]; // column names metadata: {[key: string]: { type: Type, semanticType: string, levels: any[], intrinsicDomain?: [number, number], unit?: string, }}; // metadata of the table rows: any[]; // table content, each entry is a row derive?: { // how is this table derived source: string[], // which tables are this table computed from code: string, codeSignature?: string, // HMAC-SHA256 signature proving code was generated by the server outputVariable: string, // the Python variable name containing the result DataFrame (required) explanation?: { code: string, // explanation of the code concepts: { field: string, explanation: string }[] }, dialog: any[], // the log of how the data is derived with LLM (the LLM conversation log) // tracks how this derivation is triggered, as we as user instruction used to do the formulation, // there is a subtle difference between trigger and source, trigger identifies the occasion when the derivision is called, // source specifies how the deriviation is done from the source tables, they may be the same, but not necessarily // in fact, right now dict tables are all triggered from charts trigger: Trigger, }; virtual?: { tableId: string; // the id of the virtual table in the database rowCount: number; // total number of rows in the full table }; anchored: boolean; // whether this table is anchored as a persistent table used to derive other tables createdBy: 'user' | 'agent'; // whether this table is created by the user or the agent attachedMetadata: string; // a string of attached metadata explaining what the table is about (used for prompt) // New field: tracks the source of the data and refresh configuration source?: DataSourceConfig; // Content hash for detecting data changes during refresh // Used to avoid unnecessary derived table recalculations when data hasn't changed contentHash?: string; } export function createDictTable( id: string, rows: any[], derive: { code: string, codeSignature?: string, outputVariable: string, explanation?: { code: string, concepts: {field: string, explanation: string}[]}, source: string[], dialog: any[], trigger: Trigger } | undefined = undefined, virtual: {tableId: string, rowCount: number} | undefined = undefined, anchored: boolean = false, createdBy: 'user' | 'agent' = 'user', // by default, all tables are created by the user attachedMetadata: string = '', source: DataSourceConfig | undefined = undefined, ) : DictTable { let names = Object.keys(rows[0]) return { id, displayId: `${id}`, names, rows, metadata: names.reduce((acc, name) => ({ ...acc, [name]: { type: inferTypeFromValueArray(rows.map(r => r[name])), semanticType: "", levels: [] } }), {}), derive, virtual, anchored, createdBy, attachedMetadata, source, } } export interface ChartInsight { title: string; takeaways: string[]; key: string; // "chartType|sortedFieldIds" — used to detect staleness } export type Chart = { id: string, chartType: string, encodingMap: EncodingMap, tableRef: string, saved: boolean, source: "user" | "trigger", config?: Record<string, any>, // additional chart properties defined by the chart template thumbnail?: string, // PNG data URL for thumbnail display (managed by ChartRenderService, not persisted) insight?: ChartInsight, // AI-generated insight about the visualization } /** Compute a string key for insight invalidation: chartType|sortedFieldIds */ export function computeInsightKey(chart: Chart): string { const fieldIds = Object.values(chart.encodingMap) .map(enc => enc.fieldID) .filter((id): id is string => !!id) .sort(); return `${chart.chartType}|${fieldIds.join(',')}`; } export let duplicateChart = (chart: Chart) : Chart => { return { id: `chart-${Date.now()- Math.floor(Math.random() * 10000)}`, chartType: chart.chartType, encodingMap: JSON.parse(JSON.stringify(chart.encodingMap)) as EncodingMap, tableRef: chart.tableRef, saved: false, source: chart.source, config: chart.config ? JSON.parse(JSON.stringify(chart.config)) : undefined, } } // visualization related definitions export type EncodingMap = { [key in Channel]: EncodingItem; } export interface EncodingItem { //channel: Channel, // the channel ID fieldID?: string, // the fieldID dtype?: "quantitative" | "nominal" | "ordinal" | "temporal", aggregate?: AggrOp, //sort?: "ascending" | "descending" | string, sortOrder?: "ascending" | "descending", // sortBy?: undefined | string, // what values are used to sort the encoding scheme?: string } /** * ChartTemplate extends the library's ChartTemplateDef with a UI icon. * The library definition is icon-free for reusability; this type adds * the React element used in the Data Formulator UI. */ export type ChartTemplate = ChartTemplateDef & { icon: any; } export const AGGR_OP_LIST = ["count", "sum", "average"] as const export type AggrOp = typeof AGGR_OP_LIST[number]; export type Channel = typeof channels[number]; export interface EncodingDropResult { channel: Channel } ================================================ FILE: src/components/FunComponents.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React from 'react'; import { Typography, SxProps } from "@mui/material"; export let ThinkingBufferEffect :React.FC<{text: string, sx?: SxProps}> = ({text, sx}) => { return <Typography sx={{ margin: 'auto 0', padding: 0.5, fontSize: 10, color: "darkgray", maxLines: 3, display: 'flex', alignItems: 'center', gap: 0.3, ...sx }}> {text.replace(/[^\s]/g, '·')} <Typography sx={{ fontSize: 10, opacity: 0.5, transform: 'rotate(90deg)', color: "darkgray", animation: 'writing 1.5s ease-in-out infinite', '@keyframes writing': { '0%, 100%': { transform: 'translate(0, 0) rotate(80deg)', }, '50%': { transform: 'translate(2px, 2px) rotate(95deg)', } } }}>✏️</Typography> </Typography> } ================================================ FILE: src/components/RotatingTextBlock.tsx ================================================ import React, { useEffect, useState } from 'react'; import { Box, SxProps } from '@mui/material'; interface RotatingTextBlockProps { texts: string[]; rotationInterval?: number; transitionDuration?: number; sx?: SxProps; } export const RotatingTextBlock: React.FC<RotatingTextBlockProps> = ({ texts, rotationInterval = 3000, transitionDuration = 500, sx }) => { const [currentTextIndex, setCurrentTextIndex] = useState(0); const [isTransitioning, setIsTransitioning] = useState(false); // Effect for rotating text with carousel transition useEffect(() => { const interval = setInterval(() => { setIsTransitioning(true); setTimeout(() => { setCurrentTextIndex((prevIndex) => (prevIndex + 1) % texts.length); setIsTransitioning(false); }, transitionDuration); }, rotationInterval); return () => clearInterval(interval); }, [texts.length, rotationInterval, transitionDuration]); return ( <Box component="span" sx={{ display: 'inline', position: 'relative', fontWeight: 500, opacity: isTransitioning ? 0 : 1, transition: `opacity ${transitionDuration}ms ease-in-out`, ...sx }} > {texts[currentTextIndex]} </Box> ); }; ================================================ FILE: src/components/chartUtils.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Chart recommendation utilities. * * Recommendation engine has moved to the agents-chart library. * Use vlRecommendEncodings / ecRecommendEncodings / etc. directly. */ export { vlRecommendEncodings } from '../lib/agents-chart'; ================================================ FILE: src/data/column.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { Type } from './types'; import { computeUniqueValues } from './utils'; export default class Column { protected _data: any[]; protected _type: Type; protected _uniques: any[]; constructor(data: any[], type?: Type) { this._data = data; this._type = type || Type.String; // Should sort uniques based on type? this._uniques = computeUniqueValues(this._data); } get uniques(): any[] { return this._uniques; } get type(): Type { return this._type; } get length(): number { return this._data.length; } get(row: number): any { return this._data[row]; } } ================================================ FILE: src/data/table.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import Column from './column'; import { Type } from './types'; const columnsToObjects = (columns: Map<string, Column>, names: string[], nrows: number): any[] => { const objects = []; for (let r = 0; r < nrows; r++) { const o: any = {}; for (let c = 0; c < names.length; c++) { o[names[c]] = columns.get(names[c])?.get(r); } objects.push(o); } return objects; }; export class ColumnTable { protected _data: Map<string, Column>; protected _names: string[]; protected _objects: any[]; protected _nrows: number; constructor(columns: Map<string, Column>, names: string[]) { this._data = columns; this._names = names; const nrows = columns.get(names[0])?.length; this._nrows = nrows ? nrows : 0; this._objects = columnsToObjects(this._data, this._names, this._nrows); } public numCols = (): number => { return this._names.length; }; public numRows = (): number => { return this._nrows; }; public column = (name: string): Column | undefined => { return this._data.get(name); }; public columnAt = (col: number): Column | undefined => { return this._data.get(this._names[col]); }; public columns = (): Map<string, Column> => { return this._data; }; public objects = (): any[] => { return this._objects; }; public names = (): string[] => { return this._names; }; public metadata = (): [string, Type][] => { return this._names.map((name) => [name, (this.column(name) as Column).type]) } public get = (name: string, row: number = 0): any => { const column = this.column(name); return column?.get(row); }; } ================================================ FILE: src/data/types.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. export enum Type { String = 'string', Boolean = 'boolean', Integer = 'integer', Number = 'number', Date = 'date', Auto = 'auto' // Time = 'time', // DateTime = 'datetime', } export const TypeList = [Type.Auto, Type.Number, Type.Date, Type.String]; //[Type.Boolean, Type.Integer, Type.Number, Type.Date, Type.String]; const coerceBoolean = (v: any): boolean | null => (v == null || v === '' ? null : v === 'false' ? false : !!v); const coerceNumber = (v: any): number | null => (v == null || v === '' ? null : +v); const coerceDate = (v: any, format?: any) => { // const d = format ? format : Date; //return v == null || v === '' ? null : new Date(v); //TODO: follow the standard date return v; }; const coerceString = (v: any) => (v == null || v === '' ? null : v); export const CoerceType = { boolean: coerceBoolean, number: coerceNumber, integer: coerceNumber, date: coerceDate, string: coerceString, auto: coerceString, }; const testBoolean = (v: any): boolean => v === 'true' || v === 'false' || isBoolean(v); const testNumber = (v: any): boolean => !isNaN(+v) && !isDate(v); const testInteger = (v: any): boolean => testNumber(v) && (v = +v) === ~~v; const testDate = (v: any): boolean => !isNaN(Date.parse(v)); const testString = (v: any): boolean => true; export const TestType = { boolean: testBoolean, number: testNumber, integer: testInteger, date: testDate, string: testString, auto: testString, }; export const isBoolean = (v: any) => v === true || v === false || Object.prototype.toString.call(v) === '[object Boolean]'; export const isNumber = (v: any) => typeof v === 'number' || Object.prototype.toString.call(v) === '[object Number]'; export const isDate = (v: any) => Object.prototype.toString.call(v) === '[object Date]'; export const getDType = (type: Type | undefined, domain: any[]): string => { // test the data type on the fly if it is not there return type === Type.Integer || type === Type.Number ? 'quantitative' : type === Type.Boolean ? 'nominal' : type === Type.Date ? 'temporal' // date? : type === Type.String ? 'nominal' : type === Type.Auto ? getDType(testType(domain) as Type, domain) : 'nominal'; }; export const testType = (values: any[]) => { if (values.length == 0) { return "string" } if (values.filter(v => !isBoolean(v)).length == 0) { return "boolean" } if (values.filter(v => !testNumber(v)).length == 0) { return "number" } if (values.filter(v => !testDate(v)).length == 0) { return "date" } return "string" } ================================================ FILE: src/data/utils.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import * as d3 from 'd3'; import Column from './column'; import * as ExcelJS from 'exceljs'; import { DictTable } from '../components/ComponentType'; import { CoerceType, TestType, Type } from './types'; import { ColumnTable } from './table'; export const loadTextDataWrapper = (title: string, text: string, fileType: string): DictTable | undefined => { let tableName = title; //let tableName = title.replace(/\.[^/.]+$/ , ""); let table = undefined; if (fileType == "text/csv" || fileType == "text/tab-separated-values") { table = createTableFromText(tableName, text); } else if (fileType == "application/json") { table = createTableFromFromObjectArray(tableName, JSON.parse(text), true); } return table; }; export const createTableFromText = (title: string, text: string, context?: string): DictTable | undefined => { // Check for empty strings, bad data, anything else? if (!text || text.trim() === '') { console.log('Invalid text provided for data. Could not load.'); return undefined; } // Determine if the input text is tab or comma separated values // Compute the number of tabs and lines let tabNum = 0, lineNum = 0; for (let i = 0; i < text.length; i++) { if (text.charAt(i) === '\t') tabNum++; if (text.charAt(i) === '\n') lineNum++; } // If one or more tab per line, then it is tab separated values // Should check the data file as well for the ending const isTabSeparated = tabNum / lineNum >= 1; // Use d3.dsvFormat to create a custom parser that properly handles quoted fields // This ensures commas inside quoted fields won't be treated as delimiters const rows = isTabSeparated ? d3.tsvParseRows(text) : d3.dsvFormat(',').parseRows(text, (row, index) => { // Process each row to ensure proper type handling return row; }); // Handle duplicate column names by appending _1, _2, etc. let colNames: string[] = []; for (let i = 0; i < rows[0].length; i++) { let col = rows[0][i]; if (colNames.includes(col)) { let k = 1; while (colNames.includes(`${col}_${k}`)) { k++; } colNames.push(`${col}_${k}`); } else { colNames.push(col); } } let values = rows.slice(1); let records = values.map(row => { let record: any = {}; for (let i = 0; i < colNames.length; i++) { record[colNames[i]] = row[i]; } return record; }); return createTableFromFromObjectArray(title, records, true, undefined, context); }; export const createTableFromFromObjectArray = (title: string, values: any[], anchored: boolean, derive?: any, context?: string): DictTable => { /* * title: the title of the table * values: the values of the table * anchored: whether the table is anchored * derive: the derive of the table * context: the context of the table that will be attached to table metadata */ const len = values.length; let names: string[] = []; let cleanNames: string[] = []; const columns = new Map<string, Column>(); if (len) { names = Object.keys(values[0]); cleanNames = names.map((name, i) => { if (name == "") { let newName = `c${i}`; let k = 0; while(names.includes(newName)) { newName = `c${i}_${k}` k = k + 1; } return newName; } // clean up messy column names if (name && name.includes(".")) { return name.replace(".", "_"); } return name; }) for (let i = 0; i < names.length; i++) { let col = []; for (let r = 0; r < len; r++) { col.push(values[r][names[i]]); } const type = inferTypeFromValueArray(col); col = coerceValueArrayFromTypes(col, type); columns.set(cleanNames[i], new Column(col, type)); } } let columnTable = new ColumnTable(columns, cleanNames); return { id: title, displayId: `${title}`, names: columnTable.names(), metadata: columnTable.names().reduce((acc, name) => ({ ...acc, [name]: { type: (columnTable.column(name) as Column).type, semanticType: "", levels: [] } }), {}), rows: columnTable.objects(), derive: derive, anchored: anchored, createdBy: "user", attachedMetadata: context || '' } }; export const inferTypeFromValueArray = (values: any[]): Type => { let types: Type[] = [Type.Boolean, Type.Integer, Type.Date, Type.Number, Type.String]; for (let i = 0; i < values.length; i++) { const v = values[i]; for (let t = 0; t < types.length; t++) { if (v != null && !TestType[types[t]](v)) { types.splice(t, 1); t -= 1; } } } return types[0]; }; export const convertTypeToDtype = (type: Type | undefined): string => { return type === Type.Integer || type === Type.Number ? 'quantitative' : type === Type.Boolean ? 'boolean' : type === Type.Date ? 'date' : 'nominal'; }; export const coerceValueArrayFromTypes = (values: any[], type: Type): any[] => { return values.map((v) => CoerceType[type](v)); }; export const coerceValueFromTypes = (value: any, type: Type): any => { return CoerceType[type](value); }; export const computeUniqueValues = (values: any[]): any[] => { return Array.from(new Set(values)); }; export function tupleEqual(a: any[], b: any[]) { // check if two tuples are equal if (a === b) return true; if (a == null || b == null) return false; if (a.length !== b.length) return false; for (let i = 0; i < a.length; ++i) { if (a[i] !== b[i]) return false; } return true; } export const loadBinaryDataWrapper = async (title: string, arrayBuffer: ArrayBuffer): Promise<DictTable[]> => { try { // Read the Excel file const workbook = new ExcelJS.Workbook(); await workbook.xlsx.load(arrayBuffer); // Create tables for each sheet const tables: DictTable[] = []; workbook.eachSheet((worksheet) => { try { const jsonData: any[] = []; // Get the first row as headers const headerRow = worksheet.getRow(1); const headers: string[] = []; headerRow.eachCell((cell, colNumber) => { headers[colNumber - 1] = cell.value?.toString() || `Column${colNumber}`; }); if (headers.length === 0) { return; } // Process data rows (skip header row) worksheet.eachRow((row, rowNumber) => { if (rowNumber === 1) return; // Skip header row const rowData: any = {}; row.eachCell((cell, colNumber) => { const header = headers[colNumber - 1] || `Column${colNumber}`; rowData[header] = cell.value; }); // Only add row if it has data if (Object.keys(rowData).length > 0) { jsonData.push(rowData); } }); if (jsonData.length === 0) { return; } // Create a table from the JSON data with sheet name included in the title const sheetTable = createTableFromFromObjectArray(`${title}-${worksheet.name}`, jsonData, true); tables.push(sheetTable); } catch (error) { console.error(`Error processing sheet ${worksheet.name}:`, error); } }); return tables; } catch (error) { console.error('Error processing Excel file:', error); return []; } }; /** * Exports a DictTable to DSV format using d3.dsvFormat * @param table - The DictTable to export * @param delimiter - The delimiter to use (e.g., "," for CSV, "\t" for TSV) * @returns DSV string representation of the table */ export const exportTableToDsv = (table: DictTable, delimiter: string): string => { // Use d3.dsvFormat to convert the rows array to DSV return d3.dsvFormat(delimiter).format(table.rows); }; ================================================ FILE: src/icons.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React from 'react'; import { SvgIcon, SvgIconProps } from '@mui/material'; import { Box, SxProps } from '@mui/material'; // ── MUI domain icon re-exports ────────────────────────────────────────── // Centralized so every file imports domain icons from one place. // Chart-template icons (managed in ChartTemplates.tsx) are NOT included. export { default as TableIcon } from '@mui/icons-material/TableRowsOutlined'; export { default as StreamIcon } from '@mui/icons-material/Stream'; export { default as AgentIcon } from '@mui/icons-material/PrecisionManufacturing'; export { default as InsightIcon } from '@mui/icons-material/Insights'; export { default as AnchorIcon } from '@mui/icons-material/Anchor'; // ── Custom SVG domain icons ───────────────────────────────────────────── /** Grid-lines-in-a-rectangle icon — represents a database table. */ // export const TableIcon: React.FC<{ sx?: SxProps }> = ({ sx }) => ( // <Box // component="svg" // viewBox="0 0 16 16" // sx={{ // width: 16, // height: 16, // ...sx // }} // > // <rect x="2" y="2" width="12" height="12" fill="none" stroke="currentColor" strokeWidth="1.2" rx="0.5"/> // <line x1="2" y1="6" x2="14" y2="6" stroke="currentColor" strokeWidth="1"/> // <line x1="2" y1="10" x2="14" y2="10" stroke="currentColor" strokeWidth="1"/> // <line x1="6" y1="2" x2="6" y2="14" stroke="currentColor" strokeWidth="1"/> // <line x1="10" y1="2" x2="10" y2="14" stroke="currentColor" strokeWidth="1"/> // </Box> // ); /** Two overlapping rectangles — represents a database view. */ export const DatabaseViewIcon: React.FC<{ sx?: SxProps }> = ({ sx }) => ( <Box component="svg" viewBox="0 0 16 16" sx={{ width: 16, height: 16, ...sx }} > <rect x="2" y="2" width="12" height="12" fill="none" stroke="currentColor" strokeWidth="1.2" rx="0.5" opacity="0.8"/> <rect x="4" y="4" width="12" height="12" fill="none" stroke="currentColor" strokeWidth="1.2" rx="0.5"/> </Box> ); // ── Data-type icons (existing) ────────────────────────────────────────── const BooleanIcon: React.FunctionComponent<SvgIconProps> = (props) => { return ( <SvgIcon {...props}> <path d="M8.7,2.77H5.89v8.39H4.23V2.77H1.42V1.36H8.7Z" /> <path d="M22,14.05H18.34V17H21.7v1.4H18.34v4.09H16.68V12.64H22Z" /> <rect x="0.74" y="11.45" width="22.63" height="1" transform="translate(-4.92 12.02) rotate(-45)" /> </SvgIcon> ); }; const StringIcon: React.FunctionComponent<SvgIconProps> = (props) => { return ( <SvgIcon {...props}> <path d="M1,18.55l3.67-9.8H6.57l3.64,9.8H8.38L7.56,16.2H3.64l-.8,2.35Zm3.05-3.71h3L5.77,11c0-.1-.06-.21-.09-.31s0-.23-.07-.38h0c0,.13-.05.25-.07.36L5.41,11Z" /> <path d="M10.62,10.7V.9h3.17a3.66,3.66,0,0,1,2.26.61,2,2,0,0,1,.81,1.7,2.28,2.28,0,0,1-.45,1.41,2.39,2.39,0,0,1-1.23.85v0a2.43,2.43,0,0,1,1.55.75,2.24,2.24,0,0,1,.58,1.59,2.55,2.55,0,0,1-.94,2.08,3.67,3.67,0,0,1-2.45.78Zm1.66-8.47V5h1.09a1.91,1.91,0,0,0,1.28-.4,1.34,1.34,0,0,0,.47-1.09,1.14,1.14,0,0,0-.44-1,2.17,2.17,0,0,0-1.31-.32Zm0,4.11v3h1.39A2.08,2.08,0,0,0,15.06,9a1.44,1.44,0,0,0,.5-1.15A1.26,1.26,0,0,0,15,6.72a2.48,2.48,0,0,0-1.49-.38Z" /> <path d="M22.39,21.52a6.46,6.46,0,0,1-1.45.51,6.72,6.72,0,0,1-1.53.16,4.72,4.72,0,0,1-3.54-1.31,5,5,0,0,1-1.27-3.61A5.16,5.16,0,0,1,16,13.51a4.9,4.9,0,0,1,3.69-1.45,7.28,7.28,0,0,1,1.37.12,6.54,6.54,0,0,1,1.26.4l-.55,1.36a4.91,4.91,0,0,0-2.05-.42,3.23,3.23,0,0,0-2.42,1,3.65,3.65,0,0,0-.95,2.67,3.7,3.7,0,0,0,.87,2.6,3,3,0,0,0,2.35,1,4.65,4.65,0,0,0,2.16-.56Z" /> </SvgIcon> ); }; const NumericalIcon: React.FunctionComponent<SvgIconProps> = (props) => { return ( <SvgIcon {...props}> <path d="M6.5,19.05H4.88v-8a4.23,4.23,0,0,1-.87.48,7.43,7.43,0,0,1-1.07.4l-.45-1.35A9.4,9.4,0,0,0,4.21,10,8.16,8.16,0,0,0,5.65,9H6.5Z" /> <path d="M9.55,11.62V11a3.59,3.59,0,0,1,.2-1.22,3.46,3.46,0,0,1,.54-1,4.79,4.79,0,0,1,.83-.86c.32-.26.67-.54,1.06-.82A7.58,7.58,0,0,0,13,6.47a5.14,5.14,0,0,0,.56-.62,1.88,1.88,0,0,0,.34-.62A2.55,2.55,0,0,0,14,4.49a1.51,1.51,0,0,0-.4-1.09A1.44,1.44,0,0,0,12.5,3a2.94,2.94,0,0,0-1.12.24A3.57,3.57,0,0,0,10.26,4L9.37,3a5.14,5.14,0,0,1,1.54-1,4.91,4.91,0,0,1,1.79-.32,2.89,2.89,0,0,1,2.17.79,2.54,2.54,0,0,1,.78,1.86,3.63,3.63,0,0,1-.18,1.21,3.59,3.59,0,0,1-.48.91,4.68,4.68,0,0,1-.74.81,10.42,10.42,0,0,1-.93.74l-.9.64a4.37,4.37,0,0,0-.62.55,2.29,2.29,0,0,0-.37.52,1.3,1.3,0,0,0-.12.55h4.52v1.39Z" /> <path d="M16.39,21.48l.7-1.11a4.36,4.36,0,0,0,1,.5,3.6,3.6,0,0,0,1.08.16,2,2,0,0,0,1.43-.46,1.49,1.49,0,0,0,.46-1.12,1.44,1.44,0,0,0-.63-1.28,3.25,3.25,0,0,0-1.81-.42h-.81V16.46h.75a2.84,2.84,0,0,0,1.64-.39,1.41,1.41,0,0,0,.54-1.24,1.28,1.28,0,0,0-.39-1,1.5,1.5,0,0,0-1-.34,3,3,0,0,0-1,.17,4.17,4.17,0,0,0-1,.51l-.7-1.07A5.05,5.05,0,0,1,18,12.41a4.9,4.9,0,0,1,1.52-.22,3.12,3.12,0,0,1,2.07.66,2.18,2.18,0,0,1,.8,1.76A2.42,2.42,0,0,1,22,16.12a2.56,2.56,0,0,1-1.36.9v0a2.53,2.53,0,0,1,1.54.77,2.16,2.16,0,0,1,.6,1.56,2.69,2.69,0,0,1-.93,2.14,3.62,3.62,0,0,1-2.47.8,5.44,5.44,0,0,1-1.55-.2A5.64,5.64,0,0,1,16.39,21.48Z" /> </SvgIcon> ); }; const DateIcon: React.FunctionComponent<SvgIconProps> = (props) => { return ( <SvgIcon {...props}> <path d="M18,3.5v-1H17v1H7v-1H6v1H2.5v17h19V3.5Zm2.5,16H3.5V8.5h17Zm0-12H3.5v-3H6v1H7v-1H17v1h1v-1h2.5Z" /> <rect x="9.17" y="9.68" width="2" height="2" /> <rect x="12.83" y="9.68" width="2" height="2" /> <rect x="16.5" y="9.68" width="2" height="2" /> <rect x="5.5" y="13" width="2" height="2" /> <rect x="9.17" y="13" width="2" height="2" /> <rect x="12.83" y="13" width="2" height="2" /> <rect x="16.5" y="13" width="2" height="2" /> <rect x="5.5" y="16.32" width="2" height="2" /> <rect x="9.17" y="16.32" width="2" height="2" /> </SvgIcon> ); }; const UnknownIcon: React.FunctionComponent<SvgIconProps> = (props) => { return ( <SvgIcon {...props}> <path d="M10.22,15.43c-.07-.18-.12-.42-.17-.71-.05-.29-.08-.58-.08-.86,0-.44,.09-.84,.27-1.2,.18-.36,.4-.69,.66-1.01s.55-.61,.86-.9c.31-.29,.59-.57,.86-.85s.49-.57,.66-.87,.27-.64,.27-1c0-.33-.07-.61-.2-.85-.13-.24-.31-.45-.54-.61s-.49-.29-.78-.36c-.29-.08-.61-.12-.94-.12-1.08,0-2.11,.48-3.09,1.45v-2.51c1.19-.7,2.42-1.04,3.7-1.04,.59,0,1.14,.08,1.66,.23s.98,.38,1.37,.67c.39,.3,.7,.67,.92,1.11,.22,.44,.34,.96,.34,1.54s-.09,1.06-.28,1.49-.43,.84-.71,1.2-.59,.69-.92,.97c-.33,.28-.64,.57-.92,.85-.29,.28-.52,.58-.71,.88-.19,.3-.28,.64-.28,1.01,0,.31,.04,.59,.12,.85,.08,.25,.16,.47,.24,.64h-2.29Zm1.24,4.6c-.41,0-.78-.14-1.1-.42-.3-.28-.46-.62-.46-1.02s.15-.75,.46-1.02c.31-.29,.68-.43,1.1-.43s.78,.15,1.09,.43c.3,.27,.46,.61,.46,1.02s-.15,.75-.46,1.02c-.32,.28-.68,.42-1.09,.42Z" /> </SvgIcon> ); }; export { BooleanIcon, NumericalIcon, StringIcon, DateIcon, UnknownIcon }; ================================================ FILE: src/index.css ================================================ @import '@fontsource/roboto/300.css'; @import '@fontsource/roboto/400.css'; @import '@fontsource/roboto/500.css'; @import '@fontsource/roboto/700.css'; html, body { height: 100%; margin: 0; /* transform: translateZ(0); */ overflow: hidden; } #root { height: 100%; margin: 0; overflow: auto; } /* Sharp window wipe animation */ @keyframes windowWipe { 0% { transform: translateX(-100%); opacity: 0; } 5% { opacity: 1; } 95% { opacity: 1; } 100% { transform: translateX(100%); opacity: 0; } } .window-wipe { position: relative; overflow: hidden; } .window-wipe::before { content: ''; position: absolute; top: 0; left: 0; width: 100%; height: 100%; background: linear-gradient(90deg, transparent 0%, transparent 30%, rgba(255, 255, 255, 0.3) 40%, rgba(255, 255, 255, 1) 50%, rgba(255, 255, 255, 0.3) 60%, transparent 70%, transparent 100%); animation: windowWipe 2s ease-out infinite; z-index: 1; pointer-events: none; box-shadow: 0 0 10px rgba(255, 255, 255, 0.5); } ================================================ FILE: src/index.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React from 'react'; import './index.css'; import store, { persistor } from './app/store' import { Provider } from 'react-redux' import { AppFC } from './app/App'; import { PersistGate } from 'redux-persist/integration/react' import { createRoot } from 'react-dom/client'; const domNode = document.getElementById('root') as HTMLElement; const root = createRoot(domNode); root.render(<React.StrictMode> <Provider store={store}> <PersistGate loading={null} persistor={persistor}> <AppFC /> </PersistGate> </Provider> </React.StrictMode>); ================================================ FILE: src/lib/agents-chart/README.md ================================================ # agents-chart A semantic-level visualization library that compiles data + semantic annotations into chart specifications for multiple rendering backends. The LLM outputs only chart type, field assignments, and a **semantic type** per field (e.g. `Revenue`, `Rank`, `CategoryCode`). A deterministic compiler derives all low-level parameters — sizing, zero-baseline, formatting, color schemes, and mark templates — so charts look good *and* stay editable without calling the LLM again. Pure TypeScript · No UI framework dependencies · Data-in, spec-out > For full motivation & comparisons, see [docs/story.md](docs/story.md). > For architecture details, see [docs/design_v3.md](docs/design_v3.md). --- ## Why LLM-generated chart specs face a dilemma: | Approach | Looks good | Editable | Bespoke charts | Cost to re-encode | |----------|:---:|:---:|:---:|:---:| | Library defaults | ✗ | ✓ | ✗ | 0 | | LLM-tuned spec | ✓ | ✗ | Sometimes | 1 LLM call | | **agents-chart** | **✓** | **✓** | **✓** | **0** | **Simple specs** are editable but look bad (wrong sizing, misleading encodings). **Polished specs** look great but are brittle (hard-coded values break on every field swap). agents-chart resolves this: when a user swaps fields, changes chart type, or adds facets for exploration, the compiler re-derives all parameters automatically — no LLM call needed. Because the output is native library code (Vega-Lite, ECharts, or Chart.js), users retain full control over aesthetic fine-tuning — fonts, colors, legends, annotations — using each library's own API. There is no abstraction tax or reduced expressiveness. ### Key insight: semantic types as the contract Instead of asking the LLM to set dozens of low-level parameters, we ask it one thing: **what does this data mean?** — expressed as a semantic type. ``` Semantic type (e.g. "Revenue") ├── Encoding type: quantitative ├── Zero baseline: true ├── Domain padding: 0% ├── Scale direction: normal ├── Axis formatting: "$,.0f" ├── Color scheme: sequential └── Sizing model: per-axis stretch ``` When the user swaps a field, the compiler re-derives everything from the new semantic type. No hard-coded constants go stale. No LLM call needed. ### The workflow ``` 1. LLM generates: chart type + semantic types (~10-line JSON) 2. User edits: swap field / change mark / add facet → compiler handles it (no AI) 3. Fine-tune (2%): edit the generated spec directly for bespoke styling ``` --- ## Quick start ### Vega-Lite ```ts import { assembleVegaLite } from './lib/agents-chart'; const spec = assembleVegaLite({ data: { values: myData }, semantic_types: { weight: 'Quantity', mpg: 'Quantity', origin: 'Country' }, chart_spec: { chartType: 'Scatter Plot', encodings: { x: { field: 'weight' }, y: { field: 'mpg' }, color: { field: 'origin' } }, canvasSize: { width: 400, height: 300 }, }, }); ``` ### ECharts ```ts import { assembleECharts } from './lib/agents-chart'; const option = assembleECharts({ data: { values: myData }, semantic_types: { weight: 'Quantity', mpg: 'Quantity' }, chart_spec: { chartType: 'Scatter Plot', encodings: { x: { field: 'weight' }, y: { field: 'mpg' } }, }, }); ``` ### Chart.js ```ts import { assembleChartjs } from './lib/agents-chart'; const config = assembleChartjs({ data: { values: myData }, semantic_types: { weight: 'Quantity' }, chart_spec: { chartType: 'Bar Chart', encodings: { x: { field: 'category' }, y: { field: 'value' } } }, }); ``` --- ## Architecture ``` index.ts ← public API (re-exports core/ + all backends) core/ ← target-language-agnostic types.ts ← shared type definitions (ChartAssemblyInput, ChartTemplateDef, …) semantic-types.ts ← ~70 semantic types + VisCategory helpers decisions.ts ← pure decision functions (layout, encoding type) resolve-semantics.ts ← Phase 0: semantic resolution compute-layout.ts ← Phase 1: layout computation filter-overflow.ts ← overflow filtering vegalite/ ← Vega-Lite backend assemble.ts ← assembleVegaLite() orchestrator instantiate-spec.ts ← Phase 2: VL spec instantiation templates/ ← chart templates (bar, scatter, bump, …) echarts/ ← ECharts backend assemble.ts ← assembleECharts() orchestrator instantiate-spec.ts ← Phase 2: EC option instantiation templates/ ← chart templates chartjs/ ← Chart.js backend assemble.ts ← assembleChartjs() orchestrator instantiate-spec.ts ← Phase 2: CJS config instantiation templates/ ← chart templates ``` ### Type resolution pipeline ``` semantic type → getVisCategory() → VisCategory → channel/chart rules → encoding type ↑ (fallback: inferVisCategory() inspects raw data) ``` --- ## Public API ### Assembly functions Each backend has its own assembly function. All accept the same `ChartAssemblyInput` shape: | Function | Output | Import | |----------|--------|--------| | `assembleVegaLite(input)` | Vega-Lite spec | `import { assembleVegaLite } from './lib/agents-chart'` | | `assembleECharts(input)` | ECharts option object | `import { assembleECharts } from './lib/agents-chart'` | | `assembleChartjs(input)` | Chart.js config object | `import { assembleChartjs } from './lib/agents-chart'` | ### Input types ```ts interface ChartAssemblyInput { data: { values: any[] } | { url: string }; // inline rows or URL semantic_types?: Record<string, string>; // field → semantic type chart_spec: { chartType: string; // e.g. "Scatter Plot" encodings: Record<string, ChartEncoding>; // channel → encoding map canvasSize?: { width: number; height: number }; // default 400×320 chartProperties?: Record<string, any>; // template-specific knobs }; options?: AssembleOptions; // layout tuning } ``` | Key | Description | |---|---| | `data` | Data source — either `{ values: [...] }` (inline row objects) or `{ url: "..." }` (JSON/CSV URL) | | `semantic_types` | Per-column semantic annotations (e.g., `{ revenue: "Price", country: "Country" }`) | | `chart_spec` | What to draw — chart type, encodings, canvas size, properties | | `options` | Layout tuning (elasticity, step sizes, tooltips, etc.) | ```ts interface ChartEncoding { field?: string; type?: 'quantitative' | 'nominal' | 'ordinal' | 'temporal'; aggregate?: 'count' | 'sum' | 'average'; sortOrder?: 'ascending' | 'descending'; sortBy?: string; scheme?: string; } interface AssembleOptions { addTooltips?: boolean; // default false elasticity?: number; // axis stretch exponent (default 0.5) maxStretch?: number; // axis stretch cap (default 2) facetElasticity?: number; // facet stretch exponent (default 0.3) maxStretch?: number; // unified stretch cap (default 2) minStep?: number; // min px per discrete tick (default 6) minSubplotSize?: number; // min facet subplot px (default 60) } ``` ### Template registries Each backend has its own set of supported chart types and template definitions. Templates are organized by category and can be looked up by chart type name. | Backend | Template map | Flat list | Lookup | Channels | |---------|-------------|-----------|--------|----------| | Vega-Lite | `vlTemplateDefs` | `vlAllTemplateDefs` | `vlGetTemplateDef(name)` | `vlGetTemplateChannels(name)` | | ECharts | `ecTemplateDefs` | `ecAllTemplateDefs` | `ecGetTemplateDef(name)` | `ecGetTemplateChannels(name)` | | Chart.js | `cjsTemplateDefs` | `cjsAllTemplateDefs` | `cjsGetTemplateDef(name)` | `cjsGetTemplateChannels(name)` | ```ts // Example: list available Vega-Lite chart categories import { vlTemplateDefs } from './lib/agents-chart'; Object.keys(vlTemplateDefs); // ["Scatter & Point", "Bar", "Line & Area", ...] // Example: get channels for a specific chart type import { vlGetTemplateChannels } from './lib/agents-chart'; vlGetTemplateChannels('Scatter Plot'); // ["x", "y", "color", "size", "shape"] ``` ### Semantic types (~70 types) | Group | Examples | |-------|---------| | Temporal | `DateTime`, `Date`, `Year`, `Month` | | Measures | `Quantity`, `Count`, `Price`, `Percentage` | | Discrete numerics | `Rank`, `Score`, `ID` | | Geographic | `Latitude`, `Longitude`, `Country`, `City` | | Categorical | `PersonName`, `Company`, `Status`, `Boolean` | | Ranges | `Range`, `AgeGroup`, `Bucket` | | Fallbacks | `String`, `Number`, `Unknown` | ### Core utilities (shared across backends) These are re-exported from `core/` and available at the top level: ```ts import { // Semantic type helpers inferVisCategory, // infer VisCategory from raw data getVisCategory, // look up VisCategory for a known semantic type // Shared types type ChartAssemblyInput, type ChartEncoding, type ChartTemplateDef, type AssembleOptions, type ChartWarning, // Layout constants channels, channelGroups, } from './lib/agents-chart'; ``` --- ## What the compiler handles automatically - **Sizing** — spring model for discrete axes, pressure model for continuous; composable with facets and layers. No more 6400 px charts from 80 × 4 facets. - **Zero baseline** — Revenue → include zero; Temperature → don't; Rank → don't. - **Scale direction** — Rank → reversed; others → normal. - **Formatting** — Revenue → `$,.0f`; Percentage → `.0%`; Year → `%Y`. - **Color schemes** — categorical codes → distinct hues; measures → sequential. - **Label overflow** — auto-rotation and truncation from count + string lengths. - **Bespoke marks** — lollipops, bump charts, candlesticks as single templates. - **Semantic validation** — actionable errors before rendering, not after crashing. ## Design principles 1. **No UI dependencies** — pure data-in, spec-out. 2. **Semantic types drive everything** — the caller annotates fields; the compiler derives all config. Fallback: `inferVisCategory()` inspects raw data. 3. **Callers own the data** — no aggregation transforms applied. 4. **Layout is configurable** — elastic stretch, facet sizing, step sizes exposed in `AssembleOptions`. 5. **Templates are declarative** — each chart type is a `ChartTemplateDef` with a skeleton, channel list, and optional post-processor. 6. **Backend-agnostic semantics** — the same semantic reasoning targets Vega-Lite, ECharts, and Chart.js through separate assembly functions. ================================================ FILE: src/lib/agents-chart/chartjs/README.md ================================================ # Chart.js Backend The simplest backend. Compiles the core semantic layer into [Chart.js](https://www.chartjs.org/) configuration objects using a dataset-based data model. ## Output Format ```jsonc { "type": "bar", "data": { "labels": ["A","B","C"], "datasets": [{ "data": [10,20,30], "backgroundColor": "..." }] }, "options": { "scales": {...}, "plugins": {...} } } ``` A Chart.js config object with `_width`/`_height` hints. Rendered via `new Chart(canvas, config)` with `responsive: false` and explicit canvas dimensions. ## Assembly Pipeline | Phase | Step | Description | |-------|------|-------------| | **0** | `resolveSemantics` | Shared — resolve field types, aggregates, sort orders | | 0a | `declareLayoutMode` | Template layout declaration | | 0b | `convertTemporalData` | Shared — temporal parsing | | 0c | `filterOverflow` | Shared — category truncation | | **1** | `computeLayout` | Shared — step sizes, subplot dimensions | | **2** | Build `resolvedEncodings` → `template.instantiate` → `cjsApplyLayoutToSpec` → tooltips | Final CJS config | ## File Structure ``` chartjs/ assemble.ts – assembleChartjs(): Phase 2 assembly instantiate-spec.ts – cjsApplyLayoutToSpec(), cjsApplyTooltips() index.ts – barrel exports templates/ index.ts – template registry (10 templates, 5 categories) scatter.ts – Scatter Plot bar.ts – Bar, Grouped Bar, Stacked Bar line.ts – Line Chart area.ts – Area Chart pie.ts – Pie Chart histogram.ts – Histogram radar.ts – Radar Chart rose.ts – Rose Chart (polar bar) utils.ts – shared utilities ``` ## Template Definitions (10 templates) | Category | Charts | |----------|--------| | Scatter & Point | Scatter Plot | | Bar | Bar Chart, Grouped Bar, Stacked Bar, Histogram | | Line & Area | Line Chart, Area Chart | | Part-to-Whole | Pie Chart | | Polar | Radar Chart, Rose Chart | ## Known Issues & Notes - **Smallest backend** — 10 templates, no faceting support, no maps, no custom marks, no statistical charts. - `instantiate-spec.ts` is only 159 lines — the simplest Phase 2 of all backends. - Bar sizing uses `barPercentage` / `categoryPercentage` rather than VL's `step` or ECharts' `barWidth`. - Label rotation uses `ticks.maxRotation` on scales. - Stacking is configured via `stacked: true` on both x and y scales. - Axis-less detection checks for `config.type === 'radar'` as well as `pie`/`doughnut`. - No faceting support — Chart.js has no built-in multi-panel mechanism and no custom faceting module has been implemented. - Rose Chart is implemented as a polar bar chart (`type: 'polarArea'` or `type: 'bar'` with `indexAxis` and polar scales). ================================================ FILE: src/lib/agents-chart/chartjs/assemble.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Chart.js chart assembly — Two-Stage Pipeline Coordinator. * * Reuses the **same core analysis pipeline** as Vega-Lite and ECharts: * Phase 0: resolveChannelSemantics → ChannelSemantics * Step 0a: declareLayoutMode → LayoutDeclaration * Step 0b: convertTemporalData → converted data * Step 0c: filterOverflow → filtered data, nominalCounts * Phase 1: computeLayout → LayoutResult * * Then diverges for Phase 2 (Chart.js-specific): * template.instantiate → builds Chart.js config structure * cjsApplyLayoutToSpec → applies layout decisions to config * * Key structural differences from ECharts / VL output: * VL: { mark, encoding, data: {values}, width, height } * EC: { xAxis, yAxis, series: [{type, data}], tooltip, legend, grid } * CJS: { type, data: { labels, datasets[] }, options: { scales, plugins } } * * This module has NO React, Redux, or UI framework dependencies. */ import { ChartEncoding, ChartTemplateDef, ChartAssemblyInput, AssembleOptions, LayoutDeclaration, InstantiateContext, } from '../core/types'; import type { ChartWarning } from '../core/types'; import { cjsGetTemplateDef } from './templates'; import { resolveChannelSemantics, convertTemporalData } from '../core/resolve-semantics'; import { computeZeroDecision } from '../core/semantic-types'; import { filterOverflow } from '../core/filter-overflow'; import { computeLayout, computeChannelBudgets } from '../core/compute-layout'; import { cjsApplyLayoutToSpec, cjsApplyTooltips } from './instantiate-spec'; // --------------------------------------------------------------------------- // Public API // --------------------------------------------------------------------------- /** * Assemble a Chart.js config object. * * ```ts * const config = assembleChartjs({ * data: { values: myRows }, * semantic_types: { weight: 'Quantity' }, * chart_spec: { chartType: 'Bar Chart', encodings: { x: { field: 'category' }, y: { field: 'value' } } }, * options: { addTooltips: true }, * }); * ``` * * @returns A Chart.js config object with optional `_warnings` and `_width`/`_height` hints */ export function assembleChartjs(input: ChartAssemblyInput): any { const chartType = input.chart_spec.chartType; const encodings = input.chart_spec.encodings; const data = input.data.values ?? []; const semanticTypes = input.semantic_types ?? {}; const canvasSize = input.chart_spec.canvasSize ?? { width: 400, height: 320 }; const chartProperties = input.chart_spec.chartProperties; const options = input.options ?? {}; const chartTemplate = cjsGetTemplateDef(chartType) as ChartTemplateDef; if (!chartTemplate) { throw new Error(`Unknown Chart.js chart type: ${chartType}. Use cjsAllTemplateDefs to see available types.`); } const warnings: ChartWarning[] = []; // ═══════════════════════════════════════════════════════════════════════ // PHASE 0: Resolve Semantics (shared with VL + EC — completely target-agnostic) // ═══════════════════════════════════════════════════════════════════════ const tplMark = chartTemplate.template?.mark; const templateMarkType = typeof tplMark === 'string' ? tplMark : tplMark?.type; // Convert temporal data once — feeds semantic resolution and all downstream stages const convertedData = convertTemporalData(data, semanticTypes); const channelSemantics = resolveChannelSemantics( encodings, data, semanticTypes, convertedData, ); // Finalize zero-baseline (requires template mark knowledge) const effectiveMarkType = templateMarkType || 'point'; for (const [channel, cs] of Object.entries(channelSemantics)) { if ((channel === 'x' || channel === 'y') && cs.type === 'quantitative') { const numericValues = data .map(r => r[cs.field]) .filter((v: any) => v != null && typeof v === 'number' && !isNaN(v)); cs.zero = computeZeroDecision( cs.semanticAnnotation.semanticType, channel, effectiveMarkType, numericValues, ); } } // ═══════════════════════════════════════════════════════════════════════ // STEP 0a: declareLayoutMode (shared hook) // ═══════════════════════════════════════════════════════════════════════ const declaration: LayoutDeclaration = chartTemplate.declareLayoutMode ? chartTemplate.declareLayoutMode(channelSemantics, data, chartProperties) : {}; const effectiveOptions: AssembleOptions = { ...options, ...(declaration.paramOverrides || {}), }; const { addTooltips: addTooltipsOpt = false, } = effectiveOptions; // ═══════════════════════════════════════════════════════════════════════ // STEP 0b: filterOverflow (shared) // ═══════════════════════════════════════════════════════════════════════ const allMarkTypes = new Set<string>(); if (templateMarkType) allMarkTypes.add(templateMarkType); // ── Channel budgets (shared, in layout module) ───────────────────── const budgets = computeChannelBudgets( channelSemantics, declaration, convertedData, canvasSize, effectiveOptions, ); const overflowResult = filterOverflow( channelSemantics, declaration, encodings, convertedData, budgets, allMarkTypes, ); let values = overflowResult.filteredData; warnings.push(...overflowResult.warnings); // ═══════════════════════════════════════════════════════════════════════ // PHASE 1: Compute Layout (shared — completely target-agnostic) // ═══════════════════════════════════════════════════════════════════════ const layoutResult = computeLayout( channelSemantics, declaration, values, canvasSize, effectiveOptions, ); layoutResult.truncations = overflowResult.truncations; // ═══════════════════════════════════════════════════════════════════════ // PHASE 2: Instantiate Chart.js Config (CJS-specific) // ═══════════════════════════════════════════════════════════════════════ // Build resolved encodings for interface compatibility const resolvedEncodings: Record<string, any> = {}; for (const [channel, encoding] of Object.entries(encodings)) { const cs = channelSemantics[channel]; if (cs) { resolvedEncodings[channel] = { field: cs.field, type: cs.type, aggregate: encoding.aggregate, }; } } // Template instantiate const instantiateContext: InstantiateContext = { channelSemantics, layout: layoutResult, table: values, resolvedEncodings, encodings, chartProperties, canvasSize, semanticTypes, chartType, assembleOptions: effectiveOptions, }; // Standard single-panel rendering (no faceting for initial CJS backend) const cjsConfig: any = structuredClone(chartTemplate.template); chartTemplate.instantiate(cjsConfig, instantiateContext); // Apply layout decisions (CJS-specific) cjsApplyLayoutToSpec(cjsConfig, instantiateContext, warnings); // Tooltips if (addTooltipsOpt) { cjsApplyTooltips(cjsConfig); } // Template-specific post-processing if (chartTemplate.postProcess) { chartTemplate.postProcess(cjsConfig, instantiateContext); } // ═══════════════════════════════════════════════════════════════════════ // RESULT // ═══════════════════════════════════════════════════════════════════════ if (warnings.length > 0) { cjsConfig._warnings = warnings; } cjsConfig._dataLength = values.length; return cjsConfig; } ================================================ FILE: src/lib/agents-chart/chartjs/index.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * @module agents-chart/chartjs * * Chart.js backend for agents-chart. * * Compiles the core semantic layer into Chart.js configuration objects. * Contains CJS-specific assembly, spec instantiation, and chart templates. * * Architecture contrast with other backends: * VL: encoding-channel-based — { encoding: { x: { field, type }, y: ... } } * EC: series-based — { series: [{ type, data }], xAxis, yAxis } * CJS: dataset-based — { type, data: { labels, datasets[] }, options } * * Same core pipeline (Phase 0 + Phase 1), different Phase 2 output. */ // CJS assembly function export { assembleChartjs } from './assemble'; // CJS spec instantiation (Phase 2) export { cjsApplyLayoutToSpec, cjsApplyTooltips } from './instantiate-spec'; // CJS template registry export { cjsTemplateDefs, cjsAllTemplateDefs, cjsGetTemplateDef, cjsGetTemplateChannels, } from './templates'; // CJS recommendation & adaptation export { cjsAdaptChart, cjsRecommendEncodings } from './recommendation'; ================================================ FILE: src/lib/agents-chart/chartjs/instantiate-spec.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ============================================================================= * PHASE 2: INSTANTIATE SPEC — Chart.js backend * ============================================================================= * * Translates semantic decisions (Phase 0) and layout dimensions (Phase 1) * into Chart.js-specific config properties. * * Key differences from ECharts/Vega-Lite instantiation: * - CJS uses { type, data: { labels, datasets[] }, options: { scales } } * - CJS scales use 'x'/'y' keys (not xAxis/yAxis) * - CJS sizing via canvas element dimensions + responsive: false * - CJS label rotation via scales[axis].ticks.maxRotation * - CJS stacking via stacked property on scales * - CJS bar sizing via barPercentage and categoryPercentage * * CJS dependency: **Yes — this is where Chart.js-specific syntax lives** * ============================================================================= */ import type { ChannelSemantics, LayoutResult, InstantiateContext, ChartWarning, } from '../core/types'; /** * Phase 2: Apply layout and semantic decisions to the Chart.js config object. * * Handles common Chart.js plumbing across all templates: * - Canvas sizing (_width, _height) * - Axis label rotation and font sizing * - Bar sizing (barPercentage, categoryPercentage) * - Overflow truncation warnings */ export function cjsApplyLayoutToSpec( config: any, context: InstantiateContext, warnings: ChartWarning[], ): void { const { channelSemantics, layout, canvasSize } = context; // ── Axis-less chart types (pie, radar, doughnut) ───────────────────── const hasAxes = !!(config.options?.scales?.x || config.options?.scales?.y); const isRadar = config.type === 'radar'; // ── Canvas dimensions ──────────────────────────────────────────────── // Chart.js uses the canvas element dimensions. // For non-axis charts (pie, radar), templates set their own _width/_height. if (hasAxes && !config._width) { const PADDING = 80; // approximate space for axes, labels, legend const xIsDiscrete = layout.xNominalCount > 0 || layout.xContinuousAsDiscrete > 0; const yIsDiscrete = layout.yNominalCount > 0 || layout.yContinuousAsDiscrete > 0; let plotWidth: number; let plotHeight: number; if (xIsDiscrete && layout.xStepUnit !== 'group') { const xItemCount = layout.xNominalCount || layout.xContinuousAsDiscrete || 0; plotWidth = xItemCount > 0 ? layout.xStep * xItemCount : (layout.subplotWidth || canvasSize.width); } else { plotWidth = layout.subplotWidth || canvasSize.width; } if (yIsDiscrete && layout.yStepUnit !== 'group') { const yItemCount = layout.yNominalCount || layout.yContinuousAsDiscrete || 0; plotHeight = yItemCount > 0 ? layout.yStep * yItemCount : (layout.subplotHeight || canvasSize.height); } else { plotHeight = layout.subplotHeight || canvasSize.height; } config._width = plotWidth + PADDING; config._height = plotHeight + PADDING; } // ── Bar sizing ─────────────────────────────────────────────────────── if (config.data?.datasets) { const barDatasets = config.data.datasets.filter( (ds: any) => config.type === 'bar' || ds.type === 'bar' ); if (barDatasets.length > 0 && hasAxes) { const bandPadding = layout.stepPadding; // Chart.js uses: // categoryPercentage: fraction of available space per category (default 0.8) // barPercentage: fraction of category space per bar (default 0.9) // Total bar space = categoryPercentage × barPercentage const categoryPct = 1 - bandPadding; for (const ds of barDatasets) { if (ds.categoryPercentage == null) { ds.categoryPercentage = categoryPct; } } } } // ── X-axis label sizing ────────────────────────────────────────────── if (hasAxes && config.options?.scales?.x && layout.xLabel) { if (!config.options.scales.x.ticks) config.options.scales.x.ticks = {}; if (layout.xLabel.labelAngle && layout.xLabel.labelAngle !== 0) { config.options.scales.x.ticks.maxRotation = Math.abs(layout.xLabel.labelAngle); config.options.scales.x.ticks.minRotation = Math.abs(layout.xLabel.labelAngle); } if (layout.xLabel.fontSize) { config.options.scales.x.ticks.font = { ...(config.options.scales.x.ticks.font || {}), size: layout.xLabel.fontSize, }; } } // ── Y-axis label sizing ────────────────────────────────────────────── if (hasAxes && config.options?.scales?.y && layout.yLabel) { if (!config.options.scales.y.ticks) config.options.scales.y.ticks = {}; if (layout.yLabel.fontSize) { config.options.scales.y.ticks.font = { ...(config.options.scales.y.ticks.font || {}), size: layout.yLabel.fontSize, }; } } // ── Overflow truncation warnings ───────────────────────────────────── if (layout.truncations && layout.truncations.length > 0) { for (const trunc of layout.truncations) { warnings.push({ severity: 'warning', code: 'overflow', message: trunc.message, channel: trunc.channel, field: trunc.field, }); // Chart.js: append placeholder to labels if (config.data?.labels && Array.isArray(config.data.labels)) { config.data.labels.push(trunc.placeholder); } } } } /** * Apply tooltips to a Chart.js config. * Chart.js tooltips are configured under options.plugins.tooltip. */ export function cjsApplyTooltips(config: any): void { if (!config.options) config.options = {}; if (!config.options.plugins) config.options.plugins = {}; if (!config.options.plugins.tooltip) { config.options.plugins.tooltip = { enabled: true }; } } ================================================ FILE: src/lib/agents-chart/chartjs/recommendation.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Chart.js recommendation & adaptation wrappers. */ import { adaptChannels, recommendChannels } from '../core/recommendation'; import { cjsGetTemplateChannels } from './templates'; export function cjsAdaptChart( sourceType: string, targetType: string, encodings: Record<string, string>, data?: any[], semanticTypes?: Record<string, string>, ): Record<string, string> { const targetChannels = cjsGetTemplateChannels(targetType); return adaptChannels(sourceType, targetType, targetChannels, encodings, data, semanticTypes); } export function cjsRecommendEncodings( chartType: string, data: any[], semanticTypes: Record<string, string>, ): Record<string, string> { const rec = recommendChannels(chartType, data, semanticTypes); const validChannels = cjsGetTemplateChannels(chartType); const result: Record<string, string> = {}; for (const [ch, field] of Object.entries(rec)) { if (validChannels.includes(ch)) result[ch] = field; } return result; } ================================================ FILE: src/lib/agents-chart/chartjs/templates/area.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Chart.js Area Chart template (single + multi-series). * * Chart.js renders area charts as line charts with `fill: true`. */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, groupBy, buildCategoryAlignedData, DEFAULT_COLORS, DEFAULT_BG_COLORS, } from './utils'; const isDiscrete = (type: string | undefined) => type === 'nominal' || type === 'ordinal'; export const cjsAreaChartDef: ChartTemplateDef = { chart: 'Area Chart', template: { mark: 'area', encoding: {} }, channels: ['x', 'y', 'color', 'opacity', 'column', 'row'], markCognitiveChannel: 'area', declareLayoutMode: () => ({ paramOverrides: { continuousMarkCrossSection: { x: 100, y: 20, seriesCountAxis: 'auto' } }, }), instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const xCS = channelSemantics.x; const yCS = channelSemantics.y; const colorField = channelSemantics.color?.field; if (!xCS?.field || !yCS?.field) return; const xField = xCS.field; const yField = yCS.field; const xIsDiscrete = isDiscrete(xCS.type); const categories = xIsDiscrete ? extractCategories(table, xField, xCS.ordinalSortOrder) : undefined; const opacity = chartProperties?.opacity ?? 0.4; // Stacking const stackMode = chartProperties?.stackMode; const stacked = stackMode !== 'layered'; // Interpolation const interpolate = chartProperties?.interpolate; const tension = (interpolate === 'monotone' || interpolate === 'basis' || interpolate === 'cardinal' || interpolate === 'catmull-rom') ? 0.4 : 0; const config: any = { type: 'line', data: { labels: categories || [], datasets: [], }, options: { responsive: true, maintainAspectRatio: false, scales: { x: { type: xIsDiscrete ? 'category' : 'linear', title: { display: true, text: xField }, }, y: { type: 'linear', title: { display: true, text: yField }, stacked, }, }, plugins: { tooltip: { enabled: true }, filler: { propagate: true }, }, }, }; // Zero-baseline: Chart.js defaults beginAtZero to false, so // explicitly set true when the semantic decision includes zero. if (channelSemantics.y?.zero) { config.options.scales.y.beginAtZero = channelSemantics.y.zero.zero !== false; } if (colorField) { const groups = groupBy(table, colorField); config.options.plugins.legend = { display: true }; let colorIdx = 0; for (const [name, rows] of groups) { const data = xIsDiscrete ? buildCategoryAlignedData(rows, xField, yField, categories!) : rows.map(r => ({ x: r[xField], y: r[yField] })); const bgColor = DEFAULT_BG_COLORS[colorIdx % DEFAULT_BG_COLORS.length] .replace(/[\d.]+\)$/, `${opacity})`); config.data.datasets.push({ label: name, data, borderColor: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length], backgroundColor: bgColor, tension, fill: stacked ? 'stack' : 'origin', pointRadius: 2, }); colorIdx++; } } else { const data = xIsDiscrete ? categories!.map(cat => { const row = table.find(r => String(r[xField]) === cat); return row ? row[yField] : null; }) : table.map(r => ({ x: r[xField], y: r[yField] })); config.data.datasets.push({ label: yField, data, borderColor: DEFAULT_COLORS[0], backgroundColor: `rgba(54, 162, 235, ${opacity})`, tension, fill: 'origin', pointRadius: 2, }); config.options.plugins.legend = { display: false }; } Object.assign(spec, config); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'interpolate', label: 'Curve', type: 'discrete', options: [ { value: undefined, label: 'Default (linear)' }, { value: 'linear', label: 'Linear' }, { value: 'monotone', label: 'Monotone (smooth)' }, ], } as ChartPropertyDef, { key: 'opacity', label: 'Opacity', type: 'continuous', min: 0.1, max: 1, step: 0.05, defaultValue: 0.4 } as ChartPropertyDef, { key: 'stackMode', label: 'Stack', type: 'discrete', options: [ { value: undefined, label: 'Stacked (default)' }, { value: 'layered', label: 'Layered (overlap)' }, ], } as ChartPropertyDef, ], }; ================================================ FILE: src/lib/agents-chart/chartjs/templates/bar.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Chart.js Bar Chart templates: Bar, Stacked Bar, Grouped Bar. * * Key contrast with Vega-Lite: * VL: encoding channels determine stacking/grouping implicitly * CJS: explicit datasets[] with stacked option on scales */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, groupBy, detectAxes, buildCategoryAlignedData, DEFAULT_COLORS, DEFAULT_BG_COLORS, } from './utils'; import { detectBandedAxisFromSemantics, detectBandedAxisForceDiscrete, } from '../../vegalite/templates/utils'; // --------------------------------------------------------------------------- // Helpers // --------------------------------------------------------------------------- const isDiscrete = (type: string | undefined) => type === 'nominal' || type === 'ordinal'; // ─── Bar Chart ────────────────────────────────────────────────────────────── export const cjsBarChartDef: ChartTemplateDef = { chart: 'Bar Chart', template: { mark: 'bar', encoding: {} }, channels: ['x', 'y', 'color', 'opacity', 'column', 'row'], markCognitiveChannel: 'length', declareLayoutMode: (cs, table) => { const result = detectBandedAxisFromSemantics(cs, table, { preferAxis: 'x' }); return { axisFlags: result ? { [result.axis]: { banded: true } } : { x: { banded: true } }, resolvedTypes: result?.resolvedTypes, }; }, instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const { categoryAxis, valueAxis } = detectAxes(channelSemantics); const catField = channelSemantics[categoryAxis]?.field; const valField = channelSemantics[valueAxis]?.field; if (!catField || !valField) return; const catCS = channelSemantics[categoryAxis]; const categories = extractCategories(table, catField, catCS?.ordinalSortOrder); const values = buildCategoryAlignedData(table, catField, valField, categories); const isHorizontal = categoryAxis === 'y'; const config: any = { type: 'bar', data: { labels: categories, datasets: [{ label: valField, data: values, backgroundColor: DEFAULT_BG_COLORS[0], borderColor: DEFAULT_COLORS[0], borderWidth: 1, borderRadius: chartProperties?.cornerRadius ?? 0, }], }, options: { responsive: true, maintainAspectRatio: false, indexAxis: isHorizontal ? 'y' as const : 'x' as const, scales: { x: { title: { display: true, text: isHorizontal ? valField : catField }, ...(isHorizontal ? {} : {}), }, y: { title: { display: true, text: isHorizontal ? catField : valField }, }, }, plugins: { legend: { display: false }, tooltip: { enabled: true }, }, }, }; // Apply zero-baseline from semantic decision const valScale = isHorizontal ? 'x' : 'y'; const valCS = channelSemantics[valueAxis]; if (valCS?.zero) { config.options.scales[valScale].beginAtZero = valCS.zero.zero !== false; } else { // Default: bars should include zero for length integrity config.options.scales[valScale].beginAtZero = true; } Object.assign(spec, config); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'cornerRadius', label: 'Corners', type: 'continuous', min: 0, max: 15, step: 1, defaultValue: 0 }, ] as ChartPropertyDef[], }; // ─── Stacked Bar Chart ────────────────────────────────────────────────────── export const cjsStackedBarChartDef: ChartTemplateDef = { chart: 'Stacked Bar Chart', template: { mark: 'bar', encoding: {} }, channels: ['x', 'y', 'color', 'column', 'row'], markCognitiveChannel: 'length', declareLayoutMode: (cs, table) => { const result = detectBandedAxisFromSemantics(cs, table, { preferAxis: 'x' }); return { axisFlags: result ? { [result.axis]: { banded: true } } : { x: { banded: true } }, resolvedTypes: result?.resolvedTypes, paramOverrides: { continuousMarkCrossSection: { x: 20, y: 20, seriesCountAxis: 'auto' } }, }; }, instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const { categoryAxis, valueAxis } = detectAxes(channelSemantics); const colorField = channelSemantics.color?.field; const catField = channelSemantics[categoryAxis]?.field; const valField = channelSemantics[valueAxis]?.field; if (!catField || !valField) return; const catCS = channelSemantics[categoryAxis]; const categories = extractCategories(table, catField, catCS?.ordinalSortOrder); const isHorizontal = categoryAxis === 'y'; const config: any = { type: 'bar', data: { labels: categories, datasets: [], }, options: { responsive: true, maintainAspectRatio: false, indexAxis: isHorizontal ? 'y' as const : 'x' as const, scales: { x: { stacked: true, title: { display: true, text: isHorizontal ? valField : catField }, }, y: { stacked: true, title: { display: true, text: isHorizontal ? catField : valField }, }, }, plugins: { legend: { display: !!colorField }, tooltip: { enabled: true }, }, }, }; if (colorField) { const groups = groupBy(table, colorField); let colorIdx = 0; for (const [name, rows] of groups) { const values = buildCategoryAlignedData(rows, catField, valField, categories); config.data.datasets.push({ label: name, data: values, backgroundColor: DEFAULT_BG_COLORS[colorIdx % DEFAULT_BG_COLORS.length], borderColor: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length], borderWidth: 1, }); colorIdx++; } } else { const values = buildCategoryAlignedData(table, catField, valField, categories); config.data.datasets.push({ label: valField, data: values, backgroundColor: DEFAULT_BG_COLORS[0], borderColor: DEFAULT_COLORS[0], borderWidth: 1, }); } // Apply zero-baseline from semantic decision const valScaleS = isHorizontal ? 'x' : 'y'; const valCSs = channelSemantics[valueAxis]; if (valCSs?.zero) { config.options.scales[valScaleS].beginAtZero = valCSs.zero.zero !== false; } else { config.options.scales[valScaleS].beginAtZero = true; } Object.assign(spec, config); delete spec.mark; delete spec.encoding; }, }; // ─── Grouped Bar Chart ────────────────────────────────────────────────────── export const cjsGroupedBarChartDef: ChartTemplateDef = { chart: 'Grouped Bar Chart', template: { mark: 'bar', encoding: {} }, channels: ['x', 'y', 'group', 'column', 'row'], markCognitiveChannel: 'length', declareLayoutMode: (cs, table) => { const result = detectBandedAxisForceDiscrete(cs, table, { preferAxis: 'x' }); return { axisFlags: result ? { [result.axis]: { banded: true } } : { x: { banded: true } }, resolvedTypes: result?.resolvedTypes, paramOverrides: { continuousMarkCrossSection: { x: 20, y: 20, seriesCountAxis: 'auto' } }, }; }, instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const { categoryAxis, valueAxis } = detectAxes(channelSemantics); const groupField = channelSemantics.group?.field || channelSemantics.color?.field; const catField = channelSemantics[categoryAxis]?.field; const valField = channelSemantics[valueAxis]?.field; if (!catField || !valField) return; const catCS = channelSemantics[categoryAxis]; const categories = extractCategories(table, catField, catCS?.ordinalSortOrder); const isHorizontal = categoryAxis === 'y'; const config: any = { type: 'bar', data: { labels: categories, datasets: [], }, options: { responsive: true, maintainAspectRatio: false, indexAxis: isHorizontal ? 'y' as const : 'x' as const, scales: { x: { title: { display: true, text: isHorizontal ? valField : catField }, }, y: { title: { display: true, text: isHorizontal ? catField : valField }, }, }, plugins: { legend: { display: !!groupField }, tooltip: { enabled: true }, }, }, }; if (groupField) { const groups = groupBy(table, groupField); let colorIdx = 0; for (const [name, rows] of groups) { const values = buildCategoryAlignedData(rows, catField, valField, categories); config.data.datasets.push({ label: name, data: values, backgroundColor: DEFAULT_BG_COLORS[colorIdx % DEFAULT_BG_COLORS.length], borderColor: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length], borderWidth: 1, }); colorIdx++; } } else { const values = buildCategoryAlignedData(table, catField, valField, categories); config.data.datasets.push({ label: valField, data: values, backgroundColor: DEFAULT_BG_COLORS[0], borderColor: DEFAULT_COLORS[0], borderWidth: 1, }); } // Apply zero-baseline from semantic decision const valScaleG = isHorizontal ? 'x' : 'y'; const valCSg = channelSemantics[valueAxis]; if (valCSg?.zero) { config.options.scales[valScaleG].beginAtZero = valCSg.zero.zero !== false; } else { config.options.scales[valScaleG].beginAtZero = true; } Object.assign(spec, config); delete spec.mark; delete spec.encoding; }, }; ================================================ FILE: src/lib/agents-chart/chartjs/templates/histogram.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Chart.js Histogram template. * * Chart.js has no built-in binning — we compute bins client-side * and render as a bar chart. */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { DEFAULT_COLORS, DEFAULT_BG_COLORS } from './utils'; export const cjsHistogramDef: ChartTemplateDef = { chart: 'Histogram', template: { mark: 'bar', encoding: {} }, channels: ['x', 'color', 'column', 'row'], markCognitiveChannel: 'length', instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const xField = channelSemantics.x?.field; const colorField = channelSemantics.color?.field; if (!xField) return; const binCount = chartProperties?.binCount ?? 10; // Extract numeric values const numValues = table .map(r => Number(r[xField])) .filter(v => isFinite(v)); if (numValues.length === 0) return; const minVal = Math.min(...numValues); const maxVal = Math.max(...numValues); const range = maxVal - minVal; const binWidth = range > 0 ? range / binCount : 1; const categories = Array.from({ length: binCount }, (_, i) => { const lo = (minVal + i * binWidth).toFixed(1); const hi = (minVal + (i + 1) * binWidth).toFixed(1); return `${lo}–${hi}`; }); const config: any = { type: 'bar', data: { labels: categories, datasets: [], }, options: { responsive: true, maintainAspectRatio: false, scales: { x: { title: { display: true, text: xField }, }, y: { title: { display: true, text: 'Count' }, beginAtZero: true, }, }, plugins: { tooltip: { enabled: true }, }, }, }; if (!colorField) { // Simple histogram const counts = new Array(binCount).fill(0); for (const v of numValues) { let idx = Math.floor((v - minVal) / binWidth); if (idx >= binCount) idx = binCount - 1; counts[idx]++; } config.data.datasets.push({ label: 'Count', data: counts, backgroundColor: DEFAULT_BG_COLORS[0], borderColor: DEFAULT_COLORS[0], borderWidth: 1, barPercentage: 1.0, categoryPercentage: 1.0, }); config.options.plugins.legend = { display: false }; } else { // Stacked histogram const groupValues = new Map<string, number[]>(); for (const row of table) { const v = Number(row[xField]); if (!isFinite(v)) continue; const g = String(row[colorField] ?? ''); if (!groupValues.has(g)) groupValues.set(g, []); groupValues.get(g)!.push(v); } config.options.scales.x.stacked = true; config.options.scales.y.stacked = true; let colorIdx = 0; for (const [name, vals] of groupValues) { const counts = new Array(binCount).fill(0); for (const v of vals) { let idx = Math.floor((v - minVal) / binWidth); if (idx >= binCount) idx = binCount - 1; counts[idx]++; } config.data.datasets.push({ label: name, data: counts, backgroundColor: DEFAULT_BG_COLORS[colorIdx % DEFAULT_BG_COLORS.length], borderColor: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length], borderWidth: 1, barPercentage: 1.0, categoryPercentage: 1.0, }); colorIdx++; } config.options.plugins.legend = { display: true }; } Object.assign(spec, config); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'binCount', label: 'Bins', type: 'continuous', min: 5, max: 50, step: 1, defaultValue: 10 } as ChartPropertyDef, ], }; ================================================ FILE: src/lib/agents-chart/chartjs/templates/index.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Chart.js template registry. * * Mirrors the structure of echarts/templates/index.ts and * vegalite/templates/index.ts but with Chart.js template definitions. */ import { ChartTemplateDef } from '../../core/types'; import { cjsScatterPlotDef } from './scatter'; import { cjsBarChartDef, cjsStackedBarChartDef, cjsGroupedBarChartDef } from './bar'; import { cjsLineChartDef } from './line'; import { cjsAreaChartDef } from './area'; import { cjsPieChartDef } from './pie'; import { cjsHistogramDef } from './histogram'; import { cjsRadarChartDef } from './radar'; import { cjsRoseChartDef } from './rose'; /** * Chart.js chart template definitions, grouped by category. */ export const cjsTemplateDefs: { [key: string]: ChartTemplateDef[] } = { 'Scatter & Point': [cjsScatterPlotDef], 'Bar': [cjsBarChartDef, cjsGroupedBarChartDef, cjsStackedBarChartDef, cjsHistogramDef], 'Line & Area': [cjsLineChartDef, cjsAreaChartDef], 'Part-to-Whole': [cjsPieChartDef], 'Polar': [cjsRadarChartDef, cjsRoseChartDef], }; /** * Flat list of all Chart.js chart template definitions. */ export const cjsAllTemplateDefs: ChartTemplateDef[] = Object.values(cjsTemplateDefs).flat(); /** * Look up a Chart.js chart template definition by chart type name. */ export function cjsGetTemplateDef(chartType: string): ChartTemplateDef | undefined { return cjsAllTemplateDefs.find(t => t.chart === chartType); } /** * Get the available channels for a Chart.js chart type. */ export function cjsGetTemplateChannels(chartType: string): string[] { return cjsGetTemplateDef(chartType)?.channels || []; } ================================================ FILE: src/lib/agents-chart/chartjs/templates/line.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Chart.js Line Chart template (single + multi-series). * * Contrast with VL: * VL: encoding.x + encoding.y + encoding.color → auto-groups into lines * CJS: explicit datasets[] — each dataset is one line */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, groupBy, buildCategoryAlignedData, DEFAULT_COLORS, } from './utils'; const isDiscrete = (type: string | undefined) => type === 'nominal' || type === 'ordinal'; export const cjsLineChartDef: ChartTemplateDef = { chart: 'Line Chart', template: { mark: 'line', encoding: {} }, channels: ['x', 'y', 'color', 'opacity', 'column', 'row'], markCognitiveChannel: 'position', declareLayoutMode: () => ({ paramOverrides: { continuousMarkCrossSection: { x: 100, y: 20, seriesCountAxis: 'auto' } }, }), instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const xCS = channelSemantics.x; const yCS = channelSemantics.y; const colorField = channelSemantics.color?.field; if (!xCS?.field || !yCS?.field) return; const xField = xCS.field; const yField = yCS.field; const xIsDiscrete = isDiscrete(xCS.type); const categories = xIsDiscrete ? extractCategories(table, xField, xCS.ordinalSortOrder) : undefined; // Determine tension for interpolation const interpolate = chartProperties?.interpolate; const tension = (interpolate === 'monotone' || interpolate === 'basis' || interpolate === 'cardinal' || interpolate === 'catmull-rom') ? 0.4 : 0; const stepped = interpolate === 'step' ? 'middle' as const : interpolate === 'step-before' ? 'before' as const : interpolate === 'step-after' ? 'after' as const : false as const; const config: any = { type: 'line', data: { labels: categories || [], datasets: [], }, options: { responsive: true, maintainAspectRatio: false, scales: { x: { type: xIsDiscrete ? 'category' : 'linear', title: { display: true, text: xField }, }, y: { type: 'linear', title: { display: true, text: yField }, }, }, plugins: { tooltip: { enabled: true }, }, }, }; // Zero-baseline: Chart.js defaults beginAtZero to false, so // explicitly set true when the semantic decision includes zero. if (channelSemantics.y?.zero) { config.options.scales.y.beginAtZero = channelSemantics.y.zero.zero !== false; } if (colorField) { const groups = groupBy(table, colorField); config.options.plugins.legend = { display: true }; let colorIdx = 0; for (const [name, rows] of groups) { const data = xIsDiscrete ? buildCategoryAlignedData(rows, xField, yField, categories!) : rows.map(r => ({ x: r[xField], y: r[yField] })); config.data.datasets.push({ label: name, data, borderColor: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length], backgroundColor: 'transparent', tension, stepped, pointRadius: 3, fill: false, }); colorIdx++; } } else { const data = xIsDiscrete ? categories!.map(cat => { const row = table.find(r => String(r[xField]) === cat); return row ? row[yField] : null; }) : table.map(r => ({ x: r[xField], y: r[yField] })); config.data.datasets.push({ label: yField, data, borderColor: DEFAULT_COLORS[0], backgroundColor: 'transparent', tension, stepped, pointRadius: 3, fill: false, }); config.options.plugins.legend = { display: false }; } Object.assign(spec, config); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'interpolate', label: 'Curve', type: 'discrete', options: [ { value: undefined, label: 'Default (linear)' }, { value: 'linear', label: 'Linear' }, { value: 'monotone', label: 'Monotone (smooth)' }, { value: 'step', label: 'Step' }, { value: 'step-before', label: 'Step Before' }, { value: 'step-after', label: 'Step After' }, ], } as ChartPropertyDef, ], }; ================================================ FILE: src/lib/agents-chart/chartjs/templates/pie.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Chart.js Pie Chart template. * * Contrast with VL: * VL: mark = "arc" with theta + color * CJS: type = 'pie' or 'doughnut' with data = { labels, datasets[{data}] } */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, DEFAULT_COLORS, DEFAULT_BG_COLORS } from './utils'; export const cjsPieChartDef: ChartTemplateDef = { chart: 'Pie Chart', template: { mark: 'arc', encoding: {} }, channels: ['size', 'color', 'column', 'row'], markCognitiveChannel: 'area', instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const colorField = channelSemantics.color?.field; const sizeField = channelSemantics.size?.field; const labels: string[] = []; const values: number[] = []; if (colorField && sizeField) { // color = category (slice label), size = measure (slice value) const agg = new Map<string, number>(); for (const row of table) { const cat = String(row[colorField] ?? ''); const val = Number(row[sizeField]) || 0; agg.set(cat, (agg.get(cat) ?? 0) + val); } const categories = extractCategories(table, colorField, channelSemantics.color?.ordinalSortOrder); for (const cat of categories) { labels.push(cat); values.push(agg.get(cat) ?? 0); } } else if (colorField) { // No size field → count occurrences per category const counts = new Map<string, number>(); for (const row of table) { const cat = String(row[colorField] ?? ''); counts.set(cat, (counts.get(cat) ?? 0) + 1); } const categories = extractCategories(table, colorField, channelSemantics.color?.ordinalSortOrder); for (const cat of categories) { labels.push(cat); values.push(counts.get(cat) ?? 0); } } else if (sizeField) { for (const row of table) { const val = Number(row[sizeField]) || 0; labels.push(String(val)); values.push(val); } } const innerRadius = chartProperties?.innerRadius ?? 0; const isDoughnut = innerRadius > 0; const config: any = { type: isDoughnut ? 'doughnut' : 'pie', data: { labels, datasets: [{ data: values, backgroundColor: DEFAULT_BG_COLORS.slice(0, labels.length), borderColor: DEFAULT_COLORS.slice(0, labels.length), borderWidth: 1, }], }, options: { responsive: true, maintainAspectRatio: false, plugins: { legend: { display: true, position: 'right' as const }, tooltip: { enabled: true }, }, ...(isDoughnut ? { cutout: `${innerRadius}%` } : {}), }, // Canvas size from context (no axes) _width: Math.max(ctx.canvasSize.width, 300), _height: Math.max(ctx.canvasSize.height, 250), }; Object.assign(spec, config); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'innerRadius', label: 'Donut', type: 'continuous', min: 0, max: 60, step: 5, defaultValue: 0 } as ChartPropertyDef, ], }; ================================================ FILE: src/lib/agents-chart/chartjs/templates/radar.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Chart.js Radar Chart template. * * Chart.js has native radar chart support via type: 'radar'. * * Data model (long format): * x (nominal): metric / axis name * y (quantitative): value for that metric * color (nominal): entity / group */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, groupBy, DEFAULT_COLORS, DEFAULT_BG_COLORS } from './utils'; export const cjsRadarChartDef: ChartTemplateDef = { chart: 'Radar Chart', template: { mark: 'point', encoding: {} }, channels: ['x', 'y', 'color', 'column', 'row'], markCognitiveChannel: 'position', instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const axisField = channelSemantics.x?.field; // metric names const valueField = channelSemantics.y?.field; // metric values const groupField = channelSemantics.color?.field; // entities if (!axisField || !valueField) return; // Extract unique metrics (radar axes) const metrics = extractCategories(table, axisField, channelSemantics.x?.ordinalSortOrder); if (metrics.length < 2) return; const filled = chartProperties?.filled !== false; const fillOpacity = chartProperties?.fillOpacity ?? 0.3; const config: any = { type: 'radar', data: { labels: metrics, datasets: [], }, options: { responsive: true, maintainAspectRatio: false, scales: { r: { beginAtZero: true, ticks: { display: true }, pointLabels: { display: true }, }, }, plugins: { tooltip: { enabled: true }, }, }, // Canvas size from context (no axes) _width: Math.max(ctx.canvasSize.width, 350), _height: Math.max(ctx.canvasSize.height, 300), }; if (groupField) { const groups = groupBy(table, groupField); config.options.plugins.legend = { display: true }; let colorIdx = 0; for (const [name, rows] of groups) { // Aggregate: mean per metric const metricVals = new Map<string, { sum: number; count: number }>(); for (const row of rows) { const m = String(row[axisField]); const v = Number(row[valueField]) || 0; if (!metricVals.has(m)) metricVals.set(m, { sum: 0, count: 0 }); const entry = metricVals.get(m)!; entry.sum += v; entry.count++; } const values = metrics.map(m => { const entry = metricVals.get(m); return entry ? Math.round((entry.sum / entry.count) * 100) / 100 : 0; }); const borderColor = DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length]; const bgColor = borderColor.replace(/[\d.]+\)$/, `${fillOpacity})`); config.data.datasets.push({ label: name, data: values, borderColor, backgroundColor: filled ? bgColor : 'transparent', pointBackgroundColor: borderColor, fill: filled, }); colorIdx++; } } else { // Single group const metricVals = new Map<string, { sum: number; count: number }>(); for (const row of table) { const m = String(row[axisField]); const v = Number(row[valueField]) || 0; if (!metricVals.has(m)) metricVals.set(m, { sum: 0, count: 0 }); const entry = metricVals.get(m)!; entry.sum += v; entry.count++; } const values = metrics.map(m => { const entry = metricVals.get(m); return entry ? Math.round((entry.sum / entry.count) * 100) / 100 : 0; }); config.data.datasets.push({ label: valueField, data: values, borderColor: DEFAULT_COLORS[0], backgroundColor: filled ? DEFAULT_COLORS[0].replace(/[\d.]+\)$/, `${fillOpacity})`) : 'transparent', pointBackgroundColor: DEFAULT_COLORS[0], fill: filled, }); config.options.plugins.legend = { display: false }; } Object.assign(spec, config); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'filled', label: 'Fill', type: 'discrete', options: [ { value: true, label: 'Filled (default)' }, { value: false, label: 'Outline only' }, ], } as ChartPropertyDef, { key: 'fillOpacity', label: 'Opacity', type: 'continuous', min: 0.05, max: 0.8, step: 0.05, defaultValue: 0.3 } as ChartPropertyDef, ], }; ================================================ FILE: src/lib/agents-chart/chartjs/templates/rose.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Chart.js Rose Chart (Nightingale / Coxcomb) template. * * Contrast with VL: * VL: mark = "arc" with theta (angular extent fixed per slice) + radius (value) * CJS: type = 'polarArea' — each wedge spans an equal angle, radius encodes value. * * Data model (long format): * x (nominal): angular category (direction, month, etc.) * y (quantitative): value mapped to wedge radius * color (nominal, optional): stack / group variable * * Note: Chart.js polarArea doesn't support native stacking. When a color * field is present we aggregate per category (sum across groups) and show * the total, with the legend listing the color groups for reference. */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, groupBy, DEFAULT_COLORS, DEFAULT_BG_COLORS } from './utils'; export const cjsRoseChartDef: ChartTemplateDef = { chart: 'Rose Chart', template: { mark: 'arc', encoding: {} }, channels: ['x', 'y', 'color', 'column', 'row'], markCognitiveChannel: 'area', instantiate: (spec, ctx) => { const { channelSemantics, table } = ctx; const catField = channelSemantics.x?.field; // angular categories const valField = channelSemantics.y?.field; // wedge radius value const colorField = channelSemantics.color?.field; // optional grouping if (!catField || !valField) return; // Extract unique angular categories const categories = extractCategories(table, catField, channelSemantics.x?.ordinalSortOrder); if (categories.length === 0) return; let labels: string[]; let values: number[]; let bgColors: string[]; let borderColors: string[]; if (colorField) { // Stacked: aggregate per (category × group), then sum across groups per category const groups = groupBy(table, colorField); const groupNames = [...groups.keys()]; // Build per-category totals const catTotals = new Map<string, number>(); for (const cat of categories) catTotals.set(cat, 0); for (const [, rows] of groups) { for (const row of rows) { const cat = String(row[catField] ?? ''); const val = Number(row[valField]) || 0; if (catTotals.has(cat)) { catTotals.set(cat, catTotals.get(cat)! + val); } } } labels = categories; values = categories.map(c => catTotals.get(c) ?? 0); // Assign distinct colors per wedge (category) bgColors = DEFAULT_BG_COLORS.slice(0, categories.length); borderColors = DEFAULT_COLORS.slice(0, categories.length); } else { // Simple: one value per category const catAgg = new Map<string, number>(); for (const row of table) { const cat = String(row[catField] ?? ''); const val = Number(row[valField]) || 0; catAgg.set(cat, (catAgg.get(cat) ?? 0) + val); } labels = categories; values = categories.map(c => catAgg.get(c) ?? 0); bgColors = DEFAULT_BG_COLORS.slice(0, categories.length); borderColors = DEFAULT_COLORS.slice(0, categories.length); } // Alignment: 'center' puts wedge center at 12 o'clock, // 'left' puts wedge left edge at 12 o'clock. const alignment = ctx.chartProperties?.alignment ?? 'left'; const n = categories.length; // Chart.js polarArea: startAngle 0 = 12 o'clock, CW. // Default (startAngle=0) is left alignment. // For center: offset by -halfSlice degrees. const startAngle = alignment === 'center' && n > 0 ? -(180 / n) : 0; const config: any = { type: 'polarArea', data: { labels, datasets: [{ data: values, backgroundColor: bgColors, borderColor: borderColors, borderWidth: 1, }], }, options: { responsive: true, maintainAspectRatio: false, startAngle, scales: { r: { beginAtZero: true, ticks: { display: true }, }, }, plugins: { legend: { display: true, position: 'right' as const }, tooltip: { enabled: true }, }, }, _width: Math.max(ctx.canvasSize.width, 350), _height: Math.max(ctx.canvasSize.height, 300), }; Object.assign(spec, config); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'alignment', label: 'Alignment', type: 'discrete', options: [ { value: 'left', label: 'Left (default)' }, { value: 'center', label: 'Center' }, ], } as ChartPropertyDef, ], }; ================================================ FILE: src/lib/agents-chart/chartjs/templates/scatter.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Chart.js Scatter Plot template. * * Maps scatter semantics to Chart.js config: * VL: encoding.x.field + encoding.y.field → positional channels * CJS: datasets[].data = [{x, y}, ...] with type: 'scatter' */ import { ChartTemplateDef } from '../../core/types'; import { DEFAULT_COLORS, DEFAULT_BG_COLORS } from './utils'; /** Compute a reasonable point radius based on canvas area and point count. */ function computePointRadius(width: number, height: number, pointCount: number): number { const canvasArea = width * height; const areaPerPoint = canvasArea / Math.max(1, pointCount); const idealRadius = Math.sqrt(areaPerPoint * 0.05) / 2; return Math.max(2, Math.min(6, Math.round(idealRadius))); } export const cjsScatterPlotDef: ChartTemplateDef = { chart: 'Scatter Plot', template: { mark: 'circle', encoding: {} }, channels: ['x', 'y', 'color', 'size', 'opacity', 'column', 'row'], markCognitiveChannel: 'position', instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const xField = channelSemantics.x?.field; const yField = channelSemantics.y?.field; const colorField = channelSemantics.color?.field; if (!xField || !yField) return; const opacity = chartProperties?.opacity ?? 1; const config: any = { type: 'scatter', data: { datasets: [] }, options: { responsive: true, maintainAspectRatio: false, scales: { x: { type: 'linear', title: { display: true, text: xField }, }, y: { type: 'linear', title: { display: true, text: yField }, }, }, plugins: { tooltip: { enabled: true }, }, }, }; // Apply zero-baseline decisions // Chart.js linear scales default beginAtZero to false, so we must // explicitly set true when the semantic decision includes zero. if (channelSemantics.x?.zero) { config.options.scales.x.beginAtZero = channelSemantics.x.zero.zero !== false; } if (channelSemantics.y?.zero) { config.options.scales.y.beginAtZero = channelSemantics.y.zero.zero !== false; } if (colorField) { // Multi-series: group by color field const groups = new Map<string, { x: number; y: number }[]>(); for (const row of table) { const key = String(row[colorField] ?? ''); if (!groups.has(key)) groups.set(key, []); groups.get(key)!.push({ x: row[xField], y: row[yField] }); } let colorIdx = 0; for (const [name, data] of groups) { config.data.datasets.push({ label: name, data, backgroundColor: DEFAULT_BG_COLORS[colorIdx % DEFAULT_BG_COLORS.length], borderColor: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length], borderWidth: 1, pointRadius: 4, }); colorIdx++; } config.options.plugins.legend = { display: true }; } else { const data = table.map(row => ({ x: row[xField], y: row[yField] })); config.data.datasets.push({ data, backgroundColor: `rgba(54, 162, 235, ${opacity})`, borderColor: 'rgba(54, 162, 235, 1)', borderWidth: 1, pointRadius: 4, }); config.options.plugins.legend = { display: false }; } // Write Chart.js config into spec Object.assign(spec, config); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'opacity', label: 'Opacity', type: 'continuous', min: 0.1, max: 1, step: 0.05, defaultValue: 1 }, ], postProcess: (option, ctx) => { if (!option.data?.datasets) return; const w = option._width || ctx.canvasSize.width; const h = option._height || ctx.canvasSize.height; const pointCount = ctx.table.length; const radius = computePointRadius(w, h, pointCount); for (const ds of option.data.datasets) { if (ds.pointRadius == null || ds.pointRadius === 4) { ds.pointRadius = radius; } } }, }; ================================================ FILE: src/lib/agents-chart/chartjs/templates/utils.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Shared helper functions for Chart.js template hooks. * Pure logic — no UI dependencies. */ import type { ChannelSemantics } from '../../core/types'; // --------------------------------------------------------------------------- // Discrete-dimension helpers // --------------------------------------------------------------------------- const isDiscrete = (type: string | undefined) => type === 'nominal' || type === 'ordinal'; /** * Get the number of unique non-null values for a field in the data table. */ export function getFieldCardinality(field: string, table: any[]): number { return new Set(table.map((r: any) => r[field]).filter((v: any) => v != null)).size; } // --------------------------------------------------------------------------- // Chart.js-specific helpers // --------------------------------------------------------------------------- /** * Extract unique category values from data for a given field, preserving order. * If `ordinalSortOrder` is provided, returns values sorted in that canonical order. */ export function extractCategories(data: any[], field: string, ordinalSortOrder?: string[]): string[] { const seen = new Set<string>(); const result: string[] = []; for (const row of data) { const val = row[field]; if (val != null) { const key = String(val); if (!seen.has(key)) { seen.add(key); result.push(key); } } } if (ordinalSortOrder && ordinalSortOrder.length > 0) { const orderMap = new Map(ordinalSortOrder.map((v, i) => [v, i])); result.sort((a, b) => { const ia = orderMap.get(a); const ib = orderMap.get(b); if (ia !== undefined && ib !== undefined) return ia - ib; if (ia !== undefined) return -1; if (ib !== undefined) return 1; return 0; }); } return result; } /** * Group data by a categorical field. * Returns a map: seriesName → rows[]. */ export function groupBy(data: any[], field: string): Map<string, any[]> { const groups = new Map<string, any[]>(); for (const row of data) { const key = String(row[field] ?? ''); if (!groups.has(key)) groups.set(key, []); groups.get(key)!.push(row); } return groups; } /** * Default Chart.js color palette (RGBA with alpha for fill). */ export const DEFAULT_COLORS = [ 'rgba(54, 162, 235, 1)', // blue 'rgba(255, 99, 132, 1)', // red 'rgba(255, 206, 86, 1)', // yellow 'rgba(75, 192, 192, 1)', // teal 'rgba(153, 102, 255, 1)', // purple 'rgba(255, 159, 64, 1)', // orange 'rgba(46, 204, 113, 1)', // green 'rgba(52, 73, 94, 1)', // dark blue-grey 'rgba(231, 76, 60, 1)', // red-orange 'rgba(149, 165, 166, 1)', // grey ]; export const DEFAULT_BG_COLORS = [ 'rgba(54, 162, 235, 0.6)', 'rgba(255, 99, 132, 0.6)', 'rgba(255, 206, 86, 0.6)', 'rgba(75, 192, 192, 0.6)', 'rgba(153, 102, 255, 0.6)', 'rgba(255, 159, 64, 0.6)', 'rgba(46, 204, 113, 0.6)', 'rgba(52, 73, 94, 0.6)', 'rgba(231, 76, 60, 0.6)', 'rgba(149, 165, 166, 0.6)', ]; /** * Detect which axis is the category (banded) axis and which is the value axis. */ export function detectAxes( channelSemantics: Record<string, ChannelSemantics>, ): { categoryAxis: 'x' | 'y'; valueAxis: 'x' | 'y' } { const xCS = channelSemantics.x; const yCS = channelSemantics.y; if (xCS && isDiscrete(xCS.type)) { return { categoryAxis: 'x', valueAxis: 'y' }; } if (yCS && isDiscrete(yCS.type)) { return { categoryAxis: 'y', valueAxis: 'x' }; } return { categoryAxis: 'x', valueAxis: 'y' }; } /** * Build category-aligned data array for a subset of rows. * Returns values indexed by category position (null for missing). */ export function buildCategoryAlignedData( rows: any[], xField: string, yField: string, categories: string[], ): (number | null)[] { const map = new Map<string, number>(); for (const row of rows) { const key = String(row[xField] ?? ''); const val = row[yField]; if (val != null && !isNaN(val)) { map.set(key, (map.get(key) ?? 0) + Number(val)); } } return categories.map(cat => map.get(cat) ?? null); } ================================================ FILE: src/lib/agents-chart/core/compute-layout.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ============================================================================= * PHASE 1: COMPUTE LAYOUT * ============================================================================= * * Determine how big things should be — axis lengths, step sizes, * subplot dimensions, label sizing, and overflow truncation — from data * density, axis classification, and template-provided tuning knobs. * * VL dependency: **None** * * This module reads abstract axis descriptors (AxisLayoutInput) and * produces abstract layout numbers (LayoutResult). The same layout * engine works regardless of output format. * * ── Backend Responsibility ────────────────────────────────────────── * The LayoutResult is a target-agnostic description of "how big things * should be". Each rendering backend (Vega-Lite, ECharts, etc.) MUST: * * 1. Call computeLayout() once per chart (facet-aware — it already * divides subplot sizes for the facet grid). * * 2. Translate the LayoutResult into its own rendering format: * - subplotWidth / subplotHeight → plot area size (before margins) * - xStep / yStep → bar widths, band sizes, category spacing * - stepPadding → inter-category gap (barCategoryGap, paddingInner) * - label sizing → font size, rotation, truncation * * 3. Add its own margins, padding, and chrome (axis labels, titles, * legends, CANVAS_BUFFER) around the subplot area. * * 4. Handle facet-specific concerns itself: * - Column wrapping (when user specifies column-only, the backend * decides how many columns per visual row and restructures the * panel grid accordingly). * - Per-panel vs shared axis titles. * - Panel positioning and header labels. * * The layout engine does NOT know about VL encodings, ECharts grid * objects, or any rendering-specific structure. * ============================================================================= */ import type { ChannelSemantics, LayoutDeclaration, LayoutResult, AssembleOptions, ChannelBudgets, } from './types'; import { computeElasticBudget, computeAxisStep, computeGasPressure, computeLabelSizing, DEFAULT_GAS_PRESSURE_PARAMS, type ElasticStretchParams, type GasPressureParams, type GasPressureDecision, } from './decisions'; // --------------------------------------------------------------------------- // Internal types // --------------------------------------------------------------------------- interface AxisLayoutInput { /** Spring model (banded) or gas pressure (non-banded) */ mode: 'banded' | 'non-banded'; /** Number of discrete positions (for banded) */ itemCount: number; /** Number of sub-items per group (for grouped bars) */ subItemsPerGroup?: number; /** Numeric values along this axis (for gas pressure) */ values?: number[]; /** Data extent [min, max] */ domain?: [number, number]; /** Number of distinct series (for series-based pressure) */ seriesCount?: number; } // --------------------------------------------------------------------------- // Public API: computeLayout // --------------------------------------------------------------------------- /** * Phase 1: Compute layout decisions. * * Takes channel semantics, template layout declaration, data, canvas size, * and assembly options to produce a LayoutResult with step sizes, subplot * dimensions, label sizing, and truncation warnings. * * VL dependency: **None** * * @param channelSemantics Phase 0 output * @param declaration Template's layout declaration (axisFlags, resolvedTypes, * grouping, binnedAxes) * @param table Data rows (post-overflow filtered) * @param canvasSize Target canvas dimensions * @param options Assembly options (merged with template overrides) * @param facetGrid Optional pre-decided facet grid from computeFacetGrid. * When provided, computeLayout uses these column/row * counts instead of counting from data — this * eliminates the circularity between wrapping and * banded axis sizing. */ export function computeLayout( channelSemantics: Record<string, ChannelSemantics>, declaration: LayoutDeclaration, table: any[], canvasSize: { width: number; height: number }, options: AssembleOptions = {}, facetGrid?: { columns: number; rows: number }, ): LayoutResult { const { elasticity: elasticityVal = 0.5, maxStretch: maxStretchVal = 2, facetElasticity: facetElasticityVal = 0.3, minStep: minStepVal = 6, minSubplotSize: minSubplotVal = 60, defaultStepMultiplier = 1, stepPadding: stepPaddingVal = 0.1, maintainContinuousAxisRatio = false, continuousMarkCrossSection, } = options; const defaultChartWidth = canvasSize.width; const defaultChartHeight = canvasSize.height; // Facet overhead: fixed (axis labels, titles) + per-panel gap (spacing). const fixW = options.facetFixedPadding?.width ?? 0; const fixH = options.facetFixedPadding?.height ?? 0; const gap = options.facetGap ?? 0; const baseRefSize = 300; const sizeRatio = Math.max(defaultChartWidth, defaultChartHeight) / baseRefSize; const defaultStepSize = Math.round(20 * Math.max(1, sizeRatio) * defaultStepMultiplier); const isDiscreteType = (t: string | undefined) => t === 'nominal' || t === 'ordinal'; // Apply resolved types from template declaration const effectiveTypes: Record<string, string> = {}; for (const [ch, cs] of Object.entries(channelSemantics)) { effectiveTypes[ch] = declaration.resolvedTypes?.[ch] || cs.type; } // --- Classify axes and count items --- const axisFlags = declaration.axisFlags || {}; const xBanded = axisFlags.x?.banded ?? false; const yBanded = axisFlags.y?.banded ?? false; const nominalCount: Record<string, number> = { x: 0, y: 0, column: 0, row: 0, group: 0, }; // Count discrete values per channel for (const channel of ['x', 'y', 'column', 'row', 'color'] as const) { const cs = channelSemantics[channel]; if (!cs?.field) continue; const effectiveType = effectiveTypes[channel] || cs.type; if (!isDiscreteType(effectiveType)) continue; const uniqueValues = [...new Set(table.map((r: any) => r[cs.field]))]; nominalCount[channel] = uniqueValues.length; } // Detect grouping from 'group' channel + discrete axis const groupField = channelSemantics.group?.field; let groupAxis: 'x' | 'y' | undefined; if (groupField) { nominalCount.group = new Set(table.map((r: any) => r[groupField])).size; if (isDiscreteType(effectiveTypes.x ?? channelSemantics.x?.type)) groupAxis = 'x'; else if (isDiscreteType(effectiveTypes.y ?? channelSemantics.y?.type)) groupAxis = 'y'; } // Total discrete items per axis (grouping multiplies the grouped axis) const xGroupMultiplier = (groupAxis === 'x' && nominalCount.group > 1) ? nominalCount.group : 1; const yGroupMultiplier = (groupAxis === 'y' && nominalCount.group > 1) ? nominalCount.group : 1; let xTotalNominalCount = nominalCount.x * xGroupMultiplier; let yTotalNominalCount = nominalCount.y * yGroupMultiplier; // --- Step size hints --- // Minimum group step: the inter-group gap (stepPadding × step) must be // at least MIN_GROUP_GAP_PX pixels so groups are visually separated. const MIN_GROUP_GAP_PX = 3; const xMinGroupStep = xGroupMultiplier > 1 ? Math.max(Math.ceil(MIN_GROUP_GAP_PX / stepPaddingVal), 2 * xGroupMultiplier) : minStepVal; const yMinGroupStep = yGroupMultiplier > 1 ? Math.max(Math.ceil(MIN_GROUP_GAP_PX / stepPaddingVal), 2 * yGroupMultiplier) : minStepVal; // (Overflow filtering is now handled by filterOverflow() before // computeLayout is called. The data passed here is already filtered.) // --- Count banded continuous axes --- let xContinuousAsDiscrete = 0; let yContinuousAsDiscrete = 0; for (const axis of ['x', 'y'] as const) { const cs = channelSemantics[axis]; if (!cs?.field) continue; const effectiveType = effectiveTypes[axis] || cs.type; if (isDiscreteType(effectiveType)) continue; const isBanded = (axis === 'x' ? xBanded : yBanded); // Check for binned from declaration const isBinned = declaration.binnedAxes?.[axis]; if (!isBanded && !isBinned) continue; let count: number; if (isBinned) { const binDef = declaration.binnedAxes![axis]; // Default to 10 bins (Vega-Lite's default maxbins) count = typeof binDef === 'object' && binDef.maxbins ? binDef.maxbins : 10; } else { count = new Set(table.map((r: any) => r[cs.field])).size; } if (count <= 1) continue; if (axis === 'x') { xContinuousAsDiscrete = count; } else { yContinuousAsDiscrete = count; } } // --- Facet layout --- // Use pre-decided grid from filterOverflow when available. // This avoids the circularity where wrapping depends on subplot // width which depends on facet count which depends on wrapping. let facetCols = 1; let facetRows = 1; if (facetGrid) { facetCols = facetGrid.columns; facetRows = facetGrid.rows; } else { if (nominalCount.column > 0) facetCols = nominalCount.column; if (nominalCount.row > 0) facetRows = nominalCount.row; } // --- Facet subplot sizing --- // Log-scale axes need more room so the minor grid lines (1,2,3…9 per // decade) remain legible and act as the visual cue that it's log scale. // Compute the number of orders of magnitude each axis spans; each // decade needs ~40px minimum to avoid a dense wall of grid lines. const LOG_PX_PER_DECADE = 40; let logBoostX = 0; let logBoostY = 0; for (const axis of ['x', 'y'] as const) { const cs = channelSemantics[axis]; if (!cs?.field || !cs.scaleType) continue; if (cs.scaleType !== 'log' && cs.scaleType !== 'symlog') continue; const vals = table .map((r: any) => r[cs.field]) .filter((v: any) => typeof v === 'number' && v > 0 && isFinite(v)); if (vals.length < 2) continue; const decades = Math.log10(Math.max(...vals)) - Math.log10(Math.min(...vals)); const needed = Math.ceil(Math.max(1, decades)) * LOG_PX_PER_DECADE; if (axis === 'x') logBoostX = needed; else logBoostY = needed; } const minContinuousSize = Math.max(10, minStepVal); const minContinuousSizeX = Math.max(minContinuousSize, logBoostX); const minContinuousSizeY = Math.max(minContinuousSize, logBoostY); let subplotWidth: number; if (facetCols > 1) { const stretch = Math.min(maxStretchVal, Math.pow(facetCols, facetElasticityVal)); subplotWidth = Math.round(Math.max(minContinuousSizeX, (defaultChartWidth * stretch - fixW) / facetCols - gap)); } else { subplotWidth = defaultChartWidth; } let subplotHeight: number; if (facetRows > 1) { const stretch = Math.min(maxStretchVal, Math.pow(facetRows, facetElasticityVal)); subplotHeight = Math.round(Math.max(minContinuousSizeY, (defaultChartHeight * stretch - fixH) / facetRows - gap)); } else { subplotHeight = defaultChartHeight; } // --- Gas pressure stretch for continuous non-banded axes --- const xIsContinuousNonBanded = xTotalNominalCount === 0 && xContinuousAsDiscrete === 0; const yIsContinuousNonBanded = yTotalNominalCount === 0 && yContinuousAsDiscrete === 0; if (xIsContinuousNonBanded && yIsContinuousNonBanded) { const xCS = channelSemantics.x; const yCS = channelSemantics.y; if (xCS?.field && yCS?.field) { const isTempX = (effectiveTypes.x || xCS.type) === 'temporal'; const isTempY = (effectiveTypes.y || yCS.type) === 'temporal'; const xNumeric: number[] = []; const yNumeric: number[] = []; for (const row of table) { let xv = row[xCS.field]; let yv = row[yCS.field]; if (xv == null || yv == null) continue; if (isTempX) xv = +new Date(xv); else xv = +xv; if (isTempY) yv = +new Date(yv); else yv = +yv; if (isNaN(xv) || isNaN(yv)) continue; xNumeric.push(xv); yNumeric.push(yv); } if (xNumeric.length > 1) { const xMin = Math.min(...xNumeric); const xMax = Math.max(...xNumeric); const yMin = Math.min(...yNumeric); const yMax = Math.max(...yNumeric); const xDomain: [number, number] = [xMin, xMax]; const yDomain: [number, number] = [yMin, yMax]; let gasPressureParams: GasPressureParams = DEFAULT_GAS_PRESSURE_PARAMS; if (continuousMarkCrossSection != null) { if (typeof continuousMarkCrossSection === 'number') { gasPressureParams = { ...DEFAULT_GAS_PRESSURE_PARAMS, markCrossSection: continuousMarkCrossSection }; } else { const maxCS = Math.max(continuousMarkCrossSection.x, continuousMarkCrossSection.y); gasPressureParams = { ...DEFAULT_GAS_PRESSURE_PARAMS, markCrossSection: maxCS, markCrossSectionX: continuousMarkCrossSection.x, markCrossSectionY: continuousMarkCrossSection.y, ...(continuousMarkCrossSection.elasticity != null && { elasticity: continuousMarkCrossSection.elasticity }), ...(continuousMarkCrossSection.maxStretch != null && { maxStretch: continuousMarkCrossSection.maxStretch }), }; // Series-count-based pressure if (continuousMarkCrossSection.seriesCountAxis) { const resolvedAxis = continuousMarkCrossSection.seriesCountAxis === 'auto' ? 'y' : continuousMarkCrossSection.seriesCountAxis; const nSeries = countDistinctSeries(channelSemantics, table); if (resolvedAxis === 'y') { gasPressureParams.yItemCountOverride = nSeries; } else { gasPressureParams.xItemCountOverride = nSeries; } } } } const gasPressureResult = computeGasPressure( xNumeric, yNumeric, xDomain, yDomain, subplotWidth, subplotHeight, gasPressureParams, ); if (gasPressureResult.stretchX > 1 || gasPressureResult.stretchY > 1) { let sx = gasPressureResult.stretchX; let sy = gasPressureResult.stretchY; if (maintainContinuousAxisRatio) { const maxStretch = Math.max(sx, sy); sx = maxStretch; sy = maxStretch; } if (typeof continuousMarkCrossSection === 'object' && continuousMarkCrossSection.seriesCountAxis) { const sAxis = continuousMarkCrossSection.seriesCountAxis === 'auto' ? 'y' : continuousMarkCrossSection.seriesCountAxis; if (sAxis === 'y' && sy > 1 && sx < sy) sx = sy; if (sAxis === 'x' && sx > 1 && sy < sx) sy = sx; } subplotWidth = Math.round(subplotWidth * sx); subplotHeight = Math.round(subplotHeight * sy); } } } } else if (xIsContinuousNonBanded || yIsContinuousNonBanded) { const contAxis = xIsContinuousNonBanded ? 'x' : 'y'; const otherAxisHasDiscreteItems = contAxis === 'x' ? (yTotalNominalCount > 0 || yContinuousAsDiscrete > 0) : (xTotalNominalCount > 0 || xContinuousAsDiscrete > 0); let seriesStretchApplied = false; if (typeof continuousMarkCrossSection === 'object' && continuousMarkCrossSection.seriesCountAxis) { const resolvedAxis = continuousMarkCrossSection.seriesCountAxis === 'auto' ? contAxis : continuousMarkCrossSection.seriesCountAxis; if (resolvedAxis === contAxis) { const sigmaPerSeries = contAxis === 'x' ? continuousMarkCrossSection.x : continuousMarkCrossSection.y; const baseDim = contAxis === 'x' ? subplotWidth : subplotHeight; const nSeries = countDistinctSeries(channelSemantics, table); const pressure = (nSeries * sigmaPerSeries) / baseDim; const elast = continuousMarkCrossSection.elasticity ?? DEFAULT_GAS_PRESSURE_PARAMS.elasticity; const maxS = continuousMarkCrossSection.maxStretch ?? DEFAULT_GAS_PRESSURE_PARAMS.maxStretch; if (pressure > 1) { const stretch = Math.min(maxS, Math.pow(pressure, elast)); if (contAxis === 'x') { subplotWidth = Math.round(subplotWidth * stretch); } else { subplotHeight = Math.round(subplotHeight * stretch); } } seriesStretchApplied = true; } } if (!seriesStretchApplied && !otherAxisHasDiscreteItems) { const contCS = channelSemantics[contAxis]; if (contCS?.field) { const isTemporal = (effectiveTypes[contAxis] || contCS.type) === 'temporal'; const contValues: number[] = []; for (const row of table) { let v = row[contCS.field]; if (v == null) continue; if (isTemporal) v = +new Date(v); else v = +v; if (!isNaN(v)) contValues.push(v); } const sigma1d = Math.sqrt(DEFAULT_GAS_PRESSURE_PARAMS.markCrossSection); const baseDim = contAxis === 'x' ? subplotWidth : subplotHeight; const pressure1d = (contValues.length * sigma1d) / baseDim; if (pressure1d > 1) { const stretch1d = Math.min( DEFAULT_GAS_PRESSURE_PARAMS.maxStretch, Math.pow(pressure1d, DEFAULT_GAS_PRESSURE_PARAMS.elasticity), ); if (contAxis === 'x') { subplotWidth = Math.round(subplotWidth * stretch1d); } else { subplotHeight = Math.round(subplotHeight * stretch1d); } } } } } // --- Elastic stretch for discrete axes --- const elasticParams: ElasticStretchParams = { elasticity: elasticityVal, maxStretch: maxStretchVal, defaultStepSize, minStep: minStepVal, }; const xAxis = computeAxisStep(xTotalNominalCount, xContinuousAsDiscrete, subplotWidth, elasticParams); const yAxis = computeAxisStep(yTotalNominalCount, yContinuousAsDiscrete, subplotHeight, elasticParams); const xIsDiscrete = xTotalNominalCount > 0; const yIsDiscrete = yTotalNominalCount > 0; const xHasGrouping = groupAxis === 'x' && nominalCount.group > 0; const yHasGrouping = groupAxis === 'y' && nominalCount.group > 0; let xStepSize: number; let yStepSize: number; let xStepUnit: 'item' | 'group' | undefined; let yStepUnit: 'item' | 'group' | undefined; if (xIsDiscrete && xHasGrouping) { const itemsPerGroup = nominalCount.group; const defaultGroupStep = itemsPerGroup * defaultStepSize; const minGroupStep = Math.max(Math.ceil(MIN_GROUP_GAP_PX / stepPaddingVal), 2 * itemsPerGroup); const groupAxis = computeAxisStep(nominalCount.x, 0, subplotWidth, elasticParams); const groupStep = Math.max(minGroupStep, Math.min(defaultGroupStep, groupAxis.step)); xStepSize = groupStep; xStepUnit = 'group'; } else if (xIsDiscrete) { xStepSize = Math.max(minStepVal, Math.min(defaultStepSize, xAxis.step)); } else if (xContinuousAsDiscrete > 0) { xStepSize = Math.max(minStepVal, Math.min(defaultStepSize, xAxis.step)); } else { xStepSize = defaultStepSize; } if (yIsDiscrete && yHasGrouping) { const itemsPerGroup = nominalCount.group; const defaultGroupStep = itemsPerGroup * defaultStepSize; const minGroupStep = Math.max(Math.ceil(MIN_GROUP_GAP_PX / stepPaddingVal), 2 * itemsPerGroup); const groupAxis = computeAxisStep(nominalCount.y, 0, subplotHeight, elasticParams); const groupStep = Math.max(minGroupStep, Math.min(defaultGroupStep, groupAxis.step)); yStepSize = groupStep; yStepUnit = 'group'; } else if (yIsDiscrete) { yStepSize = Math.max(minStepVal, Math.min(defaultStepSize, yAxis.step)); } else if (yContinuousAsDiscrete > 0) { yStepSize = Math.max(minStepVal, Math.min(defaultStepSize, yAxis.step)); } else { yStepSize = defaultStepSize; } // --- Banded continuous canvas size --- for (const axis of ['x', 'y'] as const) { const count = axis === 'x' ? xContinuousAsDiscrete : yContinuousAsDiscrete; if (count <= 0) continue; const stepSize = axis === 'x' ? xStepSize : yStepSize; const continuousSize = Math.round(stepSize * (count + 1)); if (axis === 'x') { subplotWidth = continuousSize; } else { subplotHeight = continuousSize; } } // --- Unified stretch budget ------------------------------------------------ // Cap the per-subplot dimensions so total canvas never exceeds // canvasWidth × maxStretch (and canvasHeight × maxStretch). // Formula: effectiveW = W × maxStretch − fixedPad; each panel costs subplot + gap. const maxSubplotW = (defaultChartWidth * maxStretchVal - fixW) / facetCols - gap; const maxSubplotH = (defaultChartHeight * maxStretchVal - fixH) / facetRows - gap; // Clamp step sizes for discrete/banded axes so VL step-based // sizing respects the same budget. // When step unit is 'group', divide by the number of groups (nominalCount) // rather than the total item count (groups × items-per-group). if (xTotalNominalCount > 0) { const divisor = xStepUnit === 'group' ? nominalCount.x : xTotalNominalCount; const cap = Math.max(minStepVal, Math.floor(maxSubplotW / divisor)); if (xStepSize > cap) xStepSize = cap; } if (xContinuousAsDiscrete > 0) { const cap = Math.max(minStepVal, Math.floor(maxSubplotW / (xContinuousAsDiscrete + 1))); if (xStepSize > cap) xStepSize = cap; } if (yTotalNominalCount > 0) { const divisor = yStepUnit === 'group' ? nominalCount.y : yTotalNominalCount; const cap = Math.max(minStepVal, Math.floor(maxSubplotH / divisor)); if (yStepSize > cap) yStepSize = cap; } if (yContinuousAsDiscrete > 0) { const cap = Math.max(minStepVal, Math.floor(maxSubplotH / (yContinuousAsDiscrete + 1))); if (yStepSize > cap) yStepSize = cap; } // Recompute banded subplot size after step clamping. for (const axis of ['x', 'y'] as const) { const count = axis === 'x' ? xContinuousAsDiscrete : yContinuousAsDiscrete; if (count <= 0) continue; const stepSize = axis === 'x' ? xStepSize : yStepSize; if (axis === 'x') subplotWidth = Math.round(stepSize * (count + 1)); else subplotHeight = Math.round(stepSize * (count + 1)); } // Clamp continuous subplot dimensions. subplotWidth = Math.min(subplotWidth, Math.round(maxSubplotW)); subplotHeight = Math.min(subplotHeight, Math.round(maxSubplotH)); // --- Label sizing --- const xHasDiscreteItems = xTotalNominalCount > 0; const yHasDiscreteItems = yTotalNominalCount > 0; const xLabel = computeLabelSizing(xStepSize, xHasDiscreteItems); const yLabel = computeLabelSizing(yStepSize, yHasDiscreteItems); return { subplotWidth, subplotHeight, xStep: xStepSize, yStep: yStepSize, xStepUnit, yStepUnit, xContinuousAsDiscrete, yContinuousAsDiscrete, xNominalCount: xTotalNominalCount, yNominalCount: yTotalNominalCount, xLabel, yLabel, stepPadding: stepPaddingVal, facet: (facetCols > 1 || facetRows > 1) ? { columns: facetCols, rows: facetRows, subplotWidth, subplotHeight, } : undefined, effectiveFacetGap: gap, truncations: [], // Overflow truncations are handled by filterOverflow }; } // --------------------------------------------------------------------------- // Helpers // --------------------------------------------------------------------------- /** * Count distinct series (color/detail categories) from channel semantics. */ function countDistinctSeries( channelSemantics: Record<string, ChannelSemantics>, data: any[], ): number { const seriesFields: string[] = []; const colorField = channelSemantics.color?.field; const detailField = channelSemantics.detail?.field; if (colorField) seriesFields.push(colorField); if (detailField && detailField !== colorField) seriesFields.push(detailField); if (seriesFields.length === 0) return 1; const seriesKeys = new Set<string>(); for (const row of data) { const key = seriesFields.map(f => String(row[f] ?? '')).join('\x00'); seriesKeys.add(key); } return seriesKeys.size; } // --------------------------------------------------------------------------- // Public: computeChannelBudgets // --------------------------------------------------------------------------- /** * Compute per-channel maximum values that can fit on the canvas. * * Uses the **most conservative** assumptions: * - minStep (smallest px per discrete item) * - minSubplotSize (smallest subplot for continuous axes) * - maxStretch (maximum canvas stretching) * * This is Step 0c-a in the pipeline — it runs before filterOverflow * and produces the budgets that filterOverflow consumes. * * Pipeline: computeChannelBudgets → filterOverflow → computeLayout * * @param channelSemantics Phase 0 output (field, type per channel) * @param declaration Template layout declaration * @param data Full data table (pre-overflow) * @param canvasSize Target canvas dimensions * @param options Assembly options * @returns ChannelBudgets with per-channel max-to-keep */ export function computeChannelBudgets( channelSemantics: Record<string, ChannelSemantics>, declaration: LayoutDeclaration, data: any[], canvasSize: { width: number; height: number }, options: AssembleOptions, ): ChannelBudgets { const { maxStretch: maxStretchVal = 2, minStep: minStepVal = 6, stepPadding: stepPaddingVal = 0.1, maxColorValues: maxColorVal = 24, } = options; const fixW = options.facetFixedPadding?.width ?? 0; const fixH = options.facetFixedPadding?.height ?? 0; const gap = options.facetGap ?? 0; const isDiscreteType = (t: string | undefined) => t === 'nominal' || t === 'ordinal'; const effectiveType = (ch: string): string | undefined => declaration.resolvedTypes?.[ch] ?? channelSemantics[ch]?.type; // --- 1. Facet grid (delegates to computeFacetGrid) --- const facetGrid = computeFacetGrid( channelSemantics, declaration, data, canvasSize, options, ); const facetCols = facetGrid?.columns ?? 1; const facetRows = facetGrid?.rows ?? 1; // --- 2. Per-subplot budget at maximum stretch --- const maxSubplotW = Math.max( options.minSubplotSize ?? 60, (canvasSize.width * maxStretchVal - fixW) / facetCols - gap, ); const maxSubplotH = Math.max( options.minSubplotSize ?? 60, (canvasSize.height * maxStretchVal - fixH) / facetRows - gap, ); // --- 3. Grouping detection --- const groupField = channelSemantics.group?.field; let groupCount = 0; let groupAxis: 'x' | 'y' | undefined; if (groupField) { groupCount = new Set(data.map(r => r[groupField])).size; if (isDiscreteType(effectiveType('x'))) groupAxis = 'x'; else if (isDiscreteType(effectiveType('y'))) groupAxis = 'y'; } const xGroupMultiplier = (groupAxis === 'x' && groupCount > 1) ? groupCount : 1; const yGroupMultiplier = (groupAxis === 'y' && groupCount > 1) ? groupCount : 1; const MIN_GROUP_GAP_PX = 3; const xMinGroupStep = xGroupMultiplier > 1 ? Math.max(Math.ceil(MIN_GROUP_GAP_PX / stepPaddingVal), 2 * xGroupMultiplier) : minStepVal; const yMinGroupStep = yGroupMultiplier > 1 ? Math.max(Math.ceil(MIN_GROUP_GAP_PX / stepPaddingVal), 2 * yGroupMultiplier) : minStepVal; // --- 4. Per-channel budgets --- let maxXToKeep = Math.floor(maxSubplotW / xMinGroupStep); let maxYToKeep = Math.floor(maxSubplotH / yMinGroupStep); // --- 5. Faceted-chart canvas cap --- // When a busy discrete axis makes each subplot wider than the // un-stretched canvas, cap axis items to fit within one canvas // width/height. This lets subplots be narrower, potentially fitting // more facet columns — reducing overall chart height. // // Example: 70 counties on X × 20 states on column. Without the cap, // minSubplotWidth = 70 × 6 = 420 → only 1 facet column fits → each // state stacks vertically → excessively tall chart. With the cap, // X is truncated to floor(400/6) = 66 items, and the facet grid is // re-derived with narrower subplots so more columns fit. if (facetGrid) { const canvasXCap = Math.max(1, Math.floor(canvasSize.width / xMinGroupStep)); const canvasYCap = Math.max(1, Math.floor(canvasSize.height / yMinGroupStep)); if (maxXToKeep > canvasXCap || maxYToKeep > canvasYCap) { maxXToKeep = Math.min(maxXToKeep, canvasXCap); maxYToKeep = Math.min(maxYToKeep, canvasYCap); // With tighter axis items, subplots can be narrower, so more // facet columns may fit. Re-derive the grid for column-only // wrapping (the most affected case). const colField = channelSemantics.column?.field; const rowField = channelSemantics.row?.field; const colCount = colField ? new Set(data.map(r => r[colField])).size : 0; if (colCount > 1 && !rowField) { const tighterW = Math.max( options.minSubplotSize ?? 60, maxXToKeep * xMinGroupStep, ); const totalW = canvasSize.width * maxStretchVal - fixW; const totalH = canvasSize.height * maxStretchVal - fixH; const revisedMaxCols = Math.max(1, Math.floor( totalW / (tighterW + gap), )); const revisedMaxRows = Math.max(1, Math.floor( totalH / ((options.minSubplotSize ?? 60) + gap), )); const maxTotal = revisedMaxCols * revisedMaxRows; const effectiveCount = Math.min(colCount, maxTotal); const visRows = Math.ceil(effectiveCount / revisedMaxCols); const visCols = Math.ceil(effectiveCount / visRows); facetGrid.columns = visCols; facetGrid.rows = visRows; facetGrid.maxColumnValues = maxTotal; } } } // maxColumnValues already carries the correct semantics for both // column+row (per-dimension cap) and column-only wrapping (total // panel count = grid cols × grid rows). No multiplication needed. const maxValues: Record<string, number> = { x: maxXToKeep, y: maxYToKeep, column: facetGrid?.maxColumnValues ?? Infinity, row: facetGrid?.maxRowValues ?? Infinity, color: maxColorVal, }; return { maxValues, facetGrid }; } // --------------------------------------------------------------------------- // Public: computeFacetGrid // --------------------------------------------------------------------------- /** * Decide the facet grid layout (including column-only wrapping). * * This runs BEFORE filterOverflow and computeLayout. It: * 1. Counts unique column/row values from data. * 2. Computes banded-aware minimum subplot dimensions. * 3. Computes max columns/rows that fit in the canvas budget. * 4. For column-only: wraps into a 2D grid (total panels = cols × rows). * 5. For column+row: caps each dimension independently. * * Returns `undefined` when there are no facet channels. * * @param channelSemantics Phase 0 output * @param declaration Template layout declaration * @param data Data rows (pre-overflow — possibly after temporal conversion) * @param canvasSize Target canvas dimensions * @param options Assembly options */ export function computeFacetGrid( channelSemantics: Record<string, ChannelSemantics>, declaration: LayoutDeclaration, data: any[], canvasSize: { width: number; height: number }, options: AssembleOptions, ): import('./types').FacetGridResult | undefined { const { maxStretch: ms = 2 } = options; const fixW = options.facetFixedPadding?.width ?? 0; const fixH = options.facetFixedPadding?.height ?? 0; const gap = options.facetGap ?? 0; const minStep = options.minStep ?? 6; const stepPadding = options.stepPadding ?? 0.1; const baseMinSubplot = options.minSubplotSize ?? 60; const isDiscreteType = (t: string | undefined) => t === 'nominal' || t === 'ordinal'; // --- Compute min subplot size per axis --- // // Continuous: baseMinSubplot (e.g. 60px). // // Discrete (not grouped): // min(minStep × valueCount, maxDim) // // Discrete (grouped): // perCategoryStep = max(minStep × groupCount, minGroupStep) // min(perCategoryStep × valueCount, maxDim) // // where minGroupStep accounts for the inter-group gap: // the gap = stepPadding × step, which must be ≥ MIN_GROUP_GAP_PX. // // Always capped at maxDim (full stretched canvas minus fixed overhead) // to guarantee at least 1 facet column/row. const maxW = canvasSize.width * ms - fixW; const maxH = canvasSize.height * ms - fixH; const MIN_GROUP_GAP_PX = 3; // Grouping detection const groupField = channelSemantics.group?.field; let groupCount = 0; let groupAxis: 'x' | 'y' | undefined; if (groupField) { groupCount = new Set(data.map((r: any) => r[groupField])).size; const xType = declaration.resolvedTypes?.x ?? channelSemantics.x?.type; const yType = declaration.resolvedTypes?.y ?? channelSemantics.y?.type; if (isDiscreteType(xType)) groupAxis = 'x'; else if (isDiscreteType(yType)) groupAxis = 'y'; } let minSubplotWidth = baseMinSubplot; let minSubplotHeight = baseMinSubplot; // Log-scale axes need more space for minor grid lines to be legible. const LOG_PX_PER_DECADE_FACET = 40; for (const axis of ['x', 'y'] as const) { const cs = channelSemantics[axis]; if (!cs?.field || !cs.scaleType) continue; if (cs.scaleType !== 'log' && cs.scaleType !== 'symlog') continue; const vals = data .map((r: any) => r[cs.field]) .filter((v: any) => typeof v === 'number' && v > 0 && isFinite(v)); if (vals.length < 2) continue; const decades = Math.log10(Math.max(...vals)) - Math.log10(Math.min(...vals)); const needed = Math.ceil(Math.max(1, decades)) * LOG_PX_PER_DECADE_FACET; if (axis === 'x') minSubplotWidth = Math.max(minSubplotWidth, needed); else minSubplotHeight = Math.max(minSubplotHeight, needed); } for (const axis of ['x', 'y'] as const) { const cs = channelSemantics[axis]; if (!cs?.field) continue; const effectiveType = declaration.resolvedTypes?.[axis] ?? cs.type; const isBanded = declaration.axisFlags?.[axis]?.banded === true; if (!isDiscreteType(effectiveType) && !isBanded) continue; const valueCount = new Set(data.map((r: any) => r[cs.field])).size; const axisGroupCount = (groupAxis === axis && groupCount > 1) ? groupCount : 1; const maxDim = axis === 'x' ? maxW : maxH; let perCategoryStep: number; if (axisGroupCount > 1) { // Grouped: each category needs room for groupCount sub-items // PLUS enough inter-group gap (stepPadding × step ≥ MIN_GROUP_GAP_PX). const minGroupStep = Math.max( Math.ceil(MIN_GROUP_GAP_PX / stepPadding), 2 * axisGroupCount, ); perCategoryStep = Math.max(minStep * axisGroupCount, minGroupStep); } else { // Ungrouped: one item per category perCategoryStep = minStep; } const dataDrivenMin = Math.min(perCategoryStep * valueCount, maxDim); const minDim = Math.max(baseMinSubplot, dataDrivenMin); if (axis === 'x') { minSubplotWidth = minDim; } else { minSubplotHeight = minDim; } } // effectiveW = totalBudget - fixedOverhead; each panel costs (subplot + gap). const effectiveW = maxW; const effectiveH = maxH; const maxFacetColumns = Math.max(1, Math.floor( effectiveW / (minSubplotWidth + gap), )); const maxFacetRows = Math.max(1, Math.floor( effectiveH / (minSubplotHeight + gap), )); // Identify column/row fields const colField = channelSemantics.column?.field; const rowField = channelSemantics.row?.field; if (!colField && !rowField) return undefined; const colCount = colField ? new Set(data.map((r: any) => r[colField])).size : 0; const rowCount = rowField ? new Set(data.map((r: any) => r[rowField])).size : 0; if (colCount === 0 && rowCount === 0) return undefined; if (colCount > 0 && rowCount === 0) { // Column-only: 2D budget for total panels, wrap visually. const maxTotalPanels = maxFacetColumns * maxFacetRows; const effectiveCount = Math.min(colCount, maxTotalPanels); // Balanced wrapping: compute rows first, then distribute evenly. // e.g. 10 items, max 6/row → 2 rows of 5 instead of 6+4. const visRows = Math.ceil(effectiveCount / maxFacetColumns); const visCols = Math.ceil(effectiveCount / visRows); return { columns: visCols, rows: visRows, maxColumnValues: maxTotalPanels, maxRowValues: maxFacetRows, }; } // Column+row or row-only: cap each dimension independently. return { columns: Math.max(1, Math.min(colCount, maxFacetColumns)), rows: Math.max(1, Math.min(rowCount, maxFacetRows)), maxColumnValues: maxFacetColumns, maxRowValues: maxFacetRows, }; } // --------------------------------------------------------------------------- // Public: computeMinSubplotDimensions // --------------------------------------------------------------------------- /** * Compute minimum subplot dimensions considering banded and discrete axes. * * For banded axes (e.g. temporal x on candlestick), each data point needs * `minStep` px, so the subplot minimum can be much larger than the generic * `minSubplotSize` (60px). For discrete axes, the count of unique values * drives the minimum similarly. * * This is used by both filterOverflow (pre-layout) and the assemblers * (post-layout) to consistently compute facet column/row caps. * * @param channelSemantics Phase 0 output (field, type per channel) * @param declaration Template layout declaration (axisFlags, resolvedTypes) * @param data Data rows * @param options Assembly options ({ minStep, minSubplotSize }) * @returns { minSubplotWidth, minSubplotHeight } */ export function computeMinSubplotDimensions( channelSemantics: Record<string, ChannelSemantics>, declaration: LayoutDeclaration, data: any[], options: { minStep?: number; minSubplotSize?: number }, ): { minSubplotWidth: number; minSubplotHeight: number } { const minStep = options.minStep ?? 6; const minSubplot = options.minSubplotSize ?? 60; let minSubplotWidth = minSubplot; let minSubplotHeight = minSubplot; // Log-scale axes need more space so minor grid lines stay legible. const LOG_PX_PER_DECADE_MIN = 40; for (const axis of ['x', 'y'] as const) { const cs = channelSemantics[axis]; if (!cs?.field || !cs.scaleType) continue; if (cs.scaleType !== 'log' && cs.scaleType !== 'symlog') continue; const vals = data .map((r: any) => r[cs.field]) .filter((v: any) => typeof v === 'number' && v > 0 && isFinite(v)); if (vals.length < 2) continue; const decades = Math.log10(Math.max(...vals)) - Math.log10(Math.min(...vals)); const needed = Math.ceil(Math.max(1, decades)) * LOG_PX_PER_DECADE_MIN; if (axis === 'x') minSubplotWidth = Math.max(minSubplotWidth, needed); else minSubplotHeight = Math.max(minSubplotHeight, needed); } const isDiscreteType = (t: string | undefined) => t === 'nominal' || t === 'ordinal'; for (const axis of ['x', 'y'] as const) { const cs = channelSemantics[axis]; if (!cs?.field) continue; const effectiveType = declaration.resolvedTypes?.[axis] ?? cs.type; const isBanded = declaration.axisFlags?.[axis]?.banded === true; const isDiscrete = isDiscreteType(effectiveType); let itemCount = 0; if (isBanded || isDiscrete) { itemCount = new Set(data.map((r: any) => r[cs.field])).size; } if (itemCount > 0) { const minDim = Math.max(minSubplot, itemCount * minStep); if (axis === 'x') { minSubplotWidth = Math.max(minSubplotWidth, minDim); } else { minSubplotHeight = Math.max(minSubplotHeight, minDim); } } } return { minSubplotWidth, minSubplotHeight }; } ================================================ FILE: src/lib/agents-chart/core/decisions.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ============================================================================= * REUSABLE DECISION LOGIC * ============================================================================= * * Pure decision functions that determine chart layout behavior. * These functions take data/config inputs and return decision objects — * NO Vega-Lite spec mutation happens here. * * The separation ensures: * 1. Decision logic is testable in isolation * 2. Same decisions can drive different output formats (VL, SVG, etc.) * 3. Templates can call decision functions without coupling to VL * * Naming conventions: * - `compute*()` — returns a decision/value from inputs * - `resolve*()` — picks from alternatives (type resolution, etc.) * - `classify*()` — categorizes an input * ============================================================================= */ import { inferVisCategory, type VisCategory, } from './semantic-types'; import { getRegistryEntry, isRegistered } from './type-registry'; // --------------------------------------------------------------------------- // Encoding Type Resolution // --------------------------------------------------------------------------- /** * Result of encoding type resolution. * Separates the decision from what gets written into VL. */ export interface EncodingTypeDecision { /** The resolved VL encoding type */ vlType: 'quantitative' | 'ordinal' | 'nominal' | 'temporal'; /** The VisCategory that drove the decision */ visCategory: VisCategory; /** Whether the type was overridden by channel rules */ channelOverride: boolean; /** Whether the type was overridden by cardinality/fraction guard */ cardinalityGuard: boolean; } // --------------------------------------------------------------------------- // Helpers for encoding type resolution // --------------------------------------------------------------------------- /** * Map a VisCategory to the corresponding VL encoding type string. * Geographic maps to quantitative since VL uses quantitative for coordinates. */ function visCategoryToVLType(vc: VisCategory): 'quantitative' | 'ordinal' | 'nominal' | 'temporal' { switch (vc) { case 'quantitative': return 'quantitative'; case 'ordinal': return 'ordinal'; case 'temporal': return 'temporal'; case 'geographic': return 'quantitative'; case 'nominal': default: return 'nominal'; } } /** * Validate that field values actually parse as dates. * * @param fromRegistry If true, uses a looser threshold (≥30%) since the * semantic type explicitly identified the field as temporal. * If false (data-inferred), requires ≥50%. */ function validateTemporalParsing( data: any[], fieldName: string, fromRegistry: boolean, ): boolean { const sampleValues = data.map(r => r[fieldName]).slice(0, 15).filter((v: any) => v != null); if (sampleValues.length === 0) return false; const looksTemporalValue = (val: any): boolean => { if (val instanceof Date) return true; if (typeof val === 'number') { // Year-like integers (1500–2200) if (val >= 1500 && val <= 2200 && val % 1 === 0) return true; // Unix-ms timestamps: 86_400_000 (Jan 2, 1970) to ~year 2103 if (val > 86400000 && val < 4200000000000) return true; return false; } if (typeof val === 'string') { const trimmed = val.trim(); if (!trimmed) return false; if (/^\d{4}$/.test(trimmed)) return true; return !Number.isNaN(Date.parse(trimmed)); } return false; }; const passingCount = sampleValues.filter(looksTemporalValue).length; const minFraction = fromRegistry ? 0.3 : 0.5; return passingCount / sampleValues.length >= minFraction; } /** * Apply temporal channel-compatibility adjustments, shared by both * registry-driven and data-inferred temporal paths. */ function resolveTemporalEncoding( visCategory: VisCategory, channel: string, data: any[], fieldName: string, fromRegistry: boolean, ): EncodingTypeDecision { // Temporal on facet/size channels → ordinal (VL limitation) if (['size', 'column', 'row'].includes(channel)) { return { vlType: 'ordinal', visCategory, channelOverride: true, cardinalityGuard: false }; } // Temporal on color with low cardinality → ordinal for distinct colors if (channel === 'color') { const uniqueCount = new Set(data.map(r => r[fieldName])).size; if (uniqueCount <= 12) { return { vlType: 'ordinal', visCategory, channelOverride: true, cardinalityGuard: false }; } } // Validate temporal parsing if (!validateTemporalParsing(data, fieldName, fromRegistry)) { return { vlType: 'ordinal', visCategory, channelOverride: false, cardinalityGuard: false }; } return { vlType: 'temporal', visCategory, channelOverride: false, cardinalityGuard: false }; } /** * Apply channel-context guards to an ordinal encoding. * * Even when the registry says a field is ordinal, channel context may * require promoting to quantitative: * - High cardinality on color/group → unreadable legend * - High cardinality on x/y → bars/lollipops need proportional * spacing and baseline anchoring (y2/x2) * - Fractional values + high cardinality → mis-classified continuous measure * * @param fromRegistry Whether the ordinal type came from the registry * (true) or was data-inferred (false). Data-inferred additionally * checks for fractional values (Guard 1). */ function applyOrdinalGuards( visCategory: VisCategory, channel: string, data: any[], fieldName: string, fieldValues: any[], fromRegistry: boolean, ): EncodingTypeDecision { const numericVals = fieldValues.filter(v => v != null && !isNaN(+v)).map(Number); if (numericVals.length > 0) { const uniqueCount = new Set(numericVals).size; const hasFractions = numericVals.some(v => v % 1 !== 0); // Guard 1 (data-inferred only): fractional + high-cardinality → // mis-classified continuous measure. Registry types are explicit, // so this guard only applies when the type was inferred from data. if (!fromRegistry && hasFractions && uniqueCount > 20) { return { vlType: 'quantitative', visCategory, channelOverride: false, cardinalityGuard: true }; } // Guard 2: integer ordinal with high cardinality on color/group → // a discrete legend with 12+ entries is unreadable; promote to // quantitative so VL renders a continuous gradient instead. if (!hasFractions && uniqueCount > 12 && ['color', 'group'].includes(channel)) { return { vlType: 'quantitative', visCategory, channelOverride: true, cardinalityGuard: true }; } // Guard 3: integer ordinal with high cardinality on position // axes (x, y) → charts like bar/lollipop need a quantitative // axis for proportional length; treating 12+ unique integers // as discrete categories produces an unreadable axis and // prevents baseline anchoring (y2/x2). if (!hasFractions && uniqueCount > 12 && ['x', 'y'].includes(channel)) { return { vlType: 'quantitative', visCategory, channelOverride: true, cardinalityGuard: true }; } } return { vlType: 'ordinal', visCategory, channelOverride: false, cardinalityGuard: false }; } /** * Disambiguate when the registry lists multiple visEncodings for a type. * * Uses channel context and data characteristics to select the most * appropriate encoding from the candidates. Each combination of * candidate encodings has dedicated logic: * * temporal + ordinal (Year, YearMonth, Decade, …) * quantitative + ordinal (Score, Rating) * quantitative + geographic (Latitude, Longitude) * ordinal + nominal (Direction) */ function disambiguateMultiEncoding( candidates: VisCategory[], channel: string, data: any[], fieldName: string, fieldValues: any[], ): EncodingTypeDecision { const has = (vc: VisCategory) => candidates.includes(vc); // ── Temporal + Ordinal (Year, YearMonth, Decade, etc.) ──────── // Time-unit granules. Temporal for continuous time axes (x/y); // ordinal for grouping channels (color, facet, size). if (has('temporal') && has('ordinal')) { return resolveTemporalEncoding('temporal', channel, data, fieldName, true); } // ── Quantitative + Ordinal (Score, Rating) ──────────────────── // Bounded discrete numerics. Use ordinal for grouping channels // with low cardinality (distinct colors/symbols); quantitative // for position axes (proportional spacing, zero-baseline). if (has('quantitative') && has('ordinal')) { if (['color', 'group'].includes(channel)) { const uniqueCount = new Set(data.map(r => r[fieldName])).size; if (uniqueCount <= 12) { return { vlType: 'ordinal', visCategory: 'ordinal', channelOverride: false, cardinalityGuard: false }; } // High-cardinality Score/Rating on color → quantitative gradient return { vlType: 'quantitative', visCategory: 'quantitative', channelOverride: false, cardinalityGuard: true }; } if (['column', 'row'].includes(channel)) { return { vlType: 'ordinal', visCategory: 'ordinal', channelOverride: false, cardinalityGuard: false }; } // x, y, size → quantitative (proportional axis) return { vlType: 'quantitative', visCategory: 'quantitative', channelOverride: false, cardinalityGuard: false }; } // ── Quantitative + Geographic (Latitude, Longitude) ─────────── // Geographic is for map projections; standard encodings use quantitative. if (has('quantitative') && has('geographic')) { return { vlType: 'quantitative', visCategory: 'quantitative', channelOverride: false, cardinalityGuard: false }; } // ── Ordinal + Nominal (Direction) ───────────────────────────── // Inherently ordered, but nominal for grouping channels to get // distinct (unordered) colors rather than a sequential scale. if (has('ordinal') && has('nominal')) { if (['color', 'group'].includes(channel)) { return { vlType: 'nominal', visCategory: 'nominal', channelOverride: false, cardinalityGuard: false }; } return { vlType: 'ordinal', visCategory: 'ordinal', channelOverride: false, cardinalityGuard: false }; } // ── Fallback: first candidate ───────────────────────────────── const fallback = candidates[0]; return { vlType: visCategoryToVLType(fallback), visCategory: fallback, channelOverride: false, cardinalityGuard: false }; } // --------------------------------------------------------------------------- // Main API // --------------------------------------------------------------------------- /** * Resolve the VL encoding type for a field. * * Two-stage pipeline: * * **Stage 1 — Registry-driven** (when semanticType is registered): * - Single visEncoding → use it directly (with channel adjustments) * - Multiple visEncodings → `disambiguateMultiEncoding()` selects best * option using channel context + data characteristics * * **Stage 2 — Data-inferred fallback** (no registered semantic type): * - `inferVisCategory()` inspects raw values → VisCategory * - Heuristic guards catch common mis-classifications (e.g., dense * fractional data inferred as ordinal) * * This is a pure decision — it does NOT mutate any spec. * * @param semanticType Semantic type string (e.g. 'Quantity', 'Country') * @param fieldValues Sampled values from the field * @param channel VL channel name (e.g. 'x', 'y', 'color') * @param data Full data table (for computing unique value counts) * @param fieldName Field name (for data lookups) */ export function resolveEncodingType( semanticType: string, fieldValues: any[], channel: string, data: any[], fieldName: string, ): EncodingTypeDecision { // ═══════════════════════════════════════════════════════════════════ // Stage 1: Registry-driven resolution // ═══════════════════════════════════════════════════════════════════ // The registry's visEncodings array is the source of truth. // - Single encoding → resolved directly // - Multiple encodings → disambiguated by channel + data if (semanticType && isRegistered(semanticType)) { const entry = getRegistryEntry(semanticType); const candidates = entry.visEncodings; if (candidates.length > 1) { // Multiple encodings listed — disambiguate semantically return disambiguateMultiEncoding(candidates, channel, data, fieldName, fieldValues); } // Single encoding — use it directly with channel adjustments const baseType = candidates[0]; // Guard: if the registry says quantitative but the actual values // are strings (e.g. semantic "Quantity" on a binned field like // "91-95"), fall back to data-inferred type. Numeric strings // that parse as numbers (e.g. "42") still count as numeric. if (baseType === 'quantitative') { const nonNull = fieldValues.filter(v => v != null); const allNumeric = nonNull.length > 0 && nonNull.every(v => typeof v === 'number' || (typeof v === 'string' && !isNaN(+v) && v.trim() !== '')); if (!allNumeric) { // Values aren't actually numeric — infer from data instead const inferred = inferVisCategory(fieldValues); return { vlType: visCategoryToVLType(inferred), visCategory: inferred, channelOverride: false, cardinalityGuard: false, }; } } if (baseType === 'temporal') { return resolveTemporalEncoding(baseType, channel, data, fieldName, true); } if (baseType === 'ordinal') { return applyOrdinalGuards(baseType, channel, data, fieldName, fieldValues, true); } return { vlType: visCategoryToVLType(baseType), visCategory: baseType, channelOverride: false, cardinalityGuard: false, }; } // ═══════════════════════════════════════════════════════════════════ // Stage 2: Data-inferred fallback // ═══════════════════════════════════════════════════════════════════ // No registered semantic type — infer from raw data values, then // apply heuristic guards for common data-inference mis-classifications. const visCategory: VisCategory = inferVisCategory(fieldValues); let channelOverride = false; let cardinalityGuard = false; switch (visCategory) { case 'temporal': return resolveTemporalEncoding(visCategory, channel, data, fieldName, false); case 'ordinal': return applyOrdinalGuards(visCategory, channel, data, fieldName, fieldValues, false); case 'quantitative': return { vlType: 'quantitative', visCategory, channelOverride, cardinalityGuard }; case 'geographic': return { vlType: 'quantitative', visCategory, channelOverride, cardinalityGuard }; case 'nominal': default: return { vlType: 'nominal', visCategory, channelOverride, cardinalityGuard }; } } // --------------------------------------------------------------------------- // Continuous Axis Gas Pressure Model (docs/design-stretch-model.md §2) // --------------------------------------------------------------------------- /** * Parameters for the per-axis stretch model (docs/design-stretch-model.md §2). * * Each axis is stretched independently based on how many distinguishable * positions (or series) compete for pixel space along that axis. */ export interface GasPressureParams { /** Mark cross-section in px² — used as default σ for both axes (default: 30) */ markCrossSection: number; /** Per-axis cross-section overrides. When set, the per-axis stretch * uses these instead of `markCrossSection`. * Useful for line charts where X needs more stretch than Y. */ markCrossSectionX?: number; markCrossSectionY?: number; /** Override X item count for stretch. * When set, X stretch uses this count (e.g. number of series) * instead of counting unique X pixel positions. */ xItemCountOverride?: number; /** Override Y item count for stretch. * When set, Y stretch uses this count (e.g. number of series) * instead of counting unique Y pixel positions. */ yItemCountOverride?: number; /** Power-law exponent for continuous stretch (default: 0.3) */ elasticity: number; /** Maximum stretch multiplier cap (default: 1.5) */ maxStretch: number; } /** Default gas pressure parameters (§2 recommendations). */ export const DEFAULT_GAS_PRESSURE_PARAMS: GasPressureParams = { markCrossSection: 30, elasticity: 0.3, maxStretch: 1.5, }; /** * Result of the per-axis stretch decision. */ export interface GasPressureDecision { /** Per-axis stretch: X axis (1 = no stretch) */ stretchX: number; /** Per-axis stretch: Y axis (1 = no stretch) */ stretchY: number; } /** * Compute per-axis stretch for a continuous 2D axis region. * * Implements docs/design-stretch-model.md §2: each axis is stretched independently based * on how many distinguishable positions (or series) compete for pixel * space along that axis. * * Two modes per axis: * - Positional: count unique pixel positions, σ_1d = √σ. * - Series-count: when xItemCountOverride / yItemCountOverride is set, * use that count directly with σ (not sqrt'd) since it's already 1D. * * @param xValues Numeric x-coordinates of data points * @param yValues Numeric y-coordinates of data points * @param xDomain Scale domain [min, max] for x-axis * @param yDomain Scale domain [min, max] for y-axis * @param canvasWidth Base canvas width W₀ * @param canvasHeight Base canvas height H₀ * @param params Gas pressure parameters (optional, uses defaults) */ export function computeGasPressure( xValues: number[], yValues: number[], xDomain: [number, number], yDomain: [number, number], canvasWidth: number, canvasHeight: number, params: GasPressureParams = DEFAULT_GAS_PRESSURE_PARAMS, ): GasPressureDecision { const N = xValues.length; if (N <= 1 || canvasWidth <= 0 || canvasHeight <= 0) { return { stretchX: 1, stretchY: 1 }; } // Per-axis stretch via unique-position linear packing. // The question for each axis is: "how many distinguishable positions // compete for pixel space along this axis?" // // Count unique positions (bucketed to ~1px resolution) along each // axis. Each unique position needs σ_1d ≈ √σ pixels of space. // 1D pressure = uniquePositions × σ_1d / axisDimension. const sigma1dDefault = Math.sqrt(params.markCrossSection); // ~5 px const computeAxisStretch = (values: number[], domain: [number, number], baseDim: number, sigma1d: number): number => { if (baseDim <= 0 || values.length <= 1) return 1; const range = domain[1] - domain[0]; if (range <= 0) return 1; // Bucket values to ~1px resolution in pixel space const pxPerUnit = baseDim / range; const seen = new Set<number>(); for (const v of values) { seen.add(Math.round((v - domain[0]) * pxPerUnit)); } const uniquePositions = seen.size; // 1D pressure: how many sigma-sized marks fight for baseDim pixels const pressure = (uniquePositions * sigma1d) / baseDim; if (pressure <= 1) return 1; return Math.min(params.maxStretch, Math.pow(pressure, params.elasticity)); }; const sigma1dX = params.markCrossSectionX != null ? Math.sqrt(params.markCrossSectionX) : sigma1dDefault; const sigma1dY = params.markCrossSectionY != null ? Math.sqrt(params.markCrossSectionY) : sigma1dDefault; // Helper: compute stretch for one axis, using series-count override if set. // When a series override is provided, σ is used directly (not sqrt'd) // because series count is already a 1D concept. const computeStretchForAxis = ( values: number[], domain: [number, number], baseDim: number, sigma1d: number, sigmaRaw: number, itemCountOverride?: number, ): number => { if (itemCountOverride != null && sigmaRaw > 0) { const pressure = (itemCountOverride * sigmaRaw) / baseDim; return pressure <= 1 ? 1 : Math.min(params.maxStretch, Math.pow(pressure, params.elasticity)); } return sigma1d > 0 ? computeAxisStretch(values, domain, baseDim, sigma1d) : 1; }; const sigmaRawX = params.markCrossSectionX ?? params.markCrossSection; const sigmaRawY = params.markCrossSectionY ?? params.markCrossSection; const stretchX = computeStretchForAxis(xValues, xDomain, canvasWidth, sigma1dX, sigmaRawX, params.xItemCountOverride); const stretchY = computeStretchForAxis(yValues, yDomain, canvasHeight, sigma1dY, sigmaRawY, params.yItemCountOverride); return { stretchX, stretchY }; } // --------------------------------------------------------------------------- // Elastic Stretch Computation // --------------------------------------------------------------------------- /** * Parameters for elastic axis stretch computation. * These control the spring-model behavior from docs/design-stretch-model.md §1. */ export interface ElasticStretchParams { /** Power-law exponent for stretch (default: 0.5) */ elasticity: number; /** Maximum stretch multiplier cap (default: 2) */ maxStretch: number; /** Default step size in px per discrete item */ defaultStepSize: number; /** Minimum pixels per discrete item (default: 6) */ minStep: number; } /** * Result of elastic budget computation for a single axis. */ export interface ElasticBudget { /** Elastic-stretched canvas budget in px */ budget: number; /** Stretch multiplier applied (1 = no stretch) */ stretchFactor: number; } /** * Compute the elastic canvas budget for an axis with N discrete items. * * When N items at defaultStepSize exceed the base dimension, the axis * stretches using a power-law: stretch = min(maxStretch, pressure^elasticity). * * @param itemCount Number of discrete items on the axis * @param baseDimension Base canvas size (width or height) in px * @param params Elastic stretch parameters */ export function computeElasticBudget( itemCount: number, baseDimension: number, params: ElasticStretchParams, ): ElasticBudget { if (itemCount <= 0) { return { budget: baseDimension, stretchFactor: 1 }; } const pressure = (itemCount * params.defaultStepSize) / baseDimension; if (pressure <= 1) { return { budget: baseDimension, stretchFactor: 1 }; } const stretchFactor = Math.min(params.maxStretch, Math.pow(pressure, params.elasticity)); return { budget: baseDimension * stretchFactor, stretchFactor, }; } /** * Result of per-axis step computation. */ export interface AxisStepDecision { /** Computed step size in px per item */ step: number; /** Total canvas budget in px */ budget: number; /** Number of items this step was computed for */ itemCount: number; } /** * Compute the step size for a single axis, covering both discrete * and continuous-as-discrete (banded) cases. * * @param nominalCount Number of discrete (nominal/ordinal) items * @param continuousCount Number of continuous-as-discrete items (banded Q/T) * @param baseDimension Base canvas size (width or height) in px * @param params Elastic stretch parameters */ export function computeAxisStep( nominalCount: number, continuousCount: number, baseDimension: number, params: ElasticStretchParams, ): AxisStepDecision { if (nominalCount > 0) { const { budget } = computeElasticBudget(nominalCount, baseDimension, params); return { step: Math.floor(budget / nominalCount), budget, itemCount: nominalCount }; } if (continuousCount > 0) { const { budget } = computeElasticBudget(continuousCount, baseDimension, params); return { step: Math.floor(budget / continuousCount), budget, itemCount: continuousCount }; } return { step: params.defaultStepSize, budget: baseDimension, itemCount: 0 }; } // --------------------------------------------------------------------------- // Facet Layout Decisions // --------------------------------------------------------------------------- /** * Result of facet layout computation. */ export interface FacetLayoutDecision { /** Number of facet columns */ columns: number; /** Number of facet rows */ rows: number; /** Per-subplot width in px */ subplotWidth: number; /** Per-subplot height in px */ subplotHeight: number; } /** * Parameters for facet layout computation. */ export interface FacetLayoutParams { /** Power-law exponent for facet stretch (default: 0.3) */ facetElasticity: number; /** Maximum total stretch multiplier cap (default: 2) */ maxStretch: number; /** Minimum subplot size in px (default: 60) */ minSubplotSize: number; } /** * Compute facet subplot dimensions. * * @param facetCols Number of facet columns * @param facetRows Number of facet rows * @param baseWidth Base canvas width in px * @param baseHeight Base canvas height in px * @param params Facet layout parameters */ export function computeFacetLayout( facetCols: number, facetRows: number, baseWidth: number, baseHeight: number, params: FacetLayoutParams, ): FacetLayoutDecision { // Minimum subplot dimension — use the caller-supplied parameter // (default 60px) so subplots remain readable. const minContinuousSize = params.minSubplotSize; let subplotWidth: number; if (facetCols > 1) { const stretch = Math.min(params.maxStretch, Math.pow(facetCols, params.facetElasticity)); subplotWidth = Math.round(Math.max(minContinuousSize, baseWidth * stretch / facetCols)); } else { subplotWidth = baseWidth; } let subplotHeight: number; if (facetRows > 1) { const stretch = Math.min(params.maxStretch, Math.pow(facetRows, params.facetElasticity)); subplotHeight = Math.round(Math.max(minContinuousSize, baseHeight * stretch / facetRows)); } else { subplotHeight = baseHeight; } return { columns: facetCols, rows: facetRows, subplotWidth, subplotHeight }; } // --------------------------------------------------------------------------- // Label Sizing Decisions // --------------------------------------------------------------------------- /** * Result of label sizing computation for a discrete axis. */ export interface LabelSizingDecision { /** Font size in px */ fontSize: number; /** Max label width in px */ labelLimit: number; /** Label rotation angle (undefined = no rotation) */ labelAngle?: number; /** Label alignment (for rotated labels) */ labelAlign?: string; /** Label baseline (for rotated labels) */ labelBaseline?: string; } /** * Compute label sizing for a discrete axis based on the effective step size. * Pure decision — returns sizing params without modifying any spec. * * @param effectiveStep Pixels per discrete item * @param hasDiscreteItems Whether the axis has discrete items */ export function computeLabelSizing( effectiveStep: number, hasDiscreteItems: boolean, ): LabelSizingDecision { const defaultFontSize = 10; const defaultLimit = 100; if (!hasDiscreteItems) { return { fontSize: defaultFontSize, labelLimit: defaultLimit }; } let fontSize = Math.max(6, Math.min(10, effectiveStep - 1)); let labelLimit = Math.max(30, Math.min(100, effectiveStep * 8)); let labelAngle: number | undefined; let labelAlign: string | undefined; let labelBaseline: string | undefined; if (effectiveStep < 10) { labelAngle = -90; fontSize = Math.max(6, Math.min(8, effectiveStep)); labelLimit = 40; labelAlign = 'right'; labelBaseline = 'middle'; } else if (effectiveStep < 16) { labelAngle = -45; fontSize = Math.max(7, Math.min(9, effectiveStep)); labelLimit = 60; labelAlign = 'right'; labelBaseline = 'top'; } return { fontSize, labelLimit, labelAngle, labelAlign, labelBaseline }; } // --------------------------------------------------------------------------- // Overflow Decision // --------------------------------------------------------------------------- /** * Result of overflow analysis for a discrete axis. */ export interface OverflowDecision { /** Whether overflow occurred (more items than can fit) */ overflowed: boolean; /** Maximum items to keep */ maxToKeep: number; /** Number of items omitted */ omittedCount: number; } /** * Compute whether a discrete axis overflows and how many items to keep. * * @param uniqueCount Number of unique values on the axis * @param maxDimension Maximum canvas dimension (with stretch) in px * @param minStepSize Minimum px per item */ export function computeOverflow( uniqueCount: number, maxDimension: number, minStepSize: number, ): OverflowDecision { const maxToKeep = Math.floor(maxDimension / minStepSize); const overflowed = uniqueCount > maxToKeep; return { overflowed, maxToKeep, omittedCount: overflowed ? uniqueCount - maxToKeep : 0, }; } // --------------------------------------------------------------------------- // Circumference-pressure model for radial charts (§3) // --------------------------------------------------------------------------- /** * Parameters for circumference-pressure scaling (spring model on polar axis). */ export interface CircumferencePressureParams { /** Minimum arc-length (px) each "effective bar" needs on the * circumference — analogous to defaultStepSize in the spring model. * Default: 45 */ minArcPx?: number; /** Minimum chart radius in px. Default: 60 */ minRadius?: number; /** Maximum chart radius in px. Caps runaway growth. Default: 400 */ maxRadius?: number; /** Power-law exponent for pressure → stretch (same as spring model). * 0.5 = square-root growth. Default: 0.5 */ elasticity?: number; /** Per-dimension maximum stretch multiplier cap (matches bar-chart * default of 2.0). The effective max stretch on the radius is * derived from min(baseW, baseH) × maxStretch so that the chart * never exceeds the cap in either dimension. Default: 2.0 */ maxStretch?: number; /** Extra margin outside the chart circle (px) for labels, legend, etc. * Added to each side when computing canvas dimensions. Default: 20 */ margin?: number; } /** * Result of circumference pressure computation. */ export interface CircumferencePressureResult { /** Computed chart radius in px */ radius: number; /** Recommended canvas width (px) */ canvasW: number; /** Recommended canvas height (px) */ canvasH: number; } /** * Compute radial chart sizing using the spring model mapped to a polar axis. * * Treats the circumference as a linear "bar axis": * baseCircumference = 2π × baseRadius * pressure = effectiveItemCount × minArcPx / baseCircumference * if pressure > 1: stretch = min(maxStretch, pressure ^ elasticity) * radius = baseRadius × stretch * * **effectiveItemCount** varies by chart type: * - Rose / Radar: N categories (uniform slices/spokes) * - Pie: total / minValue — how many of the smallest slice fit in the * full circle. This captures the worst-case thin slice that needs * minimum arc width. * - Sunburst: same as pie but computed on the outer ring leaves only. * * Both canvas dimensions grow equally (maintains 1:1 circular aspect). * * @param effectiveItemCount Effective number of uniform "bars" around * the circle (see above) * @param canvasSize Base canvas dimensions (from context) * @param params Optional tuning parameters */ export function computeCircumferencePressure( effectiveItemCount: number, canvasSize: { width: number; height: number }, params: CircumferencePressureParams = {}, ): CircumferencePressureResult { const { minArcPx = 45, minRadius = 60, maxRadius = 400, elasticity = 0.5, maxStretch = 2.0, margin = 20, } = params; const baseW = canvasSize.width; const baseH = canvasSize.height; // Base radius: largest circle that fits in the base canvas const baseRadius = Math.max(minRadius, (Math.min(baseW, baseH) / 2) - margin); // ── Effective max-stretch on the radius ────────────────────────── // The radius stretch expands the canvas in BOTH x and y equally. // Cap so that neither dimension exceeds maxStretch × baseDim. const maxCanvasW = baseW * maxStretch; const maxCanvasH = baseH * maxStretch; const maxDiameter = Math.min(maxCanvasW, maxCanvasH); const effectiveMaxRadius = Math.min(maxRadius, (maxDiameter - 2 * margin) / 2); const effectiveMaxStretch = Math.max(1, effectiveMaxRadius / baseRadius); // Spring model: pressure = items × step / baseDimension const baseCircumference = 2 * Math.PI * baseRadius; const pressure = (effectiveItemCount * minArcPx) / baseCircumference; let radius: number; if (pressure <= 1) { // No pressure — base radius is sufficient radius = baseRadius; } else { // Elastic stretch (same power law as bar-chart spring model) const stretch = Math.min(effectiveMaxStretch, Math.pow(pressure, elasticity)); radius = Math.round(baseRadius * stretch); } // Clamp radius = Math.min(maxRadius, Math.max(minRadius, radius)); // Canvas = diameter + margins const diameter = 2 * radius + 2 * margin; const canvasW = Math.max(baseW, diameter); const canvasH = Math.max(baseH, diameter); return { radius, canvasW, canvasH }; } /** * Compute effective bar count for variable-width slices (pie / sunburst). * * If all slices are equal, this returns N (number of slices). * If slices vary, this returns `total / minValue` — i.e., how many of the * thinnest slice would fill the whole circle. This is the worst-case * pressure that determines whether the chart needs to grow. * * Capped at 100 to prevent degenerate cases (near-zero slices) from * blowing up the radius. * * @param values Array of slice values (must be > 0) */ export function computeEffectiveBarCount(values: number[]): number { if (values.length === 0) return 0; const positiveValues = values.filter(v => v > 0); if (positiveValues.length === 0) return values.length; const total = positiveValues.reduce((s, v) => s + v, 0); const minVal = Math.min(...positiveValues); // effectiveCount = total / minVal → how many of the smallest slice fill the circle const effective = total / minVal; // Cap at 100 to prevent degenerate cases return Math.min(100, effective); } ================================================ FILE: src/lib/agents-chart/core/field-semantics.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ============================================================================= * FIELD SEMANTICS * ============================================================================= * * Resolves what a data field *is* by combining its semantic annotation * (from LLM or user) with the actual data values. This resolves the * one-to-many ambiguities in the type registry (e.g., Score can be * quantitative or ordinal depending on cardinality). * * The entry point is `resolveFieldSemantics()`. It produces a * `FieldSemantics` object that captures the field's identity, format, * aggregation role, domain, scale hint, and ordering — everything * about *what the data represents*, independent of how it will be * visualized on any particular channel. * * Design doc: docs/design-compilation-context.md * * VL dependency: **None** — pure TypeScript, no rendering library imports. * ============================================================================= */ import { type VisCategory, type TypeRegistryEntry, getRegistryEntry, isRegistered, } from './type-registry'; import { getZeroClass, inferOrdinalSortOrder, inferVisCategory, type ZeroClass, } from './semantic-types'; // Re-export for backward compatibility — consumers can import from here or type-registry export { getRegistryEntry } from './type-registry'; export type { TypeRegistryEntry } from './type-registry'; // ============================================================================= // §1 PUBLIC TYPES // ============================================================================= /** * Enriched semantic annotation from LLM or user. */ export interface SemanticAnnotation { /** The T2 semantic type string (e.g., "Revenue", "Rating", "Month") */ semanticType: string; /** * Intrinsic domain (value range) of this field's scale. * Only for bounded/scaled types — NOT for open-ended measures. * E.g., [1, 5] for 5-star rating, [0, 100] for score, [-90, 90] for latitude. */ intrinsicDomain?: [number, number]; /** Unit or currency code. E.g., "USD", "°C", "kg" */ unit?: string; /** Explicit ordinal ordering. E.g., ["Low", "Medium", "High"] */ sortOrder?: string[]; } /** d3-compatible format specification */ export interface FormatSpec { /** d3-format pattern: ",.2f", ".1%", "+.2f", etc. */ pattern?: string; /** Prefix before the number: "$", "€", "£" */ prefix?: string; /** Suffix after the number: "°C", "%", " kg" */ suffix?: string; /** Whether large values should be abbreviated (1K, 1M, 1B) */ abbreviate?: boolean; } /** Domain bounds constraint */ export interface DomainConstraint { min?: number; max?: number; /** Whether to hard-clamp values outside the domain */ clamp?: boolean; } /** Tick mark constraint */ export interface TickConstraint { /** Only show integer tick values */ integersOnly?: boolean; /** Exact tick values to show (for small domains like 1–5 rating) */ exactTicks?: number[]; /** Minimum step between ticks */ minStep?: number; } /** Color scheme recommendation from semantic analysis */ export interface ColorSchemeHint { /** Whether the field is best shown with sequential, diverging, or categorical colors */ type: 'sequential' | 'diverging' | 'categorical'; /** For diverging: the midpoint value */ divergingMidpoint?: number; /** Whether the field is inherently diverging (always show diverging) vs conditional */ inherentlyDiverging?: boolean; } /** Result of diverging midpoint analysis */ export interface DivergingInfo { /** The midpoint value where the diverging center sits */ midpoint: number; /** Whether this type is always diverging or only when data spans both sides */ inherent: boolean; /** Source of the midpoint determination */ source: 'unit' | 'type-intrinsic' | 'domain' | 'data'; } /** * Resolved field semantics — what the data field *is*. * * Derived from a `SemanticAnnotation` (semantic type + optional metadata) * plus actual data values. Resolves the one-to-many ambiguities in the * type registry by inspecting the concrete data representation. * * This is purely about the field’s identity and intrinsic properties — * NOT about how it will be visualized on a particular channel. * Channel-specific decisions (color scheme, axis reversal, interpolation, * tick strategy, stacking, etc.) belong in `ChannelSemantics`. * * Built once per field per dataset by `resolveFieldSemantics()`. */ export interface FieldSemantics { // --- Identity --- /** The semantic annotation (normalized from string or object input) */ semanticAnnotation: SemanticAnnotation; // --- Encoding --- /** Preferred encoding type, disambiguated from registry using data */ defaultVisType: VisCategory; // --- Formatting --- /** Number format derived from data type and unit */ format: FormatSpec; /** Tooltip format (typically higher precision than axis format) */ tooltipFormat?: FormatSpec; // --- Aggregation --- /** Default aggregate function — intrinsic to the field (additive vs intensive) */ aggregationDefault?: 'sum' | 'average'; // --- Scale --- /** Zero-baseline classification (meaningful / arbitrary / bipolar) */ zeroClass: ZeroClass | 'unknown'; /** Recommended scale type based on data distribution */ scaleType?: 'linear' | 'log' | 'sqrt' | 'symlog'; // --- Domain --- /** Intrinsic domain bounds (from annotation, type-intrinsic, or data-inferred) */ domainConstraint?: DomainConstraint; // --- Ordering --- /** Canonical ordinal sort order (months, days, etc.) */ canonicalOrder?: string[]; /** Whether the canonical order is cyclic (wraps around) */ cyclic: boolean; /** Default sort direction */ sortDirection: 'ascending' | 'descending'; // --- Histogram --- /** Whether this field’s data distribution benefits from binning */ binningSuggested: boolean; } // ============================================================================= // §2 TYPE REGISTRY → see ./type-registry.ts (single source of truth) // ============================================================================= /** * Extract the semantic type string from a bare string or annotation object. * Used when downstream code only needs the type string, not the full annotation. */ export function toTypeString(input: string | SemanticAnnotation | undefined): string { if (!input) return ''; if (typeof input === 'string') return input; return input.semanticType || ''; } // ============================================================================= // §3 ANNOTATION NORMALIZATION // ============================================================================= /** * Normalize a bare string or enriched annotation object into a * consistent SemanticAnnotation. * * Accepts: * "Revenue" → { semanticType: "Revenue" } * { semanticType: "Rating", intrinsicDomain: [1,5] } → as-is * undefined / "" → { semanticType: "Unknown" } */ export function normalizeAnnotation( input: string | SemanticAnnotation | undefined, ): SemanticAnnotation { if (!input) return { semanticType: 'Unknown' }; if (typeof input === 'string') return { semanticType: input || 'Unknown' }; return { ...input, semanticType: input.semanticType || 'Unknown' }; } // ============================================================================= // §4 FORMAT RESOLUTION // ============================================================================= /** Map currency codes to display symbols */ const CURRENCY_MAP: Record<string, string> = { USD: '$', EUR: '€', GBP: '£', JPY: '¥', CNY: '¥', KRW: '₩', INR: '₹', BRL: 'R$', CAD: 'CA$', AUD: 'A$', CHF: 'CHF', SEK: 'kr', NOK: 'kr', DKK: 'kr', }; /** * Map common unit strings to suffix display. * * Limited to a small set of well-known, universally understood units. * Unknown/arbitrary annotation.unit values are intentionally excluded * to keep axis labels clean and avoid displaying obscure or verbose * unit strings on tick marks. */ const UNIT_SUFFIX_MAP: Record<string, string> = { // Temperature '°C': '°C', '°F': '°F', C: '°C', F: '°F', // Mass kg: ' kg', lb: ' lb', // Distance km: ' km', mi: ' mi', m: ' m', ft: ' ft', // Speed 'km/h': ' km/h', mph: ' mph', // Time sec: ' s', min: ' min', hr: ' hr', seconds: ' s', minutes: ' min', hours: ' hr', // Percentage (handled by formatClass, but allow explicit suffix) '%': '%', }; /** * Detect whether percentage data uses 0–1 (fractional) or 0–100 (whole-number) * representation. * * Values can exceed the intrinsic range (e.g., 155 % growth), so we look at * the *majority* of absolute values rather than just the max. */ function detectPercentageRepresentation(values: number[]): '0-1' | '0-100' { if (values.length === 0) return '0-100'; const abs = values.map(Math.abs); // If the majority of values are ≤ 1, treat as fractional 0–1 representation const countBelow1 = abs.filter(v => v <= 1).length; if (countBelow1 / abs.length >= 0.8) return '0-1'; return '0-100'; } /** * Detect the maximum number of meaningful decimal places in a set of values. * * Returns 0 for all-integer data, 1 for data like [3.7, 4.2], 2 for [1.25, 3.50], etc. * Caps at 4 to avoid floating-point noise (e.g., 0.1 + 0.2 = 0.30000000000000004). */ function detectPrecision(values: number[]): number { let maxDecimals = 0; for (const v of values) { if (!Number.isFinite(v)) continue; // Convert to string, trim trailing zeros, count decimal places const s = v.toFixed(10); // enough digits to detect real precision const dot = s.indexOf('.'); if (dot === -1) continue; // Trim trailing zeros let end = s.length - 1; while (end > dot && s[end] === '0') end--; const decimals = end > dot ? end - dot : 0; if (decimals > maxDecimals) maxDecimals = decimals; } return Math.min(maxDecimals, 4); } /** * Build a d3-format pattern that matches the detected data precision. * * @param values Numeric data values * @param useGrouping Whether to include thousands separator (,) * @param signMode '' = default, '+' = always show sign * @returns Format pattern string like ',d', ',.1f', ',.2f' */ function precisionFormat(values: number[], useGrouping = true, signMode: '' | '+' = ''): string { const p = detectPrecision(values); const group = useGrouping ? ',' : ''; if (p === 0) return `${signMode}${group}d`; return `${signMode}${group}.${p}f`; } /** * Resolve the format specification for a field based on its semantic type, * annotation metadata, and data values. * * Priority: annotation.unit > type-specific defaults */ export function resolveFormat( semanticType: string, annotation: SemanticAnnotation, values: any[], ): { format: FormatSpec; tooltipFormat?: FormatSpec } { const entry = getRegistryEntry(semanticType); const unit = annotation.unit; // Resolve currency prefix from annotation.unit const currencyPrefix = unit ? CURRENCY_MAP[unit.toUpperCase()] ?? CURRENCY_MAP[unit] : undefined; // Resolve unit suffix from annotation.unit — only use known units; // unknown units are dropped to avoid polluting tick labels with // obscure or verbose strings. const unitSuffix = unit ? UNIT_SUFFIX_MAP[unit] : undefined; const nums = values.filter((v: any) => typeof v === 'number' && !isNaN(v)); switch (entry.formatClass) { case 'currency': { const pfx = currencyPrefix ?? '$'; // Price always shows cents; other currency uses data precision (min 0 for axis) const axisPattern = semanticType === 'Price' ? ',.2f' : precisionFormat(nums); return { format: { pattern: axisPattern, prefix: pfx, abbreviate: true }, tooltipFormat: { pattern: ',.2f', prefix: pfx }, }; } case 'signed-currency': { const pfx = currencyPrefix ?? '$'; return { format: { pattern: precisionFormat(nums, true, '+'), prefix: pfx, abbreviate: true }, tooltipFormat: { pattern: '+,.2f', prefix: pfx }, }; } case 'percent': { // Without an explicit intrinsicDomain we cannot reliably tell // whether values are fractional 0-1 or whole-number 0-100, so // fall back to plain formatting to avoid misinterpretation. if (!annotation.intrinsicDomain) { return { format: {}, tooltipFormat: { pattern: precisionFormat(nums) }, }; } const rep = detectPercentageRepresentation(nums); if (rep === '0-1') { // d3's .% format multiplies by 100 automatically const p = detectPrecision(nums); // After ×100 we still need (p-2) decimals, minimum 0 const axisP = Math.max(0, p - 2); const tipP = Math.min(axisP + 1, 4); return { format: { pattern: `.${axisP}%` }, tooltipFormat: { pattern: `.${tipP}%` }, }; } // Whole-number percentage (0–100): use data precision return { format: { pattern: precisionFormat(nums, false), suffix: '%' }, tooltipFormat: { pattern: precisionFormat(nums, false), suffix: '%' }, }; } case 'signed-percent': { // Without an explicit intrinsicDomain we cannot reliably tell // whether values are fractional 0-1 or whole-number 0-100, so // fall back to plain signed formatting to avoid misinterpretation. if (!annotation.intrinsicDomain) { return { format: { pattern: precisionFormat(nums, true, '+') }, tooltipFormat: { pattern: precisionFormat(nums, true, '+') }, }; } const rep = detectPercentageRepresentation(nums); if (rep === '0-1') { const p = detectPrecision(nums); const axisP = Math.max(0, p - 2); const tipP = Math.min(axisP + 1, 4); return { format: { pattern: `+.${axisP}%` }, tooltipFormat: { pattern: `+.${tipP}%` }, }; } return { format: { pattern: precisionFormat(nums, false, '+'), suffix: '%' }, tooltipFormat: { pattern: precisionFormat(nums, false, '+'), suffix: '%' }, }; } case 'signed-decimal': return { format: { pattern: precisionFormat(nums, false, '+') }, tooltipFormat: { pattern: precisionFormat(nums, false, '+') }, }; case 'unit-suffix': { const sfx = unitSuffix ?? ''; return { format: { pattern: precisionFormat(nums), suffix: sfx, abbreviate: true }, tooltipFormat: { pattern: precisionFormat(nums), suffix: sfx }, }; } case 'integer': if (semanticType === 'Year') { return { format: { pattern: 'd' } }; } return { format: { pattern: ',d' }, tooltipFormat: { pattern: ',d' }, }; case 'decimal': // Don't override axis format — VL's native formatting is superior // for generic decimals (it adapts precision and uses ~s notation // for large values). We only keep a tooltipFormat so tooltip // pop-ups show clean precision rather than raw floats. return { format: {}, tooltipFormat: { pattern: precisionFormat(nums) }, }; case 'plain': default: return { format: {} }; } } // ============================================================================= // §5 DEFAULT VIS TYPE // ============================================================================= /** * Resolve the default Vega-Lite encoding type for a field. * * When the registry lists multiple candidates (e.g., Score → ['quantitative', 'ordinal']), * disambiguate using data statistics (distinct value count). */ export function resolveDefaultVisType( semanticType: string, values: any[], ): VisCategory { // For unregistered types, defer entirely to data characteristics if (!isRegistered(semanticType)) { return inferVisCategory(values); } const entry = getRegistryEntry(semanticType); const candidates = entry.visEncodings; if (candidates.length === 1) { // Guard: if registry says quantitative but actual values are // strings (e.g. binned ranges like "91-95"), defer to data inference. if (candidates[0] === 'quantitative') { const nonNull = values.filter(v => v != null); const allNumeric = nonNull.length > 0 && nonNull.every(v => typeof v === 'number' || (typeof v === 'string' && !isNaN(+v) && v.trim() !== '')); if (!allNumeric) { return inferVisCategory(values); } } return candidates[0]; } // Disambiguate between quantitative and ordinal based on distinct count if (candidates.includes('quantitative') && candidates.includes('ordinal')) { const distinct = new Set(values.filter(v => v != null)).size; // Small number of distinct values → ordinal feels more natural return distinct <= 12 ? 'ordinal' : 'quantitative'; } // Disambiguate between temporal and ordinal if (candidates.includes('temporal') && candidates.includes('ordinal')) { const distinct = new Set(values.filter(v => v != null)).size; // Few values → ordinal (e.g., only 3 years: 2022, 2023, 2024) return distinct <= 6 ? 'ordinal' : 'temporal'; } // If geographic + quantitative (lat/lon), prefer quantitative for standard charts if (candidates.includes('geographic') && candidates.includes('quantitative')) { return 'quantitative'; } return candidates[0]; } // ============================================================================= // §6 AGGREGATION DEFAULT // ============================================================================= /** * Resolve the default aggregation function based on the field's role. * * - Additive measures → sum (parts sum to a meaningful total) * - Intensive measures → average (rates/averages shouldn't be summed) * - Signed-additive → sum (preserves sign semantics) * - Dimensions/IDs → undefined (aggregation not meaningful) */ export function resolveAggregationDefault( semanticType: string, ): 'sum' | 'average' | undefined { const entry = getRegistryEntry(semanticType); switch (entry.aggRole) { case 'additive': return 'sum'; case 'signed-additive': return 'sum'; case 'intensive': return 'average'; case 'dimension': return undefined; case 'identifier': return undefined; default: return undefined; } } // ============================================================================= // §7 ZERO-BASELINE CLASSIFICATION // ============================================================================= /** * Resolve zero-baseline class, enhanced with annotation domain. * * If annotation provides a domain starting above 0 (e.g., Rating [1, 5]), * zero is arbitrary regardless of what the base type says. */ export function resolveZeroClassFromAnnotation( semanticType: string, domain?: [number, number], ): ZeroClass | 'unknown' { // If domain starts above zero (e.g., Rating [1,5]), zero is arbitrary if (domain && domain[0] > 0) return 'arbitrary'; // Delegate to existing classification return getZeroClass(semanticType); } // ============================================================================= // §8 SCALE TYPE // ============================================================================= /** * Recommend a scale type based on semantic type and data distribution. * * Conservative policy — only triggers when ALL of these hold: * 1. The semantic type is in the ALLOW-list (Population, GDP, etc.) * 2. Data spans ≥ 4 orders of magnitude (10 000×) * 3. At least 10 data points * * This avoids surprising users on normal datasets while still helping * with genuinely wide-range data like city populations or GDP figures. */ export function resolveScaleType( semanticType: string, values: number[], ): 'linear' | 'log' | 'sqrt' | 'symlog' | undefined { // Only consider log for additive measures with open domains — // these are the types that can legitimately span many orders of magnitude. // (E.g., revenue, population, quantities across different scales.) // Exclude generic fallback types (Number, Unknown) — they just mean // "we know it's numeric but not what it measures", so applying // log/symlog would be presumptuous. const entry = getRegistryEntry(semanticType); const eligible = entry.aggRole === 'additive' && entry.domainShape === 'open' && entry.t1 !== 'GenericMeasure'; if (!eligible) return undefined; if (values.length < 10) return undefined; const filtered = values.filter(v => typeof v === 'number' && !isNaN(v) && isFinite(v)); if (filtered.length < 10) return undefined; const min = Math.min(...filtered); const max = Math.max(...filtered); if (max <= 0 || min === max) return undefined; // Only all-positive data — don't auto-log mixed-sign if (min < 0) return undefined; // Require ≥ 6 orders of magnitude (1000 000×) — very conservative const positiveMin = Math.min(...filtered.filter(v => v > 0)); if (positiveMin > 0 && max / positiveMin >= 1000000) { // If data contains zeros, log(0) = -∞ breaks the scale. // Use symlog (linear near zero, logarithmic for large values) // so zeros remain representable. const hasZeros = filtered.some(v => v === 0); return hasZeros ? 'symlog' : 'log'; } return undefined; } // ============================================================================= // §9 DOMAIN CONSTRAINTS // ============================================================================= /** * Merge an intrinsic (semantic) domain with the actual data range. * * For **hard** domains (Latitude, Correlation) the intrinsic bounds are * physically absolute — data cannot exceed them, so we clamp. * * For **soft** domains (Percentage, Score, Rating, annotation-supplied) * the intrinsic bounds describe the *typical* range but real data can * legitimately exceed them (e.g., 155 % growth). The effective domain * is the union: min(intrinsic[0], dataMin) … max(intrinsic[1], dataMax). */ function mergeIntrinsicWithData( intrinsic: [number, number], values: any[], hard: boolean, ): DomainConstraint { if (hard) { return { min: intrinsic[0], max: intrinsic[1], clamp: true }; } const nums = values.filter((v: any) => typeof v === 'number' && !isNaN(v)); if (nums.length === 0) { return { min: intrinsic[0], max: intrinsic[1], clamp: false }; } const dataMin = Math.min(...nums); const dataMax = Math.max(...nums); return { min: Math.min(intrinsic[0], dataMin), max: Math.max(intrinsic[1], dataMax), clamp: false, }; } /** * Snap-to-bound heuristic for bounded types like Percentage / PercentageChange. * * Each bound is snapped independently: * - If data approaches the intrinsic lower bound → snap min * - If data approaches the intrinsic upper bound → snap max * - If data exceeds a bound → don't snap that side (let VL auto-extend) * * Threshold: 25% of the *effective side range*. * * We err on the side of snapping, because: * - Semantic types are opt-in — the bound carries meaning by definition. * - A wrong snap (extra white space) is less harmful than a wrong * no-snap (viewer loses semantic reference, differences are * exaggerated and proximity to the bound is hidden). * - Only when data is clearly in the interior (> 25% away from each * bound) does the bound stop being a useful reference. * * When the intrinsic domain straddles zero (lo < 0 < hi), zero acts as a * visual baseline (bar charts, contextual zero). Each bound's threshold * is computed relative to its distance from zero — not the full range — * so that snapping one side doesn't make values on the other side of zero * invisible (e.g., snapping to -100% when data has a tiny +0.2% bar). * * When the domain doesn't straddle zero (e.g., [0, 100]), the full range * is used as the reference. * * Examples for Percentage [0, 100] (threshold = 25, full range): * 20–45% → snap min=0 only (20 within 25 of 0; 45 far from 100) * 35–65% → no snap (both far from edges, in interior) * 55–82% → snap max=100 only (82 within 25 of 100; 55 far from 0) * 15–80% → snap both [0, 100] (15 near 0, 80 near 100) * 30–130% → no snap (130 exceeds 100 → no snap; 30 far from 0) * * Examples for PercentageChange [-1, 1] (threshold = 0.25 per side): * -0.03 to +0.05 → no snap (both far from ±0.75) * -0.70 to +0.30 → no snap (-0.70 > -0.75, not close enough) * -0.80 to +0.30 → snap min=-1 (-0.80 ≤ -0.75; +0.30 < 0.75) * -0.80 to +0.78 → snap both (both within 0.25 of edges) */ export function snapToBoundHeuristic( intrinsic: [number, number], values: any[], ): DomainConstraint | undefined { const nums = values.filter((v: any) => typeof v === 'number' && !isNaN(v)); if (nums.length === 0) return undefined; const [lo, hi] = intrinsic; const range = hi - lo; if (range <= 0) return undefined; const dataMin = Math.min(...nums); const dataMax = Math.max(...nums); // When the domain straddles zero, compute each side's threshold relative // to its distance from zero. This prevents snapping one side from // stretching the axis so wide that values near zero on the other side // become invisible (sub-pixel bars). const zeroInside = lo < 0 && hi > 0; const thresholdLo = 0.25 * (zeroInside ? (0 - lo) : range); const thresholdHi = 0.25 * (zeroInside ? hi : range); let snapMin: number | undefined; let snapMax: number | undefined; // Snap lower bound: data min is close to intrinsic lower bound // AND data doesn't go below it (if it does, VL auto-extends) if (dataMin >= lo && dataMin <= lo + thresholdLo) { snapMin = lo; } // Snap upper bound: data max is close to intrinsic upper bound // AND data doesn't exceed it if (dataMax <= hi && dataMax >= hi - thresholdHi) { snapMax = hi; } if (snapMin === undefined && snapMax === undefined) return undefined; return { min: snapMin, max: snapMax, clamp: false }; } /** * Resolve domain constraints from annotation, type-intrinsic rules, or data. * * Only truly fixed physical domains (Latitude, Longitude, Correlation) * use hard clamping. Bounded types like Percentage use a snap-to-bound * heuristic: the axis extends to the theoretical endpoint (e.g., 100%) * only when data is close to it, avoiding wasted space when data is * concentrated in a small region. * * Priority: annotation.intrinsicDomain > type-intrinsic > data-inferred */ export function resolveDomainConstraint( semanticType: string, annotation: SemanticAnnotation, values: any[], ): DomainConstraint | undefined { const entry = getRegistryEntry(semanticType); // 1. Explicit annotation intrinsicDomain if (annotation.intrinsicDomain) { // Proportion (Percentage) and SignedMeasure (PercentageChange, Profit): // use snap-to-bound heuristic on both ends independently. // Don't force the full theoretical range — only snap to a bound // when data approaches it (e.g., 97% → snap to 100%, -0.95 → snap to -1). if (entry.t1 === 'Proportion' || entry.t1 === 'SignedMeasure') { return snapToBoundHeuristic(annotation.intrinsicDomain, values); } // All other types: soft merge (union of intrinsic + data) return mergeIntrinsicWithData(annotation.intrinsicDomain, values, false); } // 2. Type-intrinsic hard domains (physically impossible to exceed) if (semanticType === 'Latitude') return mergeIntrinsicWithData([-90, 90], values, true); if (semanticType === 'Longitude') return mergeIntrinsicWithData([-180, 180], values, true); if (semanticType === 'Correlation') return mergeIntrinsicWithData([-1, 1], values, true); // 3. Percentage without explicit annotation — detect scale and apply snap if (semanticType === 'Percentage') { const nums = values.filter((v: any) => typeof v === 'number' && !isNaN(v)); if (nums.length > 0) { const rep = detectPercentageRepresentation(nums); const M = rep === '0-1' ? 1 : 100; return snapToBoundHeuristic([0, M], values); } } return undefined; } // ============================================================================= // §10 TICK CONSTRAINTS // ============================================================================= /** * Resolve tick constraints based on semantic type and domain. * * For bounded integer domains (e.g., Rating [1, 5]), generates exact ticks. * For integer types (Count, Rank, Year), enforces integer-only ticks. */ export function resolveTickConstraint( semanticType: string, domain?: [number, number], ): TickConstraint | undefined { const entry = getRegistryEntry(semanticType); if (entry.formatClass === 'integer') { const tc: TickConstraint = { integersOnly: true, minStep: 1 }; // If domain provided and span is small, generate exact ticks if (domain) { const span = domain[1] - domain[0]; if (span <= 20 && span > 0) { tc.exactTicks = []; for (let i = domain[0]; i <= domain[1]; i++) { tc.exactTicks.push(i); } } } return tc; } // Score/Rating with bounded domain → integer ticks if ((semanticType === 'Score' || semanticType === 'Rating') && domain) { const span = domain[1] - domain[0]; const tc: TickConstraint = { integersOnly: true, minStep: 1 }; if (span <= 20 && span > 0) { tc.exactTicks = []; for (let i = domain[0]; i <= domain[1]; i++) { tc.exactTicks.push(i); } } return tc; } return undefined; } // ============================================================================= // §11 CANONICAL ORDERING & CYCLIC // ============================================================================= /** * Resolve the canonical sort order for a field. * * Priority: annotation.sortOrder > well-known type sequence > auto-detect from data */ export function resolveCanonicalOrder( semanticType: string, annotation: SemanticAnnotation, values: any[], ): string[] | undefined { // 1. Explicit annotation sortOrder if (annotation.sortOrder && annotation.sortOrder.length > 0) { return annotation.sortOrder; } // 2. Delegate to existing well-known sequence detection return inferOrdinalSortOrder(semanticType, values); } /** * Determine whether a field's values form a cyclic (wrap-around) sequence. * * Derived purely from semantic type — NOT an LLM annotation. * Types with domainShape='cyclic' in the registry are cyclic. */ export function resolveCyclic(semanticType: string): boolean { const entry = getRegistryEntry(semanticType); return entry.domainShape === 'cyclic'; } // ============================================================================= // §12 REVERSED AXIS // ============================================================================= /** * Whether the axis should be reversed for this field. * * Rank is the primary case: 1st place should appear at the top of the * y-axis. On the x-axis, rank 1 should stay on the left (no reversal). */ export function resolveReversed(semanticType: string, channel?: string): boolean { if (semanticType === 'Rank') { // Only reverse on the y-axis (rank 1 at top). // On x-axis, natural left-to-right order is correct. return channel !== 'x'; } return false; } // ============================================================================= // §13 NICE (domain rounding) // ============================================================================= /** * Whether to apply "nice" rounding to scale domain endpoints. * * Nice is false when: * - There's a fixed domain constraint (Rating [1, 5] → axis should show exactly 1–5) * - The type has a fixed domain shape (Latitude, Correlation) */ export function resolveNice( semanticType: string, domainConstraint?: DomainConstraint, ): boolean { if (domainConstraint?.clamp) return false; if (domainConstraint && domainConstraint.min !== undefined && domainConstraint.max !== undefined) { return false; } const entry = getRegistryEntry(semanticType); if (entry.domainShape === 'fixed') return false; return true; } // ============================================================================= // §14 DIVERGING & COLOR SCHEME HINT // ============================================================================= /** * Resolve diverging midpoint information for a field. * * Priority chain: * 1. annotation.unit → type lookup (°C → 0, °F → 32) * 2. type-intrinsic midpoint (Sentiment → 0, Correlation → 0) * 3. annotation.intrinsicDomain midpoint (Rating [1,5] → 3) * 4. data-driven: data spans 0 → midpoint 0 * * Returns undefined if no diverging treatment applies. */ export function resolveDivergingInfo( semanticType: string, annotation: SemanticAnnotation, values: number[], ): DivergingInfo | undefined { const entry = getRegistryEntry(semanticType); // Types with diverging='none' don't get diverging treatment // 1. Unit-derived (Temperature) if (semanticType === 'Temperature' && annotation.unit) { const unitMidpoints: Record<string, number> = { '°C': 0, '°F': 32, 'K': 273.15, C: 0, F: 32, }; const mid = unitMidpoints[annotation.unit]; if (mid !== undefined) { return { midpoint: mid, inherent: false, source: 'unit' }; } } // 3. Type-intrinsic if (entry.diverging === 'inherent') { return { midpoint: 0, inherent: true, source: 'type-intrinsic' }; } if (entry.diverging === 'conditional') { return { midpoint: 0, inherent: false, source: 'type-intrinsic' }; } // 3. Domain-derived midpoint (e.g., Rating [1,5] → 3) if (annotation.intrinsicDomain) { return { midpoint: (annotation.intrinsicDomain[0] + annotation.intrinsicDomain[1]) / 2, inherent: false, source: 'domain', }; } // 4. Data-driven: if data spans 0, use 0 as midpoint if (values.length > 0) { const min = Math.min(...values); const max = Math.max(...values); if (min < 0 && max > 0) { return { midpoint: 0, inherent: false, source: 'data' }; } } return undefined; } /** * Resolve color scheme hint based on semantic type, diverging analysis, * and data values. */ export function resolveColorSchemeHint( semanticType: string, annotation: SemanticAnnotation, values: any[], ): ColorSchemeHint { const entry = getRegistryEntry(semanticType); const nums = values.filter((v: any) => typeof v === 'number' && !isNaN(v)); // Try diverging analysis const divInfo = resolveDivergingInfo(semanticType, annotation, nums); if (divInfo) { const min = nums.length > 0 ? Math.min(...nums) : 0; const max = nums.length > 0 ? Math.max(...nums) : 0; const spansBothSides = min < divInfo.midpoint && max > divInfo.midpoint; if (divInfo.inherent || spansBothSides) { return { type: 'diverging', divergingMidpoint: divInfo.midpoint, inherentlyDiverging: divInfo.inherent, }; } } // Sequential for quantitative, categorical for nominal/ordinal if (entry.visEncodings.includes('quantitative')) { return { type: 'sequential' }; } return { type: 'categorical' }; } // ============================================================================= // §15 BINNING SUITABILITY // ============================================================================= /** * Whether this field benefits from histogram-style binning. * * False for small bounded domains (Rating 1–5), non-numeric types, * and identifiers. */ export function resolveBinningSuggested( semanticType: string, domain?: [number, number], ): boolean { const entry = getRegistryEntry(semanticType); // Non-quantitative types don't get binned if (!entry.visEncodings.includes('quantitative')) return false; // Identifiers/dimensions don't get binned if (entry.aggRole === 'identifier' || entry.aggRole === 'dimension') return false; // Year should use temporal axis, not bins if (semanticType === 'Year' || semanticType === 'Decade') return false; // Small bounded domains have too few values to bin if (domain && (domain[1] - domain[0]) <= 20) return false; // Rating/Score with known small range if (semanticType === 'Rating' && !domain) return false; return true; } // ============================================================================= // §17 STACKING COMPATIBILITY // ============================================================================= /** * Whether values of this type can be stacked in a bar/area chart, and how. * * - 'sum': Additive measures (parts sum to whole) * - 'normalize': Proportions (show 100% breakdown) * - false: Stacking is meaningless (rates, scores, identifiers) */ export function resolveStackable( semanticType: string, ): 'sum' | 'normalize' | false { const entry = getRegistryEntry(semanticType); switch (entry.aggRole) { case 'additive': return 'sum'; case 'signed-additive': return 'sum'; case 'intensive': // Percentage is the exception — normalizable if (semanticType === 'Percentage') return 'normalize'; return false; case 'dimension': return false; case 'identifier': return false; default: return false; } } // ============================================================================= // §18 SORT DIRECTION // ============================================================================= /** * Default sort direction for this field when used on an axis. */ export function resolveSortDirection( semanticType: string, ): 'ascending' | 'descending' { // Rank: show best first if (semanticType === 'Rank') return 'descending'; return 'ascending'; } // ============================================================================= // §19 BUILDER: resolveFieldSemantics() // ============================================================================= /** * Resolve field semantics from annotation + data. * * This is the sole entry point for data-identity decisions. It resolves * the one-to-many ambiguities in the type registry by inspecting the * concrete data representation. * * Visualization-specific decisions (color scheme, axis reversal, * interpolation, tick strategy, nice rounding, stacking) are NOT * computed here — those belong in `resolveChannelSemantics()`. * * @param input The semantic type annotation (string or enriched object) * @param fieldName Column name (used for unit detection heuristics) * @param values Sampled data values from this field * @returns Resolved field semantics */ export function resolveFieldSemantics( input: string | SemanticAnnotation | undefined, fieldName: string, values: any[], ): FieldSemantics { // 1. Normalize annotation const annotation = normalizeAnnotation(input); const semanticType = annotation.semanticType; // 2. Numeric values (filtered once, reused across resolvers) const numericValues = values .filter((v: any) => typeof v === 'number' && !isNaN(v) && isFinite(v)); // 3. Resolve field-intrinsic properties const defaultVisType = resolveDefaultVisType(semanticType, values); const { format, tooltipFormat } = resolveFormat(semanticType, annotation, values); let aggregationDefault = resolveAggregationDefault(semanticType); let zeroClass = resolveZeroClassFromAnnotation(semanticType, annotation.intrinsicDomain); const scaleType = resolveScaleType(semanticType, numericValues); const domainConstraint = resolveDomainConstraint(semanticType, annotation, values); const canonicalOrder = resolveCanonicalOrder(semanticType, annotation, values); const cyclic = resolveCyclic(semanticType); let binningSuggested = resolveBinningSuggested(semanticType, annotation.intrinsicDomain); const sortDirection = resolveSortDirection(semanticType); // 4. For unregistered types, provide data-driven fallbacks. // The registry treats unknown types as categorical, but if the data // is actually numeric, we should behave like a generic measure. if (!isRegistered(semanticType) && defaultVisType === 'quantitative') { // Data looks numeric → treat like Number (GenericMeasure) if (!aggregationDefault) aggregationDefault = 'sum'; if (zeroClass === 'unknown') zeroClass = 'meaningful'; binningSuggested = true; } return { semanticAnnotation: annotation, defaultVisType, format, tooltipFormat, aggregationDefault, zeroClass, scaleType: scaleType ?? undefined, domainConstraint, canonicalOrder, cyclic, sortDirection, binningSuggested, }; } ================================================ FILE: src/lib/agents-chart/core/filter-overflow.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ============================================================================= * OVERFLOW FILTERING * ============================================================================= * * Decides *which* discrete values to keep when there are too many for * the available canvas space, then filters the data accordingly. * * This module does **no layout math**. Per-channel capacity budgets * are computed upstream by `computeChannelBudgets` and passed in as * a `ChannelBudgets` object. This module focuses on: * 1. Iterating each discrete channel * 2. Applying the overflow strategy (which values to keep) * 3. Filtering data rows * 4. Producing truncation warnings * * Runs AFTER computeChannelBudgets and BEFORE computeLayout. * * VL dependency: **None** * ============================================================================= */ import type { ChannelSemantics, ChartEncoding, LayoutDeclaration, TruncationWarning, OverflowResult, OverflowStrategy, OverflowStrategyContext, ChannelBudgets, } from './types'; import type { ChartWarning } from './types'; import { inferVisCategory } from './semantic-types'; // --------------------------------------------------------------------------- // Public API // --------------------------------------------------------------------------- /** * Filter data to keep only the values that fit within the canvas. * * @param channelSemantics Phase 0 output (field, type per channel) * @param declaration Template layout declaration (resolvedTypes, overflowStrategy) * @param encodings Original user-level encodings (for sort info) * @param data Full data table * @param budgets Per-channel capacity budgets from computeChannelBudgets * @param allMarkTypes Set of all mark types in the template (for connected-mark detection) * @returns OverflowResult with filtered data, nominal counts, truncations, and warnings */ export function filterOverflow( channelSemantics: Record<string, ChannelSemantics>, declaration: LayoutDeclaration, encodings: Record<string, ChartEncoding>, data: any[], budgets: ChannelBudgets, allMarkTypes: Set<string>, ): OverflowResult { // --- Build effective channel info from semantics + declaration --- const effectiveType = (ch: string): string | undefined => declaration.resolvedTypes?.[ch] ?? channelSemantics[ch]?.type; const effectiveField = (ch: string): string | undefined => { if (channelSemantics[ch]?.field) return channelSemantics[ch].field; return undefined; }; const isDiscreteType = (t: string | undefined) => t === 'nominal' || t === 'ordinal'; // --- Filter data --- const nominalCounts: Record<string, number> = { x: 0, y: 0, column: 0, row: 0, group: 0, }; const truncations: TruncationWarning[] = []; const warnings: ChartWarning[] = []; let filteredData = data; // Compute group nominal count const groupField = channelSemantics.group?.field; if (groupField) { nominalCounts.group = new Set(data.map(r => r[groupField])).size; } // Strategy context for custom or default overflow const strategyContext: OverflowStrategyContext = { data, channelSemantics, encodings, allMarkTypes, }; const strategy = declaration.overflowStrategy ?? defaultOverflowStrategy; for (const channel of ['x', 'y', 'column', 'row', 'color'] as const) { const fieldName = effectiveField(channel); const type = effectiveType(channel); if (!fieldName) continue; // Budget for this channel (Infinity if uncapped) const maxToKeep = budgets.maxValues[channel] ?? Infinity; // For non-discrete types on column/row, apply overflow cap — // every unique value becomes a facet panel. if (!isDiscreteType(type)) { if (channel === 'column' || channel === 'row') { const uniqueValues = [...new Set(filteredData.map(r => r[fieldName]))]; nominalCounts[channel] = Math.min(uniqueValues.length, maxToKeep); if (uniqueValues.length > maxToKeep) { // For non-discrete facets, keep the first N values (sorted) const sorted = [...uniqueValues].sort(); const valuesToKeep = sorted.slice(0, maxToKeep); const omittedCount = uniqueValues.length - valuesToKeep.length; warnings.push({ severity: 'warning', code: 'overflow', message: `${omittedCount} of ${uniqueValues.length} values in '${fieldName}' were omitted (showing first ${valuesToKeep.length}).`, channel, field: fieldName, }); const keepSet = new Set(valuesToKeep); filteredData = filteredData.filter(row => keepSet.has(row[fieldName])); } } continue; } const uniqueValues = [...new Set(filteredData.map(r => r[fieldName]))]; nominalCounts[channel] = Math.min(uniqueValues.length, maxToKeep); if (uniqueValues.length > maxToKeep) { const valuesToKeep = strategy(channel, fieldName, uniqueValues, maxToKeep, strategyContext); const omittedCount = uniqueValues.length - valuesToKeep.length; const placeholder = `...${omittedCount} items omitted`; warnings.push({ severity: 'warning', code: 'overflow', message: `${omittedCount} of ${uniqueValues.length} values in '${fieldName}' were omitted (showing top ${valuesToKeep.length}).`, channel, field: fieldName, }); truncations.push({ severity: 'warning', code: 'overflow', message: `${omittedCount} of ${uniqueValues.length} values in '${fieldName}' were omitted (showing top ${valuesToKeep.length}).`, channel, field: fieldName, keptValues: valuesToKeep, omittedCount, placeholder, }); // Filter data rows (except for color — we keep all rows but style the legend) if (channel !== 'color') { filteredData = filteredData.filter(row => valuesToKeep.includes(row[fieldName])); } } } return { filteredData, nominalCounts, truncations, warnings }; } // --------------------------------------------------------------------------- // Default overflow strategy // --------------------------------------------------------------------------- /** * Default overflow strategy: decides which discrete values to keep. * * - Connected marks (line/area/trail): preserve natural order (first N) * - User-specified sort: respect it * - Auto-sort: sort by quantitative opposite axis or color, keep top N * - Bar charts: sum-aggregate for sort (total bar height matters) */ const defaultOverflowStrategy: OverflowStrategy = ( channel, fieldName, uniqueValues, maxToKeep, context, ) => { const { data, channelSemantics, encodings, allMarkTypes } = context; // Connected marks need chronological/natural order preserved const hasConnectedMark = allMarkTypes.has('line') || allMarkTypes.has('area') || allMarkTypes.has('trail'); // Determine sort intent from user encodings const encoding = encodings[channel]; const sortBy = encoding?.sortBy; const sortOrder = encoding?.sortOrder; // Infer sort field and direction let sortField: string | undefined; let sortFieldType: string | undefined; let isDescending = true; if (sortBy) { // User explicitly specified sort if (sortBy === 'x' || sortBy === 'y' || sortBy === 'color') { const sortCS = channelSemantics[sortBy]; sortField = sortCS?.field; sortFieldType = sortCS?.type; isDescending = sortOrder === 'descending' || (sortOrder !== 'ascending' && sortBy !== channel); } else { // Custom sort list — respect insertion order try { const sortedList = JSON.parse(sortBy); if (Array.isArray(sortedList)) { const orderedValues = (sortOrder === 'descending') ? sortedList.reverse() : sortedList; return orderedValues.filter((v: any) => uniqueValues.includes(v)).slice(0, maxToKeep); } } catch { // not a JSON list, fall through } isDescending = sortOrder === 'descending'; } } else { // Auto-detect sort: quantitative opposite axis or quantitative color const oppositeChannel = channel === 'x' ? 'y' : channel === 'y' ? 'x' : undefined; const colorCS = channelSemantics.color; const oppositeCS = oppositeChannel ? channelSemantics[oppositeChannel] : undefined; // For rect marks (heatmaps), color is the primary value — don't auto-sort by it const markType = allMarkTypes.has('rect') ? 'rect' : undefined; if (markType !== 'rect' && colorCS?.type === 'quantitative') { sortField = colorCS.field; sortFieldType = colorCS.type; } else if (oppositeCS?.type === 'quantitative') { sortField = oppositeCS.field; sortFieldType = oppositeCS.type; } else { isDescending = false; } } // For quantitative field values treated as nominal, sort numerically const fieldOriginalType = inferVisCategory(data.map(r => r[fieldName])); if (fieldOriginalType === 'quantitative' || channel === 'color') { return uniqueValues.sort((a, b) => a - b) .slice(0, maxToKeep); } // Facet channels: first N if (channel === 'column' || channel === 'row') { return uniqueValues.slice(0, maxToKeep); } // Connected marks: preserve natural order if (hasConnectedMark) { return uniqueValues.slice(0, maxToKeep); } // Sort by aggregate of the quantitative sort field if (sortField && sortFieldType === 'quantitative') { // Bar charts: sum aggregate (total bar height). Others: max. let aggregateOp = Math.max; let initialValue = -Infinity; if (allMarkTypes.has('bar') && sortField !== channelSemantics.color?.field) { aggregateOp = (x: number, y: number) => x + y; initialValue = 0; } const valueAggregates = new Map<string, number>(); for (const row of data) { const fieldValue = row[fieldName]; const sortValue = row[sortField] || 0; if (valueAggregates.has(fieldValue)) { valueAggregates.set(fieldValue, aggregateOp(valueAggregates.get(fieldValue)!, sortValue)); } else { valueAggregates.set(fieldValue, aggregateOp(initialValue, sortValue)); } } return Array.from(valueAggregates.entries()) .map(([value, agg]) => ({ value, agg })) .sort((a, b) => isDescending ? b.agg - a.agg : a.agg - b.agg) .slice(0, maxToKeep) .map(v => v.value); } // Descending explicit sort: reverse then take first N if (sortOrder === 'descending') { return uniqueValues.reverse().slice(0, maxToKeep); } // Default: first N values return uniqueValues.slice(0, maxToKeep); }; ================================================ FILE: src/lib/agents-chart/core/index.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * @module agents-chart/core * * Target-language-agnostic core of the chart engine. * * Contains semantic type system, layout computation, overflow filtering, * decision functions, and shared type definitions. No Vega-Lite or other * rendering-library dependencies. */ // Types & constants export { channels, channelGroups, type ChartAssemblyInput, type ChartEncoding, type AssembleOptions, type ChartTemplateDef, type ChartWarning, type ChannelSemantics, type SemanticResult, type MarkCognitiveChannel, type LayoutDeclaration, type TruncationWarning, type LayoutResult, type InstantiateContext, type ChartPropertyDef, type OverflowStrategy, type OverflowStrategyContext, type OverflowResult, type ChannelBudgets, } from './types'; // Semantic type system export { SemanticTypes, type SemanticType, type VisCategory, type ZeroClass, type ZeroDecision, getVisCategory, inferVisCategory, getZeroClass, computeZeroDecision, computePaddedDomain, isMeasureType, isTimeSeriesType, isCategoricalType, isOrdinalType, isGeoType, getRecommendedColorScheme, inferOrdinalSortOrder, } from './semantic-types'; // Reusable decision functions export { resolveEncodingType, computeElasticBudget, computeAxisStep, computeFacetLayout, computeLabelSizing, computeOverflow, computeGasPressure, DEFAULT_GAS_PRESSURE_PARAMS, type EncodingTypeDecision, type ElasticStretchParams, type ElasticBudget, type AxisStepDecision, type FacetLayoutDecision, type LabelSizingDecision, type OverflowDecision, type GasPressureParams, type GasPressureDecision, } from './decisions'; // Phase modules (analysis pipeline — VL-free) export { resolveChannelSemantics, convertTemporalData } from './resolve-semantics'; export { filterOverflow } from './filter-overflow'; export { computeLayout, computeChannelBudgets } from './compute-layout'; // Recommendation & adaptation engine export { adaptChannels, recommendChannels, type SemanticRole, } from './recommendation'; // Field semantics export { type SemanticAnnotation, type FormatSpec, type DomainConstraint, type TickConstraint, type ColorSchemeHint, type DivergingInfo, type FieldSemantics, resolveFieldSemantics, normalizeAnnotation, getRegistryEntry, toTypeString, resolveFormat, resolveDefaultVisType, resolveAggregationDefault, resolveZeroClassFromAnnotation, resolveScaleType, resolveDomainConstraint, resolveTickConstraint, resolveCanonicalOrder, resolveCyclic, resolveReversed, resolveNice, resolveDivergingInfo, resolveColorSchemeHint, resolveBinningSuggested, resolveStackable, resolveSortDirection, } from './field-semantics'; ================================================ FILE: src/lib/agents-chart/core/recommendation.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ============================================================================= * CHART RECOMMENDATION & ADAPTATION ENGINE * ============================================================================= * * Backend-agnostic logic for two chart operations: * * 1. **Adaptation** — remapping encoding channels when switching chart types * (e.g. Bar→Pie: x→color, y→size because the semantic roles differ). * * 2. **Recommendation** — suggesting which data fields best fit each * encoding channel for a given chart type, using semantic types, * cardinality constraints, and data-fitness tests. * * Both functions operate on plain field names (string→string maps) with no * UI-layer dependencies. Backend-specific wrappers (vegalite/recommendation.ts * etc.) filter results to channels that actually exist in that backend's * template registry. * * ============================================================================= */ import { inferVisCategory, getVisCategory, isMeasureType, isTimeSeriesType, isCategoricalType, isOrdinalType, isGeoType, isGeoCoordinateType, isNonMeasureNumeric, } from './semantic-types'; // ============================================================================ // 1. Semantic Role System (used by adaptation) // ============================================================================ /** * Semantic role of a channel within a specific chart type. */ export type SemanticRole = | 'category' | 'measure' | 'measure2' | 'series' | 'facetCol' | 'facetRow' | 'auxiliary' | 'geo' | 'price'; type ChannelRoleMap = Partial<Record<string, SemanticRole>>; // ── Chart Families ────────────────────────────────────────────────────── /** Standard x/y charts: x=category, y=measure, color=series */ const FAMILY_XY_STANDARD: ChannelRoleMap = { x: 'category', y: 'measure', color: 'series', opacity: 'auxiliary', size: 'auxiliary', shape: 'auxiliary', detail: 'auxiliary', group: 'series', column: 'facetCol', row: 'facetRow', }; /** Pie-like charts: color=category, size=measure */ const FAMILY_PIE: ChannelRoleMap = { color: 'category', size: 'measure', column: 'facetCol', row: 'facetRow', }; /** Rose chart: polar radial bar — x=category, y=measure, color=series */ const FAMILY_ROSE: ChannelRoleMap = { x: 'category', y: 'measure', color: 'series', column: 'facetCol', row: 'facetRow', }; /** Radar chart */ const FAMILY_RADAR: ChannelRoleMap = { x: 'category', y: 'measure', color: 'series', column: 'facetCol', row: 'facetRow', }; /** Map charts: latitude/longitude = geo */ const FAMILY_MAP: ChannelRoleMap = { latitude: 'geo', longitude: 'geo', color: 'series', size: 'auxiliary', opacity: 'auxiliary', }; /** Candlestick: x=category, open/high/low/close=price */ const FAMILY_CANDLESTICK: ChannelRoleMap = { x: 'category', open: 'price', high: 'price', low: 'price', close: 'price', column: 'facetCol', row: 'facetRow', }; /** Histogram: x=measure (binned) */ const FAMILY_HISTOGRAM: ChannelRoleMap = { x: 'measure', color: 'series', column: 'facetCol', row: 'facetRow', }; /** Density */ const FAMILY_DENSITY: ChannelRoleMap = { x: 'measure', color: 'series', column: 'facetCol', row: 'facetRow', }; /** Heatmap: x=category, y=category, color=measure */ const FAMILY_HEATMAP: ChannelRoleMap = { x: 'category', y: 'category', color: 'measure', column: 'facetCol', row: 'facetRow', }; /** Gauge: size=measure */ const FAMILY_GAUGE: ChannelRoleMap = { size: 'measure', column: 'facetCol', }; /** Funnel: y=category, size=measure */ const FAMILY_FUNNEL: ChannelRoleMap = { y: 'category', size: 'measure', }; /** Treemap / Sunburst */ const FAMILY_TREEMAP: ChannelRoleMap = { color: 'category', size: 'measure', detail: 'auxiliary', }; /** Sankey */ const FAMILY_SANKEY: ChannelRoleMap = { x: 'category', y: 'category', size: 'measure', }; /** Range charts with x2/y2 */ const FAMILY_RANGE: ChannelRoleMap = { x: 'category', y: 'measure', x2: 'measure2', y2: 'measure2', color: 'series', opacity: 'auxiliary', column: 'facetCol', row: 'facetRow', }; // ── Chart → Family lookup ─────────────────────────────────────────────── const CHART_ROLE_MAP: Record<string, ChannelRoleMap> = { // Axis-based (x/y standard) 'Bar Chart': FAMILY_XY_STANDARD, 'Pyramid Chart': FAMILY_XY_STANDARD, 'Grouped Bar Chart': FAMILY_XY_STANDARD, 'Stacked Bar Chart': FAMILY_XY_STANDARD, 'Lollipop Chart': FAMILY_XY_STANDARD, 'Waterfall Chart': FAMILY_XY_STANDARD, 'Line Chart': FAMILY_XY_STANDARD, 'Dotted Line Chart': FAMILY_XY_STANDARD, 'Bump Chart': FAMILY_XY_STANDARD, 'Area Chart': FAMILY_XY_STANDARD, 'Streamgraph': FAMILY_XY_STANDARD, 'Scatter Plot': FAMILY_XY_STANDARD, 'Regression': FAMILY_XY_STANDARD, 'Ranged Dot Plot': FAMILY_XY_STANDARD, 'Boxplot': FAMILY_XY_STANDARD, 'Strip Plot': FAMILY_XY_STANDARD, // Custom marks 'Custom Point': FAMILY_XY_STANDARD, 'Custom Line': FAMILY_XY_STANDARD, 'Custom Bar': FAMILY_XY_STANDARD, 'Custom Rect': FAMILY_RANGE, 'Custom Area': FAMILY_RANGE, // Pie-like 'Pie Chart': FAMILY_PIE, // Polar 'Rose Chart': FAMILY_ROSE, 'Radar Chart': FAMILY_RADAR, // Heatmap 'Heatmap': FAMILY_HEATMAP, // Histogram / Density 'Histogram': FAMILY_HISTOGRAM, 'Density Plot': FAMILY_DENSITY, // Geographic 'US Map': FAMILY_MAP, 'World Map': FAMILY_MAP, // Financial 'Candlestick Chart': FAMILY_CANDLESTICK, // ECharts-only 'Gauge Chart': FAMILY_GAUGE, 'Funnel Chart': FAMILY_FUNNEL, 'Treemap': FAMILY_TREEMAP, 'Sunburst Chart': FAMILY_TREEMAP, 'Sankey Diagram': FAMILY_SANKEY, }; // ── Role helpers ──────────────────────────────────────────────────────── function getChannelRole(chartType: string, channel: string): SemanticRole { const roleMap = CHART_ROLE_MAP[chartType]; if (roleMap && channel in roleMap) return roleMap[channel]!; if (channel === 'column') return 'facetCol'; if (channel === 'row') return 'facetRow'; return 'auxiliary'; } function findChannelsByRole(chartType: string, templateChannels: string[], role: SemanticRole): string[] { return templateChannels.filter(ch => getChannelRole(chartType, ch) === role); } /** Fallback chain when target has no channel with the exact source role. */ const FALLBACK_CHAIN: Partial<Record<SemanticRole, SemanticRole[]>> = { measure2: ['measure', 'auxiliary'], series: ['auxiliary'], category: ['series', 'auxiliary'], measure: ['auxiliary'], geo: ['category'], price: ['measure', 'auxiliary'], }; const ROLE_PRIORITY: Record<SemanticRole, number> = { category: 0, measure: 1, series: 2, facetCol: 3, facetRow: 4, measure2: 5, auxiliary: 6, geo: 7, price: 8, }; // ============================================================================ // 2. Adaptation — adapt channel encodings across chart types // ============================================================================ /** * Adapt encoding channels from one chart type to another. * * When `data` is provided, uses **recommendation-based adaptation**: re-runs * the recommendation engine with a strong preference for the currently-assigned * fields, letting the target chart's field-type preferences take effect * (e.g. Line Chart prefers temporal on x). Remaining empty channels are * optionally filled from all available fields. * * When no data is provided, falls back to **structural role-based** adaptation * (pure channel remapping by semantic role). * * @param sourceType The current chart type name (e.g. "Bar Chart") * @param targetType The target chart type name (e.g. "Pie Chart") * @param targetChannels Available channels on the target template * @param encodings Current channel→fieldName map (only filled channels) * @param data (optional) Array of data row objects * @param semanticTypes (optional) Field→semantic-type map * @returns New channel→fieldName map for the target */ export function adaptChannels( sourceType: string, targetType: string, targetChannels: string[], encodings: Record<string, string>, data?: any[], semanticTypes?: Record<string, string>, recommendFn?: RecommendFn, ): Record<string, string> { // Recommendation-based adaptation when data is available if (data && data.length > 0) { return adaptViaRecommendation(sourceType, targetType, targetChannels, encodings, data, semanticTypes ?? {}, recommendFn ?? getRecommendation); } // Fallback: structural role-based adaptation return adaptViaRoles(sourceType, targetType, targetChannels, encodings); } /** * Edit-distance-based adaptation: find the minimum-cost mapping from existing * field→channel assignments to target chart channels. * * Cost model: * 0 — field stays in the same channel (compatible type) * 0.5 — field moves to a channel with the same semantic role * (e.g., color→group — both "series" role) * 1 — field moves to a channel with a different but compatible role * 1.5 — field is dropped (not assigned to any target channel) * (slightly above move-cost so we prefer keeping fields when tied) * ∞ — field type is incompatible with target channel * * After the minimum-cost assignment, remaining empty target channels are * filled via the recommendation engine. */ function adaptViaRecommendation( sourceType: string, targetType: string, targetChannels: string[], encodings: Record<string, string>, data: any[], semanticTypes: Record<string, string>, recommendFn: RecommendFn, ): Record<string, string> { // --- Pre-process: handle facet channels and filter data --- const FACET_CHANNELS = ['column', 'row']; let facetedData = data; const prePinned: Record<string, string> = {}; const prePinnedFields = new Set<string>(); for (const ch of FACET_CHANNELS) { const field = encodings[ch]; if (field && targetChannels.includes(ch)) { prePinned[ch] = field; prePinnedFields.add(field); if (facetedData.length > 0) { const firstVal = facetedData[0][field]; facetedData = facetedData.filter(row => row[field] === firstVal); } } } const tv = buildTableView(facetedData, semanticTypes); // --- Type compatibility check --- const isFieldCompatibleWithRole = (role: SemanticRole, field: string): boolean => { const ft = tv.fieldType[field] ?? 'nominal'; const st = tv.fieldSemanticType[field] ?? ''; const card = tv.fieldLevels[field]?.length ?? 0; switch (role) { case 'category': return isDiscreteLike(ft, st, card); case 'measure': return isQuantitativeField(ft, st); case 'series': return isDiscreteLike(ft, st, card); case 'geo': return isGeoCoordinateType(st) || ft === 'quantitative'; case 'facetCol': case 'facetRow': return isDiscreteLike(ft, st, card); case 'auxiliary': return true; default: return true; } }; // --- Cost function for assigning field (from srcCh) to targetCh --- const assignCost = (srcCh: string, field: string, targetCh: string): number => { const targetRole = getChannelRole(targetType, targetCh); if (!isFieldCompatibleWithRole(targetRole, field)) return Infinity; // Same channel name → free if (srcCh === targetCh) return 0; // Same semantic role (e.g., color→group, both "series") → small cost const srcRole = getChannelRole(sourceType, srcCh); if (srcRole === targetRole) return 0.5; // Different role but type-compatible → higher cost return 1; }; const COST_DROP = 1.5; // slightly above move cost to prefer keeping fields // --- Collect source entries (excluding pre-pinned facets) --- const entries = Object.entries(encodings) .filter(([ch, f]) => f && !FACET_CHANNELS.includes(ch) && !prePinnedFields.has(f)); // Available target channels (exclude pre-pinned ones) const availableTargets = targetChannels.filter(ch => !(ch in prePinned)); // --- Solve minimum-cost assignment via branch-and-bound --- // With typically ≤5 fields and ≤8 channels, this is instant. let bestCost = Infinity; let bestAssignment: Record<string, string> = {}; const usedTargets = new Set<string>(); function solve( idx: number, currentCost: number, assignment: Record<string, string>, ): void { if (currentCost >= bestCost) return; // prune if (idx === entries.length) { bestCost = currentCost; bestAssignment = { ...assignment }; return; } const [srcCh, field] = entries[idx]; // Option A: assign to each available target channel for (const tch of availableTargets) { if (usedTargets.has(tch)) continue; const cost = assignCost(srcCh, field, tch); if (cost === Infinity) continue; usedTargets.add(tch); assignment[tch] = field; solve(idx + 1, currentCost + cost, assignment); delete assignment[tch]; usedTargets.delete(tch); } // Option B: drop the field solve(idx + 1, currentCost + COST_DROP, assignment); } solve(0, 0, {}); // --- Merge pre-pinned facets + solver result --- const result: Record<string, string> = { ...prePinned, ...bestAssignment }; const usedFields = new Set(Object.values(result)); // --- Fill remaining empty channels via recommendation --- const emptyChannels = targetChannels.filter(ch => !(ch in result)); if (emptyChannels.length > 0) { const rec = recommendFn(targetType, buildTableView(facetedData, semanticTypes)); for (const ch of emptyChannels) { if (rec[ch] && !usedFields.has(rec[ch])) { result[ch] = rec[ch]; usedFields.add(rec[ch]); } } } return result; } /** * Structural role-based adaptation: remap channels by semantic role when no * data is available. Each field keeps its role (category, measure, series…) * and is placed into the target channel that has the same role. */ function adaptViaRoles( sourceType: string, targetType: string, targetChannels: string[], encodings: Record<string, string>, ): Record<string, string> { const result: Record<string, string> = {}; // Collect filled encodings with their semantic roles const filledEncodings: { channel: string; role: SemanticRole; field: string }[] = []; for (const [ch, field] of Object.entries(encodings)) { if (field) { filledEncodings.push({ channel: ch, role: getChannelRole(sourceType, ch), field }); } } // Sort by priority: category > measure > series > … filledEncodings.sort((a, b) => ROLE_PRIORITY[a.role] - ROLE_PRIORITY[b.role]); const assigned = new Set<string>(); for (const { channel: srcCh, role: srcRole, field } of filledEncodings) { let placed = false; // (a) Direct match: same channel name with same role if (targetChannels.includes(srcCh) && !assigned.has(srcCh)) { if (getChannelRole(targetType, srcCh) === srcRole) { result[srcCh] = field; assigned.add(srcCh); placed = true; } } if (!placed) { // (b) Role match: find empty target channel with same role placed = tryAssign(srcRole, field, targetType, targetChannels, result, assigned, srcCh); } if (!placed) { // (c) Fallback chain const chain = FALLBACK_CHAIN[srcRole]; if (chain) { for (const fallbackRole of chain) { placed = tryAssign(fallbackRole, field, targetType, targetChannels, result, assigned, srcCh); if (placed) break; } } } // (d) If not placed, encoding is dropped } return result; } function tryAssign( role: SemanticRole, field: string, targetType: string, targetChannels: string[], result: Record<string, string>, assigned: Set<string>, preferredName?: string, ): boolean { const candidates = findChannelsByRole(targetType, targetChannels, role) .filter(ch => !assigned.has(ch)); if (candidates.length === 0) return false; const best = preferredName && candidates.includes(preferredName) ? preferredName : candidates[0]; result[best] = field; assigned.add(best); return true; } // ============================================================================ // 3. Internal Table View — field classification from data + semantic types // ============================================================================ export interface InternalTableView { names: string[]; fieldType: Record<string, string>; fieldSemanticType: Record<string, string>; fieldLevels: Record<string, any[]>; rows: any[]; /** Fields the user has already assigned — preferred during pick(). */ preferredFields?: Set<string>; } /** * Recommendation function signature — used to inject backend-specific * chart type handlers into adaptViaRecommendation. */ export type RecommendFn = (chartType: string, tv: InternalTableView) => Record<string, string>; export function buildTableView(data: any[], semanticTypes: Record<string, string>): InternalTableView { const names = data.length > 0 ? Object.keys(data[0]) : []; const fieldType: Record<string, string> = {}; const fieldSemanticType: Record<string, string> = {}; const fieldLevels: Record<string, any[]> = {}; for (const name of names) { const values = data.map(r => r[name]); const semanticType = semanticTypes[name] || ''; fieldType[name] = (semanticType && getVisCategory(semanticType)) || inferVisCategory(values); fieldSemanticType[name] = semanticType; fieldLevels[name] = [...new Set(data.map(r => r[name]).filter(v => v != null))]; } return { names, fieldType, fieldSemanticType, fieldLevels, rows: data }; } // ============================================================================ // 4. Preference-Based Assignment Solver // ============================================================================ /** Preference tiers for a (channel, field) pair. */ export const Pref = { STRONG: 3, OK: 2, WEAK: 1, EXCLUDE: -Infinity } as const; export type PrefScore = number; /** * A scoring function that returns a preference score for a field being * assigned to a particular channel. Return Pref.EXCLUDE to forbid the * assignment. */ export type ChannelPrefFn = ( name: string, type: string, semanticType: string, cardinality: number, hasLevels: boolean, ) => PrefScore; /** * Solve the assignment problem: given N channels each with a preference * scoring function, and a set of candidate fields, find the assignment of * distinct fields to channels that maximises total score. * * Returns the best assignment as a Record<channel, fieldName>, or {} if * no valid (all-channels-filled) assignment exists. * * Complexity: O(C! · F^C) where C = number of channels, F = number of * fields. Fine for C ≤ 5 and typical field counts. */ export function resolveAssignment( tv: InternalTableView, used: Set<string>, channelPrefs: { channel: string; pref: ChannelPrefFn }[], ): Record<string, string> { // Build the score matrix: scores[channelIdx][fieldIdx] const candidates: string[] = tv.names.filter(n => !used.has(n) && (!isLikelyIdentifierOrRank(n) || tv.preferredFields?.has(n))); const C = channelPrefs.length; const F = candidates.length; if (F < C) return {}; // Pre-compute all scores const scores: number[][] = []; for (let ci = 0; ci < C; ci++) { scores[ci] = []; for (let fi = 0; fi < F; fi++) { const name = candidates[fi]; const type = tv.fieldType[name] ?? 'nominal'; const st = tv.fieldSemanticType[name] ?? ''; const card = tv.fieldLevels[name]?.length ?? 0; scores[ci][fi] = channelPrefs[ci].pref(name, type, st, card, card > 0); } } // Brute-force search over all C-permutations of F fields let bestScore = -Infinity; let bestAssign: number[] | undefined; const perm = new Array<number>(C); const usedF = new Uint8Array(F); function search(depth: number, totalScore: number) { if (depth === C) { if (totalScore > bestScore) { bestScore = totalScore; bestAssign = [...perm]; } return; } for (let fi = 0; fi < F; fi++) { if (usedF[fi]) continue; const s = scores[depth][fi]; if (s === -Infinity) continue; // excluded if (totalScore + s <= bestScore - (C - depth - 1) * Pref.STRONG) continue; // prune perm[depth] = fi; usedF[fi] = 1; search(depth + 1, totalScore + s); usedF[fi] = 0; } } search(0, 0); if (!bestAssign) return {}; const result: Record<string, string> = {}; for (let ci = 0; ci < C; ci++) { const fieldName = candidates[bestAssign[ci]]; result[channelPrefs[ci].channel] = fieldName; used.add(fieldName); } return result; } // ============================================================================ // 5. Field Classification Utilities (renumbered) // ============================================================================ function isTemporalField(type: string, semanticType: string): boolean { return type === 'temporal' || isTimeSeriesType(semanticType); } function isQuantitativeField(type: string, semanticType: string): boolean { if (isTemporalField(type, semanticType)) return false; if (type !== 'quantitative') return false; if (isNonMeasureNumeric(semanticType)) return false; return isMeasureType(semanticType) || semanticType === ''; } function isOrdinalField(type: string, semanticType: string, hasLevels: boolean): boolean { if (hasLevels) return true; return isOrdinalType(semanticType); } function isCategoricalFieldCheck(type: string, semanticType: string): boolean { if (isTemporalField(type, semanticType)) return false; if (isQuantitativeField(type, semanticType)) return false; return type === 'nominal' || isCategoricalType(semanticType); } function isDiscreteLike(type: string, semanticType: string, cardinality: number, maxCard = 50): boolean { if (isCategoricalFieldCheck(type, semanticType)) return true; if (isTemporalField(type, semanticType)) return true; if (isOrdinalType(semanticType)) return true; if (type === 'quantitative' && cardinality > 0 && cardinality <= maxCard) return true; return false; } export function nameMatches(name: string, patterns: string[]): boolean { const lower = name.toLowerCase(); return patterns.some(p => lower === p) || patterns.some(p => lower.includes(p)); } function isLikelyIdentifierOrRank(name: string): boolean { const lower = name.toLowerCase(); const idPatterns = ['rank', 'id', 'index', 'idx', 'row', 'order', 'position', 'pos']; return idPatterns.some(p => lower === p || lower.endsWith('_' + p) || lower.endsWith(p)); } // ============================================================================ // 6. Field Picker Utilities // ============================================================================ export function pick( tv: InternalTableView, used: Set<string>, predicate: (name: string, type: string, semanticType: string, cardinality: number, hasLevels: boolean) => boolean, ): string | undefined { const candidates: string[] = []; for (const name of tv.names) { if (used.has(name)) continue; const type = tv.fieldType[name] ?? 'nominal'; const semanticType = tv.fieldSemanticType[name] ?? ''; const cardinality = tv.fieldLevels[name]?.length ?? 0; const hasLevels = cardinality > 0; if (predicate(name, type, semanticType, cardinality, hasLevels)) { candidates.push(name); } } if (candidates.length === 0) return undefined; // Prefer fields from the user's existing encodings when available if (tv.preferredFields) { const preferred = candidates.filter(n => tv.preferredFields!.has(n)); if (preferred.length > 0) { const chosen = preferred[Math.floor(Math.random() * preferred.length)]; used.add(chosen); return chosen; } } const chosen = candidates[Math.floor(Math.random() * candidates.length)]; used.add(chosen); return chosen; } export const pickQuantitative = (tv: InternalTableView, u: Set<string>) => pick(tv, u, (name, ty, st) => isQuantitativeField(ty, st) && (!isLikelyIdentifierOrRank(name) || !!tv.preferredFields?.has(name))); export const pickTemporal = (tv: InternalTableView, u: Set<string>) => pick(tv, u, (_n, ty, st) => isTemporalField(ty, st)); export const pickNominal = (tv: InternalTableView, u: Set<string>) => pick(tv, u, (_n, ty, st) => isCategoricalFieldCheck(ty, st)); export const pickLowCardNominal = (tv: InternalTableView, u: Set<string>, maxCard = 30) => pick(tv, u, (_n, ty, st, card) => isCategoricalFieldCheck(ty, st) && card > 0 && card <= maxCard); export const pickOrdinal = (tv: InternalTableView, u: Set<string>) => pick(tv, u, (_n, ty, st, _card, hasLevels) => isOrdinalField(ty, st, hasLevels)); export const pickGeo = (tv: InternalTableView, u: Set<string>) => pick(tv, u, (_n, _ty, st) => isGeoType(st)); export const pickDiscrete = (tv: InternalTableView, u: Set<string>) => pick(tv, u, (name, ty, st, card) => isDiscreteLike(ty, st, card) && (!isLikelyIdentifierOrRank(name) || !!tv.preferredFields?.has(name))); export const pickLowCardDiscrete = (tv: InternalTableView, u: Set<string>, maxCard = 30) => pick(tv, u, (name, ty, st, card) => isDiscreteLike(ty, st, card, maxCard) && card > 0 && card <= maxCard && (!isLikelyIdentifierOrRank(name) || !!tv.preferredFields?.has(name)) ); export const pickSeriesAxis = (tv: InternalTableView, u: Set<string>) => pickTemporal(tv, u) ?? pickOrdinal(tv, u) ?? pickNominal(tv, u); export const pickQuantitativeByName = (tv: InternalTableView, u: Set<string>, patterns: string[]) => pick(tv, u, (name, ty, st) => isQuantitativeField(ty, st) && nameMatches(name, patterns)); export function pickAllQuantitative(tv: InternalTableView, used: Set<string>): string[] { const result: string[] = []; for (const name of tv.names) { if (used.has(name)) continue; const type = tv.fieldType[name] ?? 'nominal'; const semanticType = tv.fieldSemanticType[name] ?? ''; if (isQuantitativeField(type, semanticType) && (!isLikelyIdentifierOrRank(name) || tv.preferredFields?.has(name))) { result.push(name); } } for (const name of result) used.add(name); return result; } // ============================================================================ // 7. Data Fitness Tests // ============================================================================ export function hasMultipleValuesPerField(tv: InternalTableView, fieldName: string): boolean { if (!fieldName || !tv.rows || tv.rows.length === 0) return false; const seen = new Set<any>(); for (const row of tv.rows) { const val = row[fieldName]; if (seen.has(val)) return true; seen.add(val); } return false; } function isValidGroupingField(tv: InternalTableView, xField: string, colorField: string): boolean { if (!xField || !colorField || !tv.rows || tv.rows.length === 0) return false; const seen = new Set<string>(); for (const row of tv.rows) { const key = `${row[xField]}|||${row[colorField]}`; if (seen.has(key)) return false; seen.add(key); } return true; } export function pickValidGroupingField( tv: InternalTableView, used: Set<string>, xField: string, maxCard = 20, ): string | undefined { const candidates: string[] = []; for (const name of tv.names) { if (used.has(name)) continue; const type = tv.fieldType[name] ?? 'nominal'; const semanticType = tv.fieldSemanticType[name] ?? ''; const cardinality = tv.fieldLevels[name]?.length ?? 0; if (!isDiscreteLike(type, semanticType, cardinality, maxCard)) continue; if (cardinality <= 0 || cardinality > maxCard) continue; if (isLikelyIdentifierOrRank(name) && !tv.preferredFields?.has(name)) continue; if (isValidGroupingField(tv, xField, name)) candidates.push(name); } if (candidates.length === 0) return undefined; // Prefer fields from existing encodings if (tv.preferredFields) { const preferred = candidates.filter(n => tv.preferredFields!.has(n)); if (preferred.length > 0) { const chosen = preferred[Math.floor(Math.random() * preferred.length)]; used.add(chosen); return chosen; } } const chosen = candidates[Math.floor(Math.random() * candidates.length)]; used.add(chosen); return chosen; } export function isValidLineSeriesData(tv: InternalTableView, xField: string, colorField?: string): boolean { if (!tv.rows || tv.rows.length === 0) return false; const xColorCombinations = new Set<string>(); const colorGroupCounts = new Map<string, number>(); for (const row of tv.rows) { const xVal = row[xField]; const colorVal = colorField ? row[colorField] : '__single__'; const xColorKey = `${xVal}|||${colorVal}`; if (xColorCombinations.has(xColorKey)) return false; xColorCombinations.add(xColorKey); colorGroupCounts.set(colorVal, (colorGroupCounts.get(colorVal) ?? 0) + 1); } let validGroups = 0; let totalGroups = 0; for (const count of colorGroupCounts.values()) { totalGroups++; if (count >= 2) validGroups++; } return totalGroups > 0 && (validGroups / totalGroups) > 0.5; } export function pickLineChartColorField( tv: InternalTableView, used: Set<string>, xField: string, maxCard = 20, ): string | undefined { const candidates: string[] = []; for (const name of tv.names) { if (used.has(name)) continue; const type = tv.fieldType[name] ?? 'nominal'; const semanticType = tv.fieldSemanticType[name] ?? ''; const cardinality = tv.fieldLevels[name]?.length ?? 0; if (!isDiscreteLike(type, semanticType, cardinality, maxCard)) continue; if (cardinality <= 0 || cardinality > maxCard) continue; if (isLikelyIdentifierOrRank(name) && !tv.preferredFields?.has(name)) continue; if (isValidLineSeriesData(tv, xField, name)) candidates.push(name); } if (candidates.length === 0) return undefined; // Prefer fields from existing encodings if (tv.preferredFields) { const preferred = candidates.filter(n => tv.preferredFields!.has(n)); if (preferred.length > 0) { const chosen = preferred[Math.floor(Math.random() * preferred.length)]; used.add(chosen); return chosen; } } const chosen = candidates[Math.floor(Math.random() * candidates.length)]; used.add(chosen); return chosen; } function calculateMultiplicity(tv: InternalTableView, xField: string, colorField?: string): number { if (!tv.rows || tv.rows.length === 0) return 1; const groups = new Set<string>(); for (const row of tv.rows) { const key = colorField ? `${row[xField]}|||${row[colorField]}` : `${row[xField]}`; groups.add(key); } return tv.rows.length / groups.size; } export function pickBestGroupingField( tv: InternalTableView, used: Set<string>, xField: string, maxMultiplicity = 5, ): string | undefined { const baseMultiplicity = calculateMultiplicity(tv, xField); if (baseMultiplicity <= 1.0) return undefined; let bestField: string | undefined; let bestMultiplicity = baseMultiplicity; for (const name of tv.names) { if (used.has(name)) continue; const type = tv.fieldType[name] ?? 'nominal'; const semanticType = tv.fieldSemanticType[name] ?? ''; const cardinality = tv.fieldLevels[name]?.length ?? 0; if (!isDiscreteLike(type, semanticType, cardinality)) continue; if (isLikelyIdentifierOrRank(name) && !tv.preferredFields?.has(name)) continue; const multiplicity = calculateMultiplicity(tv, xField, name); if (multiplicity < bestMultiplicity) { bestMultiplicity = multiplicity; bestField = name; if (multiplicity <= 1.0) break; } } if (bestField && bestMultiplicity < baseMultiplicity && bestMultiplicity <= maxMultiplicity) { used.add(bestField); return bestField; } return undefined; } // ============================================================================ // 8. Per-Chart-Type Recommendation Heuristics // ============================================================================ /** * Recommend channel→fieldName assignments for a given chart type. * * Pure logic: takes raw data rows + semantic type annotations, returns * a channel→fieldName map. Backend wrappers filter to valid channels. * * @param chartType Chart template name (e.g. "Bar Chart") * @param data Array of row objects * @param semanticTypes Field→semantic-type map (e.g. { weight: "Quantity" }) * @returns channel→fieldName map */ export function recommendChannels( chartType: string, data: any[], semanticTypes: Record<string, string>, recommendFn?: RecommendFn, ): Record<string, string> { const fn = recommendFn ?? getRecommendation; return fn(chartType, buildTableView(data, semanticTypes)); } /** * Core recommendation engine — handles chart types shared across 2+ backends. * Backend-specific chart types should be handled in their own recommendation files * by extending this function. * * Exported so that backend wrappers can call it as a fallback in their own * extended `getRecommendation` implementations. */ export function getRecommendation(chartType: string, tv: InternalTableView): Record<string, string> { const used = new Set<string>(); const rec: Record<string, string> = {}; const assign = (channel: string, fieldName: string | undefined) => { if (fieldName) rec[channel] = fieldName; }; switch (chartType) { case 'Scatter Plot': { const yField = pickQuantitative(tv, used) ?? pickTemporal(tv, used) ?? pickNominal(tv, used); const xField = pickQuantitative(tv, used) ?? pickTemporal(tv, used) ?? pickNominal(tv, used); if (!xField || !yField) return {}; assign('x', xField); assign('y', yField); assign('color', pickLowCardNominal(tv, used)); break; } case 'Bar Chart': case 'Stacked Bar Chart': { const xField = pickDiscrete(tv, used); const yField = pickQuantitative(tv, used); if (!xField || !yField) return {}; assign('x', xField); assign('y', yField); if (hasMultipleValuesPerField(tv, xField)) { assign('color', pickBestGroupingField(tv, used, xField)); } break; } case 'Grouped Bar Chart': { const xField = pickDiscrete(tv, used); const yField = pickQuantitative(tv, used); if (!xField || !yField) return {}; const colorField = pickValidGroupingField(tv, used, xField, 20); if (!colorField) return {}; assign('x', xField); assign('y', yField); assign('color', colorField); break; } case 'Histogram': { const xField = pickQuantitative(tv, used); if (!xField) return {}; assign('x', xField); break; } case 'Heatmap': { // Semantic-first preference scoring. // 1. Semantic type determines preference (temporal, categorical, measure) // 2. Data type (ty) is a safeguard for compatibility const heatmapResult = resolveAssignment(tv, used, [ { channel: 'x', pref: (_n, ty, st, card) => { // Semantic preference if (isTimeSeriesType(st)) return Pref.STRONG; if (isCategoricalType(st)) return Pref.OK; if (isOrdinalType(st)) return Pref.OK; if (isNonMeasureNumeric(st)) return Pref.OK; // Data type safeguard: allow nominal or low-card continuous if (ty === 'nominal') return Pref.OK; if (ty === 'temporal') return Pref.STRONG; if (ty === 'quantitative' && card > 0 && card <= 50) return Pref.WEAK; return Pref.EXCLUDE; }, }, { channel: 'y', pref: (_n, ty, st, card) => { // Semantic preference if (isCategoricalType(st)) return Pref.STRONG; if (isTimeSeriesType(st)) return Pref.OK; if (isOrdinalType(st)) return Pref.OK; if (isNonMeasureNumeric(st)) return Pref.OK; // Data type safeguard if (ty === 'nominal') return Pref.STRONG; if (ty === 'temporal') return Pref.OK; if (ty === 'quantitative' && card > 0 && card <= 50) return Pref.WEAK; return Pref.EXCLUDE; }, }, { channel: 'color', pref: (_n, ty, st) => { // Semantic preference: measures are ideal for heatmap color if (isMeasureType(st)) return Pref.STRONG; if (isOrdinalType(st)) return Pref.OK; // Data type safeguard: quantitative with no semantic type if (ty === 'quantitative' && !st) return Pref.STRONG; if (ty === 'temporal') return Pref.WEAK; if (ty === 'nominal') return Pref.WEAK; return Pref.EXCLUDE; }, }, ]); if (!heatmapResult['x'] || !heatmapResult['y'] || !heatmapResult['color']) return {}; assign('x', heatmapResult['x']); assign('y', heatmapResult['y']); assign('color', heatmapResult['color']); break; } case 'Line Chart': { const xField = pickSeriesAxis(tv, used); const yField = pickQuantitative(tv, used); if (!xField || !yField) return {}; assign('x', xField); assign('y', yField); if (!isValidLineSeriesData(tv, xField, undefined)) { const colorField = pickLineChartColorField(tv, used, xField, 20) ?? pickLineChartColorField(tv, used, xField, 200); if (!colorField) return {}; assign('color', colorField); } break; } case 'Boxplot': { const xField = pickDiscrete(tv, used); const yField = pickQuantitative(tv, used); if (!xField || !yField) return {}; assign('x', xField); assign('y', yField); break; } case 'Pie Chart': { const sizeField = pickQuantitative(tv, used); const colorField = pickLowCardDiscrete(tv, used, 12); if (!sizeField || !colorField) return {}; assign('size', sizeField); assign('color', colorField); break; } case 'Area Chart': { const xField = pickSeriesAxis(tv, used); const yField = pickQuantitative(tv, used); if (!xField || !yField) return {}; assign('x', xField); assign('y', yField); assign('color', pickLineChartColorField(tv, used, xField, 20)); break; } case 'Streamgraph': { // Semantic-first preference scoring. // 1. Semantic type determines preference (temporal→x, measure→y, categorical→color) // 2. Data type (ty) is a safeguard for compatibility const streamResult = resolveAssignment(tv, used, [ { channel: 'x', pref: (_n, ty, st, card) => { // Semantic preference: temporal types are ideal series axes if (isTimeSeriesType(st)) return Pref.STRONG; if (isOrdinalType(st)) return Pref.OK; if (isCategoricalType(st)) return Pref.OK; if (isNonMeasureNumeric(st)) return Pref.OK; // Data type safeguard if (ty === 'temporal') return Pref.STRONG; if (ty === 'nominal') return Pref.OK; if (ty === 'quantitative' && card > 0 && card <= 50) return Pref.OK; return Pref.EXCLUDE; }, }, { channel: 'y', pref: (_n, ty, st, card) => { // Semantic preference: true measures are ideal if (isMeasureType(st)) return Pref.STRONG; // Semantic exclusion: temporal / categorical / non-measure // numeric types should not be used as y-axis measures if (isTimeSeriesType(st)) return Pref.EXCLUDE; if (isCategoricalType(st)) return Pref.EXCLUDE; if (isNonMeasureNumeric(st)) return Pref.EXCLUDE; // Data type safeguard: quantitative with no semantic type if (ty === 'quantitative' && !st) return card > 20 ? Pref.STRONG : Pref.OK; return Pref.EXCLUDE; }, }, { channel: 'color', pref: (_n, ty, st, card) => { // Semantic preference if (isCategoricalType(st)) return Pref.STRONG; if (isOrdinalType(st)) return Pref.OK; if (isTimeSeriesType(st)) return Pref.OK; // Data type safeguard if (ty === 'nominal') return Pref.STRONG; if (ty === 'temporal' || ty === 'ordinal') return Pref.OK; if (isDiscreteLike(ty, st, card, 20)) return Pref.WEAK; return Pref.EXCLUDE; }, }, ]); if (!streamResult['x'] || !streamResult['y'] || !streamResult['color']) return {}; assign('x', streamResult['x']); assign('y', streamResult['y']); assign('color', streamResult['color']); break; } case 'Radar Chart': { const xField = pickDiscrete(tv, used) ?? pickLowCardDiscrete(tv, used, 20); const yField = pickQuantitative(tv, used); if (!xField || !yField) return {}; assign('x', xField); assign('y', yField); assign('color', pickLowCardDiscrete(tv, used, 20)); break; } case 'Candlestick Chart': { const xField = pickTemporal(tv, used) ?? pick(tv, used, (name) => nameMatches(name, ['date', 'time', 'day', 'datetime', 'timestamp', 'period'])) ?? pickQuantitativeByName(tv, used, ['date', 'time', 'day']) ?? pickDiscrete(tv, used); if (!xField) return {}; assign('x', xField); const openField = pickQuantitativeByName(tv, used, ['open']); const highField = pickQuantitativeByName(tv, used, ['high']); const lowField = pickQuantitativeByName(tv, used, ['low']); const closeField = pickQuantitativeByName(tv, used, ['close']); if (openField && highField && lowField && closeField) { assign('open', openField); assign('high', highField); assign('low', lowField); assign('close', closeField); } else { const quants = pickAllQuantitative(tv, used); if (quants.length >= 4) { assign('open', quants[0]); assign('high', quants[1]); assign('low', quants[2]); assign('close', quants[3]); } } break; } default: break; } return rec; } ================================================ FILE: src/lib/agents-chart/core/resolve-semantics.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ============================================================================= * CHANNEL SEMANTICS RESOLVER * ============================================================================= * * Stage 2 of the semantic pipeline: * SemanticAnnotation + data → FieldSemantics → **ChannelSemantics** * * Takes each channel’s field, builds FieldSemantics (stage 1), then adds * channel-specific visualization decisions: encoding type, color scheme, * temporal format, ordinal sort, tick constraints, axis reversal, nice * rounding, interpolation, and stacking. * * Zero-baseline is NOT resolved here — it requires template mark knowledge * and is finalized by the assembler after this function returns. * * VL dependency: **None** * ============================================================================= */ import type { ChartEncoding, ChannelSemantics, SemanticResult, } from './types'; import { getVisCategory, inferVisCategory, getRecommendedColorScheme, inferOrdinalSortOrder, } from './semantic-types'; import { resolveEncodingType as resolveEncodingTypeDecision, type EncodingTypeDecision, } from './decisions'; import { resolveFieldSemantics, normalizeAnnotation, toTypeString, resolveNice, resolveTickConstraint, resolveReversed, resolveStackable, resolveColorSchemeHint, resolveDivergingInfo, type SemanticAnnotation, } from './field-semantics'; // --------------------------------------------------------------------------- // Internal helpers (moved from assemble.ts) // --------------------------------------------------------------------------- /** Upper bounds for plausible timestamps (~2099-12-31). */ const MAX_TIMESTAMP_SEC = 4102444800; const MAX_TIMESTAMP_MS = 4102444800000; function isLikelyTimestamp(val: number): boolean { if (val >= 1e9 && val <= MAX_TIMESTAMP_SEC) return true; if (val > MAX_TIMESTAMP_SEC && val <= MAX_TIMESTAMP_MS) return true; return false; } function timestampToMs(val: number): number { return val <= MAX_TIMESTAMP_SEC ? val * 1000 : val; } function looksLikeDateString(s: string): boolean { const t = s.trim(); return /^\d|^(jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)/i.test(t); } // --------------------------------------------------------------------------- // Temporal field analysis // --------------------------------------------------------------------------- interface TemporalAnalysis { dates: Date[]; same: { month: boolean; day: boolean; hour: boolean; minute: boolean; second: boolean; }; sameYear: boolean; sameMonth: boolean; sameDay: boolean; } function analyzeTemporalField(fieldValues: any[]): TemporalAnalysis | null { const dates: Date[] = []; let nonNull = 0; for (const v of fieldValues.slice(0, 100)) { if (v == null) continue; nonNull++; const d = v instanceof Date ? v : new Date(v); if (!isNaN(d.getTime())) dates.push(d); } if (dates.length < 2 || dates.length < nonNull * 0.5) return null; const monthSet = new Set(dates.map(d => d.getUTCMonth())); const daySet = new Set(dates.map(d => d.getUTCDate())); const hourSet = new Set(dates.map(d => d.getUTCHours())); const minuteSet = new Set(dates.map(d => d.getUTCMinutes())); const secondSet = new Set(dates.map(d => d.getUTCSeconds())); const yearSet = new Set(dates.map(d => d.getUTCFullYear())); const isSmallSpread = (s: Set<number>, maxSpread: number = 1) => { if (s.size <= 1) return true; const arr = [...s]; return Math.max(...arr) - Math.min(...arr) <= maxSpread; }; const same = { month: monthSet.size === 1, day: daySet.size === 1, hour: isSmallSpread(hourSet, 1), minute: minuteSet.size === 1, second: secondSet.size === 1, }; const sameYear = yearSet.size === 1; const sameMonth = sameYear && same.month; const sameDay = sameMonth && same.day; return { dates, same, sameYear, sameMonth, sameDay }; } function computeDataVotes(same: TemporalAnalysis['same']): number[] { const votes = [0, 0, 0, 0, 0, 0]; if (same.second) votes[5] += 1; if (same.minute && same.second) votes[5] += 1; if (same.hour && same.minute && same.second) votes[5] += 1; if (same.day && same.hour && same.minute && same.second) votes[5] += 2; if (same.month && same.day && same.hour && same.minute && same.second) votes[5] += 3; if (same.second) votes[4] += 1; if (same.minute && same.second) votes[4] += 1; if (same.hour && same.minute && same.second) votes[4] += 1; if (same.day && same.hour && same.minute && same.second) votes[4] += 2; if (!same.month && same.day && same.hour && same.minute && same.second) votes[4] += 3; if (same.second) votes[3] += 1; if (same.minute && same.second) votes[3] += 1; if (same.hour && same.minute && same.second) votes[3] += 1; if (!same.day && same.hour && same.minute && same.second) votes[3] += 3; if (same.second) votes[2] += 1; if (same.minute && same.second) votes[2] += 1; if (!same.hour && same.minute && same.second) votes[2] += 3; if (same.second) votes[1] += 1; if (!same.minute && same.second) votes[1] += 3; if (!same.second) votes[0] += 4; return votes; } const SEMANTIC_LEVEL: Record<string, number> = { Year: 5, Decade: 5, YearMonth: 4, Month: 4, YearQuarter: 4, Quarter: 4, Date: 3, Day: 3, Hour: 2, DateTime: 1, Timestamp: 0, }; function pickBestLevel(votes: number[]): { level: number; score: number } { let bestLevel = 0; let bestScore = votes[0]; for (let i = 1; i <= 5; i++) { if (votes[i] >= bestScore) { bestScore = votes[i]; bestLevel = i; } } return { level: bestLevel, score: bestScore }; } function levelToFormat(level: number, analysis: TemporalAnalysis): string | null { switch (level) { case 5: return '%Y'; case 4: return analysis.sameYear ? '%b' : '%b %Y'; case 3: return analysis.sameYear ? '%b %d' : '%b %d, %Y'; case 2: return analysis.sameDay ? '%H:00' : '%b %d %H:00'; case 1: return analysis.sameDay ? '%H:%M' : '%b %d %H:%M'; case 0: return analysis.sameDay ? '%H:%M:%S' : '%b %d %H:%M:%S'; default: return null; } } /** * Resolve temporal format for a field. * Used for both temporal and ordinal-temporal fields. */ function resolveTemporalFormat( fieldValues: any[], semanticType: string, ): string | null { const analysis = analyzeTemporalField(fieldValues); if (!analysis) return null; const votes = computeDataVotes(analysis.same); const semLevel = SEMANTIC_LEVEL[semanticType]; if (semLevel !== undefined) votes[semLevel] += 3; const { level } = pickBestLevel(votes); return levelToFormat(level, analysis); } // --------------------------------------------------------------------------- // Temporal data conversion // --------------------------------------------------------------------------- /** * Expand a year string to an unambiguous 4-digit representation. * * - "98" → "1998", "07" → "2007", "00" → "2000" * - "1998" → "1998" (already 4+ digits, pass through) * - "FY 2018" → "FY 2018" (non-numeric, pass through) * * Two-digit cutoff: 0–49 → 2000s, 50–99 → 1900s (same heuristic JS Date uses). */ function expandToFullYear(val: string): string { const trimmed = val.trim(); if (/^\d{2}$/.test(trimmed)) { const n = parseInt(trimmed, 10); return String(n <= 49 ? 2000 + n : 1900 + n); } return val; } /** * Convert temporal field values in the data table to canonical string * representations for Vega-Lite consumption. * * This is a data-level concern (not VL-specific) — it ensures consistent * date parsing across backends. */ export function convertTemporalData( data: any[], semanticTypes: Record<string, string | SemanticAnnotation>, ): any[] { if (data.length === 0) return data; const keys = Object.keys(data[0]); const temporalKeys = keys.filter((k: string) => { const st = toTypeString(semanticTypes[k]); const vc = inferVisCategory(data.map(r => r[k])); const stCategory = st ? getVisCategory(st) : null; return vc === 'temporal' || stCategory === 'temporal' || st === 'Decade'; }); if (temporalKeys.length === 0) return data; const values = structuredClone(data); return values.map((r: any) => { for (const temporalKey of temporalKeys) { const val = r[temporalKey]; const st = toTypeString(semanticTypes[temporalKey]); if (typeof val === 'number') { if (st === 'Year' || st === 'Decade') { r[temporalKey] = `${Math.floor(val)}`; } else if (isLikelyTimestamp(val)) { r[temporalKey] = new Date(timestampToMs(val)).toISOString(); } else { r[temporalKey] = String(val); } } else if (val instanceof Date) { r[temporalKey] = val.toISOString(); } else { // For Year/Decade strings, normalise to 4-digit years so // Vega-Lite parses them unambiguously and doesn't auto-tick // at sub-year intervals (e.g. "98" → "1998"). if ((st === 'Year' || st === 'Decade') && typeof val === 'string') { r[temporalKey] = expandToFullYear(val); } else { r[temporalKey] = String(val); } } } return r; }); } // --------------------------------------------------------------------------- // Public API: resolveChannelSemantics // --------------------------------------------------------------------------- /** * Resolve all channel-level semantic decisions. * * For each channel, builds FieldSemantics (data identity) then layers on * channel-specific visualization decisions (color scheme, temporal format, * tick constraints, axis reversal, interpolation, etc.). * * Zero-baseline (cs.zero) is NOT resolved here -- it requires template * mark knowledge (bar vs point) that belongs to the assembler. * The assembler finalizes zero after calling this function. * * @param encodings Channel -> ChartEncoding from user / AI agent * @param data Array of data rows (original, unconverted) * @param semanticTypes Field name -> semantic type string * @param convertedData Pre-converted temporal data (from convertTemporalData). * If omitted, falls back to data for temporal format detection. */ export function resolveChannelSemantics( encodings: Record<string, ChartEncoding>, data: any[], semanticTypes: Record<string, string | SemanticAnnotation>, convertedData?: any[], ): SemanticResult { const result: SemanticResult = {}; // Use pre-converted temporal data for format detection, or fall back to raw data const temporalData = convertedData ?? data; for (const [channel, encoding] of Object.entries(encodings)) { const fieldName = encoding.field; if (!fieldName && encoding.aggregate !== 'count') continue; // Handle count aggregate without a field if (!fieldName && encoding.aggregate === 'count') { result[channel] = { field: '_count', semanticAnnotation: { semanticType: 'Count' }, type: 'quantitative', aggregationDefault: 'sum', }; continue; } if (!fieldName) continue; const rawAnnotation = semanticTypes[fieldName]; const semanticType = typeof rawAnnotation === 'string' ? (rawAnnotation || '') : (rawAnnotation?.semanticType ?? ''); const fieldValues = data.map(r => r[fieldName]); // Resolve encoding type const typeDecision = resolveEncodingTypeDecision( semanticType, fieldValues, channel, data, fieldName, ); // Apply explicit type override let resolvedType = typeDecision.vlType; if (encoding.type) { resolvedType = encoding.type; } else if (channel === 'column' || channel === 'row') { if (resolvedType !== 'nominal' && resolvedType !== 'ordinal') { resolvedType = 'nominal'; } } // ISO date hack if (resolvedType === 'quantitative') { const sampleValues = data.slice(0, 15).filter(r => r[fieldName] != undefined).map(r => r[fieldName]); const isoDateRegex = /^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}(\.\d+)?(Z|[+-]\d{2}:\d{2})?$/; if (sampleValues.length > 0 && sampleValues.every((val: any) => isoDateRegex.test(`${val}`.trim()))) { resolvedType = 'temporal'; } } // Build ChannelSemantics entry // Stage 1: resolve field-level semantics (data identity) const fc = resolveFieldSemantics(rawAnnotation, fieldName, fieldValues); const annotation = fc.semanticAnnotation; // Stage 2: layer on channel-specific visualization decisions const tickConstraint = resolveTickConstraint(annotation.semanticType, annotation.intrinsicDomain); const reversed = resolveReversed(annotation.semanticType, channel); const nice = resolveNice(annotation.semanticType, fc.domainConstraint); const stackable = resolveStackable(annotation.semanticType); const cs: ChannelSemantics = { field: fieldName, semanticAnnotation: annotation, type: resolvedType, // From FieldSemantics (data identity) format: fc.format, tooltipFormat: fc.tooltipFormat, aggregationDefault: fc.aggregationDefault, scaleType: fc.scaleType, domainConstraint: fc.domainConstraint, cyclic: fc.cyclic || undefined, sortDirection: fc.sortDirection, binningSuggested: fc.binningSuggested || undefined, // Channel-specific visualization decisions nice, tickConstraint, reversed: reversed || undefined, stackable, }; // Adjust field name for aggregated fields if (encoding.aggregate) { if (encoding.aggregate === 'count') { cs.field = '_count'; cs.type = 'quantitative'; } else { cs.field = `${fieldName}_${encoding.aggregate}`; cs.type = 'quantitative'; } } // --- Channel-specific semantic decisions --- // Color scheme (color and group channels) if ((channel === 'color' || channel === 'group') && fieldName) { if (encoding.scheme && encoding.scheme !== 'default') { cs.colorScheme = { scheme: encoding.scheme, type: 'categorical', reason: 'explicit user scheme', }; } else { const encodingVLType = cs.type as 'nominal' | 'ordinal' | 'quantitative' | 'temporal'; // Use design-aligned classification from field-semantics.ts const colorHint = resolveColorSchemeHint(semanticType, annotation, fieldValues); const uniqueValues = [...new Set(fieldValues)]; cs.colorScheme = getRecommendedColorScheme( semanticType, encodingVLType, uniqueValues.length, fieldName, fieldValues, { type: colorHint.type }, ); // Apply midpoint from design-aligned diverging analysis if (cs.colorScheme.type === 'diverging' && encodingVLType === 'quantitative') { const nums = fieldValues.filter((v: any) => typeof v === 'number' && !isNaN(v)); const divInfo = resolveDivergingInfo(semanticType, annotation, nums); if (divInfo) { cs.colorScheme.domainMid = divInfo.midpoint; } } } } // Temporal format if (cs.type === 'temporal' || (semanticType && getVisCategory(semanticType) === 'temporal')) { const convertedFieldValues = temporalData.map(r => r[fieldName]); const fmt = resolveTemporalFormat(convertedFieldValues, semanticType); if (fmt) cs.temporalFormat = fmt; } // Ordinal sort order (canonical ordering for months, days, quarters, etc.) if (cs.type === 'ordinal' || cs.type === 'nominal') { if (!encoding.sortOrder && !encoding.sortBy) { const ordinalSort = inferOrdinalSortOrder(semanticType, fieldValues); if (ordinalSort) { cs.ordinalSortOrder = ordinalSort; } } } result[channel] = cs; } return result; } // Re-export helpers needed by other modules export { analyzeTemporalField, computeDataVotes, pickBestLevel, levelToFormat, looksLikeDateString, SEMANTIC_LEVEL, type TemporalAnalysis, }; ================================================ FILE: src/lib/agents-chart/core/semantic-types.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { getRegistryEntry, getRegisteredTypes, isRegistered, type VisCategory } from './type-registry'; export type { VisCategory } from './type-registry'; /** * ============================================================================= * SEMANTIC TYPE SYSTEM * ============================================================================= * * Semantic types classify data fields for intelligent chart recommendations. * Uses strings for flexibility and easy JSON serialization. * * DESIGN GOALS: * 1. Comprehensive: Cover common data types seen in real-world datasets * 2. Visualization-aware: Map to Vega-Lite encoding types (Q, O, N, T) * 3. Hierarchical: Support generalization via lattice structure * 4. Simple: Use strings with helper functions, no complex enums * * ============================================================================= * SEMANTIC TYPE LATTICE * ============================================================================= * * ┌─────────────┐ * │ AnyType │ * └──────┬──────┘ * ┌────────────────────┼────────────────────┐ * ▼ ▼ ▼ * ┌──────────┐ ┌──────────┐ ┌───────-───┐ * │ Temporal │ │ Numeric │ │Categorical│ * └────┬─────┘ └────┬─────┘ └─────┬────┘ * │ │ │ * ┌─────┴─────┐ ┌─────┴─────┐ ┌─────┴─────┐ * │ │ │ │ │ │ * Point Granule Measure Discrete Entity Coded * │ │ │ │ │ │ * DateTime Year Quantity Rank Person Status * Date Month Count Index Company Boolean * Time Day Price Score Product Category * Quarter Percentage Rating * Decade Amount ID * Temperature * Revenue * * ============================================================================= */ // --------------------------------------------------------------------------- // All Semantic Types (as string constants) // --------------------------------------------------------------------------- /** * All recognized semantic types. * Use these constants when comparing or assigning types. */ export const SemanticTypes = { // ========================================================================= // TEMPORAL TYPES - Time-related concepts // ========================================================================= // Point-in-time (full timestamp precision) DateTime: 'DateTime', // Full date and time: "2024-01-15T14:30:00" Date: 'Date', // Date only: "2024-01-15" Time: 'Time', // Time only: "14:30:00" Timestamp: 'Timestamp', // Unix timestamp (seconds or milliseconds since epoch) // Temporal granules (discrete time units, inherently ordered) Year: 'Year', // "2024" (as a time unit, not a measure) Quarter: 'Quarter', // "Q1", "Q2", "2024-Q1" Month: 'Month', // "January", "Jan", 1-12 Week: 'Week', // "Week 1", 1-52 Day: 'Day', // "Monday", "Mon", 1-31 Hour: 'Hour', // 0-23 // Combined temporal YearMonth: 'YearMonth', // "2024-01", "Jan 2024" YearQuarter: 'YearQuarter', // "2024-Q1" YearWeek: 'YearWeek', // "2024-W01" Decade: 'Decade', // "1990s", "2000s" // Temporal duration/span Duration: 'Duration', // Time span: "2 hours", "3 days", milliseconds // ========================================================================= // NUMERIC MEASURE TYPES - Continuous values for aggregation // ========================================================================= Quantity: 'Quantity', // Generic continuous measure Count: 'Count', // Discrete count of items Amount: 'Amount', // Monetary or general amounts Price: 'Price', // Unit price Revenue: 'Revenue', // Total revenue/sales Cost: 'Cost', // Expenses/costs Percentage: 'Percentage', // 0-100% or 0-1 ratio Temperature: 'Temperature', // Degrees // Signed measures (can be positive or negative, zero has meaning) Profit: 'Profit', // Gain/loss, profit/deficit PercentageChange: 'PercentageChange', // Growth rate, change % Sentiment: 'Sentiment', // Positive/negative sentiment score Correlation: 'Correlation', // Positive/negative correlation coefficient // ========================================================================= // NUMERIC DISCRETE TYPES - Numbers with ordinal/identifier meaning // ========================================================================= Rank: 'Rank', // Position in ordered list: 1st, 2nd, 3rd Index: 'Index', // Row number, sequence number ID: 'ID', // Unique identifier (not for aggregation!) Score: 'Score', // Rating score: 1-5, 1-10, 0-100 Rating: 'Rating', // Star rating, letter grade // ========================================================================= // GEOGRAPHIC TYPES - Location-based data // ========================================================================= Latitude: 'Latitude', // -90 to 90 Longitude: 'Longitude', // -180 to 180 Country: 'Country', // Country name or code State: 'State', // State/Province City: 'City', // City name Region: 'Region', // Geographic region Address: 'Address', // Street address ZipCode: 'ZipCode', // Postal code // ========================================================================= // CATEGORICAL ENTITY TYPES - Named entities // ========================================================================= PersonName: 'PersonName', // Full name, first/last name Company: 'Company', // Company/Organization name Product: 'Product', // Product name Category: 'Category', // Product/item category Name: 'Name', // Generic named entity (fallback) // ========================================================================= // CATEGORICAL CODED TYPES - Discrete categories/statuses // ========================================================================= Status: 'Status', // State: "Active", "Pending", "Closed" Type: 'Type', // Type classification Boolean: 'Boolean', // True/False, Yes/No Direction: 'Direction', // Compass direction: "N", "NE", "East", etc. // ========================================================================= // BINNED/RANGE TYPES - Discretized continuous values // ========================================================================= Range: 'Range', // Numeric range: "10000-20000", "<50", "50+" AgeGroup: 'AgeGroup', // Age range: "18-24", "25-34" // ========================================================================= // FALLBACK TYPES // ========================================================================= String: 'String', // Generic string (categorical fallback) Number: 'Number', // Generic number (measure fallback) Unknown: 'Unknown', // Cannot determine type } as const; // Type for any semantic type string export type SemanticType = typeof SemanticTypes[keyof typeof SemanticTypes]; // --------------------------------------------------------------------------- // Visualization Categories → defined in type-registry.ts (single source of truth) // --------------------------------------------------------------------------- // --------------------------------------------------------------------------- // Type Sets for Classification — derived from type-registry.ts // --------------------------------------------------------------------------- // timeseriesXTypes: REMOVED — derived from type-registry.ts via isTimeSeriesType() /** * Types suitable for quantitative encoding (true continuous measures). * * Derived from the registry: aggRole ∈ {additive, intensive, signed-additive}, * excluding Score/Rating (t1='Score') which behave as bounded ordinal scales * for vis purposes (e.g., 1–5 star rating). This is an intentional vis-level * distinction, not a mathematical one. */ export const measureTypes = new Set<string>( getRegisteredTypes().filter(t => { const e = getRegistryEntry(t); return ['additive', 'intensive', 'signed-additive'].includes(e.aggRole) && e.t1 !== 'Score'; }) ); /** Numeric types that should NOT be used as measures (don't aggregate) */ export const nonMeasureNumericTypes = new Set<string>([ 'Rank', 'Index', 'ID', 'Score', 'Rating', 'Year', 'Month', 'Day', 'Hour', 'Latitude', 'Longitude', ]); /** * Types suitable for categorical color/grouping encoding. * * Derived from the registry: types that include 'nominal' in visEncodings * (at any position — Direction has ['ordinal','nominal']), * plus binned types (Range, AgeGroup) which also work as categorical for * color/grouping despite having 'ordinal' as their primary encoding. * Excludes identifiers (ID) which are nominal but not useful for grouping. */ export const categoricalTypes = new Set<string>( getRegisteredTypes().filter(t => { const e = getRegistryEntry(t); return (e.visEncodings.includes('nominal') && e.aggRole !== 'identifier') || e.t1 === 'Binned'; }) ); /** * Types suitable for ordinal encoding (have inherent order). * * Derived from the registry: types whose visEncodings include 'ordinal'. */ export const ordinalTypes = new Set<string>( getRegisteredTypes().filter(t => { const e = getRegistryEntry(t); return e.visEncodings.includes('ordinal'); }) ); // geoTypes, geoCoordinateTypes, geoLocationTypes: REMOVED — derived from type-registry.ts // via isGeoType(), isGeoCoordinateType(), isGeoLocationString() // --------------------------------------------------------------------------- // Type Hierarchy — REMOVED // --------------------------------------------------------------------------- // The typeHierarchy map and its helper functions (getParentType, // getAncestorTypes, isSubtypeOf) have been removed. They were unused // externally — no consumer ever imported them. // // The registry's t0/t1 dimensions capture family grouping (e.g., all // Amount types share t1='Amount'). If fine-grained parent-child lattice // traversal is ever needed in the future, it can be rebuilt from // type-registry.ts with an explicit `parent` field per entry. // --------------------------------------------------------------------------- // visCategoryMap: REMOVED — derived from type-registry.ts via getRegistryEntry().visEncodings[0] // --------------------------------------------------------------------------- // Helper Functions // --------------------------------------------------------------------------- /** * Get the Vega-Lite visualization category for a semantic type. * Derived from the registry's visEncodings[0] (primary encoding). * Returns null for unrecognised types so callers can fall back * to data-driven inference. */ export function getVisCategory(semanticType: string): VisCategory | null { // Return null for empty, 'Unknown', or any unregistered type string // so callers fall back to data-driven inference (inferVisCategory). if (!semanticType || !isRegistered(semanticType)) return null; return getRegistryEntry(semanticType).visEncodings[0] ?? null; } /** * Infer a VisCategory from raw data values when no semantic type is available. * Mirrors the DataType → VL encoding type mapping: * number/integer → quantitative, boolean → nominal, date → temporal, string → nominal. */ export function inferVisCategory(values: any[]): VisCategory { if (values.length === 0) return 'nominal'; const isBoolean = (v: any) => v === true || v === false || Object.prototype.toString.call(v) === '[object Boolean]'; const isNumber = (v: any) => !isNaN(+v) && !(Object.prototype.toString.call(v) === '[object Date]'); // Date.parse is too permissive in V8 — "FY 2018", "hello world 2018" all parse. // Require the string to start with a digit or a known month-name prefix. const looksLikeDate = (s: string) => /^\d|^(jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)/i.test(s.trim()); const isDate = (v: any) => { if (v instanceof Date) return !isNaN(v.getTime()); if (typeof v === 'string') return looksLikeDate(v) && !isNaN(Date.parse(v)); return !isNaN(Date.parse(v)); }; const nonNull = values.filter(v => v != null); if (nonNull.length === 0) return 'nominal'; if (nonNull.every(isBoolean)) return 'nominal'; if (nonNull.every(isNumber)) return 'quantitative'; if (nonNull.every(isDate)) return 'temporal'; return 'nominal'; } /** * Check if a semantic type is a true measure (suitable for quantitative encoding). */ export function isMeasureType(semanticType: string): boolean { return measureTypes.has(semanticType); } /** * Check if a semantic type is suitable for time-series X axis. * Derived from type-registry: t0 === 'Temporal' but not Duration. */ export function isTimeSeriesType(semanticType: string): boolean { const entry = getRegistryEntry(semanticType); return entry.t0 === 'Temporal' && entry.t1 !== 'Duration'; } /** * Check if a semantic type is categorical (suitable for color/grouping). */ export function isCategoricalType(semanticType: string): boolean { return categoricalTypes.has(semanticType); } /** * Check if a semantic type is ordinal (has inherent order). */ export function isOrdinalType(semanticType: string): boolean { return ordinalTypes.has(semanticType); } /** * Check if a semantic type is geographic. * Derived from type-registry: t0 === 'Geographic'. */ export function isGeoType(semanticType: string): boolean { return getRegistryEntry(semanticType).t0 === 'Geographic'; } /** * Check if a semantic type is a geographic coordinate (lat/lon). * Derived from type-registry: t1 === 'GeoCoordinate'. */ export function isGeoCoordinateType(semanticType: string): boolean { return getRegistryEntry(semanticType).t1 === 'GeoCoordinate'; } /** * Check if a semantic type is a named geographic location. * Derived from type-registry: t1 === 'GeoPlace'. */ export function isGeoLocationString(semanticType: string): boolean { return getRegistryEntry(semanticType).t1 === 'GeoPlace'; } /** * Check if a semantic type is numeric but should not be aggregated. */ export function isNonMeasureNumeric(semanticType: string): boolean { return nonMeasureNumericTypes.has(semanticType); } // --------------------------------------------------------------------------- // Zero-Baseline Classification → data lives in type-registry.ts (zeroBaseline, zeroPad) // --------------------------------------------------------------------------- /** * Classification of whether zero is a meaningful baseline for a semantic type. * * - `meaningful`: 0 has a real-world interpretation (absence of the measured thing). * Comparisons to zero and ratios between values are meaningful. * Examples: Count, Revenue, Distance, Weight. * * - `arbitrary`: 0 is either meaningless, doesn't exist, or is an arbitrary * reference point. The data's range is what matters. * Examples: Temperature (0°F is arbitrary), Year (year 0 doesn't exist), * Rank (0th place doesn't exist). * * - `contextual`: 0 is meaningful but data-fitting may be better when data * is concentrated far from zero and the mark is not bar/area. * Examples: Percentage (0–100% natural, but 48–52% benefits from zoom), * Score (1–5 scale, but 4.2–4.8 benefits from zoom). */ export type ZeroClass = 'meaningful' | 'arbitrary' | 'contextual'; /** * Result of the zero-baseline decision. * Encapsulates both the boolean decision and domain padding for non-zero axes. */ export interface ZeroDecision { /** Whether the axis should include zero */ zero: boolean; /** * For non-zero axes: fraction of data range to pad on each side * so edge values aren't crushed against the axis boundary. * e.g. 0.05 = 5% padding on each side. */ domainPadFraction: number; /** The zero class that drove this decision */ zeroClass: ZeroClass | 'unknown'; } // zeroMeaningfulTypes, zeroArbitraryTypes, zeroContextualTypes, zeroPadMap: // REMOVED — now stored as zeroBaseline/zeroPad in type-registry.ts /** * Classify a semantic type's relationship to zero. * Derived from the registry's zeroBaseline dimension. */ export function getZeroClass(semanticType: string): ZeroClass | 'unknown' { const baseline = getRegistryEntry(semanticType).zeroBaseline; if (baseline === 'none') return 'unknown'; return baseline; } /** * Compute whether a quantitative axis should start at zero, based on * semantic type, mark type, channel, and data values. * * Priority: semantic type > mark type > data range > VL default. * * This is a pure decision function — it returns a ZeroDecision object * without modifying any spec. The caller applies the decision to VL. * * @param semanticType The semantic type of the field (e.g. 'Revenue', 'Temperature') * @param channel The VL channel ('x', 'y', 'size', etc.) * @param markType The mark type ('bar', 'line', 'point', etc.) * @param values Optional numeric data values for data-range analysis */ export function computeZeroDecision( semanticType: string, channel: string, markType: string, values?: number[], ): ZeroDecision { const isBarLike = ['bar', 'area', 'rect'].includes(markType); const isPositional = ['x', 'y'].includes(channel); const entry = getRegistryEntry(semanticType); const zeroClass = getZeroClass(semanticType); // --- Zero-meaningful types: always zero --- if (zeroClass === 'meaningful') { return { zero: true, domainPadFraction: 0, zeroClass }; } // --- Zero-arbitrary types: never zero, apply padding --- if (zeroClass === 'arbitrary') { // Exception: bar/area marks with data that touches/crosses zero if (isBarLike && values && values.length > 0) { const dataMin = Math.min(...values); if (dataMin <= 0) { return { zero: true, domainPadFraction: 0, zeroClass }; } } return { zero: false, domainPadFraction: entry.zeroPad || 0.05, zeroClass, }; } // --- Contextual types: use data range + mark to decide --- if (zeroClass === 'contextual' && values && values.length > 0) { const dataMin = Math.min(...values); const dataMax = Math.max(...values); // Data touches/crosses zero → include it if (dataMin <= 0) { return { zero: true, domainPadFraction: 0, zeroClass }; } // How far is data from zero? const proximity = dataMax > 0 ? dataMin / dataMax : 0; // Close to zero → include it if (proximity < 0.3) { return { zero: true, domainPadFraction: 0, zeroClass }; } // Far from zero + bar/area → still include (bar length integrity) if (isBarLike) { return { zero: true, domainPadFraction: 0, zeroClass }; } // Far from zero + non-bar → data-fit with padding return { zero: false, domainPadFraction: 0.05, zeroClass }; } // --- No semantic type or unrecognized → no opinion, let VL decide --- if (isBarLike && isPositional) { return { zero: true, domainPadFraction: 0, zeroClass: 'unknown' }; } return { zero: false, domainPadFraction: 0.05, zeroClass: 'unknown' }; } /** * Compute padded domain bounds for a non-zero axis. * Pure computation — returns [paddedMin, paddedMax] without modifying any spec. * * @param values Numeric data values * @param padFraction Fraction of data range to pad on each side * @returns [paddedMin, paddedMax] or null if padding is not applicable */ export function computePaddedDomain( values: number[], padFraction: number, ): [number, number] | null { if (padFraction <= 0 || values.length < 2) return null; const dataMin = Math.min(...values); const dataMax = Math.max(...values); const span = dataMax - dataMin; if (span <= 0) return null; const padding = span * padFraction; return [dataMin - padding, dataMax + padding]; } // --------------------------------------------------------------------------- // Color Scheme Recommendations // --------------------------------------------------------------------------- export type ColorSchemeType = 'categorical' | 'sequential' | 'diverging'; export interface ColorSchemeRecommendation { scheme: string; type: ColorSchemeType; reason: string; /** For diverging schemes, the recommended midpoint value */ domainMid?: number; } // getDivergingMidpoint: REMOVED — superseded by resolveDivergingInfo() in field-semantics.ts // which uses a priority chain (unit → type-intrinsic → domain → data) and // distinguishes inherent vs conditional diverging. /** * Vega-Lite color schemes organized by use case * See: https://vega.github.io/vega/docs/schemes/ */ const colorSchemes = { // Categorical (nominal) - good for distinct categories categorical: { default: 'category10', large: 'category20', pastel: 'pastel1', accent: 'accent', paired: 'paired', // Good for paired comparisons set1: 'set1', // Distinct, saturated set2: 'set2', // Pastel set3: 'set3', // Larger set tableau10: 'tableau10', tableau20: 'tableau20', }, // Sequential - good for ordered/quantitative data sequential: { blues: 'blues', greens: 'greens', oranges: 'oranges', reds: 'reds', purples: 'purples', greys: 'greys', // Multi-hue sequential viridis: 'viridis', inferno: 'inferno', magma: 'magma', plasma: 'plasma', turbo: 'turbo', // Domain-specific yellowGreen: 'yellowgreen', yellowOrangeBrown: 'yelloworangebrown', goldGreen: 'goldgreen', goldOrange: 'goldorange', goldRed: 'goldred', }, // Diverging - good for data with meaningful center point diverging: { redBlue: 'redblue', redGrey: 'redgrey', redYellowBlue: 'redyellowblue', redYellowGreen: 'redyellowgreen', pinkYellowGreen: 'pinkyellowgreen', purpleGreen: 'purplegreen', purpleOrange: 'purpleorange', brownBlueGreen: 'brownbluegreen', }, }; /** * Get recommended color scheme based on semantic type and encoding context. * * @param semanticType - The semantic type of the field * @param encodingType - The Vega-Lite encoding type ('nominal', 'ordinal', 'quantitative') * @param uniqueValueCount - Number of unique values (for categorical sizing) * @param fieldName - Field name (for consistent hashing) * @param values - Optional actual data values (for inspecting data range) * @param colorHint - Optional classification from resolveColorSchemeHint(). * When provided, the hint's type ('diverging'|'sequential'|'categorical') * overrides inline detection, avoiding duplicate diverging logic. */ export function getRecommendedColorScheme( semanticType: string | undefined, encodingType: 'nominal' | 'ordinal' | 'quantitative' | 'temporal', uniqueValueCount: number = 10, fieldName: string = '', values: any[] = [], colorHint?: { type: 'categorical' | 'sequential' | 'diverging' }, ): ColorSchemeRecommendation { // Helper for consistent scheme selection from array const pickScheme = (schemes: string[], name: string): string => { let hash = 0; for (let i = 0; i < name.length; i++) { hash = ((hash << 5) - hash) + name.charCodeAt(i); hash = hash & hash; } return schemes[Math.abs(hash) % schemes.length]; }; // If no semantic type, use defaults based on encoding type if (!semanticType) { if (encodingType === 'quantitative') { return { scheme: 'viridis', type: 'sequential', reason: 'default for quantitative' }; } if (encodingType === 'ordinal') { return { scheme: 'blues', type: 'sequential', reason: 'default for ordinal' }; } // nominal/temporal default to categorical — use saturated schemes for readability return { scheme: uniqueValueCount > 10 ? 'tableau20' : 'tableau10', type: 'categorical', reason: 'default for categorical' }; } // --- Diverging-capable types --- // When a colorHint is provided (from resolveColorSchemeHint), it drives the // diverging/sequential decision. Without a hint, fall back to sequential. // This avoids duplicating the diverging detection logic from field-semantics.ts. // Temperature if (semanticType === 'Temperature') { if (colorHint?.type === 'diverging') { return { scheme: 'redblue', type: 'diverging', reason: 'temperature diverging around freezing point' }; } return { scheme: 'reds', type: 'sequential', reason: 'temperature single-direction uses sequential' }; } // Percentage if (semanticType === 'Percentage') { if (colorHint?.type === 'diverging') { return { scheme: 'redblue', type: 'diverging', reason: 'percentage spans positive and negative' }; } return { scheme: 'oranges', type: 'sequential', reason: 'percentage all same sign uses sequential' }; } // Revenue/Price/Cost/Amount if (['Revenue', 'Price', 'Cost', 'Amount'].includes(semanticType)) { if (colorHint?.type === 'diverging') { return { scheme: 'redblue', type: 'diverging', reason: 'financial data spans positive and negative' }; } return { scheme: 'goldgreen', type: 'sequential', reason: 'financial data uses gold-green' }; } // Score/Rating - evaluation metrics; diverging when hint says so (e.g., domain midpoint) if (['Score', 'Rating'].includes(semanticType)) { if (colorHint?.type === 'diverging') { return { scheme: 'redblue', type: 'diverging', reason: 'score/rating diverging around midpoint' }; } return { scheme: 'yelloworangebrown', type: 'sequential', reason: 'scores use warm sequential' }; } // Rank - use single-hue sequential if (['Rank', 'Index'].includes(semanticType)) { return { scheme: 'purples', type: 'sequential', reason: 'ranks use single-hue sequential' }; } // Age groups / ranges - use sequential if (['AgeGroup', 'Range'].includes(semanticType)) { return { scheme: 'blues', type: 'sequential', reason: 'age/range groups use sequential' }; } // Temporal granules (Year, Month, Quarter, etc.) - sequential for continuity if (ordinalTypes.has(semanticType) && ['Year', 'Quarter', 'Month', 'Week', 'Day', 'Hour', 'Decade'].includes(semanticType)) { return { scheme: 'viridis', type: 'sequential', reason: 'temporal granules use perceptually uniform' }; } // Geographic locations - use geographic-friendly palettes if (getRegistryEntry(semanticType ?? '').t1 === 'GeoPlace') { if (uniqueValueCount <= 10) { return { scheme: 'set2', type: 'categorical', reason: 'geographic regions use distinct pastels' }; } return { scheme: 'tableau20', type: 'categorical', reason: 'many regions use large categorical' }; } // Status/Boolean - use accent colors for clear distinction if (['Status', 'Boolean'].includes(semanticType)) { return { scheme: 'set1', type: 'categorical', reason: 'status uses high-contrast categorical' }; } // Categories/Types - use standard categorical if (['Category', 'Type'].includes(semanticType)) { return { scheme: uniqueValueCount > 10 ? 'tableau20' : 'tableau10', type: 'categorical', reason: 'categories use standard categorical' }; } // Companies/Products - use paired for small sets, tableau for large if (['Company', 'Product'].includes(semanticType)) { return { scheme: uniqueValueCount > 10 ? 'tableau20' : 'paired', type: 'categorical', reason: 'entities use distinct categorical' }; } // Person names - use saturated schemes for readability; only pastel for very small sets if (['Name', 'PersonName'].includes(semanticType)) { return { scheme: uniqueValueCount > 8 ? 'tableau20' : 'set2', type: 'categorical', reason: 'names use readable categorical' }; } // Duration - use sequential (longer = more intense) if (semanticType === 'Duration') { return { scheme: 'oranges', type: 'sequential', reason: 'duration uses intensity-based sequential' }; } // Quantity/Count/Distance/etc. - general measures // Check colorHint first — signed measures (Profit, Sentiment, Correlation, // PercentageChange) pass through here and should honor their diverging hint. if (measureTypes.has(semanticType)) { if (colorHint?.type === 'diverging') { return { scheme: 'redblue', type: 'diverging', reason: 'measure with diverging nature' }; } const sequentialSchemes = ['viridis', 'blues', 'greens', 'reds', 'yelloworangebrown', 'goldgreen']; return { scheme: pickScheme(sequentialSchemes, fieldName), type: 'sequential', reason: 'measures use perceptually uniform sequential' }; } // Ordinal types not already handled if (ordinalTypes.has(semanticType) || encodingType === 'ordinal') { const ordinalSchemes = ['blues', 'greens', 'purples', 'oranges']; return { scheme: pickScheme(ordinalSchemes, fieldName), type: 'sequential', reason: 'ordinal data uses sequential scheme' }; } // Default categorical for nominal if (encodingType === 'nominal' || encodingType === 'temporal') { return { scheme: uniqueValueCount > 10 ? 'tableau20' : 'tableau10', type: 'categorical', reason: 'default categorical palette' }; } // Fallback return { scheme: 'viridis', type: 'sequential', reason: 'universal fallback' }; } // getRecommendedColorSchemeWithMidpoint: REMOVED — diverging midpoint is now // resolved via resolveDivergingInfo() in field-semantics.ts and applied directly // by the caller in resolve-semantics.ts. See resolveChannelSemantics(). // =========================================================================== // Canonical Ordinal Sort Orders // =========================================================================== /** * Well-known canonical ordinal sequences. * * Used to detect when data values belong to a known ordinal domain * (months, days of the week, quarters, etc.) and sort them in their * natural order instead of alphabetically or by a quantitative axis. */ /** Full and abbreviated English month names (case-insensitive lookup). */ const MONTH_FULL = ['January','February','March','April','May','June','July','August','September','October','November','December']; const MONTH_ABBR3 = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']; const MONTH_NUM = ['1','2','3','4','5','6','7','8','9','10','11','12']; /** Full and abbreviated English day-of-week names. */ const DOW_FULL = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']; const DOW_ABBR3 = ['Mon','Tue','Wed','Thu','Fri','Sat','Sun']; const DOW_ABBR2 = ['Mo','Tu','We','Th','Fr','Sa','Su']; /** Sunday-first variant (US convention). */ const DOW_FULL_SUN = ['Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Saturday']; const DOW_ABBR3_SUN = ['Sun','Mon','Tue','Wed','Thu','Fri','Sat']; /** Quarter labels. */ const QUARTER_LABELS = ['Q1','Q2','Q3','Q4']; /** Compass directions — clockwise from North (top of chart). */ const COMPASS_8 = ['N','NE','E','SE','S','SW','W','NW']; const COMPASS_8_FULL = ['North','Northeast','East','Southeast','South','Southwest','West','Northwest']; const COMPASS_4 = ['N','E','S','W']; const COMPASS_4_FULL = ['North','East','South','West']; interface OrdinalSequence { /** Canonical labels in order */ labels: string[]; /** Case-insensitive matching */ caseInsensitive: boolean; } /** All known ordinal sequences, keyed by semantic type. */ const ORDINAL_SEQUENCES: Record<string, OrdinalSequence[]> = { Month: [ { labels: MONTH_FULL, caseInsensitive: true }, { labels: MONTH_ABBR3, caseInsensitive: true }, { labels: MONTH_NUM, caseInsensitive: false }, ], Day: [ { labels: DOW_FULL, caseInsensitive: true }, { labels: DOW_ABBR3, caseInsensitive: true }, { labels: DOW_ABBR2, caseInsensitive: true }, { labels: DOW_FULL_SUN, caseInsensitive: true }, { labels: DOW_ABBR3_SUN, caseInsensitive: true }, ], Quarter: [ { labels: QUARTER_LABELS, caseInsensitive: true }, ], Direction: [ { labels: COMPASS_8, caseInsensitive: true }, { labels: COMPASS_8_FULL, caseInsensitive: true }, { labels: COMPASS_4, caseInsensitive: true }, { labels: COMPASS_4_FULL, caseInsensitive: true }, ], }; /** * Build a case-insensitive lookup map from a sequence's labels. * Returns map: lowercased label → index. */ function buildLookup(seq: OrdinalSequence): Map<string, number> { const m = new Map<string, number>(); for (let i = 0; i < seq.labels.length; i++) { const key = seq.caseInsensitive ? seq.labels[i].toLowerCase() : seq.labels[i]; m.set(key, i); } return m; } /** * Try to match a set of data values against a well-known ordinal sequence. * * Returns the canonical sort order (subset of the sequence, in order) if * enough values match, or `undefined` if no match. * * Matching rules: * - At least 60% of unique data values must be found in the sequence * - All matched values are returned in canonical order * - Unmatched values are appended at the end (preserving data order) * * @param values The data values (strings or numbers) on this channel * @param sequences The candidate sequences for the semantic type */ function matchSequence(values: any[], sequences: OrdinalSequence[]): string[] | undefined { const uniqueValues = [...new Set(values.map(v => v != null ? String(v) : ''))].filter(v => v !== ''); if (uniqueValues.length === 0) return undefined; for (const seq of sequences) { const lookup = buildLookup(seq); const matched: { value: string; index: number }[] = []; const unmatched: string[] = []; for (const val of uniqueValues) { const key = seq.caseInsensitive ? val.toLowerCase() : val; const idx = lookup.get(key); if (idx !== undefined) { matched.push({ value: val, index: idx }); } else { unmatched.push(val); } } // Require at least 60% match rate if (matched.length >= uniqueValues.length * 0.6 && matched.length >= 2) { // Sort matched values by canonical index matched.sort((a, b) => a.index - b.index); const result = matched.map(m => m.value); // Append unmatched at the end result.push(...unmatched); return result; } } return undefined; } /** * Infer a canonical ordinal sort order for a field based on its semantic type * and data values. * * Works for: * - Month names (full/abbreviated/numeric): Jan, Feb, ... or January, February, ... * - Day-of-week names (full/abbreviated): Mon, Tue, ... or Monday, Tuesday, ... * - Quarter labels: Q1, Q2, Q3, Q4 * * Falls back to `undefined` if no known sequence is detected, letting the * caller use its own default sort logic. * * @param semanticType The semantic type of the field (e.g. 'Month', 'Day') * @param values The data values on this channel * @returns Sorted unique values in canonical order, or undefined */ export function inferOrdinalSortOrder( semanticType: string, values: any[], ): string[] | undefined { // 1. Check by explicit semantic type const sequences = ORDINAL_SEQUENCES[semanticType]; if (sequences) { return matchSequence(values, sequences); } // 2. Auto-detect: try all sequences if semantic type is generic if (!semanticType || semanticType === 'Category' || semanticType === 'String' || semanticType === 'Unknown') { for (const seqs of Object.values(ORDINAL_SEQUENCES)) { const result = matchSequence(values, seqs); if (result) return result; } } return undefined; } ================================================ FILE: src/lib/agents-chart/core/type-registry.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ============================================================================= * TYPE REGISTRY — Single Source of Truth * ============================================================================= * * Every recognized semantic type is registered here with its orthogonal * compilation dimensions. This is the ONLY place where per-type properties * are defined. All other files (field-semantics.ts, semantic-types.ts) * derive helper functions by querying this registry. * * To add a new semantic type: add an entry here. * To query a type's properties: use `getRegistryEntry()`. * ============================================================================= */ // --------------------------------------------------------------------------- // Visualization Categories // --------------------------------------------------------------------------- export type VisCategory = 'quantitative' | 'ordinal' | 'nominal' | 'temporal' | 'geographic'; // --------------------------------------------------------------------------- // Registry Dimension Types // --------------------------------------------------------------------------- /** Top-level type family */ export type T0Family = 'Temporal' | 'Measure' | 'Discrete' | 'Geographic' | 'Categorical' | 'Identifier'; /** Mid-level category within a family */ export type T1Category = | 'DateTime' | 'DateGranule' | 'Duration' | 'Amount' | 'Physical' | 'Proportion' | 'SignedMeasure' | 'GenericMeasure' | 'Rank' | 'Score' | 'Index' | 'GeoCoordinate' | 'GeoPlace' | 'Entity' | 'Coded' | 'Binned' | 'ID'; export type DomainShape = 'open' | 'bounded' | 'fixed' | 'cyclic'; export type AggRole = 'additive' | 'intensive' | 'signed-additive' | 'dimension' | 'identifier'; export type DivergingClass = 'none' | 'inherent' | 'conditional'; export type FormatClass = 'currency' | 'percent' | 'signed-percent' | 'signed-currency' | 'signed-decimal' | 'unit-suffix' | 'integer' | 'decimal' | 'plain'; /** * Zero-baseline classification for quantitative axes. * * - `meaningful`: 0 = absence of the measured thing; axis should include 0 (Count, Revenue). * - `arbitrary`: 0 is arbitrary or nonexistent; data-fit the axis (Temperature, Year, Rank). * - `contextual`: 0 is meaningful but data-fitting may be better when data is far from 0 (Percentage, Score). * - `none`: Not a quantitative type; zero question is irrelevant (all categorical/temporal types). */ export type ZeroBaseline = 'meaningful' | 'arbitrary' | 'contextual' | 'none'; export interface TypeRegistryEntry { t0: T0Family; t1: T1Category; visEncodings: VisCategory[]; aggRole: AggRole; domainShape: DomainShape; diverging: DivergingClass; formatClass: FormatClass; /** Zero-baseline classification for quantitative axes */ zeroBaseline: ZeroBaseline; /** Domain padding fraction for non-zero axes (0 = no padding) */ zeroPad: number; } // --------------------------------------------------------------------------- // The Registry // --------------------------------------------------------------------------- /** * Static registry mapping every recognized semantic type to its * tier membership and orthogonal compilation dimensions. * * Types not in this registry are treated as 'Unknown' → nominal/plain. */ const TYPE_REGISTRY: Record<string, TypeRegistryEntry> = { // --- Temporal: DateTime --- DateTime: { t0: 'Temporal', t1: 'DateTime', visEncodings: ['temporal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, Date: { t0: 'Temporal', t1: 'DateTime', visEncodings: ['temporal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, Time: { t0: 'Temporal', t1: 'DateTime', visEncodings: ['temporal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, Timestamp: { t0: 'Temporal', t1: 'DateTime', visEncodings: ['temporal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, // --- Temporal: DateGranule --- Year: { t0: 'Temporal', t1: 'DateGranule', visEncodings: ['temporal', 'ordinal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'integer', zeroBaseline: 'arbitrary', zeroPad: 0.03 }, Quarter: { t0: 'Temporal', t1: 'DateGranule', visEncodings: ['ordinal'], aggRole: 'dimension', domainShape: 'cyclic', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, Month: { t0: 'Temporal', t1: 'DateGranule', visEncodings: ['ordinal'], aggRole: 'dimension', domainShape: 'cyclic', diverging: 'none', formatClass: 'plain', zeroBaseline: 'arbitrary', zeroPad: 0 }, Week: { t0: 'Temporal', t1: 'DateGranule', visEncodings: ['ordinal'], aggRole: 'dimension', domainShape: 'cyclic', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, Day: { t0: 'Temporal', t1: 'DateGranule', visEncodings: ['ordinal'], aggRole: 'dimension', domainShape: 'cyclic', diverging: 'none', formatClass: 'plain', zeroBaseline: 'arbitrary', zeroPad: 0 }, Hour: { t0: 'Temporal', t1: 'DateGranule', visEncodings: ['ordinal'], aggRole: 'dimension', domainShape: 'cyclic', diverging: 'none', formatClass: 'integer', zeroBaseline: 'arbitrary', zeroPad: 0 }, YearMonth: { t0: 'Temporal', t1: 'DateGranule', visEncodings: ['temporal', 'ordinal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, YearQuarter: { t0: 'Temporal', t1: 'DateGranule', visEncodings: ['temporal', 'ordinal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, YearWeek: { t0: 'Temporal', t1: 'DateGranule', visEncodings: ['temporal', 'ordinal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, Decade: { t0: 'Temporal', t1: 'DateGranule', visEncodings: ['temporal', 'ordinal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'integer', zeroBaseline: 'arbitrary', zeroPad: 0.03 }, // --- Temporal: Duration --- Duration: { t0: 'Temporal', t1: 'Duration', visEncodings: ['quantitative'], aggRole: 'additive', domainShape: 'open', diverging: 'none', formatClass: 'unit-suffix', zeroBaseline: 'meaningful', zeroPad: 0 }, // --- Measure: Amount --- Amount: { t0: 'Measure', t1: 'Amount', visEncodings: ['quantitative'], aggRole: 'additive', domainShape: 'open', diverging: 'none', formatClass: 'currency', zeroBaseline: 'meaningful', zeroPad: 0 }, Price: { t0: 'Measure', t1: 'Amount', visEncodings: ['quantitative'], aggRole: 'intensive', domainShape: 'open', diverging: 'none', formatClass: 'currency', zeroBaseline: 'meaningful', zeroPad: 0 }, Revenue: { t0: 'Measure', t1: 'Amount', visEncodings: ['quantitative'], aggRole: 'additive', domainShape: 'open', diverging: 'none', formatClass: 'currency', zeroBaseline: 'meaningful', zeroPad: 0 }, Cost: { t0: 'Measure', t1: 'Amount', visEncodings: ['quantitative'], aggRole: 'additive', domainShape: 'open', diverging: 'none', formatClass: 'currency', zeroBaseline: 'meaningful', zeroPad: 0 }, // --- Measure: Physical --- Quantity: { t0: 'Measure', t1: 'Physical', visEncodings: ['quantitative'], aggRole: 'additive', domainShape: 'open', diverging: 'none', formatClass: 'unit-suffix', zeroBaseline: 'meaningful', zeroPad: 0 }, Temperature: { t0: 'Measure', t1: 'Physical', visEncodings: ['quantitative'], aggRole: 'intensive', domainShape: 'open', diverging: 'conditional', formatClass: 'unit-suffix', zeroBaseline: 'arbitrary', zeroPad: 0.05 }, // --- Measure: Proportion --- Percentage: { t0: 'Measure', t1: 'Proportion', visEncodings: ['quantitative'], aggRole: 'intensive', domainShape: 'bounded', diverging: 'none', formatClass: 'percent', zeroBaseline: 'contextual', zeroPad: 0 }, // --- Measure: SignedMeasure --- Profit: { t0: 'Measure', t1: 'SignedMeasure', visEncodings: ['quantitative'], aggRole: 'signed-additive', domainShape: 'open', diverging: 'conditional', formatClass: 'signed-currency', zeroBaseline: 'meaningful', zeroPad: 0 }, PercentageChange: { t0: 'Measure', t1: 'SignedMeasure', visEncodings: ['quantitative'], aggRole: 'intensive', domainShape: 'open', diverging: 'conditional', formatClass: 'signed-percent', zeroBaseline: 'contextual', zeroPad: 0.05 }, Sentiment: { t0: 'Measure', t1: 'SignedMeasure', visEncodings: ['quantitative'], aggRole: 'intensive', domainShape: 'open', diverging: 'inherent', formatClass: 'signed-decimal', zeroBaseline: 'meaningful', zeroPad: 0 }, Correlation: { t0: 'Measure', t1: 'SignedMeasure', visEncodings: ['quantitative'], aggRole: 'intensive', domainShape: 'bounded', diverging: 'inherent', formatClass: 'signed-decimal', zeroBaseline: 'meaningful', zeroPad: 0 }, // --- Measure: GenericMeasure --- Count: { t0: 'Measure', t1: 'GenericMeasure', visEncodings: ['quantitative'], aggRole: 'additive', domainShape: 'open', diverging: 'none', formatClass: 'integer', zeroBaseline: 'meaningful', zeroPad: 0 }, Number: { t0: 'Measure', t1: 'GenericMeasure', visEncodings: ['quantitative'], aggRole: 'additive', domainShape: 'open', diverging: 'none', formatClass: 'decimal', zeroBaseline: 'meaningful', zeroPad: 0 }, // --- Discrete --- Rank: { t0: 'Discrete', t1: 'Rank', visEncodings: ['ordinal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'integer', zeroBaseline: 'arbitrary', zeroPad: 0.08 }, Score: { t0: 'Discrete', t1: 'Score', visEncodings: ['quantitative', 'ordinal'], aggRole: 'intensive', domainShape: 'bounded', diverging: 'conditional', formatClass: 'decimal', zeroBaseline: 'contextual', zeroPad: 0.05 }, Rating: { t0: 'Discrete', t1: 'Score', visEncodings: ['quantitative', 'ordinal'], aggRole: 'intensive', domainShape: 'bounded', diverging: 'conditional', formatClass: 'decimal', zeroBaseline: 'contextual', zeroPad: 0.05 }, Index: { t0: 'Discrete', t1: 'Index', visEncodings: ['ordinal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'integer', zeroBaseline: 'arbitrary', zeroPad: 0.08 }, ID: { t0: 'Identifier', t1: 'ID', visEncodings: ['nominal'], aggRole: 'identifier', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'arbitrary', zeroPad: 0 }, // --- Geographic --- Latitude: { t0: 'Geographic', t1: 'GeoCoordinate', visEncodings: ['quantitative', 'geographic'], aggRole: 'dimension', domainShape: 'fixed', diverging: 'none', formatClass: 'decimal', zeroBaseline: 'arbitrary', zeroPad: 0.02 }, Longitude: { t0: 'Geographic', t1: 'GeoCoordinate', visEncodings: ['quantitative', 'geographic'], aggRole: 'dimension', domainShape: 'fixed', diverging: 'none', formatClass: 'decimal', zeroBaseline: 'arbitrary', zeroPad: 0.02 }, Country: { t0: 'Geographic', t1: 'GeoPlace', visEncodings: ['nominal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, State: { t0: 'Geographic', t1: 'GeoPlace', visEncodings: ['nominal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, City: { t0: 'Geographic', t1: 'GeoPlace', visEncodings: ['nominal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, Region: { t0: 'Geographic', t1: 'GeoPlace', visEncodings: ['nominal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, Address: { t0: 'Geographic', t1: 'GeoPlace', visEncodings: ['nominal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, ZipCode: { t0: 'Geographic', t1: 'GeoPlace', visEncodings: ['nominal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, // --- Categorical: Entity --- PersonName: { t0: 'Categorical', t1: 'Entity', visEncodings: ['nominal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, Company: { t0: 'Categorical', t1: 'Entity', visEncodings: ['nominal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, Product: { t0: 'Categorical', t1: 'Entity', visEncodings: ['nominal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, Category: { t0: 'Categorical', t1: 'Entity', visEncodings: ['nominal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, Name: { t0: 'Categorical', t1: 'Entity', visEncodings: ['nominal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, // --- Categorical: Coded --- Status: { t0: 'Categorical', t1: 'Coded', visEncodings: ['nominal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, Type: { t0: 'Categorical', t1: 'Coded', visEncodings: ['nominal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, Boolean: { t0: 'Categorical', t1: 'Coded', visEncodings: ['nominal'], aggRole: 'dimension', domainShape: 'fixed', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, Direction: { t0: 'Categorical', t1: 'Coded', visEncodings: ['ordinal', 'nominal'], aggRole: 'dimension', domainShape: 'cyclic', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, // --- Categorical: Binned --- Range: { t0: 'Categorical', t1: 'Binned', visEncodings: ['ordinal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, AgeGroup: { t0: 'Categorical', t1: 'Binned', visEncodings: ['ordinal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, // --- Fallbacks --- String: { t0: 'Categorical', t1: 'Entity', visEncodings: ['nominal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, Unknown: { t0: 'Categorical', t1: 'Entity', visEncodings: ['nominal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0 }, }; /** Default entry for unrecognized types */ const UNKNOWN_ENTRY: TypeRegistryEntry = { t0: 'Categorical', t1: 'Entity', visEncodings: ['nominal'], aggRole: 'dimension', domainShape: 'open', diverging: 'none', formatClass: 'plain', zeroBaseline: 'none', zeroPad: 0, }; // --------------------------------------------------------------------------- // Public API // --------------------------------------------------------------------------- /** Look up a semantic type in the registry. Falls back to UNKNOWN_ENTRY. */ export function getRegistryEntry(semanticType: string): TypeRegistryEntry { return TYPE_REGISTRY[semanticType] ?? UNKNOWN_ENTRY; } /** Check whether a semantic type string is explicitly registered. */ export function isRegistered(semanticType: string): boolean { return semanticType in TYPE_REGISTRY; } /** * Get all registered type names. * Useful for validation or iterating over the type system. */ export function getRegisteredTypes(): string[] { return Object.keys(TYPE_REGISTRY); } ================================================ FILE: src/lib/agents-chart/core/types.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import type { ZeroDecision, ColorSchemeRecommendation } from './semantic-types'; import type { LabelSizingDecision } from './decisions'; import type { SemanticAnnotation, FormatSpec, DomainConstraint, TickConstraint } from './field-semantics'; /** * Core types for the chart engine library. * No React or UI framework dependencies — pure TypeScript. */ // --------------------------------------------------------------------------- // Data Types // --------------------------------------------------------------------------- // --------------------------------------------------------------------------- // Channel & Encoding // --------------------------------------------------------------------------- export const channels = [ "x", "y", "x2", "y2", "id", "color", "opacity", "size", "shape", "strokeDash", "column", "row", "latitude", "longitude", "radius", "detail", "group", "open", "high", "low", "close", "angle", ] as const; export const channelGroups: Record<string, string[]> = { "": ["x", "x2", "y", "y2", "latitude", "longitude", "id", "radius", "detail"], "legends": ["color", "group", "size", "shape", "text", "opacity", "strokeDash"], "price": ["open", "high", "low", "close"], "facets": ["column", "row"], }; /** * Encoding definition for a single channel, using field names directly. * This is the library-level encoding — no fieldID indirection. */ export interface ChartEncoding { field?: string; type?: "quantitative" | "nominal" | "ordinal" | "temporal"; aggregate?: 'count' | 'sum' | 'average'; sortOrder?: "ascending" | "descending"; sortBy?: string; scheme?: string; } // ============================================================================ // Phase 0: Semantic Resolution Types // ============================================================================ /** * Everything Phase 0 decides for a single channel. * * Combines the original ChartEncoding (user intent) with resolved * decisions derived from semantic type, data values, and channel context. * All downstream phases (layout, assembly, instantiation) read this — * no nested FieldSemantics reference needed. */ export interface ChannelSemantics { // --- Identity --- /** Field name bound to this channel */ field: string; /** The semantic annotation for this field */ semanticAnnotation: SemanticAnnotation; // --- Encoding type --- /** * Final encoding type for this channel. * Resolved from semantic type + data characteristics + channel rules. */ type: 'quantitative' | 'nominal' | 'ordinal' | 'temporal'; // --- Formatting --- /** Axis/legend number format */ format?: FormatSpec; /** Tooltip format (typically higher precision) */ tooltipFormat?: FormatSpec; /** * Temporal format string (temporal fields on any channel). * E.g., "%Y", "%b %d", "%H:%M". */ temporalFormat?: string; // --- Aggregation --- /** Default aggregate function when used as a measure */ aggregationDefault?: 'sum' | 'average'; // --- Scale --- /** * Zero-baseline decision (positional quantitative channels only). * Present only on 'x' and 'y' channels with type 'quantitative'. */ zero?: ZeroDecision; /** Recommended scale type */ scaleType?: 'linear' | 'log' | 'sqrt' | 'symlog'; /** Whether to apply "nice" rounding to domain endpoints */ nice?: boolean; /** Domain bounds constraint */ domainConstraint?: DomainConstraint; /** Tick mark constraints */ tickConstraint?: TickConstraint; // --- Ordering --- /** * Canonical ordinal sort order for this field's values. * E.g., month names, day-of-week, quarters. */ ordinalSortOrder?: string[]; /** Whether the canonical order is cyclic (wraps around) */ cyclic?: boolean; /** Whether the axis should be reversed (e.g., Rank: 1 at top) */ reversed?: boolean; /** Default sort direction */ sortDirection?: 'ascending' | 'descending'; // --- Color --- /** Color scheme recommendation (color channel only) */ colorScheme?: ColorSchemeRecommendation; // --- Histogram --- /** Whether this field benefits from binning */ binningSuggested?: boolean; // --- Stacking --- /** Whether values can be stacked, and how */ stackable?: 'sum' | 'normalize' | false; } /** Phase 0 output: one entry per channel. */ export type SemanticResult = Record<string, ChannelSemantics>; // ============================================================================ // Phase 1: Layout Types // ============================================================================ /** * How the template's primary mark encodes its quantitative value * on the positional (value) axis. * * Grounded in perceptual accuracy ranking: * 1. Position along a common scale — most accurate * 2. Length from a shared baseline * 3. Area * 4. Color saturation / luminance * * Drives zero-baseline, scale tightness, and compression behavior. */ export type MarkCognitiveChannel = 'position' | 'length' | 'area' | 'color'; /** * Template's layout intent — returned by declareLayoutMode(). */ export interface LayoutDeclaration { /** * Which axes allocate fixed bands per data position. * Banded axes use the spring model; non-banded use gas pressure. */ axisFlags?: { x?: { banded: boolean }; y?: { banded: boolean }; }; /** * Resolved encoding types after any template-driven type conversion. * E.g., detectBandedAxis may convert Q→O for a bar chart axis. * These override the Phase 0 decisions for layout purposes. */ resolvedTypes?: Record<string, 'nominal' | 'ordinal' | 'quantitative' | 'temporal'>; /** * Template-specific overrides to layout parameters. */ paramOverrides?: Partial<AssembleOptions>; /** * Which axes use binned encoding (e.g. histogram). * The assembler auto-detects this from template.encoding if not set. */ binnedAxes?: Record<string, boolean | { maxbins?: number }>; /** * Custom overflow strategy for deciding which discrete values to keep * when a channel overflows. If not provided, the default strategy is used. * * @param channel The overflowing channel ('x', 'y', 'color', etc.) * @param fieldName The field on that channel * @param uniqueValues All unique values in the data for that field * @param maxToKeep Maximum number of values that fit * @param context Abstract context with data and channel info * @returns The values to keep (in display order) */ overflowStrategy?: OverflowStrategy; } /** * Custom overflow strategy function type. * Returns the values to keep when a channel has too many discrete values. */ export type OverflowStrategy = ( channel: string, fieldName: string, uniqueValues: any[], maxToKeep: number, context: OverflowStrategyContext, ) => any[]; /** Context passed to overflow strategy functions. */ export interface OverflowStrategyContext { /** Full data table */ data: any[]; /** Per-channel semantic info */ channelSemantics: Record<string, ChannelSemantics>; /** Original user encodings (for sort info) */ encodings: Record<string, ChartEncoding>; /** Mark types present in the template */ allMarkTypes: Set<string>; } /** * Per-channel maximum values that can fit on the canvas. * * Computed once by `computeChannelBudgets` using the most conservative * assumptions (minStep, minSubplotSize, maxStretch). Passed to * `filterOverflow` so it only needs to decide *which* values to keep * and filter rows — no layout math. * * Pipeline: computeChannelBudgets → filterOverflow → computeLayout */ export interface ChannelBudgets { /** Maximum discrete values to keep per channel. * Channels not present here are uncapped (`Infinity`). */ maxValues: Record<string, number>; /** Facet grid decision (if facet channels exist) */ facetGrid?: FacetGridResult; } /** Result of overflow filtering. */ export interface OverflowResult { /** Data after removing overflow rows */ filteredData: any[]; /** Nominal value counts per channel (post-overflow) */ nominalCounts: Record<string, number>; /** Detailed truncation info for overflow styling */ truncations: TruncationWarning[]; /** Warning messages for the UI */ warnings: ChartWarning[]; } /** * Result of facet grid computation (from computeFacetGrid). * * Decides the visual grid layout (including column-only wrapping) * and the maximum number of unique values to keep per facet channel. * * Pipeline: computeFacetGrid → filterOverflow (uses caps) → computeLayout (uses grid) */ export interface FacetGridResult { /** Visual columns per row (after wrapping for column-only) */ columns: number; /** Visual rows (after wrapping for column-only) */ rows: number; /** Max unique values to keep for the column channel */ maxColumnValues: number; /** Max unique values to keep for the row channel */ maxRowValues: number; } /** * Describes one axis that was truncated due to overflow. */ export interface TruncationWarning { /** Severity level for UI display */ severity: 'warning'; /** Machine-readable code */ code: 'overflow'; /** Human-readable message */ message: string; /** Which channel overflowed ('x', 'y', 'color', etc.) */ channel: string; /** Field name on the overflowing axis */ field: string; /** Values retained (in display order) */ keptValues: any[]; /** Number of items omitted */ omittedCount: number; /** Placeholder string to append to the axis domain */ placeholder: string; } /** * Phase 1 output: all layout decisions. * * LayoutResult is **target-agnostic** — it describes abstract dimensions * and step sizes that any rendering backend can consume. It is the * backend's responsibility to translate these values into its own * coordinate system: * * subplotWidth / subplotHeight * The intended data-area (plot area) size in pixels. This does NOT * include axis labels, titles, legends, or margins. Each backend * must add its own margins/padding around this area. * * xStep / yStep * Pixel distance per discrete position on each axis. A backend * rendering bars should derive bar width from step and stepPadding. * VL uses `width: {step: N}` natively; ECharts must compute * explicit barWidth / barCategoryGap. * * stepPadding * Fraction of each step reserved for inter-category spacing (0–1). * Usable bar width = step × (1 − stepPadding). * * facet (columns / rows / subplot sizes) * When faceting is active, the subplot dimensions are already * divided for the facet grid. Each backend is responsible for * facet wrapping (e.g. column-only → wrapped rows), panel * positioning, header labels, and shared/per-panel axis titles. * * Backends should NOT modify LayoutResult. They read it and translate * to their native format (VL encoding props, ECharts grid/axis config, etc.). */ export interface LayoutResult { /** Final subplot width in px (after stretch) */ subplotWidth: number; /** Final subplot height in px (after stretch) */ subplotHeight: number; /** Computed step size for X axis (px per discrete position) */ xStep: number; /** Computed step size for Y axis (px per discrete position) */ yStep: number; /** Whether the step size is per-item or per-group. */ xStepUnit?: 'item' | 'group'; yStepUnit?: 'item' | 'group'; /** Number of banded continuous items on each axis (0 if not banded-continuous) */ xContinuousAsDiscrete: number; yContinuousAsDiscrete: number; /** Number of nominal/ordinal items on each axis */ xNominalCount: number; yNominalCount: number; /** Label sizing decisions per axis */ xLabel: LabelSizingDecision; yLabel: LabelSizingDecision; /** Facet layout (if applicable) */ facet?: { columns: number; rows: number; subplotWidth: number; subplotHeight: number; }; /** * Gap between facet panels in px, as set by the backend. * Backends use this to configure their own spacing * (VL config.facet.spacing, ECharts GAP, etc.). */ effectiveFacetGap: number; /** * Inter-category padding fraction (0–1) used by the layout engine. * Renderers (especially ECharts) should use this to size bars: * barWidth = step × (1 − stepPadding) */ stepPadding: number; /** Items truncated due to overflow */ truncations: TruncationWarning[]; } // ============================================================================ // Phase 2: Instantiation Types // ============================================================================ /** * Context passed to template instantiate() and to the shared assembler's * Phase 2 logic. Combines semantic decisions, layout results, and original * inputs. */ export interface InstantiateContext { /** Per-channel semantic decisions (Phase 0) */ channelSemantics: Record<string, ChannelSemantics>; /** Layout decisions (Phase 1) */ layout: LayoutResult; /** The data table (array of row objects, post-overflow filtering) */ table: any[]; /** Resolved VL encoding objects (built by assembler from Phase 0 decisions) */ resolvedEncodings: Record<string, any>; /** Original user-level encodings */ encodings: Record<string, ChartEncoding>; /** User-configured chart properties */ chartProperties?: Record<string, any>; /** Target canvas dimensions */ canvasSize: { width: number; height: number }; /** Field name → semantic type (string or enriched annotation) */ semanticTypes: Record<string, string | SemanticAnnotation>; /** Chart type name */ chartType: string; /** Assembly options (layout tuning parameters from the caller) */ assembleOptions?: AssembleOptions; } // --------------------------------------------------------------------------- // Chart Template // --------------------------------------------------------------------------- /** * Defines a configurable property for a chart template. * Describes the value domain; the app decides how to render it. */ export type ChartPropertyDef = { key: string; label: string; } & ( | { type: 'continuous'; min: number; max: number; step?: number; defaultValue?: number } | { type: 'discrete'; options: { value: any; label: string }[]; defaultValue?: any } | { type: 'binary'; defaultValue?: boolean } ); /** * Chart template definition — pure data, no UI/icon dependencies. * This is the reusable core that defines chart structure, encoding channels, * and processing logic. * * Three-phase pipeline hooks: * 1. declareLayoutMode — declare axis flags, type overrides, param overrides * 2. instantiate — build final spec from resolved encodings + layout */ export interface ChartTemplateDef { /** Display name of the chart type, e.g. "Scatter Plot" */ chart: string; /** Vega-Lite spec skeleton (mark + encoding structure) */ template: any; /** Which encoding channels are available for this chart */ channels: string[]; /** * How the primary mark encodes its quantitative value. * Determines zero-baseline, scale tightness, and compression behavior. * * Examples: * - Bar, Histogram, Lollipop, Waterfall, Pyramid: 'length' * - Area, Streamgraph, Density: 'area' * - Line, Scatter, Boxplot, Candlestick, Strip: 'position' * - Heatmap: 'color' */ markCognitiveChannel: MarkCognitiveChannel; /** * Phase 1a: Declare layout intent. * Runs BEFORE layout computation. * * Inspects channel semantics and data to decide: * - Which axes are banded (need spring model) * - Any type conversions (Q→O for banded axis) * - Layout parameter overrides (σ, step multiplier, etc.) * Grouping (from group channel + discrete axis detection) */ declareLayoutMode?: ( channelSemantics: Record<string, ChannelSemantics>, table: any[], chartProperties?: Record<string, any>, ) => LayoutDeclaration; /** * Build the final spec from resolved encodings + layout. * Runs AFTER layout computation. * * Receives the spec skeleton (deep clone of template), * and a context with resolved encodings, semantic decisions, * and layout result. Handles both encoding mapping and mark sizing. * * @param spec The Vega-Lite spec skeleton (deep clone of template) * @param context Complete context with all phase outputs */ instantiate: ( spec: any, context: InstantiateContext, ) => void; /** Optional configurable properties for the chart type */ properties?: ChartPropertyDef[]; /** * Optional post-processing hook. * Called after instantiation and layout application, before the final * result is returned. Receives the assembled spec/option and the * effective canvas size so the template can adjust visual parameters * (e.g. symbol size, line width) proportionally. */ postProcess?: ( spec: any, context: InstantiateContext, ) => void; } // --------------------------------------------------------------------------- // Warnings // --------------------------------------------------------------------------- /** A warning produced during chart assembly */ export interface ChartWarning { /** Warning severity */ severity: 'info' | 'warning' | 'error'; /** Short machine-readable warning code */ code: string; /** Human-readable description */ message: string; /** Optional: which channel(s) or field(s) triggered the warning */ channel?: string; field?: string; } // --------------------------------------------------------------------------- // Unified Assembly Input // --------------------------------------------------------------------------- /** * Unified input for all chart assembly functions (Vega-Lite, ECharts, Chart.js). * * Instead of passing multiple positional arguments, callers provide a single * JSON-serializable object with four top-level keys: * * ```ts * const result = assembleVegaLite({ * data: { values: myRows }, * semantic_types: { weight: 'Quantity', origin: 'Country' }, * chart_spec: { * chartType: 'Scatter Plot', * encodings: { x: { field: 'weight' }, y: { field: 'mpg' } }, * canvasSize: { width: 400, height: 300 }, * }, * options: { addTooltips: true }, * }); * ``` */ export interface ChartAssemblyInput { /** * Data source — either inline rows or a URL to fetch. * * - `{ values: any[] }` — an array of row objects (like Vega-Lite `data.values`). * - `{ url: string }` — a URL pointing to a JSON or CSV resource. * The assembler will resolve this internally before processing. * * At least one of `values` or `url` must be provided. */ data: { values: any[]; url?: never } | { url: string; values?: never }; /** * Per-column semantic type annotations. * * Maps field names to semantic type strings (e.g., `"Quantity"`, `"Country"`, * `"Year"`, `"Percentage"`). These drive encoding type resolution, zero-baseline * decisions, color schemes, formatting, and more. * * Fields not listed here fall back to `inferVisCategory()` which inspects * raw data values. */ semantic_types?: Record<string, string | SemanticAnnotation>; /** * Chart specification — describes *what* to draw. */ chart_spec: { /** Template name, e.g. `"Scatter Plot"`, `"Bar Chart"` */ chartType: string; /** Channel → encoding map (e.g., `{ x: { field: 'weight' }, y: { field: 'mpg' } }`) */ encodings: Record<string, ChartEncoding>; /** Target canvas size in pixels (default: `{ width: 400, height: 320 }`) */ canvasSize?: { width: number; height: number }; /** Template-specific configurable properties (e.g., bar corner radius, show labels) */ chartProperties?: Record<string, any>; }; /** * Options for the assembler — layout tuning, tooltips, etc. * All fields are optional and have sensible defaults. */ options?: AssembleOptions; } // --------------------------------------------------------------------------- // Assembly Options // --------------------------------------------------------------------------- /** * Options for the chart assembly function. * Includes layout tuning parameters — all have sensible defaults. */ export interface AssembleOptions { /** Whether to add tooltips to the chart (default: false) */ addTooltips?: boolean; /** * Fraction of each step reserved for inter-category padding (0–1). * VL pads *inside* the step (band = step × (1 − padding)), so this * value should match VL's paddingInner. ECharts pads *outside* the * band, so the layout engine passes this through so ECharts can * compute barWidth = step × (1 − stepPadding) explicitly. * * Default: 0.1 (matching VL's default band paddingInner). */ stepPadding?: number; /** Power-law exponent for discrete axis stretch (default: 0.5) */ elasticity?: number; /** * Maximum total stretch multiplier cap (default: 2). * * This is a **unified** budget: the combined stretch from facet * layout AND discrete/banded axis sizing must stay within this * factor. For example, with maxStretch=2 and a 400px canvas, * the total chart width never exceeds 800px regardless of how * many facet columns or discrete axis items there are. */ maxStretch?: number; /** Power-law exponent for facet subplot stretch — lower = more conservative (default: 0.3) */ facetElasticity?: number; /** Minimum pixels per discrete axis item (default: 6) */ minStep?: number; /** Maximum number of distinct color values before overflow truncation (default: 24) */ maxColorValues?: number; /** Minimum facet subplot size in px (default: 60) */ minSubplotSize?: number; /** * Fixed overhead in px for axis labels, titles, legend, etc. * Subtracted once from the total canvas budget (not per-panel). * Each backend sets its own default; core uses { width: 0, height: 0 }. */ facetFixedPadding?: { width: number; height: number }; /** * Gap in px between adjacent facet panels (spacing, headers). * Used directly by the core layout engine to compute subplot sizes * and max canvas dimensions. * Each backend sets its own value (VL ≈ 10, ECharts ≈ 14); core uses 0. */ facetGap?: number; /** Multiplier for the default step size (default: 1). Values >1 give more room per category. */ defaultStepMultiplier?: number; /** * When true, continuous X and Y axes stretch together using the * larger of the two per-axis stretch factors. This preserves the * aspect ratio of the data space. (default: false — axes stretch * independently based on their own density.) */ maintainContinuousAxisRatio?: boolean; /** * Gas-pressure tuning for continuous axes (default: scatter-plot settings). * - A single number overrides markCrossSection (σ) for both axes. * - An object allows per-axis σ plus optional elasticity / maxStretch: * `{ x: 100, y: 0, elasticity: 0.7, maxStretch: 2 }` * x/y = 0 means "don't stretch this axis". * Useful for line/area charts where horizontal crowding matters * far more than vertical. */ continuousMarkCrossSection?: number | { x: number; y: number; /** Per-axis stretch elasticity (default: 0.3). Higher → more responsive. */ elasticity?: number; /** Per-axis stretch cap (default: 1.5). */ maxStretch?: number; /** * Which axis uses series-count-based pressure instead of pixel counting. * - 'x' or 'y': that axis uses nSeries × σ / dim for pressure. * - 'auto': auto-detect — in 2D (both continuous), defaults to 'y'; * in 1D (one continuous + one discrete), uses the continuous axis. * The σ for the series axis is used directly (not sqrt'd) since series * count is inherently 1D. */ seriesCountAxis?: 'x' | 'y' | 'auto'; }; } ================================================ FILE: src/lib/agents-chart/docs/README.md ================================================ # Agents-Chart: A Visualization Library for Agent Developers > You're building an AI agent that creates charts. Every approach you've > tried is brittle — prompt-engineered Vega-Lite that breaks when users > edit fields, sizing heuristics that fail on new data shapes, retry > loops that burn tokens. **Agents-chart** is a library that eliminates > this brittleness. > > For the semantic type system, see > [design-semantics.md](design-semantics.md). For the axis layout > compression models, see > [design-stretch-model.md](design-stretch-model.md). --- ## TL;DR If you're building an AI agent that creates visualizations, you face a fundamental problem. You can have your agent generate simple chart specs that users can edit — but they look bad (wrong sizing, misleading encodings). Or you can have it generate polished specs — but they're brittle (hard-coded values break on every field swap, and every edit requires another LLM call). Either way, you're encoding design knowledge in prompts — and **prompts are not a reliable way to encode design knowledge.** Your agent may or may not follow them, and the result varies across models, prompt versions, and even runs. Worse, if you need to support multiple charting backends (Vega-Lite for composition, ECharts for interactivity, Chart.js for lightweight embedding), **every prompt, every example, every post-processing rule must be duplicated per backend** — multiplying the brittleness. **Agents-chart** is a library that moves design knowledge out of your prompts and into deterministic code. Instead of generating low-level charting code, your agent outputs a minimal semantic description: chart type, field assignments, and a **semantic type** per field (e.g., `Revenue`, `Rank`, `CategoryCode`). Agents-chart's compiler deterministically derives all low-level parameters — axis sizing, zero-baseline behavior, formatting, color schemes, bespoke mark templates — producing charts that look good *and* stay editable without calling your agent again. The quality is consistent because it comes from the library, not from the model. Because the semantic layer is **library-agnostic**, the same spec compiles to multiple rendering backends — Vega-Lite, ECharts, Chart.js, and GoFish today, with Plotly or D3 tomorrow — without re-deriving any design rules and without duplicating any prompts. The expensive work (semantic reasoning, layout computation) is done once; only the final instantiation step differs per backend. --- ## The Problem ### What you deal with today You're building an agent that needs to create charts. Maybe it's a data analysis copilot, a dashboard generator, or an automated reporting pipeline. At some point your agent has to produce a visualization — and that's where the brittleness starts. The typical approach: your agent generates Vega-Lite (or ECharts options, or Plotly traces) directly. You write prompt templates with examples, add post-processing logic for edge cases, build retry loops for malformed output. It works for your demo. Then real users arrive with real data, and the charts break in ways you didn't anticipate. Here is what goes wrong: 1. **Inconsistency across runs.** Your agent produces variable output quality — incorrect encodings, broken layouts, poor aesthetic defaults. You tune prompts for one chart type and break another. Even with detailed prompts specifying exact sizing rules, formatting conventions, and encoding guidelines, **your agent may or may not follow them** — and the degree of compliance varies across models, prompt versions, context length, and even individual runs. Weaker models (the ones you want to use for cost) struggle with anything beyond basic charts; even frontier models fail on composition, faceting, and layered designs. Design knowledge encoded in prompts is inherently unreliable. 2. **The quality–editability trap.** If your agent generates simple code, users can edit it (swap a field, change chart type) — but the chart looks mediocre. If your agent generates polished code, the chart looks great — but every user edit breaks it, forcing another round-trip to your agent. You can't have both, and neither option makes users happy. 3. **Expensive and slow for what it does.** Only frontier models *sometimes* produce correct specs for non-trivial charts, because the parameter space (axis types, domain settings, sizing, formatting, mark config) is large and inter-dependent. Even then, compliance with your design guidelines is probabilistic — you're paying frontier-model prices for output that still needs validation and retry. Your agent is spending its most expensive tokens on visualization plumbing instead of the data computation that actually matters. 4. **Ugly failure modes that you can't catch.** When your agent's output breaks, the chart doesn't degrade gracefully — it produces extreme dimensions (10,000 px wide from high-cardinality facets), crashes the renderer, or silently misrepresents the data. The charting library gives only low-level errors that neither you, your users, nor your agent can act on. ### The brittleness cascade Every fix you apply to your agent's visualization pipeline creates new problems: | What you try | What breaks next | |-------------|------------------| | Add sizing logic to prompts | Hard-coded for this data shape; breaks on different cardinality | | Add more VL examples to prompts | Token count balloons; model cost rises; unrelated examples confuse the model | | Post-process the output (fix widths, rotate labels) | You're now maintaining VL manipulation code that couples to every chart type | | Validate output and retry on failure | More LLM calls, more latency, more cost; retry loops don't fix *semantic* errors (wrong encoding type) | | Constrain output with JSON schema | Schema can enforce structure but not *correctness* — `{"type": "quantitative"}` is valid JSON for a CategoryCode field but produces a meaningless chart | | Support a second charting library (e.g., ECharts for interactivity) | All your prompt templates, post-processing, and validation must be duplicated for the new API | This is the **agent developer's treadmill**: you keep patching visualization edge cases instead of building the data analysis features that differentiate your product. ### Why the obvious approaches don't work #### Approach 1: Have your agent generate minimal VL (rely on defaults) **Result: The spec is simple and editable, but the charts look bad — and bespoke charts are impossible.** This is the "keep it simple" approach: your agent generates just field names, mark type, and data. The spec is easy for users to edit — swap a field name and the chart re-renders. But the *quality* of what renders is poor, because VL's defaults are generic heuristics that ignore both data characteristics and semantic meaning. **Sizing failures** — the chart is the wrong size for the data: | Scenario | VL default | What's wrong | |----------|-----------|-------------| | **Bar chart, 80 products** | `step: 20` → 1600 px wide | Overflows any container. Forces horizontal scrolling; unusable without a giant monitor. | | **Grouped bar, 30 × 5** | 30 × 5 × 20 = 3000 px | Grouped bars multiply the problem: each product group has 5 sub-bars at 20 px each. | | **Bars on temporal X (daily)** | VL doesn't auto-band temporal | Bars overlap or collapse to 1 px. Temporal axes are continuous; VL has no step-based sizing for them. | | **Line chart, 15 series** | Fixed `height: 300` | 15 lines in 300 px → ~20 px per series. Unreadable spaghetti. | | **Scatter, 2000 points** | Fixed 400 × 300 | Total mark area (156K px²) exceeds canvas area (120K px²). A solid blob. | | **80 product labels** | No auto-rotation | Labels overlap into an unreadable smear. | **Composition makes it worse.** These problems compound — every single-view sizing failure gets multiplied by the number of facets, and `xOffset` (grouped bars) adds another multiplicative factor: | Scenario | VL default | What's wrong | |----------|-----------|-------------| | **80 products, 4 facets** | 4 × 1600 px subplots | 6400 px total — comparison between facets is impossible because they're screens apart. | | **Grouped bar, 30 × 5, faceted × 4** | Facet × xOffset × step all multiply | 4 facets × 30 products × 5 groups × 20 px = 12,000 px. No way to express "fit everything within 800 px." | | **Facet columns + subplot width** | Must hard-code both `columns` and `width` | Interdependent: 4 columns → each subplot 190 px (unreadable). 2 columns → wider but taller. VL has no coordination mechanism. | **Semantic failures** — the chart misrepresents the data: The same number can mean completely different things, and VL defaults can't tell the difference. `17329487239` could be a **Unix timestamp**, a **Customer ID**, **Revenue ($)**, or a **sensor reading** — each requiring different encoding type, zero behavior, formatting, and color scheme. VL sees a number and defaults to `quantitative`. If it's a customer ID, you get a continuous axis from 0 to 17 billion with a single dot. If it's a timestamp, you get raw numbers instead of dates. `1, 2, 3, 4, 5` could be **Rank**, **Star rating**, **Quantity**, **Category code** (1=North, 2=South…), or a **Likert score** — each needing different zero behavior, scale direction, tick formatting, and chart compatibility. VL treats all as `quantitative, zero: true, ascending`. For Rank, this means rank 1 (best) at the bottom, zero wasted, ticks at "2.5" — absurd. You can't solve this in your agent's prompt or in post-processing. Simple heuristics don't help: Rank, Rating, Quantity, Category code, and Likert are all integers with identical cardinality. The only way to know the correct encoding is to know what the data *means*. **Bespoke charts are out of reach.** This approach can't produce bump charts, candlestick charts, streamgraphs, waterfall charts, or radar plots. Minimal VL generation only covers basic bar / line / scatter / area. **Bottom line:** The simple approach keeps your agent cheap and fast, but your users get wrong-sized, semantically misleading charts limited to basic types. #### Approach 2: Have your agent generate polished VL **Result: The charts look great once, but every user edit breaks them — and your agent gets called for every interaction.** This is the "invest tokens in quality" approach. Your agent generates detailed VL with tuned sizing, formatting, and encoding. It achieves quality precisely by **hard-coding values tuned to the current data**: | Hard-coded value | Breaks when… | |------------------|-------------| | `"width": 800` | User filters to 5 products (massive empty bars) or adds 200 (unreadable) | | `"labelAngle": -45` | User swaps X to Region (3-letter labels don't need rotation) | | `"domain": [0, 950000]` | User swaps Y to Temperature (wrong by 6 orders of magnitude) | | `"mark.size": 8` | User switches to scatter with 1000 points (dots overlap completely) | | `"scale.zero": true` | User swaps Y to Rank (zero-based rank wastes space, reads inverted) | | `"format": "$,.0f"` | User swaps Y to Percentage (shows "$48" instead of "48%") | The better your agent's output, the *more* hard-coded constants it contains, and the *harder* the chart is to edit. This creates a vicious cycle: **high-quality generation → brittle spec → forced regeneration on every edit → high cost and latency → poor exploration experience.** As the agent developer, you're paying for this cycle in API costs, user frustration, and engineering time spent on retry logic. **The parameters live at different levels and must coordinate.** A polished VL spec scatters its configuration across multiple layers that all couple to each other: | Level | Examples | Coordinates with | |-------|---------|-----------------| | **Global** | `width`, `height`, `autosize`, `padding` | Step size, facet columns, mark size | | **Per-axis** | `scale.zero`, `scale.domain`, `scale.type`, `axis.format`, `axis.labelAngle` | Global width (label overflow), mark type (zero behavior), encoding type | | **Per-mark** | `mark.size`, `mark.strokeWidth`, `mark.opacity` | Global dimensions (overlap), data cardinality | | **Composition** | `facet.columns`, `resolve.scale`, `xOffset.step` | Global width (subplot size), per-axis step (bar width), label config | Editing any one parameter without adjusting the others produces a broken chart. Change `width` from 800 to 400? The label angle, step size, and font size were tuned for 800 — now labels overlap. Add a facet column? The per-subplot width halves, so bars become unreadable unless you also shrink the step, rotate labels, and adjust font size. Switch from bar to scatter? `scale.zero`, `mark.size`, and `domain` all need updating, but `width` and `labelAngle` might also change because scatter points have different spatial needs than bars. This coordination is **the core brittleness problem for agent developers.** You can't encode it in a prompt. A rule-based post-processor would need to enumerate the full cross-product of parameter interactions. Neither is practical — which is why every edit becomes another LLM call from your agent. **Structural rewrites are the worst case.** Swapping Product→Year and Revenue→Rank transforms a bar chart into a bump chart: the mark changes from bar to line+circle (layered), X type changes from nominal to ordinal, Y gets reversed with `zero: false` and domain padding, a color channel appears, and width/height both change. No single-parameter edit path exists — it's a complete structural rewrite. **The alternative: call your agent for every edit.** This works, but: - **Expensive for you.** ~500–1000 tokens per call × 10–15 edits in a session = significant API cost. Each call takes 2–5 seconds. - **Forces you to use expensive models.** Only frontier models reliably handle bespoke charts with their layered marks, data transforms, and complex scale configurations. - **The $F \times C$ combinatorial problem.** With 15 fields and 5 chart types, there are 75 possible configurations. Your agent handles each one individually — dozens of calls per exploration session. **Bottom line:** Polished VL generation looks good once, but the spec is too complex to edit, and regeneration per edit is slow, expensive, and requires frontier models. --- ## The Solution The core insight: **design knowledge belongs in the library, not in your prompts.** Your agent may or may not follow a detailed prompt — and even when it does, the result varies across models and runs. Agents-chart eliminates this uncertainty by encoding all visualization design decisions in deterministic code. Your agent generates a minimal spec, and the library handles everything else — consistently, every time, regardless of which model produced the spec. ### What your agent does differently - **A simple output contract.** Your agent outputs a small JSON: chart type, field assignments, and a semantic type per field. No axis config, no sizing, no formatting, no mark layering. This is easy for any model to produce reliably — even cheap, fast models. The contract is small enough that compliance is near-certain, unlike detailed prompts with dozens of design rules that models follow inconsistently. - **Automatic, consistent quality.** The library compiles that JSON into a polished chart with correct sizing, formatting, zero-baseline behavior, color schemes, and label handling — every time, deterministically. The design knowledge is built into the compiler, not hoped for from the model. Your agent doesn't need to know Vega-Lite (or ECharts, or Chart.js) at all. - **User edits without calling your agent.** Users can swap fields, change chart types, add facets — and the chart re-derives all low-level config automatically. **90% of edits need zero LLM calls.** Your agent is only invoked for the initial chart creation and for data transformations. - **Bespoke charts at no extra prompt cost.** Grouped bars, bump charts, streamgraphs, candlesticks, ridge plots — they all take the same ~7-line spec as a basic bar chart. The templates in the library handle the mark layering, custom transforms, and specialized encodings. - **Actionable error messages.** When a chart configuration is wrong, the library produces semantic explanations (*"Pyramid chart requires exactly 2 categories; 'Region' has 5"*) that your agent can read and repair — no VL stack traces, no silent misrepresentation. - **Multi-backend output from one spec.** The same semantic spec compiles to Vega-Lite, ECharts, Chart.js, or GoFish. Your deployment context picks the backend; your agent and your prompts don't change. - **The generated output is still accessible.** Agents-chart compiles to standard Vega-Lite (or ECharts options, or Chart.js configs). If a user needs to fine-tune a specific visual detail, they can edit the generated output directly. The library handles the 98% of decisions derivable from semantics; users override the remaining 2%. ### At a glance | Property | Approach 1 (defaults) | Approach 2 (polished VL) | Agents-chart | |----------|-----------------------|--------------------------|--------------| | **Looks good** | ✗ | ✓ | ✓ | | **Editable** | ✓ (simple spec) | ✗ (brittle, hard-coded) | ✓ (semantic spec) | | **Bespoke charts** | ✗ | Sometimes (frontier model) | ✓ (templates) | | **Cost per user edit** | 0 (no agent call) | 1 agent call ($, latency) | 0 (no agent call) | | **Agent complexity** | Low (just fields + mark) | High (coordinate 10–30 params) | Low (fields + semantic types) | | **Model requirement** | N/A | Frontier for bespoke | Any (classification only) | ### How your agent integrates ``` 1. Your agent generates: chart spec + semantic types for each field (small JSON) (e.g., Revenue, Year, Company) └─→ This is what your agent's prompt produces. ~7–12 lines. └─→ No VL knowledge, no sizing, no formatting. 2. User edits to explore: swap field / change chart type / add facet └─→ agents-chart re-derives all config from semantic types (NO agent call!) └─→ Chart looks good automatically (98% of edits) 3. (Rare) Fine-tune: user asks agent to edit underlying VL/ECharts for detailed style customization (2% of edits) ``` The chart spec is intentionally minimal. In Data Formulator, it's a small JSON object returned alongside the data transformation code, so that precious tokens go where they matter most: data computation and transformation, not visualization plumbing. --- ## System Architecture ### Design Principles 1. **VL-free analysis.** Layout computation, overflow filtering, and semantic resolution operate on abstract channel names (`x`, `y`, `color`, `group`, `size`) — never on Vega-Lite encoding objects. This keeps the core logic backend-agnostic. 2. **Minimal spec surface.** The input is: chart type + field assignments + semantic types (~7–12 lines). The compiler derives all low-level parameters deterministically. 3. **Templates absorb VL complexity.** Bespoke charts (lollipop, bump, candlestick, waterfall, etc.) are defined as template skeletons with an `instantiate()` hook. The user/LLM never touches layered marks, custom transforms, or scale configurations. 4. **No UI dependencies.** Zero React, Redux, or framework imports. Pure TypeScript library usable from any context. ### Two-Stage Pipeline Each backend has its own `assemble*()` entry point (`assembleVegaLite`, `assembleECharts`, `assembleChartjs`, `assembleGoFish`), but they all follow the same two-stage structure. The analysis stage is shared; only the instantiation stage is backend-specific. ``` assembleVegaLite(input: ChartAssemblyInput) // or assembleECharts, assembleChartjs, assembleGoFish │ ▼ ══ ANALYSIS (backend-free, shared core) ═════════════════════ │ ├── Phase 0: resolveSemantics() → ChannelSemantics │ Infers encoding type, zero-baseline, color scheme, │ format, aggregation default, scale type, domain, │ temporal format for each channel from semantic types │ + data characteristics. │ ├── Step 0a: template.declareLayoutMode() → LayoutDeclaration │ Template hook: declares axis flags (banded?), │ type overrides, param overrides, overflow strategy. │ ├── Step 0b: convertTemporalData() → converted data │ Parses temporal string values into Date objects. │ ├── Step 0c: filterOverflow() → OverflowResult │ Truncates discrete channels that exceed the canvas │ budget. Produces filtered data, nominal counts, │ truncation warnings. │ └── Phase 1: computeLayout() → LayoutResult Computes subplot width/height, step sizes, label sizing, facet columns/rows. Uses spring model for banded axes, gas-pressure model for continuous axes. │ ▼ ══ INSTANTIATE (backend-specific) ═══════════════════════════ │ ├── build*Encodings() (per backend) │ Translates abstract channel semantics into │ backend encoding objects. │ ├── template.instantiate() │ Template-specific spec construction. │ (e.g., pie remaps size→theta, bar adjusts marks) │ ├── restructureFacets() (VL/ECharts) │ Restructures column/row into facet spec for │ layered charts. Computes facet columns. │ ├── applyLayoutToSpec() │ Applies width/height/step/config/formatting from │ LayoutResult to the backend spec. │ └── Post-layout adjustments Facet binning, independent y-scales, tooltips. │ ▼ Return: complete backend spec + warnings ``` The compiler operates in three conceptual phases. Phases 0 and 1 contain ~90% of the design logic and are **identical across all backends**. Phase 2 is a thin translation layer — typically 50–100 lines per chart type. ``` Phase 0 (shared): Resolve semantic types → encoding types, zero behavior, formatting, color schemes, sort order Phase 1 (shared): Compute layout → spring/pressure model → canvas size, step sizes, label rotation, overflow warnings Phase 2 (per-backend): Instantiate → translate layout into VL spec, ECharts option, or Chart.js config ``` Three design decisions make this architecture work: semantic types as the contract between your agent and the library (Phase 0), parametric physics for layout (Phase 1), and a library-agnostic multi-backend framework (Phase 2). --- ## How It Works ### 1. Semantic types as the contract The root cause of your agent's brittleness is that charting APIs scatter the semantic contract between data and chart across dozens of low-level parameters. Consider a column containing `17234982372` — it could be a Unix timestamp, a monetary value, a serial number, or a group ID. Today, your agent must decide the encoding type (`temporal`, `quantitative`, `nominal`), set axis formatting, configure zero-baseline behavior, choose sizing — and these become hard-coded constants in the output that break when the user edits anything. **The fix:** instead of asking your agent to set these low-level details, ask it to communicate one thing: **what does this data mean?** — expressed through a fine-grained semantic type system (e.g., `Revenue`, `Rank`, `Temperature`, `Year`). From the semantic type plus data characteristics (cardinality, range, distribution), agents-chart's compiler automatically derives everything: ``` Semantic type ↓ ├── Encoding type (Revenue → quantitative, Rank → ordinal, Month → ordinal) ├── Zero baseline (Revenue → true, Temperature → false, Rank → false) ├── Domain padding (Rank → 8%, Temperature → 5%, Revenue → 0%) ├── Scale direction (Rank → reversed, others → normal) ├── Axis formatting (Revenue → "$,.0f", Percentage → ".0%", Year → "%Y") ├── Sort order (Month → calendar order, Product → by value) ├── Color scheme (Company → categorical, Revenue → sequential) └── Sizing model (nominal → spring, quantitative → per-axis stretch) ``` Your agent's only job is **semantic classification** — assigning a type like `"Revenue"`, `"Rank"`, `"Temperature"`, or `"Month"` to each data field. This is one of the easiest tasks for any LLM (even small, fast models do it reliably). **Semantic types survive user edits.** When the user swaps Y from Revenue to Temperature, agents-chart re-derives all parameters from `"Temperature"` instead of `"Revenue"` and gets the right answer. No hard-coded constant goes stale because there are no hard-coded constants. **Your agent is not called.** **The $F \times C$ problem becomes $F + C$.** Your agent classifies each field once ($F$ decisions), the user picks a chart type ($C$ choices), and agents-chart handles the cross-product. Instead of 75 configurations that each need an agent call, the system needs 15 type assignments (done once) and handles all 75 deterministically. → Full semantic type system details: [design-semantics.md](design-semantics.md) ### 2. Parametric physics instead of manual heuristics The layout challenge is **coordinating sizing across axes, layers, mark types, and facets** — parameters that are deeply interdependent (e.g., facet count affects subplot width, which affects bar width, which affects label rotation). The conventional approach — whether done by your agent, by hand-coded post-processing, or by the charting library's defaults — is **heuristic-based**: a pile of if/else rules and magic numbers (`if bars > 30, rotate labels; if width > 800, shrink step`). These heuristics are brittle because they don't compose: every new chart type, every new facet structure, every new mark combination requires new rules. You're always guessing thresholds, and the guesses break on data shapes you didn't anticipate. Agents-chart replaces this with a **parametric physics-inspired model**. Instead of guessing layout constants, you control the system through a small set of physics parameters with intuitive physical meaning: | Parameter | Physical meaning | What it controls | |-----------|-----------------|------------------| | $\ell_0$ (rest length) | Natural spacing between items when unconstrained | Default bar/cell width before any compression | | $k$ (spring stiffness) | Resistance to compression | How aggressively items shrink to fit the canvas | | $\ell_{\min}$ (min length) | Hard floor — items never compress below this | Minimum readable bar/cell width | | $W_{\max}$ (max canvas) | Maximum allowed canvas width | Upper bound on chart dimensions | | $\beta_f$ (facet stretch) | How much extra canvas a facet adds | Trade-off between subplot density and total chart width | | $P$ (pressure) | Outward force from continuous data density | How much a dense scatter/line plot stretches its canvas | These parameters **compose naturally**. A faceted grouped bar chart doesn't need special-case rules — the spring model runs per subplot, facet stretch adjusts the container, and the parameters interact through the same physics equations regardless of chart type. Change one parameter (e.g., raise $\ell_{\min}$ to guarantee wider bars) and the system re-equilibrates automatically — no cascade of broken heuristics. **Spring Model (Discrete Axes).** Applies to banded marks (bar, histogram, heatmap, boxplot, grouped bar). Models the axis as $N$ springs in a box: $$\ell = \frac{\kappa \cdot \ell_0 + L_0 / N}{1 + \kappa}$$ Three regimes: **Fits** (items at natural size), **Elastic** (items compress + axis stretches), **Overflow** (items at minimum, excess truncated). **Gas-Pressure Model (Continuous Axes).** Applies to positional marks (scatter, line, area, bump). Each axis stretches based on 1D crowding pressure: $$s = \min\!\big(1 + \beta_c,\; p^{\,\alpha_c}\big)$$ Two pressure modes: **positional** (unique pixel positions × √σ / dim) and **series-count** (nSeries × σ / dim for line/area Y axes). **Facet Model.** Second-level stretch for faceted charts. Each subplot runs its own sizing model internally; the facet layer determines subplot count, columns, and overall canvas growth. → Full layout model details: [design-stretch-model.md](design-stretch-model.md) ### 3. Library-agnostic multi-backend architecture Both semantic types and physics-based sizing are **library-agnostic** — they reason about data meaning and visual density, not about any particular charting API. This means the same compiler logic targets multiple rendering backends without re-deriving the design rules. As an agent developer, you may need to deploy to different contexts — a lightweight mobile app, a desktop analytics tool, a static report. No single charting library fits all of them: | Backend | Strengths | Weaknesses | |---------|-----------|------------| | **Vega-Lite** | Grammar of graphics; declarative composition; strong faceting and layering | Heavy runtime (~400 KB); limited interactivity beyond tooltips; poor mobile performance | | **ECharts** | Rich interactivity (zoom, brush, dataZoom); Canvas + SVG dual renderer; strong CJK locale support | Imperative option-bag API; no grammar-of-graphics composition; verbose config for layered designs | | **Chart.js** | Lightweight (~60 KB); Canvas-native (fast for large datasets); simple API; massive plugin ecosystem | No faceting; limited statistical charts; no declarative composition | | **GoFish** | Imperative rendering; full control over mark placement | No built-in layout or scale system | **These are not interchangeable APIs with different syntax — they use fundamentally different visual representation models and data models.** Adapting between them is highly non-trivial: - **Vega-Lite** is a *grammar of graphics*: a chart is a composition of independent encoding channels (x, y, color, size, shape) bound to data fields through scales. Layering and faceting are declarative compositional operators. Data flows through a transform pipeline into a single flat table. - **ECharts** is an *option bag*: a chart is a top-level config object with `series[]` arrays, each containing its own data, mark type, and axis bindings. There are no encoding channels — you configure axis objects directly and reference them by index. Layering means adding series entries; faceting has no native concept. - **Chart.js** is a *dataset-oriented canvas renderer*: a chart has a single chart type, a `labels` array for the categorical axis, and `datasets[]` with parallel value arrays. There is no independent encoding model — color, border, and point style are per-dataset properties, not data-driven channels. Composition beyond basic stacking doesn't exist. These differences mean you can't transliterate a spec from one library to another — you must *re-think* the chart in each library's conceptual model. Without a backend-agnostic library, supporting multiple renderers means **duplicating your entire agent pipeline per backend** — prompts, examples, post-processing, validation, retry logic, sizing heuristics. This is the $B \times T \times R$ explosion: $B$ backends × $T$ templates × $R$ rules. **Agents-chart collapses this to $T + (B \times I)$.** The $T$ templates and $R$ rules live in shared Phases 0–1, and each backend only implements $I$ instantiation functions — thin translators that map the already-computed layout into the target library's config format. Adding a new backend means writing instantiation code, not re-implementing the design system. **What this means for you:** - **Deployment flexibility.** A mobile app uses Chart.js (60 KB); a desktop analytics tool uses Vega-Lite (full composition). Your agent generates one spec; the deployment target picks the backend. - **Capability coverage.** Radar and gauge charts are native in ECharts but missing from Vega-Lite. Faceted compositions are native in Vega-Lite but painful in Chart.js. Agents-chart routes each chart type to the backend that handles it best — your agent doesn't need to know which. - **Rendering trade-offs handled for you.** Canvas renderers handle 10K+ points without DOM pressure; SVG renderers produce crisper output for publication. The choice depends on dataset size — not on your prompts. - **Vendor independence.** When agents-chart is the contract — not the backend API — swapping or upgrading a renderer is a localized change, not a rewrite of your entire pipeline. - **New backends are easy to add — even by coding agents.** Adding a backend is a *mechanical translation* task: given the computed layout and a target library's API, write the instantiation functions. This is exactly what coding agents (Copilot, Cursor, Codex) excel at — they can scaffold a new backend from existing ones as reference. Developers and designers then enhance the generated adapters with domain-specific design knowledge: animation defaults, theme integration, accessibility, or library-specific optimizations. The result is a **human-in-the-loop backend pipeline** where the mechanical work is automated and design expertise is applied where it matters most. ### 4. Error handling When your agent generates raw VL and the chart fails, failures are either **catastrophic** (the chart crashes or renders at 2000 px × 80 px) or **silent** (the chart misrepresents the data). VL gives only low-level errors that neither your agent nor your user can act on. With agents-chart, your agent gets **structured, semantic error messages** that it can parse and repair automatically. **Semantic validation — agents-chart catches errors before rendering:** | Violation | What agents-chart detects | What raw VL does | |-----------|--------------------------|-----------------| | **Chart–data incompatibility** | *"Pyramid chart requires exactly 2 categories; 'Region' has 5"* | Renders a broken layered bar chart. No error. | | **Redundant encoding** | *"Revenue is mapped to both Y and color — color adds no information"* | Renders silently with a meaningless gradient legend. | | **Field–channel mismatch** | *"Product has 80 values — too many for a color encoding"* or *"Group is a CategoryCode — use nominal, not quantitative"* | Renders 80 indistinguishable colors, or maps numeric categories to a continuous gradient. No warning. | | **Missing required encoding** | *"Candlestick chart requires Open, High, Low, Close fields"* | Crashes or renders partial marks. No explanation. | **Overflow detection — agents-chart explains *why* a chart was clipped:** | Scenario | Agents-chart response | Raw VL result | |----------|----------------------|---------------| | **80 products × 4 facets** | *"X axis clipped: 80 products compressed from 20 px to 8 px per bar; canvas stretched to 800 px (max). Consider filtering to top 20."* | 6400 px wide, or 400 px with 5 px bars. No explanation. | | **720 temporal cells** | *"Heatmap X axis: 720 hourly cells compressed to 1.1 px each. Consider aggregating to daily."* | 14,400 px wide, or 0.5 px cells — a solid color band. | | **50 facets × 10 categories** | *"Facet overflow: 50 subplots cannot fit readable bars. Showing top 12 facets; 38 truncated."* | 50 squished facets with 0.8 px bars. Technically renders but useless. | --- ## Abstract Channels The library defines its own channel vocabulary, distinct from VL encoding channels. This decouples user/AI intent from rendering specifics. | Channel | Purpose | VL translation | |---------|---------|----------------| | `x`, `y` | Positional axes | Direct mapping | | `x2`, `y2` | Range endpoints (ranged dot, waterfall) | Direct mapping | | `color` | Color encoding | Direct mapping | | `group` | Grouped subdivision (e.g., grouped bar) | VL `color` + `xOffset`/`yOffset` | | `size` | Mark size (scatter) or slice weight (pie) | Scatter: VL `size` with sqrt scale; Pie: VL `theta` | | `shape` | Mark shape | Direct mapping | | `opacity` | Opacity | Direct mapping | | `column`, `row` | Faceting | VL `facet`/`column`/`row` | | `detail` | Detail level without visual change | Direct mapping | | `latitude`, `longitude` | Geo coordinates | Direct mapping | | `open`, `high`, `low`, `close` | Candlestick price channels | Layered rule + rect encoding | | `radius` | Radar chart radius | VL `radius` with sqrt scale | ### The `group` channel First-class channel for grouped bar charts. The analysis stage resolves its semantics (type, color scheme) without any VL knowledge. The grouping axis is auto-detected: whichever of `x`/`y` is discrete gets subdivided. During instantiation, `buildVLEncodings()` translates: - `group` → VL `color` encoding (for coloring) - `group` → VL `xOffset` or `yOffset` encoding (for position subdivision) This avoids the old `additionalEncodings` hack and keeps the analysis stage completely VL-free. ### The `size` channel Abstract channel for two distinct visual mappings: - **Scatter plot**: maps to VL `size` with adaptive `sqrt` scale range based on canvas area and point count. - **Pie chart**: `pie.ts` instantiate remaps `size` → VL `theta`, stripping the sqrt scale (theta is linear area). --- ## Core Types ### `ChannelSemantics` Phase 0 output for a single channel. Combines user input with resolved decisions. This is the central IR — all four backends read the same `ChannelSemantics` record. ```typescript interface ChannelSemantics { // --- Identity --- field: string; semanticAnnotation: SemanticAnnotation; // --- Encoding type --- type: 'quantitative' | 'nominal' | 'ordinal' | 'temporal'; // --- Formatting --- format?: FormatSpec; tooltipFormat?: FormatSpec; temporalFormat?: string; // --- Aggregation --- aggregationDefault?: 'sum' | 'average'; // --- Scale --- zero?: ZeroDecision; scaleType?: 'linear' | 'log' | 'sqrt' | 'symlog'; nice?: boolean; domainConstraint?: DomainConstraint; tickConstraint?: TickConstraint; // --- Ordering --- ordinalSortOrder?: string[]; cyclic?: boolean; reversed?: boolean; sortDirection?: 'ascending' | 'descending'; // --- Color --- colorScheme?: ColorSchemeRecommendation; // --- Histogram --- binningSuggested?: boolean; // --- Stacking --- stackable?: 'sum' | 'normalize' | false; } ``` 21 fields total (2 required: `field`, `type`; plus `semanticAnnotation`). ### `LayoutDeclaration` Template's layout intent, returned by `declareLayoutMode()`: ```typescript interface LayoutDeclaration { axisFlags?: { x?: { banded: boolean }; y?: { banded: boolean }; }; resolvedTypes?: Record<string, 'nominal' | 'ordinal' | 'quantitative' | 'temporal'>; paramOverrides?: Partial<AssembleOptions>; binnedAxes?: Record<string, boolean | { maxbins?: number }>; overflowStrategy?: OverflowStrategy; } ``` No `grouping` field — grouping is auto-detected from `channelSemantics.group` + which axis is discrete. No `additionalEncodings` — the `group` channel + auto-detection replaces the old approach entirely. ### `LayoutResult` Phase 1 output — all layout decisions: ```typescript interface LayoutResult { subplotWidth: number; subplotHeight: number; xStep: number; yStep: number; xStepUnit?: 'item' | 'group'; yStepUnit?: 'item' | 'group'; xContinuousAsDiscrete: number; yContinuousAsDiscrete: number; xNominalCount: number; yNominalCount: number; xLabel: LabelSizingDecision; yLabel: LabelSizingDecision; facet?: { columns: number; rows: number; subplotWidth: number; subplotHeight: number }; truncations: TruncationWarning[]; } ``` ### `InstantiateContext` Everything a template's `instantiate()` receives: ```typescript interface InstantiateContext { channelSemantics: Record<string, ChannelSemantics>; layout: LayoutResult; table: any[]; resolvedEncodings: Record<string, any>; // VL encoding objects encodings: Record<string, ChartEncoding>; chartProperties?: Record<string, any>; canvasSize: { width: number; height: number }; semanticTypes: Record<string, string>; chartType: string; } ``` ### `ChartTemplateDef` Template definition — pure data, no UI dependencies: ```typescript interface ChartTemplateDef { chart: string; // display name template: any; // VL spec skeleton channels: string[]; // available encoding channels markCognitiveChannel: MarkCognitiveChannel; // 'position' | 'length' | 'area' | 'color' declareLayoutMode?: (cs, data, props) => LayoutDeclaration; instantiate: (spec, context: InstantiateContext) => void; properties?: ChartPropertyDef[]; } ``` ### `OverflowStrategy` Customizable per-template. The default strategy in `filter-overflow.ts` handles: connected marks (keep all for continuity), user sorts, auto-sorts, bar sum-aggregate, numeric sort, first-N. ```typescript type OverflowStrategy = ( channel: string, fieldName: string, uniqueValues: any[], maxToKeep: number, context: OverflowStrategyContext, ) => any[]; ``` --- ## Semantic Type System Semantic types classify fields by what they *mean* (not just their data type), organized in a three-tier hierarchy (T0 → T1 → T2): ``` T0 Family T1 Category T2 Types (examples) ───────── ─────────── ────────────────── Temporal ────┬── DateTime ──────────── DateTime, Date, Time, Timestamp ├── DateGranule ────────── Year, Quarter, Month, Week, Day, Hour, ... └── Duration ──────────── Duration Measure ─────┬── Amount ────────────── Amount, Price, Revenue, Cost ├── Physical ──────────── Quantity, Temperature ├── Proportion ────────── Percentage ├── SignedMeasure ──────── Profit, PercentageChange, Sentiment, Correlation └── GenericMeasure ────── Count, Number Discrete ────┬── Rank ──────────────── Rank ├── Score ─────────────── Score, Rating └── Index ─────────────── Index Geographic ──┬── GeoCoordinate ─────── Latitude, Longitude └── GeoPlace ──────────── Country, State, City, Region, ZipCode, Address Categorical ─┬── Entity ────────────── PersonName, Company, Product, Category, Name, ... ├── Coded ─────────────── Status, Type, Boolean, Direction └── Binned ────────────── Range, AgeGroup Identifier ──┴── ID ────────────────── ID ``` **46 registered types** across 6 T0 families and 17 T1 categories. Each type entry in the registry carries orthogonal dimensions that drive visualization decisions: - **visEncodings** — encoding type candidates (quantitative, ordinal, nominal, temporal) - **aggRole** — aggregation role (`additive`, `intensive`, `signed-additive`, `dimension`, `identifier`) - **domainShape** — domain shape (`open`, `bounded`, `fixed`, `cyclic`) - **diverging** — diverging nature (`none`, `conditional`, `inherent`) - **formatClass** — format class (`currency`, `percent`, `unit-suffix`, `date`, `integer`, `plain`, ...) - **zeroBaseline** — zero baseline policy (`meaningful`, `arbitrary`, `contextual`, `none`) - **zeroPad** — domain padding fraction → Full details: [design-semantics.md](design-semantics.md) --- ## Template Catalog ### Vega-Lite (30 chart types) | Category | Charts | |----------|--------| | **Scatter & Point** | Scatter Plot, Regression, Ranged Dot Plot, Boxplot, Strip Plot | | **Bar** | Bar Chart, Grouped Bar Chart, Stacked Bar Chart, Histogram, Heatmap, Lollipop Chart, Pyramid Chart | | **Line & Area** | Line Chart, Dotted Line Chart, Bump Chart, Area Chart, Streamgraph | | **Part-to-Whole** | Pie Chart, Rose Chart, Waterfall Chart | | **Statistical** | Density Plot, Candlestick Chart, Radar Chart | | **Map** | US Map, World Map | | **Custom** | Custom Point, Custom Line, Custom Bar, Custom Rect, Custom Area | ### ECharts (28 chart types) | Category | Charts | |----------|--------| | **Scatter & Point** | Scatter Plot, Regression, Ranged Dot Plot, Boxplot, Strip Plot | | **Bar** | Bar Chart, Grouped Bar Chart, Stacked Bar Chart, Histogram, Heatmap, Lollipop Chart, Pyramid Chart | | **Line & Area** | Line Chart, Dotted Line Chart, Bump Chart, Area Chart, Streamgraph | | **Part-to-Whole** | Pie Chart, Funnel Chart, Treemap, Sunburst Chart | | **Polar** | Radar Chart, Rose Chart | | **Financial** | Candlestick Chart | | **Indicator** | Gauge Chart | | **Flow** | Sankey Diagram | | **Other** | Waterfall Chart, Density Plot | ### Chart.js (10 chart types) | Category | Charts | |----------|--------| | **Scatter & Point** | Scatter Plot | | **Bar** | Bar Chart, Grouped Bar Chart, Stacked Bar Chart, Histogram | | **Line & Area** | Line Chart, Area Chart | | **Part-to-Whole** | Pie Chart | | **Polar** | Radar Chart, Rose Chart | ### GoFish (8 chart types) | Category | Charts | |----------|--------| | **Scatter & Point** | Scatter Plot, Scatter Pie Chart | | **Bar** | Bar Chart, Grouped Bar Chart, Stacked Bar Chart | | **Line & Area** | Line Chart, Area Chart | | **Part-to-Whole** | Pie Chart | **76 template definitions** across 4 backends. Each template defines: 1. **`template`** — spec skeleton (mark + encoding structure) 2. **`channels`** — available encoding channels 3. **`markCognitiveChannel`** — how the mark encodes value (`position`, `length`, `area`, `color`) 4. **`declareLayoutMode()`** — optional hook for layout intent 5. **`instantiate()`** — build final backend spec from resolved context --- ## Public API All four backends share the same input type (`ChartAssemblyInput`) and follow the same calling convention: ```typescript import { assembleVegaLite, assembleECharts, assembleChartjs, assembleGoFish } from './lib/agents-chart'; const input: ChartAssemblyInput = { chartType: 'Scatter Plot', encodings: { x: { field: 'weight' }, y: { field: 'mpg' }, color: { field: 'origin' } }, table: myData, semanticTypes: { weight: 'Quantity', mpg: 'Quantity', origin: 'Country' }, canvasSize: { width: 400, height: 300 }, }; const vlSpec = assembleVegaLite(input); // → Vega-Lite JSON spec const ecSpec = assembleECharts(input); // → ECharts option object const cjsSpec = assembleChartjs(input); // → Chart.js config object const gfSpec = assembleGoFish(input); // → GoFish imperative spec ``` **`ChartAssemblyInput` fields:** - `chartType` — template name (e.g., `"Grouped Bar Chart"`) - `encodings` — channel → `ChartEncoding` (field, aggregate, sort, scheme) - `table` — array of row objects - `semanticTypes` — field name → semantic type string (e.g., `"Revenue"`, `"Year"`) - `canvasSize` — `{ width, height }` in pixels - `options` — `AssembleOptions` (layout tuning, all have defaults) **Output:** Complete backend-specific spec, ready to render. May include `_warnings: ChartWarning[]` for overflow/truncation diagnostics. --- ## Overflow & Warning System When discrete channels overflow the canvas budget, the library: 1. Computes the max items that fit (from spring model equilibrium) 2. Applies the overflow strategy (default or template-custom) to choose which values to keep 3. Filters the data to only kept values 4. Emits `TruncationWarning` with: channel, field, kept values, omitted count, placeholder string 5. Emits `ChartWarning` for the UI The default overflow strategy priority: 1. Connected marks (line, area) → keep all (truncation breaks continuity) 2. User-specified sort → keep top/bottom N by sort order 3. Quantitative opposite axis → sort by opposite, keep top N 4. Bar with count aggregate → sum-aggregate and keep top N 5. Numeric field → numeric sort, keep first N 6. Fallback → keep first N in data order --- ## File Map ### Core (shared across all backends) | File | Lines | Role | |------|-------|------| | `core/types.ts` | 686 | All type definitions (ChannelSemantics, ChartAssemblyInput, etc.) | | `core/field-semantics.ts` | 1,040 | Field semantic resolution (T0/T1/T2 tiered logic) | | `core/semantic-types.ts` | 921 | Semantic type system (hierarchy, zero decisions, color schemes) | | `core/type-registry.ts` | 197 | Type registry (46 types, 6 T0 families) | | `core/resolve-semantics.ts` | 476 | Phase 0: channel semantic resolution + temporal conversion | | `core/compute-layout.ts` | 1,001 | Phase 1: backend-free layout computation | | `core/decisions.ts` | 919 | Reusable decision functions (elastic budget, step, facet, label, gas pressure) | | `core/filter-overflow.ts` | 296 | Phase 0c: backend-free overflow filtering | | `core/recommendation.ts` | 1,178 | Chart recommendation engine | | `core/index.ts` | 120 | Core re-exports | ### Vega-Lite backend | File | Lines | Role | |------|-------|------| | `vegalite/assemble.ts` | 758 | VL two-stage pipeline coordinator | | `vegalite/instantiate-spec.ts` | 719 | `applyLayoutToSpec`, `applyTooltips` | | `vegalite/recommendation.ts` | 194 | VL-specific recommendation | | `vegalite/templates/*.ts` | ~2,500 | 15 template files (30 chart types) | ### ECharts backend | File | Lines | Role | |------|-------|------| | `echarts/assemble.ts` | 710 | ECharts two-stage pipeline coordinator | | `echarts/instantiate-spec.ts` | 660 | ECharts spec instantiation | | `echarts/facet.ts` | 251 | ECharts facet support | | `echarts/recommendation.ts` | 101 | ECharts-specific recommendation | | `echarts/templates/*.ts` | ~4,800 | 23 template files (28 chart types) | ### Chart.js backend | File | Lines | Role | |------|-------|------| | `chartjs/assemble.ts` | 213 | Chart.js two-stage pipeline coordinator | | `chartjs/instantiate-spec.ts` | 158 | Chart.js spec instantiation | | `chartjs/recommendation.ts` | 34 | Chart.js recommendation | | `chartjs/templates/*.ts` | ~1,400 | 8 template files (10 chart types) | ### GoFish backend | File | Lines | Role | |------|-------|------| | `gofish/assemble.ts` | 521 | GoFish imperative rendering pipeline | | `gofish/recommendation.ts` | 34 | GoFish recommendation | | `gofish/templates/*.ts` | ~850 | 6 template files (8 chart types) | ### Top-level | File | Lines | Role | |------|-------|------| | `index.ts` | 60 | Public API re-exports (all 4 backends) | **Total: ~21,000 lines** across 87 `.ts` files (excluding test-data). --- ## Architectural Boundaries ``` ┌─────────────────────────────────────────────────┐ │ ANALYSIS (core/ — backend-free) │ │ │ │ resolveSemantics → ChannelSemantics │ │ declareLayoutMode → LayoutDeclaration │ │ convertTemporalData │ │ filterOverflow → OverflowResult │ │ computeLayout → LayoutResult │ │ │ │ Inputs: abstract channels, data, semantic types│ │ Outputs: types, decisions, layout numbers │ │ Imports: NO backend-specific syntax │ ├─────────────────────────────────────────────────┤ │ INSTANTIATE (vegalite/ | echarts/ | chartjs/ | gofish/) │ │ │ │ Each backend has its own: │ │ build*Encodings → backend encoding objects │ │ template.instantiate → backend spec │ │ restructureFacets → backend facet structure │ │ applyLayoutToSpec → backend config/sizing │ │ │ │ Inputs: ChannelSemantics + LayoutResult + data │ │ Outputs: complete backend-specific spec │ │ Backend code ONLY constructs its own syntax │ └─────────────────────────────────────────────────┘ ``` The boundary is enforced by function signatures: analysis-stage functions accept `ChannelSemantics` and `LayoutDeclaration` — never backend encoding objects or spec structures. All four backends (Vega-Lite, ECharts, Chart.js, GoFish) share the same analysis stage and read the same `ChannelSemantics` IR. Adding a new backend only requires implementing the instantiation layer — no analysis code changes. --- ## In Practice: Four Examples Four examples that progressively demonstrate the gap between your agent generating charting code directly versus outputting a minimal semantic spec. In every case, the agents-chart spec is the same shape: chart type + field assignments + semantic types (~7–12 lines). ### Example 1: Simple bar chart — no advantage yet **Task:** Bar chart of Revenue by 5 Regions. **With agents-chart:** Chart type, two field encodings, two semantic types. **With your agent generating VL directly:** Mark type, two field encodings with explicit `type` annotations. The two are nearly identical. VL's defaults happen to work: 5 bars fit comfortably, `zero: true` is correct, alphabetical sort is acceptable. **No win here — and that's the point.** Agents-chart isn't designed to help with cases your agent already handles well. ### Example 2: Lollipop chart — templates eliminate prompt complexity **Task:** Lollipop chart of Revenue by Product (top 10), colored by Group (values: 1, 2, 3, 4, 5 — categorical groups encoded as numbers). **With agents-chart:** Chart type = "Lollipop Chart", three field encodings (x, y, color), three semantic types (`Revenue`, `Product`, `CategoryCode`). Same ~10 lines as any chart. **What your agent's prompt must produce if generating VL directly:** | VL parameter | What and why | |-------------|-------------| | Layered spec structure | Must use `layer: [...]` — a lollipop is two marks, not one | | Rule mark config | `mark.type: "rule"`, `strokeWidth`, `color` for the stem | | Circle mark config | `mark.type: "circle"`, `size`, `color` for the dot | | Duplicated encoding | Both layers need identical `x`, `y`, and `color` encodings | | Sort | `sort: "-x"` on the Y axis to rank products by value | | Zero baseline | `scale.zero: true` on the X axis (Revenue is zero-meaningful) | | Step size | VL default step (20 px) works here for 10 items, but breaks if data grows | | Axis formatting | `axis.format: "~s"` for compact numbers | | Color encoding type | Group values are `1, 2, 3, 4, 5` — VL defaults to `quantitative`, producing a continuous blue gradient. Must set `"type": "nominal"` for categorical colors. | | Color scale scheme | Must override to categorical palette (`"category10"`) — but only after fixing the type. | The color problem is especially insidious: VL sees numbers and defaults to `quantitative` with a sequential color scheme. Groups 1 and 2 get nearly identical shades of blue — visually indistinguishable. The chart *renders* without error but the color encoding is meaningless. With agents-chart, `CategoryCode` → nominal → categorical palette, and groups get distinct hues automatically. ### Example 3: Faceted bar chart — physics layout vs. hard-coded sizing **Task:** Revenue by Product (80 products), faceted by Region (4 regions). **With agents-chart:** Chart type, three field encodings (x, y, column), three semantic types. Same ~12 lines. **What your agent must hard-code if generating VL directly:** | Problem | What VL requires | Why it's hard | |---------|-----------------|---------------| | **Canvas size** | Hard-code `width` per subplot + `columns` for facet wrap | VL default: 20 px × 80 = 1600 px per subplot → 4 facets = 6400 px total. Must override, but numbers depend on each other. | | **Step / bar width** | Hard-code `step` or `width` | With `width: 380` for 80 bars, each bar is ~4.75 px — unreadable. Must add `labelAngle: -90`, `labelLimit: 60`, `labelFontSize: 9`. | | **Facet wrapping** | Hard-code `columns` | 4 columns → each subplot 190 px (unreadable). 1 column → page 4× taller. The right choice depends on bar count, label length, container size. | | **Scale resolution** | `resolve.scale.x: "independent"` | Each facet may have different products — shared scale wastes space. | Your agent's prompt can't express these dependencies — each value must be hard-coded, and they all break when the data changes. With agents-chart, the spring model handles all four automatically: - Facet stretch ($\beta_f$) determines overall canvas growth. - Each subplot's spring model: 80 products × $\ell_0 = 20$ px overflows → items compress to $\ell = 8$ px, subplot stretches to fit. - Label rotation and truncation derived from count and string lengths. - Result: readable bars (8 px, not 2 px), controlled total width (not 6400 px), facet columns balanced. Your agent doesn't touch any of this. ### Example 4: Heatmap with temporal × category — semantic types vs. guessing **Task:** Heatmap of event counts — UTC timestamps (hourly, 30 days) on X, Category (15 event types) on Y, count as color. **With agents-chart:** Chart type = "Heatmap", three encodings (x, y, color), three semantic types (`DateTime`, `Category`, `Count`). ~12 lines. **What your agent must get right if generating VL directly** — 11 decisions, all stemming from needing to know what the data *means*: | Decision | What VL requires | What agents-chart derives | |----------|-----------------|--------------------------| | X encoding type | `"type": "temporal"` — must recognize timestamp is a date, not a number | `DateTime` → temporal | | Time formatting | `"format": "%m/%d %H:%M"` — must pick format for hourly granularity | `DateTime` + hourly range → appropriate format | | UTC scale | `"scale": { "type": "utc" }` — must know timestamps are UTC | `DateTime` → UTC handling | | Time unit | `"timeUnit": "yearmonthdatehoursminutes"` — verbose, error-prone | Derived from data range + granularity | | Label rotation | `"labelAngle": -45` — must guess from label width | Auto from label count + string length | | Y encoding type | `"type": "nominal"` — must decide Category isn't quantitative | `Category` → nominal | | Color zero baseline | `"scale.zero": true` — Count should start from 0 | `Count` → zero-meaningful → `zero: true` | | Color scheme | `"scheme": "blues"` — sequential scheme for counts | `Count` → quantitative sequential → blues | | Color format | `"format": "d"` — integer formatting for counts | `Count` → integer → `"d"` | | Cell step size | VL default: 720 × 20 = 14,400 px wide. Must hard-code width. | Spring model: 720 cells → equilibrium at ~800 px | | Canvas width | Hard-code `"width": 800` after manually computing 720 cells | Derived automatically from cell count + compression | That's **11 decisions your agent must make correctly**, all derived automatically from three semantic type annotations. If the timestamp column contained Unix epoch numbers (e.g., `1739600400`), VL would default to `quantitative` — a continuous axis from 0 to 1.7 billion. The semantic type `DateTime` tells the compiler to treat it as temporal regardless of the raw data format. ================================================ FILE: src/lib/agents-chart/docs/design-semantics.md ================================================ # Design: Semantic-Type-Driven Compilation Context > **Status:** Draft — for discussion and revision > **Date:** 2026-02-27 > **Scope:** Redesign the semantic type system to produce a structured per-field compilation context that drives all visualization property decisions > **Related:** `core/semantic-types.ts`, `core/field-semantics.ts`, `core/type-registry.ts`, `core/resolve-semantics.ts`, `core/decisions.ts` --- ## §0 Semantic Type Inventory ### 0.1 Tiered type system: resolution levels Having 80+ fine-grained types is powerful but impractical for every scenario. Different tasks warrant different levels of type specificity. We organize semantic types into **three tiers** so the LLM can annotate at the level of detail appropriate for the cost/quality tradeoff: | Tier | Count | Purpose | LLM cost | Viz config quality | |---|---|---|---|---| | **T0 — Family** | 6 | Coarsest. Enough to pick encoding type (Q/O/N/T) and basic defaults | Lowest — can even be rule-based without LLM | Correct encoding, generic formatting | | **T1 — Category** | 17 | Mid-level. Enough for format prefix/suffix, aggregation default, zero-baseline, color scheme class | Moderate — small closed list, high accuracy | Good formatting, sensible defaults | | **T2 — Specific** | 46 | Finest. Enables diverging midpoints, domain constraints, tick strategies, interpolation hints | Higher — larger vocabulary, needs examples | Full compilation context | **The key design principle:** the compilation logic works at any tier. If the LLM provides `"Revenue"` (T2), we get everything: `$` prefix, sum aggregation, meaningful zero, sequential color, log-scale hint. If it provides `"Amount"` (T1), we still get `$` prefix, sum, meaningful zero — but miss the log-scale hint. If it provides `"Measure"` (T0), we get quantitative encoding, sum aggregation, meaningful zero — but no format prefix. **Graceful degradation, not failure.** ### 0.2 Tier 0 — Families (6 types) These are the broadest categories. They map directly to visualization encoding logic and can be inferred by simple heuristics (no LLM needed): | T0 Family | Data type | Default vis encoding | What it determines | |---|---|---|---| | **Temporal** | date/string | temporal | Time axis, date parsing, temporal sort | | **Measure** | number | quantitative | Numeric axis, aggregation=sum, meaningful zero | | **Discrete** | number | ordinal | Integer ticks, no aggregation, arbitrary zero | | **Geographic** | number/string | geographic/nominal | Map layer, geocoding | | **Categorical** | string | nominal | Color/shape/facet, no axis ordering | | **Identifier** | number/string | nominal | Tooltip only, never encode on axis/color | **What T0 alone gives you:** correct encoding type, basic aggregation default, zero-baseline class, which channels are appropriate (you wouldn't put a Categorical on Y for a bar chart). This is roughly what a simple rule-based system (no LLM) could produce from data type + cardinality + column-name heuristics. **What T0 misses:** format prefix/suffix, specific aggregation (sum vs avg), diverging detection, domain constraints, scale type hints, interpolation. ### 0.3 Tier 1 — Categories (17 types) Mid-level types within each family. Each T1 type maps to exactly one T0 family. The LLM picks from a manageable list and gets high accuracy. | T0 Family | T1 Categories | What T1 adds over T0 | |---|---|---| | **Temporal** | `DateTime`, `DateGranule`, `Duration` | Point-in-time vs granule (month/year) vs time span; determines temporal parse, ordinal-vs-temporal encoding | | **Measure** | `Amount`, `Physical`, `Proportion`, `SignedMeasure`, `GenericMeasure` | Format prefix/suffix class ($, %, °), aggregation (sum vs avg), diverging detection | | **Discrete** | `Rank`, `Score`, `Index` | Reversed axis (Rank), integer ticks, domain hints | | **Geographic** | `GeoCoordinate`, `GeoPlace` | Lat/lon pairing vs geocodable name; map layer type | | **Categorical** | `Entity`, `Coded`, `Binned` | Cardinality expectations, ordinal-ness of binned values | | **Identifier** | `ID` | Never aggregate, never encode | **Full T1 type table:** | T1 Type | T0 Family | Vis encoding | What it determines | |---|---|---|---| | `DateTime` | Temporal | temporal | Full date/time parsing, temporal axis | | `DateGranule` | Temporal | ordinal or temporal | Month/Year/Quarter/etc. — ordinal sort, built-in canonical order | | `Duration` | Temporal | quantitative | Time span, "2h 30m" formatting, sum/avg aggregation | | `Amount` | Measure | quantitative | Currency prefix ($, €), sum aggregation, meaningful zero | | `Physical` | Measure | quantitative | Unit suffix (kg, km, °C, mph), avg aggregation, arbitrary zero for Temperature | | `Proportion` | Measure | quantitative | % or ratio formatting, bounded domain [0,1] or [0,100], avg aggregation | | `SignedMeasure` | Measure | quantitative | Diverging midpoint (0), signed data, conditional/inherent diverging color | | `GenericMeasure` | Measure | quantitative | No special format, sum/avg heuristic from field name | | `Rank` | Discrete | ordinal | Reversed axis, integer ticks, not aggregable | | `Score` | Discrete | quantitative | Bounded domain, integer ticks, avg aggregation | | `Index` | Discrete | ordinal/nominal | Row number, sequence — not aggregable, not for axis | | `GeoCoordinate` | Geographic | quantitative | Fixed domain (lat [-90,90], lon [-180,180]), map projection | | `GeoPlace` | Geographic | nominal | Geocodable name, choropleth/symbol-map | | `Entity` | Categorical | nominal | High cardinality expected, tooltip-friendly | | `Coded` | Categorical | nominal | Low cardinality, discrete colors, legend-friendly. Includes Status, Type, Boolean, Direction | | `Binned` | Categorical | ordinal | Pre-binned ranges ("18-24"), ordinal axis, sequential color | | `ID` | Identifier | nominal | Never aggregate, tooltip only | **What T1 alone gives you:** correct encoding, format class (currency/percent/unit/plain), aggregation default (sum vs avg), zero-baseline class, diverging detection, domain shape (bounded/open/fixed/cyclic). **What T1 misses:** specific format symbol ($€£¥ vs just "currency"), exact domain bounds, unit-specific formatting (°C vs °F), type-specific interpolation hints. Diverging midpoints for domain-specific scales (like pH=7) are derived from `intrinsicDomain` or type-intrinsic logic rather than T2 types. ### 0.4 Tier 2 — Specific types (46 types) The full vocabulary. Each T2 type maps to exactly one T1 category. These provide the finest-grained compilation context. The T2 inventory is deliberately **pruned to types that change compilation behavior** vs. their T1 parent. Types that compile identically to a sibling are dropped — the LLM can use the T1 name instead. Domain-specific diverging midpoints (e.g., pH=7, NPS=0) are derived from the `intrinsicDomain` midpoint or type-intrinsic logic rather than dedicated T2 types. | T1 Category | T2 Specific Types | |---|---| | `DateTime` | DateTime, Date, Time, Timestamp | | `DateGranule` | Year, Quarter, Month, Week, Day, Hour, YearMonth, YearQuarter, YearWeek, Decade | | `Duration` | Duration | | `Amount` | Amount, Price, Revenue, Cost | | `Physical` | Quantity, Temperature | | `Proportion` | Percentage | | `SignedMeasure` | Profit, PercentageChange, Sentiment, Correlation | | `GenericMeasure` | Count, Number | | `Rank` | Rank | | `Score` | Score, Rating | | `Index` | Index | | `GeoCoordinate` | Latitude, Longitude | | `GeoPlace` | Country, State, City, Region, ZipCode, Address | | `Entity` | PersonName, Company, Product, Category, Name, String, Unknown | | `Coded` | Status, Type, Boolean, Direction | | `Binned` | Range, AgeGroup | | `ID` | ID | **What was dropped and why:** > **Implementation note:** These types are fully removed from both the > `TYPE_REGISTRY` (type-registry.ts) and the `SemanticTypes` constant > (semantic-types.ts). If the LLM produces a dropped type string, it will > hit the `UNKNOWN_ENTRY` fallback (Categorical/Entity/nominal). The LLM > prompt should be updated to only offer the "Use instead" types. | Dropped T2 | Use instead | Rationale | |---|---|---| | TimeRange | Duration | Same compilation (ordinal encoding) | | Distance, Area, Volume, Weight, Speed | Quantity (or `Physical` T1) | Unit captured by annotation; same `unit-suffix` format class | | Rate | Percentage | Same format + aggregation | | Ratio | Number (via `decimal` format) | Open domain, no percent scaling | | Level | Score | Same compilation (bounded, avg, integer) | | Coordinates | Latitude + Longitude | Ambiguous pair; use specific coordinate types | | Location | Country / State / City | Generic fallback; same compilation | | Username, Email, Brand, Department | PersonName / Company / Name | Same nominal compilation | | Binary, Code | Boolean / Status | Same categorical compilation | | Bucket | Range | Same compilation | | SKU | ID | Same compilation (identifier role) | **What T2 adds over T1:** - `Revenue` vs `Price` → both `Amount`, but Revenue is `additive` (totals) while Price is `intensive` (per-unit); `Cost` kept for LLM annotation clarity (compiles like Revenue) - `Temperature` vs `Quantity` → both `Physical`, but Temperature has conditional diverging (freezing-point midpoint from unit); Quantity is generic with no diverging - `Month` vs `Year` vs `Quarter` → all `DateGranule`, but Month has cyclic(12) domain, Quarter has cyclic(4), Year is open-ended - `Sentiment` vs `Profit` vs `Correlation` → all `SignedMeasure`, but Sentiment is inherently diverging, Profit is conditionally diverging, Correlation has fixed domain [-1,1] ### 0.5 The hierarchy as a DAG ``` T0 Family T1 Category T2 Specific ───────── ─────────── ────────────────────── Temporal ─────┬── DateTime ──────────── DateTime, Date, Time, Timestamp ├── DateGranule ───────── Year, Quarter, Month, Week, Day, Hour, │ YearMonth, YearQuarter, YearWeek, Decade └── Duration ─────────── Duration Measure ──────┬── Amount ────────────── Amount, Price, Revenue, Cost ├── Physical ─────────── Quantity, Temperature ├── Proportion ────────── Percentage ├── SignedMeasure ─────── Profit, PercentageChange, Sentiment, Correlation └── GenericMeasure ────── Count, Number Discrete ─────┬── Rank ─────────────── Rank ├── Score ────────────── Score, Rating └── Index ────────────── Index Geographic ───┬── GeoCoordinate ────── Latitude, Longitude └── GeoPlace ─────────── Country, State, City, Region, ZipCode, Address Categorical ──┬── Entity ───────────── PersonName, Company, Product, Category, Name, String, Unknown ├── Coded ────────────── Status, Type, Boolean, Direction └── Binned ───────────── Range, AgeGroup Identifier ───┴── ID ───────────────── ID ``` **Resolution logic:** The builder function `resolveFieldSemantics()` resolves the tier of the provided type and applies progressively more specific logic: ```typescript function resolveFieldSemantics(annotation, fieldName, values) { const { semanticType } = normalizeAnnotation(annotation); // Resolve tier membership const t2 = T2_REGISTRY[semanticType]; // e.g., { t1: 'Amount', t0: 'Measure', ... } const t1 = t2?.t1 ?? T1_REGISTRY[semanticType]; // maybe the input IS a T1 type const t0 = t1?.t0 ?? T0_REGISTRY[semanticType]; // maybe the input IS a T0 type // T0 decisions (always available) const encoding = resolveEncoding(t0, values); const aggRole = resolveAggRole(t0); const zeroBaseline = resolveZeroBaseline_T0(t0); // T1 decisions (available if T1 or finer) const formatClass = t1 ? resolveFormatClass(t1) : null; const aggDefault = t1 ? resolveAggDefault(t1) : resolveAggDefault_fromT0(t0); const diverging = t1 ? resolveDivergingClass(t1) : null; // T2 decisions (available if T2) const formatDetail = t2 ? resolveFormatDetail(t2, annotation) : null; const domainHint = t2 ? resolveDomainHint(t2, annotation, values) : null; const tickHint = t2 ? resolveTickHint(t2, annotation) : null; const interpolation = t2 ? resolveInterpolation(t2) : null; // Merge: finer overrides coarser, nulls fall back return mergeContext(t0Defaults, t1Refinements, t2Specifics); } ``` ### 0.6 LLM annotation strategies The tiered system enables different annotation strategies depending on the task: | Strategy | Types used | When to use | LLM prompt size | |---|---|---|---| | **Full T2** | All specific types | High-value dashboards, one-time setup | Largest (~46 types in prompt) | | **T1 only** | Category-level only | Bulk dataset annotation, cost-sensitive | Medium (~16 types) | | **T0 only** | Family-level only | Quick preview, rule-based fallback | Smallest (~6 types), may not need LLM | | **Mixed** | T2 for key fields, T1 for rest | Typical interactive session | Adaptive prompt | **Mixed strategy example:** The LLM is given a dataset with 20 columns. The user is building a revenue chart, so the system asks for T2 annotation on the 3-4 likely chart fields (revenue, month, category) and T1 for the remaining 16 columns: ```json { "revenue": { "semantic_type": "Revenue", "unit": "USD" }, "month": { "semantic_type": "Month" }, "product_category": { "semantic_type": "Coded" }, "customer_name": { "semantic_type": "Entity" }, "customer_age": { "semantic_type": "GenericMeasure" }, "region": { "semantic_type": "GeoPlace" }, "order_date": { "semantic_type": "DateTime" }, "satisfaction": { "semantic_type": "Score", "domain": [1, 5] } } ``` Here `revenue`, `month`, and `satisfaction` get T2 types (fine-grained decisions). `product_category` and `customer_name` get T1 (enough for encoding and format class). When the user later drags `customer_age` onto a chart, the system can re-annotate that one field at T2 level. ### 0.7 Multi-membership dimensions: orthogonal axes that drive visualization The tier hierarchy (T0→T1→T2) is ONE axis of the type system — it controls *which* compilation rules fire and at what granularity. But every semantic type also sits at a specific position along several **orthogonal classification dimensions**, each of which directly controls a distinct set of visualization properties. The compilation context is the *product* of tier-derived decisions **and** dimension-derived decisions. #### 0.7.1 The five orthogonal dimensions | Dimension | Values | What viz properties it controls | |---|---|---| | **Vis encoding candidates** | `quantitative`, `ordinal`, `nominal`, `temporal` (one or more, with preference order) | Axis type, scale type, mark compatibility, channel compatibility, sort | | **Aggregation role** | `additive`, `intensive`, `signed-additive`, `dimension`, `identifier` | Whether to aggregate, which aggregate function, whether to group-by, tooltip-only | | **Domain shape** | `open`, `bounded`, `fixed`, `cyclic` | Scale domain clamping, tick generation, extrapolation, axis extent, radar/polar recommendation | | **Diverging nature** | `none`, `conditional`, `inherent` | Color scheme class (sequential vs diverging), midpoint, legend center, bipolar axis | | **Format class** | `currency`, `percent`, `unit-suffix`, `date`, `time`, `integer`, `plain` | Axis label format, tooltip format, prefix/suffix, decimal precision | These dimensions are NOT derivable from the tier hierarchy alone — they are properties of each type that must be explicitly catalogued. Two types in the same T1 category can differ on multiple dimensions (e.g., `Temperature` and `Weight` are both `Physical` but differ on diverging nature). #### 0.7.2 Dimension → Visualization property mapping Each dimension controls a specific, non-overlapping set of downstream visualization decisions: **Vis encoding candidates** → determines: - Whether a field can go on X/Y axis (quantitative/temporal/ordinal) vs only color/shape/facet (nominal) - Scale type: `linear`, `log`, `time`, `point`, `band` - Compatible mark types: quantitative fields → line/area/bar; nominal → bar/point; temporal → line/area - Sort behavior: temporal → chronological; ordinal → canonical order; nominal → data order or alphabetical - When a type has multiple valid encodings (e.g., `Rating` → Q or O), the builder picks based on chart type + channel: scatter Y → quantitative; heatmap color → ordinal **Aggregation role** → determines: - Whether the field appears in the `aggregate` vs `groupby` clause - Default aggregate function: `additive` → `"sum"`, `intensive` → `"mean"`, `signed-additive` → `"sum"` - Whether the field should be offered as a measure or dimension in the UI - `identifier` → excluded from aggregation entirely, tooltip-only **Domain shape** → determines: - Scale domain: `open` → auto from data; `bounded` → clamp to known range; `fixed` → hard limits; `cyclic` → modular - Tick generation: `bounded [0,100]` → nice ticks within range; `fixed [-90,90]` → constrained; `cyclic` → all cycle values - Extrapolation: `cyclic` → no extrapolation beyond period; `open` → allow forecast extension - Chart type hints: `cyclic` → radar/polar natural; `bounded` → gauge natural - Axis padding: `bounded` → don't pad beyond bounds; `open` → allow padding - Color interpolation: `cyclic` → wrap-around palette; `bounded` → clamp at edges **Diverging nature** → determines: - Color scheme: `none` → sequential; `conditional` → sequential by default, diverging if data spans both sides; `inherent` → always diverging - Midpoint: `inherent` → fixed center (0 for Profit, 0 for Sentiment); `conditional` → 0 if data crosses zero; `none` → N/A - Domain-specific midpoints (e.g., pH=7, NPS=0, custom satisfaction scales) are derived from `annotation.intrinsicDomain` midpoint rather than type-intrinsic logic - Legend symmetry: diverging → symmetric around midpoint; sequential → start at min - Reference line: diverging → draw reference at midpoint; sequential → no reference **Format class** → determines: - Axis label format string: `currency` → `$,.2f`; `percent` → `.1%`; `unit-suffix` → `,.1f kg` - Tooltip format: same prefix/suffix, possibly more precision - Number precision: `currency` → 2 decimal; `percent` → 1 decimal; `integer` → 0 decimal - Prefix/suffix: `currency` → prefix ($, €, £); `unit-suffix` → suffix (kg, km, °C); `percent` → suffix (%) #### 0.7.3 Complete multi-membership table Every type occupies a position in the tier hierarchy AND a position along each orthogonal dimension. This table shows both: | Type (T2) | T1 | T0 | Vis encoding (pref order) | Agg role | Domain | Diverging | Format | |---|---|---|---|---|---|---|---| | Month | DateGranule | Temporal | ordinal, temporal | dimension | cyclic (12) | none | date | | Year | DateGranule | Temporal | temporal, ordinal | dimension | open | none | integer | | Rating | Score | Discrete | quantitative, ordinal | intensive | bounded [1,N] | conditional | integer | | Temperature | Physical | Measure | quantitative | intensive | open | conditional | unit-suffix | | Quantity | Physical | Measure | quantitative | intensive | open, ≥0 | none | unit-suffix | | Sentiment | SignedMeasure | Measure | quantitative | signed-additive | bounded [-1,1] | inherent | plain | | Correlation | SignedMeasure | Measure | quantitative | signed-additive | bounded [-1,1] | inherent | plain | | Profit | SignedMeasure | Measure | quantitative | signed-additive | open | conditional | currency | | PercentageChange | SignedMeasure | Measure | quantitative | signed-additive | open | conditional | percent | | Revenue | Amount | Measure | quantitative | additive | open, ≥0 | none | currency | | Price | Amount | Measure | quantitative | intensive | open, ≥0 | none | currency | | Percentage | Proportion | Measure | quantitative | intensive | bounded [0,1] or [0,100] (data-inferred) | none | percent | | Count | GenericMeasure | Measure | quantitative | additive | open, ≥0 | none | integer | | Country | GeoPlace | Geographic | nominal | dimension | open | none | plain | | Latitude | GeoCoordinate | Geographic | quantitative | dimension | fixed [-90,90] | none | plain | | ZipCode | GeoPlace | Geographic | nominal (NOT quant!) | dimension | open | none | plain | | AgeGroup | Binned | Categorical | ordinal | dimension | bounded | none | plain | | Duration | Duration | Temporal | quantitative | additive | open, ≥0 | none | time | | Rank | Rank | Discrete | ordinal | dimension | open | none | integer | | Status | Coded | Categorical | nominal | dimension | fixed | none | plain | | Direction | Coded | Categorical | nominal | dimension | cyclic (8/16) | none | plain | | Boolean | Coded | Categorical | nominal | dimension | fixed (2) | none | plain | #### 0.7.4 How the builder uses both axes The compilation logic queries **two independent sources** to produce the `FieldSemantics`: ``` ┌─────────── Tier hierarchy ───────────┐ │ T0: encoding type, basic defaults │ │ T1: format class, agg default, zero │ │ T2: specific format, interpolation │ └──────────────┬───────────────────────┘ │ ▼ ┌── FieldSemantics ──┐ │ encoding, format, aggregate │ │ domain, scale, color, ticks │ │ zero, diverging, sort, ... │ └──────────────▲──────────────┘ │ ┌──────────────┴───────────────────────┐ │ Orthogonal dimensions │ │ Vis candidates → encoding resolution │ │ Agg role → aggregate function │ │ Domain shape → scale domain, ticks │ │ Diverging → color, midpoint, ref │ │ Format class → label format │ └──────────────────────────────────────┘ ``` The tier hierarchy provides **progressive refinement** (more detail at finer tiers). The orthogonal dimensions provide **cross-cutting properties** that apply regardless of tier. Both are stored in the type registry: ```typescript // Each type in the registry carries its tier position AND its dimension values. // Actual interface from core/type-registry.ts: interface TypeRegistryEntry { // Tier position t0: T0Family; // 'Temporal' | 'Measure' | 'Discrete' | 'Geographic' | 'Categorical' | 'Identifier' t1: T1Category; // e.g., 'Amount', 'DateGranule', 'Entity', etc. // Orthogonal dimensions (these drive viz properties directly) visEncodings: VisCategory[]; // preference-ordered, e.g., ['quantitative', 'ordinal'] aggRole: AggRole; // 'additive' | 'intensive' | 'signed-additive' | 'dimension' | 'identifier' domainShape: DomainShape; // 'open' | 'bounded' | 'fixed' | 'cyclic' diverging: DivergingClass; // 'none' | 'conditional' | 'inherent' formatClass: FormatClass; // 'currency' | 'percent' | 'signed-percent' | 'unit-suffix' | 'date' | 'time' | 'integer' | 'plain' zeroBaseline: ZeroBaseline; // 'meaningful' | 'arbitrary' | 'contextual' | 'none' zeroPad: number; // domain padding fraction (e.g., 0.08 for Rank, 0.05 for Temperature) } ``` > **Note on aggRole naming:** The design doc originally proposed `measure-sum` / `measure-avg` terminology. > The implementation uses `additive` (summed: Revenue, Count), `intensive` (averaged: Temperature, Price), > and `signed-additive` (summed but can go negative: Profit). The mapping is: > `measure-sum` → `additive`, `measure-avg` → `intensive`, signed measures → `signed-additive`. When a type is recognized at T1 level, the builder inherits the T1 entry's dimension values. When recognized at T2, the T2 entry's values override. When only T0 is known, the builder uses conservative defaults for each dimension (e.g., `visEncodings: ['quantitative']` for Measure, `domainShape: 'open'`, `diverging: 'none'`). #### 0.7.5 Key design consequences 1. **Vis encoding is not 1:1 with semantic type.** `Month` can be ordinal (bar chart categories) or temporal (time-series X). `Rating` can be quantitative (scatter Y) or ordinal (heatmap). The `visEncodings` array provides a *preference order* that the builder resolves based on chart type and channel. This is a first-class property of the type, not an afterthought. 2. **Domain shape directly determines scale and tick behavior.** Both `Percentage` (T1: Proportion) and `Latitude` (T1: GeoCoordinate) have bounded domains but are in completely different T0 families. The builder queries `domainShape` independently of the tier — same logic applies to both. 3. **Diverging nature directly determines color scheme.** This is not just a T2-level detail — it's an orthogonal dimension that T1 types can carry too. `SignedMeasure` (T1) carries diverging information; `Physical` (T1) is conditionally diverging (only `Temperature` within it, at T2 level). Domain-specific diverging midpoints (e.g., pH=7) are derived from `intrinsicDomain` midpoint rather than dedicated types. 4. **Aggregation role determines aggregate function — and auto-aggregation.** `Revenue` and `Price` are both `Amount` (T1), but Revenue is `additive` while Price is `intensive`. This distinction lives in the orthogonal dimension, not in the tier hierarchy. Critically, the aggregation role is essential for **auto-aggregation in under-specified charts**: when a user creates a bar chart with X=`Month` and Y=`Revenue` but provides no color encoding, the dataset likely has multiple rows per month (e.g., per-product or per-region rows). Without explicit color to distinguish them, the system must auto-aggregate Y. Knowing Revenue is `additive` lets the instantiator emit `{"aggregate": "sum", "field": "Revenue"}` automatically. The same applies to line charts — multiple Y values per X point produce a jagged, unreadable line unless aggregated. The correct aggregate function (sum vs mean vs count) depends entirely on the field's aggregation role: Revenue→sum, Temperature→mean, row-count→count. Getting this wrong (e.g., summing temperatures) produces nonsensical charts. Auto-aggregation should be a **compiler option** — an explicit flag the caller passes to the instantiation phase, since some contexts (e.g., raw data preview, user explicitly wanting per-row marks) should suppress it: ```typescript interface CompilerOptions { autoAggregate: boolean; // when true, instantiator injects aggregate transforms // for measure fields when multiple rows map to the same // positional encoding (e.g., same X in a bar/line chart) // ... other compiler options ... } ``` 5. **The `FieldSemantics` is the resolved product of both axes.** Downstream code never reasons about tiers or dimensions directly — it gets a fully resolved context where encoding, format, aggregation, domain, diverging, etc. are all concrete values. #### 0.7.6 Intrinsic vs. data-dependent dimension values The dimension values in the type registry (§0.7.3, §0.7.4) are **intrinsic** — they reflect the type's nature independent of any specific dataset or chart. But some dimension values are only fully determined when combined with **actual data characteristics**: cardinality, distribution, range, null rate, etc. **Examples of data-dependent shifts:** | Intrinsic (from type) | Data signal | Effective (after data) | Reason | |---|---|---|---| | `Coded` → vis: nominal | Cardinality > 20 | Treat as quantitative or use top-N + "Other" | Too many categories → cluttered legend/axis | | `Rating` → vis: [quant, ordinal] | Only 5 distinct values | Prefer ordinal | Small discrete set → ordinal ticks natural | | `Rating` → vis: [quant, ordinal] | 100-point scale, continuous-looking | Prefer quantitative | Large range → continuous axis cleaner | | `Country` → vis: nominal | 150+ countries | Consider top-N filtering or map instead of bar | Nominal with extreme cardinality → unreadable | | `Year` → vis: [temporal, ordinal] | Only 3 values: 2022, 2023, 2024 | Prefer ordinal | Sparse temporal → ordinal ticks better | | `Percentage` → domain: bounded [0,100] | Actual data range [45, 55] | Narrow domain, zoom in | Bounded but data clustered → auto-zoom | | `GenericMeasure` → agg: heuristic | Field name contains "count" | additive | Name-based heuristic refines generic role | **Design decision: where does this happen?** This data-dependent resolution happens at **two distinct stages**, not one: 1. **Context determination time** (`resolveFieldSemantics`): The builder can use data statistics (cardinality, min/max, distinct count) to **disambiguate** the intrinsic dimension values. For example, `Rating` has `visEncodings: ['quantitative', 'ordinal']` — the builder picks between them based on distinct-value count. This is a resolution of ambiguity already present in the type registry. The result is a concrete `FieldSemantics` with a single chosen encoding, a resolved domain, etc. 2. **Instantiation time** (spec generation for a specific chart): The instantiation phase may **override** the context's resolved values based on the specific chart type, channel assignment, and visual constraints. For example: - A `Coded` field with 30 values is resolved as `nominal` in the context. But when assigned to a color channel, the instantiator may decide to show only top-10 + "Other" to prevent legend clutter — this is a presentation decision, not a type-level one. - A `Month` field resolved as `ordinal` in the context may be re-encoded as `temporal` if placed on X in a line chart — this is a chart-type-driven override. - An ordinal field with high cardinality assigned to color may be treated as quantitative (continuous ramp) to reduce clutter — this is a channel-specific adaptation. **The boundary:** Context determination resolves *what the field IS* (its preferred encoding, format, domain, etc.). Instantiation resolves *how to render it given the chart constraints*. The context should carry enough information (including data statistics like cardinality) for the instantiator to make informed overrides without re-querying the type registry. ```typescript interface FieldSemantics { // ... resolved dimension values ... // Data statistics carried forward for instantiation-time decisions dataStats: { cardinality: number; // distinct value count nullRate: number; // fraction of nulls min?: number; // for numeric fields max?: number; sortedDistinctValues?: string[]; // for ordinal/nominal (if small enough) }; } ``` This way, the instantiator can check `context.dataStats.cardinality` and decide to bin, filter, or re-encode — without the context itself making premature presentation decisions. ### 0.8 Cyclic domain types A special class of types has **cyclic** (wrap-around) domains: | Type | Cycle | Values | Visualization concern | |---|---|---|---| | Month | 12 | Jan–Dec or 1–12 | Axis shouldn't show "13"; color interpolation wraps | | Day (weekday) | 7 | Mon–Sun | Same | | Hour | 24 | 0–23 | Circular charts natural | | Direction | 8/16+ | N, NE, E, ... | Polar/radar natural | | Quarter | 4 | Q1–Q4 | Axis ordering | Cyclic types need: - Built-in canonical sort (not alphabetical) - No extrapolation beyond the cycle - Cyclic color palettes for color encoding - Radar/polar chart recommendations --- ## §1 Motivation ### 1.1 Current state — Four-stage pipeline The chart engine uses a four-stage compilation pipeline, analogous to LLVM's frontend → IR → middle-end → backend architecture: | Stage | Function | Input → Output | Concern | |-------|----------|---------------|--------| | **1. Field Semantics** | `resolveFieldSemantics()` | Annotation + data → `FieldSemantics` | What *is* this field? (data identity) | | **2. Channel Semantics** | `resolveChannelSemantics()` | FieldSemantics + channel → `ChannelSemantics` | How should it render on this channel? | | **3. Layout** | `computeLayout()` | ChannelSemantics + data → `LayoutResult` | How big? What gets filtered? | | **4. Spec Generation** | `assembleVegaLite()` etc. | ChannelSemantics + template → backend spec | Backend-specific output | **`ChannelSemantics`** is the IR — a flat, target-agnostic interface that decouples all upstream semantics (Stages 1–2) from all downstream rendering (Stages 3–4). Four backends (VL, ECharts, ChartJS, GoFish) all read the same `ChannelSemantics` record without knowing each other exist. **Stage boundaries:** - Stage 2 does NOT know about template mark types — zero-baseline finalization requires mark knowledge and happens in Stage 4. - `convertTemporalData()` runs once before Stage 2; the converted data is passed to Stage 2 for temporal format detection and then reused by Stages 3–4. - `FieldSemantics` is internal to Stage 2 — it is never exposed downstream. `ChannelSemantics` is a flat interface — no nested `FieldSemantics` reference: | Decision | Source | Output | |---|---|---| | **From field semantics (data identity)** | | | | Semantic annotation | `resolveFieldSemantics()` | `ChannelSemantics.semanticAnnotation` | | Number format | `resolveFieldSemantics()` → `resolveFormat()` | `ChannelSemantics.format` | | Tooltip format | `resolveFieldSemantics()` → `resolveFormat()` | `ChannelSemantics.tooltipFormat` | | Aggregation default | `resolveFieldSemantics()` → `resolveAggregationDefault()` | `ChannelSemantics.aggregationDefault` | | Scale type | `resolveFieldSemantics()` → `resolveScaleType()` | `ChannelSemantics.scaleType` | | Domain constraint | `resolveFieldSemantics()` → `resolveDomainConstraint()` | `ChannelSemantics.domainConstraint` | | Canonical order | `resolveFieldSemantics()` → `resolveCanonicalOrder()` | `FieldSemantics.canonicalOrder` (internal) | | Cyclic ordering | `resolveFieldSemantics()` → `resolveCyclic()` | `ChannelSemantics.cyclic` | | Sort direction | `resolveFieldSemantics()` → `resolveSortDirection()` | `ChannelSemantics.sortDirection` | | Zero baseline | `resolveFieldSemantics()` → `resolveZeroClassFromAnnotation()` | `FieldSemantics.zeroBaseline` (internal) | | Binning suggested | `resolveFieldSemantics()` → `resolveBinningSuggested()` | `ChannelSemantics.binningSuggested` | | **Channel-specific (visualization)** | | | | Encoding type (Q/O/N/T) | `resolveEncodingTypeDecision()` | `ChannelSemantics.type` | | Zero-baseline | `computeZeroDecision()` (Stage 4) | `ChannelSemantics.zero` | | Color scheme | `getRecommendedColorSchemeWithMidpoint()` | `ChannelSemantics.colorScheme` (color/group only) | | Temporal format | `resolveTemporalFormat()` | `ChannelSemantics.temporalFormat` | | Ordinal sort order | `inferOrdinalSortOrder()` | `ChannelSemantics.ordinalSortOrder` | | Nice rounding | `resolveNice()` | `ChannelSemantics.nice` | | Tick constraint | `resolveTickConstraint()` | `ChannelSemantics.tickConstraint` | | Axis reversal | `resolveReversed()` | `ChannelSemantics.reversed` | | Interpolation | `resolveInterpolation()` | `ChannelSemantics.interpolation` | | Stackable | `resolveStackable()` | `ChannelSemantics.stackable` | These decisions are effective but they represent only a fraction of the visualization properties that semantic types should influence. Many properties are either hardcoded, delegated to VL defaults, or handled ad-hoc in template instantiation. ### 1.2 Properties not currently driven by semantic type | Category | Property | Example gap | |---|---|---| | **Formatting** | Axis tick format, tooltip format, legend label format | `Price` should show "$1,234", `Percentage` should show "45%" | | **Scale behavior** | Scale type (linear/log/sqrt), domain clamping, nice rounding | `Percentage` domain should clamp to [0, 100]; `Revenue` spanning 3 orders of magnitude could use log | | **Axis direction** | Reversed axis | `Rank` should have 1 at top (reversed Y axis) | | **Tick strategy** | Tick count, tick interval constraints | `Year` must have integer ticks only (no 2018.5); `Rating` 1-5 → exactly 5 ticks | | **Aggregation** | Default aggregate function | `Revenue` → `sum`; `Temperature` → `average`; `Count` → `sum` | | **Mark behavior** | Line interpolation, binning suitability, stack compatibility | `Rank` → step interpolation; `Age` (continuous) → suggest binning; `Rate` → don't stack | | **Display metadata** | Unit labels, axis title suffixes | `Temperature` → "°C" or "°F"; `Weight` → "kg" or "lbs" | ### 1.3 Goal Introduce a **FieldSemantics** — a structured object that captures all visualization-relevant decisions for a field, derived from its semantic type plus data characteristics. These properties are **promoted** into a flat `ChannelSemantics` struct — the single public interface consumed by all downstream phases (VL assembler, ECharts assembler, recommendation engine, tooltip renderer). `FieldSemantics` itself is an internal type used only inside `resolveChannelSemantics()`. ### 1.4 Non-goals - Changing Stage 3 (layout/stretch model) — it stays data-driven - Building a full "smart defaults" system that replaces user configuration — the compilation context provides *defaults* that users and templates can override > **Note:** While §0 proposes adding new semantic types (Profit, Sentiment, NPS, etc.), the core taxonomy structure and string-based representation remain the same. The new types extend the existing catalog, they don't restructure it. --- ## §2 Design Principles 1. **Semantic type is the source of truth.** The compilation context is a deterministic function of (semanticType, dataValues, channel, markType). No hidden state. 2. **Decisions are structured, not scattered.** Instead of N separate functions that each know about semantic types, one builder produces a typed context object. Downstream code reads fields, never re-inspects the semantic type. 3. **Per-field, then per-channel.** Some decisions are intrinsic to the field (format, aggregation default, scale type) regardless of which channel it's mapped to. Others are channel-dependent (zero-baseline, reversed axis). The context has both layers. 4. **Override-friendly.** Every decision has a default from the semantic type. Users, templates, or the AI agent can override any individual decision without replacing the whole context. Overrides are explicit and traceable. 5. **Backend-agnostic.** The compilation context describes abstract visualization intent (e.g., "format as currency with 0 decimals", "reverse the axis"). Backend-specific translation (VL `axis.format` vs. ECharts `axisLabel.formatter`) happens in Stage 4 (spec generation). 6. **Semantic type + optional metadata.** The semantic type string alone (e.g., `'Rating'`) is not always sufficient. Certain types carry additional properties (domain, unit) that are critical for correct visualization. This metadata is provided alongside the semantic type as a structured annotation. --- ## §3 Semantic Type Annotation: Enriched Input ### 3.1 The problem with bare semantic type strings Today, the LLM annotates each field with a single semantic type string: ```json { "rating": { "type": "number", "semantic_type": "Rating" } } ``` But `Rating` alone is ambiguous: - Is it 0–5? 1–5? 1–10? 0–100? - Should we show exact tick marks [1, 2, 3, 4, 5] or let the renderer choose? - Is 0 a valid value (meaningful zero) or is the scale 1-based (arbitrary zero)? Similar ambiguity exists for other bounded/scaled types: | Type | What's missing | Why it matters | |---|---|---| | **Rating** | Scale range (1–5, 1–10, 0–100) | Tick marks, domain constraint, zero decision | | **Score** | Scale range (0–100, 0–10) | Same as Rating | | **Percentage** | Representation (0–1 vs 0–100) | Format string: `.1%` vs `.1f` + "%" | | **Temperature** | Unit (°C, °F, K) | Format suffix, diverging midpoint (0°C vs 32°F) | | **Physical measures** (any) | Unit (kg, km, mph, etc.) | Format suffix | | **Price/Revenue/Cost/Amount** | Currency (USD, EUR, GBP, JPY) | Format prefix ($, €, £, ¥) | | **Duration** | Unit (seconds, minutes, hours, days) | Format strategy ("2h 30m" vs "150 min") | Notice this is NOT needed for open-ended measures like `Quantity`, `Count`, `Revenue` (generic), `Rank`, or categorical types like `Country`, `Status`. Those types have no inherent scale or unit ambiguity. ### 3.2 SemanticAnnotation: the enriched input We extend the annotation format to carry optional metadata alongside the semantic type: ```typescript /** * Enriched semantic annotation for a single field. * * The LLM (or user) provides this when annotating a dataset. * Only `semanticType` is required — all other fields are optional * hints that improve compilation decisions. * * Compact form: When no metadata is needed, a bare string is equivalent * to `{ semanticType: "..."}`. The system accepts both: * "Rating" // bare string * { semanticType: "Rating" } // object, no metadata * { semanticType: "Rating", intrinsicDomain: [1, 5] } // object with metadata */ interface SemanticAnnotation { /** The semantic type string (e.g., 'Rating', 'Temperature', 'Price') */ semanticType: string; /** * The intrinsic domain (value range) of this field's scale. * Only for bounded/scaled types — NOT for open-ended measures. * * Examples: * Rating 1–5: [1, 5] * Score 0–100: [0, 100] * Percentage 0–1: [0, 1] * Percentage 0–100: [0, 100] * Level 1–10: [1, 10] * Latitude: [-90, 90] (always fixed) * * NOT used for: Revenue (open-ended), Count (open-ended), * Quantity (open-ended), Temperature (unit determines meaning, * not a fixed domain). * * When provided, drives: * - domainConstraint in FieldSemantics * - tickConstraint.exactTicks (for small discrete scales) * - zeroBaseline refinement (scale starting at 1 → arbitrary zero) * - colorSchemeHint.divergingMidpoint (via intrinsicDomain midpoint) */ intrinsicDomain?: [number, number]; /** * The unit of measurement for this field. * Strictly optional — the system works correctly without it. * * When present, provides cosmetic improvements: * - format.suffix (e.g., "°C", " kg") * - format.prefix (e.g., "$" for USD, "€" for EUR) * - tooltip display with unit label * - diverging midpoint hint (0 for °C, 32 for °F) * * When absent, the system still determines encoding, aggregation, * domain, zero-baseline, and color scheme correctly from the * semantic type alone. The only loss is axis/tooltip formatting. * * IMPORTANT: Users often have mixed units within the same field * (e.g., distances in both km and miles, prices in mixed currencies). * When the LLM cannot determine a single consistent unit, it should * omit this field rather than guess. The compilation logic must * never assume unit is present. * * Examples (when unit IS consistent): * Temperature: "°C", "°F", "K" * Weight: "kg", "lbs", "g", "oz" * Distance: "km", "mi", "m", "ft" * Speed: "km/h", "mph", "m/s" * Duration: "seconds", "minutes", "hours", "days" * Price: "USD", "EUR", "GBP", "JPY" */ unit?: string; /** * Canonical sort order for ordinal/categorical fields. * The LLM provides this when values have a meaningful non-alphabetical * ordering that cannot be inferred from the semantic type alone. * * For well-known ordinals (Month, DayOfWeek, etc.), the system can * infer the order from the type. But for domain-specific ordinals * the LLM must provide it explicitly. * * Examples: * Education level: ["High School", "Bachelor's", "Master's", "PhD"] * Severity: ["Low", "Medium", "High", "Critical"] * T-shirt size: ["XS", "S", "M", "L", "XL", "XXL"] * Satisfaction: ["Very Unsatisfied", "Unsatisfied", "Neutral", "Satisfied", "Very Satisfied"] * * NOT needed for: Month (built-in), DayOfWeek (built-in), Year (numeric), * Country (no inherent order), PersonName (no inherent order). * * When provided, drives: * - canonicalOrder in FieldSemantics * - axis/legend ordering * - ordinal scale domain */ sortOrder?: string[]; } ``` ### 3.3 Which types need metadata? | Type | `intrinsicDomain` | `unit` | `sortOrder` | Why | |---|---|---|---|---| | **Rating** | **yes** — [1,5], [1,10], [0,100] | no | no | Scale determines ticks, domain, zero | | **Score** | **yes** — [0,100], [0,10], [0,1000] | no | no | Same as Rating | | **Percentage** | semi — inferred from data (0–1 vs 0–100) | no | no | Representation affects format | | **Temperature** | no (open-ended) | optional — °C, °F, K | no | Suffix + diverging midpoint hint; omit if mixed | | **Physical** (any) | no | optional — kg, km, mph, etc. | no | Suffix only; omit if mixed | | **Duration** | no | optional — sec, min, hr, day | no | Display hint; omit if mixed | | **Price** | no | optional — USD, EUR, GBP | no | Prefix ($, €, £); omit if mixed currencies | | **Revenue** | no | optional — USD, EUR, GBP | no | Prefix; omit if mixed currencies | | **Cost** | no | optional — USD, EUR, GBP | no | Prefix; omit if mixed currencies | | **Amount** | no | optional — USD, EUR, GBP | no | Prefix; omit if mixed currencies | | **Latitude** | fixed [-90, 90] | no | no | Always known; no annotation needed | | **Longitude** | fixed [-180, 180] | no | no | Always known | | Count, Quantity, Rank, ID, ... | no | no | no | No ambiguity | | **Ordinal categoricals** (Severity, Size, Education) | no | no | **yes** — domain-specific order | LLM provides canonical ordering | | Well-known ordinals (Month, DayOfWeek) | no | no | no (built-in) | System infers order; cyclic derived from type | | Nominal categoricals (Country, Name, Status) | no | no | no | No inherent order | | **Sentiment**, **Correlation**, **Profit** | no | no (or currency) | no | Diverging midpoint inferred from type (see §5.10) | | **Domain-specific diverging** (pH, NPS, custom) | **yes** — e.g., [0, 14] for pH | no | no | Diverging midpoint derived from `intrinsicDomain` midpoint | ### 3.4 LLM prompt update The annotation prompt changes minimally. The output schema becomes: ```json { "fields": { "rating": { "type": "number", "semantic_type": "Rating", "intrinsic_domain": [1, 5] }, "temperature": { "type": "number", "semantic_type": "Temperature", "unit": "°F" }, "price": { "type": "number", "semantic_type": "Price", "unit": "USD" }, "score": { "type": "number", "semantic_type": "Score", "intrinsic_domain": [0, 100] }, "severity": { "type": "string", "semantic_type": "Coded", "sort_order": ["Low", "Medium", "High", "Critical"] }, "sentiment": { "type": "number", "semantic_type": "Sentiment" }, "ph_level": { "type": "number", "semantic_type": "Score", "intrinsic_domain": [0, 14] }, "name": { "type": "string", "semantic_type": "PersonName" }, "revenue": { "type": "number", "semantic_type": "Revenue" }, "season": { "type": "string", "semantic_type": "Coded", "sort_order": ["Spring", "Summer", "Fall", "Winter"] } } } ``` Guidelines added to the prompt: ``` - For Rating and Score: provide "intrinsic_domain" as [min, max] of the scale (e.g., [1, 5] for 5-star rating, [0, 100] for percentage score) - For Temperature and other Physical measures: provide "unit" (e.g., "°C", "kg", "km", "mph", "seconds") - For Price, Revenue, Cost, Amount: provide "unit" with currency code (e.g., "USD", "EUR", "GBP", "JPY") - For ordinal categorical fields with a meaningful non-alphabetical order: provide "sort_order" as an array from lowest to highest (e.g., ["Low", "Medium", "High"] for severity, ["XS", "S", "M", "L", "XL"] for clothing size) - For well-known ordinals (Month, DayOfWeek): sort_order is NOT needed (the system knows built-in orderings) - Cyclic detection (Month, DayOfWeek, Quarter, Hour, Direction, seasons) is handled automatically by the system — no annotation needed - For all other types: no additional metadata needed ``` ### 3.5 Types with multiple intrinsic data representations Some semantic types can appear in fundamentally **different numeric encodings** in the raw data. This is NOT unit ambiguity (kg vs lbs, °C vs °F) — unit differences are already handled by the optional `unit` field (§3.2). Representation ambiguity is about the **same concept encoded at different scales or data types**, which affects format strings, domain bounds, and tick generation. | Type | Representation A | Representation B | Other representations | How the builder detects | |---|---|---|---|---| | **Percentage / Rate** | Fractional: 0.48 (0–1 range) | Whole-number: 48 (0–100 range) | Per-mille: 480 (0–1000) | `max(data) ≤ 1.0` → fractional; `max(data) ≤ 100` → whole; or `intrinsicDomain` annotation | | **Timestamp** | Unix seconds: 1705312200 | Unix milliseconds: 1705312200000 | ISO string: "2024-01-15T14:30:00" | Magnitude: >1e12 → ms; >1e9 → s; string → parse | | **Month** | Numeric: 1–12 | Abbreviated string: "Jan"–"Dec" | Full name: "January"–"December" | Data type (number vs string); string pattern matching | | **Day** | Numeric: 0–6 or 1–7 | Abbreviated: "Mon"–"Sun" | Full: "Monday"–"Sunday"; day-of-month: 1–31 | Data type + value range + string pattern | | **Year** | Number: 2024 | String: "2024" | Two-digit: 24 | Data type; value range (0–99 → two-digit ambiguity) | | **Boolean** | Boolean: true/false | Numeric: 0/1 | String: "Yes"/"No", "Y"/"N", "True"/"False" | Data type + distinct values | | **Coordinates** | Decimal degrees: 47.6062 | DMS string: "47°36'22\"N" | [lat, lon] tuple | Data type: number vs string pattern | **Why this matters — concrete example (Percentage):** | Concern | Fractional (0–1) | Whole-number (0–100) | |---|---|---| | Format pattern | `.1%` (d3 auto-multiplies ×100) | `.0f` + suffix `%` | | Domain constraint | `[0, 1]` | `[0, 100]` | | Tick values | 0, 0.25, 0.5, 0.75, 1.0 | 0, 25, 50, 75, 100 | | Tooltip | "48.3%" | "48%" | Getting the representation wrong means the axis shows `4800%` instead of `48%`, or domain clips to `[0, 1]` when data is 0–100. **Why this matters — concrete example (Timestamp):** | Concern | Unix seconds | Unix milliseconds | |---|---|---| | Conversion | `new Date(v * 1000)` | `new Date(v)` | | Misdetection effect | Dates in 1970 (ms interpreted as s) or year 55970 (s interpreted as ms) | Same | **Design decision:** The builder resolves representation at context-determination time using a priority chain: 1. **Explicit annotation** — `domain: [0, 1]` disambiguates percentage; `unit: "milliseconds"` disambiguates timestamp 2. **Data inspection** — value range, magnitude, data type, distinct values, string patterns 3. **Conservative default** — when ambiguous, pick the most common representation The resolved representation is baked into the `FieldSemantics` (format, domain, ticks). Downstream consumers never need to reason about which representation was in the data. ### 3.6 Backward compatibility Bare string annotations continue to work: ```typescript // Normalize: accept both string and object forms function normalizeAnnotation( input: string | SemanticAnnotation ): SemanticAnnotation { if (typeof input === 'string') { return { semanticType: input }; } return input; } ``` The `semantic_types` map in `ChartAssemblyInput` changes type: ```typescript // Before: semantic_types?: Record<string, string>; // After (with backward compat): semantic_types?: Record<string, string | SemanticAnnotation>; ``` ### 3.6 How annotation metadata flows into FieldSemantics The `resolveFieldSemantics` function accepts the full annotation: ``` resolveFieldSemantics(annotation: SemanticAnnotation, fieldName, values) │ ├── annotation.intrinsicDomain provided? │ → domainConstraint = mergeIntrinsicWithData(intrinsicDomain, values, soft) │ (effective domain = union of intrinsic bounds and actual data range) │ → tickConstraint.exactTicks (if intrinsic range is small, e.g., [1,5] → [1,2,3,4,5]) │ → zeroBaseline: intrinsicDomain[0] > 0 → 'arbitrary' (1-based scale) │ → binningSuggested: false (bounded discrete scale) │ → colorSchemeHint.divergingMidpoint: (intrinsicDomain[0] + intrinsicDomain[1]) / 2 │ ├── annotation.unit provided? │ → format.suffix or format.prefix (unit → display mapping) │ → tooltipFormat.suffix (more verbose: "°F" instead of "°") │ → diverging midpoint hint (°C → 0, °F → 32) │ ├── annotation.sortOrder provided? │ → canonicalOrder = sortOrder │ → defaultVisType = 'ordinal' (not 'nominal') │ └── none provided? → fall back to type-only + data-driven inference → diverging midpoint inferred by resolveDivergingInfo() (see §5.10) ``` --- ## §4 FieldSemantics: The Core Structure ### 4.1 Type definition ```typescript /** * Complete field semantics for a single data field. * * Computed once per field during Stage 1 (field semantic resolution). * Read-only downstream — backends translate this to their native format. * * These are FIELD-INTRINSIC properties — they depend on the semantic type, * annotation metadata, and data values, NOT on which channel the field is * mapped to. Channel-specific decisions (color scheme, axis reversal, * interpolation, tick strategy, nice rounding, stacking, zero-baseline) * are resolved separately in Stage 2 (resolveChannelSemantics) and live * on ChannelSemantics. */ interface FieldSemantics { // --- Identity --- /** The semantic annotation (normalized from string or object input). * Contains the semantic type string plus optional metadata * (intrinsicDomain, unit, sortOrder). */ semanticAnnotation: SemanticAnnotation; // --- Encoding --- /** Preferred encoding type, disambiguated from registry using data */ defaultVisType: 'quantitative' | 'ordinal' | 'nominal' | 'temporal'; // --- Formatting --- /** * Primary number format spec. * Non-empty only when the semantic type adds value over VL's native * formatting: currency prefix ($), unit suffix (kg), signed prefix (+/-), * percentage (%), abbreviation (1.2M). * For generic decimals (formatClass 'decimal'), format is empty {} * because VL's native formatting adapts precision better. */ format: FormatSpec; /** * Tooltip format — typically with explicit precision for pop-ups * even when the axis format is left to VL defaults. */ tooltipFormat?: FormatSpec; // --- Aggregation --- /** * Default aggregate function — intrinsic to the field (additive vs intensive). * 'sum' for additive measures (Revenue, Count), 'average' for intensive * measures (Temperature, Rating). undefined for non-aggregable fields. */ aggregationDefault?: 'sum' | 'average'; // --- Scale --- /** * Zero-baseline classification (meaningful / arbitrary / contextual). * NOT a boolean decision — that requires channel + mark type knowledge * and is finalized as ChannelSemantics.zero in Stage 4. */ zeroBaseline: ZeroBaseline | 'unknown'; /** * Recommended scale type based on data distribution. * Only set for specific semantic types (e.g., Population → 'log' when * data spans ≥ 4 orders of magnitude). */ scaleType?: 'linear' | 'log' | 'sqrt' | 'symlog'; // --- Domain --- /** * Intrinsic domain bounds (from annotation, type-intrinsic, or data-inferred). * E.g., Rating [1, 5], Latitude [-90, 90], Percentage [0, 100]. */ domainConstraint?: DomainConstraint; // --- Ordering --- /** * Canonical ordinal sort order (months, days, seasons, etc.). * Resolved from annotation.sortOrder or well-known type sequences. */ canonicalOrder?: string[]; /** Whether the canonical order is cyclic (wraps around) */ cyclic: boolean; /** Default sort direction ('descending' for Rank, 'ascending' for rest) */ sortDirection: 'ascending' | 'descending'; // --- Histogram --- /** Whether this field's data distribution benefits from binning */ binningSuggested: boolean; } ``` **What is NOT on FieldSemantics (and why):** These properties are resolved at the channel level in `resolveChannelSemantics()` because they depend on which channel the field is mapped to, or need channel-level context: | Property | Why channel-level | Resolved by | |---|---|---| | `nice` | Depends on whether `domainConstraint` exists (field-level) but also whether the domain is bounded or fixed — resolved together with domain | `resolveNice()` | | `tickConstraint` | Depends on type + annotation domain; resolved alongside channel | `resolveTickConstraint()` | | `reversed` | Only meaningful on positional axes (x/y) | `resolveReversed()` | | `interpolation` | Only meaningful for line/area marks | `resolveInterpolation()` | | `stackable` | Only meaningful for positional channels with compatible marks | `resolveStackable()` | | `colorScheme` | Only meaningful on color/group channel; needs VL type + data | `getRecommendedColorSchemeWithMidpoint()` | | `zero` | Requires mark type (bar → include zero); finalized in Stage 4 | `computeZeroDecision()` | | `temporalFormat` | Needs converted temporal data for format detection | `resolveTemporalFormat()` | | `ordinalSortOrder` | Uses `inferOrdinalSortOrder()` with field values, respects user sort overrides | `inferOrdinalSortOrder()` | ### 4.2 Supporting types ```typescript /** * T0 Family — coarsest tier (§0.2). * Used internally by the builder to resolve compilation context. * NOT exposed on FieldSemantics — consumers use the * materialized properties instead. */ type T0Family = | 'Temporal' // DateTime, Year, Month, Duration, etc. | 'Measure' // Quantity, Price, Revenue, Temperature, etc. | 'Discrete' // Rank, Score, Rating, Index, etc. | 'Categorical' // Name, Status, Category, etc. | 'Ordinal' // Domain-specific ordered categories | 'Geographic' // Latitude, Country, etc. | 'Identifier'; // ID, SKU — never encode /** * T1 Category — mid-level tier (§0.3). * Used internally by the builder. NOT exposed on FieldSemantics. */ type T1Category = | 'DateTime' | 'DateGranule' | 'Duration' // Temporal | 'Amount' | 'Physical' | 'Proportion' // Measure | 'SignedMeasure' | 'GenericMeasure' // Measure | 'Rank' | 'Score' | 'Index' // Discrete | 'GeoCoordinate' | 'GeoPlace' // Geographic | 'Entity' | 'Coded' | 'Binned' // Categorical | 'ID'; // Identifier /** * Format specification — backend-agnostic. * * Provides the numeric format pattern, optional prefix/suffix, and unit. * How these are rendered (tick labels, axis title, tooltip) is the * renderer's decision — the context just carries the information. */ interface FormatSpec { /** * d3-format pattern (used by VL natively; ECharts translates). * Examples: "$,.0f", ".1%", ".2f", ",d" */ pattern?: string; /** Prefix for formatted value. E.g., "$", "€", "£", "¥" */ prefix?: string; /** Suffix for formatted value. E.g., "%", "°C", " kg" */ suffix?: string; /** Number of decimal places (overrides pattern precision) */ decimals?: number; /** * Whether to abbreviate large/small numbers. * true → 1234567 → "1.2M", 0.00123 → "1.2m" */ abbreviate?: boolean; /** * Temporal format string (strftime-style). * Used when the field is temporal. E.g., "%Y", "%b %d", "%H:%M" */ temporalPattern?: string; } /** Aggregate operations */ type AggregateOp = 'sum' | 'average' | 'median' | 'min' | 'max' | 'count'; /** Scale types for quantitative axes */ type ScaleType = 'linear' | 'log' | 'sqrt' | 'symlog'; /** Hard domain constraints */ interface DomainConstraint { min?: number; max?: number; /** * Whether to hard-clamp values outside the constraint. * true: values outside [min, max] are clipped to the boundary. * false: constraint is a suggestion — renderer may extend if data exceeds. */ clamp?: boolean; } /** Tick generation constraints */ interface TickConstraint { /** Force ticks to be integers only (e.g., Year, Count, Rating) */ integersOnly?: boolean; /** * Exact set of tick values (e.g., Rating 1-5 → [1, 2, 3, 4, 5]). * When specified, overrides automatic tick calculation. */ exactTicks?: number[]; /** * Suggested tick count. Renderer may adjust based on available space. */ suggestedCount?: number; /** * Minimum step between ticks (e.g., 1 for integer types). */ minStep?: number; } /** Color scheme hint (drives getRecommendedColorScheme) */ interface ColorSchemeHint { /** Primary scheme type */ type: 'categorical' | 'sequential' | 'diverging'; /** * Whether to reverse the color scale direction. * true for Rank (1 = best = darkest/most saturated). */ reversed?: boolean; /** * Natural midpoint for diverging schemes. * This is the semantic center of the data — the value that should * map to the neutral color in diverging palettes. * * Sources (in priority order): * 1. annotation.unit → type lookup (°C → 0, °F → 32) * 2. Type-intrinsic (Profit → 0, Correlation → 0) * 3. Domain midpoint (Rating [1,5] → 3) * 4. Data-driven: spansBothSides(0) → 0, else data midpoint * * Only meaningful when type = 'diverging'. */ divergingMidpoint?: number; /** * Whether the diverging nature is inherent to the type (true) * or conditional on the data spanning both sides (false). * * Inherent: Sentiment (always has pos/neg meaning), * Correlation (always -1 to 1) * Conditional: Temperature (diverging only if data crosses 0°C), * Revenue (diverging only if data has losses), * Percentage (diverging only if data has negatives) * * When inherentlyDiverging = true: * - Always use diverging scheme, even if all data is on one side * - The midpoint carries semantic meaning regardless * * When inherentlyDiverging = false (default): * - Only use diverging scheme if data actually spans both sides * of the midpoint * - Fall back to sequential if all values are on one side */ inherentlyDiverging?: boolean; } /** Line interpolation methods */ type Interpolation = 'linear' | 'monotone' | 'step' | 'step-after' | 'step-before'; ``` --- ## §5 Semantic Type → Context Mapping ### 5.1 Format rules The format is the most impactful new capability. The context provides format information (pattern, prefix, suffix); the **renderer** decides how to use them — whether to put the unit on tick labels, in the axis title, or only in tooltips is a rendering concern, not a compilation concern. **Design principle — only override VL's native formatting when semantic context adds value.** VL's default axis formatting is excellent — it adapts precision, uses ~s notation for large values, and produces clean integer labels for integer data. We only provide an explicit format when: - There's a **prefix** ($, €, +) or **suffix** (%, °C, kg) that VL can't know about - There's an **abbreviation** need (1.2M instead of 1200000) - There's a **sign** requirement (+12% / -5%) that VL won't add by default - There's a **no-comma** override (Year: 2024 not 2,024) For generic decimal types (Number, Score, Rating, Ratio, Latitude, Longitude), the format is **empty** — VL handles axis formatting natively and does it better than any hardcoded precision. **Data-driven precision:** When format IS provided (currency, percent, unit-suffix, etc.), the precision is **data-driven** rather than hardcoded. The `detectPrecision()` helper examines actual data values and returns the maximum meaningful decimal places (0–4, capped to avoid floating-point noise). This means: - Revenue data `[120000, 230000]` → `$120K, $230K` (0 decimals) - Price data `[12.50, 8.99]` → `$12.50, $8.99` (2 decimals, always for Price) - Temperature data `[23.5, 18.2]` → `23.5°C, 18.2°C` (1 decimal) | Semantic Type | `pattern` | `prefix` | `suffix` | `abbreviate` | Tooltip override | Notes | |---|---|---|---|---|---|---| | **Count** | `,d` | — | — | — | — | Integer with thousands sep | | **Amount** | data-driven precision | `$` | — | yes | `,.2f` + prefix `$` | | | **Price** | `,.2f` | `$` | — | yes | — | Always shows cents | | **Revenue** | data-driven precision | `$` | — | yes | `,.2f` + prefix `$` | | | **Cost** | data-driven precision | `$` | — | yes | `,.2f` + prefix `$` | | | **Percentage** (0–1) | `.Xp%` | — | — | — | `.X+1p%` | Auto-detects 0–1 vs 0–100 | | **Percentage** (0–100) | data-driven + `d`/`.Xf` | — | `%` | — | same | Suffix, no ×100 | | **PercentageChange** | `+.X%` or `+.Xf` | — | `%` (if 0–100) | — | higher-precision | Always-show sign | | **Temperature** | data-driven precision | — | from unit (`°C`) | — | higher-precision | Unit from annotation | | **Score** | — (empty) | — | — | — | data-driven precision | VL handles axis natively | | **Rating** | — (empty) | — | — | — | data-driven precision | VL handles axis natively | | **Rank** | `,d` | — | — | — | — | Integer | | **Year** | `d` | — | — | — | — | No comma (2024 not 2,024) | | **Number** | — (empty) | — | — | — | data-driven precision | VL handles axis natively | | **Quantity** | data-driven precision | — | from unit | yes | — | Unit from annotation | | **Profit** | `+` + data-driven | `$` | — | yes | `+,.2f` + prefix `$` | Signed currency | | **Sentiment** | `+` + data-driven | — | — | — | higher-precision | Signed decimal | | **Correlation** | `+` + data-driven | — | — | — | higher-precision | Signed decimal | | **Latitude, Longitude** | — (empty) | — | — | — | data-driven precision | VL handles axis natively | | **Ratio** | — (empty) | — | — | — | data-driven precision | VL handles axis natively | **Unit and currency from annotation metadata:** When the LLM provides `unit` in the annotation (e.g., `"unit": "EUR"` for Price, `"unit": "kg"` for Weight), the format spec uses that directly. See §3 for the full annotation schema. **Fallback priority for units:** annotation.unit > column-name heuristics ("Weight (kg)") > data-value scanning ("$1,234") > type-specific defaults ("$" for Price). ### 5.1.1 Parsing Parsing is the **compiler's responsibility**, not part of the compilation context. The semantic type already tells the compiler what the data represents — the compiler decides how to clean it. For example, knowing a field is `Amount` tells the compiler to strip `$` and `,` from `"$1,234.56"`. Knowing it's `Percentage` with string representation tells it to strip `%`. The semantic type + data representation (§3.5) provide all the information needed; no separate parse hint interface is required. **Semantic type as parsing guide:** | Semantic Type | Raw data examples | Compiler knows to... | |---|---|---| | Amount, Price, Revenue, Cost | `"$1,234.56"`, `"€1.234,56"` | Strip currency symbol + separators → number | | Percentage, PercentageChange | `"45.2%"`, `"+12.3%"` | Strip `%` and sign → number | | Temperature, Quantity (with unit) | `"23.5°C"`, `"75 kg"` | Strip unit suffix → number | | Duration | `"2h 30m"`, `"02:30:00"` | Parse compound time → seconds | | Timestamp | `1705312200`, `"2024-01-15"` | Detect epoch vs string → Date | | Boolean | `"Yes"`, `"No"`, `1`, `0` | Normalize → boolean | | Month | `"January"`, `1` | Normalize → canonical form | | Coordinates | `"47°36'22"N"` | DMS → decimal degrees | The compiler may provide built-in parsing utilities internally, but that's an implementation detail — not something the compilation context needs to describe. ### 5.2 Aggregation defaults | Semantic Family | Semantic Types | Default Aggregate | Rationale | |---|---|---|---| | **Additive measures** | Count, Amount, Revenue, Cost, Quantity, Duration | `sum` | These represent totals — summing is natural | | **Intensive measures** | Percentage, PercentageChange, Temperature, Score, Rating, Price, Correlation, Sentiment | `average` | These represent rates/conditions — averaging is natural | | **Signed additive** | Profit | `sum` | Additive but can be negative; summing preserves sign semantics | | **Discrete numeric** | Rank, Index, ID | — (none) | Aggregation is meaningless | | **Temporal** | DateTime, Date, Year, etc. | — (none) | Not aggregable | | **Categorical** | Name, Status, Category, etc. | — (none) | Not aggregable | **When this helps:** When a bar chart is created with Revenue on Y, the system auto-applies `aggregate: 'sum'` (total revenue per category). For Temperature on Y, it auto-applies `aggregate: 'average'` (mean temperature per category). Currently, the AI agent or the user must specify this. ### 5.3 Scale type recommendations | Condition | Recommended Scale | Example | |---|---|---| | Measure type + data spans >2 orders of magnitude | `log` | Revenue: $1K to $1B | | Measure type + data has long tail (skew > 2) | `sqrt` | Population: most cities small, few very large | | Measure type + data spans both positive and negative + wide range | `symlog` | Profit/Loss: -$10M to +$500M | | Percentage (0-100) | `linear` (always) | Completion rate | | All other quantitative | `linear` | Default | **Implementation note:** Scale type recommendation requires inspecting data distribution (min, max, skewness). This makes it a **data-dependent** decision that belongs in the compilation context builder, not in a static mapping. ### 5.4 Domain constraints Domain constraints come from two sources: **annotation metadata** (explicit `intrinsicDomain` from the LLM) and **type-intrinsic knowledge** (geographic bounds, etc.). The effective domain stored in `FieldSemantics.domainConstraint` is always the **union** of the intrinsic domain and the actual data range. This ensures data points that exceed the type's natural bounds (e.g., 155% growth for a Percentage type) are never clipped. Two categories: - **Hard domains** (`clamp: true`): physically impossible to exceed — Latitude [-90, 90], Longitude [-180, 180], Correlation [-1, 1]. The intrinsic bounds _are_ the final bounds. - **Soft domains** (`clamp: false`): intrinsic bounds describe the _typical_ range, but data can legitimately exceed them. Effective domain = `[min(intrinsic[0], dataMin), max(intrinsic[1], dataMax)]`. | Source | Semantic Type | Intrinsic Domain | Data Range | Effective Domain | Clamp? | |---|---|---|---|---|---| | **Annotation** | Rating (domain: [1, 5]) | [1, 5] | [1, 4] | `{ min: 1, max: 5 }` | soft | | **Annotation** | Score (domain: [0, 100]) | [0, 100] | [0, 120] | `{ min: 0, max: 120 }` | soft | | **Data-inferred** | Percentage (0–100 data) | [0, 100] | [0, 80] | `{ min: 0, max: 100 }` | soft | | **Data-inferred** | Percentage (> 100 data) | [0, 100] | [0, 155] | `{ min: 0, max: 155 }` | soft | | **Data-inferred** | Percentage (0–1 data) | [0, 1] | [0, 0.8] | `{ min: 0, max: 1 }` | soft | | **Type-intrinsic** | Latitude | [-90, 90] | any | `{ min: -90, max: 90 }` | hard | | **Type-intrinsic** | Longitude | [-180, 180] | any | `{ min: -180, max: 180 }` | hard | | **Type-intrinsic** | Correlation | [-1, 1] | any | `{ min: -1, max: 1 }` | hard | **Priority:** annotation.intrinsicDomain > type-intrinsic > data-inferred. **Percentage scale detection:** The representation (0–1 fractional vs 0–100 whole-number) is detected from data: if ≥ 80% of absolute values are ≤ 1, treat as fractional; otherwise whole-number. This works even when values exceed the intrinsic range (e.g., [10, 20, 155] → whole-number → intrinsic [0, 100] → effective [0, 155]). When `annotation.intrinsicDomain` is provided, the builder also derives: - **zeroBaseline:** If `intrinsicDomain[0] > 0` (e.g., Rating [1, 5]), zero is arbitrary. If `intrinsicDomain[0] === 0` (e.g., Score [0, 100]), zero is contextual. - **tickConstraint:** If the intrinsic domain span is small (≤ 20), generate `exactTicks` for every integer. E.g., Rating [1, 5] → `exactTicks: [1, 2, 3, 4, 5]`. - **binningSuggested:** If intrinsic domain span ≤ 20, binning is not useful → `false`. - **colorSchemeHint.divergingMidpoint:** `(intrinsicDomain[0] + intrinsicDomain[1]) / 2`. E.g., Score [0, 100] → midpoint 50. ### 5.5 Tick constraints Tick constraints combine type-intrinsic rules with annotation-provided domain: | Semantic Type | `integersOnly` | `exactTicks` | `minStep` | Source | |---|---|---|---|---| | Count | true | — | 1 | Type-intrinsic | | Year | true | — | 1 | Type-intrinsic | | Rank | true | — | 1 | Type-intrinsic | | Rating (domain: [1, 5]) | true | [1, 2, 3, 4, 5] | 1 | Annotation domain | | Rating (domain: [1, 10]) | true | [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] | 1 | Annotation domain | | Score (domain: [0, 100]) | true | — (too many) | 1 | Annotation domain (span > 20 → no exactTicks) | | Month (1-12) | true | [1..12] | 1 | Type-intrinsic | | Index | true | — | 1 | Type-intrinsic | **Rule for exactTicks from annotation domain:** When `domain` is provided and `domain[1] - domain[0] ≤ 20`, generate integer ticks from `domain[0]` to `domain[1]`. When span > 20, use `integersOnly: true` with `minStep: 1` and let the renderer choose tick count. ### 5.6 Reversed axis | Semantic Type | Reversed? | Rationale | |---|---|---| | Rank | `true` | 1st place should be at the top (Y axis) or leftmost (X axis) | | All others | `false` | Standard direction | **Note:** Reversed axis is a *default suggestion*. A bump chart template may already handle rank reversal internally (via `scale.reverse`). The compilation context provides the intent; the template decides whether to apply it. ### 5.7 Stack compatibility | Semantic Type | Stackable | Mode | Rationale | |---|---|---|---| | Count, Amount, Revenue, Cost, Quantity | `'sum'` | Additive | Parts sum to whole | | Percentage | `'normalize'` | Normalize | Show proportion breakdown | | Temperature, Score, Rating, PercentageChange, Correlation, Sentiment | `false` | — | Stacking rates/conditions is meaningless | | Rank, Index | `false` | — | Not aggregable | | Duration, Profit | `'sum'` | Additive | Duration is additive; Profit sums to net | ### 5.8 Interpolation hints | Semantic Type | Interpolation | Rationale | |---|---|---| | Rank, Index | `'step'` | Value stays constant until next transition | | Rating, Score | `'step'` or `'linear'` | Quasi-continuous; context-dependent | | Temperature, Quantity | `'monotone'` | Smooth physical process | | Count | `'step-after'` or `'linear'` | Discrete events; depends on context | | Revenue, Price, Amount, Profit | `'monotone'` | Smooth trend | | Percentage, PercentageChange, Correlation | `'monotone'` | Smooth trend | | All others / unknown | `'linear'` | Default | ### 5.9 Binning suitability | Semantic Type | Suggest Binning? | Rationale | |---|---|---| | Quantity, Amount, Price, Revenue, Cost | `true` | Continuous, benefits from distribution view | | Temperature | `true` | Continuous measure | | Percentage, PercentageChange | `true` | Continuous, though bounded | | Duration | `true` | Continuous time span | | Count | `true` (if high-card) | Many distinct values → bin | | Score (continuous range) | `true` | e.g., 0-100 scores | | Rating (1-5, 1-10) | `false` | Too few values to bin | | Rank | `false` | Ordinal; binning loses identity | | Year | `false` | Should use temporal axis, not bins | | All categorical | `false` | Not numeric | ### 5.10 Diverging point inference Diverging treatment (color scheme + axis centering) requires knowing a **midpoint** — the value that separates two opposing meanings. This midpoint can come from multiple sources, resolved in priority order: **Priority chain for diverging midpoint:** ``` 1. annotation.unit → type lookup (°C → 0, °F → 32, K → 273.15) 2. type-intrinsic midpoint (see table below) 3. annotation.intrinsicDomain midpoint (Rating [1,5] → 3) 4. data-driven: spansBothSides(0) (data has both neg + pos → midpoint 0) 5. data-driven: midpoint of data range (fallback) ``` **Type-intrinsic midpoints (no annotation needed):** | Semantic Type | Midpoint | Inherently diverging? | Rationale | |---|---|---|---| | Temperature | 0 (°C) / 32 (°F) / 273.15 (K) | **conditional** — only when data spans both sides | freezing/thawing boundary; but all-positive temp data is fine as sequential | | Profit | 0 | **conditional** | gain vs loss; but all-profitable data doesn't need diverging | | Sentiment | 0 | **inherent** — always meaningful | positive vs negative sentiment, even if all values happen to be positive | | Correlation | 0 | **inherent** | positive vs negative correlation | | PercentageChange | 0 | **conditional** | growth vs decline; but all-growth data is fine as sequential | | Score (0–100 scale) | 50 | **conditional** | above/below average; only when data spans both sides | | Rating (1–5 scale) | 3 | **conditional** | derived from domain midpoint; rarely used as diverging | **Key distinction — inherent vs. conditional:** - **Inherently diverging** types always benefit from a diverging palette because the two sides carry distinct semantic meanings (e.g., positive vs negative sentiment). Even if all data points are positive, the color encodes "how positive" vs "how negative" relative to the center. - **Conditionally diverging** types only use a diverging palette when the data actually spans both sides of the midpoint. If Revenue is all positive, a sequential palette (darker = more revenue) is more informative than a diverging palette with an unused half. **`resolveDivergingInfo()` utility:** ```typescript interface DivergingInfo { /** The midpoint value where the diverging center sits */ midpoint: number; /** Whether this type is always diverging or only when data spans both sides */ inherent: boolean; /** Source of the midpoint determination */ source: 'unit' | 'type-intrinsic' | 'domain' | 'data'; } /** * Resolve the diverging midpoint and whether the type is inherently diverging. * * Called by resolveColorSchemeHint() to populate ColorSchemeHint. * * @param semanticType - The semantic type string * @param annotation - Full annotation (may have intrinsicDomain, unit) * @param values - Data values for data-driven fallback * @returns DivergingInfo or undefined if no diverging treatment applies */ function resolveDivergingInfo( semanticType: string, annotation: SemanticAnnotation, values: number[] ): DivergingInfo | undefined { // 1. Unit-derived (Temperature) if (semanticType === 'Temperature' && annotation.unit) { const unitMidpoints: Record<string, number> = { '°C': 0, '°F': 32, 'K': 273.15, 'C': 0, 'F': 32 }; if (annotation.unit in unitMidpoints) { return { midpoint: unitMidpoints[annotation.unit], inherent: false, // only show diverging if data crosses it source: 'unit' }; } } // 2. Type-intrinsic const intrinsicMap: Record<string, { midpoint: number; inherent: boolean }> = { 'Sentiment': { midpoint: 0, inherent: true }, 'Correlation': { midpoint: 0, inherent: true }, 'Profit': { midpoint: 0, inherent: false }, 'PercentageChange': { midpoint: 0, inherent: false }, }; if (semanticType in intrinsicMap) { return { ...intrinsicMap[semanticType], source: 'type-intrinsic' }; } // 3. Domain-derived (e.g., Rating [1,5] → midpoint 3) if (annotation.intrinsicDomain) { return { midpoint: (annotation.intrinsicDomain[0] + annotation.intrinsicDomain[1]) / 2, inherent: false, source: 'domain' }; } // 4. Data-driven: if data spans 0, use 0 const min = Math.min(...values); const max = Math.max(...values); if (min < 0 && max > 0) { return { midpoint: 0, inherent: false, source: 'data' }; } return undefined; // no diverging treatment } ``` **How it feeds into `resolveColorSchemeHint()`:** ```typescript function resolveColorSchemeHint(semanticType, annotation, values): ColorSchemeHint { const divInfo = resolveDivergingInfo(semanticType, annotation, values); if (divInfo) { const min = Math.min(...values); const max = Math.max(...values); const spansBothSides = min < divInfo.midpoint && max > divInfo.midpoint; if (divInfo.inherent || spansBothSides) { return { type: 'diverging', divergingMidpoint: divInfo.midpoint, inherentlyDiverging: divInfo.inherent, }; } // Data doesn't span both sides → sequential, but remember the midpoint // in case user explicitly requests diverging later } // Default: sequential for measures, categorical for nominals return { type: isQuantitative ? 'sequential' : 'categorical' }; } ``` --- ## §6 Builder Function ### 6.1 Signature ```typescript /** * Build the field semantics for a single field. * * This is the sole entry point for semantic-type-driven decisions. * All downstream code reads from the returned context. * * @param annotation The semantic type annotation (string or enriched object) * @param fieldName Column name (used for unit detection heuristics) * @param values Sampled data values from this field * @returns Complete field semantics with all defaults resolved */ function resolveFieldSemantics( annotation: string | SemanticAnnotation, fieldName: string, values: any[], ): FieldSemantics; ``` ### 6.2 Internal structure `resolveFieldSemantics` computes only **field-intrinsic** properties — things determined by the data alone, without knowing which channel the field is mapped to. Channel-specific resolve functions (tickConstraint, reversed, nice, colorScheme, interpolation, stackable) are exported from the same file but called by `resolveChannelSemantics()` in Stage 2. ``` resolveFieldSemantics(annotation, fieldName, values) │ ├── normalizeAnnotation(annotation) │ → { semanticType, intrinsicDomain?, unit?, sortOrder? } │ ├── resolveTiers(semanticType) // internal — determines which rules fire │ → { t0: T0Family, t1: T1Category | null } │ ├── resolveDefaultVisType(semanticType, values) │ → 'quantitative' | 'ordinal' | 'nominal' | 'temporal' │ ├── resolveFormat(semanticType, unit, fieldName, values) │ → { format: FormatSpec, tooltipFormat: FormatSpec } │ ├── resolveCurrencyPrefix(unit, fieldName) │ ├── resolveUnitSuffix(unit, fieldName) │ ├── detectPrecision(values) → data-driven decimal places (0–4) │ └── precisionFormat(values) → d3-format pattern from precision │ Note: `decimal` formatClass returns format:{} (empty — VL native) │ ├── resolveAggregationDefault(semanticType) │ → 'sum' | 'average' | undefined │ ├── resolveZeroBaseline(semanticType, domain) │ → 'meaningful' | 'arbitrary' | 'contextual' │ └── intrinsicDomain[0] > 0? → 'arbitrary' (1-based scale) │ ├── resolveScaleType(semanticType, values) │ → 'linear' | 'log' | 'sqrt' | undefined │ ├── resolveDomainConstraint(semanticType, domain, values) │ → DomainConstraint | undefined │ ├── annotation.intrinsicDomain provided? → use directly │ ├── type-intrinsic? (Lat/Lon) → use fixed bounds │ └── data-inferred? (Percentage) → detect from values │ ├── resolveCanonicalOrder(semanticType, sortOrder, values) │ → string[] | undefined │ ├── annotation.sortOrder provided? → use directly │ ├── well-known type? (Month, DayOfWeek) → built-in order │ └── otherwise → undefined (no canonical order) │ ├── resolveCyclic(semanticType, sortOrder) │ → boolean │ ├── resolveSortDirection(semanticType) │ → 'ascending' | 'descending' | undefined │ └── resolveBinningSuggested(semanticType, domain, values) → boolean └── domain span ≤ 20? → false ``` **Functions exported from core/field-semantics.ts but called by Stage 2 (`resolveChannelSemantics`):** ``` resolveTickConstraint(semanticType, domain, values) → TickConstraint | undefined resolveReversed(semanticType) → boolean resolveNice(semanticType, domainShape) → boolean getRecommendedColorSchemeWithMidpoint(type, vlType, values, field) → ColorSchemeRecommendation resolveInterpolation(semanticType) → Interpolation | undefined resolveStackable(semanticType) → 'sum' | 'normalize' | false ``` These require channel context (encoding type, axis direction, mark type) and are therefore not part of `FieldSemantics`. ### 6.3 Caching The field context is expensive to compute (data scanning for format detection, distribution analysis for scale type). It should be built once per field per dataset and cached: ```typescript /** Cache key: `${fieldName}::${semanticType}::${dataHash}` */ const contextCache = new Map<string, FieldSemantics>(); ``` The data hash can be a fast fingerprint of the first 100 values to avoid recomputation when the same data is reused. --- ## §7 Integration: Four-Stage Pipeline The chart engine follows a four-stage compilation pipeline inspired by LLVM's architecture. `ChannelSemantics` serves as the IR — the stable contract between frontend (semantic resolution) and backend (spec generation). ``` ┌──────────────────────────────────────────────────────────────────────┐ │ Stage 1: Field Semantics │ │ resolveFieldSemantics(annotation, fieldName, values) │ │ → FieldSemantics (data identity: format, agg, domain, ordering) │ │ VL dependency: None │ ├──────────────────────────────────────────────────────────────────────┤ │ Stage 2: Channel Semantics │ │ resolveChannelSemantics(encodings, data, semanticTypes, converted) │ │ → ChannelSemantics (encoding type, color scheme, temporal format, │ │ tick constraints, axis reversal, nice, interpolation, stacking) │ │ Calls Stage 1 internally per field, then promotes into flat struct │ │ VL dependency: None │ ├──────────────────────────────────────────────────────────────────────┤ │ IR boundary: ChannelSemantics (flat, target-agnostic) │ ├──────────────────────────────────────────────────────────────────────┤ │ Stage 3: Layout │ │ computeLayout(channelSemantics, declaration, data, canvasSize, opts) │ │ → LayoutResult (subplot sizes, step widths, facet grid) │ │ Also: convertTemporalData, declareLayoutMode, filterOverflow │ │ VL dependency: None │ ├──────────────────────────────────────────────────────────────────────┤ │ Stage 4: Spec Generation (backend-specific) │ │ assembleVegaLite / assembleECharts / assembleChartjs / assembleGoFish│ │ → Backend-native spec (VL JSON / ECharts option / CJS config / GF) │ │ Also: finalize zero-baseline, template.instantiate, apply layout │ │ VL dependency: Yes (only this stage) │ └──────────────────────────────────────────────────────────────────────┘ ``` ### 7.1 Stage 1–2: Semantic resolution Stage 2 is the public entry point. It calls Stage 1 internally per field, then layers on channel-specific decisions: ``` resolveChannelSemantics(encodings, data, semanticTypes, convertedData?) → for each channel: // Stage 1: field identity (internal) annotation = normalizeAnnotation(semanticTypes[field]) fc = resolveFieldSemantics(annotation, field, values) // Stage 2: channel-specific decisions cs = { field, semanticAnnotation: fc.semanticAnnotation, type: resolveEncodingType(...), // promoted from FieldSemantics (data identity): format, tooltipFormat, aggregationDefault, scaleType, domainConstraint, canonicalOrder, cyclic, sortDirection, binningSuggested, // channel-resolved (NOT from FieldSemantics): nice: resolveNice(semanticType, domainShape), tickConstraint: resolveTickConstraint(semanticType, domain, values), reversed: resolveReversed(semanticType), colorScheme: resolveColorScheme(semanticType, annotation, values), temporalFormat: resolveTemporalFormat(...), ordinalSortOrder: resolveOrdinalSortOrder(...), interpolation: resolveInterpolation(semanticType), stackable: resolveStackable(semanticType), } → Record<channel, ChannelSemantics> ``` **Key design decision:** Stage 2 does NOT resolve `zero` (zero-baseline). The zero decision requires knowing the template's mark type (bar → include zero for length integrity; scatter → data-fitted), which is Stage 4 knowledge. Stage 2 provides `zeroBaseline` (from FieldSemantics) as a hint; Stage 4 finalizes `cs.zero` using `computeZeroDecision()`. **Temporal data conversion:** `convertTemporalData()` runs once in the assembler, before calling `resolveChannelSemantics()`. The converted data is passed as the optional `convertedData` parameter for temporal format detection, and reused by Stages 3–4 for filtering and layout. `FieldSemantics` is internal — never exposed downstream. All properties are promoted into the flat `ChannelSemantics` interface. ### 7.2 ChannelSemantics: the IR `ChannelSemantics` is the **sole public interface** consumed by all assemblers, templates, layout, and recommendation. Field-level properties are **promoted** directly into the struct during Stage 2. ```typescript interface ChannelSemantics { // --- Identity --- field: string; semanticAnnotation: SemanticAnnotation; // --- Encoding type --- type: 'quantitative' | 'nominal' | 'ordinal' | 'temporal'; // --- Formatting --- format?: FormatSpec; tooltipFormat?: FormatSpec; temporalFormat?: string; // --- Aggregation --- aggregationDefault?: 'sum' | 'average'; // --- Scale --- zero?: ZeroDecision; // finalized by Stage 4, not Stage 2 scaleType?: 'linear' | 'log' | 'sqrt' | 'symlog'; nice?: boolean; domainConstraint?: DomainConstraint; tickConstraint?: TickConstraint; // --- Ordering --- ordinalSortOrder?: string[]; cyclic?: boolean; reversed?: boolean; sortDirection?: 'ascending' | 'descending'; // --- Color --- colorScheme?: ColorSchemeRecommendation; // --- Line chart --- interpolation?: 'linear' | 'step' | 'step-after' | 'monotone'; // --- Histogram --- binningSuggested?: boolean; // --- Stacking --- stackable?: 'sum' | 'normalize' | false; } ``` ### 7.3 Stage 3: Layout (target-agnostic) Stage 3 operates entirely on `ChannelSemantics` and data — no backend knowledge: ``` // Pre-stage: once per assembly convertedData = convertTemporalData(data, semanticTypes) // Template hook (narrow interface for backend → layout communication) declaration = chartTemplate.declareLayoutMode(channelSemantics, data, props) // Overflow filtering budgets = computeChannelBudgets(channelSemantics, declaration, convertedData, ...) overflowResult = filterOverflow(channelSemantics, declaration, encodings, convertedData, ...) // Layout sizing layoutResult = computeLayout(channelSemantics, declaration, filteredData, canvasSize, ...) ``` `declareLayoutMode` is a template hook analogous to LLVM's `TargetTransformInfo` — it lets the backend (Stage 4) influence middle-end (Stage 3) decisions through a narrow, well-defined interface (e.g., "I use binned axes" → affects layout sizing). ### 7.4 Stage 4: Spec generation (backend-specific) Each backend assembler performs: 1. **Zero-baseline finalization** — reads `zeroBaseline` from `ChannelSemantics`, combines with template mark type, calls `computeZeroDecision()`: ```typescript // In each assembler, after resolveChannelSemantics(): const effectiveMarkType = templateMarkType || 'point'; for (const [channel, cs] of Object.entries(channelSemantics)) { if ((channel === 'x' || channel === 'y') && cs.type === 'quantitative') { cs.zero = computeZeroDecision( cs.semanticAnnotation.semanticType, channel, effectiveMarkType, numericValues, ); } } ``` 2. **Encoding translation** — `buildVLEncodings()` / ECharts series config / etc. 3. **Template instantiation** — `template.instantiate(vgObj, context)` 4. **Layout application** — `vlApplyLayoutToSpec()` / `ecApplyLayoutToSpec()` / etc. 5. **Post-layout adjustments** — facet refinement, tooltips, independent scales The `InstantiateContext` passed to templates contains `channelSemantics`, `layout`, `table`, `resolvedEncodings`, `canvasSize`, and `assembleOptions`. Templates read the flat `ChannelSemantics` directly — no nested types. ### 7.5 Recommendation engine impact The recommendation engine (`recommendation.ts`) currently uses: - `isMeasureType()`, `isTimeSeriesType()`, `isCategoricalType()`, etc. With the flat `ChannelSemantics`, recommendation can also use: - `cs.aggregationDefault` to auto-populate aggregate in encodings - `cs.stackable` to decide whether to suggest stacked variants - `cs.binningSuggested` to suggest histogram for continuous fields - `cs.semanticAnnotation` for type identity when needed These don't require API changes — the recommendation functions can optionally accept channel semantics and use them for better scoring. --- ## §8 SemanticResult: Updated Structure ```typescript /** * Stage 2 output. * * A flat Record<channel, ChannelSemantics>. Each entry contains all * resolved decisions — no separate FieldSemantics map needed because * those properties are promoted directly into the struct. */ type SemanticResult = Record<string, ChannelSemantics>; ``` --- ## §9 Worked Examples ### Example 1: Revenue bar chart (with currency annotation) **Input:** - Field: `revenue`, Annotation: `{ semanticType: "Revenue", unit: "EUR" }` - Data: [124500, 89200, 450000, 312000, ...] - Channel: Y, Mark: bar **resolveFieldSemantics output:** ```json { "semanticAnnotation": { "semanticType": "Revenue", "unit": "EUR" }, "defaultVisType": "quantitative", "format": { "pattern": "€,.0f", "prefix": "€", "abbreviate": true }, "tooltipFormat": { "pattern": "€,.2f", "prefix": "€" }, "aggregationDefault": "sum", "zeroBaseline": "meaningful", "scaleType": "linear", "domainConstraint": null, "canonicalOrder": null, "cyclic": false, "sortDirection": null, "binningSuggested": true } ``` **Channel-resolved additions (by resolveChannelSemantics):** `nice: true`, `reversed: false`, `tickConstraint: null`, `colorScheme: { type: 'sequential', scheme: 'goldgreen' }`, `interpolation: 'monotone'`, `stackable: 'sum'` **Result:** Y axis shows "€0", "€100K", "€200K", ...; zero-baseline included; tooltip shows "€124,500.00"; bars are summable via stacking. Note: `unit: "EUR"` → `prefix: "€"` mapping. Precision is data-driven: `detectPrecision([124500, 89200, ...])` → 0 → `€,.0f`. ### Example 2: Temperature line chart (with unit annotation) **Input:** - Field: `avg_temp`, Annotation: `{ semanticType: "Temperature", unit: "°C" }` - Data: [16.8, 18.4, 22.1, 25.8, 29.6, 31.7, 33.1, 31.5, ...] - Channel: Y, Mark: line **resolveFieldSemantics output:** ```json { "semanticAnnotation": { "semanticType": "Temperature", "unit": "°C" }, "defaultVisType": "quantitative", "format": { "pattern": ".1f", "suffix": "°C" }, "tooltipFormat": { "pattern": ".2f", "suffix": "°C" }, "aggregationDefault": "average", "zeroBaseline": "arbitrary", "scaleType": "linear", "domainConstraint": null, "canonicalOrder": null, "cyclic": false, "sortDirection": null, "binningSuggested": true } ``` **Channel-resolved additions:** `nice: true`, `reversed: false`, `tickConstraint: null`, `colorScheme: { type: 'diverging', midpoint: 0, scheme: 'blueorange' }`, `interpolation: 'monotone'`, `stackable: false` **Result:** Y axis data-fitted (no 0°C baseline — zero is arbitrary for temperature); ticks show "16°C", "20°C", "25°C", "30°C"; tooltip shows "16.80°C"; diverging color midpoint at 0°C (freezing point, meaningful for Celsius); smooth monotone interpolation. Precision data-driven: `detectPrecision([16.8, 18.4, ...])` → 1 → `.1f`. ### Example 3: Rank bump chart **Input:** - Field: `rank`, SemanticType: `Rank` - Data: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] - Channel: Y, Mark: line (bump) **resolveFieldSemantics output:** ```json { "semanticAnnotation": { "semanticType": "Rank" }, "defaultVisType": "ordinal", "format": { "pattern": "d" }, "tooltipFormat": { "pattern": "d" }, "aggregationDefault": null, "zeroBaseline": "arbitrary", "scaleType": "linear", "domainConstraint": null, "canonicalOrder": null, "cyclic": false, "sortDirection": "ascending", "binningSuggested": false } ``` **Channel-resolved additions:** `nice: false`, `reversed: true`, `tickConstraint: { integersOnly: true, minStep: 1 }`, `colorScheme: { type: 'sequential', reversed: true }`, `interpolation: 'step'`, `stackable: false` **Result:** Y axis reversed (1 at top); integer ticks only; no zero; step interpolation; no stacking. ### Example 4: Month categorical axis **Input:** - Field: `month`, SemanticType: `Month` - Data: ["Jan", "Feb", "Mar", "Apr", "May", "Jun", ...] - Channel: X, Mark: bar **resolveFieldSemantics output:** ```json { "semanticAnnotation": { "semanticType": "Month" }, "defaultVisType": "ordinal", "format": {}, "tooltipFormat": {}, "aggregationDefault": null, "zeroBaseline": null, "scaleType": null, "domainConstraint": null, "canonicalOrder": ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"], "cyclic": true, "sortDirection": "ascending", "binningSuggested": false } ``` **Channel-resolved additions:** `reversed: false`, `ordinalSortOrder: ["Jan", "Feb", ...]` (from canonicalOrder), `colorScheme: null`, `interpolation: null`, `stackable: false` **Result:** X axis sorts months in calendar order (not alphabetical); ordinal type; cyclic; no binning. ### Example 5: Percentage in scatter plot (with representation detection) Percentage data comes in two common representations that require different formatting: - **Fractional (0–1):** values like 0.48, 0.51 — d3's `.%` format multiplies by 100 automatically - **Whole-number (0–100):** values like 48, 51 — need `.f` + "%" suffix, no multiplication The builder **infers the representation from data values**: if `max(values) ≤ 1.0` (and most values are in [0,1]), it's fractional; if values are in [0,100] range, it's whole-number. The LLM can also provide `domain: [0, 1]` or `domain: [0, 100]` to disambiguate explicitly. **Example 5a: Fractional percentage (0–1)** **Input:** - Field: `completion_rate`, SemanticType: `Percentage` - Data: [0.48, 0.49, 0.51, 0.52, 0.50, 0.47, ...] - Channel: Y, Mark: point **resolveFieldSemantics output:** ```json { "semanticAnnotation": { "semanticType": "Percentage" }, "defaultVisType": "quantitative", "format": { "pattern": ".1p%" }, "tooltipFormat": { "pattern": ".2p%" }, "aggregationDefault": "average", "zeroBaseline": "contextual", "scaleType": "linear", "domainConstraint": { "min": 0, "max": 1, "clamp": false }, "canonicalOrder": null, "cyclic": false, "sortDirection": null, "binningSuggested": true } ``` **Channel-resolved additions:** `nice: true`, `reversed: false`, `colorScheme: { type: 'sequential' }`, `interpolation: 'monotone'`, `stackable: 'normalize'` Note: d3's `.1%` format handles the ×100 conversion: `0.48` → `"48.0%"`. Precision data-driven. **Example 5b: Whole-number percentage (0–100)** **Input:** - Field: `pass_rate`, SemanticType: `Percentage` - Data: [85, 92, 78, 91, 88, ...] - Channel: Y, Mark: bar **resolveFieldSemantics output:** ```json { "semanticAnnotation": { "semanticType": "Percentage" }, "defaultVisType": "quantitative", "format": { "pattern": "d", "suffix": "%" }, "tooltipFormat": { "pattern": ".1f", "suffix": "%" }, "aggregationDefault": "average", "zeroBaseline": "contextual", "scaleType": "linear", "domainConstraint": { "min": 0, "max": 100, "clamp": false }, "canonicalOrder": null, "cyclic": false, "sortDirection": null, "binningSuggested": false } ``` **Channel-resolved additions:** `nice: true`, `reversed: false`, `colorScheme: { type: 'sequential' }`, `interpolation: null`, `stackable: 'normalize'` Note: here `suffix: "%"` is explicit and the pattern is plain `d` (integer, no ×100). `85` → `"85%"`. Precision data-driven: `detectPrecision([85, 92, ...])` → 0. **Channel override (5a):** Since data is clustered at 0.47–0.52 (proximity = 0.47/0.52 ≈ 0.90 > 0.3) and mark is point (not bar), `computeZeroDecision` returns `zero: false`. Axis zooms to ~46%–53%. ### Example 6: Rating bar chart (with domain annotation) **Input:** - Field: `rating`, Annotation: `{ semanticType: "Rating", domain: [1, 5] }` - Data: [4, 3, 5, 2, 4, 5, 3, 4, ...] - Channel: Y, Mark: bar **resolveFieldSemantics output:** ```json { "semanticAnnotation": { "semanticType": "Rating", "intrinsicDomain": [1, 5] }, "defaultVisType": "quantitative", "format": {}, "tooltipFormat": { "pattern": ".1f" }, "aggregationDefault": "average", "zeroBaseline": "arbitrary", "scaleType": "linear", "domainConstraint": { "min": 1, "max": 5, "clamp": false }, "canonicalOrder": null, "cyclic": false, "sortDirection": null, "binningSuggested": false } ``` **Channel-resolved additions:** `nice: false` (bounded domainShape), `reversed: false`, `tickConstraint: { integersOnly: true, exactTicks: [1, 2, 3, 4, 5], minStep: 1 }`, `colorScheme: { type: 'sequential' }`, `interpolation: null`, `stackable: false` Note: `format: {}` (empty) — VL handles axis formatting natively for Rating. `tooltipFormat` uses data-driven precision for the popup. **Key derivations from `domain: [1, 5]`:** - `domainConstraint`: axis range fixed to 1–5 - `zeroBaseline: "arbitrary"`: domain starts at 1 (not 0), so zero is not meaningful - `tickConstraint.exactTicks: [1,2,3,4,5]`: span = 4 ≤ 20, so every integer gets a tick (channel-resolved) - `binningSuggested: false`: only 5 possible values, binning is useless - `nice: false`: bounded domain shape → don't extend to "nice" numbers (channel-resolved) - **Mark-aware zero**: For bar marks, despite `zeroBaseline: 'arbitrary'`, the Stage 4 assembler keeps `scale.zero = true` for proportional bar lengths (bars grow from 0, VL auto-extends to [0,5]). For scatter/line marks, `scale.zero` is cleared and the axis stays [1,5]. --- ## §10 Migration Plan ### Phase A: Type system foundation (non-breaking) 1. Define `SemanticAnnotation` interface in `types.ts` (see §3.2) 2. Add `normalizeAnnotation()` to accept both bare strings and enriched objects 3. Update `semantic_types` field type in `ChartAssemblyInput` to `Record<string, string | SemanticAnnotation>` 4. Implement `resolveFieldSemantics()` in `field-semantics.ts` (Stage 1) 5. Implement all `resolve*()` sub-functions, with annotation.intrinsicDomain / annotation.unit flowing in 6. Add `FieldSemantics` to `types.ts` 7. Promote `FieldSemantics` properties into flat `ChannelSemantics` 8. In `resolveChannelSemantics` (Stage 2), call `resolveFieldSemantics` and promote properties into each channel **Phase A also includes updating the Python-side LLM prompt:** 9. Update `generate_semantic_types_prompt()` in `semantic_types.py` to request `intrinsic_domain` and `unit` for applicable types 10. Update `SYSTEM_PROMPT` in `agent_data_load.py` to show the enriched JSON format 11. Add backward-compatible parsing: accept both old `"Rating"` and new `{ "semantic_type": "Rating", "domain": [1, 5] }` formats **No existing behavior changes.** The context is computed but not yet consumed. ### Phase B: Consume context in VL assembler (Stage 4) 1. In `vlApplyLayoutToSpec`, read `cs.format` → apply `axis.format` 2. Read `cs.tickConstraint` → apply `axis.tickMinStep`, `axis.values` 3. Read `cs.reversed` → apply `scale.reverse` 4. Read `cs.domainConstraint` → apply `scale.domain` + `scale.clamp` 5. Read `cs.nice` → apply `scale.nice` **Existing zero/color/temporal/sort logic continues to work.** New properties layer on top. ### Phase C: Consume context in ECharts assembler (Stage 4) Same as Phase B but translating to ECharts API (`axisLabel.formatter`, `yAxis.inverse`, etc.). ### Phase D: Consume context in recommendation engine 1. Use `cs.aggregationDefault` when auto-populating encodings 2. Use `cs.stackable` in stacked chart suitability checks 3. Use `cs.binningSuggested` in histogram recommendation ### Phase E: Consolidate existing decisions 1. `computeZeroDecision` reads `zeroBaseline` from `ChannelSemantics`; finalized in Stage 4 by each assembler 2. Move `getRecommendedColorScheme` to read from `cs.colorScheme` (promoted from FieldSemantics) 3. Move `inferOrdinalSortOrder` to read from `cs.ordinalSortOrder` (promoted from FieldSemantics) 4. Move temporal format resolution to `cs.temporalFormat` (promoted from FieldSemantics) After this phase, all semantic-type-driven decisions flow through the flat `ChannelSemantics` IR. No downstream code directly imports `isMeasureType()`, `isTimeSeriesType()`, etc. for decision-making. --- ## §11 Open Questions 1. **Unit/domain annotation reliability.** How reliably will the LLM provide `domain` and `unit`? Mitigation strategies: - (a) Require domain/unit for a small set of types (Rating, Score, Temperature, Price) — reject annotations without them - (b) Treat domain/unit as best-effort hints — fall back gracefully to data-inferred or type-intrinsic defaults (current proposal) - (c) Prompt the user to confirm/correct LLM-provided annotations in certain cases - Fallback priority: annotation.unit > column-name heuristics ("Weight (kg)") > data scan ("$1,234") > type defaults - Note: `intrinsicDomain` replaces the old `domain` property for clarity 2. **Scale type auto-detection.** Should we auto-switch to log scale when data spans >2 orders of magnitude? This is powerful but can surprise users. Options: - (a) Never auto-switch; provide `scaleType` as a hint for recommendation only - (b) Auto-switch with a prominent UI indicator ("Log scale applied") - (c) Auto-switch only for specific types (Revenue, Population) where log is commonly expected 3. **Reversed axis scope.** Rank reversal on Y makes sense, but what about X? A horizontal bump chart with rank on X should also reverse. Should `reversed` be axis-aware or axis-agnostic? - Current proposal: axis-agnostic (template/backend decides how to apply) 4. **Format vs. LLM context.** Should the compilation context's format information be passed to the LLM when generating chart code? This could help the LLM produce better Python/JS code that formats values correctly. But it adds token overhead. 5. **Interaction with explicit user overrides.** When the user manually sets an axis format or domain, how does that interact with the compilation context? - Proposed: User overrides always win. The context provides defaults; any explicit setting in `ChartEncoding` or `chartProperties` takes precedence. 6. **Where does `resolveFieldSemantics` live?** *(Resolved)* - Lives in `field-semantics.ts` (Option B) — clean separation; `semantic-types.ts` stays lean. - Called internally by `resolveChannelSemantics()` in `resolve-semantics.ts` (Stage 2). --- ## §12 Summary The compilation context is a structured bridge between semantic type knowledge and visualization property configuration. Instead of sprinkling `if (semanticType === 'Revenue') { ... }` across 6 files, we build a single typed context object per field and let all downstream consumers read from it. **What changes:** - One new type: `SemanticAnnotation` (enriched input with optional `intrinsicDomain`, `unit`, `sortOrder`) - One new type: `FieldSemantics` (structured output) - One new builder: `resolveFieldSemantics(annotation, fieldName, values)` - `ChannelSemantics` becomes flat — promotes field-semantics properties directly (no nested `fieldSemantics`) - Dead properties removed: `aggregate`, `sortOrder`, `sortBy`, `typeReason` - `semantic_types` map accepts both bare strings and annotation objects - VL/ECharts assemblers gain a `vlApplyFieldContext()` step - LLM prompts updated to request `intrinsic_domain`/`unit` for applicable types **What stays the same:** - The semantic type string taxonomy - The four-stage pipeline structure (Stage 1: Field Semantics → Stage 2: Channel Semantics → Stage 3: Layout → Stage 4: Spec Generation) - The existing zero/color/temporal/sort decisions (they migrate to read from `ChannelSemantics`) - The recommendation engine API **What's new:** - Enriched semantic type annotation with optional `intrinsicDomain`, `unit`, and `sortOrder` metadata - Field-aware formatting (axis ticks, tooltips, data labels) driven by annotation - Aggregation defaults per semantic type - Tick constraints (integer-only, exact ticks) - Reversed axes for rank-like types - Domain clamping for bounded types - Scale type hints (log, sqrt) - Interpolation hints for line charts - Binning and stacking compatibility flags - Diverging midpoint resolution from unit, type, intrinsicDomain, or data - Cyclic domain support for wrap-around ordinals (seasons, compass directions) ================================================ FILE: src/lib/agents-chart/docs/design-stretch-model.md ================================================ # Design: Axis Layout Compression > **Physics-based models for automatically sizing chart axes when data > overflows the available canvas.** Four models cover the four geometric contexts in which layout pressure arises: | § | Model | Geometry | Chart types | |---|---|---|---| | [§1](#1-discrete-axis-elastic-budget-model) | Elastic Budget | 1D banded axis | Bar, Histogram, Heatmap, Boxplot | | [§2](#2-continuous-axis-gas-pressure-model) | Gas Pressure | 2D point cloud | Scatter, Line, Area | | [§3](#3-circumference-radial-pressure-model) | Circumference | 1D closed loop | Pie, Rose, Sunburst, Radar, Gauge | | [§4](#4-area-layout-2d-pressure-model) | Area (2D) | 2D filled space | Treemap | All four share a common pattern: 1. **Pressure = demand / supply.** Items need space; the base canvas provides it. Pressure > 1 means overflow. 2. **Elastic stretch.** `stretch = min(maxStretch, pressure ^ elasticity)`. The power-law exponent controls how aggressively the chart grows. 3. **Per-dimension cap.** No axis grows beyond `maxStretch × base`. For radial/area models this becomes a radius or area cap. --- ## Table of Contents - [§0 Layout Mode Classification](#0-layout-mode-classification) - [§0.1 Banded vs Non-Banded](#01-banded-vs-non-banded) - [§0.2 Decision Tree](#02-decision-tree) - [§0.3 Vega-Lite Implementation Notes](#03-vega-lite-implementation-notes) - [§1 Discrete Axis (Elastic Budget Model)](#1-discrete-axis-elastic-budget-model) - [§1.1 Problem](#11-problem) - [§1.2 Parameters](#12-parameters) - [§1.3 Three Regimes](#13-three-regimes) - [§1.4 Power-Law Elastic Budget](#14-power-law-elastic-budget) - [§1.5 Linear Spring Model (Theoretical Foundation)](#15-linear-spring-model-theoretical-foundation) - [§1.6 Relationship Between Formulations](#16-relationship-between-formulations) - [§1.7 Grouped Items](#17-grouped-items) - [§1.8 Per-Mark-Type Guidelines](#18-per-mark-type-guidelines) - [§1.9 Faceted Charts](#19-faceted-charts) - [§1.10 Summary](#110-summary) - [§2 Continuous Axis (Gas Pressure Model)](#2-continuous-axis-gas-pressure-model) - [§2.1 Problem](#21-problem) - [§2.2 Parameters](#22-parameters) - [§2.3 Per-Axis Stretch](#23-per-axis-stretch) - [§2.4 Positional ≥ Series Constraint](#24-positional--series-constraint) - [§2.5 Parameter Table](#25-parameter-table) - [§2.6 Worked Examples](#26-worked-examples) - [§2.7 Summary](#27-summary) - [§3 Circumference (Radial Pressure Model)](#3-circumference-radial-pressure-model) - [§3.1 Problem](#31-problem) - [§3.2 Parameters](#32-parameters) - [§3.3 Effective Item Count](#33-effective-item-count) - [§3.4 Pressure and Stretch](#34-pressure-and-stretch) - [§3.5 Canvas Sizing](#35-canvas-sizing) - [§3.6 Gauge Faceting](#36-gauge-faceting) - [§3.7 Parameter Table](#37-parameter-table) - [§3.8 Summary](#38-summary) - [§4 Area Layout (2D Pressure Model)](#4-area-layout-2d-pressure-model) - [§4.1 Problem](#41-problem) - [§4.2 Parameters](#42-parameters) - [§4.3 Effective Item Count](#43-effective-item-count) - [§4.4 Pressure and Biased Split](#44-pressure-and-biased-split) - [§4.5 Worked Examples](#45-worked-examples) - [§4.6 Summary](#46-summary) - [§5 Unified Summary](#5-unified-summary) --- # §0 Layout Mode Classification ## §0.1 Banded vs Non-Banded The layout model needs to decide **how** to allocate space for each positional axis. This depends on two independent properties: 1. **Scale type** — the Vega-Lite encoding type of the field. 2. **Mark geometry** — whether the mark occupies a fixed-width band or a point-like position. ### Banded layout A **banded** axis allocates a fixed-width slot (band) per data position. The layout model controls the step size per slot. Items are read by the width/area of their band. | Condition | Example | |---|---| | **Discrete scale** (nominal / ordinal) | Categories on a bar chart, ordinal months | | **Continuous scale + band mark** | Bar chart with quantitative or temporal X (years as numbers) | | **Binned axis** (`bin: true`) | Histogram bins — each bin is a band regardless of scale | ### Non-banded layout A **non-banded** axis places items at data-determined positions within a continuous range. The layout model controls the overall canvas size but does **not** allocate per-item slots. | Condition | Example | |---|---| | **Continuous scale + point mark** | Scatter plot, line chart, area chart | ### Summary matrix | | Band mark (bar, rect, boxplot) | Point mark (circle, line, area) | |---|---|---| | **Discrete scale** (N/O) | Banded — §1 | Banded — §1 (*) | | **Continuous scale** (Q/T) | Banded — §1 | Non-banded — §2 | (*) Discrete scales are always banded regardless of mark type — VL allocates a band per category. ## §0.2 Decision Tree ``` For each positional axis (x, y): 1. Is the VL encoding type nominal or ordinal? → YES: Banded (discrete). Use §1 directly. 2. Is the axis binned (enc.bin = true)? → YES: Banded (continuous). Use §1 with bin count as N. 3. Does the template declare this axis as banded? (axisFlags.banded = true, e.g. bar/rect/boxplot marks) → YES: Banded (continuous). Use §1 with field cardinality as N. 4. Otherwise: → Non-banded (continuous). Use §2. ``` > **Implementation:** The decision is made in `compute-layout.ts` via `axisFlags.x.banded` / `axisFlags.y.banded` and `isDiscreteType()` checks. See `computeLayout()` lines ~155–230. ## §0.3 Vega-Lite Implementation Notes The §1 elastic budget model applies to both discrete-banded and continuous-banded axes, but the **Vega-Lite implementation differs**: ### Discrete banded (nominal / ordinal) VL natively supports step-based sizing: ```json { "width": { "step": ℓ } } ``` VL creates a band scale, allocates $\ell$ pixels per category, and sizes the chart to $N \times \ell$. For grouped bars (xOffset / yOffset): ```json { "width": { "step": ℓ_group, "for": "position" } } ``` ### Continuous banded (quantitative / temporal + band mark) VL does **not** support `{ "step": N }` on continuous scales. We handle this in two phases: **Phase 1 — Canvas sizing (assemble.ts):** ``` continuousWidth = stepSize × (N + 1) ``` The `+1` adds half-step padding on each side. The scale domain is extended by ±halfStep so positions align as they would on a discrete band scale. **Phase 2 — Mark sizing (postProcessing):** Since VL won't auto-size bars on a continuous scale: 1. Sort unique field values; find `minGap` (smallest consecutive difference). 2. Convert to pixels: `pixelsPerUnit = subplotDim × (N−1) / (dataRange × N)`. 3. `markSize = min(stepSize × 0.9, floor(minGap × pixelsPerUnit))`. 4. Apply via `{ "mark": { "size": markSize } }` (or `width`/`height` for rect with 0.98 fill ratio). ### Comparison | Aspect | Discrete banded | Continuous banded | |---|---|---| | VL scale type | `nominal` / `ordinal` (band scale) | `quantitative` / `temporal` (linear/time scale) | | Step control | `{ "step": ℓ }` on width/height | Manual: `config.view.continuousWidth = ℓ × (N+1)` | | Mark sizing | Automatic (VL fills bands) | Manual: `mark.size` from min-gap calculation | | Domain padding | Automatic (band scale) | Manual: extend domain by ±halfStep | | Sort control | `encoding.sort` | Data-determined (continuous scale) | ### When to prefer continuous banded - The data has **natural ordering and arithmetic meaning** (years, dates, prices). - The data has **irregular spacing** — a continuous scale preserves proportional positions. - The template declares `axisFlags.banded = true` while keeping the VL encoding type as Q/T. The `detectBandedAxis` function in `templates/utils.ts` handles this decision. --- # §1 Discrete Axis (Elastic Budget Model) ## §1.1 Problem A discrete axis displays $N$ banded items (categories, bins, groups) along a 1D segment of length $L_0$ pixels. Each item ideally occupies $\ell_0$ pixels (the natural length). When $N \cdot \ell_0 > L_0$, the items overflow. Two competing goals must be balanced: 1. **Items resist compression** — each item pushes outward to maintain $\ell_0$, and cannot shrink below $\ell_{\min}$. 2. **The axis resists expansion** — the axis can stretch beyond $L_0$ but has a hard maximum $L_{\max}$. ## §1.2 Parameters | Symbol | Meaning | Code mapping | Default | |---|---|---|---| | $L_0$ | Natural axis length | `width` / `height` (canvas size) | 400 px | | $L_{\max}$ | Maximum axis length | `width × maxStretch` | 800 px | | $N$ | Number of banded items | Field cardinality | data-dependent | | $\ell_0$ | Natural length per item | `defaultStepSize` | ~20 px | | $\ell_{\min}$ | Minimum length per item | `minStep` option | 6 px | | $\alpha$ | Elasticity exponent | `elasticity` option | 0.5 | | $\beta$ | Maximum stretch multiplier | `maxStretch` option | 2.0 | > **Code defaults:** `ElasticStretchParams` in `core/decisions.ts` — `elasticity: 0.5`, `maxStretch: 2`, `minStep: 6`. The `defaultStepSize` is computed dynamically based on canvas size: `round(20 × max(1, sizeRatio) × defaultStepMultiplier)`. ## §1.3 Three Regimes ### Regime 1: No compression needed **Condition:** $N \cdot \ell_0 \leq L_0$ All items fit at their natural length: $$\ell = \ell_0, \quad L = N \cdot \ell_0$$ ### Regime 2: Overflow beyond recovery **Condition:** $N \cdot \ell_{\min} \geq L_{\max}$ Even at minimum item length and maximum stretch, not all items fit. Excess items are truncated: $$N' = \left\lfloor \frac{L_{\max}}{\ell_{\min}} \right\rfloor, \quad \ell = \ell_{\min}, \quad L = L_{\max}$$ ### Regime 3: Elastic equilibrium **Condition:** $N \cdot \ell_0 > L_0$ and $N \cdot \ell_{\min} < L_{\max}$ Items overflow but can be accommodated by compressing items and/or stretching the axis. This is where the elastic model applies. ## §1.4 Power-Law Elastic Budget This is the **implemented model**. The axis stretches using a power-law of the pressure ratio: **Pressure:** $$p = \frac{N \cdot \ell_0}{L_0}$$ **Stretch factor:** $$s = \min(\beta,\; p^{\alpha})$$ **Resulting step size:** $$\ell = \frac{L_0 \cdot s}{N} = \frac{L_0 \cdot p^{\alpha}}{N}$$ With $\alpha = 0.5$, doubling the overflow only increases the stretch by $\sqrt{2} \approx 1.41\times$ — a naturally progressive response. **Clamping:** The step is clamped to $[\ell_{\min},\; \ell_0]$ and the axis length to $[L_0,\; L_{\max}]$. > **Implementation:** `computeElasticBudget()` in `core/decisions.ts` (lines ~549–569). Called by `computeAxisStep()` which handles both nominal and continuous-as-discrete cases. ## §1.5 Linear Spring Model (Theoretical Foundation) The power-law model can be motivated by a physical analogy: $N$ identical springs packed inside a box. **Setup:** - Each spring (item) has natural length $\ell_0$, solid length $\ell_{\min}$, spring constant $k_1$. - The box (axis) has natural length $L_0$, max length $L_{\max}$, spring constant $k_2$. **Force balance at equilibrium:** $$N \cdot k_1 \cdot (\ell_0 - \ell) = k_2 \cdot (N \cdot \ell - L_0)$$ **Equilibrium step size** (using stiffness ratio $\kappa = k_1 / k_2$): $$\boxed{\ell = \frac{\kappa \cdot \ell_0 + L_0 / N}{1 + \kappa}}$$ **Interpretation of $\kappa$:** - $\kappa \to \infty$: items don't compress; the wall absorbs everything ($\ell \to \ell_0$). - $\kappa \to 0$: items compress to fit the fixed axis ($\ell \to L_0 / N$). - $\kappa = 1$: compression is split evenly ($\ell = (\ell_0 + L_0/N) / 2$). The linear spring model is more physically intuitive and allows independent tuning of item vs. wall stiffness ($\kappa$). It is presented here as the theoretical motivation for the power-law model. **Nonlinear (progressive-rate) variant:** Replacing the linear spring with a hardening spring $F_1(\ell) = k_1 \cdot ((\ell_0 - \ell) / (\ell_0 - \ell_{\min}))^{\gamma}$ leads directly to the power-law formulation used in the implementation. ## §1.6 Relationship Between Formulations | Linear spring model | Power-law implementation | |---|---| | $\kappa$ (stiffness ratio $k_1/k_2$) | $\alpha$ (elasticity exponent) | | $\ell = (\kappa \cdot \ell_0 + L_0/N) / (1 + \kappa)$ | $s = \min(\beta, p^{\alpha})$; $\ell = L_0 \cdot s / N$ | | Uniform interpolation between $\ell_0$ and $L_0/N$ | Power-curve interpolation favoring $\ell_0$ | | Two parameters ($k_1$, $k_2$) | One parameter ($\alpha$) | | More physically intuitive | More compact; naturally progressive | ## §1.7 Grouped Items Grouped items (e.g., grouped bar with $m$ sub-bars per group) are treated as a special case — the **group** is the unit of compression, not the individual item. | Parameter | Simple discrete | Grouped bar ($m$ sub-bars) | |---|---|---| | $\ell_0$ (natural) | `defaultStepSize` | $m \times$ `defaultStepSize` | | $\ell_{\min}$ (solid) | `minStep` (6 px) | $2m$ px (2 px per sub-bar) | | $N$ (item count) | Field cardinality | Number of **groups** | The elastic budget formula is unchanged — only the parameter values change. **Example:** 15 groups × 3 sub-bars on a 400 px axis: - $N = 15$, $\ell_0 = 60$, ideal $= 900 > 400$ → Regime 3. - With $\alpha = 0.5$: $p = 900/400 = 2.25$, $s = \min(2, 2.25^{0.5}) = 1.50$. - Budget $= 400 \times 1.5 = 600$, step $= 600/15 = 40$ px per group. > **Implementation:** In `computeLayout()`, grouping is detected via the `group` channel. When `xHasGrouping` is true, step is computed per-group with `xStepUnit = 'group'` and a minimum group gap of 3 px is enforced. ## §1.8 Per-Mark-Type Guidelines Different mark types have different visual footprints and compression tolerances. Templates can tune behavior via `defaultStepMultiplier` and `overrideDefaultSettings`. **Design guidelines** (for a 300 px reference canvas, `defaultStepSize` ≈ 20 px): | Mark type | $\ell_0$ | $\ell_{\min}$ | Compression tolerance | Rationale | |---|---|---|---|---| | **Bar** | 20 px | 6 px | Moderate | Width encodes the item — can't shrink too much | | **Stacked bar** | 20 px | 6 px | Low | Stacked segments unreadable when thin | | **Grouped bar** ($m$) | $20m$ px | $2m$ px | Low | Losing sub-bar distinction is costly | | **Lollipop** | 14 px | 4 px | High | Dot (position) carries encoding, not width | | **Heatmap / rect** | 20 px | 8 px | Very low | Color cell needs area for color to be perceivable | | **Boxplot** | 24 px | 10 px | Low | Internal structure (box/whiskers/median) lost early | | **Strip / jitter** | 24 px | 6 px | Moderate | Points collapse into a line when too narrow | | **Histogram** | 16 px | 4 px | High | Distribution shape survives compression well | | **Candlestick** | 18 px | 8 px | Low | Open/close body + wicks need room | **Design principles:** 1. Marks encoding value by **width/area** (bar, rect) → higher $\ell_0$, lower compression tolerance. 2. Marks encoding value by **position** (lollipop, bump) → higher compression tolerance. 3. Marks with **internal structure** (boxplot, candlestick) → higher $\ell_{\min}$. 4. Marks showing **distribution shape** (histogram) → can be narrower. > **Note:** Currently, templates primarily adjust layout via `defaultStepMultiplier` (scales $\ell_0$ proportionally) and `overrideDefaultSettings`. Per-mark-type spring stiffness ($\kappa$) is a design aspiration, not yet individually parameterized in the code. ## §1.9 Faceted Charts Faceting splits one chart into a grid of subplots. This introduces an additional layer of layout compression: the canvas must accommodate $F$ panels, each containing its own axis. ### §1.9.1 Facet stretch factor The total canvas stretches to accommodate facets: $$\lambda_f = \min(\beta,\; F^{\alpha_f})$$ where $\alpha_f$ = `facetElasticity` (default 0.3) and $\beta$ = `maxStretch` (default 2.0). The facet stretch uses a **gentler exponent** ($\alpha_f = 0.3$ vs $\alpha = 0.5$ for discrete items) because each subplot is a self-contained chart — even a small subplot can be readable, whereas a 3 px bar cannot. > **Implementation:** `computeLayout()` lines ~256–270 in `compute-layout.ts`. Uses `facetElasticityVal = 0.3` and `maxStretchVal = 2`. ### §1.9.2 Subplot sizing Each subplot gets a share of the stretched canvas: $$W_{\text{sub}} = \max\!\left(S_{\min},\; \frac{W_0 \cdot \lambda_f - \text{fixedPad}}{F_c} - \text{gap}\right)$$ | Symbol | Meaning | Default | |---|---|---| | $F_c, F_r$ | Facet columns / rows | data-dependent | | $\alpha_f$ | Facet elasticity | 0.3 | | $S_{\min}$ | Minimum subplot size (continuous axis) | 60 px | ### §1.9.3 Facet-mode shrink limits Under faceting, axes can shrink **further** than in single-chart mode because the reader compares patterns across panels rather than reading individual values precisely. | Mark type | $\ell_{\min}^{f}$ (banded) | $S_{\min}$ (continuous) | |---|---|---| | Bar / stacked bar | 3 px | 60 px | | Heatmap / rect | 4 px | 40 px | | Boxplot | 6 px | 60 px | | Line / area | — | 40 px | | Ridge / density | — | 20 px | | Scatter | — | 60 px | ### §1.9.4 Faceted discrete axis The spring model runs **per subplot**: $W_{\text{sub}}$ becomes $L_0$ and $N_{\text{items}}$ is the per-panel count. If items still overflow, they are truncated to $N' = \lfloor W_{\text{sub}} / \ell_{\min} \rfloor$. ### §1.9.5 Faceted continuous axis The gas pressure model (§2) runs within each subplot using $W_{\text{sub}} \times H_{\text{sub}}$ as the container. Subplot dimensions are uniform across panels for visual consistency. ### §1.9.6 Facet wrap (column-only folding) When only a column facet is specified and $F$ is large, panels wrap into a 2D grid: 1. **Determine columns:** $F_c = \min(F,\; \lfloor (W_0 \cdot \beta) / S_{\min} \rfloor)$, further constrained by internal discrete pressure. 2. **Compute rows:** $F_r = \lceil F / F_c \rceil$. 3. **Size subplots:** Each dimension uses $\lambda_f = \min(\beta, F^{\alpha_f})$. Wrapping transfers pressure from horizontal to vertical — fewer columns give each subplot more width, but total chart height grows. > **Implementation:** `computeFacetGrid()` and `computeChannelBudgets()` in `compute-layout.ts`. The facet grid is computed **before** `computeLayout()` to break the circularity between wrapping and banded axis sizing. ## §1.10 Summary | Symbol | Meaning | Default | |---|---|---| | $N$ | Number of discrete items | data-dependent | | $\ell_0$ | Natural step size | ~20 px | | $\ell_{\min}$ | Minimum step size | 6 px | | $\alpha$ | Elasticity exponent | 0.5 | | $\beta$ | Maximum stretch | 2.0 | ``` Given: N items, natural length ℓ₀, solid length ℓ_min, axis rest length L₀, maxStretch β, elasticity α pressure = N · ℓ₀ / L₀ if pressure ≤ 1: ℓ = ℓ₀ # Regime 1: fits elif N · ℓ_min ≥ β · L₀: ℓ = ℓ_min, truncate to N' items # Regime 2: overflow else: stretch = min(β, pressure^α) # Regime 3: elastic ℓ = L₀ · stretch / N ℓ = clamp(ℓ, ℓ_min, ℓ₀) ``` > **Key functions:** `computeElasticBudget()`, `computeAxisStep()` in `core/decisions.ts`; `computeLayout()` in `core/compute-layout.ts`. --- # §2 Continuous Axis (Gas Pressure Model) ## §2.1 Problem A continuous axis displays $N$ point-like items (scatter dots, line vertices) across a 2D canvas. Unlike discrete items, these marks do not occupy fixed bands — they float at data-determined positions. Each mark has a visual cross-section $\sigma$ (px²). **Why springs don't apply:** Continuous marks don't own slots. A scatter plot with 100 points and one with 10 can both fit in the same canvas — the difference is **density**, not per-item allocation. This is the domain of gas physics. ## §2.2 Parameters | Symbol | Meaning | Code mapping | Default | |---|---|---|---| | $W_0, H_0$ | Natural canvas dimensions | `subplotWidth`, `subplotHeight` | 400 × 320 px | | $\sigma$ | Mark cross-section (px²) | `markCrossSection` | 30 px² | | $\sigma_x, \sigma_y$ | Per-axis cross-sections | `markCrossSectionX/Y` | chart-type specific | | $\alpha_c$ | Elasticity exponent | `elasticity` | 0.3 | | $\beta_c$ | Maximum stretch | `maxStretch` | 1.5 | > **Code defaults:** `DEFAULT_GAS_PRESSURE_PARAMS` in `core/decisions.ts` — `markCrossSection: 30`, `elasticity: 0.3`, `maxStretch: 1.5`. **Why $\beta_c$ is smaller than discrete $\beta$:** Continuous axes encode by **position along a scale** — the most perceptually robust channel (Cleveland & McGill, 1984). A scatter plot remains readable even when compressed because relative positions are preserved. Discrete axes encode by **length/area of bands**, which degrades faster. | | Discrete axis | Continuous axis | |---|---|---| | Primary encoding | Length / area of band | Position along scale | | Recommended $\beta$ | 2.0 | 1.5 | ## §2.3 Per-Axis Stretch Crowding is almost always asymmetric — e.g., on a line chart, X is driven by time points while Y is driven by overlapping series. Each axis is stretched independently. ### Mode 1: Positional (default) Count unique pixel positions along the axis (bucketed at ~1 px resolution). Each position needs $\sigma_{1d} = \sqrt{\sigma}$ pixels: $$p_{1d} = \frac{\text{uniquePos} \cdot \sigma_{1d}}{\text{dim}_0}$$ $$s = \begin{cases} 1 & \text{if } p_{1d} \leq 1 \\ \min(\beta_c,\; p_{1d}^{\,\alpha_c}) & \text{if } p_{1d} > 1 \end{cases}$$ ### Mode 2: Series-count (`seriesCountAxis`) When `seriesCountAxis` is set (`'x'`, `'y'`, or `'auto'`), the designated axis uses the number of distinct series (color ∪ detail fields) for pressure. `'auto'` resolves to: - 2D path (both axes continuous): Y axis. - 1D path (one continuous + one discrete): the continuous axis. $$p_{\text{series}} = \frac{n_{\text{series}} \cdot \sigma}{\text{dim}_0}$$ Here $\sigma$ is used **directly** (not square-rooted) since series count is inherently 1D. > **Implementation:** `computeGasPressure()` in `core/decisions.ts` (lines ~442–508). The 2D path (both axes continuous) and 1D path (one axis continuous) are handled separately in `computeLayout()` lines ~275–425. ## §2.4 Positional ≥ Series Constraint For charts where both axes are continuous (line, area), stretching the positional axis also reduces visual overlap between series. So: $$s_{\text{positional}} = \max(s_{\text{positional}},\; s_{\text{series}})$$ When `maintainContinuousAxisRatio` is set, both axes use the maximum of the two stretches. ## §2.5 Parameter Table | Chart type | $\sigma_x$ | $\sigma_y$ | $\alpha_c$ | $\beta_c$ | seriesCountAxis | |---|---|---|---|---|---| | Scatter | 30 | 30 | 0.3 | 1.5 | — | | Line | 100 | 20 | 0.3 | 1.5 | auto (→ Y) | | Dotted Line | 100 | 20 | 0.3 | 1.5 | auto (→ Y) | | Area | 100 | 20 | 0.3 | 1.5 | auto (→ Y) | | Streamgraph | 100 | 20 | 0.3 | 1.5 | auto (→ Y) | | Bump | 80 | 20 | 0.3 | 1.5 | auto (→ Y) | | Stacked Bar | 20 | 20 | 0.3 | 1.5 | auto (→ Y*) | \* For stacked bar, X is discrete (§1), Y is continuous. `auto` resolves to Y via the 1D path. ## §2.6 Worked Examples ### Series-axis stretch ($\sigma = 20$, $\text{dim}_0 = 300$, $\alpha_c = 0.3$, $\beta_c = 1.5$) | Scenario | nSeries | pressure | stretch | Final dim | |---|---|---|---|---| | 8 series (typical) | 8 | 0.53 | 1.0 | 300 | | 15 series (moderate) | 15 | 1.0 | 1.0 | 300 | | 20 series (busy) | 20 | 1.33 | 1.09 | 328 | | 40 series (extreme) | 40 | 2.67 | 1.35 | 406 | ### Combined positional + series (positional ≥ series constraint) | Scenario | nDates | nSeries | raw X | raw Y | final X | final Y | |---|---|---|---|---|---|---| | 12 dates × 20 series | 12 | 20 | 1.0 | 1.09 | **1.09** | 1.09 | | 100 dates × 40 series | 100 | 40 | 1.32 | 1.35 | **1.35** | 1.35 | | 100 dates × 60 series | 100 | 60 | 1.32 | 1.50 | **1.50** | 1.50 | | 200 dates × 3 series | 200 | 3 | 1.50 | 1.0 | 1.50 | 1.0 | | 200 dates × 20 series | 200 | 20 | 1.50 | 1.09 | 1.50 | 1.09 | ## §2.7 Summary | Symbol | Meaning | Default | |---|---|---| | $\sigma$ | 2D mark cross-section (px²) | 30 | | $\sigma_{1d}$ | 1D projection: $\sqrt{\sigma}$ | ~5.5 | | $\alpha_c$ | Elasticity exponent | 0.3 | | $\beta_c$ | Max stretch | 1.5 | ``` Given: data points with x/y values, per-axis cross-sections σ_x σ_y, canvas W₀×H₀, elasticity αc, maxStretch βc, optional seriesCountAxis For each axis (X, Y): if seriesCountAxis resolves to this axis: nSeries = |distinct color ∪ detail values| pressure = nSeries · σ / dim₀ else: uniquePos = |{ round(v · px_per_unit) : v ∈ data }| σ_1d = √σ pressure = uniquePos · σ_1d / dim₀ if pressure ≤ 1: stretch = 1 else: stretch = min(βc, pressure^αc) # Positional ≥ Series constraint (when seriesCountAxis is set): stretch_positional = max(stretch_positional, stretch_series) W = W₀ · stretch_x H = H₀ · stretch_y ``` > **Key functions:** `computeGasPressure()` in `core/decisions.ts`; gas-pressure integration in `computeLayout()` in `core/compute-layout.ts`. --- # §3 Circumference (Radial Pressure Model) ## §3.1 Problem Radial charts (pie, rose, sunburst, radar) arrange data items around a **circle**. The relevant dimension is the **circumference**. When many items crowd the circumference, the chart must grow to keep slices/spokes legible. **Why axis models don't apply:** - **§1 (Spring):** Assumes a 1D axis with endpoints. Radial charts have a closed loop — growing means increasing the **radius**, which increases circumference as $C = 2\pi r$. - **§2 (Gas):** Assumes 2D free-floating points. Radial items are angularly constrained to their slice/spoke positions. The circumference model maps the spring intuition to polar geometry: treat the circumference as a "bent axis" and stretch the radius. ## §3.2 Parameters | Symbol | Meaning | Default | |---|---|---| | $r_0$ | Base radius: $\max(r_{\min},\; \min(W_0, H_0)/2 - m)$ | derived | | $C_0$ | Base circumference: $2\pi r_0$ | derived | | $N_{\text{eff}}$ | Effective item count (§3.3) | data-dependent | | $\ell_{\text{arc}}$ | Minimum arc-length per item (px) | 45 | | $\alpha$ | Elasticity exponent | 0.5 | | $\beta$ | Per-dimension max stretch | 2.0 | | $r_{\min}$ | Minimum radius | 60 px | | $r_{\max}$ | Maximum radius (absolute cap) | 400 px | | $m$ | Margin around circle (px) | 20 | > **Code defaults:** `CircumferencePressureParams` in `core/decisions.ts` — `minArcPx: 45`, `minRadius: 60`, `maxRadius: 400`, `elasticity: 0.5`, `maxStretch: 2.0`, `margin: 20`. ## §3.3 Effective Item Count Different radial chart types have different crowding dynamics, abstracted into a single number $N_{\text{eff}}$. **Uniform slices/spokes** (rose, radar): $N_{\text{eff}} = N$. **Variable-width slices** (pie, sunburst): $$N_{\text{eff}} = \frac{\sum v_i}{\min(v_i)}$$ This answers: "how many of the smallest slice would fill the entire circle?" Capped at 100 to prevent degenerate cases. **Sunburst:** Compute $N_{\text{eff}}$ on the **outer ring** (leaf nodes only) — the most crowded ring. > **Implementation:** `computeEffectiveBarCount()` in `core/decisions.ts` (lines ~906–920). ## §3.4 Pressure and Stretch **Pressure:** $$p = \frac{N_{\text{eff}} \cdot \ell_{\text{arc}}}{C_0} = \frac{N_{\text{eff}} \cdot \ell_{\text{arc}}}{2\pi r_0}$$ **Effective max stretch** (respects per-dimension canvas cap): $$s_{\max} = \min\!\left(\frac{r_{\max}}{r_0},\; \frac{\min(W_0 \cdot \beta,\; H_0 \cdot \beta) - 2m}{2 r_0}\right)$$ **Stretch:** $$s = \begin{cases} 1 & \text{if } p \leq 1 \\ \min(s_{\max},\; p^{\alpha}) & \text{if } p > 1 \end{cases}$$ **Radius:** $r = \text{clamp}(r_0 \cdot s,\; r_{\min},\; r_{\max})$ > **Implementation:** `computeCircumferencePressure()` in `core/decisions.ts` (lines ~850–893). ## §3.5 Canvas Sizing After computing the final radius $r$: $$W = \max(W_0,\; 2r + 2m), \quad H = \max(H_0,\; 2r + 2m)$$ Both canvas dimensions grow equally (maintaining circular aspect ratio). ## §3.6 Gauge Faceting Gauge charts are a special case: each gauge is a single-item radial chart. Multiple gauges are laid out in a facet-style grid computed by the template (since the assembler's facet path doesn't apply to axis-less charts). All gauge element sizes scale **continuously** with the computed radius: $$\text{elementSize} = \text{baseline} \times (r / r_{\text{ref}})$$ where $r_{\text{ref}} = 100$ px. Each element is clamped to a minimum. This avoids threshold artifacts. ## §3.7 Parameter Table | Chart type | $N_{\text{eff}}$ source | $\ell_{\text{arc}}$ | $\alpha$ | $\beta$ | $m$ | |---|---|---|---|---|---| | **Pie** | `total / min(values)` | 45 | 0.5 | 2.0 | 50 | | **Rose** | N categories | 45 | 0.5 | 2.0 | 20 | | **Sunburst** | outer-ring `total / min` | 45 | 0.5 | 2.0 | 20 | | **Radar** | N spokes | 45 | 0.5 | 2.0 | 20 | | **Gauge** | N dials (facet grid) | — | — | 2.0 | 20 | ## §3.8 Summary ``` Given: N_eff items, minArc ℓ_arc, base canvas W₀×H₀, margin m, elasticity α, maxStretch β, minRadius, maxRadius r₀ = max(minRadius, (min(W₀, H₀) / 2) - m) C₀ = 2π · r₀ p = N_eff · ℓ_arc / C₀ # Effective max stretch on radius (per-dimension cap) s_max = min(maxRadius / r₀, (min(W₀·β, H₀·β) - 2m) / (2·r₀)) if p ≤ 1: r = r₀ else: r = r₀ · min(s_max, p^α) r = clamp(r, minRadius, maxRadius) W = max(W₀, 2r + 2m) H = max(H₀, 2r + 2m) ``` > **Key functions:** `computeCircumferencePressure()`, `computeEffectiveBarCount()` in `core/decisions.ts`. --- # §4 Area Layout (2D Pressure Model) ## §4.1 Problem Area-filling charts (treemap) divide a 2D canvas into rectangles whose area encodes value. Unlike Cartesian charts, the fundamental resource is **total area**. When many items crowd the space, every item ends up too small to display labels or be visually distinguishable. **Why other models don't apply:** - **§1 / §2:** Reason about 1D axes independently. Treemap items don't have stable positions on either axis — the squarify algorithm decides the partition on-the-fly. - **§3:** Reasons about a closed loop. Treemap items occupy 2D area, not angular sectors. ## §4.2 Parameters | Symbol | Meaning | Default | |---|---|---| | $W_0, H_0$ | Base canvas dimensions | from context | | $A_0$ | Base canvas area: $W_0 \times H_0$ | derived | | $N_{\text{eff}}$ | Effective item count (§4.3) | data-dependent | | $\ell_{\min}$ | Minimum width per effective item (px) | 30 | | $\alpha$ | Elasticity exponent | 0.5 | | $\beta$ | Per-dimension max stretch | 2.0 | | $b$ | X-bias factor | 1.5 | > **Implementation note:** The area model is currently implemented **inline** in `echarts/templates/treemap.ts` (lines ~91–115), not as a shared core function in `decisions.ts`. The formulas and defaults match this document exactly. ## §4.3 Effective Item Count Uses the same formula as §3.3: $$N_{\text{eff}} = \min\!\left(100,\; \frac{\sum v_i}{\min(v_i)}\right)$$ This captures the worst case: how many of the smallest item would fill the entire space. > **Implementation:** Calls `computeEffectiveBarCount()` from `core/decisions.ts`. ## §4.4 Pressure and Biased Split ### Step 1: 1D Pressure Imagine all treemap items laid out as vertical bars along X. Pressure is measured against the base width: $$p = \frac{N_{\text{eff}} \cdot \ell_{\min}}{W_0}$$ ### Step 2: Area stretch $$A_{\text{stretch}} = \begin{cases} 1 & \text{if } p \leq 1 \\ \min(\beta^2,\; p^{\alpha}) & \text{if } p > 1 \end{cases}$$ The cap is $\beta^2$ because $A = W \times H$ and each dimension is capped at $\beta$. ### Step 3: Biased split to X and Y X gets more stretch because most reading happens left-to-right and labels are horizontal. Given X-bias factor $b$: $$s_x = \min(\beta,\; A_{\text{stretch}}^{\,b/(b+1)})$$ $$s_y = \min(\beta,\; A_{\text{stretch}}^{\,1/(b+1)})$$ **Invariant:** $s_x \times s_y = A_{\text{stretch}}$. | $b$ | X share | Y share | Effect | |---|---|---|---| | 1.0 | 50% | 50% | Uniform: $s_x = s_y = \sqrt{A_{\text{stretch}}}$ | | 1.5 (default) | 60% | 40% | X takes more | | 2.0 | 67% | 33% | Strongly X-biased | ### Step 4: Canvas sizing $$W = \lfloor W_0 \cdot s_x \rceil, \quad H = \lfloor H_0 \cdot s_y \rceil$$ ## §4.5 Worked Examples Base canvas 400×300, $\ell_{\min} = 30$, $\alpha = 0.5$, $\beta = 2.0$, $b = 1.5$: | Scenario | $N_{\text{eff}}$ | Pressure | $A_{\text{stretch}}$ | $s_x$ | $s_y$ | W | H | |---|---|---|---|---|---|---|---| | 5 equal items | 5 | 0.38 | 1.0 | 1.0 | 1.0 | 400 | 300 | | 10 equal items | 10 | 0.75 | 1.0 | 1.0 | 1.0 | 400 | 300 | | 20 equal items | 20 | 1.50 | 1.22 | 1.13 | 1.08 | 452 | 324 | | 50 equal items | 50 | 3.75 | 1.94 | 1.52 | 1.27 | 608 | 381 | | Skewed (1 large + 20 tiny) | 100 | 7.50 | 2.74 | 1.87 | 1.46 | 748 | 438 | **Why biased split?** Treemap squarify algorithms produce nearly square cells when the canvas is square. Giving X more stretch prioritizes horizontal readability: labels inside treemap cells are horizontal, so extra width is more valuable for label fitting. ## §4.6 Summary | Symbol | Meaning | Default | |---|---|---| | $N_{\text{eff}}$ | Effective item count ($\sum v / \min v$, cap 100) | data-dependent | | $\ell_{\min}$ | Minimum width per effective item (px) | 30 | | $\alpha$ | Elasticity exponent | 0.5 | | $\beta$ | Per-dimension max stretch | 2.0 | | $b$ | X-bias factor (1 = uniform, >1 = X takes more) | 1.5 | ``` Given: leaf values, base canvas W₀×H₀, minBarPx, elasticity α, maxStretch β, xBias b N_eff = min(100, sum(values) / min(values)) p = N_eff · minBarPx / W₀ if p ≤ 1: A_stretch = 1 else: A_stretch = min(β², p^α) s_x = min(β, A_stretch^(b/(b+1))) s_y = min(β, A_stretch^(1/(b+1))) W = round(W₀ · s_x) H = round(H₀ · s_y) ``` > **Key function:** Inline in `echarts/templates/treemap.ts`. Uses `computeEffectiveBarCount()` from `core/decisions.ts`. --- # §5 Unified Summary The four models adapt the same core idea — **pressure → elastic stretch → clamped output** — to different geometric contexts: | § | Model | Geometry | Pressure formula | Stretch dimension(s) | Chart types | |---|---|---|---|---|---| | §1 | Elastic Budget | 1D axis | $N \cdot \ell_0 / L_0$ | 1D (axis length) | Bar, Histogram, Heatmap, Boxplot | | §2 | Gas Pressure | 2D point cloud | $\text{uniquePos} \cdot \sigma_{1d} / \text{dim}$ | Per-axis (X, Y independent) | Scatter, Line, Area | | §3 | Circumference | 1D closed loop | $N_{\text{eff}} \cdot \ell_{\text{arc}} / C_0$ | Radius (both W, H equally) | Pie, Rose, Sunburst, Radar, Gauge | | §4 | Area | 2D filled space | $N_{\text{eff}} \cdot \ell_{\min} / W_0$ | Area (biased X/Y split) | Treemap | ### Shared concepts 1. **Pressure = demand / supply.** Items need space; the base canvas provides it. Pressure > 1 means overflow. 2. **Elastic stretch.** $s = \min(\beta,\; p^\alpha)$. The power-law exponent $\alpha$ controls how aggressively the chart grows (0.3 for gas, 0.5 for discrete/radial/area). 3. **Per-dimension cap $\beta$.** No axis grows beyond $\beta \times$ base. For radial/area models this translates to radius or area caps. 4. **Effective item count.** For variable-width items (pie, treemap), $N_{\text{eff}} = \sum v_i / \min(v_i)$ measures worst-case crowding. ### Decision tree ``` Is the chart axis-based? ├── YES: Does it have banded (discrete) axes? │ ├── YES → §1 Elastic Budget Model │ └── NO → §2 Gas Pressure Model └── NO: Is the layout radial (items around a circle)? ├── YES → §3 Circumference Model └── NO → §4 Area Model (2D space-filling) ``` ### Implementation map | Function | File | Model | |---|---|---| | `computeElasticBudget()` | `core/decisions.ts` | §1 | | `computeAxisStep()` | `core/decisions.ts` | §1 | | `computeGasPressure()` | `core/decisions.ts` | §2 | | `computeCircumferencePressure()` | `core/decisions.ts` | §3 | | `computeEffectiveBarCount()` | `core/decisions.ts` | §3, §4 | | `computeLayout()` | `core/compute-layout.ts` | §1, §2 orchestration | | `computeFacetGrid()` | `core/compute-layout.ts` | §1.9 faceting | | `computeChannelBudgets()` | `core/compute-layout.ts` | §1.9 overflow budgets | | Area pressure (inline) | `echarts/templates/treemap.ts` | §4 | ================================================ FILE: src/lib/agents-chart/docs/test_plan.md ================================================ # Chart Engine Test Plan ## Overview Test data lives in `test-data/` as fixture generators (not executable test suites). Each file exports generator functions that produce `TestCase[]` arrays. The gallery UI (`ChartGallery.tsx`) uses `TEST_GENERATORS` and `GALLERY_SECTIONS` from `test-data/index.ts` to render all tests interactively. **20 test-data files**, **~11,100 lines**, **53 named test generators**. ### Test categories | Category | Files | Description | |----------|-------|-------------| | **VL chart matrices** | scatter-tests, line-tests, bar-tests, area-tests | Matrix-driven tests for core VL chart types | | **Distribution charts** | distribution-tests | Histogram, Boxplot, Density, Strip Plot | | **Specialized charts** | specialized-tests | Pie, Heatmap, Lollipop, Candlestick, Waterfall, Ranged Dot, Bump, Radar, Pyramid, Rose, Custom | | **Semantic context** | semantic-tests | 39 tests validating semantic type → ChannelSemantics resolution | | **ECharts backend** | echarts-tests | All ECharts chart types (reuses VL inputs + ECharts-only types) | | **Chart.js backend** | chartjs-tests | Chart.js chart types | | **GoFish backend** | gofish-tests | GoFish imperative rendering | | **Facets** | facet-tests | Column, row, col+row, wrap, clip, overflow faceting | | **Stress/sizing** | stress-tests, gas-pressure-tests, line-area-stretch-tests, discrete-axis-tests | Overflow, elasticity, pressure model, discrete axis sizing | | **Temporal** | date-tests | Year, Month, YearMonth, Decade, DateTime, Hours parsing/formatting | | **Line/area variants** | line-area-tests | Dotted Line, Bump Chart | --- ## Scatter Plot A scatter plot places marks (points/bubbles) in a 2D space. The core axes are continuous (quantitative), but one or both axes can be discrete (nominal), which changes how the engine computes layout, step sizing, and overflow. Temporal axes are omitted from scatter tests because T behaves identically to Q in scatter layout — no special handling. Temporal is still tested as a color channel (`color: 'T'`). Default test canvas: 300 × 300 px. ### Matrix-driven approach Tests are generated from a **declarative matrix** (`SCATTER_MATRIX` in `scatter-tests.ts`). Each row describes one test via its axis types, optional third channels, cardinality, and special flags. A generator function converts each matrix entry into a full `TestCase`. ### Matrix dimensions | Dimension | Values | Notes | |-----------|--------|-------| | **x axis type** | Q, N | Quantitative, Nominal | | **y axis type** | Q, N | Same | | **color channel** | —, Q, T, N | Optional 3rd encoding | | **size channel** | —, Q, N | Optional 4th encoding | | **n (density)** | 10–500 | Or 0 for N×N grid mode | | **cardinality** | xCard, yCard, colorCard, sizeCard | Cardinality of nominal dims | | **flags** | hugeRange | Special data distributions | ### Full test matrix (25 tests) #### Q × Q — 15 tests | # | color | size | n | flags | what it tests | |---|-------|------|---|-------|---------------| | 1 | — | — | 20 | | Baseline scatter | | 2 | N(3) | — | 20 | | Nominal color groups | | 3 | Q | — | 20 | | Continuous color gradient | | 4 | T | — | 30 | | Temporal color gradient | | 5 | — | Q | 50 | | Bubble chart | | 6 | — | N(4) | 20 | | Ordinal size — 4 ranked levels | | 7 | N(3) | Q | 15 | | Gapminder-style | | 8 | Q | Q | 30 | | Dual continuous (4D) | | 9 | N(20) | Q | 20 | hugeRange | Size 1K–1B, sqrt scale | | 10 | — | — | 100 | | Moderate density | | 11 | — | — | 500 | | High density | | 12 | N(20) | — | 200 | | Dense, many groups | | 13 | N(50) | — | 100 | | Legend overflow | | 14 | — | Q | 10 | | Sparse bubbles | | 15 | — | Q | 200 | | Dense bubbles | #### N × Q — 4 tests | # | xCard | color | size | n | what it tests | |---|-------|-------|------|---|---------------| | 1 | 5 | Q | — | 25 | Strip + continuous color | | 2 | 5 | — | Q | 25 | Bubble strip | | 3 | 2 | — | — | 30 | Binary category strip (edge) | | 4 | 60 | — | — | 60 | 60 cats — overflow | #### Q × N — 3 tests (mirrors N×Q with flipped orientation) | # | yCard | color | size | n | what it tests | |---|-------|-------|------|---|---------------| | 1 | 5 | Q | — | 25 | Horizontal strip + continuous color | | 2 | 5 | — | Q | 25 | Horizontal bubble strip | | 3 | 60 | — | — | 60 | Horizontal 60-cat overflow | #### N × N — 3 tests | # | xCard | yCard | color | size | what it tests | |---|-------|-------|-------|------|---------------| | 1 | 5 | 6 | — | Q | Bubble grid | | 2 | 5 | 4 | Q | — | Heatmap-like grid | | 3 | 15 | 12 | — | Q | Large grid — overflow | ### Coverage summary Axis combos: Q×Q, N×Q, Q×N, N×N. Third-channel variants (color and size, typed Q/T/N) crossed with Q×Q. Density from 10 to 500. Edge cases: binary categories, legend overflow, huge value ranges. ### How to add a test Add one row to `SCATTER_MATRIX` in `scatter-tests.ts`: ```typescript { x: 'N', y: 'Q', n: 40, xCard: 8, color: 'Q', desc: 'Strip + continuous color, moderate density' }, ``` The generator handles field naming, data synthesis, metadata, tags, and title automatically. --- ## Line Chart A line chart connects data points with lines. Lines imply sequential progression, so axes use T (temporal), O (ordinal), or Q (quantitative) — never purely nominal. N (nominal) is used only for color groups. Channels: `x, y, color, opacity, column, row` ### Matrix-driven approach Tests are generated from `LINE_MATRIX` in `line-tests.ts`. Each row specifies axis types, optional color channel, point count, and flags like `sparse` (20% dropout). ### Full test matrix (16 tests) #### T × Q — 6 tests (core time series) | # | color | n | flags | what it tests | |---|-------|---|-------|---------------| | 1 | — | 30 | | Simple time series | | 2 | N(4) | 200 | | 4 series × 50 dates | | 3 | N(8) | 800 | | 8 series crowded | | 4 | N(20) | 4000 | stress | 20 series spaghetti | | 5 | N(3) | 180 | sparse | 3 series, ~20% missing | | 6 | Q | 30 | | Continuous color gradient | #### O × Q — 4 tests (ordinal x) | # | xCard | color | n | what it tests | |---|-------|-------|---|---------------| | 7 | 5 | — | 5 | Ordinal line | | 8 | 12 | N(4) | 48 | 12 ordinal × 4 series | | 9 | 30 | — | 30 | Label overflow | | 10 | 5 | Q | 5 | Ordinal + gradient | #### Q × Q — 3 tests | # | color | n | what it tests | |---|-------|---|---------------| | 11 | — | 30 | Quantitative x line | | 12 | N(3) | 150 | 3 parametric curves | | 13 | — | 200 | Dense single curve | #### Q × O — 3 tests (mirror) | # | yCard | color | n | what it tests | |---|-------|-------|---|---------------| | 14 | 5 | — | 5 | Horizontal ordinal | | 15 | 12 | N(4) | 48 | Horizontal 12 ordinal × 4 | | 16 | 30 | — | 30 | Horizontal 30 ordinal overflow | #### Excluded combos - **T×T, Q×T** — date-pair data (start vs end date) doesn't suit line charts. Each row is an independent event, not a sequential series; lines connect points in data order producing random zig-zags. Better served by scatter or dumbbell charts. - **O×O** — ordinal×ordinal lines are degenerate. - **N×N, T×N, N×T** — purely nominal axes don't suit line charts. Lines imply sequence/progression; connecting unordered categories is misleading. ### Coverage summary Axis combos: T×Q, O×Q, Q×Q, Q×O. Color variants (N, Q) crossed with primary combos. Density from 5 to 4000 (stress). Sparse dropout tests irregular gaps. Total **16 tests**. --- ## Bar Chart / Stacked Bar Chart / Grouped Bar Chart Bar charts encode values as rectangular bars. Three variants share a common matrix format in `bar-tests.ts`: - **Bar Chart**: `x, y, color, opacity` — basic bars with optional color - **Stacked Bar Chart**: `x, y, color` — bars stacked by color dimension - **Grouped Bar Chart**: `x, y, group` — bars side-by-side by group dimension ### Matrix-driven approach Three matrices (`BAR_MATRIX`, `STACKED_BAR_MATRIX`, `GROUPED_BAR_MATRIX`) share one generator function `barMatrixToTestCase`. The third channel key is `'color'` for bar/stacked and `'group'` for grouped. ### Bar Chart matrix (19 tests) #### N × Q — 6 tests (classic vertical) | # | xCard | color | n | what it tests | |---|-------|-------|---|---------------| | 1 | 5 | — | 5 | Basic 5 bars | | 2 | 20 | — | 20 | Label rotation | | 3 | 30 | — | 30 | Thin bar handling | | 4 | 100 | — | 100 | Discrete cutoff | | 5 | 5 | N(3) | 15 | 5 cats × 3 colors | | 6 | 5 | N(20) | 100 | Color saturation | #### Q × N — 3 tests (horizontal) | # | yCard | color | n | what it tests | |---|-------|-------|---|---------------| | 7 | 10 | — | 10 | Horizontal 10 bars | | 8 | 100 | — | 100 | Horizontal cutoff | | 9 | 10 | N(3) | 30 | Horizontal + 3 colors | #### T × Q — 3 tests (temporal) | # | color | n | what it tests | |---|-------|---|---------------| | 10 | — | 24 | Temporal bars | | 11 | — | 100 | 100 dates — dynamic sizing | | 12 | N(3) | 72 | Temporal + 3 colors | #### Q × T — 2 tests (horizontal temporal) | # | color | n | what it tests | |---|-------|---|---------------| | 13 | — | 18 | Horizontal temporal | | 14 | N(3) | 54 | Horizontal temporal + color | #### Q × Q — 2 tests (continuous banded) | # | n | what it tests | |---|---|---------------| | 15 | 20 | Both quant — dynamic resizing | | 16 | 30 | Equally spaced 1..30 | #### Edge combos — 3 tests | # | x | y | n | what it tests | |---|---|---|---|---------------| | 17 | N | N | grid | Cat × cat (degenerate) | | 18 | T | T | 20 | Date × date (degenerate) | | 19 | T | N | 25 | Temporal × categorical | ### Stacked Bar Chart matrix (12 tests) | # | x | y | color | n | what it tests | |---|---|---|-------|---|---------------| | 1 | N | Q | N(3) | 12 | Basic stack 4×3 | | 2 | N | Q | N(5) | 75 | Large 15×5 | | 3 | N | Q | N(3) | 240 | Very large 80×3 (cutoff) | | 4 | N | Q | Q(4) | 24 | Numeric color (1–4) | | 5 | N | Q | Q(30) | 150 | Numeric color (1–30) | | 6 | T | Q | N(3) | 30 | Temporal stack | | 7 | T | Q | N(4) | 80 | 20 dates × 4 | | 8 | Q | Q | N(3) | 30 | Both quant stacked | | 9 | Q | N | N(3) | 24 | Horizontal stack | | 10 | Q | T | N(3) | 45 | Horizontal temporal stack | | 11 | N | N | N(3) | grid | Cat×cat stacked (edge) | | 12 | T | T | N(3) | 30 | Date×date stacked (edge) | ### Grouped Bar Chart matrix (12 tests) | # | x | y | group | n | what it tests | |---|---|---|-------|---|---------------| | 1 | N | Q | N(3) | 12 | Basic grouped 4×3 | | 2 | N | Q | — | 8 | No group — fallback | | 3 | N | Q | N(3) | 270 | Very large 90×3 (cutoff) | | 4 | N | Q | Q(5) | 30 | Numeric group (1–5) | | 5 | T | Q | N(3) | 36 | Temporal grouped | | 6 | Q | Q | N(4) | 20 | Both quant + group | | 7 | Q | N | N(4) | 24 | Horizontal grouped | | 8 | Q | T | N(3) | 30 | Horizontal temporal grouped | | 9 | N | Q | Q(50) | 400 | Numeric group (1–50) | | 10 | N | Q | Q | 50 | Continuous float on group | | 11 | N | N | N(3) | grid | Cat×cat grouped (edge) | | 12 | T | T | N(3) | 30 | Date×date grouped (edge) | ### Coverage summary All three bar variants cover common xy-type combinations. Bar Chart has 19 tests, Stacked Bar has 12, Grouped Bar has 12 — total **43 bar tests**. Covers horizontal/vertical orientation, discrete cutoff, numeric/continuous color, edge combos. ## Area Chart & Streamgraph **File:** `area-tests.ts` **Approach:** Matrix-driven — `AREA_MATRIX` (17 entries) + `STREAMGRAPH_MATRIX` (6 entries). **Shared generator:** `areaMatrixToTestCase(entry, chartType, rand)` — same infrastructure for both chart types. **Data characteristic:** Uses `genAreaTrend()` with upward drift (natural for cumulative / stacked-area metrics). Area charts use O (ordinal) for categorical axes (like line charts) — area fills imply continuity. N (nominal) is used only for color groups. Purely nominal axis combos are excluded. ### Area Chart matrix (17 tests) #### T × Q — 7 tests (core stacked / layered area) | # | color | n | flags | what it tests | |---|-------|---|-------|---------------| | 1 | — | 30 | | Simple time-series area | | 2 | N(4) | 96 | | 4 stacked series | | 3 | N(8) | 480 | | 8 series large stacked | | 4 | N(15) | 1800 | stress | 15 series stress | | 5 | N(3) | 120 | | 3 layered/overlapping | | 6 | N(3) | 180 | sparse | 3 series, ~20% missing | | 7 | Q | 30 | | Continuous color gradient | #### O × Q — 4 tests (ordinal x) | # | xCard | color | n | what it tests | |---|-------|-------|---|---------------| | 8 | 5 | — | 5 | Ordinal area 5 cats | | 9 | 12 | N(4) | 48 | 12 ordinal × 4 stacked | | 10 | 30 | — | 30 | 30 ordinal overflow | | 11 | 5 | Q | 5 | Ordinal + continuous color | #### Q × O — 3 tests (mirror) | # | yCard | color | n | what it tests | |---|-------|-------|---|---------------| | 12 | 5 | — | 5 | Horizontal ordinal 5 cats | | 13 | 12 | N(4) | 48 | Horizontal 12 ordinal × 4 | | 14 | 30 | — | 30 | Horizontal 30 ordinal overflow | #### Q × Q — 3 tests | # | color | n | what it tests | |---|-------|---|---------------| | 15 | — | 30 | Quantitative x area | | 16 | N(3) | 150 | 3 stacked curves | | 17 | — | 200 | Dense single-series | #### Excluded combos - **T×T, Q×T** — date-pair data doesn't suit area charts. Area fills imply sequential progression; T×T/Q×T lack monotonic relationships. - **N×N, T×N, N×T** — purely nominal axes don't suit area charts. Area fills imply continuity/progression; nominal axes lack this. ### Streamgraph matrix (6 tests) | # | x | y | color | n | what it tests | |---|---|---|-------|---|---------------| | 1 | T | Q | N(5) | 200 | 5 genres basic streamgraph | | 2 | T | Q | N(10) | 800 | 10 industries large | | 3 | T | Q | N(20) | 3000 | 20 series stress | | 4 | T | Q | N(5) | 200 | 5 series ~20% sparse | | 5 | O | Q | N(5) | 60 | Ordinal streamgraph | | 6 | Q | Q | N(3) | 150 | Quant-x streamgraph | ### Coverage summary Area Chart covers T×Q, O×Q, Q×O, Q×Q axis combos (17 tests). Streamgraph adds 6 tests exercising T×Q, O×Q, and Q×Q with multi-series color. Total **23 area/streamgraph tests**. --- ## Grand total (matrix-driven chart tests) | Chart type | Tests | |------------|-------| | Scatter | 25 | | Line | 16 | | Bar | 19 | | Stacked Bar | 12 | | Grouped Bar | 12 | | Area | 17 | | Streamgraph | 6 | | **Matrix subtotal** | **107** | Plus additional non-matrix test generators: - Distribution charts (Histogram, Boxplot, Density, Strip) - Specialized charts (Pie, Heatmap, Lollipop, Candlestick, Waterfall, etc.) - Semantic context (39 tests) - Facets (9 generators) - Stress/sizing (4 generators) - Temporal (7 generators) - ECharts backend (24 generators) - Chart.js backend (11 generators) - GoFish backend (10 generators) **53 named test generators** total across all categories. ================================================ FILE: src/lib/agents-chart/echarts/README.md ================================================ # ECharts Backend Compiles the core semantic layer into [Apache ECharts](https://echarts.apache.org/) option objects. Uses a series-based data model rather than VL's encoding-channel approach. ## Output Format ```jsonc { "xAxis": {...}, "yAxis": {...}, "series": [{ "type": "bar", "data": [...] }], "tooltip": {...}, "legend": {...}, "grid": {...} } ``` An ECharts option object with `_width`/`_height` hints and optional `_warnings`. Consumed by `echarts.init(dom).setOption(spec)`. ## Assembly Pipeline | Phase | Step | Description | |-------|------|-------------| | **0** | `resolveSemantics` | Shared — resolve field types, aggregates, sort orders | | 0a | `declareLayoutMode` | Template layout declaration | | 0b | `convertTemporalData` | Shared — temporal parsing | | 0c | `filterOverflow` | Shared — category truncation | | **1** | `computeLayout` | Shared — step sizes, subplot dimensions | | **2** | Build `resolvedEncodings` → `template.instantiate` → `ecApplyLayoutToSpec` → `ecCombineFacetPanels` → tooltips | Final ECharts option | **Key difference from VL:** No `buildVLEncodings` step. ECharts templates read `channelSemantics` directly and produce series/axis config themselves. ## File Structure ``` echarts/ assemble.ts – assembleECharts(): Phase 2 assembly instantiate-spec.ts – ecApplyLayoutToSpec(), ecApplyTooltips() facet.ts – ecCombineFacetPanels(): synthetic multi-grid faceting index.ts – barrel exports templates/ index.ts – template registry (13 templates, 6 categories) scatter.ts – Scatter Plot bar.ts – Bar, Grouped Bar, Stacked Bar line.ts – Line Chart area.ts – Area, Streamgraph pie.ts – Pie Chart histogram.ts – Histogram heatmap.ts – Heatmap boxplot.ts – Boxplot candlestick.ts – Candlestick Chart radar.ts – Radar Chart rose.ts – Rose Chart streamgraph.ts – Streamgraph (themeRiver) utils.ts – shared utilities ``` ## Template Definitions (13 templates) | Category | Charts | |----------|--------| | Scatter & Point | Scatter Plot, Boxplot | | Bar | Bar Chart, Grouped Bar, Stacked Bar, Histogram, Heatmap | | Line & Area | Line Chart, Area Chart, Streamgraph | | Part-to-Whole | Pie Chart | | Financial | Candlestick Chart | | Polar | Radar Chart, Rose Chart | ## Known Issues & Notes - **Custom faceting module** (`facet.ts`, 252 lines): ECharts has no native faceting, so this module synthesizes multi-grid layouts with `grid[]`, `xAxis[]`, `yAxis[]`, `series[]` (indexed), and `graphic[]` for header labels. Only axis-based charts support faceting — pie, radar, and themeRiver do not. - Bar sizing uses explicit `barWidth`, `barCategoryGap`, `barGap` pixel values rather than VL's declarative `step` sizing. - Zero-baseline uses `axis.min` / `scale: true` instead of VL's `scale.zero`. - Axis-less charts (pie, radar) are detected separately in `instantiate-spec.ts` — they skip axis/grid config entirely. - ECharts Streamgraph uses `themeRiver` series type, which has different data shape requirements. - The `ecCombineFacetPanels` step is unique to ECharts — it converts separate facet panel specs into a single multi-grid option. ================================================ FILE: src/lib/agents-chart/echarts/assemble.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts chart assembly — Two-Stage Pipeline Coordinator. * * Reuses the **same core analysis pipeline** as Vega-Lite: * Phase 0: resolveChannelSemantics → ChannelSemantics * Step 0a: declareLayoutMode → LayoutDeclaration * Step 0b: convertTemporalData → converted data * Step 0c: filterOverflow → filtered data, nominalCounts * Phase 1: computeLayout → LayoutResult * * Then diverges for Phase 2 (ECharts-specific): * template.instantiate → builds ECharts option structure * ecApplyLayoutToSpec → applies layout decisions to option * * ── Backend Translation Responsibilities ──────────────────────────── * The LayoutResult from Phase 1 is target-agnostic. This assembler is * responsible for translating it into ECharts-specific structures: * * subplotWidth / subplotHeight * → ECharts `grid.width` / `grid.height` (the inner plot area). * The assembler adds ECharts-specific margins (CANVAS_BUFFER, * axis label space, legend width) to compute the outer canvas * `_width` / `_height`. * * xStep / yStep / stepPadding * → ECharts `barWidth`, `barCategoryGap`, `barGap` on series. * VL handles this via `width: {step: N}` natively; ECharts has * no such declarative feature, so we compute explicit pixel * values from the layout numbers. * * Facet wrapping * → Column wrapping is decided by filterOverflow (shared with VL). * This assembler reads `overflowResult.facetGrid` for the grid * dimensions, restructures the flat 1×N panels into a wrapped * 2D grid, and passes the result to ecCombineFacetPanels. * The combiner itself has NO wrapping logic — it renders * whatever grid it receives. * * Per-panel vs shared axis titles * → The facet combiner keeps Y-axis titles on the left column only * and renders the X-axis title as a shared centered element. * * Key structural differences from Vega-Lite output: * VL: { mark, encoding, data: {values}, width, height } * EC: { xAxis, yAxis, series: [{type, data}], tooltip, legend, grid } * * This module has NO React, Redux, or UI framework dependencies. */ import { ChartEncoding, ChartTemplateDef, ChartAssemblyInput, AssembleOptions, LayoutDeclaration, InstantiateContext, } from '../core/types'; import type { ChartWarning } from '../core/types'; import { ecGetTemplateDef } from './templates'; import { resolveChannelSemantics, convertTemporalData } from '../core/resolve-semantics'; import { toTypeString, type SemanticAnnotation } from '../core/field-semantics'; import { filterOverflow } from '../core/filter-overflow'; import { computeLayout, computeChannelBudgets } from '../core/compute-layout'; import { ecApplyLayoutToSpec, ecApplyTooltips } from './instantiate-spec'; import { ecCombineFacetPanels } from './facet'; import { DEFAULT_COLORS } from './templates/utils'; import { inferVisCategory, computeZeroDecision } from '../core/semantic-types'; // --------------------------------------------------------------------------- // Public API // --------------------------------------------------------------------------- /** * Assemble an ECharts option object. * * ```ts * const option = assembleECharts({ * data: { values: myRows }, * semantic_types: { weight: 'Quantity' }, * chart_spec: { chartType: 'Bar Chart', encodings: { x: { field: 'category' }, y: { field: 'value' } } }, * options: { addTooltips: true }, * }); * ``` * * @returns An ECharts option object with optional `_warnings` and `_width`/`_height` hints */ export function assembleECharts(input: ChartAssemblyInput): any { const chartType = input.chart_spec.chartType; const encodings = input.chart_spec.encodings; const data = input.data.values ?? []; const semanticTypes = input.semantic_types ?? {}; const canvasSize = input.chart_spec.canvasSize ?? { width: 400, height: 320 }; const chartProperties = input.chart_spec.chartProperties; const options = input.options ?? {}; const chartTemplate = ecGetTemplateDef(chartType) as ChartTemplateDef; if (!chartTemplate) { throw new Error(`Unknown ECharts chart type: ${chartType}. Use ecAllTemplateDefs to see available types.`); } const warnings: ChartWarning[] = []; // ═══════════════════════════════════════════════════════════════════════ // PHASE 0: Resolve Semantics (shared with VL — completely target-agnostic) // ═══════════════════════════════════════════════════════════════════════ // Extract mark type from template (for zero finalization) // ECharts templates still use VL-style `template.mark` for compatibility const tplMark = chartTemplate.template?.mark; const templateMarkType = typeof tplMark === 'string' ? tplMark : tplMark?.type; // Convert temporal data once — feeds semantic resolution and all downstream stages const convertedData = convertTemporalData(data, semanticTypes); const channelSemantics = resolveChannelSemantics( encodings, data, semanticTypes, convertedData, ); // Finalize zero-baseline (requires template mark knowledge) const effectiveMarkType = templateMarkType || 'point'; for (const [channel, cs] of Object.entries(channelSemantics)) { if ((channel === 'x' || channel === 'y') && cs.type === 'quantitative') { const numericValues = data .map(r => r[cs.field]) .filter((v: any) => v != null && typeof v === 'number' && !isNaN(v)); cs.zero = computeZeroDecision( cs.semanticAnnotation.semanticType, channel, effectiveMarkType, numericValues, ); } } // ═══════════════════════════════════════════════════════════════════════ // STEP 0a: declareLayoutMode (shared hook) // ═══════════════════════════════════════════════════════════════════════ const declaration: LayoutDeclaration = chartTemplate.declareLayoutMode ? chartTemplate.declareLayoutMode(channelSemantics, data, chartProperties) : {}; // Merge paramOverrides into effective options const effectiveOptions: AssembleOptions = { ...options, ...(declaration.paramOverrides || {}), }; // ECharts facet overhead: // Fixed: mLeft (~35-55px width) + mBottom (~22px height). // Gap: GAP + PAD between panels (ref 14px at 400px canvas, // core scales proportionally). if (effectiveOptions.facetFixedPadding == null) { effectiveOptions.facetFixedPadding = { width: 55, height: 22 }; } if (effectiveOptions.facetGap == null) { effectiveOptions.facetGap = 14; // reference at 400px canvas } // Default true so that _encodingTooltip is applied and all charts get encoding-style tooltips const { addTooltips: addTooltipsOpt = true, } = effectiveOptions; // ═══════════════════════════════════════════════════════════════════════ // STEP 0b: filterOverflow (shared) // ═══════════════════════════════════════════════════════════════════════ const allMarkTypes = new Set<string>(); if (templateMarkType) allMarkTypes.add(templateMarkType); // ── Channel budgets (shared, in layout module) ───────────────────── const budgets = computeChannelBudgets( channelSemantics, declaration, convertedData, canvasSize, effectiveOptions, ); const facetGridResult = budgets.facetGrid; const overflowResult = filterOverflow( channelSemantics, declaration, encodings, convertedData, budgets, allMarkTypes, ); let values = overflowResult.filteredData; warnings.push(...overflowResult.warnings); // ═══════════════════════════════════════════════════════════════════════ // PHASE 1: Compute Layout (shared — completely target-agnostic) // ═══════════════════════════════════════════════════════════════════════ const layoutResult = computeLayout( channelSemantics, declaration, values, canvasSize, effectiveOptions, facetGridResult, ); layoutResult.truncations = overflowResult.truncations; // ═══════════════════════════════════════════════════════════════════════ // PHASE 2: Instantiate ECharts Option (EC-specific) // ═══════════════════════════════════════════════════════════════════════ // --- Build resolved encodings (EC analogue of VL buildVLEncodings) --- const resolvedEncodings = buildECEncodings( encodings, channelSemantics, declaration, values, canvasSize, semanticTypes, templateMarkType, chartTemplate, ); // --- Template instantiate --- const instantiateContext: InstantiateContext = { channelSemantics, layout: layoutResult, table: values, resolvedEncodings, encodings, chartProperties, canvasSize, semanticTypes, chartType, assembleOptions: effectiveOptions, }; // --- Detect faceting (column / row channels) --- const colField = channelSemantics.column?.field; const rowField = channelSemantics.row?.field; const hasFacet = !!(colField || rowField); // Multi-grid faceting only works for axis-based charts. // Pie, radar, themeRiver use different positioning. const hasAxes = chartTemplate.channels.includes('x') || chartTemplate.channels.includes('y'); let ecOption: any; if (hasFacet && hasAxes) { // ── Faceted rendering — multi-grid layout ──────────────────────── const maxFacetCols = facetGridResult?.columns ?? 1; const maxFacetRows = facetGridResult?.rows ?? 1; const maxFacetNominalValues = maxFacetCols * maxFacetRows; // Bin quantitative facet channels when unique values exceed grid capacity (VL enc.bin = true) let colValues: string[]; let rowValues: string[]; if (colField && channelSemantics.column?.type === 'quantitative') { const raw = values.map((r: any) => r[colField]).filter((v: any) => v != null && typeof v === 'number' && !isNaN(v)); const uniques = new Set(raw); if (uniques.size > maxFacetNominalValues) { const numBins = Math.min(maxFacetNominalValues, 20); const minVal = Math.min(...raw); const maxVal = Math.max(...raw); const step = (maxVal - minVal) / numBins || 1; const getColBin = (v: number) => Math.min(numBins - 1, Math.floor((v - minVal) / step)); values = values.map((r: any) => { const v = r[colField]; const bin = (v != null && typeof v === 'number' && !isNaN(v)) ? getColBin(v) : 0; return { ...r, _ecColumnBin: bin }; }); colValues = Array.from({ length: numBins }, (_, i) => String(i)); } else { colValues = [...new Set(values.map((r: any) => String(r[colField])))]; } } else { colValues = colField ? [...new Set(values.map((r: any) => String(r[colField])))] : []; } if (rowField && channelSemantics.row?.type === 'quantitative') { const raw = values.map((r: any) => r[rowField]).filter((v: any) => v != null && typeof v === 'number' && !isNaN(v)); const uniques = new Set(raw); if (uniques.size > maxFacetNominalValues) { const numBins = Math.min(maxFacetNominalValues, 20); const minVal = Math.min(...raw); const maxVal = Math.max(...raw); const step = (maxVal - minVal) / numBins || 1; const getRowBin = (v: number) => Math.min(numBins - 1, Math.floor((v - minVal) / step)); values = values.map((r: any) => { const v = r[rowField]; const bin = (v != null && typeof v === 'number' && !isNaN(v)) ? getRowBin(v) : 0; return { ...r, _ecRowBin: bin }; }); rowValues = Array.from({ length: numBins }, (_, i) => String(i)); } else { rowValues = [...new Set(values.map((r: any) => String(r[rowField])))]; } } else { rowValues = rowField ? [...new Set(values.map((r: any) => String(r[rowField])))] : []; } // ── Shared layout (facet-aware, same as VL path) ───────────────── const facetLayout = computeLayout( channelSemantics, declaration, values, canvasSize, effectiveOptions, facetGridResult, ); facetLayout.truncations = overflowResult.truncations; const nRows = rowValues.length || 1; const nCols = colValues.length || 1; // ── Column wrapping ───────────────────────────────────────────── // Use the facet grid decided by computeFacetGrid. const maxColsPerRow = facetGridResult?.columns ?? nCols; const panels: any[][] = []; const colBinned = colField && values.length > 0 && (values[0] as any)._ecColumnBin !== undefined; const rowBinned = rowField && values.length > 0 && (values[0] as any)._ecRowBin !== undefined; for (let ri = 0; ri < nRows; ri++) { const row: any[] = []; for (let ci = 0; ci < nCols; ci++) { const cv = colValues[ci]; const rv = rowValues[ri]; const panelData = values.filter((r: any) => { if (colField) { if (colBinned) { if ((r as any)._ecColumnBin !== ci) return false; } else if (String(r[colField]) !== cv) return false; } if (rowField) { if (rowBinned) { if ((r as any)._ecRowBin !== ri) return false; } else if (String(r[rowField]) !== rv) return false; } return true; }); const panelOption: any = structuredClone(chartTemplate.template); const panelCtx: InstantiateContext = { ...instantiateContext, table: panelData, layout: facetLayout, canvasSize, }; // Let ecApplyLayoutToSpec compute step-based dimensions // naturally (plotWidth = step × itemCount for discrete, // subplotWidth for continuous). _width/_height are NOT // pre-set so the computation runs. chartTemplate.instantiate(panelOption, panelCtx); ecApplyLayoutToSpec(panelOption, panelCtx, []); if (addTooltipsOpt) ecApplyTooltips(panelOption); if (chartTemplate.postProcess) chartTemplate.postProcess(panelOption, panelCtx); // Extract pure plot-area dimensions for facet.ts. // ecApplyLayoutToSpec set: _width = plotArea + margins (with // CANVAS_BUFFER). facet.ts needs only the plot area so it // can set grid.width/height exactly — not derived from // panelW − margins, which would inflate when margins are // scaled down for non-edge panels. const g = panelOption.grid || {}; panelOption._plotWidth = Math.max(20, (panelOption._width || 200) - (g.left || 0) - (g.right || 0)); panelOption._plotHeight = Math.max(20, (panelOption._height || 150) - (g.top || 0) - (g.bottom || 0)); // Attach facet header labels for the combiner if (colField) panelOption._colHeader = cv; if (rowField) panelOption._rowHeader = rv; row.push(panelOption); } panels.push(row); } // ── Wrap column-only facets into a proper 2D grid ──────────────── // The wrapping decision was already computed above (maxColsPerRow). // Restructure the flat 1×N panels into displayCols×wrapRows so // that ecCombineFacetPanels receives a plain grid with no // wrapping logic of its own. let finalPanels = panels; let colHeaderPerRow = false; if (colField && !rowField && maxColsPerRow < nCols) { const displayCols = maxColsPerRow; const wrapRows = Math.ceil(nCols / displayCols); finalPanels = []; for (let wr = 0; wr < wrapRows; wr++) { const wrapRow: any[] = []; for (let vc = 0; vc < displayCols; vc++) { const origCi = wr * displayCols + vc; if (origCi < nCols) { wrapRow.push(panels[0][origCi]); } } if (wrapRow.length > 0) finalPanels.push(wrapRow); } colHeaderPerRow = true; } ecOption = ecCombineFacetPanels(finalPanels, { colField, rowField, colHeaderPerRow, }); } else { // ── Standard single-panel rendering ────────────────────────────── ecOption = structuredClone(chartTemplate.template); chartTemplate.instantiate(ecOption, instantiateContext); // --- Apply layout decisions (EC-specific) --- ecApplyLayoutToSpec(ecOption, instantiateContext, warnings); // --- Tooltips --- if (addTooltipsOpt) { ecApplyTooltips(ecOption); } // --- ECharts-specific post-processing --- // Template-specific post-processing (e.g. scatter symbolSize) if (chartTemplate.postProcess) { chartTemplate.postProcess(ecOption, instantiateContext); } } // ═══════════════════════════════════════════════════════════════════════ // RESULT // ═══════════════════════════════════════════════════════════════════════ // Attach metadata if (warnings.length > 0) { ecOption._warnings = warnings; } // Store data reference (unlike VL which embeds data.values, // ECharts data is embedded directly in series[].data) ecOption._dataLength = values.length; // Clean internal-only props delete ecOption._legendWidth; return ecOption; } // =========================================================================== // buildECEncodings — Translate abstract semantics → EC-resolved encodings // =========================================================================== // Analogous to VegaLite's buildVLEncodings in vegalite/assemble.ts. Output is // passed as context.resolvedEncodings so ecApplyLayoutToSpec and templates // read colorPalette, ordinalSortOrder, sizeRange, etc. from one place. interface ECResolvedChannelEncoding { field?: string; type?: 'quantitative' | 'nominal' | 'ordinal' | 'temporal'; aggregate?: string; colorPalette?: string[]; /** Diverging scale mid value (from Phase 0 colorScheme.domainMid). */ colorDomainMid?: number; ordinalSortOrder?: string[]; /** Explicit sort order when sortBy is a JSON array of values (e.g. custom order). */ sortValues?: string[]; /** When true, discrete axis should preserve data encounter order (no alphabetical sort). */ preserveDataOrder?: boolean; sortOrder?: 'ascending' | 'descending'; sortBy?: string; sizeRange?: [number, number]; /** For radius channel: ECharts can use sqrt scale for area-proportional radius. */ radiusScale?: { type: 'sqrt'; zero: boolean }; /** When true, quantitative x axis should not "nice" the domain (line/area/point). */ scaleNice?: boolean; /** High-cardinality legend: suggest smaller symbol/label (EC legend itemStyle.symbolSize, etc.). */ legendSymbolSize?: number; legendLabelFontSize?: number; groupAxis?: 'x' | 'y'; offsetChannel?: 'xOffset' | 'yOffset'; } const TABLEAU10 = [ '#4e79a7', '#f28e2b', '#e15759', '#76b7b2', '#59a14f', '#edc948', '#b07aa1', '#ff9da7', '#9c755f', '#bab0ac', ]; const TABLEAU20 = [ '#4e79a7', '#a0cbe8', '#f28e2b', '#ffbe7d', '#59a14f', '#8cd17d', '#b6992d', '#f1ce63', '#499894', '#86bcb6', '#e15759', '#ff9d9a', '#79706e', '#bab0ac', '#d37295', '#fabfd2', '#b07aa1', '#d4a6c8', '#9d7660', '#cee0b4', ]; const SET1 = [ '#e41a1c', '#377eb8', '#4daf4a', '#984ea3', '#ff7f00', '#ffff33', '#a65628', '#f781bf', '#999999', ]; const SET2 = [ '#66c2a5', '#fc8d62', '#8da0cb', '#e78ac3', '#a6d854', '#ffd92f', '#e5c494', '#b3b3b3', ]; const CATEGORY10 = [ '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf', ]; const SCHEME_TO_PALETTE: Record<string, string[]> = { tableau10: TABLEAU10, tableau20: TABLEAU20, set1: SET1, set2: SET2, set3: SET2.concat(SET1), category10: CATEGORY10, category20: TABLEAU20, pastel1: SET2, accent: SET1, paired: TABLEAU20, default: DEFAULT_COLORS, }; function schemeToPalette(schemeName: string): string[] { const key = schemeName.toLowerCase().replace(/[- ]/g, ''); for (const [name, palette] of Object.entries(SCHEME_TO_PALETTE)) { if (key === name.toLowerCase()) return palette; } return DEFAULT_COLORS; } /** * Translate Phase 0 channel semantics + declaration overrides into * ECharts-resolved encodings. Mirrors Vega-Lite buildVLEncodings logic * but outputs EC-specific fields (colorPalette, sizeRange, ordinalSortOrder, * etc.) for template.instantiate and ecApplyLayoutToSpec. */ function buildECEncodings( encodings: Record<string, ChartEncoding>, channelSemantics: Record<string, import('../core/types').ChannelSemantics>, declaration: LayoutDeclaration, data: any[], canvasSize: { width: number; height: number }, semanticTypes: Record<string, string | SemanticAnnotation>, templateMarkType: string | undefined, chartTemplate: ChartTemplateDef, ): Record<string, ECResolvedChannelEncoding> { const resolved: Record<string, ECResolvedChannelEncoding> = {}; const encodingsEntries = Object.entries(encodings); for (const [channel, encoding] of encodingsEntries) { const entry: ECResolvedChannelEncoding = {}; const fieldName = encoding.field; const cs = channelSemantics[channel]; // --- Radius: ECharts can use sqrt scale for area-proportional radius (mirror VL) --- if (channel === 'radius') { entry.radiusScale = { type: 'sqrt', zero: true }; } // --- Count aggregate without a field (mirror VL) --- if (!fieldName && encoding.aggregate === 'count') { entry.field = '_count'; entry.type = 'quantitative'; } if (fieldName) { entry.field = fieldName; entry.type = (cs?.type ?? 'nominal') as ECResolvedChannelEncoding['type']; // Explicit type override; column/row forced to nominal when not discrete (mirror VL) if (encoding.type) { entry.type = encoding.type as ECResolvedChannelEncoding['type']; } else if (channel === 'column' || channel === 'row') { if (entry.type !== 'nominal' && entry.type !== 'ordinal') { entry.type = 'nominal'; } } if (encoding.aggregate) { if (encoding.aggregate === 'count') { entry.field = '_count'; entry.type = 'quantitative'; } else { entry.field = `${fieldName}_${encoding.aggregate}`; entry.type = 'quantitative'; } } // Quantitative X axis: no "nice" for line/area/point (mirror VL scale.nice: false) if (entry.type === 'quantitative' && channel === 'x') { if (templateMarkType === 'line' || templateMarkType === 'area' || templateMarkType === 'trail' || templateMarkType === 'point') { entry.scaleNice = false; } } // High-cardinality nominal color/group: suggest smaller legend (mirror VL) if (entry.type === 'nominal' && (channel === 'color' || channel === 'group')) { const actualDomain = [...new Set(data.map((r: any) => r[fieldName]))]; if (actualDomain.length >= 16) { entry.legendSymbolSize = 12; entry.legendLabelFontSize = 8; } } } // --- Size channel: pixel range for ECharts symbolSize (mirror VL size scale logic) --- if (channel === 'size') { const EC_SIZE_MIN_PX = 10; const EC_SIZE_MAX_PX = 50; const plotArea = canvasSize.width * canvasSize.height; const n = Math.max(data.length, 1); const fairShare = plotArea / n; const targetPct = 0.05; const idealDiameterPx = Math.sqrt(fairShare * targetPct); const isQuant = entry.type === 'quantitative' || entry.type === 'temporal'; const maxSize = Math.round(Math.max(EC_SIZE_MIN_PX, Math.min(EC_SIZE_MAX_PX, idealDiameterPx))); const minSize = isQuant ? Math.max(EC_SIZE_MIN_PX, Math.round(maxSize / 3)) : Math.round(maxSize / 4); entry.sizeRange = [Math.max(EC_SIZE_MIN_PX, minSize), Math.max(minSize, maxSize)]; } // --- Sorting (mirror VL: sortBy/sortOrder + custom sortValues + auto ordinal/preserve order) --- if (encoding.sortBy || encoding.sortOrder) { entry.sortOrder = encoding.sortOrder as 'ascending' | 'descending'; entry.sortBy = encoding.sortBy; if (encoding.sortBy) { if (encoding.sortBy === 'x' || encoding.sortBy === 'y' || encoding.sortBy === 'color') { // Stored for templates to apply axis/series order } else { try { if (fieldName) { const fieldSemType = toTypeString(semanticTypes[fieldName]); const fieldVisCat = inferVisCategory(data.map((r: any) => r[fieldName])); let sortedValues = JSON.parse(encoding.sortBy) as any[]; if (fieldVisCat === 'temporal' || fieldSemType === 'Year' || fieldSemType === 'Decade') { sortedValues = sortedValues.map((v: any) => String(v)); } entry.sortValues = (encoding.sortOrder === 'descending') ? [...sortedValues].reverse() : sortedValues; } } catch { // ignore invalid sortBy JSON } } } } else { const isDiscrete = entry.type === 'nominal' || entry.type === 'ordinal'; if (isDiscrete) { if (cs?.ordinalSortOrder?.length) { entry.ordinalSortOrder = cs.ordinalSortOrder; } else { entry.preserveDataOrder = true; } } } // --- Color / group: palette + diverging domainMid (mirror VL) --- if (channel === 'color' || channel === 'group') { const schemeSource = encoding.scheme && encoding.scheme !== 'default' ? encoding.scheme : (fieldName && cs?.colorScheme?.scheme) ? cs.colorScheme.scheme : undefined; if (schemeSource) { entry.colorPalette = schemeToPalette(schemeSource); } if (fieldName && cs?.colorScheme?.type === 'diverging' && cs.colorScheme.domainMid !== undefined) { entry.colorDomainMid = cs.colorScheme.domainMid; } } if (Object.keys(entry).length > 0) { resolved[channel] = entry; } } // --- Declaration overrides (mirror VL) --- if (declaration.resolvedTypes) { for (const [ch, type] of Object.entries(declaration.resolvedTypes)) { if (resolved[ch]) { resolved[ch].type = type as ECResolvedChannelEncoding['type']; } } } // --- Group → color + offset (mirror VL: ensure color exists from group; keep group for EC offset) --- const groupCS = channelSemantics.group; if (groupCS?.field && resolved.group) { const xType = resolved.x?.type; const yType = resolved.y?.type; const isDiscrete = (t: string | undefined) => t === 'nominal' || t === 'ordinal'; const groupAxis = isDiscrete(xType) ? 'x' : isDiscrete(yType) ? 'y' : 'x'; const offsetChannel = groupAxis === 'x' ? 'xOffset' : 'yOffset'; resolved.group.groupAxis = groupAxis; resolved.group.offsetChannel = offsetChannel; if (!resolved.color) { resolved.color = { field: groupCS.field, type: (groupCS.type ?? 'nominal') as ECResolvedChannelEncoding['type'], colorPalette: resolved.group.colorPalette ?? (groupCS.colorScheme?.scheme ? schemeToPalette(groupCS.colorScheme.scheme) : undefined), colorDomainMid: resolved.group.colorDomainMid, ordinalSortOrder: resolved.group.ordinalSortOrder, sortOrder: resolved.group.sortOrder, sortBy: resolved.group.sortBy, sortValues: resolved.group.sortValues, preserveDataOrder: resolved.group.preserveDataOrder, legendSymbolSize: resolved.group.legendSymbolSize, legendLabelFontSize: resolved.group.legendLabelFontSize, }; } else if (!resolved.color.colorPalette && groupCS.colorScheme?.scheme) { resolved.color.colorPalette = schemeToPalette(groupCS.colorScheme.scheme); } } // --- Merge template encoding defaults (mirror VL: template encoding as base) --- const templateEncoding = chartTemplate.template?.encoding as Record<string, any> | undefined; if (templateEncoding && typeof templateEncoding === 'object') { for (const [ch, enc] of Object.entries(templateEncoding)) { if (enc && typeof enc === 'object' && Object.keys(enc).length > 0 && resolved[ch]) { resolved[ch] = { ...enc, ...resolved[ch] }; } } } return resolved; } ================================================ FILE: src/lib/agents-chart/echarts/facet.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Facet Support — Multi-grid layout for column/row channels. * * ECharts has no native faceting like VL's column/row encodings. * This module transforms per-panel single-grid options into one * combined multi-grid option: * * - grid[] one positioned grid per facet panel * - xAxis[] per-grid, labels only on bottom-row panels * - yAxis[] per-grid, labels only on left-column panels * - series[] assigned to grids via xAxisIndex/yAxisIndex * - graphic[] column/row facet header text labels * * Only axis-based charts (bar, scatter, line, area, etc.) are supported. * Pie, radar, and themeRiver charts use different positioning and cannot * be faceted with multi-grid. */ // ============================================================================ // Types // ============================================================================ export interface FacetConfig { colField?: string; rowField?: string; /** When true, each visual row shows its own column-header row. * Set by the assembler when column-only facets are wrapped. */ colHeaderPerRow?: boolean; } // ============================================================================ // Public API // ============================================================================ /** * Combine per-panel single-grid ECharts options into a multi-grid faceted layout. * * The assembler is responsible for wrapping column-only facets into a proper * 2D panels array before calling this function. Each panel may carry * `_colHeader` / `_rowHeader` strings for facet header labels. * * @param panels 2D array `panels[rowIdx][colIdx]` of single-panel ECharts options. * Each panel is a complete option with xAxis, yAxis, grid, series, etc. * @param config Facet field names and layout hints. * @returns Combined ECharts option with multi-grid layout. */ export function ecCombineFacetPanels( panels: any[][], config: FacetConfig, ): any { const nRows = panels.length; const nCols = Math.max(1, ...panels.map(r => r.length)); const ref = panels[0]?.[0]; if (!ref) return {}; // ── Plot-area dimensions (extracted by assembler) ───────────────────── const plotW = ref._plotWidth || ref._width || 200; const plotH = ref._plotHeight || ref._height || 150; // ── Simple spacing constants ───────────────────────────────────────── const GAP = 6; // between panels const COL_HEADER_H = config.colField ? 18 : 0; // facet column headers const ROW_HEADER_W = config.rowField ? 18 : 0; // facet row headers const colHeaderPerRow = config.colHeaderPerRow ?? false; const refX = ref.xAxis || {}; const refY = ref.yAxis || {}; const hasYTitle = !!(refY.name); // Shared axis titles — rendered once as graphic elements const sharedXTitle = refX.name || ''; const sharedYTitle = (config.rowField && hasYTitle) ? (refY.name || '') : ''; const SHARED_X_H = sharedXTitle ? 18 : 0; const SHARED_Y_W = sharedYTitle ? 18 : 0; // ── Uniform cell size ──────────────────────────────────────────────── // ── Facet-specific margins ───────────────────────────────────────── // The assembler's refGrid margins include CANVAS_BUFFER (16px) meant // for standalone charts. In a faceted layout the grids are internal // elements — no buffer needed. Use tighter margins based on content. const mLeft = hasYTitle && !sharedYTitle ? 55 : 35; // y-axis labels (+ title if not shared) const mBottom = 22; // x-axis labels const PAD = 4; // minimal padding for inner panels // Cell widths differ: first column includes y-axis label margin, // inner columns use minimal padding. Row heights differ: only the // bottom row reserves mBottom for x-axis labels. const col0W = mLeft + plotW + PAD; const colInnerW = PAD + plotW + PAD; const rowInnerH = PAD + plotH + PAD; // non-bottom rows: compact const rowBottomH = PAD + plotH + mBottom; // bottom row: x-axis labels // ── Overall dimensions ─────────────────────────────────────────────── const baseLeft = SHARED_Y_W + ROW_HEADER_W; const innerRowBlock = colHeaderPerRow ? COL_HEADER_H + rowInnerH : rowInnerH; const bottomRowBlock = colHeaderPerRow ? COL_HEADER_H + rowBottomH : rowBottomH; const totalW = baseLeft + col0W + (nCols > 1 ? (nCols - 1) * (colInnerW + GAP) : 0); const totalH = (colHeaderPerRow ? 0 : COL_HEADER_H) + (nRows > 1 ? (nRows - 1) * (innerRowBlock + GAP) : 0) + bottomRowBlock + SHARED_X_H; // ── Combined option ────────────────────────────────────────────────── const combined: any = { grid: [], xAxis: [], yAxis: [], series: [], _width: totalW, _height: totalH, }; if (ref.tooltip) combined.tooltip = { ...ref.tooltip }; if (ref.color) combined.color = ref.color; const fontSize = Math.max(8, Math.round(10 * Math.min(1, plotW / 200))); const headerFontSize = Math.max(9, Math.round(11 * Math.min(1, plotW / 200))); // ── Place grids & axes ─────────────────────────────────────────────── const gridMap: number[][] = []; let gridIdx = 0; for (let ri = 0; ri < nRows; ri++) { gridMap[ri] = []; for (let ci = 0; ci < nCols; ci++) { const panel = panels[ri]?.[ci]; if (!panel) { gridMap[ri][ci] = -1; continue; } gridMap[ri][ci] = gridIdx; const isLeft = ci === 0; const isBottom = ri === nRows - 1; // Cell position — row height depends on whether it's the bottom row const cx = baseLeft + (ci === 0 ? 0 : col0W + GAP + (ci - 1) * (colInnerW + GAP)); let cy: number; if (colHeaderPerRow) { const rowOff = ri * (innerRowBlock + GAP); cy = rowOff + COL_HEADER_H; } else { const rowOff = COL_HEADER_H + ri * (innerRowBlock + GAP); cy = rowOff; } // Grid — position the plot area inside the cell. const pLeft = ci === 0 ? mLeft : PAD; combined.grid.push({ left: cx + pLeft, top: cy + PAD, width: plotW, height: plotH, }); // xAxis const srcX = panel.xAxis ? { ...panel.xAxis } : { type: 'category' }; combined.xAxis.push({ ...srcX, gridIndex: gridIdx, name: undefined, nameGap: 0, axisLabel: { ...(srcX.axisLabel || {}), show: isBottom, fontSize }, axisTick: { ...(srcX.axisTick || {}), show: isBottom }, axisLine: { show: true }, }); // yAxis — per-panel name only when row facet is absent const srcY = panel.yAxis ? { ...panel.yAxis } : { type: 'value' }; const showYName = isLeft && !sharedYTitle; combined.yAxis.push({ ...srcY, gridIndex: gridIdx, name: showYName ? srcY.name : undefined, nameGap: showYName ? (srcY.nameGap ?? 4) : 0, axisLabel: { ...(srcY.axisLabel || {}), show: isLeft, fontSize }, axisTick: { ...(srcY.axisTick || {}), show: isLeft }, axisLine: { show: true }, }); // Series if (Array.isArray(panel.series)) { for (const s of panel.series) { combined.series.push({ ...s, xAxisIndex: gridIdx, yAxisIndex: gridIdx }); } } gridIdx++; } } // ── Helpers: grid center from placed grids ─────────────────────────── const gridOf = (ri: number, ci: number) => { const gi = gridMap[ri]?.[ci]; return gi != null && gi >= 0 ? combined.grid[gi] : null; }; const gCX = (g: any) => g.left + g.width / 2; const gCY = (g: any) => g.top + g.height / 2; // ── Facet headers — positioned from actual grids ───────────────────── const graphics: any[] = []; const hStyle = { fontSize: headerFontSize, fontWeight: 'bold' as const, fill: '#555', textAlign: 'center' as const, textVerticalAlign: 'middle' as const }; // Column headers if (config.colField) { const hRows = colHeaderPerRow ? nRows : 1; for (let ri = 0; ri < hRows; ri++) { for (let ci = 0; ci < nCols; ci++) { const p = panels[ri]?.[ci], g = gridOf(ri, ci); if (!p?._colHeader || !g) continue; graphics.push({ type: 'text', left: gCX(g), top: g.top - COL_HEADER_H / 2, style: { ...hStyle, text: String(p._colHeader) }, }); } } } // Row headers if (config.rowField) { for (let ri = 0; ri < nRows; ri++) { const p = panels[ri]?.[0], g = gridOf(ri, 0); if (!p?._rowHeader || !g) continue; graphics.push({ type: 'text', left: SHARED_Y_W + ROW_HEADER_W / 2, top: gCY(g), style: { ...hStyle, text: String(p._rowHeader) }, rotation: Math.PI / 2, }); } } // Shared Y-axis title if (sharedYTitle) { const first = gridOf(0, 0), last = gridOf(nRows - 1, 0); if (first && last) { graphics.push({ type: 'text', left: SHARED_Y_W / 2, top: (gCY(first) + gCY(last)) / 2, style: { text: sharedYTitle, fontSize: headerFontSize, fill: '#333', textAlign: 'center', textVerticalAlign: 'middle' }, rotation: Math.PI / 2, }); } } // Shared X-axis title if (sharedXTitle) { graphics.push({ type: 'text', left: totalW / 2, top: totalH - SHARED_X_H + 4, style: { text: sharedXTitle, fontSize: headerFontSize, fill: '#333', textAlign: 'center' }, }); } if (graphics.length > 0) combined.graphic = graphics; return combined; } ================================================ FILE: src/lib/agents-chart/echarts/index.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * @module agents-chart/echarts * * ECharts backend for agents-chart. * * Compiles the core semantic layer into ECharts option objects. * Contains EC-specific assembly, spec instantiation, and chart templates. * * Architecture contrast with Vega-Lite backend: * VL: encoding-channel-based — { encoding: { x: { field, type }, y: ... } } * EC: series-based — { series: [{ type, data }], xAxis, yAxis } * * Same core pipeline (Phase 0 + Phase 1), different Phase 2 output. */ // EC assembly function export { assembleECharts } from './assemble'; // EC spec instantiation (Phase 2) export { ecApplyLayoutToSpec, ecApplyTooltips } from './instantiate-spec'; // EC template registry export { ecTemplateDefs, ecAllTemplateDefs, ecGetTemplateDef, ecGetTemplateChannels, } from './templates'; // EC recommendation & adaptation export { ecAdaptChart, ecRecommendEncodings } from './recommendation'; ================================================ FILE: src/lib/agents-chart/echarts/instantiate-spec.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ============================================================================= * PHASE 2: INSTANTIATE SPEC — ECharts backend * ============================================================================= * * Translates semantic decisions (Phase 0) and layout dimensions (Phase 1) * into ECharts-specific option properties. * * Key differences from Vega-Lite instantiation: * - VL uses declarative encoding width: {step: N} — EC uses explicit pixel widths * - VL color schemes are strings — EC uses explicit color arrays * - VL handles zero-baseline via scale.zero — EC uses axis.min / scale=true * - VL temporal formatting uses timeUnit — EC uses axisLabel.formatter * - VL label rotation is axis.labelAngle — EC uses axisLabel.rotate * * EC dependency: **Yes — this is where ECharts-specific syntax lives** * ============================================================================= */ import type { ChannelSemantics, LayoutResult, InstantiateContext, ChartWarning, } from '../core/types'; import { computePaddedDomain } from '../core/semantic-types'; import { getVisCategory } from '../core/semantic-types'; import { toTypeString } from '../core/field-semantics'; import { looksLikeDateString, analyzeTemporalField, computeDataVotes, pickBestLevel, levelToFormat, SEMANTIC_LEVEL, } from '../core/resolve-semantics'; /** * Phase 2: Apply layout and semantic decisions to the ECharts option object. * * Handles common ECharts plumbing across all templates: * - Grid/canvas sizing * - Axis label rotation and sizing * - Overflow truncation markers * - Color scheme application * - Temporal format application */ export function ecApplyLayoutToSpec( option: any, context: InstantiateContext, warnings: ChartWarning[], ): void { const { channelSemantics, layout, canvasSize } = context; // ── Axis-less chart types (pie, radar) ──────────────────────────────── // These set their own _width/_height and need no grid/axis processing. const hasAxes = !!(option.xAxis || option.yAxis); // ── Zero-baseline and domain padding (value axes) ─────────────────────── // Unify zero-baseline and domainPadFraction for all axis-based charts // (including bar); VL does this in vlApplyLayoutToSpec. for (const axis of ['x', 'y'] as const) { const axisObj = option[`${axis}Axis`]; if (!axisObj || axisObj.type !== 'value') continue; const cs = channelSemantics[axis]; if (!cs?.zero) continue; const decision = cs.zero; if (axisObj.scale === undefined) { axisObj.scale = !decision.zero; // false = include zero, true = data-fit } if (!decision.zero && decision.domainPadFraction > 0 && cs.field) { const numericValues = context.table .map((r: any) => r[cs.field]) .filter((v: any) => v != null && typeof v === 'number' && !isNaN(v)); const padded = computePaddedDomain(numericValues, decision.domainPadFraction); if (padded) { axisObj.min = padded[0]; axisObj.max = padded[1]; } } } // ── Banded continuous axis domain (e.g. heatmap) ────────────────────── // Half-step padding so edge cells are not clipped. // Only apply to value axes — category/time axes (e.g. bar with temporal // categories) should not receive numeric min/max, otherwise ECharts will // window the category index range and hide all bars. for (const axis of ['x', 'y'] as const) { const bandedCount = axis === 'x' ? layout.xContinuousAsDiscrete : layout.yContinuousAsDiscrete; if (bandedCount <= 1) continue; const axisObj = option[`${axis}Axis`]; if (!axisObj || axisObj.type !== 'value' || axisObj.min != null) continue; const cs = channelSemantics[axis]; if (!cs?.field || (cs.type !== 'quantitative' && cs.type !== 'temporal')) continue; const isTemporal = cs.type === 'temporal'; const numericVals = context.table .map((r: any) => { const raw = r[cs.field]; if (raw == null) return NaN; return isTemporal ? +new Date(raw) : +raw; }) .filter((v: number) => !isNaN(v)); if (numericVals.length <= 1) continue; const minVal = Math.min(...numericVals); const maxVal = Math.max(...numericVals); const dataRange = maxVal - minVal; if (dataRange === 0) continue; const pad = dataRange / (bandedCount - 1) / 2; axisObj.min = minVal - pad; axisObj.max = maxVal + pad; } // ── Axis title positioning ─────────────────────────────────────────── // Ensure axis names are centered (VL default), not at the endpoint. if (option.xAxis) { if (option.xAxis.name) { option.xAxis.nameLocation = option.xAxis.nameLocation || 'middle'; option.xAxis.nameGap = option.xAxis.nameGap || 25; option.xAxis.nameTextStyle = { fontSize: 12, ...(option.xAxis.nameTextStyle || {}) }; } } if (option.yAxis) { if (option.yAxis.name) { option.yAxis.nameLocation = option.yAxis.nameLocation || 'middle'; option.yAxis.nameGap = option.yAxis.nameGap || 45; option.yAxis.nameTextStyle = { fontSize: 12, ...(option.yAxis.nameTextStyle || {}) }; } } // ── singleAxis title styling (themeRiver / streamgraph) ────────────── if (option.singleAxis) { if (option.singleAxis.name) { option.singleAxis.nameLocation = option.singleAxis.nameLocation || 'middle'; option.singleAxis.nameGap = option.singleAxis.nameGap || 25; option.singleAxis.nameTextStyle = { fontSize: 12, ...(option.singleAxis.nameTextStyle || {}) }; } if (!option.singleAxis.axisLabel) option.singleAxis.axisLabel = {}; option.singleAxis.axisLabel.fontSize = option.singleAxis.axisLabel.fontSize || 11; } // ── Legend positioning ──────────────────────────────────────────────── const hasLegend = !!option.legend; const hasVisualMap = !!option.visualMap; // Dual legend: when both a categorical legend and a visualMap (continuous // size/color legend) coexist, move the categorical legend to the bottom // to avoid crowding the right side of the chart. const isDualLegend = hasLegend && hasVisualMap; if (hasLegend) { // If the template already fully positioned the legend (e.g. pie), // skip repositioning — detect by checking if orient was already set. const alreadyPositioned = option.legend.orient && (option.legend.right !== undefined || option.legend.left !== undefined); const legendTitle = option._legendTitle as string | undefined; if (legendTitle != null) delete option._legendTitle; if (!alreadyPositioned) { const rawLegendData = option.legend.data || []; const legendLabels: string[] = rawLegendData.map((d: any) => typeof d === 'string' ? d : (d?.name ?? '')); if (isDualLegend) { const highCardinality = legendLabels.length >= 16; option._legendWidth = 0; // no right-side space needed for the legend option.legend = { ...option.legend, bottom: 0, left: 'center', orient: 'horizontal', textStyle: { fontSize: highCardinality ? 8 : 11, ...(option.legend.textStyle || {}), }, ...(legendLabels.length > 10 ? { type: 'scroll' } : {}), ...(highCardinality ? { itemWidth: 12, itemHeight: 12 } : {}), }; if (legendTitle != null) { const titleGraphic = { type: 'text' as const, bottom: 22, left: 'center', z: 100, style: { text: legendTitle, fontSize: 11, fontWeight: 'bold', fill: '#333', textAlign: 'center', }, }; const existing = option.graphic; option.graphic = Array.isArray(existing) ? [...existing, titleGraphic] : (existing ? [existing, titleGraphic] : [titleGraphic]); } } else { // Single legend: use left positioning so title and legend circles share the same left edge const maxLabelLen = Math.max(...legendLabels.map((l: string) => l.length), 3); const highCardinality = legendLabels.length >= 16; const legendSymbolWidth = highCardinality ? 12 : 14; const legendItemGap = 5; const estimatedTextWidth = Math.min(120, maxLabelLen * 7 + 30); option._legendWidth = legendSymbolWidth + legendItemGap + estimatedTextWidth; const LEGEND_GAP = 12; const CANVAS_BUFFER = 16; const rightMarginPx = option._legendWidth + LEGEND_GAP + CANVAS_BUFFER; const hasYTitle = !!option.yAxis?.name; const gridLeft = (hasYTitle ? 70 : 50) + CANVAS_BUFFER; // Use same effective width as later (plotWidth + grid.left + grid.right) so legend aligns with grid const plotW = layout?.subplotWidth ?? canvasSize?.width ?? 400; const effectiveChartWidth = plotW + gridLeft + rightMarginPx; const legendLeftPx = Math.max(0, effectiveChartWidth - rightMarginPx); option.legend = { ...option.legend, top: legendTitle != null ? 20 : 0, left: legendLeftPx, orient: option.legend.orient || 'vertical', align: 'left', // icon on left, text on right textStyle: { fontSize: highCardinality ? 8 : 11, ...(option.legend.textStyle || {}), }, ...(legendLabels.length > 10 ? { type: 'scroll' } : {}), ...(highCardinality ? { itemWidth: 12, itemHeight: 12 } : {}), }; if (legendTitle != null) { const titleGraphic = { type: 'text' as const, left: legendLeftPx, top: 4, z: 100, style: { text: legendTitle, fontSize: 11, fontWeight: 'bold', fill: '#333', textAlign: 'left', }, }; const existing = option.graphic; option.graphic = Array.isArray(existing) ? [...existing, titleGraphic] : (existing ? [existing, titleGraphic] : [titleGraphic]); } } } else { // Already positioned — estimate width for grid margin const rawData = option.legend.data || []; const legendLabels = rawData.map((d: any) => typeof d === 'string' ? d : (d?.name ?? '')); const maxLabelLen = Math.max(...legendLabels.map((l: string) => l.length), 3); option._legendWidth = Math.min(150, maxLabelLen * 7 + 30); } } // ── Grid sizing ────────────────────────────────────────────────────── // ECharts uses an explicit grid with left/right/top/bottom margins. // Unlike VL where width/height set the *plot area* and the SVG wraps // around it, ECharts' width/height set the *total canvas* and the grid // sits inside. We define explicit grid margins and inflate the canvas // so that the inner plot area matches the intended subplot dimensions. // // Skip grid processing for axis-less charts (pie, radar) which handle // their own sizing in their template instantiate() methods. const hasXTitle = !!option.xAxis?.name; const hasYTitle = !!option.yAxis?.name; // ECharts init() creates an internal div with overflow:hidden at the // exact _width × _height pixel dimensions. Adding a small buffer to // each grid margin keeps the plot area unchanged but gives breathing // room for axis labels / legends / ticks that extend to the canvas edge. const CANVAS_BUFFER = 16; const LEGEND_GAP = 12; // gap between plot and legend/visualMap so they don't overlap const VISUALMAP_GAP = 18; // extra gap between plot and visualMap bar when only visualMap (no legend) const VISUALMAP_RIGHT_OFFSET = 10; // must match scatter (and other templates) visualMap right position const legendWidth = (hasLegend ? (option._legendWidth || 120) : 20); const visualMapWidth = (option._visualMapWidth as number) || 0; if (visualMapWidth) delete option._visualMapWidth; // When dual legend (segment at bottom + size/color bar on right), reserve right space so plot does not overlap visualMap const rightMargin = isDualLegend ? (hasVisualMap ? VISUALMAP_RIGHT_OFFSET + visualMapWidth + VISUALMAP_GAP : 10) : (hasLegend ? legendWidth : (hasVisualMap ? visualMapWidth + VISUALMAP_GAP : 10)) + LEGEND_GAP; const bottomLegendExtra = isDualLegend ? 30 : 0; const gridMargin = { left: (hasYTitle ? 70 : 50) + CANVAS_BUFFER, right: rightMargin + CANVAS_BUFFER, top: 20 + CANVAS_BUFFER, bottom: (hasXTitle ? 45 : 30) + CANVAS_BUFFER + bottomLegendExtra, }; if (hasAxes) { if (!option.grid) option.grid = {}; option.grid.left = gridMargin.left; option.grid.right = gridMargin.right; option.grid.top = gridMargin.top; option.grid.bottom = gridMargin.bottom; } // ── Canvas dimensions ──────────────────────────────────────────────── // For axis-less charts (pie, radar), _width/_height are set by the // template itself. Only compute for axis-based charts. if ((hasAxes || option.singleAxis) && !option._width) { // For discrete axes, VL uses width:{step:N} which auto-sizes the plot. // ECharts has no such feature — we derive the plot size from layout. // // For non-grouped discrete axes: plotWidth = xStep × categoryCount // For grouped discrete axes (stepUnit='group'): the step already // accounts for multiple bars per category, but xNominalCount includes // the group multiplier. Use subplotWidth which is already correct. // For continuous axes: use subplotWidth directly. const xIsDiscrete = layout.xNominalCount > 0 || layout.xContinuousAsDiscrete > 0; const yIsDiscrete = layout.yNominalCount > 0 || layout.yContinuousAsDiscrete > 0; let plotWidth: number; let plotHeight: number; if (xIsDiscrete && layout.xStepUnit !== 'group') { const xItemCount = layout.xNominalCount || layout.xContinuousAsDiscrete || 0; plotWidth = xItemCount > 0 ? layout.xStep * xItemCount : (layout.subplotWidth || canvasSize.width); } else { // Continuous axis or group-stepped axis — subplotWidth is already correct plotWidth = layout.subplotWidth || canvasSize.width; } if (yIsDiscrete && layout.yStepUnit !== 'group') { const yItemCount = layout.yNominalCount || layout.yContinuousAsDiscrete || 0; plotHeight = yItemCount > 0 ? layout.yStep * yItemCount : (layout.subplotHeight || canvasSize.height); } else { plotHeight = layout.subplotHeight || canvasSize.height; } option._width = plotWidth + gridMargin.left + gridMargin.right; option._height = plotHeight + gridMargin.top + gridMargin.bottom; } // ── Bar sizing ─────────────────────────────────────────────────────── // ECharts needs barCategoryGap / barWidth to match the step layout. // VL pads *inside* the step (bandwidth = step × (1 − paddingInner)), // while ECharts uses barCategoryGap as a percentage of the category // slot. For single/stacked bars we use barCategoryGap only — // letting ECharts auto-size bars proportionally to the grid width. // For grouped bars we need explicit barWidth per series. if (option.series && Array.isArray(option.series)) { const barSeries = option.series.filter((s: any) => s.type === 'bar'); if (barSeries.length > 0) { const catAxis = option.xAxis?.type === 'category' ? 'x' : 'y'; const step = catAxis === 'x' ? layout.xStep : layout.yStep; const stepUnit = catAxis === 'x' ? layout.xStepUnit : layout.yStepUnit; const isStacked = barSeries.some((s: any) => s.stack != null); if (step > 0) { const bandPadding = layout.stepPadding; const catGapPct = `${Math.round(bandPadding * 100)}%`; if (!isStacked && (stepUnit === 'group' || barSeries.length > 1)) { // Grouped: each bar gets an equal share of the usable band const usableStep = step * (1 - bandPadding); const barW = Math.max(1, Math.floor(usableStep / barSeries.length)); for (const s of barSeries) { s.barWidth = barW; s.barGap = '0%'; } barSeries[0].barCategoryGap = catGapPct; } else { // Single series or stacked: let ECharts auto-size bars. // barCategoryGap controls the fraction of each slot // reserved for inter-category gap. for (const s of barSeries) { s.barCategoryGap = catGapPct; } } } } } // ── X-axis label sizing ────────────────────────────────────────────── if (option.xAxis && layout.xLabel) { if (!option.xAxis.axisLabel) option.xAxis.axisLabel = {}; // ECharts uses degrees (positive = counter-clockwise). Don't override category axis rotate (template set it for scatter). if (layout.xLabel.labelAngle && layout.xLabel.labelAngle !== 0 && option.xAxis.type !== 'category') { option.xAxis.axisLabel.rotate = -layout.xLabel.labelAngle; // VL convention → EC convention } if (layout.xLabel.fontSize) { option.xAxis.axisLabel.fontSize = layout.xLabel.fontSize; } if (layout.xLabel.labelLimit && layout.xLabel.labelLimit < 100) { const maxLen = layout.xLabel.labelLimit; option.xAxis.axisLabel.formatter = (value: string) => { if (typeof value === 'string' && value.length > maxLen) { return value.substring(0, maxLen) + '…'; } return value; }; } } // ── Y-axis label sizing ────────────────────────────────────────────── if (option.yAxis && layout.yLabel) { if (!option.yAxis.axisLabel) option.yAxis.axisLabel = {}; // Don't override category axis rotate — scatter keeps y labels horizontal (0) to avoid overlap. if (layout.yLabel.labelAngle && layout.yLabel.labelAngle !== 0 && option.yAxis.type !== 'category') { option.yAxis.axisLabel.rotate = -layout.yLabel.labelAngle; } if (layout.yLabel.fontSize) { option.yAxis.axisLabel.fontSize = layout.yLabel.fontSize; } } // ── Ordinal temporal: category axes with date-like labels ────────────── // VL applyOrdinalTemporalFormat: format nominal/ordinal axis as dates when data looks like dates. for (const axis of ['x', 'y'] as const) { const axisObj = option[`${axis}Axis`]; if (!axisObj || axisObj.type !== 'category') continue; const cs = channelSemantics[axis]; if (!cs?.field || (cs.type !== 'nominal' && cs.type !== 'ordinal')) continue; const semanticType = toTypeString(context.semanticTypes[cs.field]); if (getVisCategory(semanticType) !== 'temporal') continue; const fieldVals = context.table.map((r: any) => r[cs.field]).filter((v: any) => v != null); const datelikeCnt = fieldVals.filter((v: any) => typeof v !== 'string' || looksLikeDateString(String(v)) ).length; if (datelikeCnt < fieldVals.length * 0.5) continue; const analysis = analyzeTemporalField(fieldVals); if (!analysis) continue; const votes = computeDataVotes(analysis.same); const semLevel = SEMANTIC_LEVEL[semanticType]; if (semLevel !== undefined) votes[semLevel] += 3; const { level, score } = pickBestLevel(votes); if (score < 5) continue; const fmt = levelToFormat(level, analysis); if (!fmt) continue; if (!axisObj.axisLabel) axisObj.axisLabel = {}; const existingFormatter = axisObj.axisLabel.formatter; axisObj.axisLabel.formatter = (value: string) => { const formatted = formatCategoryTemporal(value, fmt); return typeof existingFormatter === 'function' ? existingFormatter(formatted) : formatted; }; } // ── Temporal format (time / value axes) ────────────────────────────── for (const axis of ['x', 'y'] as const) { const cs = channelSemantics[axis]; if (cs?.temporalFormat && option[`${axis}Axis`]) { const axisObj = option[`${axis}Axis`]; if (axisObj.type === 'time') { if (!axisObj.axisLabel) axisObj.axisLabel = {}; axisObj.axisLabel.formatter = convertTemporalFormat(cs.temporalFormat); } if (axisObj.type === 'value' && cs.type === 'temporal') { if (!axisObj.axisLabel) axisObj.axisLabel = {}; const fmt = cs.temporalFormat; axisObj.axisLabel.formatter = (val: number) => formatTimestamp(val, fmt); } } } // ── Color scheme ───────────────────────────────────────────────────── // Use palette from buildECEncodings when present (VL scheme → EC hex array). const colorPalette = context.resolvedEncodings?.color?.colorPalette ?? context.resolvedEncodings?.group?.colorPalette; if (colorPalette?.length) { option.color = [...colorPalette]; } // ── Overflow truncation markers ────────────────────────────────────── if (layout.truncations && layout.truncations.length > 0) { const axisPlaceholders: Record<string, Set<string>> = { xAxis: new Set(), yAxis: new Set() }; for (const trunc of layout.truncations) { warnings.push({ severity: 'warning', code: 'overflow', message: trunc.message, channel: trunc.channel, field: trunc.field, }); const axisKey = trunc.channel === 'x' ? 'xAxis' : 'yAxis'; if (trunc.channel === 'x' || trunc.channel === 'y') { axisPlaceholders[axisKey].add(trunc.placeholder); if (option[axisKey]?.data && Array.isArray(option[axisKey].data)) { option[axisKey].data.push(trunc.placeholder); } } } // Grey styling for placeholder labels (VL labelColor equivalent) for (const axisKey of ['xAxis', 'yAxis'] as const) { const placeholders = axisPlaceholders[axisKey]; if (placeholders.size === 0 || !option[axisKey]) continue; if (!option[axisKey].axisLabel) option[axisKey].axisLabel = {}; const existingColor = option[axisKey].axisLabel.color; option[axisKey].axisLabel.color = (params: string) => placeholders.has(params) ? '#999999' : (typeof existingColor === 'function' ? existingColor(params) : (existingColor ?? '#000')); } } } /** * Convert a d3-style temporal format string to an ECharts template string. * Only suitable for ECharts 'time' type axes which support {yyyy}, {MM}, etc. * * d3: %Y → 2024, %b → Jan, %d → 01, %H → 14, %M → 30 * EC uses {yyyy}, {MM}, {dd}, {HH}, {mm} in templates */ function convertTemporalFormat(d3Format: string): string { return d3Format .replace(/%Y/g, '{yyyy}') .replace(/%y/g, '{yy}') .replace(/%b/g, '{MMM}') .replace(/%B/g, '{MMMM}') .replace(/%m/g, '{MM}') .replace(/%d/g, '{dd}') .replace(/%H/g, '{HH}') .replace(/%M/g, '{mm}') .replace(/%S/g, '{ss}'); } const MONTH_ABBR = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']; const MONTH_FULL = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']; /** * Format a category-axis label as date when it parses as valid date. * Used for ordinal temporal (nominal/ordinal channel with date-like strings). */ function formatCategoryTemporal(value: string, d3Format: string): string { const d = new Date(value); if (isNaN(d.getTime())) return value; return formatTimestamp(d.getTime(), d3Format); } /** * Format a numeric timestamp using a d3-style format string. * Used for 'value' axes that hold temporal data (timestamps). * Exported for scatter (temporal color visualMap labels). */ export function formatTimestamp(val: number, d3Format: string): string { const d = new Date(val); const pad = (n: number) => n < 10 ? '0' + n : String(n); return d3Format .replace(/%Y/g, String(d.getFullYear())) .replace(/%y/g, String(d.getFullYear()).slice(-2)) .replace(/%B/g, MONTH_FULL[d.getMonth()]) .replace(/%b/g, MONTH_ABBR[d.getMonth()]) .replace(/%m/g, pad(d.getMonth() + 1)) .replace(/%d/g, pad(d.getDate())) .replace(/%H/g, pad(d.getHours())) .replace(/%M/g, pad(d.getMinutes())) .replace(/%S/g, pad(d.getSeconds())); } function fmtNumForTooltip(v: unknown): string { if (v == null) return ''; const n = Number(v); return isNaN(n) ? String(v) : (Number.isInteger(n) ? String(n) : n.toFixed(1)); } /** * Build a single encoding-style tooltip formatter from _encodingTooltip. * Supports item trigger (parts from data/series) and axis trigger (category + series values). */ function buildEncodingTooltipFormatter(option: any): ((params: any) => string) | null { const enc = option._encodingTooltip as any; if (!enc) return null; if (enc.trigger === 'axis' && enc.categoryLabel != null) { const categoryLabel = enc.categoryLabel; const valueLabel = enc.valueLabel ?? 'Value'; return (params: any) => { const list = Array.isArray(params) ? params : [params]; if (list.length === 0) return ''; const p = list[0]; const cat = p.axisValue ?? p.name ?? ''; const parts = [`${categoryLabel}: ${cat}`]; for (const item of list) { const name = item.seriesName ?? valueLabel; const val = item.value != null ? item.value : (Array.isArray(item.data) ? item.data[item.dataIndex] : item.data); parts.push(`${name}: ${fmtNumForTooltip(val)}`); } return parts.join('<br/>'); }; } const parts = enc.parts as Array<{ from: string; index?: number; label: string; format?: string; temporalFormat?: string; categoryNames?: string[] }>; if (!parts || !Array.isArray(parts) || parts.length === 0) return null; return (params: any) => { if (params == null) return ''; const d = Array.isArray(params.data) ? params.data : (params.data != null ? [params.data] : []); const out: string[] = []; for (const p of parts) { let val: unknown; if (p.from === 'series') { val = params.seriesName ?? params.name; } else if (p.from === 'name') { val = params.name; } else if (p.from === 'value') { val = params.value; } else { const idx = p.index ?? 0; val = d[idx]; if (val != null && typeof val === 'object' && 'value' in val) val = (val as any).value; } if (val == null && p.from !== 'series' && p.from !== 'name') continue; let str: string; if (p.format === 'temporal') { str = formatTimestamp(Number(val), p.temporalFormat ?? '%b %d, %Y'); } else if (p.format === 'category' && p.categoryNames) { const i = Number(val); str = Number.isInteger(i) && p.categoryNames[i] != null ? p.categoryNames[i] : String(val ?? ''); } else if (p.format === 'number' || (p.from === 'data' && p.format !== 'category')) { str = fmtNumForTooltip(val); } else { str = String(val ?? ''); } out.push(`${p.label}: ${str}`); } return out.join('<br/>'); }; } /** * Apply tooltips to an ECharts option. * ECharts tooltip is typically configured at the top level. * When option._encodingTooltip is set, a Vega-Lite–style formatter (label: value per encoding) is applied. */ export function ecApplyTooltips(option: any): void { if (!option.tooltip) { option.tooltip = {}; } const encodingFormatter = buildEncodingTooltipFormatter(option); if (encodingFormatter) { delete option._encodingTooltip; option.tooltip.formatter = encodingFormatter; } // Ensure trigger is set if (!option.tooltip.trigger) { const hasScatter = option.series?.some((s: any) => s.type === 'scatter'); const hasPie = option.series?.some((s: any) => s.type === 'pie'); const hasRadar = option.series?.some((s: any) => s.type === 'radar'); const hasCandlestick = option.series?.some((s: any) => s.type === 'candlestick'); const hasThemeRiver = option.series?.some((s: any) => s.type === 'themeRiver'); option.tooltip.trigger = (hasScatter || hasPie || hasRadar || hasThemeRiver) ? 'item' : 'axis'; // Candlestick charts benefit from crosshair pointer if (hasCandlestick && !option.tooltip.axisPointer) { option.tooltip.axisPointer = { type: 'cross' }; } // ThemeRiver default tooltip shows raw template tokens — provide a // custom formatter. ThemeRiver params: { data: [date, value, name], color } if (hasThemeRiver && !option.tooltip.formatter) { option.tooltip.formatter = (params: any) => { if (!params || !params.data) return ''; const [date, value, name] = params.data; const color = params.color || '#333'; const dateStr = date instanceof Date ? date.toLocaleDateString() : String(date); return `<span style="display:inline-block;width:10px;height:10px;border-radius:50%;background:${color};margin-right:6px;"></span>` + `<b>${name}</b><br/>${dateStr}: ${value}`; }; } } } ================================================ FILE: src/lib/agents-chart/echarts/recommendation.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts recommendation & adaptation wrappers. * * Extends core/recommendation.ts with ECharts-only chart types * (Gauge, Funnel, Treemap, Sunburst, Sankey). */ import { adaptChannels, recommendChannels, getRecommendation, type InternalTableView, type RecommendFn, pickQuantitative, pickDiscrete, pickLowCardDiscrete, } from '../core/recommendation'; import { ecGetTemplateChannels } from './templates'; // ── EC-extended recommendation ────────────────────────────────────────── function ecGetRecommendation(chartType: string, tv: InternalTableView): Record<string, string> { const used = new Set<string>(); const rec: Record<string, string> = {}; const assign = (channel: string, fieldName: string | undefined) => { if (fieldName) rec[channel] = fieldName; }; switch (chartType) { case 'Gauge Chart': { const valueField = pickQuantitative(tv, used); if (!valueField) return {}; assign('value', valueField); assign('color', pickLowCardDiscrete(tv, used, 10)); return rec; } case 'Funnel Chart': { const valueField = pickQuantitative(tv, used); const colorField = pickLowCardDiscrete(tv, used, 15); if (!valueField || !colorField) return {}; assign('value', valueField); assign('color', colorField); return rec; } case 'Treemap': case 'Sunburst Chart': { const sizeField = pickQuantitative(tv, used); const colorField = pickLowCardDiscrete(tv, used, 20); if (!sizeField || !colorField) return {}; assign('size', sizeField); assign('color', colorField); return rec; } case 'Sankey Diagram': { const sourceField = pickDiscrete(tv, used); const targetField = pickDiscrete(tv, used); const valueField = pickQuantitative(tv, used); if (!sourceField || !targetField || !valueField) return {}; assign('source', sourceField); assign('target', targetField); assign('value', valueField); return rec; } default: return getRecommendation(chartType, tv); } } // ── Public API ────────────────────────────────────────────────────────── export function ecAdaptChart( sourceType: string, targetType: string, encodings: Record<string, string>, data?: any[], semanticTypes?: Record<string, string>, ): Record<string, string> { const targetChannels = ecGetTemplateChannels(targetType); return adaptChannels(sourceType, targetType, targetChannels, encodings, data, semanticTypes, ecGetRecommendation); } export function ecRecommendEncodings( chartType: string, data: any[], semanticTypes: Record<string, string>, ): Record<string, string> { const rec = recommendChannels(chartType, data, semanticTypes, ecGetRecommendation); const validChannels = ecGetTemplateChannels(chartType); const result: Record<string, string> = {}; for (const [ch, field] of Object.entries(rec)) { if (validChannels.includes(ch)) result[ch] = field; } return result; } ================================================ FILE: src/lib/agents-chart/echarts/templates/area.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Area Chart template (single + multi-series, stacked / layered). * * Contrast with VL: * VL: mark = "area" with encoding; stacking via y.stack property * EC: line series with areaStyle; stacking via series[].stack property */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, groupBy, DEFAULT_COLORS, getCategoryOrder } from './utils'; const isDiscrete = (type: string | undefined) => type === 'nominal' || type === 'ordinal'; export const ecAreaChartDef: ChartTemplateDef = { chart: 'Area Chart', template: { mark: 'area', encoding: {} }, channels: ['x', 'y', 'color', 'opacity', 'column', 'row'], markCognitiveChannel: 'area', declareLayoutMode: () => ({ paramOverrides: { continuousMarkCrossSection: { x: 100, y: 20, seriesCountAxis: 'auto' } }, }), instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const xCS = channelSemantics.x; const yCS = channelSemantics.y; const colorField = channelSemantics.color?.field; if (!xCS?.field || !yCS?.field) return; const xField = xCS.field; const yField = yCS.field; const xIsDiscrete = isDiscrete(xCS.type); const xIsTemporal = xCS.type === 'temporal'; const categories = xIsDiscrete ? extractCategories(table, xField, getCategoryOrder(ctx, 'x')) : undefined; const option: any = { tooltip: { trigger: 'axis' }, xAxis: { type: xIsDiscrete ? 'category' : xIsTemporal ? 'time' : 'value', name: xField, nameLocation: 'middle', nameGap: 30, boundaryGap: xIsDiscrete, ...(categories ? { data: categories } : {}), }, yAxis: { type: 'value', name: yField, nameLocation: 'middle', nameGap: 40, }, series: [], }; option._encodingTooltip = { trigger: 'axis', categoryLabel: xField, valueLabel: yField }; // ECharts: scale=true means "data-fit", scale=false means "include zero" if (channelSemantics.y?.zero) { option.yAxis.scale = !channelSemantics.y.zero.zero; } // Stack / layer mode const stackMode = chartProperties?.stackMode; const stackGroup = stackMode === 'layered' ? undefined : 'total'; // Opacity const opacity = chartProperties?.opacity ?? 0.7; // Interpolation / smooth const interpolate = chartProperties?.interpolate; const smooth = interpolate === 'monotone' || interpolate === 'basis' || interpolate === 'cardinal' || interpolate === 'catmull-rom'; const step = interpolate === 'step' ? 'middle' : interpolate === 'step-before' ? 'start' : interpolate === 'step-after' ? 'end' : undefined; if (colorField) { const groups = groupBy(table, colorField); option.legend = { data: [...groups.keys()] }; let colorIdx = 0; for (const [name, rows] of groups) { const seriesData = xIsDiscrete ? buildCategoryAlignedData(rows, xField, yField, categories!) : rows.map(r => [r[xField], r[yField]]); const series: any = { name, type: 'line', data: seriesData, areaStyle: { opacity }, itemStyle: { color: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length] }, }; if (stackGroup) series.stack = stackGroup; if (smooth) series.smooth = true; if (step) series.step = step; option.series.push(series); colorIdx++; } } else { const seriesData = xIsDiscrete ? categories!.map(cat => { const row = table.find(r => String(r[xField]) === cat); return row ? row[yField] : null; }) : table.map(r => [r[xField], r[yField]]); const series: any = { type: 'line', data: seriesData, areaStyle: { opacity }, }; if (smooth) series.smooth = true; if (step) series.step = step; option.series.push(series); } Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'interpolate', label: 'Curve', type: 'discrete', options: [ { value: undefined, label: 'Default (linear)' }, { value: 'linear', label: 'Linear' }, { value: 'monotone', label: 'Monotone (smooth)' }, { value: 'step', label: 'Step' }, { value: 'step-before', label: 'Step Before' }, { value: 'step-after', label: 'Step After' }, ], } as ChartPropertyDef, { key: 'opacity', label: 'Opacity', type: 'continuous', min: 0.1, max: 1, step: 0.05, defaultValue: 0.7 } as ChartPropertyDef, { key: 'stackMode', label: 'Stack', type: 'discrete', options: [ { value: undefined, label: 'Stacked (default)' }, { value: 'normalize', label: 'Normalize (100%)' }, { value: 'center', label: 'Center' }, { value: 'layered', label: 'Layered (overlap)' }, ], } as ChartPropertyDef, ], }; /** Align series data to category array, returning y values by category position. */ function buildCategoryAlignedData( rows: any[], xField: string, yField: string, categories: string[], ): (number | null)[] { const map = new Map<string, number>(); for (const row of rows) { map.set(String(row[xField]), row[yField]); } return categories.map(cat => map.get(cat) ?? null); } ================================================ FILE: src/lib/agents-chart/echarts/templates/bar.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Bar Chart templates: Bar, Stacked Bar, Grouped Bar. * * Key contrast with Vega-Lite: * VL: encoding channels determine stacking/grouping implicitly * - stacked bar: color channel → auto-stacks * - grouped bar: xOffset/group channel → side-by-side * EC: explicit series[] with stack property for stacking, * and barGap/barCategoryGap for grouped layout */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, groupBy, detectAxes, DEFAULT_COLORS, getCategoryOrder, } from './utils'; import { detectBandedAxisFromSemantics, detectBandedAxisForceDiscrete, } from '../../vegalite/templates/utils'; // --------------------------------------------------------------------------- // Helpers // --------------------------------------------------------------------------- const isDiscrete = (type: string | undefined) => type === 'nominal' || type === 'ordinal'; /** * For a category-axis bar chart, build an array of values aligned to the * category array. Each entry is the sum of values for that category in the * given rows (to handle pre-aggregated or raw data). */ function buildCategoryValues( rows: any[], categoryField: string, valueField: string, categories: string[], ): (number | null)[] { const map = new Map<string, number>(); for (const row of rows) { const cat = String(row[categoryField] ?? ''); const val = row[valueField]; if (val != null && !isNaN(val)) { map.set(cat, (map.get(cat) ?? 0) + Number(val)); } } return categories.map(cat => map.get(cat) ?? null); } /** Count rows per category (when value axis has no numeric field, e.g. temporal). */ function buildCategoryCounts( rows: any[], categoryField: string, categories: string[], ): number[] { const map = new Map<string, number>(); for (const row of rows) { const cat = String(row[categoryField] ?? ''); map.set(cat, (map.get(cat) ?? 0) + 1); } return categories.map(cat => map.get(cat) ?? 0); } /** When both x and y are discrete: count per (category, group). Returns one row per group. */ function buildCategoryGroupCounts( rows: any[], categoryField: string, groupField: string, categories: string[], groups: string[], ): number[][] { return groups.map(group => categories.map(cat => rows.filter(r => String(r[categoryField] ?? '') === cat && String(r[groupField] ?? '') === group).length, ), ); } // ─── Bar Chart ────────────────────────────────────────────────────────────── export const ecBarChartDef: ChartTemplateDef = { chart: 'Bar Chart', template: { mark: 'bar', encoding: {} }, channels: ['x', 'y', 'color', 'opacity', 'column', 'row'], markCognitiveChannel: 'length', declareLayoutMode: (cs, table) => { const result = detectBandedAxisFromSemantics(cs, table, { preferAxis: 'x' }); return { axisFlags: result ? { [result.axis]: { banded: true } } : { x: { banded: true } }, resolvedTypes: result?.resolvedTypes, }; }, instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const { categoryAxis, valueAxis } = detectAxes(channelSemantics); const catField = channelSemantics[categoryAxis]?.field; const valField = channelSemantics[valueAxis]?.field; if (!catField || !valField) return; const catCS = channelSemantics[categoryAxis]; const bothDiscrete = isDiscrete(channelSemantics.x?.type) && isDiscrete(channelSemantics.y?.type); if (bothDiscrete) { // x=Category, y=Group both nominal → heatmap: cell color = count per (Category, Group) const categories = extractCategories(table, catField, getCategoryOrder(ctx, categoryAxis)); const groups = extractCategories(table, valField, getCategoryOrder(ctx, valueAxis)); const countMatrix = buildCategoryGroupCounts(table, catField, valField, categories, groups); const heatData: [number, number, number][] = []; let minVal = Infinity; let maxVal = -Infinity; for (let yi = 0; yi < groups.length; yi++) { for (let xi = 0; xi < categories.length; xi++) { const v = countMatrix[yi][xi]; heatData.push([xi, yi, v]); if (v < minVal) minVal = v; if (v > maxVal) maxVal = v; } } if (minVal === Infinity) minVal = 0; if (maxVal === -Infinity) maxVal = 1; const option: any = { tooltip: { position: 'top', formatter: (params: any) => { const d = params.data; return `${categories[d[0]]}, ${groups[d[1]]}: ${d[2]}`; }, }, xAxis: { type: 'category', data: categories, name: catField, splitArea: { show: true }, axisLabel: { rotate: 90 }, }, yAxis: { type: 'category', data: groups, name: valField, splitArea: { show: true }, }, visualMap: { min: minVal, max: maxVal, calculable: true, orient: 'vertical', right: 10, top: 'center', itemGap: 15, inRange: { color: ['#f0f9ff', '#0ea5e9', '#0369a1'] }, }, _visualMapWidth: 50, series: [{ type: 'heatmap', data: heatData, label: { show: heatData.length <= 100 }, emphasis: { itemStyle: { shadowBlur: 10, shadowColor: 'rgba(0, 0, 0, 0.5)' }, }, }], }; Object.assign(spec, option); delete spec.mark; delete spec.encoding; return; } const valCS = channelSemantics[valueAxis]; let categories = extractCategories(table, catField, getCategoryOrder(ctx, categoryAxis)); let values: (number | null)[]; if (valCS?.type === 'temporal') { // Value axis is date — use count per category (no numeric to sum) values = buildCategoryCounts(table, catField, categories); } else { values = buildCategoryValues(table, catField, valField, categories); } if (catCS?.type === 'temporal') { const pairs: [string, number | null][] = categories.map((c, i) => [c, values[i]]); pairs.sort((a, b) => new Date(a[0]).getTime() - new Date(b[0]).getTime()); categories = pairs.map(p => p[0]); values = pairs.map(p => p[1]); } const isHorizontal = categoryAxis === 'y'; const valueLabel = valCS?.type === 'temporal' ? 'Count' : valField; const option: any = { tooltip: { trigger: 'axis', axisPointer: { type: 'shadow' } }, xAxis: isHorizontal ? { type: 'value', name: valueLabel } : { type: 'category', data: categories, name: catField, // Numeric categories: keep labels horizontal so numbers read left-to-right. axisLabel: { rotate: catCS?.type === 'quantitative' ? 0 : 90 }, axisTick: { show: true, alignWithLabel: true }, axisLine: { show: true }, }, yAxis: isHorizontal ? { type: 'category', data: categories, name: catField } : { type: 'value', name: valueLabel }, series: [{ type: 'bar', data: values, itemStyle: { borderRadius: chartProperties?.cornerRadius ?? 0, }, }], }; option._encodingTooltip = { trigger: 'axis', categoryLabel: catField, valueLabel }; Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'cornerRadius', label: 'Corners', type: 'continuous', min: 0, max: 15, step: 1, defaultValue: 0 }, ] as ChartPropertyDef[], }; // ─── Stacked Bar Chart ────────────────────────────────────────────────────── export const ecStackedBarChartDef: ChartTemplateDef = { chart: 'Stacked Bar Chart', template: { mark: 'bar', encoding: {} }, channels: ['x', 'y', 'color', 'column', 'row'], markCognitiveChannel: 'length', declareLayoutMode: (cs, table) => { const result = detectBandedAxisFromSemantics(cs, table, { preferAxis: 'x' }); return { axisFlags: result ? { [result.axis]: { banded: true } } : { x: { banded: true } }, resolvedTypes: result?.resolvedTypes, paramOverrides: { continuousMarkCrossSection: { x: 20, y: 20, seriesCountAxis: 'auto' } }, }; }, instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const { categoryAxis, valueAxis } = detectAxes(channelSemantics); const colorField = channelSemantics.color?.field; const catField = channelSemantics[categoryAxis]?.field; const valField = channelSemantics[valueAxis]?.field; if (!catField || !valField) return; const catCS = channelSemantics[categoryAxis]; let categories = extractCategories(table, catField, getCategoryOrder(ctx, categoryAxis)); if (catCS?.type === 'temporal') { categories = [...categories].sort((a, b) => new Date(a).getTime() - new Date(b).getTime()); } const isHorizontal = categoryAxis === 'y'; const option: any = { tooltip: { trigger: 'axis', axisPointer: { type: 'shadow' } }, xAxis: isHorizontal ? { type: 'value', name: valField } : { type: 'category', data: categories, name: catField, axisLabel: { rotate: catCS?.type === 'quantitative' ? 0 : 90 }, axisTick: { show: true, alignWithLabel: true }, axisLine: { show: true }, }, yAxis: isHorizontal ? { type: 'category', data: categories, name: catField } : { type: 'value', name: valField }, series: [], }; option._encodingTooltip = { trigger: 'axis', categoryLabel: catField, valueLabel: valField }; // Stack mode from chart properties const stackMode = chartProperties?.stackMode; // In ECharts, stack is a group name; normalize maps to '%' formatting const stackGroup = stackMode === 'layered' ? undefined : 'total'; if (colorField) { const groups = groupBy(table, colorField); option.legend = { data: [...groups.keys()] }; let colorIdx = 0; for (const [name, rows] of groups) { const data = buildCategoryValues(rows, catField, valField, categories); const series: any = { name, type: 'bar', data, itemStyle: { color: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length] }, }; if (stackGroup) { series.stack = stackGroup; } // Normalize: ECharts doesn't have a built-in "normalize" stack, // but we can signal it via a custom label format if (stackMode === 'normalize') { series.stack = 'total'; // Note: true normalize requires computing percentages; // for now we just stack — full normalize would need data transform } option.series.push(series); colorIdx++; } } else { // Single series stacked (no color = just a regular bar) const data = buildCategoryValues(table, catField, valField, categories); option.series.push({ type: 'bar', data, stack: stackGroup }); } Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'stackMode', label: 'Stack', type: 'discrete', options: [ { value: undefined, label: 'Stacked (default)' }, { value: 'normalize', label: 'Normalize (100%)' }, { value: 'layered', label: 'Layered (overlap)' }, ], }, ] as ChartPropertyDef[], }; // ─── Grouped Bar Chart ────────────────────────────────────────────────────── export const ecGroupedBarChartDef: ChartTemplateDef = { chart: 'Grouped Bar Chart', template: { mark: 'bar', encoding: {} }, channels: ['x', 'y', 'group', 'column', 'row'], markCognitiveChannel: 'length', declareLayoutMode: (cs, table) => { const result = detectBandedAxisForceDiscrete(cs, table, { preferAxis: 'x' }); const axis = result?.axis || 'x'; return { axisFlags: { [axis]: { banded: true } }, resolvedTypes: result?.resolvedTypes, }; }, instantiate: (spec, ctx) => { const { channelSemantics, table } = ctx; const { categoryAxis, valueAxis } = detectAxes(channelSemantics); // The "group" channel in the core maps to color in ECharts const groupField = channelSemantics.group?.field || channelSemantics.color?.field; const catField = channelSemantics[categoryAxis]?.field; const valField = channelSemantics[valueAxis]?.field; if (!catField || !valField) return; const catCS = channelSemantics[categoryAxis]; let categories = extractCategories(table, catField, getCategoryOrder(ctx, categoryAxis)); if (catCS?.type === 'temporal') { categories = [...categories].sort((a, b) => new Date(a).getTime() - new Date(b).getTime()); } const isHorizontal = categoryAxis === 'y'; const option: any = { tooltip: { trigger: 'axis', axisPointer: { type: 'shadow' } }, xAxis: isHorizontal ? { type: 'value', name: valField } : { type: 'category', data: categories, name: catField, axisLabel: { rotate: catCS?.type === 'quantitative' ? 0 : 90 }, axisTick: { show: true, alignWithLabel: true }, axisLine: { show: true }, }, yAxis: isHorizontal ? { type: 'category', data: categories, name: catField } : { type: 'value', name: valField }, series: [], }; option._encodingTooltip = { trigger: 'axis', categoryLabel: catField, valueLabel: valField }; if (groupField) { // Each group becomes a separate series — ECharts places them // side-by-side within each category automatically const groups = groupBy(table, groupField); option.legend = { data: [...groups.keys()] }; let colorIdx = 0; for (const [name, rows] of groups) { const data = buildCategoryValues(rows, catField, valField, categories); option.series.push({ name, type: 'bar', data, itemStyle: { color: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length] }, }); colorIdx++; } } else { // No grouping — single series const data = buildCategoryValues(table, catField, valField, categories); option.series.push({ type: 'bar', data }); } Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, }; ================================================ FILE: src/lib/agents-chart/echarts/templates/boxplot.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Boxplot template. * * Contrast with VL: * VL: mark = "boxplot" — VL computes quartiles automatically from raw data * EC: series type = 'boxplot' — we compute quartiles client-side and pass * [min, Q1, median, Q3, max] per category. * Optionally an "outlier" scatter series. */ import { ChartTemplateDef } from '../../core/types'; import { extractCategories, groupBy, DEFAULT_COLORS } from './utils'; import { detectBandedAxisForceDiscrete } from '../../vegalite/templates/utils'; const isDiscrete = (type: string | undefined) => type === 'nominal' || type === 'ordinal'; /** Compute the five-number summary for an array of values. */ function fiveNumberSummary(values: number[]): [number, number, number, number, number] { const sorted = [...values].sort((a, b) => a - b); const n = sorted.length; if (n === 0) return [0, 0, 0, 0, 0]; if (n === 1) return [sorted[0], sorted[0], sorted[0], sorted[0], sorted[0]]; const median = quantile(sorted, 0.5); const q1 = quantile(sorted, 0.25); const q3 = quantile(sorted, 0.75); const iqr = q3 - q1; // Whisker extent: min/max within 1.5×IQR const lowerFence = q1 - 1.5 * iqr; const upperFence = q3 + 1.5 * iqr; const whiskerLow = sorted.find(v => v >= lowerFence) ?? sorted[0]; const whiskerHigh = [...sorted].reverse().find(v => v <= upperFence) ?? sorted[n - 1]; return [whiskerLow, q1, median, q3, whiskerHigh]; } function quantile(sorted: number[], p: number): number { const n = sorted.length; const idx = p * (n - 1); const lo = Math.floor(idx); const hi = Math.ceil(idx); const frac = idx - lo; return sorted[lo] * (1 - frac) + sorted[hi] * frac; } /** Find outliers outside 1.5×IQR fences. */ function findOutliers(values: number[]): number[] { const sorted = [...values].sort((a, b) => a - b); const q1 = quantile(sorted, 0.25); const q3 = quantile(sorted, 0.75); const iqr = q3 - q1; const lo = q1 - 1.5 * iqr; const hi = q3 + 1.5 * iqr; return values.filter(v => v < lo || v > hi); } export const ecBoxplotDef: ChartTemplateDef = { chart: 'Boxplot', template: { mark: 'boxplot', encoding: {} }, channels: ['x', 'y', 'color', 'opacity', 'column', 'row'], markCognitiveChannel: 'position', declareLayoutMode: (cs, table) => { if (!cs.x?.field || !cs.y?.field) return {}; const result = detectBandedAxisForceDiscrete(cs, table, { preferAxis: 'x' }); if (!result) return {}; return { axisFlags: { [result.axis]: { banded: true } }, resolvedTypes: result.resolvedTypes, }; }, instantiate: (spec, ctx) => { const { channelSemantics, table } = ctx; const xCS = channelSemantics.x; const yCS = channelSemantics.y; if (!xCS?.field || !yCS?.field) return; // Determine which axis is categorical and which is quantitative const xIsDiscrete = isDiscrete(xCS.type); const yIsDiscrete = isDiscrete(yCS.type); // Default: x is category, y is value let catAxis: 'x' | 'y' = 'x'; let valAxis: 'x' | 'y' = 'y'; if (yIsDiscrete && !xIsDiscrete) { catAxis = 'y'; valAxis = 'x'; } const catField = channelSemantics[catAxis]!.field!; const valField = channelSemantics[valAxis]!.field!; const catCS = channelSemantics[catAxis]; const categories = extractCategories(table, catField, catCS?.ordinalSortOrder); // Group data by category const catGroups = groupBy(table, catField); // Compute boxplot data per category const boxData: [number, number, number, number, number][] = []; const outlierData: [number, number][] = []; for (let i = 0; i < categories.length; i++) { const cat = categories[i]; const rows = catGroups.get(cat) || []; const values = rows.map(r => Number(r[valField])).filter(v => isFinite(v)); boxData.push(fiveNumberSummary(values)); const outliers = findOutliers(values); for (const o of outliers) { outlierData.push([i, o]); } } const isHorizontal = catAxis === 'y'; const option: any = { tooltip: { trigger: 'item' }, [isHorizontal ? 'yAxis' : 'xAxis']: { type: 'category', data: categories, name: catField, boundaryGap: true, }, [isHorizontal ? 'xAxis' : 'yAxis']: { type: 'value', name: valField, }, series: [ { type: 'boxplot', data: boxData, itemStyle: { borderColor: '#5470c6', }, }, ], }; // Add outlier scatter series if there are outliers if (outlierData.length > 0) { option.series.push({ name: 'Outliers', type: 'scatter', data: outlierData, symbolSize: 4, itemStyle: { color: '#ee6666' }, }); } Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, }; ================================================ FILE: src/lib/agents-chart/echarts/templates/candlestick.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Candlestick Chart template. * * Contrast with VL: * VL: layered spec with rule (high→low) + bar (open→close) + conditional color * EC: native `candlestick` series type — OHLC data format built-in * * ECharts candlestick is one of its strongest native chart types: * - Built-in up/down coloring * - Automatic tooltip with OHLC labels * - Integrated with dataZoom for pan/zoom * * Channels: x (date/category), open, high, low, close */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories } from './utils'; const isDiscrete = (type: string | undefined) => type === 'nominal' || type === 'ordinal'; export const ecCandlestickDef: ChartTemplateDef = { chart: 'Candlestick Chart', template: { mark: 'candlestick', encoding: {} }, channels: ['x', 'open', 'high', 'low', 'close', 'column', 'row'], markCognitiveChannel: 'position', declareLayoutMode: () => ({ axisFlags: { x: { banded: true } }, }), instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const xCS = channelSemantics.x; const openCS = channelSemantics.open; const highCS = channelSemantics.high; const lowCS = channelSemantics.low; const closeCS = channelSemantics.close; if (!xCS?.field) return; const xField = xCS.field; const openField = openCS?.field; const highField = highCS?.field; const lowField = lowCS?.field; const closeField = closeCS?.field; // Need at least open+close for a meaningful candlestick if (!openField || !closeField) return; const xIsDiscrete = isDiscrete(xCS.type); const xIsTemporal = xCS.type === 'temporal'; const categories = xIsDiscrete ? extractCategories(table, xField, xCS.ordinalSortOrder) : undefined; // ── Build OHLC data ────────────────────────────────────────────── // ECharts candlestick data format: [open, close, low, high] // (note: EC ordering is open, close, low, high — NOT the typical OHLC) const candleData: [number, number, number, number][] = []; const xValues: string[] = []; for (const row of table) { const o = Number(row[openField]); const c = Number(row[closeField]); const h = highField ? Number(row[highField]) : Math.max(o, c); const l = lowField ? Number(row[lowField]) : Math.min(o, c); candleData.push([o, c, l, h]); xValues.push(String(row[xField])); } // ── Build option ───────────────────────────────────────────────── const option: any = { tooltip: { trigger: 'axis', axisPointer: { type: 'cross' }, }, xAxis: { type: xIsDiscrete ? 'category' : xIsTemporal ? 'category' : 'category', data: categories || xValues, name: xField, nameLocation: 'middle', nameGap: 30, boundaryGap: true, axisLine: { onZero: false }, }, yAxis: { type: 'value', scale: true, // candlestick charts should never start at zero name: 'Price', nameLocation: 'middle', nameGap: 50, }, series: [{ type: 'candlestick', data: candleData, itemStyle: { color: '#06982d', // bullish (close > open) — green color0: '#ae1325', // bearish (close < open) — red borderColor: '#06982d', borderColor0: '#ae1325', }, }], }; // ── Optional MA overlay ────────────────────────────────────────── if (chartProperties?.showMA) { const maWindow = chartProperties.maWindow ?? 5; const closePrices = table.map((r: any) => Number(r[closeField])); const maData = computeMA(closePrices, maWindow); option.series.push({ name: `MA${maWindow}`, type: 'line', data: maData, smooth: true, lineStyle: { width: 1.5, opacity: 0.7 }, symbol: 'none', }); option.legend = { data: [`MA${maWindow}`] }; } // ── DataZoom for large datasets ────────────────────────────────── if (table.length > 60) { const startPercent = Math.max(0, 100 - Math.round(60 / table.length * 100)); option.dataZoom = [ { type: 'inside', start: startPercent, end: 100 }, { type: 'slider', start: startPercent, end: 100, bottom: 5, height: 20 }, ]; // Extra grid bottom padding so axis title + slider don't overlap option._dataZoomExtra = 35; } // ── Bar width auto-sizing ──────────────────────────────────────── const plotWidth = ctx.canvasSize?.width || 400; const barWidth = Math.max(2, Math.min(20, Math.round(plotWidth * 0.6 / table.length))); option.series[0].barWidth = barWidth; Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, postProcess: (option) => { // Grow canvas to fit dataZoom slider below the axis title const extra = option._dataZoomExtra ?? 0; if (extra > 0) { if (!option.grid) option.grid = {}; const curBottom = typeof option.grid.bottom === 'number' ? option.grid.bottom : 45; option.grid.bottom = curBottom + extra; if (typeof option._height === 'number') { option._height += extra; } delete option._dataZoomExtra; } }, properties: [ { key: 'showMA', label: 'Show Moving Avg', type: 'binary', defaultValue: false, } as ChartPropertyDef, { key: 'maWindow', label: 'MA Window', type: 'continuous', min: 3, max: 30, step: 1, defaultValue: 5, } as ChartPropertyDef, ], }; /** * Compute simple moving average. * Returns null for the first (window-1) entries. */ function computeMA(prices: number[], window: number): (number | null)[] { const result: (number | null)[] = []; for (let i = 0; i < prices.length; i++) { if (i < window - 1) { result.push(null); } else { let sum = 0; for (let j = i - window + 1; j <= i; j++) { sum += prices[j]; } result.push(Math.round(sum / window * 100) / 100); } } return result; } ================================================ FILE: src/lib/agents-chart/echarts/templates/density.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Density Plot — area from binned distribution (mirror vegalite/templates/density.ts). * Single channel x; optional color. Uses histogram bins then area chart as approximation. */ import { ChartTemplateDef } from '../../core/types'; import { groupBy, DEFAULT_COLORS } from './utils'; function binData(values: number[], numBins: number): { x: number[]; y: number[] } { if (values.length === 0) return { x: [], y: [] }; const min = Math.min(...values); const max = Math.max(...values); const step = (max - min) / numBins || 1; const bins = new Array(numBins + 1).fill(0); const edges = new Array(numBins + 1).fill(0).map((_, i) => min + i * step); for (const v of values) { const i = Math.min(Math.floor((v - min) / step), numBins - 1); bins[i]++; } const total = values.length; const x = edges.slice(0, -1).map((e, i) => (e + edges[i + 1]) / 2); const y = bins.slice(0, -1).map(c => c / total); return { x, y }; } export const ecDensityPlotDef: ChartTemplateDef = { chart: 'Density Plot', template: { mark: 'area', encoding: {} }, channels: ['x', 'color', 'column', 'row'], markCognitiveChannel: 'area', instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const xField = channelSemantics.x?.field; const colorField = channelSemantics.color?.field; if (!xField) return; const numBins = Math.min(40, Math.max(10, Math.ceil(Math.sqrt(table.length)))); const bandwidth = chartProperties?.bandwidth ?? 0.4; const bins = Math.max(10, Math.round(numBins / (1 + bandwidth))); const option: any = { tooltip: { trigger: 'axis' }, xAxis: { type: 'value', name: xField, nameLocation: 'middle', nameGap: 30 }, yAxis: { type: 'value', name: 'Density', nameLocation: 'middle', nameGap: 40 }, series: [], }; option._encodingTooltip = { trigger: 'axis', categoryLabel: xField, valueLabel: 'Density' }; if (colorField) { const groups = groupBy(table, colorField); option.legend = { data: [...groups.keys()] }; let colorIdx = 0; for (const [name, rows] of groups) { const values = rows.map((r: any) => Number(r[xField])).filter((v: number) => !isNaN(v)); const { x, y } = binData(values, bins); const data = x.map((xi, i) => [xi, y[i]]); option.series.push({ name, type: 'line', data, areaStyle: { color: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length], opacity: 0.5 }, itemStyle: { color: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length] }, }); colorIdx++; } } else { const values = table.map((r: any) => Number(r[xField])).filter((v: number) => !isNaN(v)); const { x, y } = binData(values, bins); const data = x.map((xi, i) => [xi, y[i]]); option.series.push({ type: 'line', data, areaStyle: { opacity: 0.5 }, }); } Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, }; ================================================ FILE: src/lib/agents-chart/echarts/templates/funnel.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Funnel Chart template. * * Unique to ECharts — no Vega-Lite equivalent. * Displays a funnel (descending trapezoids) showing sequential stage values, * commonly used for conversion pipelines (e.g., visits → signups → purchases). * * Data model: * y (nominal): stage name / category — drives vertical sizing via spring model * size (quantitative): value for each stage — drives trapezoid width * * Each row represents one stage. Multiple rows per stage are aggregated (sum). * * Scaling: the `y` channel is declared as banded, so the spring model in * `computeLayout` decides `yStep` (px per stage). Height = yStep × stageCount. * The `defaultStepMultiplier: 2.5` gives each stage generous vertical space * (≈50-67px default vs 20-27px for bars). * * The template does NOT create yAxis/xAxis in the ECharts option, so * `ecApplyLayoutToSpec` correctly skips grid processing. */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, DEFAULT_COLORS } from './utils'; export const ecFunnelChartDef: ChartTemplateDef = { chart: 'Funnel Chart', template: { mark: 'rect', encoding: {} }, channels: ['y', 'size'], markCognitiveChannel: 'area', declareLayoutMode: () => ({ axisFlags: { y: { banded: true } }, paramOverrides: { defaultStepMultiplier: 2.5, // taller bands for funnel stages }, }), instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties, layout } = ctx; const stageField = channelSemantics.y?.field; const valField = channelSemantics.size?.field; if (!stageField) return; // Extract stages (categories from y channel) const stages = extractCategories( table, stageField, channelSemantics.y?.ordinalSortOrder, ); if (stages.length === 0) return; // Aggregate values per stage const funnelData: { name: string; value: number }[] = []; if (valField) { const agg = new Map<string, number>(); for (const row of table) { const stage = String(row[stageField] ?? ''); const val = Number(row[valField]) || 0; agg.set(stage, (agg.get(stage) ?? 0) + val); } for (const stage of stages) { funnelData.push({ name: stage, value: agg.get(stage) ?? 0 }); } } else { // No value field — count occurrences per stage const counts = new Map<string, number>(); for (const row of table) { const stage = String(row[stageField] ?? ''); counts.set(stage, (counts.get(stage) ?? 0) + 1); } for (const stage of stages) { funnelData.push({ name: stage, value: counts.get(stage) ?? 0 }); } } // Sort by value (largest first) for funnel shape const sortOrder = chartProperties?.sort ?? 'descending'; if (sortOrder === 'descending') { funnelData.sort((a, b) => b.value - a.value); } else if (sortOrder === 'ascending') { funnelData.sort((a, b) => a.value - b.value); } // 'none' preserves original stage order // ── Layout-driven sizing ───────────────────────────────────────── // Use spring model results: yStep × stageCount → funnel body height const stageCount = layout.yNominalCount || stages.length; const yStep = layout.yStep; const funnelBodyH = Math.max(120, yStep * stageCount); const topMargin = 30; const bottomMargin = 20; const canvasH = funnelBodyH + topMargin + bottomMargin; // Estimate legend width from label text const maxLabelLen = Math.max(...funnelData.map(d => d.name.length), 3); const estimatedLegendWidth = Math.min(150, maxLabelLen * 7 + 30); const canvasW = Math.max(ctx.canvasSize.width, 300); // Leave room for legend on the right const funnelLeft = 40; const funnelRight = estimatedLegendWidth + 30; const funnelWidth = `${Math.max(100, canvasW - funnelLeft - funnelRight)}px`; const orient = chartProperties?.orient ?? 'vertical'; const option: any = { tooltip: { trigger: 'item', formatter: '{b}: {c} ({d}%)', }, legend: { data: funnelData.map(d => d.name), type: funnelData.length > 8 ? 'scroll' : 'plain', orient: 'vertical', right: 10, top: 'middle', textStyle: { fontSize: 11 }, }, series: [{ type: 'funnel', left: funnelLeft, top: topMargin, bottom: bottomMargin, width: funnelWidth, sort: sortOrder, orient, gap: chartProperties?.gap ?? 2, data: funnelData, label: { show: true, position: 'inside', formatter: '{b}\n{c}', fontSize: 11, }, emphasis: { label: { fontSize: 13, }, }, itemStyle: { borderColor: '#fff', borderWidth: 1, }, }], color: DEFAULT_COLORS, _width: canvasW, _height: canvasH, }; Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'sort', label: 'Sort', type: 'discrete', options: [ { value: 'descending', label: 'Descending (default)' }, { value: 'ascending', label: 'Ascending' }, { value: 'none', label: 'Original order' }, ], } as ChartPropertyDef, { key: 'orient', label: 'Orient', type: 'discrete', options: [ { value: 'vertical', label: 'Vertical (default)' }, { value: 'horizontal', label: 'Horizontal' }, ], } as ChartPropertyDef, { key: 'gap', label: 'Gap', type: 'continuous', min: 0, max: 20, step: 1, defaultValue: 2 } as ChartPropertyDef, ], }; ================================================ FILE: src/lib/agents-chart/echarts/templates/gauge.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Gauge Chart template. * * Unique to ECharts — no Vega-Lite equivalent. * Displays numeric KPI values on speedometer-style dials. * * Data model: * size (quantitative): the value to display * column (nominal, optional): splits into separate gauge subplots with * facet-style wrapping — each unique value creates one gauge dial * * When multiple rows share the same column value, the size values are * averaged. With no column channel, all rows are averaged into a single * gauge. * * Scaling: uses facet-style wrapping — the template computes a grid layout * internally (since gauge is axis-less, the assembler's facet path doesn't * apply). The grid respects a minimum gauge cell size and wraps to multiple * rows when there are too many dials for a single row. */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, groupBy, DEFAULT_COLORS } from './utils'; export const ecGaugeChartDef: ChartTemplateDef = { chart: 'Gauge Chart', template: { mark: 'point', encoding: {} }, channels: ['size', 'column'], markCognitiveChannel: 'position', instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const valueField = channelSemantics.size?.field; const columnField = channelSemantics.column?.field; if (!valueField) return; // Compute data range for axis scaling const allValues = table.map(r => Number(r[valueField])).filter(v => isFinite(v)); const dataMax = allValues.length > 0 ? Math.max(...allValues) : 100; const scaleMin = chartProperties?.min ?? 0; const scaleMax = chartProperties?.max ?? niceGaugeMax(dataMax); // ── Build gauge items: one per column category, or single ──────── const gaugeItems: { name: string; value: number; color?: string }[] = []; if (columnField) { const groups = groupBy(table, columnField); const categories = extractCategories( table, columnField, channelSemantics.column?.ordinalSortOrder, ); categories.forEach((cat, idx) => { const rows = groups.get(cat) || []; const vals = rows.map(r => Number(r[valueField])).filter(v => isFinite(v)); const avg = vals.length > 0 ? Math.round(vals.reduce((a, b) => a + b, 0) / vals.length * 100) / 100 : 0; gaugeItems.push({ name: cat, value: avg, color: DEFAULT_COLORS[idx % DEFAULT_COLORS.length], }); }); } else { const avg = allValues.length > 0 ? Math.round(allValues.reduce((a, b) => a + b, 0) / allValues.length * 100) / 100 : 0; gaugeItems.push({ name: valueField, value: avg }); } // ── Facet-style grid wrapping ──────────────────────────────────── const n = gaugeItems.length; const baseW = ctx.canvasSize.width; const baseH = ctx.canvasSize.height; const minCellDim = 180; // minimum px per gauge cell const maxStretchFactor = ctx.assembleOptions?.maxStretch ?? 2.0; // max canvas stretch let gridCols: number, gridRows: number; if (n === 1) { gridCols = 1; gridRows = 1; } else { const maxCols = Math.max(1, Math.floor(baseW * maxStretchFactor / minCellDim)); if (n <= maxCols) { gridCols = n; gridRows = 1; } else { gridRows = Math.ceil(n / maxCols); gridCols = Math.ceil(n / gridRows); } } const canvasW = Math.max(baseW, gridCols * minCellDim); const canvasH = Math.max(baseH, gridRows * (minCellDim + 20)); const cellW = canvasW / gridCols; const cellH = canvasH / gridRows; const gaugeRadius = Math.max(40, Math.round(Math.min(cellW * 0.38, cellH * 0.38))); // Scale all gauge element sizes proportionally to the radius. // Reference radius ~100px maps to baseline sizes. const s = gaugeRadius / 100; const progressWidth = Math.max(4, Math.round(12 * s)); const pointerWidth = Math.max(2, Math.round(5 * s)); const detailFontSize = Math.max(10, Math.round(20 * s)); const titleFontSize = Math.max(8, Math.round(14 * s)); const axisLabelFontSize = Math.max(6, Math.round(9 * s)); const tickLength = Math.max(3, Math.round(5 * s)); const tickDistance = -Math.round(16 * s); const splitLength = Math.max(5, Math.round(12 * s)); const splitDistance = -Math.round(20 * s); const labelDistance = -Math.round(24 * s); const showProgress = chartProperties?.showProgress !== false; // ── Build one ECharts gauge series per item ────────────────────── const series = gaugeItems.map((item, i) => { const col = i % gridCols; const row = Math.floor(i / gridCols); const cx = Math.round((col + 0.5) * cellW); const cy = Math.round((row + 0.5) * cellH); return { type: 'gauge' as const, min: scaleMin, max: scaleMax, center: [`${cx}px`, `${cy}px`], radius: `${gaugeRadius}px`, data: [{ name: item.name, value: item.value, ...(item.color ? { itemStyle: { color: item.color } } : {}), }], detail: { formatter: '{value}', fontSize: detailFontSize, offsetCenter: [0, '70%'], }, title: { fontSize: titleFontSize, offsetCenter: [0, '85%'], }, axisLine: { lineStyle: { width: progressWidth }, }, progress: { show: showProgress, width: progressWidth, ...(item.color ? { itemStyle: { color: item.color } } : {}), }, pointer: { length: '60%', width: pointerWidth, ...(item.color ? { itemStyle: { color: item.color } } : {}), }, axisTick: { distance: tickDistance, length: tickLength, lineStyle: { color: '#999', width: 1 }, }, splitLine: { distance: splitDistance, length: splitLength, lineStyle: { color: '#999', width: 2 }, }, axisLabel: { distance: labelDistance, fontSize: axisLabelFontSize, color: '#666', }, }; }); const option: any = { tooltip: { trigger: 'item', formatter: '{b}: {c}' }, series, color: DEFAULT_COLORS, _width: canvasW, _height: canvasH, }; Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'min', label: 'Min', type: 'continuous', min: 0, max: 1000, step: 10, defaultValue: 0 } as ChartPropertyDef, { key: 'max', label: 'Max', type: 'continuous', min: 0, max: 10000, step: 100, defaultValue: 100 } as ChartPropertyDef, { key: 'showProgress', label: 'Progress', type: 'discrete', options: [ { value: true, label: 'Show (default)' }, { value: false, label: 'Hide' }, ], } as ChartPropertyDef, ], }; /** Round up to a nice gauge maximum. */ function niceGaugeMax(v: number): number { if (v <= 0) return 100; const pow = Math.pow(10, Math.floor(Math.log10(v))); const mantissa = v / pow; const nice = mantissa <= 1 ? 1 : mantissa <= 2 ? 2 : mantissa <= 5 ? 5 : 10; return nice * pow; } ================================================ FILE: src/lib/agents-chart/echarts/templates/heatmap.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Heatmap template. * * Contrast with VL: * VL: mark = "rect" with x (nominal), y (nominal), color (quantitative) * EC: series type = 'heatmap' with data = [[xIdx, yIdx, value], ...] * and a visualMap component for the color scale. */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, DEFAULT_COLORS } from './utils'; const isDiscrete = (type: string | undefined) => type === 'nominal' || type === 'ordinal'; /** Map VL-style scheme names to ECharts built-in color ranges. */ const SCHEME_COLORS: Record<string, string[]> = { viridis: ['#440154', '#3b528b', '#21918c', '#5ec962', '#fde725'], inferno: ['#000004', '#420a68', '#932667', '#dd513a', '#fca50a', '#fcffa4'], magma: ['#000004', '#3b0f70', '#8c2981', '#de4968', '#fe9f6d', '#fcfdbf'], plasma: ['#0d0887', '#6a00a8', '#b12a90', '#e16462', '#fca636', '#f0f921'], turbo: ['#30123b', '#4662d7', '#35abed', '#1ae4b6', '#72fe5e', '#c8ef34', '#faba39', '#f66b19', '#d23105', '#7a0403'], blues: ['#f7fbff', '#6baed6', '#08519c'], reds: ['#fff5f0', '#fb6a4a', '#a50f15'], greens: ['#f7fcf5', '#74c476', '#00441b'], oranges: ['#fff5eb', '#fd8d3c', '#7f2704'], purples: ['#fcfbfd', '#9e9ac8', '#3f007d'], greys: ['#ffffff', '#969696', '#252525'], blueorange: ['#08519c', '#f7fbff', '#ff7f00'], redblue: ['#a50f15', '#ffffff', '#08519c'], }; export const ecHeatmapDef: ChartTemplateDef = { chart: 'Heatmap', template: { mark: 'rect', encoding: {} }, channels: ['x', 'y', 'color', 'column', 'row'], markCognitiveChannel: 'color', declareLayoutMode: () => ({ axisFlags: { x: { banded: true }, y: { banded: true } }, paramOverrides: { minStep: 20, // heatmap cells need room for value labels defaultStepMultiplier: 2, // inflate default step so cells aren't cramped }, }), instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const xCS = channelSemantics.x; const yCS = channelSemantics.y; const colorCS = channelSemantics.color; const xField = xCS?.field; const yField = yCS?.field; const colorField = colorCS?.field; if (!xField || !yField) return; const xCategories = extractCategories(table, xField, xCS?.ordinalSortOrder); const yCategories = extractCategories(table, yField, yCS?.ordinalSortOrder); // Build heatmap data: [xIndex, yIndex, value] const xIndexMap = new Map(xCategories.map((c, i) => [c, i])); const yIndexMap = new Map(yCategories.map((c, i) => [c, i])); const heatData: [number, number, number][] = []; let minVal = Infinity; let maxVal = -Infinity; // Aggregate if multiple rows per cell (sum) const cellMap = new Map<string, number>(); for (const row of table) { const xKey = String(row[xField]); const yKey = String(row[yField]); const val = colorField ? (Number(row[colorField]) || 0) : 1; const cellKey = `${xKey}|||${yKey}`; cellMap.set(cellKey, (cellMap.get(cellKey) ?? 0) + val); } for (const [cellKey, val] of cellMap) { const [xKey, yKey] = cellKey.split('|||'); const xi = xIndexMap.get(xKey); const yi = yIndexMap.get(yKey); if (xi !== undefined && yi !== undefined) { heatData.push([xi, yi, val]); if (val < minVal) minVal = val; if (val > maxVal) maxVal = val; } } if (minVal === Infinity) minVal = 0; if (maxVal === -Infinity) maxVal = 1; // Color scheme const schemeName = chartProperties?.colorScheme || 'viridis'; const schemeColors = SCHEME_COLORS[schemeName] || SCHEME_COLORS.viridis; const option: any = { tooltip: { position: 'top', formatter: (params: any) => { const d = params.data; return `${xCategories[d[0]]}, ${yCategories[d[1]]}: ${d[2]}`; }, }, xAxis: { type: 'category', data: xCategories, name: xField, splitArea: { show: true }, }, yAxis: { type: 'category', data: yCategories, name: yField, splitArea: { show: true }, }, visualMap: { min: minVal, max: maxVal, calculable: true, orient: 'horizontal', left: 'center', bottom: 0, inRange: { color: schemeColors, }, }, series: [{ type: 'heatmap', data: heatData, label: { show: heatData.length <= 100, // Show labels for small heatmaps }, emphasis: { itemStyle: { shadowBlur: 10, shadowColor: 'rgba(0, 0, 0, 0.5)', }, }, }], }; Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, postProcess: (option, ctx) => { // Scale heatmap label font size based on cell dimensions const heatSeries = option.series?.find((s: any) => s.type === 'heatmap'); const { layout } = ctx; const cellW = layout.xStep || 50; const cellH = layout.yStep || 50; const minDim = Math.min(cellW, cellH); if (heatSeries?.label) { // Scale font: 12px at 60px cell, down to 8px at 35px, hide below 30px if (minDim < 30) { heatSeries.label.show = false; } else { const fontSize = Math.max(8, Math.min(12, Math.round(minDim * 0.2))); heatSeries.label.fontSize = fontSize; // If cell is narrow, truncate displayed value if (cellW < 50) { const maxChars = Math.max(2, Math.floor(cellW / (fontSize * 0.6))); heatSeries.label.formatter = (params: any) => { const val = params.data[2]; const s = String(val); return s.length > maxChars ? s.slice(0, maxChars) : s; }; } } } // Make room for the visualMap bar below the chart // The bottom stack is: grid → x-axis labels → x-axis title → visualMap if (option.visualMap && option.grid) { const vmHeight = 50; // space for visualMap below the x-axis title option.grid.bottom = (option.grid.bottom || 30) + vmHeight; // Position visualMap at absolute bottom with a small margin option.visualMap.bottom = 5; if (option._height) { option._height += vmHeight; } } }, properties: [ { key: 'colorScheme', label: 'Scheme', type: 'discrete', options: [ { value: undefined, label: 'Default (Viridis)' }, { value: 'viridis', label: 'Viridis' }, { value: 'inferno', label: 'Inferno' }, { value: 'magma', label: 'Magma' }, { value: 'plasma', label: 'Plasma' }, { value: 'turbo', label: 'Turbo' }, { value: 'blues', label: 'Blues' }, { value: 'reds', label: 'Reds' }, { value: 'greens', label: 'Greens' }, { value: 'oranges', label: 'Oranges' }, { value: 'purples', label: 'Purples' }, { value: 'greys', label: 'Greys' }, { value: 'blueorange', label: 'Blue-Orange (diverging)' }, { value: 'redblue', label: 'Red-Blue (diverging)' }, ], } as ChartPropertyDef, ], }; ================================================ FILE: src/lib/agents-chart/echarts/templates/histogram.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Histogram template. * * Contrast with VL: * VL: encoding.x.bin = true + encoding.y.aggregate = "count" * (VL handles binning internally) * EC: No built-in bin transform — we compute bins client-side and render * as a bar chart with contiguous bars (barCategoryGap: 0). */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { DEFAULT_COLORS } from './utils'; export const ecHistogramDef: ChartTemplateDef = { chart: 'Histogram', template: { mark: 'bar', encoding: {} }, channels: ['x', 'color', 'column', 'row'], markCognitiveChannel: 'length', instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const xField = channelSemantics.x?.field; const colorField = channelSemantics.color?.field; if (!xField) return; const binCount = chartProperties?.binCount ?? 10; // Extract numeric values const values = table .map(r => Number(r[xField])) .filter(v => isFinite(v)); if (values.length === 0) return; const minVal = Math.min(...values); const maxVal = Math.max(...values); const range = maxVal - minVal; const binWidth = range > 0 ? range / binCount : 1; if (!colorField) { // Simple histogram — single series const counts = new Array(binCount).fill(0); for (const v of values) { let idx = Math.floor((v - minVal) / binWidth); if (idx >= binCount) idx = binCount - 1; counts[idx]++; } const categories = counts.map((_, i) => { const lo = (minVal + i * binWidth).toFixed(1); const hi = (minVal + (i + 1) * binWidth).toFixed(1); return `${lo}–${hi}`; }); const option: any = { tooltip: { trigger: 'axis', axisPointer: { type: 'shadow' }, }, xAxis: { type: 'category', data: categories, name: xField, nameLocation: 'middle', nameGap: 25, axisLabel: { rotate: categories.length > 10 ? 45 : 0 }, }, yAxis: { type: 'value', name: 'Count', nameLocation: 'middle', nameGap: 40, }, series: [{ type: 'bar', data: counts, barCategoryGap: '0%', // contiguous bars itemStyle: { borderColor: '#fff', borderWidth: 0.5, }, }], }; option._encodingTooltip = { trigger: 'axis', categoryLabel: xField, valueLabel: 'Count' }; Object.assign(spec, option); } else { // Stacked histogram — one series per color group const groupValues = new Map<string, number[]>(); for (const row of table) { const v = Number(row[xField]); if (!isFinite(v)) continue; const g = String(row[colorField] ?? ''); if (!groupValues.has(g)) groupValues.set(g, []); groupValues.get(g)!.push(v); } const categories = Array.from({ length: binCount }, (_, i) => { const lo = (minVal + i * binWidth).toFixed(1); const hi = (minVal + (i + 1) * binWidth).toFixed(1); return `${lo}–${hi}`; }); const series: any[] = []; let colorIdx = 0; const legendData: string[] = []; for (const [name, vals] of groupValues) { const counts = new Array(binCount).fill(0); for (const v of vals) { let idx = Math.floor((v - minVal) / binWidth); if (idx >= binCount) idx = binCount - 1; counts[idx]++; } legendData.push(name); series.push({ name, type: 'bar', data: counts, stack: 'total', barCategoryGap: '0%', itemStyle: { color: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length], borderColor: '#fff', borderWidth: 0.5, }, }); colorIdx++; } const option: any = { tooltip: { trigger: 'axis', axisPointer: { type: 'shadow' } }, legend: { data: legendData }, xAxis: { type: 'category', data: categories, name: xField, nameLocation: 'middle', nameGap: 25, axisLabel: { rotate: categories.length > 10 ? 45 : 0 }, }, yAxis: { type: 'value', name: 'Count', nameLocation: 'middle', nameGap: 40, }, series, }; option._encodingTooltip = { trigger: 'axis', categoryLabel: xField, valueLabel: 'Count' }; Object.assign(spec, option); } delete spec.mark; delete spec.encoding; }, properties: [ { key: 'binCount', label: 'Bins', type: 'continuous', min: 5, max: 50, step: 1, defaultValue: 10 } as ChartPropertyDef, ], }; ================================================ FILE: src/lib/agents-chart/echarts/templates/index.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts template registry. * * Mirrors the structure of vegalite/templates/index.ts but with ECharts * template definitions. */ import { ChartTemplateDef } from '../../core/types'; import { ecScatterPlotDef, ecRegressionDef } from './scatter'; import { ecBarChartDef, ecStackedBarChartDef, ecGroupedBarChartDef } from './bar'; import { ecLineChartDef, ecDottedLineChartDef, ecBumpChartDef } from './line'; import { ecAreaChartDef } from './area'; import { ecPieChartDef } from './pie'; import { ecHeatmapDef } from './heatmap'; import { ecHistogramDef } from './histogram'; import { ecBoxplotDef } from './boxplot'; import { ecRadarChartDef } from './radar'; import { ecCandlestickDef } from './candlestick'; import { ecStreamgraphDef } from './streamgraph'; import { ecRoseChartDef } from './rose'; import { ecGaugeChartDef } from './gauge'; import { ecFunnelChartDef } from './funnel'; import { ecTreemapDef } from './treemap'; import { ecSunburstDef } from './sunburst'; import { ecSankeyDef } from './sankey'; import { ecLollipopChartDef } from './lollipop'; import { ecStripPlotDef } from './jitter'; import { ecWaterfallChartDef } from './waterfall'; import { ecPyramidChartDef } from './pyramid'; import { ecRangedDotPlotDef } from './ranged-dot'; import { ecDensityPlotDef } from './density'; /** * ECharts chart template definitions, grouped by category. * Mirrors vegalite/templates/index.ts so VegaLite test cases can run through ECharts. */ export const ecTemplateDefs: { [key: string]: ChartTemplateDef[] } = { 'Scatter & Point': [ecScatterPlotDef, ecRegressionDef, ecRangedDotPlotDef, ecBoxplotDef, ecStripPlotDef], 'Bar': [ecBarChartDef, ecGroupedBarChartDef, ecStackedBarChartDef, ecHistogramDef, ecLollipopChartDef, ecPyramidChartDef, ecHeatmapDef], 'Line & Area': [ecLineChartDef, ecDottedLineChartDef, ecBumpChartDef, ecAreaChartDef, ecStreamgraphDef], 'Part-to-Whole': [ecPieChartDef, ecFunnelChartDef, ecTreemapDef, ecSunburstDef], 'Statistical': [ecDensityPlotDef], 'Financial': [ecCandlestickDef], 'Other': [ecWaterfallChartDef], 'Polar': [ecRadarChartDef, ecRoseChartDef], 'Indicator': [ecGaugeChartDef], 'Flow': [ecSankeyDef], }; /** * Flat list of all ECharts chart template definitions. */ export const ecAllTemplateDefs: ChartTemplateDef[] = Object.values(ecTemplateDefs).flat(); /** * Look up an ECharts chart template definition by chart type name. */ export function ecGetTemplateDef(chartType: string): ChartTemplateDef | undefined { return ecAllTemplateDefs.find(t => t.chart === chartType); } /** * Get the available channels for an ECharts chart type. */ export function ecGetTemplateChannels(chartType: string): string[] { return ecGetTemplateDef(chartType)?.channels || []; } ================================================ FILE: src/lib/agents-chart/echarts/templates/jitter.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Strip Plot — scatter with jitter on categorical axis (mirror vegalite/templates/jitter.ts). */ import { ChartTemplateDef } from '../../core/types'; import { extractCategories, groupBy, DEFAULT_COLORS } from './utils'; const isDiscrete = (type: string | undefined) => type === 'nominal' || type === 'ordinal'; /** Seeded jitter for reproducible strip plot. */ function jitter(seed: number): () => number { let s = seed; return () => { s = (s * 1103515245 + 12345) & 0x7fffffff; return (s / 0x7fffffff) * 2 - 1; }; } export const ecStripPlotDef: ChartTemplateDef = { chart: 'Strip Plot', template: { mark: 'circle', encoding: {} }, channels: ['x', 'y', 'color', 'size', 'column', 'row'], markCognitiveChannel: 'position', declareLayoutMode: () => ({ paramOverrides: { defaultStepMultiplier: 2, minStep: 16 }, }), instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const xCS = channelSemantics.x; const yCS = channelSemantics.y; const xField = xCS?.field; const yField = yCS?.field; const colorField = channelSemantics.color?.field; if (!xField || !yField) return; const xIsDiscrete = isDiscrete(xCS?.type); const yIsDiscrete = isDiscrete(yCS?.type); const catAxis = xIsDiscrete ? 'x' : yIsDiscrete ? 'y' : 'x'; const contAxis = catAxis === 'x' ? 'y' : 'x'; const catField = catAxis === 'x' ? xField : yField; const contField = contAxis === 'x' ? xField : yField; const categories = extractCategories(table, catField!, (catAxis === 'x' ? xCS : yCS)?.ordinalSortOrder); const catToIndex = new Map(categories.map((c, i) => [c, i])); const jitterWidth = (chartProperties?.stepWidth ?? 20) * 0.8; const rand = jitter(42); const option: any = { tooltip: { trigger: 'item' }, xAxis: { type: 'category', data: categories, name: catField, nameLocation: 'middle', nameGap: 30, }, yAxis: { type: 'value', name: contField, nameLocation: 'middle', nameGap: 40, }, series: [], }; if (catAxis === 'y') { option.xAxis = { type: 'value', name: contField, nameLocation: 'middle', nameGap: 30 }; option.yAxis = { type: 'category', data: categories, name: catField, nameLocation: 'middle', nameGap: 40 }; } const buildPoint = (row: any) => { const cat = String(row[catField!] ?? ''); const idx = catToIndex.get(cat) ?? 0; const offset = rand() * jitterWidth; const x = catAxis === 'x' ? idx + offset : row[contField]; const y = catAxis === 'y' ? idx + offset : row[contField]; return [x, y]; }; if (colorField) { const groups = groupBy(table, colorField); option.legend = { data: [...groups.keys()] }; let colorIdx = 0; for (const [name, rows] of groups) { option.series.push({ name, type: 'scatter', data: rows.map(buildPoint), itemStyle: { color: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length], opacity: 0.7 }, symbolSize: 8, }); colorIdx++; } } else { option.series.push({ type: 'scatter', data: table.map(buildPoint), itemStyle: { opacity: 0.7 }, symbolSize: 8, }); } Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, }; ================================================ FILE: src/lib/agents-chart/echarts/templates/line.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Line Chart template (supports multi-series). * * Contrast with VL: * VL: encoding.x + encoding.y + encoding.color → auto-groups into separate lines * EC: explicit series[] with each series.data = [v1, v2, ...] aligned to xAxis.data */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, groupBy, DEFAULT_COLORS, getCategoryOrder } from './utils'; import { toTypeString } from '../../core/field-semantics'; const isDiscrete = (type: string | undefined) => type === 'nominal' || type === 'ordinal'; const interpolateMap: Record<string, string> = { 'linear': 'linear', // default 'monotone': 'monotone', // ECharts smooth: true approximates this 'step': 'step', 'step-before': 'stepBefore', // Not directly supported; mapped 'step-after': 'stepAfter', // Not directly supported; mapped 'basis': 'smooth', 'cardinal': 'smooth', 'catmull-rom': 'smooth', }; export const ecLineChartDef: ChartTemplateDef = { chart: 'Line Chart', template: { mark: 'line', encoding: {} }, channels: ['x', 'y', 'color', 'opacity', 'column', 'row'], markCognitiveChannel: 'position', declareLayoutMode: () => ({ paramOverrides: { continuousMarkCrossSection: { x: 100, y: 20, seriesCountAxis: 'auto' } }, }), instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const xCS = channelSemantics.x; const yCS = channelSemantics.y; const colorField = channelSemantics.color?.field; if (!xCS?.field || !yCS?.field) return; const xField = xCS.field; const yField = yCS.field; // Determine x-axis type const xIsDiscrete = isDiscrete(xCS.type); const xIsTemporal = xCS.type === 'temporal'; // Build x-axis categories for discrete/temporal axes const categories = xIsDiscrete ? extractCategories(table, xField, getCategoryOrder(ctx, 'x')) : undefined; const option: any = { tooltip: { trigger: 'axis', }, xAxis: { type: xIsDiscrete ? 'category' : xIsTemporal ? 'time' : 'value', name: xField, nameLocation: 'middle', nameGap: 30, ...(categories ? { data: categories } : {}), }, yAxis: { type: 'value', name: yField, nameLocation: 'middle', nameGap: 40, }, series: [], }; option._encodingTooltip = { trigger: 'axis', categoryLabel: xField, valueLabel: yField }; // Apply zero-baseline // ECharts: scale=true means "data-fit", scale=false means "include zero" if (channelSemantics.y?.zero) { option.yAxis.scale = !channelSemantics.y.zero.zero; } // Interpolation / smooth const interpolate = chartProperties?.interpolate; const smooth = interpolate === 'monotone' || interpolate === 'basis' || interpolate === 'cardinal' || interpolate === 'catmull-rom'; const step = interpolate === 'step' ? 'middle' : interpolate === 'step-before' ? 'start' : interpolate === 'step-after' ? 'end' : undefined; if (colorField) { // Multi-series line chart const groups = groupBy(table, colorField); option.legend = { data: [...groups.keys()] }; let colorIdx = 0; for (const [name, rows] of groups) { const seriesData = xIsDiscrete ? buildCategoryAlignedData(rows, xField, yField, categories!) : rows.map(r => [r[xField], r[yField]]); const series: any = { name, type: 'line', data: seriesData, itemStyle: { color: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length] }, }; if (smooth) series.smooth = true; if (step) series.step = step; option.series.push(series); colorIdx++; } } else { // Single series const seriesData = xIsDiscrete ? categories!.map(cat => { const row = table.find(r => String(r[xField]) === cat); return row ? row[yField] : null; }) : table.map(r => [r[xField], r[yField]]); const series: any = { type: 'line', data: seriesData }; if (smooth) series.smooth = true; if (step) series.step = step; option.series.push(series); } Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'interpolate', label: 'Curve', type: 'discrete', options: [ { value: undefined, label: 'Default (linear)' }, { value: 'linear', label: 'Linear' }, { value: 'monotone', label: 'Monotone (smooth)' }, { value: 'step', label: 'Step' }, { value: 'step-before', label: 'Step Before' }, { value: 'step-after', label: 'Step After' }, ], } as ChartPropertyDef, ], }; /** * For category-axis line charts, align series data to the shared category array. * Returns an array of y-values (or null) indexed by category position. */ function buildCategoryAlignedData( rows: any[], xField: string, yField: string, categories: string[], ): (number | null)[] { // Build a map: category → y value const map = new Map<string, number>(); for (const row of rows) { map.set(String(row[xField]), row[yField]); } return categories.map(cat => map.get(cat) ?? null); } /** RANK_SEMANTIC_TYPES: used to detect rank axis for Bump Chart (mirror vegalite/templates/bump.ts). */ const RANK_SEMANTIC_TYPES = new Set(['Rank', 'Index', 'Score', 'Rating', 'Level']); /** * Dotted Line Chart — same as Line Chart with showSymbol and dashed line (mirror vegalite Dotted Line). */ export const ecDottedLineChartDef: ChartTemplateDef = { chart: 'Dotted Line Chart', template: { mark: 'line', encoding: {} }, channels: ['x', 'y', 'color', 'opacity', 'column', 'row'], markCognitiveChannel: 'position', declareLayoutMode: () => ({ paramOverrides: { continuousMarkCrossSection: { x: 100, y: 20, seriesCountAxis: 'auto' } }, }), instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const xCS = channelSemantics.x; const yCS = channelSemantics.y; const colorField = channelSemantics.color?.field; if (!xCS?.field || !yCS?.field) return; const xField = xCS.field; const yField = yCS.field; const xIsDiscrete = isDiscrete(xCS.type); const xIsTemporal = xCS.type === 'temporal'; const categories = xIsDiscrete ? extractCategories(table, xField, getCategoryOrder(ctx, 'x')) : undefined; const option: any = { tooltip: { trigger: 'axis' }, xAxis: { type: xIsDiscrete ? 'category' : xIsTemporal ? 'time' : 'value', name: xField, nameLocation: 'middle', nameGap: 30, ...(categories ? { data: categories } : {}), }, yAxis: { type: 'value', name: yField, nameLocation: 'middle', nameGap: 40, }, series: [], }; option._encodingTooltip = { trigger: 'axis', categoryLabel: xField, valueLabel: yField }; if (channelSemantics.y?.zero) { option.yAxis.scale = !channelSemantics.y.zero.zero; } const interpolate = chartProperties?.interpolate; const smooth = interpolate === 'monotone' || interpolate === 'basis' || interpolate === 'cardinal' || interpolate === 'catmull-rom'; const baseSeriesOpt = { showSymbol: true, symbol: 'circle', symbolSize: 6, lineStyle: { type: 'dashed' as const }, smooth: !!smooth, }; if (colorField) { const groups = groupBy(table, colorField); option.legend = { data: [...groups.keys()] }; let colorIdx = 0; for (const [name, rows] of groups) { const seriesData = xIsDiscrete ? buildCategoryAlignedData(rows, xField, yField, categories!) : rows.map(r => [r[xField], r[yField]]); option.series.push({ name, type: 'line', data: seriesData, ...baseSeriesOpt, itemStyle: { color: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length] }, }); colorIdx++; } } else { const seriesData = xIsDiscrete ? categories!.map(cat => { const row = table.find(r => String(r[xField]) === cat); return row ? row[yField] : null; }) : table.map(r => [r[xField], r[yField]]); option.series.push({ type: 'line', data: seriesData, ...baseSeriesOpt }); } Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, }; /** * Bump Chart — line with points, rank axis reversed when y is rank-like (mirror vegalite/templates/bump.ts). */ export const ecBumpChartDef: ChartTemplateDef = { chart: 'Bump Chart', template: { mark: 'line', encoding: {} }, channels: ['x', 'y', 'color', 'detail', 'column', 'row'], markCognitiveChannel: 'position', declareLayoutMode: () => ({ paramOverrides: { continuousMarkCrossSection: { x: 80, y: 20, seriesCountAxis: 'auto' } }, }), instantiate: (spec, ctx) => { const { channelSemantics, table, semanticTypes } = ctx; const xCS = channelSemantics.x; const yCS = channelSemantics.y; const colorField = channelSemantics.color?.field; if (!xCS?.field || !yCS?.field) return; const xField = xCS.field; const yField = yCS.field; const ySemType = toTypeString(semanticTypes?.[yField]); const xSemType = toTypeString(semanticTypes?.[xField]); const yIsRank = RANK_SEMANTIC_TYPES.has(ySemType); const xIsRank = RANK_SEMANTIC_TYPES.has(xSemType); const rankOnY = yIsRank && !xIsRank; const rankOnX = xIsRank && !yIsRank; const xIsDiscrete = isDiscrete(xCS.type); const categories = xIsDiscrete ? extractCategories(table, xField, getCategoryOrder(ctx, 'x')) : undefined; const option: any = { tooltip: { trigger: 'axis' }, xAxis: { type: xIsDiscrete ? 'category' : 'value', name: xField, nameLocation: 'middle', nameGap: 30, ...(categories ? { data: categories } : {}), }, yAxis: { type: 'value', name: yField, nameLocation: 'middle', nameGap: 40, inverse: !!rankOnY, }, series: [], }; option._encodingTooltip = { trigger: 'axis', categoryLabel: xField, valueLabel: yField }; const baseSeriesOpt = { showSymbol: true, symbolSize: 6, smooth: true }; if (colorField) { const groups = groupBy(table, colorField); option.legend = { data: [...groups.keys()] }; let colorIdx = 0; for (const [name, rows] of groups) { const seriesData = xIsDiscrete ? buildCategoryAlignedData(rows, xField, yField, categories!) : (rankOnX ? [...rows].sort((a, b) => Number(a[xField]) - Number(b[xField])) : rows) .map(r => [r[xField], r[yField]]); option.series.push({ name, type: 'line', data: seriesData, ...baseSeriesOpt, itemStyle: { color: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length] }, }); colorIdx++; } } else { let rows = table; if (rankOnX) rows = [...rows].sort((a, b) => Number(a[xField]) - Number(b[xField])); const seriesData = xIsDiscrete ? categories!.map(cat => { const row = rows.find(r => String(r[xField]) === cat); return row ? row[yField] : null; }) : rows.map(r => [r[xField], r[yField]]); option.series.push({ type: 'line', data: seriesData, ...baseSeriesOpt }); } Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, }; ================================================ FILE: src/lib/agents-chart/echarts/templates/lollipop.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Lollipop Chart — rule from 0 to value + dot at end (mirror vegalite/templates/lollipop.ts). */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, DEFAULT_COLORS } from './utils'; import { detectAxes } from './utils'; import { detectBandedAxisFromSemantics } from '../../vegalite/templates/utils'; const isDiscrete = (type: string | undefined) => type === 'nominal' || type === 'ordinal'; export const ecLollipopChartDef: ChartTemplateDef = { chart: 'Lollipop Chart', template: { mark: 'bar', encoding: {} }, channels: ['x', 'y', 'color', 'column', 'row'], markCognitiveChannel: 'length', declareLayoutMode: (cs, table) => { const result = detectBandedAxisFromSemantics(cs, table, { preferAxis: 'x' }); return { axisFlags: result ? { [result.axis]: { banded: true } } : { x: { banded: true } }, resolvedTypes: result?.resolvedTypes, }; }, instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const { categoryAxis, valueAxis } = detectAxes(channelSemantics); const catField = channelSemantics[categoryAxis]?.field; const valField = channelSemantics[valueAxis]?.field; if (!catField || !valField) return; const catCS = channelSemantics[categoryAxis]; const categories = extractCategories(table, catField, catCS?.ordinalSortOrder); const valueMap = new Map<string, number>(); for (const row of table) { const cat = String(row[catField] ?? ''); const val = row[valField]; if (val != null && !isNaN(val)) { valueMap.set(cat, (valueMap.get(cat) ?? 0) + Number(val)); } } const values = categories.map(cat => valueMap.get(cat) ?? null); const isHorizontal = categoryAxis === 'y'; const dotSize = chartProperties?.dotSize ?? 80; const barWidth = Math.max(2, Math.min(8, dotSize / 10)); const option: any = { tooltip: { trigger: 'axis', axisPointer: { type: 'shadow' } }, xAxis: isHorizontal ? { type: 'value', name: valField } : { type: 'category', data: categories, name: catField }, yAxis: isHorizontal ? { type: 'category', data: categories, name: catField } : { type: 'value', name: valField }, series: [ { type: 'bar', data: values, barWidth, itemStyle: { color: '#5470c6' }, }, { type: 'scatter', data: categories.map((cat, i) => { const v = values[i]; return isHorizontal ? [v, cat] : [cat, v]; }), symbolSize: Math.min(20, dotSize / 4), itemStyle: { color: '#5470c6', borderColor: '#fff', borderWidth: 1 }, z: 2, }, ], }; Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'dotSize', label: 'Dot Size', type: 'continuous', min: 20, max: 300, step: 10, defaultValue: 80 }, ] as ChartPropertyDef[], }; ================================================ FILE: src/lib/agents-chart/echarts/templates/pie.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Pie Chart template. * * Contrast with VL: * VL: mark = "arc" with theta (angular extent) + color (slice groups) * EC: series type = 'pie' with data = [{ name, value }, ...] * * Pie charts have no axes — a fundamentally different layout from bar/line/scatter. */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, DEFAULT_COLORS, computeCircumferencePressure, computeEffectiveBarCount } from './utils'; export const ecPieChartDef: ChartTemplateDef = { chart: 'Pie Chart', template: { mark: 'arc', encoding: {} }, channels: ['size', 'color', 'column', 'row'], markCognitiveChannel: 'area', instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const colorField = channelSemantics.color?.field; const sizeField = channelSemantics.size?.field; // Build pie data: { name, value } pairs const pieData: { name: string; value: number }[] = []; if (colorField && sizeField) { // color = category (slice label), size = measure (slice value) // Aggregate: sum values per category const agg = new Map<string, number>(); for (const row of table) { const cat = String(row[colorField] ?? ''); const val = Number(row[sizeField]) || 0; agg.set(cat, (agg.get(cat) ?? 0) + val); } // Preserve ordinal sort if available const categories = extractCategories(table, colorField, channelSemantics.color?.ordinalSortOrder); for (const cat of categories) { pieData.push({ name: cat, value: agg.get(cat) ?? 0 }); } } else if (colorField) { // No size field → count occurrences per category const counts = new Map<string, number>(); for (const row of table) { const cat = String(row[colorField] ?? ''); counts.set(cat, (counts.get(cat) ?? 0) + 1); } const categories = extractCategories(table, colorField, channelSemantics.color?.ordinalSortOrder); for (const cat of categories) { pieData.push({ name: cat, value: counts.get(cat) ?? 0 }); } } else if (sizeField) { // Only size field, no categories for (const row of table) { const val = Number(row[sizeField]) || 0; pieData.push({ name: String(val), value: val }); } } const innerRadius = chartProperties?.innerRadius ?? 0; // ── Circumference-pressure sizing (spring model) ────────────── // Pie slices have variable width — use effective bar count based // on the smallest slice to determine worst-case pressure. const sliceValues = pieData.map(d => d.value); const effectiveCount = computeEffectiveBarCount(sliceValues); const { radius: pressureRadius, canvasW: rawCanvasW, canvasH } = computeCircumferencePressure(effectiveCount, ctx.canvasSize, { minArcPx: 45, minRadius: 60, maxStretch: ctx.assembleOptions?.maxStretch, margin: 50, // extra room for pie label lines + text }); const canvasW = rawCanvasW; // ── Adaptive label sizing ───────────────────────────────────── // Scale font size and label width based on slice count so that // labels stay readable without crowding. const n = pieData.length; const labelFontSize = n <= 4 ? 13 : n <= 8 ? 11 : n <= 15 ? 10 : 9; // Label width budget: available space outside the pie on each side. // Shrink pie more when there are many slices to leave label room. const radiusFraction = n <= 4 ? 0.70 : n <= 8 ? 0.60 : n <= 15 ? 0.55 : 0.50; const labelBudget = Math.max(40, Math.round((canvasW - 40) / 2 * (1 - radiusFraction))); // Pie radius const outerRadiusPx = Math.max(60, Math.round( Math.min(pressureRadius, (canvasW - 40) / 2 * radiusFraction, (canvasH - 40) / 2 * radiusFraction))); const outerRadius = `${outerRadiusPx}px`; const categoryLabel = colorField ?? 'Category'; const valueLabel = sizeField ?? 'Value'; const option: any = { tooltip: { trigger: 'item' }, _encodingTooltip: { trigger: 'item', parts: [ { from: 'name', label: categoryLabel }, { from: 'value', label: valueLabel, format: 'number' }, ], }, series: [{ type: 'pie', radius: innerRadius > 0 ? [`${Math.round(outerRadiusPx * innerRadius / 100)}px`, outerRadius] : ['0%', outerRadius], center: ['50%', '50%'], data: pieData, emphasis: { itemStyle: { shadowBlur: 10, shadowOffsetX: 0, shadowColor: 'rgba(0, 0, 0, 0.5)', }, }, label: { show: true, formatter: '{b}: {d}%', fontSize: labelFontSize, width: labelBudget, overflow: 'break', // word-wrap long labels }, labelLine: { show: true, }, itemStyle: { borderRadius: chartProperties?.cornerRadius ?? 0, }, }], // Assign colors color: DEFAULT_COLORS, }; // Canvas size from context option._width = canvasW; option._height = canvasH; Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'innerRadius', label: 'Donut', type: 'continuous', min: 0, max: 60, step: 5, defaultValue: 0 } as ChartPropertyDef, { key: 'cornerRadius', label: 'Corners', type: 'continuous', min: 0, max: 10, step: 1, defaultValue: 0 } as ChartPropertyDef, ], }; ================================================ FILE: src/lib/agents-chart/echarts/templates/pyramid.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Pyramid Chart — horizontal bar symmetric (mirror vegalite/templates/bar.ts pyramidChartDef). */ import { ChartTemplateDef } from '../../core/types'; import { extractCategories } from './utils'; export const ecPyramidChartDef: ChartTemplateDef = { chart: 'Pyramid Chart', template: { mark: 'bar', encoding: {} }, channels: ['x', 'y', 'color'], markCognitiveChannel: 'length', declareLayoutMode: () => ({ axisFlags: { y: { banded: true } } }), instantiate: (spec, ctx) => { const { channelSemantics, table } = ctx; const xCS = channelSemantics.x; const yCS = channelSemantics.y; const xField = xCS?.field; const yField = yCS?.field; if (!xField || !yField) return; const yDiscrete = yCS?.type === 'nominal' || yCS?.type === 'ordinal'; const catField = yDiscrete ? yField : xField; const valField = yDiscrete ? xField : yField; const categories = extractCategories(table, catField, yCS?.ordinalSortOrder); const valueMap = new Map<string, number>(); for (const row of table) { const cat = String(row[catField] ?? ''); const v = row[valField]; if (v != null && !isNaN(v)) valueMap.set(cat, (valueMap.get(cat) ?? 0) + Number(v)); } const values = categories.map(cat => valueMap.get(cat) ?? 0); const option: any = { tooltip: { trigger: 'axis', axisPointer: { type: 'shadow' } }, xAxis: { type: 'value', name: valField, axisLabel: { formatter: (v: number) => Math.abs(v).toString() }, }, yAxis: { type: 'category', data: categories, name: catField }, series: [ { type: 'bar', data: values.map(v => -v), itemStyle: { color: '#4e79a7' }, barGap: '-100%' }, { type: 'bar', data: values, itemStyle: { color: '#e15759' } }, ], }; Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, }; ================================================ FILE: src/lib/agents-chart/echarts/templates/radar.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Radar Chart template. * * Contrast with VL: * VL: No native radar — the VL template manually computes polar coordinates * using trig and draws with layered point/line/rule marks. * EC: Native radar series with polar coordinate system — much simpler. * ECharts handles the axis spokes, grid rings, and polar projection natively. * * Data model (long format): * x (nominal): metric / axis name * y (quantitative): value for that metric * color (nominal): entity / group */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, groupBy, DEFAULT_COLORS, computeCircumferencePressure } from './utils'; /** Round up to a nice ceiling for radar axis max. */ function niceMax(v: number): number { if (v <= 0) return 1; const pow = Math.pow(10, Math.floor(Math.log10(v))); const mantissa = v / pow; const nice = mantissa <= 1 ? 1 : mantissa <= 2 ? 2 : mantissa <= 2.5 ? 2.5 : mantissa <= 5 ? 5 : 10; return nice * pow; } export const ecRadarChartDef: ChartTemplateDef = { chart: 'Radar Chart', template: { mark: 'point', encoding: {} }, channels: ['x', 'y', 'color', 'column', 'row'], markCognitiveChannel: 'position', instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const axisField = channelSemantics.x?.field; // metric names const valueField = channelSemantics.y?.field; // metric values const groupField = channelSemantics.color?.field; // entities if (!axisField || !valueField) return; // Extract unique metrics (radar axes) const metrics = extractCategories(table, axisField, channelSemantics.x?.ordinalSortOrder); if (metrics.length < 2) return; // Compute max value per metric for axis scaling const metricMax = new Map<string, number>(); for (const m of metrics) { const vals = table .filter(r => String(r[axisField]) === m) .map(r => Number(r[valueField])) .filter(v => isFinite(v)); metricMax.set(m, niceMax(vals.length > 0 ? Math.max(...vals) : 1)); } // Build radar indicator (axis) definitions const indicator = metrics.map(m => ({ name: m, max: metricMax.get(m) || 1, })); // Build series data const filled = chartProperties?.filled !== false; const fillOpacity = chartProperties?.fillOpacity ?? 0.3; const seriesData: any[] = []; const legendData: string[] = []; if (groupField) { // Multi-group: one polygon per group const groups = groupBy(table, groupField); let colorIdx = 0; for (const [name, rows] of groups) { legendData.push(name); // Aggregate: mean per metric const metricVals = new Map<string, { sum: number; count: number }>(); for (const row of rows) { const m = String(row[axisField]); const v = Number(row[valueField]) || 0; if (!metricVals.has(m)) metricVals.set(m, { sum: 0, count: 0 }); const entry = metricVals.get(m)!; entry.sum += v; entry.count++; } const values = metrics.map(m => { const entry = metricVals.get(m); return entry ? Math.round((entry.sum / entry.count) * 100) / 100 : 0; }); seriesData.push({ name, value: values, itemStyle: { color: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length] }, areaStyle: filled ? { opacity: fillOpacity } : undefined, }); colorIdx++; } } else { // Single group const metricVals = new Map<string, { sum: number; count: number }>(); for (const row of table) { const m = String(row[axisField]); const v = Number(row[valueField]) || 0; if (!metricVals.has(m)) metricVals.set(m, { sum: 0, count: 0 }); const entry = metricVals.get(m)!; entry.sum += v; entry.count++; } const values = metrics.map(m => { const entry = metricVals.get(m); return entry ? Math.round((entry.sum / entry.count) * 100) / 100 : 0; }); seriesData.push({ value: values, areaStyle: filled ? { opacity: fillOpacity } : undefined, }); } // ── Circumference-pressure sizing (spring model) ────────────── // Radar: uniform spokes — each metric is one "bar". // Spokes need more spacing for axis labels than pie/rose. const { radius: pressureRadius, canvasW, canvasH } = computeCircumferencePressure(metrics.length, ctx.canvasSize, { minArcPx: 60, // spokes need more spacing for axis labels minRadius: 80, maxStretch: ctx.assembleOptions?.maxStretch, }); const option: any = { tooltip: { trigger: 'item' }, radar: { indicator, shape: chartProperties?.shape === 'circle' ? 'circle' : 'polygon', radius: pressureRadius, }, series: [{ type: 'radar', data: seriesData, emphasis: { lineStyle: { width: 3 }, }, }], // Enforce minimum canvas for radar readability _width: canvasW, _height: canvasH, }; if (legendData.length > 0) { option.legend = { data: legendData }; } Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'shape', label: 'Grid', type: 'discrete', options: [ { value: undefined, label: 'Polygon (default)' }, { value: 'circle', label: 'Circle' }, ], } as ChartPropertyDef, { key: 'filled', label: 'Fill', type: 'discrete', options: [ { value: true, label: 'Filled (default)' }, { value: false, label: 'Outline only' }, ], } as ChartPropertyDef, { key: 'fillOpacity', label: 'Opacity', type: 'continuous', min: 0.05, max: 0.8, step: 0.05, defaultValue: 0.3 } as ChartPropertyDef, ], }; ================================================ FILE: src/lib/agents-chart/echarts/templates/ranged-dot.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Ranged Dot Plot — line segments + points (mirror vegalite/templates/scatter.ts rangedDotPlotDef). * Expects x (e.g. category), y (value); optional color. Renders as line + scatter. */ import { ChartTemplateDef } from '../../core/types'; import { extractCategories, groupBy, DEFAULT_COLORS } from './utils'; const isDiscrete = (type: string | undefined) => type === 'nominal' || type === 'ordinal'; export const ecRangedDotPlotDef: ChartTemplateDef = { chart: 'Ranged Dot Plot', template: { mark: 'line', encoding: {} }, channels: ['x', 'y', 'color'], markCognitiveChannel: 'position', instantiate: (spec, ctx) => { const { channelSemantics, table } = ctx; const xField = channelSemantics.x?.field; const yField = channelSemantics.y?.field; const colorField = channelSemantics.color?.field; if (!xField || !yField) return; const xIsDiscrete = isDiscrete(channelSemantics.x?.type); const categories = xIsDiscrete ? extractCategories(table, xField, channelSemantics.x?.ordinalSortOrder) : undefined; const option: any = { tooltip: { trigger: 'item' }, xAxis: { type: xIsDiscrete ? 'category' : 'value', name: xField, nameLocation: 'middle', nameGap: 30, ...(categories ? { data: categories } : {}), }, yAxis: { type: 'value', name: yField, nameLocation: 'middle', nameGap: 40 }, series: [], }; if (colorField) { const groups = groupBy(table, colorField); option.legend = { data: [...groups.keys()] }; let colorIdx = 0; for (const [name, rows] of groups) { const lineData = xIsDiscrete ? categories!.map(cat => { const row = rows.find((r: any) => String(r[xField]) === cat); return row ? row[yField] : null; }) : rows.map((r: any) => [r[xField], r[yField]]); const scatterData = xIsDiscrete ? categories!.map((cat, i) => [cat, lineData[i]]) : lineData; option.series.push({ name, type: 'line', data: lineData, showSymbol: false, itemStyle: { color: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length] } }); option.series.push({ name: `${name} pts`, type: 'scatter', data: scatterData, symbolSize: 8, itemStyle: { color: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length] } }); colorIdx++; } } else { const lineData = xIsDiscrete ? categories!.map(cat => { const row = table.find((r: any) => String(r[xField]) === cat); return row ? row[yField] : null; }) : table.map((r: any) => [r[xField], r[yField]]); const scatterData = xIsDiscrete ? categories!.map((cat, i) => [cat, lineData[i]]) : lineData; option.series.push({ type: 'line', data: lineData, showSymbol: false }); option.series.push({ type: 'scatter', data: scatterData, symbolSize: 8 }); } Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, }; ================================================ FILE: src/lib/agents-chart/echarts/templates/rose.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Rose Chart (Nightingale / Coxcomb) template. * * Contrast with VL: * VL: mark = "arc" with theta (angular extent fixed per slice) + radius (value) * EC: series type = 'bar' with coordinateSystem = 'polar', * angleAxis (categorical — directions/categories) + * radiusAxis (value — the measure mapped to wedge radius). * * Data model (long format): * x (nominal): angular category (direction, month, etc.) * y (quantitative): value mapped to wedge radius * color (nominal, optional): stack / group variable */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, groupBy, DEFAULT_COLORS, computeCircumferencePressure } from './utils'; export const ecRoseChartDef: ChartTemplateDef = { chart: 'Rose Chart', template: { mark: 'arc', encoding: {} }, channels: ['x', 'y', 'color', 'column', 'row'], markCognitiveChannel: 'area', instantiate: (spec, ctx) => { const { channelSemantics, table } = ctx; const catField = channelSemantics.x?.field; // angular categories const valField = channelSemantics.y?.field; // wedge radius value const colorField = channelSemantics.color?.field; // stack groups if (!catField || !valField) return; // Extract unique angular categories (directions, months, etc.) const categories = extractCategories(table, catField, channelSemantics.x?.ordinalSortOrder); if (categories.length === 0) return; // Build series data const seriesArr: any[] = []; const legendData: string[] = []; if (colorField) { // Stacked rose: one series per color group const groups = groupBy(table, colorField); let colorIdx = 0; for (const [name, rows] of groups) { legendData.push(name); // Aggregate: sum per category const catAgg = new Map<string, number>(); for (const row of rows) { const cat = String(row[catField] ?? ''); const val = Number(row[valField]) || 0; catAgg.set(cat, (catAgg.get(cat) ?? 0) + val); } const values = categories.map(c => catAgg.get(c) ?? 0); seriesArr.push({ type: 'bar', name, data: values, coordinateSystem: 'polar', stack: 'rose', itemStyle: { color: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length] }, emphasis: { focus: 'series' }, }); colorIdx++; } } else { // Single series: one value per category const catAgg = new Map<string, number>(); for (const row of table) { const cat = String(row[catField] ?? ''); const val = Number(row[valField]) || 0; catAgg.set(cat, (catAgg.get(cat) ?? 0) + val); } const values = categories.map(c => catAgg.get(c) ?? 0); seriesArr.push({ type: 'bar', data: values, coordinateSystem: 'polar', itemStyle: { color: DEFAULT_COLORS[0] }, }); } // Alignment: 'center' puts wedge center at 12 o'clock, // 'left' puts wedge left edge at 12 o'clock. const alignment = ctx.chartProperties?.alignment ?? 'left'; const n = categories.length; // ECharts angleAxis: startAngle is where the first wedge's LEFT edge begins, // and categories proceed clockwise (decreasing angle). // Left alignment: startAngle=90 → left edge at top. // Center alignment: shift forward (increase) by half a wedge so the center lands at top. const startAngle = alignment === 'center' && n > 0 ? 90 + 180 / n : 90; const hasLegend = legendData.length > 0; // Estimate legend width from label text const maxLabelLen = hasLegend ? Math.max(...legendData.map(d => d.length), 3) : 0; const estimatedLegendWidth = hasLegend ? Math.min(150, maxLabelLen * 7 + 40) : 0; // ── Circumference-pressure sizing (spring model) ────────────── // Rose: uniform angular width — each petal is one "bar". const { radius: pressureRadius, canvasW: rawCanvasW, canvasH } = computeCircumferencePressure(categories.length, ctx.canvasSize, { minArcPx: 45, minRadius: 80, maxStretch: ctx.assembleOptions?.maxStretch, }); // Canvas size — grow width to fit legend without squeezing the chart const canvasW = rawCanvasW + (hasLegend ? estimatedLegendWidth : 0); // Shrink polar radius and shift center left to leave room for legend const polarRadius = hasLegend ? Math.min(pressureRadius, (canvasW - estimatedLegendWidth - 40) / 2, (canvasH - 40) / 2) : pressureRadius; const polarCenter = hasLegend ? [`${Math.round((canvasW - estimatedLegendWidth) / 2)}px`, '50%'] : undefined; const option: any = { tooltip: { trigger: 'item', }, angleAxis: { type: 'category', data: categories, startAngle, }, radiusAxis: { // hide axis line for cleaner look axisLine: { show: false }, axisTick: { show: false }, }, polar: { radius: polarRadius, ...(polarCenter != null ? { center: polarCenter } : {}), }, series: seriesArr, color: DEFAULT_COLORS, // Canvas size _width: canvasW, _height: canvasH, }; if (hasLegend) { option.legend = { data: legendData, type: legendData.length > 8 ? 'scroll' : 'plain', orient: 'vertical', right: 10, top: 'middle', textStyle: { fontSize: 11 }, }; } Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'alignment', label: 'Alignment', type: 'discrete', options: [ { value: 'left', label: 'Left (default)' }, { value: 'center', label: 'Center' }, ], } as ChartPropertyDef, ], }; ================================================ FILE: src/lib/agents-chart/echarts/templates/sankey.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Sankey Diagram template. * * Unique to ECharts — no Vega-Lite equivalent. * Displays flow/transfer data as a node-link diagram where link width * encodes flow magnitude. * * Data model (each row = one flow link): * x (nominal): source node name * y (nominal): target node name * size (quantitative): flow value / weight * * Nodes are auto-derived from unique source/target values. * Multiple rows with the same source→target pair are aggregated (sum). * * Scaling: uses the spring model separately for x and y, treating each * node block as a spring. The x-axis counts source nodes (proxy for * layer width), y-axis counts target nodes (proxy for vertical stacking). * Large step multipliers ensure adequate space for edge routing between * node columns. */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { DEFAULT_COLORS } from './utils'; export const ecSankeyDef: ChartTemplateDef = { chart: 'Sankey Diagram', template: { mark: 'rect', encoding: {} }, channels: ['x', 'y', 'size'], markCognitiveChannel: 'area', declareLayoutMode: () => ({ axisFlags: { x: { banded: true }, y: { banded: true }, }, paramOverrides: { // Each node block needs generous space: // x-step covers node width (~20px) + edge routing gap (~60px) // y-step covers node height + nodeGap defaultStepMultiplier: 3, }, }), instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties, layout } = ctx; const sourceField = channelSemantics.x?.field; // source node const targetField = channelSemantics.y?.field; // target node const valueField = channelSemantics.size?.field; // flow value if (!sourceField || !targetField) return; // Aggregate links: source→target → sum(value) const linkAgg = new Map<string, number>(); for (const row of table) { const src = String(row[sourceField] ?? ''); const tgt = String(row[targetField] ?? ''); if (!src || !tgt || src === tgt) continue; // skip self-links const key = `${src}\x00${tgt}`; const val = valueField ? (Number(row[valueField]) || 0) : 1; linkAgg.set(key, (linkAgg.get(key) ?? 0) + val); } // Build links & collect unique nodes const nodeSet = new Set<string>(); const links: { source: string; target: string; value: number }[] = []; for (const [key, value] of linkAgg) { const [source, target] = key.split('\x00'); nodeSet.add(source); nodeSet.add(target); links.push({ source, target, value }); } if (links.length === 0) return; // Build nodes with colors const nodeArr = [...nodeSet]; const nodes = nodeArr.map((name, i) => ({ name, itemStyle: { color: DEFAULT_COLORS[i % DEFAULT_COLORS.length] }, })); // ── Layout-driven sizing ───────────────────────────────────────── // x-step × sourceCount → width (proxy for layer structure) // y-step × targetCount → height (proxy for vertical stacking) const sourceCount = layout.xNominalCount || new Set(table.map(r => String(r[sourceField]))).size; const targetCount = layout.yNominalCount || new Set(table.map(r => String(r[targetField]))).size; const nodeGap = chartProperties?.nodeGap ?? 10; const nodeWidth = chartProperties?.nodeWidth ?? 20; // Width: need room for source column + target column + edge routing // Use xStep as "per source node spacing" — scale by max(2, layerEstimate) const layerEstimate = 2; // typical sankey has at least 2 layers const canvasW = Math.max(300, layout.xStep * Math.max(sourceCount, layerEstimate) + 60); // Height: need room for max stacking of nodes vertically const maxNodesPerColumn = Math.max(sourceCount, targetCount); const canvasH = Math.max(250, layout.yStep * maxNodesPerColumn); const orient = chartProperties?.orient ?? 'horizontal'; const margin = 30; const option: any = { tooltip: { trigger: 'item', triggerOn: 'mousemove', formatter: (params: any) => { if (params.dataType === 'edge') { return `${params.data.source} → ${params.data.target}<br/>Value: ${params.data.value}`; } return params.name; }, }, series: [{ type: 'sankey', data: nodes, links, orient, emphasis: { focus: 'adjacency', }, lineStyle: { color: 'gradient', curveness: 0.5, }, nodeWidth, nodeGap, label: { show: true, fontSize: 11, }, left: margin, right: margin, top: 20, bottom: 20, }], color: DEFAULT_COLORS, _width: canvasW, _height: canvasH, }; Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'orient', label: 'Orient', type: 'discrete', options: [ { value: 'horizontal', label: 'Horizontal (default)' }, { value: 'vertical', label: 'Vertical' }, ], } as ChartPropertyDef, { key: 'nodeWidth', label: 'Node Width', type: 'continuous', min: 5, max: 40, step: 5, defaultValue: 20 } as ChartPropertyDef, { key: 'nodeGap', label: 'Node Gap', type: 'continuous', min: 2, max: 30, step: 2, defaultValue: 10 } as ChartPropertyDef, ], }; ================================================ FILE: src/lib/agents-chart/echarts/templates/scatter.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Scatter Plot template. * * Maps scatter semantics to ECharts series-based config: * VL: encoding.x.field + encoding.y.field → positional channels * EC: series[].data = [[x, y], [x, y], ...] with type: 'scatter' */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { formatTimestamp } from '../instantiate-spec'; import { DEFAULT_COLORS, groupBy, getCategoryOrder, extractCategories } from './utils'; /** Compute a reasonable scatter symbolSize based on canvas area and point count. */ function computeSymbolSize(width: number, height: number, pointCount: number): number { // Target: each point occupies ~0.05% of canvas area (in px²), take sqrt for diameter. // 50pts @ 400×300 → areaPerPt=2400, 2400*0.05=120, √120≈11 → 11 // 500pts @ 600×450 → areaPerPt=540, 540*0.05=27, √27≈5 → 5 // 1000pts @ 400×300 → areaPerPt=120, 120*0.05=6, √6≈2.4 → 3 (min) const canvasArea = width * height; const areaPerPoint = canvasArea / Math.max(1, pointCount); const idealDiameter = Math.sqrt(areaPerPoint * 0.05); return Math.max(3, Math.min(12, Math.round(idealDiameter))); } export const ecScatterPlotDef: ChartTemplateDef = { chart: 'Scatter Plot', template: { mark: 'circle', encoding: {} }, // skeleton for compatibility channels: ['x', 'y', 'color', 'size', 'opacity', 'column', 'row'], markCognitiveChannel: 'position', instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const xField = channelSemantics.x?.field; const yField = channelSemantics.y?.field; const colorField = channelSemantics.color?.field; const sizeField = channelSemantics.size?.field; const sizeRange = (ctx.resolvedEncodings as any)?.size?.sizeRange as [number, number] | undefined; const sizeType = channelSemantics.size?.type; if (!xField || !yField) return; // ECharts symbolSize is pixel diameter; buildECEncodings outputs sizeRange in pixels [4, 28] const EC_SIZE_MIN = 4; const EC_SIZE_MAX = 30; let rangeMin = Math.max(EC_SIZE_MIN, Math.min(EC_SIZE_MAX, sizeRange?.[0] ?? 6)); let rangeMaxClamped = Math.max(EC_SIZE_MIN, Math.min(EC_SIZE_MAX, sizeRange?.[1] ?? 20)); rangeMaxClamped = Math.max(rangeMin, rangeMaxClamped); // Fallback: ensure a visible spread if sizeRange was degenerate if (rangeMaxClamped <= rangeMin) { rangeMin = EC_SIZE_MIN; rangeMaxClamped = EC_SIZE_MAX; } // VL: ordinal/nominal (Rank, Level, etc.) → ordered domain, map by index. Quantitative/temporal with many unique values → sqrt scale. // When resolved type is quantitative but unique count is small (e.g. 4 levels), treat as discrete priority levels → ordinal by index. // If type is missing/wrong but data are 2–12 discrete non-numeric levels (e.g. "Low","Medium","High","Critical"), treat as ordinal. const sizeUniqueCount = sizeField && table.length > 0 ? new Set(table.map((r: any) => String(r[sizeField]))).size : 0; const sizeValuesSample = sizeField && table.length > 0 ? table.slice(0, 50).map((r: any) => r[sizeField]).filter((v: any) => v != null) : []; const allSizeValuesNumeric = sizeValuesSample.length > 0 && sizeValuesSample.every((v: any) => !isNaN(Number(v)) && String(v).trim() !== ''); const useOrdinalSize = sizeType === 'ordinal' || sizeType === 'nominal' || (sizeType === 'quantitative' && sizeUniqueCount >= 2 && sizeUniqueCount <= 12) || (sizeField && sizeUniqueCount >= 2 && sizeUniqueCount <= 12 && !allSizeValuesNumeric); let scaleSize: (raw: number | string | null | undefined) => number; let sizeOrderForLegend: string[] | undefined; /** For quantitative size: data domain for size legend (VL-style). */ let sizeDomainMin: number | undefined; let sizeDomainMax: number | undefined; if (useOrdinalSize && sizeField) { // Discrete levels (Rank, Level, etc.): ordered domain → size by index (VL ordinal scale). // VL: sort = ordinalSortOrder when set, else sort = null → domain order = data encounter order. // We align: getCategoryOrder(ctx, 'size') when present; otherwise extractCategories(..., undefined) // preserves first-occurrence order, matching VL. const sizeOrder = extractCategories(table, sizeField, getCategoryOrder(ctx, 'size')); sizeOrderForLegend = sizeOrder; const orderMap = new Map<string, number>(sizeOrder.map((val, i) => [String(val), i])); const n = sizeOrder.length; scaleSize = (raw: number | string | null | undefined): number => { if (raw == null) return rangeMin; const key = String(raw); const index = orderMap.get(key); if (index === undefined) return rangeMin; const t = n > 1 ? index / (n - 1) : 0; return Math.round(rangeMin + t * (rangeMaxClamped - rangeMin)); }; } else if (sizeField) { // Continuous quantitative/temporal: sqrt scale over numeric domain (VL size scale type: sqrt, zero: true). const vals = table .map((r: any) => r[sizeField]) .map((v: any) => (v != null ? Number(v) : NaN)) .filter((v: number) => !isNaN(v)); const sizeMin = vals.length ? Math.min(...vals) : 0; const sizeMax = vals.length ? Math.max(...vals) : 1; sizeDomainMin = sizeMin; sizeDomainMax = sizeMax; scaleSize = (raw: number | string | null | undefined): number => { const v = raw != null ? Number(raw) : NaN; if (isNaN(v)) return rangeMin; let t: number; if (sizeMax === sizeMin) t = 0.5; else { const sqrtMin = Math.sqrt(Math.max(0, sizeMin)); const sqrtMax = Math.sqrt(Math.max(0, sizeMax)); const sqrtV = Math.sqrt(Math.max(0, v)); t = (sqrtV - sqrtMin) / (sqrtMax - sqrtMin); } t = Math.max(0, Math.min(1, t)); return Math.round(rangeMin + t * (rangeMaxClamped - rangeMin)); }; } else { scaleSize = () => rangeMin; } // When ordinal size has a discrete order, use piecewise visualMap; when quantity, use continuous visualMap (scatter-aqi-color style). const usePiecewiseSizeVisualMap = sizeOrderForLegend && sizeOrderForLegend.length > 0 && sizeField; const useContinuousSizeVisualMap = sizeField != null && sizeDomainMin !== undefined && sizeDomainMax !== undefined; const useVisualMapForSize = usePiecewiseSizeVisualMap || useContinuousSizeVisualMap; // X/Y can be value (quantitative/temporal) or category (nominal/ordinal) — align with VL const xType = channelSemantics.x?.type; const yType = channelSemantics.y?.type; const xIsCategorical = xType === 'nominal' || xType === 'ordinal'; const yIsCategorical = yType === 'nominal' || yType === 'ordinal'; const xCategories = xIsCategorical ? extractCategories(table, xField, getCategoryOrder(ctx, 'x')) : []; const yCategories = yIsCategorical ? extractCategories(table, yField, getCategoryOrder(ctx, 'y')) : []; const xCategoryToIndex = new Map<string, number>(xCategories.map((c, i) => [String(c), i])); const yCategoryToIndex = new Map<string, number>(yCategories.map((c, i) => [String(c), i])); // ECharts scatter uses direct data arrays const option: any = { tooltip: { trigger: 'item' }, xAxis: xIsCategorical ? { type: 'category', data: xCategories, name: xField, nameLocation: 'middle', nameGap: 30, axisLabel: { interval: 0, rotate: 90 }, axisTick: { show: true, alignWithLabel: true }, axisLine: { show: true }, } : { type: 'value', name: xField, nameLocation: 'middle', nameGap: 30 }, yAxis: yIsCategorical ? { type: 'category', data: yCategories, name: yField, nameLocation: 'middle', nameGap: 40, axisLabel: { interval: 0, rotate: 0 }, axisTick: { show: true, alignWithLabel: true }, axisLine: { show: true }, } : { type: 'value', name: yField, nameLocation: 'middle', nameGap: 40 }, series: [], }; if (usePiecewiseSizeVisualMap) { // Piecewise visualMap maps dimension 2 to symbolSize; hide default UI and use custom graphic legend (Vega-Lite style: different sized circles, label on the right). option.visualMap = [ { type: 'piecewise', show: false, dimension: 2, pieces: sizeOrderForLegend!.map((name) => ({ value: name, symbolSize: scaleSize(name), })), orient: 'vertical', right: 10, top: 'center', itemGap: 8, itemSymbol: 'circle', formatter: (value: string) => value, title: sizeField, }, ]; option._visualMapWidth = 88; // One group for the whole legend; all coordinates use left/top so text aligns left const ordLegendRight = 28; const ordGap = 8; const ordRowGap = 6; const ordFontSize = 10; const ordTitleHeight = 20; const ordLabelWidth = 44; const canvasH = ctx.canvasSize?.height ?? 300; const maxCircleR = Math.max(...sizeOrderForLegend!.map((name) => scaleSize(name) / 2)); const legendWidth = ordLabelWidth + ordGap + 2 * maxCircleR; const scatterColor = (ctx.resolvedEncodings as any)?.color?.colorPalette?.[0] ?? DEFAULT_COLORS[0]; const rowHeights = sizeOrderForLegend!.map((name) => Math.max(scaleSize(name), 16) + ordRowGap); const totalLegendHeight = ordTitleHeight + rowHeights.reduce((a, b) => a + b, 0); const ordLegendTop = Math.max(10, (canvasH - totalLegendHeight) / 2); const legendChildren: any[] = [ { type: 'text' as const, left: 0, top: 0, style: { text: sizeField, fontSize: 11, fontWeight: 'bold', fill: '#333', textAlign: 'left', }, }, ]; let rowTop = ordTitleHeight; for (let i = 0; i < sizeOrderForLegend!.length; i++) { const name = sizeOrderForLegend![i]; const r = scaleSize(name) / 2; const rowH = rowHeights[i]; const circleTop = rowTop + (rowH - scaleSize(name)) / 2; const textTop = rowTop + (rowH - ordFontSize) / 2; legendChildren.push({ type: 'circle' as const, left: maxCircleR - r, top: circleTop - r, shape: { cx: r, cy: r, r }, style: { fill: scatterColor }, }); legendChildren.push({ type: 'text' as const, left: 2 * maxCircleR + ordGap, top: textTop, style: { text: name, fontSize: ordFontSize, fill: '#333', textAlign: 'left', }, }); rowTop += rowH; } const ordLegendGraphic = { type: 'group' as const, right: ordLegendRight, top: ordLegendTop, width: legendWidth, z: 100, children: legendChildren, }; const existingGraphic = option.graphic; option.graphic = Array.isArray(existingGraphic) ? [...existingGraphic, ordLegendGraphic] : existingGraphic ? [existingGraphic, ordLegendGraphic] : [ordLegendGraphic]; } else if (useContinuousSizeVisualMap) { // Quantity size: continuous visualMap (scatter-aqi-color style), inRange.symbolSize maps dimension to size. // Enforce a minimum spread so circles are visibly different (ECharts maps data linearly to [min, max]). const SIZE_SPREAD_MIN = 20; const sizeMaxForMap = Math.max(rangeMaxClamped, rangeMin + SIZE_SPREAD_MIN); const fmtSize = (v: number) => (Number.isInteger(v) ? String(v) : v.toFixed(1)); const sizeVisualMap = { type: 'continuous' as const, show: true, min: sizeDomainMin!, max: sizeDomainMax!, dimension: 2, inRange: { symbolSize: [rangeMin, sizeMaxForMap] as [number, number] }, orient: 'vertical', right: 50, top: '10.0%', bottom: '10.0%', padding: 0, itemGap: 0, text: [fmtSize(sizeDomainMax!), fmtSize(sizeDomainMin!)] as [string, string], textStyle: { fontSize: 10 }, seriesIndex: 0, name: sizeField, }; if (option.visualMap) { (option.visualMap as any[]).push(sizeVisualMap); } else { option.visualMap = [sizeVisualMap]; } option._visualMapWidth = 70; option.graphic = option.graphic || []; const existingGraphic = Array.isArray(option.graphic) ? option.graphic : [option.graphic]; option.graphic = [ ...existingGraphic, { type: 'text' as const, right: 50, top: 10, z: 100, style: { text: sizeField, fontSize: 11, fontWeight: 'bold', fill: '#333', textAlign: 'right', }, }, ]; } // Apply zero-baseline decisions (only for value axes) // ECharts: scale=true means "data-fit, don't force zero" // scale=false (default) means "include zero" if (!xIsCategorical && channelSemantics.x?.zero) { option.xAxis.scale = !channelSemantics.x.zero.zero; } if (!yIsCategorical && channelSemantics.y?.zero) { option.yAxis.scale = !channelSemantics.y.zero.zero; } // Opacity from chart properties const opacity = chartProperties?.opacity ?? 1; const xVal = (row: any) => xIsCategorical ? (xCategoryToIndex.get(String(row[xField] ?? '')) ?? 0) : row[xField]; const yVal = (row: any) => yIsCategorical ? (yCategoryToIndex.get(String(row[yField] ?? '')) ?? 0) : row[yField]; const pointData = (row: any) => sizeField != null ? [xVal(row), yVal(row), row[sizeField]] : [xVal(row), yVal(row)]; // Palette from resolvedEncodings (scheme) or fallback to DEFAULT_COLORS const colorPalette = (ctx.resolvedEncodings as any)?.color?.colorPalette ?? (ctx.resolvedEncodings as any)?.group?.colorPalette ?? DEFAULT_COLORS; const legendOpts = (ctx.resolvedEncodings as any)?.color ?? (ctx.resolvedEncodings as any)?.group; const colorType = channelSemantics.color?.type ?? (ctx.resolvedEncodings as any)?.color?.type; const isTemporalColor = colorField && colorType === 'temporal'; const isContinuousColor = colorField && (colorType === 'quantitative' || colorType === 'temporal'); if (isContinuousColor) { // VL: color type quantitative → numeric scale; temporal → timestamp scale (mirror VL type "temporal" + legend format "%b %d, %Y"). const colorDim = sizeField != null ? 3 : 2; // data: [x, y, size?, colorVal] const toColorVal = isTemporalColor ? (v: any) => (v != null ? new Date(v).getTime() : NaN) : (v: any) => (v != null ? Number(v) : NaN); const pointDataWithColor = (row: any) => { const x = xVal(row); const y = yVal(row); const c = toColorVal(row[colorField]); if (sizeField != null) return [x, y, row[sizeField], c]; return [x, y, c]; }; const colorVals = table .map((r: any) => toColorVal(r[colorField])) .filter((v: number) => !isNaN(v)); const colorMin = colorVals.length ? Math.min(...colorVals) : (isTemporalColor ? Date.now() : 0); const colorMax = colorVals.length ? Math.max(...colorVals) : (isTemporalColor ? Date.now() : 1); const scheme = (ctx.encodings as any)?.color?.scheme ?? ''; // Default visualMap color bar: gray gradient (light → dark) const defaultGrayRange = ['#f5f5f5', '#e0e0e0', '#9e9e9e', '#616161', '#424242']; const greensRange = ['#f7fcf5', '#c7e9c0', '#41ab5d', '#006d2c', '#00441b']; // Continuous colormap: default gray; optional scheme override (e.g. green) or palette. const inRange = /green/i.test(scheme) ? greensRange : colorPalette.length >= 2 ? [colorPalette[colorPalette.length - 1], colorPalette[0]] // light → dark : defaultGrayRange; // Layout: when both size and color visualMap exist, place them side by side (size right, color left) const VM_BAR_RIGHT = 50; const VM_BAR_WIDTH = 70; const VM_GAP = 16; const VM_VAL_RIGHT = 28; const VM_TITLE_TOP = 10; const VM_FONT_SIZE = 10; const REF_H = 400; const VM_BAR_TOP_PX = 40; const VM_BAR_BOTTOM_PX = 40; const VM_TOP_PCT = ((VM_BAR_TOP_PX / REF_H) * 100).toFixed(1) + '%'; const VM_BOTTOM_PCT = ((VM_BAR_BOTTOM_PX / REF_H) * 100).toFixed(1) + '%'; const hasSizeVisualMap = option.visualMap && Array.isArray(option.visualMap) && option.visualMap.some((vm: any) => vm.inRange?.symbolSize != null); const colorBarRight = hasSizeVisualMap ? VM_BAR_RIGHT + VM_BAR_WIDTH + VM_GAP : VM_BAR_RIGHT; const temporalFormat = channelSemantics.color?.temporalFormat ?? '%b %d, %Y'; const formatColorLabel = (val: number) => isTemporalColor ? formatTimestamp(val, temporalFormat) : String(val); // Use visualMap's built-in text for max/min so labels are positioned by ECharts and align with the bar. // text[0] = high (top), text[1] = low (bottom) per ContinuousView. const colorVisualMap = { type: 'continuous' as const, min: colorMin, max: colorMax, dimension: colorDim, inRange: { color: inRange }, orient: 'vertical', right: colorBarRight, top: VM_TOP_PCT, bottom: VM_BOTTOM_PCT, padding: 0, itemGap: 0, text: [formatColorLabel(colorMax), formatColorLabel(colorMin)] as [string, string], formatter: formatColorLabel, textStyle: { fontSize: VM_FONT_SIZE }, show: true, seriesIndex: 0, name: colorField, }; if (option.visualMap) { (option.visualMap as any[]).push(colorVisualMap); } else { option.visualMap = colorVisualMap; } option._visualMapWidth = hasSizeVisualMap ? VM_BAR_WIDTH + VM_GAP + VM_BAR_WIDTH : VM_BAR_WIDTH; // Only the title is custom graphic; max/min come from visualMap.text above. const vmGraphics: any[] = [ { type: 'text' as const, right: colorBarRight, top: VM_TITLE_TOP, z: 100, style: { text: colorField, fontSize: 11, fontWeight: 'bold', fill: '#333', textAlign: 'right', }, }, ]; const existingGraphic = option.graphic; option.graphic = Array.isArray(existingGraphic) ? [...existingGraphic, ...vmGraphics] : existingGraphic ? [existingGraphic, ...vmGraphics] : vmGraphics; const data = table.map((row: any) => pointDataWithColor(row)); const seriesOpt: any = { type: 'scatter', data, itemStyle: { opacity }, }; if (sizeField != null && !useVisualMapForSize) { seriesOpt.symbolSize = (value: number[] | number) => scaleSize(Array.isArray(value) ? value[2] : value); } option.series.push(seriesOpt); } else if (colorField) { // Categorical color: one series per category, legend with category names const colorOrder = extractCategories(table, colorField, getCategoryOrder(ctx, 'color')); const groups = new Map<string, number[][]>(); for (const row of table) { const key = String(row[colorField] ?? ''); if (!groups.has(key)) groups.set(key, []); groups.get(key)!.push(pointData(row) as number[]); } const legendNames = colorOrder.length > 0 ? colorOrder : [...groups.keys()]; const hasSizeBySeries = sizeField != null && !useVisualMapForSize; option.legend = { data: legendNames.map((name) => { const data = groups.get(name) ?? []; if (!hasSizeBySeries || data.length === 0) return name; const sizes = data.map((d: number[]) => (d.length >= 3 ? scaleSize(d[2]) : rangeMin)); sizes.sort((a, b) => a - b); const medianSize = sizes[Math.floor(sizes.length / 2)] ?? rangeMin; return { name, symbolSize: medianSize, itemStyle: { symbolSize: medianSize } }; }), show: true, }; option._legendTitle = colorField; if (legendOpts?.legendSymbolSize != null && !hasSizeBySeries) { option.legend.itemWidth = legendOpts.legendSymbolSize; option.legend.itemHeight = legendOpts.legendSymbolSize; option.legend.itemGap = 8; } if (legendOpts?.legendLabelFontSize != null) { option.legend.textStyle = option.legend.textStyle ?? {}; option.legend.textStyle.fontSize = legendOpts.legendLabelFontSize; } legendNames.forEach((name) => { const data = groups.get(name) ?? []; if (data.length === 0) return; const colorIdx = colorOrder.indexOf(name); const seriesOpt: any = { name, type: 'scatter', data, itemStyle: { color: colorPalette[colorIdx % colorPalette.length], opacity, }, }; if (hasSizeBySeries) { seriesOpt.symbolSize = (value: number[] | number) => scaleSize(Array.isArray(value) ? value[2] : value); } option.series.push(seriesOpt); }); } else { const data = table.map((row: any) => pointData(row)); const seriesOpt: any = { type: 'scatter', data, itemStyle: { opacity }, }; if (sizeField != null && !useVisualMapForSize) { seriesOpt.symbolSize = (value: number[] | number) => scaleSize(Array.isArray(value) ? value[2] : value); } else if (useContinuousSizeVisualMap && sizeDomainMin !== undefined && sizeDomainMax !== undefined) { // Apply same linear mapping as visualMap so circles are sized correctly (visualMap may not drive symbolSize in all environments) const SIZE_SPREAD_MIN = 20; const sizeSpread = Math.max(rangeMaxClamped - rangeMin, SIZE_SPREAD_MIN); const sizeMaxMapped = rangeMin + sizeSpread; seriesOpt.symbolSize = (value: number[] | number) => { const v = Array.isArray(value) ? value[2] : value; const num = Number(v); if (v == null || isNaN(num)) return rangeMin; const span = sizeDomainMax! - sizeDomainMin!; const t = span <= 0 ? 0.5 : Math.max(0, Math.min(1, (num - sizeDomainMin!) / span)); return Math.round(rangeMin + t * (sizeMaxMapped - rangeMin)); }; } option.series.push(seriesOpt); } // Tooltip: use shared encoding-style formatter (same as bar/line/pie) via _encodingTooltip const xName = option.xAxis?.name ?? xField ?? 'X'; const yName = option.yAxis?.name ?? yField ?? 'Y'; const sizeName = sizeField ?? null; const colorName = colorField ?? null; const temporalFormat = channelSemantics.color?.temporalFormat ?? '%b %d, %Y'; const tooltipParts: Array<{ from: string; index?: number; label: string; format?: string; temporalFormat?: string; categoryNames?: string[] }> = [ { from: 'data', index: 0, label: xName, format: xIsCategorical ? 'category' : 'number', categoryNames: xIsCategorical ? xCategories : undefined }, { from: 'data', index: 1, label: yName, format: yIsCategorical ? 'category' : 'number', categoryNames: yIsCategorical ? yCategories : undefined }, ]; if (sizeName != null) tooltipParts.push({ from: 'data', index: 2, label: sizeName, format: 'number' }); if (colorName != null) { if (isContinuousColor) { tooltipParts.push({ from: 'data', index: sizeField != null ? 3 : 2, label: colorName, format: isTemporalColor ? 'temporal' : 'number', temporalFormat, }); } else { tooltipParts.push({ from: 'series', label: colorName }); } } option.tooltip = option.tooltip ?? {}; option._encodingTooltip = { trigger: 'item', parts: tooltipParts }; // When there are multiple series (e.g. categorical color), size visualMap must apply to all of them const vmList = Array.isArray(option.visualMap) ? option.visualMap : (option.visualMap ? [option.visualMap] : []); const seriesCount = option.series?.length ?? 0; if (seriesCount > 1) { const allIndices = option.series!.map((_: any, i: number) => i); for (const vm of vmList) { if (vm.type === 'continuous' && vm.inRange?.symbolSize != null) { vm.seriesIndex = allIndices; } } } // Write the ECharts option into the spec object Object.assign(spec, option); // Clear VL skeleton delete spec.mark; delete spec.encoding; }, properties: [ { key: 'opacity', label: 'Opacity', type: 'continuous', min: 0.1, max: 1, step: 0.05, defaultValue: 1 }, ], postProcess: (option, ctx) => { if (!option.series || !Array.isArray(option.series)) return; // When visualMap controls symbolSize (piecewise ordinal or continuous quantity), do not override series.symbolSize const vmList = Array.isArray(option.visualMap) ? option.visualMap : (option.visualMap ? [option.visualMap] : []); const visualMapControlsSize = vmList.some( (vm: any) => (vm.type === 'piecewise' && Array.isArray(vm.pieces) && vm.pieces.some((p: any) => p.symbolSize != null)) || (vm.type === 'continuous' && vm.inRange?.symbolSize != null), ); if (visualMapControlsSize) return; const w = option._width || ctx.canvasSize.width; const h = option._height || ctx.canvasSize.height; const pointCount = ctx.table.length; const size = computeSymbolSize(w, h, pointCount); for (const series of option.series) { if (series.type !== 'scatter') continue; // Series has size encoding (data is [x, y, size]): keep template-set symbolSize function, do not override const hasSizeEncoding = series.data?.length && Array.isArray(series.data[0]) && (series.data[0] as number[]).length >= 3; if (hasSizeEncoding) continue; if (series.symbolSize == null) { series.symbolSize = size; } } }, }; /** Simple linear regression: slope and intercept. */ function linearRegression(data: number[][]): { slope: number; intercept: number; xMin: number; xMax: number } { const n = data.length; if (n === 0) return { slope: 0, intercept: 0, xMin: 0, xMax: 0 }; let sumX = 0, sumY = 0, sumXY = 0, sumXX = 0; let xMin = data[0][0], xMax = data[0][0]; for (const [x, y] of data) { sumX += x; sumY += y; sumXY += x * y; sumXX += x * x; if (x < xMin) xMin = x; if (x > xMax) xMax = x; } const slope = (n * sumXY - sumX * sumY) / (n * sumXX - sumX * sumX) || 0; const intercept = (sumY - slope * sumX) / n; return { slope, intercept, xMin, xMax }; } /** Polynomial regression via least-squares normal equations. */ function polyRegression(data: number[][], order: number): { coeffs: number[]; xMin: number; xMax: number } { const n = data.length; if (n === 0) return { coeffs: [0], xMin: 0, xMax: 0 }; let xMin = data[0][0], xMax = data[0][0]; for (const [x] of data) { if (x < xMin) xMin = x; if (x > xMax) xMax = x; } const k = order + 1; // Build normal equations: X^T X * a = X^T y const xtx: number[][] = Array.from({ length: k }, () => new Array(k).fill(0)); const xty: number[] = new Array(k).fill(0); for (const [x, y] of data) { let xp = 1; for (let i = 0; i < k; i++) { xty[i] += xp * y; let xp2 = xp; for (let j = i; j < k; j++) { xtx[i][j] += xp2; if (i !== j) xtx[j][i] += xp2; xp2 *= x; } xp *= x; } } // Gaussian elimination with partial pivoting const aug = xtx.map((row, i) => [...row, xty[i]]); for (let col = 0; col < k; col++) { let maxRow = col; for (let row = col + 1; row < k; row++) { if (Math.abs(aug[row][col]) > Math.abs(aug[maxRow][col])) maxRow = row; } [aug[col], aug[maxRow]] = [aug[maxRow], aug[col]]; const pivot = aug[col][col]; if (Math.abs(pivot) < 1e-12) continue; for (let j = col; j <= k; j++) aug[col][j] /= pivot; for (let row = 0; row < k; row++) { if (row === col) continue; const factor = aug[row][col]; for (let j = col; j <= k; j++) aug[row][j] -= factor * aug[col][j]; } } const coeffs = aug.map(row => row[k]); return { coeffs, xMin, xMax }; } /** Evaluate polynomial at x given coefficients [a0, a1, a2, ...] */ function polyEval(coeffs: number[], x: number): number { let result = 0, xp = 1; for (const c of coeffs) { result += c * xp; xp *= x; } return result; } /** Generate regression curve points for a given method. */ function regressionCurvePoints( data: number[][], method: string, order: number, numPoints: number = 50, ): number[][] { if (data.length === 0) return []; if (method === 'linear' || !method) { const reg = linearRegression(data); return [[reg.xMin, reg.slope * reg.xMin + reg.intercept], [reg.xMax, reg.slope * reg.xMax + reg.intercept]]; } // For non-linear methods, transform data, fit linear, then transform back if (method === 'log') { // y = a + b * ln(x) const filtered = data.filter(([x]) => x > 0); if (filtered.length < 2) return []; const logData = filtered.map(([x, y]) => [Math.log(x), y]); const reg = linearRegression(logData); let xMin = Infinity, xMax = -Infinity; for (const [x] of filtered) { if (x < xMin) xMin = x; if (x > xMax) xMax = x; } const pts: number[][] = []; for (let i = 0; i < numPoints; i++) { const x = xMin + (xMax - xMin) * i / (numPoints - 1); pts.push([x, reg.intercept + reg.slope * Math.log(x)]); } return pts; } if (method === 'exp') { // ln(y) = a + b*x → y = exp(a) * exp(b*x) const filtered = data.filter(([, y]) => y > 0); if (filtered.length < 2) return []; const logData = filtered.map(([x, y]) => [x, Math.log(y)]); const reg = linearRegression(logData); let xMin = Infinity, xMax = -Infinity; for (const [x] of filtered) { if (x < xMin) xMin = x; if (x > xMax) xMax = x; } const pts: number[][] = []; for (let i = 0; i < numPoints; i++) { const x = xMin + (xMax - xMin) * i / (numPoints - 1); pts.push([x, Math.exp(reg.intercept + reg.slope * x)]); } return pts; } if (method === 'pow') { // ln(y) = a + b*ln(x) → y = exp(a) * x^b const filtered = data.filter(([x, y]) => x > 0 && y > 0); if (filtered.length < 2) return []; const logData = filtered.map(([x, y]) => [Math.log(x), Math.log(y)]); const reg = linearRegression(logData); let xMin = Infinity, xMax = -Infinity; for (const [x] of filtered) { if (x < xMin) xMin = x; if (x > xMax) xMax = x; } const pts: number[][] = []; for (let i = 0; i < numPoints; i++) { const x = xMin + (xMax - xMin) * i / (numPoints - 1); pts.push([x, Math.exp(reg.intercept) * Math.pow(x, reg.slope)]); } return pts; } if (method === 'quad') { const reg = polyRegression(data, 2); const pts: number[][] = []; for (let i = 0; i < numPoints; i++) { const x = reg.xMin + (reg.xMax - reg.xMin) * i / (numPoints - 1); pts.push([x, polyEval(reg.coeffs, x)]); } return pts; } if (method === 'poly') { const reg = polyRegression(data, order); const pts: number[][] = []; for (let i = 0; i < numPoints; i++) { const x = reg.xMin + (reg.xMax - reg.xMin) * i / (numPoints - 1); pts.push([x, polyEval(reg.coeffs, x)]); } return pts; } // Fallback to linear const reg = linearRegression(data); return [[reg.xMin, reg.slope * reg.xMin + reg.intercept], [reg.xMax, reg.slope * reg.xMax + reg.intercept]]; } /** * Regression — scatter + trend line (mirror vegalite/templates/scatter.ts regressionDef). */ export const ecRegressionDef: ChartTemplateDef = { chart: 'Regression', template: { mark: 'circle', encoding: {} }, channels: ['x', 'y', 'size', 'color', 'column', 'row'], markCognitiveChannel: 'position', instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const xField = channelSemantics.x?.field; const yField = channelSemantics.y?.field; const colorField = channelSemantics.color?.field; if (!xField || !yField) return; const method = chartProperties?.regressionMethod ?? 'linear'; const polyOrder = chartProperties?.polyOrder ?? 3; const option: any = { tooltip: { trigger: 'item' }, xAxis: { type: 'value', name: xField, nameLocation: 'middle', nameGap: 30 }, yAxis: { type: 'value', name: yField, nameLocation: 'middle', nameGap: 40 }, series: [], }; if (channelSemantics.x?.zero) option.xAxis.scale = !channelSemantics.x.zero.zero; if (channelSemantics.y?.zero) option.yAxis.scale = !channelSemantics.y.zero.zero; const opacity = chartProperties?.opacity ?? 1; if (colorField) { const groups = groupBy(table, colorField); option.legend = { data: [...groups.keys()] }; option._legendTitle = colorField; let colorIdx = 0; for (const [name, rows] of groups) { const data = rows.map((r: any) => [r[xField], r[yField]]); const lineData = regressionCurvePoints(data, method, polyOrder); option.series.push({ name, type: 'scatter', data, itemStyle: { color: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length], opacity }, }); option.series.push({ name: `${name} (trend)`, type: 'line', data: lineData, showSymbol: false, smooth: method !== 'linear', lineStyle: { color: DEFAULT_COLORS[colorIdx % DEFAULT_COLORS.length], width: 2 }, }); colorIdx++; } } else { const data = table.map((r: any) => [r[xField], r[yField]]); const lineData = regressionCurvePoints(data, method, polyOrder); option.series.push({ type: 'scatter', data, itemStyle: { opacity } }); option.series.push({ name: 'Trend', type: 'line', data: lineData, showSymbol: false, smooth: method !== 'linear', lineStyle: { color: '#ee6666', width: 2 }, }); } const xName = option.xAxis?.name ?? xField ?? 'X'; const yName = option.yAxis?.name ?? yField ?? 'Y'; const tooltipParts: Array<{ from: string; index?: number; label: string; format?: string }> = [ { from: 'data', index: 0, label: xName, format: 'number' }, { from: 'data', index: 1, label: yName, format: 'number' }, ]; if (colorField) tooltipParts.push({ from: 'series', label: colorField }); option._encodingTooltip = { trigger: 'item', parts: tooltipParts }; Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'regressionMethod', label: 'Method', type: 'discrete', options: [ { value: 'linear', label: 'Linear' }, { value: 'log', label: 'Logarithmic' }, { value: 'exp', label: 'Exponential' }, { value: 'pow', label: 'Power' }, { value: 'quad', label: 'Quadratic' }, { value: 'poly', label: 'Polynomial' }, ], defaultValue: 'linear', } as ChartPropertyDef, { key: 'polyOrder', label: 'Poly Order', type: 'continuous', min: 2, max: 10, step: 1, defaultValue: 3, } as ChartPropertyDef, ], }; ================================================ FILE: src/lib/agents-chart/echarts/templates/streamgraph.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Streamgraph template — uses native ThemeRiver series. * * Contrast with VL: * VL: area mark with y.stack = "center" and y.axis = null * EC: themeRiver series — purpose-built for streamgraphs * * ThemeRiver is ECharts' native streamgraph implementation: * - Automatic center-aligned stacking (wiggle / silhouette baseline) * - Built-in legend integration * - Smooth transitions between series * * Channels: x (temporal/ordinal), y (quantitative), color (series groups) */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { groupBy, DEFAULT_COLORS } from './utils'; export const ecStreamgraphDef: ChartTemplateDef = { chart: 'Streamgraph', template: { mark: 'area', encoding: {} }, channels: ['x', 'y', 'color', 'column', 'row'], markCognitiveChannel: 'area', declareLayoutMode: () => ({ paramOverrides: { continuousMarkCrossSection: { x: 100, y: 20, seriesCountAxis: 'auto' } }, }), instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const xCS = channelSemantics.x; const yCS = channelSemantics.y; const colorField = channelSemantics.color?.field; if (!xCS?.field || !yCS?.field) return; const xField = xCS.field; const yField = yCS.field; // ── Build ThemeRiver data ──────────────────────────────────────── // ThemeRiver data format: [[date, value, seriesName], ...] // All series must have entries for every x-value (fill with 0 if missing) if (!colorField) { // Without a color/series field, fall back to a simple area chart const option: any = { tooltip: { trigger: 'axis' }, xAxis: { type: xCS.type === 'temporal' ? 'time' : 'value', name: xField, nameLocation: 'middle', nameGap: 30, }, yAxis: { type: 'value', show: false }, series: [{ type: 'line', data: table.map(r => [r[xField], r[yField]]), areaStyle: { opacity: 0.85 }, lineStyle: { width: 0.5 }, symbol: 'none', }], }; Object.assign(spec, option); delete spec.mark; delete spec.encoding; return; } // Collect unique x-values in order (preserving data order) const xValSet = new Set<string>(); const xVals: string[] = []; for (const row of table) { const xv = String(row[xField]); if (!xValSet.has(xv)) { xValSet.add(xv); xVals.push(xv); } } // Collect series names const groups = groupBy(table, colorField); const seriesNames = [...groups.keys()]; // Build a lookup: (xVal, seriesName) → value const valMap = new Map<string, number>(); for (const row of table) { const key = `${row[xField]}|||${row[colorField]}`; valMap.set(key, row[yField] ?? 0); } // Build themeRiver data array — every series must appear at every x-value const riverData: [string, number, string][] = []; for (const xv of xVals) { for (const sn of seriesNames) { const key = `${xv}|||${sn}`; riverData.push([xv, valMap.get(key) ?? 0, sn]); } } // ── Build option ───────────────────────────────────────────────── const option: any = { tooltip: { trigger: 'axis', axisPointer: { type: 'line', lineStyle: { color: 'rgba(0,0,0,0.2)', width: 1, type: 'solid' } }, }, legend: { data: seriesNames, }, singleAxis: { type: xCS.type === 'temporal' ? 'time' : 'category', ...(xCS.type !== 'temporal' ? { data: xVals } : {}), bottom: 45, // enough room for tick labels + axis name below name: xField, nameLocation: 'middle', nameGap: 25, nameTextStyle: { fontSize: 12 }, axisLabel: { fontSize: 11 }, }, series: [{ type: 'themeRiver', data: riverData, label: { show: false }, emphasis: { focus: 'series' }, itemStyle: { borderWidth: 0.5, borderColor: 'rgba(255,255,255,0.3)', }, }], }; // Apply color palette option.color = seriesNames.map((_, i) => DEFAULT_COLORS[i % DEFAULT_COLORS.length]); Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, postProcess: (option) => { // ThemeRiver uses singleAxis (not xAxis/yAxis). The layout engine // now computes _width/_height for singleAxis charts too, but it uses // the grid-based margins (which assume xAxis/yAxis). We adjust the // singleAxis margins and canvas to match consistently. if (option.singleAxis) { const BUFFER = 15; const hasLegend = !!option.legend; const rightMargin = hasLegend ? 130 : 20; // singleAxis positions: left/right control horizontal extent, // top/bottom control vertical extent option.singleAxis.left = option.singleAxis.left || 50; option.singleAxis.right = option.singleAxis.right || rightMargin; // Ensure canvas is large enough const minW = 600 + BUFFER; const minH = 350 + BUFFER; if (typeof option._width === 'number' && option._width < minW) { option._width = minW; } if (typeof option._height === 'number' && option._height < minH) { option._height = minH; } if (!option._width) option._width = minW; if (!option._height) option._height = minH; // Push the axis up from the canvas bottom to avoid clipping if (typeof option.singleAxis.bottom === 'number') { option.singleAxis.bottom += BUFFER; } } }, properties: [ ], }; ================================================ FILE: src/lib/agents-chart/echarts/templates/sunburst.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Sunburst Chart template. * * Unique to ECharts — no native Vega-Lite equivalent. * Displays hierarchical data as concentric rings where angular extent * encodes value and ring level encodes hierarchy depth. * * Data model: * color (nominal): top-level category (outer ring or first ring) * size (quantitative): value (mapped to angular extent) * detail (nominal, optional): sub-category for two-level hierarchy * * If only color + size: single-ring sunburst (same as pie but styled differently). * If color + size + detail: two-ring sunburst (color = inner, detail = outer). */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, DEFAULT_COLORS, computeCircumferencePressure, computeEffectiveBarCount } from './utils'; export const ecSunburstDef: ChartTemplateDef = { chart: 'Sunburst Chart', template: { mark: 'arc', encoding: {} }, channels: ['color', 'size', 'detail'], markCognitiveChannel: 'area', instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const catField = channelSemantics.color?.field; const valField = channelSemantics.size?.field; const subCatField = channelSemantics.detail?.field; if (!catField) return; const categories = extractCategories(table, catField, channelSemantics.color?.ordinalSortOrder); if (categories.length === 0) return; // Build sunburst data (hierarchical tree structure) let sunburstData: any[]; if (subCatField) { // Two-level hierarchy: color (inner ring) → detail (outer ring) sunburstData = categories.map((cat, catIdx) => { const catRows = table.filter(r => String(r[catField]) === cat); const subCats = extractCategories(catRows, subCatField); const children = subCats.map(sub => { const subRows = catRows.filter(r => String(r[subCatField]) === sub); let value: number; if (valField) { value = subRows.reduce((sum, r) => sum + (Number(r[valField]) || 0), 0); } else { value = subRows.length; } return { name: sub, value }; }); return { name: cat, children, itemStyle: { color: DEFAULT_COLORS[catIdx % DEFAULT_COLORS.length] }, }; }); } else { // Flat: single ring const agg = new Map<string, number>(); if (valField) { for (const row of table) { const cat = String(row[catField] ?? ''); const val = Number(row[valField]) || 0; agg.set(cat, (agg.get(cat) ?? 0) + val); } } else { for (const row of table) { const cat = String(row[catField] ?? ''); agg.set(cat, (agg.get(cat) ?? 0) + 1); } } sunburstData = categories.map((cat, i) => ({ name: cat, value: agg.get(cat) ?? 0, itemStyle: { color: DEFAULT_COLORS[i % DEFAULT_COLORS.length] }, })); } // ── Circumference-pressure sizing (spring model) ────────────── // Compute effective bar count from outer-ring leaf values. // For two-level hierarchy: use the outer-ring leaves. // For flat: use slice values directly. let outerValues: number[]; if (subCatField) { // Outer ring: collect all leaf values outerValues = sunburstData.flatMap( d => d.children?.map((c: any) => c.value as number) ?? []); } else { outerValues = sunburstData.map((d: any) => d.value as number); } const effectiveCount = computeEffectiveBarCount(outerValues); const { radius: pressureRadius, canvasW, canvasH } = computeCircumferencePressure(effectiveCount, ctx.canvasSize, { minArcPx: 45, minRadius: 80, maxStretch: ctx.assembleOptions?.maxStretch, }); const outerRadius = Math.max(80, Math.round(Math.min(pressureRadius, (Math.min(canvasW, canvasH) / 2 - 20)))); const innerRadius = chartProperties?.innerRadius ?? Math.round(outerRadius * 0.15); const option: any = { tooltip: { trigger: 'item', formatter: (params: any) => { const { name, value, treePathInfo } = params; const path = treePathInfo ? treePathInfo.map((n: any) => n.name).filter(Boolean).join(' → ') : name; return `${path}<br/>Value: ${value}`; }, }, series: [{ type: 'sunburst', data: sunburstData, radius: [`${innerRadius}px`, `${outerRadius}px`], center: ['50%', '50%'], label: { show: true, rotate: chartProperties?.labelRotate ?? 'radial', fontSize: 11, }, emphasis: { focus: 'ancestor', }, levels: subCatField ? [ {}, // root { // Inner ring (top-level categories) r0: `${innerRadius}px`, r: `${Math.round(innerRadius + (outerRadius - innerRadius) * 0.5)}px`, label: { fontSize: 12, fontWeight: 'bold' }, itemStyle: { borderWidth: 2, borderColor: '#fff' }, }, { // Outer ring (sub-categories) r0: `${Math.round(innerRadius + (outerRadius - innerRadius) * 0.5)}px`, r: `${outerRadius}px`, label: { fontSize: 10 }, itemStyle: { borderWidth: 1, borderColor: 'rgba(255,255,255,0.5)' }, }, ] : [ {}, // root { label: { fontSize: 12 }, itemStyle: { borderWidth: 2, borderColor: '#fff' }, }, ], }], color: DEFAULT_COLORS, _width: canvasW, _height: canvasH, }; Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'innerRadius', label: 'Inner R', type: 'continuous', min: 0, max: 80, step: 5, defaultValue: 0 } as ChartPropertyDef, { key: 'labelRotate', label: 'Labels', type: 'discrete', options: [ { value: 'radial', label: 'Radial (default)' }, { value: 'tangential', label: 'Tangential' }, { value: 0, label: 'Horizontal' }, ], } as ChartPropertyDef, ], }; ================================================ FILE: src/lib/agents-chart/echarts/templates/treemap.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Treemap template. * * Unique to ECharts — no native Vega-Lite equivalent. * Displays hierarchical data as nested rectangles where area encodes value. * * Data model: * color (nominal): top-level category * size (quantitative): value (mapped to rectangle area) * detail (nominal, optional): sub-category for two-level hierarchy * * If only color + size: flat treemap with one level. * If color + size + detail: two-level hierarchy (color → detail → value). */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, DEFAULT_COLORS } from './utils'; import { computeEffectiveBarCount } from '../../core/decisions'; export const ecTreemapDef: ChartTemplateDef = { chart: 'Treemap', template: { mark: 'rect', encoding: {} }, channels: ['color', 'size', 'detail'], markCognitiveChannel: 'area', instantiate: (spec, ctx) => { const { channelSemantics, table, chartProperties } = ctx; const catField = channelSemantics.color?.field; const valField = channelSemantics.size?.field; const subCatField = channelSemantics.detail?.field; if (!catField) return; const categories = extractCategories(table, catField, channelSemantics.color?.ordinalSortOrder); if (categories.length === 0) return; // Build treemap data let treemapData: any[]; if (subCatField) { // Two-level hierarchy: color → detail → value treemapData = categories.map((cat, catIdx) => { const catRows = table.filter(r => String(r[catField]) === cat); const subCats = extractCategories(catRows, subCatField); const children = subCats.map(sub => { const subRows = catRows.filter(r => String(r[subCatField]) === sub); let value: number; if (valField) { value = subRows.reduce((sum, r) => sum + (Number(r[valField]) || 0), 0); } else { value = subRows.length; } return { name: sub, value }; }); return { name: cat, children, itemStyle: { color: DEFAULT_COLORS[catIdx % DEFAULT_COLORS.length] }, }; }); } else { // Flat treemap: one level const agg = new Map<string, number>(); if (valField) { for (const row of table) { const cat = String(row[catField] ?? ''); const val = Number(row[valField]) || 0; agg.set(cat, (agg.get(cat) ?? 0) + val); } } else { for (const row of table) { const cat = String(row[catField] ?? ''); agg.set(cat, (agg.get(cat) ?? 0) + 1); } } treemapData = categories.map((cat, i) => ({ name: cat, value: agg.get(cat) ?? 0, itemStyle: { color: DEFAULT_COLORS[i % DEFAULT_COLORS.length] }, })); } // ── Scaling: treat treemap items as variable-width vertical bars ── // Use effective bar count (total/min) to determine if the treemap // needs more area. Both axes share the area stretch but X takes // a larger share via the xBias factor: // stretchX = areaStretch^(xBias/(xBias+1)) // stretchY = areaStretch^(1/(xBias+1)) // stretchX × stretchY = areaStretch (total area preserved) // xBias=1 → uniform, xBias=2 → X gets 2/3 of the log-stretch. const leafValues = treemapData.flatMap((d: any) => d.children ? d.children.map((c: any) => c.value as number) : [d.value as number] ).filter((v: number) => v > 0); const effectiveCount = leafValues.length > 0 ? computeEffectiveBarCount(leafValues) : categories.length; const baseW = ctx.canvasSize.width; const baseH = ctx.canvasSize.height; const minBarPx = 30; // minimum width per effective bar const elasticity = 0.5; const maxStretch = ctx.assembleOptions?.maxStretch ?? 2.0; // max per-axis stretch const xBias = 1.5; // X takes more of the stretch than Y const pressure = (effectiveCount * minBarPx) / baseW; const areaStretch = pressure <= 1 ? 1 : Math.min(maxStretch * maxStretch, Math.pow(pressure, elasticity)); const stretchX = Math.min(maxStretch, Math.pow(areaStretch, xBias / (xBias + 1))); const stretchY = Math.min(maxStretch, Math.pow(areaStretch, 1 / (xBias + 1))); const canvasW = Math.round(baseW * stretchX); const canvasH = Math.round(baseH * stretchY); const showBreadcrumb = chartProperties?.breadcrumb !== false; const option: any = { tooltip: { trigger: 'item', formatter: (params: any) => { const { name, value, treePathInfo } = params; const path = treePathInfo ? treePathInfo.map((n: any) => n.name).filter(Boolean).join(' → ') : name; return `${path}<br/>Value: ${value}`; }, }, series: [{ type: 'treemap', data: treemapData, width: '90%', height: showBreadcrumb ? '80%' : '90%', top: 10, left: 'center', roam: false, leafDepth: subCatField ? 2 : 1, breadcrumb: { show: showBreadcrumb, bottom: 5, }, label: { show: true, formatter: '{b}', fontSize: 12, }, upperLabel: subCatField ? { show: true, height: 20, fontSize: 11, color: '#fff', } : undefined, levels: subCatField ? [ { // Root level (hidden) itemStyle: { borderWidth: 0, gapWidth: 2 }, }, { // Top-level categories itemStyle: { borderWidth: 2, borderColor: '#fff', gapWidth: 2, }, upperLabel: { show: true }, }, { // Leaf level (sub-categories) itemStyle: { borderWidth: 1, borderColor: 'rgba(255,255,255,0.5)', gapWidth: 1, }, label: { show: true, fontSize: 10 }, colorSaturation: [0.3, 0.6], colorMappingBy: 'value', }, ] : [ { itemStyle: { borderWidth: 2, borderColor: '#fff', gapWidth: 2, }, }, ], }], color: DEFAULT_COLORS, _width: canvasW, _height: canvasH, }; Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, properties: [ { key: 'breadcrumb', label: 'Breadcrumb', type: 'discrete', options: [ { value: true, label: 'Show (default)' }, { value: false, label: 'Hide' }, ], } as ChartPropertyDef, ], }; ================================================ FILE: src/lib/agents-chart/echarts/templates/utils.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Shared helper functions for ECharts template hooks. * Pure logic — no UI dependencies. */ import type { ChannelSemantics, InstantiateContext } from '../../core/types'; /** * Get canonical category order for a channel (from buildECEncodings or channelSemantics). * Use when calling extractCategories so sort order is consistent across templates. */ export function getCategoryOrder(ctx: InstantiateContext, channel: string): string[] | undefined { return (ctx.resolvedEncodings as any)?.[channel]?.ordinalSortOrder ?? ctx.channelSemantics?.[channel]?.ordinalSortOrder; } // Re-export circumference-pressure functions from core (shared with VL backend) export { computeCircumferencePressure, computeEffectiveBarCount, type CircumferencePressureParams, type CircumferencePressureResult, } from '../../core/decisions'; // --------------------------------------------------------------------------- // Discrete-dimension helpers (mirrored from vegalite/templates/utils.ts) // --------------------------------------------------------------------------- const isDiscrete = (type: string | undefined) => type === 'nominal' || type === 'ordinal'; /** * Get the number of unique non-null values for a field in the data table. */ export function getFieldCardinality(field: string, table: any[]): number { return new Set(table.map((r: any) => r[field]).filter((v: any) => v != null)).size; } /** * Determine the discrete type for a given encoding type. */ export function resolveDiscreteType( currentType: string, field: string | undefined, table: any[], ): 'nominal' | 'ordinal' { if (currentType === 'nominal') return 'nominal'; if (currentType === 'ordinal') return 'ordinal'; if (currentType === 'temporal') return 'ordinal'; if (currentType === 'quantitative' && field && table.length > 0) { const cardinality = getFieldCardinality(field, table); return cardinality <= 20 ? 'ordinal' : 'nominal'; } return 'nominal'; } // --------------------------------------------------------------------------- // ECharts-specific helpers // --------------------------------------------------------------------------- /** * Extract unique category values from data for a given field, preserving order. * If `ordinalSortOrder` is provided, returns values sorted in that canonical order. */ export function extractCategories(data: any[], field: string, ordinalSortOrder?: string[]): string[] { const seen = new Set<string>(); const result: string[] = []; for (const row of data) { const val = row[field]; if (val != null) { const key = String(val); if (!seen.has(key)) { seen.add(key); result.push(key); } } } // Apply canonical ordinal sort if available if (ordinalSortOrder && ordinalSortOrder.length > 0) { const orderMap = new Map(ordinalSortOrder.map((v, i) => [v, i])); result.sort((a, b) => { const ia = orderMap.get(a); const ib = orderMap.get(b); if (ia !== undefined && ib !== undefined) return ia - ib; if (ia !== undefined) return -1; if (ib !== undefined) return 1; return 0; }); } return result; } /** * Group data by a categorical field. * Returns a map: seriesName → rows[]. */ export function groupBy(data: any[], field: string): Map<string, any[]> { const groups = new Map<string, any[]>(); for (const row of data) { const key = String(row[field] ?? ''); if (!groups.has(key)) groups.set(key, []); groups.get(key)!.push(row); } return groups; } /** * Build ECharts axis config from a channel semantic. */ export function buildAxisConfig( cs: ChannelSemantics | undefined, position: 'x' | 'y', categories?: string[], ): any { if (!cs) return { type: 'value' }; const axisType = isDiscrete(cs.type) ? 'category' : cs.type === 'temporal' ? 'time' : 'value'; const axis: any = { type: axisType }; if (axisType === 'category' && categories) { axis.data = categories; } // Add field name as axis name if (cs.field) { axis.name = cs.field; axis.nameLocation = 'middle'; axis.nameGap = 30; } return axis; } /** * Pick a default ECharts color palette. */ export const DEFAULT_COLORS = [ '#5470c6', '#91cc75', '#fac858', '#ee6666', '#73c0de', '#3ba272', '#fc8452', '#9a60b4', '#ea7ccc', '#48b8d0', ]; /** * Detect which axis is the category (banded) axis and which is the value axis. * Returns { categoryAxis, valueAxis } with 'x' or 'y'. */ export function detectAxes( channelSemantics: Record<string, ChannelSemantics>, ): { categoryAxis: 'x' | 'y'; valueAxis: 'x' | 'y' } { const xCS = channelSemantics.x; const yCS = channelSemantics.y; // If x is discrete → x is category if (xCS && isDiscrete(xCS.type)) { return { categoryAxis: 'x', valueAxis: 'y' }; } // If y is discrete → y is category (horizontal bars) if (yCS && isDiscrete(yCS.type)) { return { categoryAxis: 'y', valueAxis: 'x' }; } // x quantitative + y temporal → horizontal bars (VL: x=bar length, y=dates) if (xCS?.type === 'quantitative' && yCS?.type === 'temporal') { return { categoryAxis: 'y', valueAxis: 'x' }; } // x temporal + y quantitative → vertical bars (VL: x=dates, y=bar length) if (xCS?.type === 'temporal' && yCS?.type === 'quantitative') { return { categoryAxis: 'x', valueAxis: 'y' }; } // Default: x is category return { categoryAxis: 'x', valueAxis: 'y' }; } ================================================ FILE: src/lib/agents-chart/echarts/templates/waterfall.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts Waterfall Chart — cumulative bar with start/delta/end (mirror vegalite/templates/waterfall.ts). */ import { ChartTemplateDef } from '../../core/types'; import { extractCategories } from './utils'; export const ecWaterfallChartDef: ChartTemplateDef = { chart: 'Waterfall Chart', template: { mark: 'bar', encoding: {} }, channels: ['x', 'y', 'color', 'column', 'row'], markCognitiveChannel: 'length', declareLayoutMode: () => ({ axisFlags: { x: { banded: true } } }), instantiate: (spec, ctx) => { const { channelSemantics, table } = ctx; const xField = channelSemantics.x?.field || 'Category'; const yField = channelSemantics.y?.field || 'Amount'; const colorField = channelSemantics.color?.field; const categories = extractCategories(table, xField, undefined); const rows = categories.map(cat => table.find((r: any) => String(r[xField]) === cat)).filter(Boolean); const values = rows.map((r: any) => Number(r[yField]) || 0); const hasTypeCol = !!colorField; let types: string[]; if (hasTypeCol) { types = rows.map((r: any) => String(r[colorField] ?? 'delta')); } else { types = values.map((_, i) => i === 0 ? 'start' : i === values.length - 1 ? 'end' : 'delta'); } let running = 0; const baseData: number[] = []; const deltaData: number[] = []; const colors: string[] = []; for (let i = 0; i < values.length; i++) { const v = values[i]; const t = types[i]; if (t === 'start') { baseData.push(0); deltaData.push(v); running = v; colors.push('#5470c6'); } else if (t === 'end') { baseData.push(running); deltaData.push(v); colors.push('#5470c6'); } else { baseData.push(running); running += v; deltaData.push(v); colors.push(v >= 0 ? '#91cc75' : '#ee6666'); } } const option: any = { tooltip: { trigger: 'axis', axisPointer: { type: 'shadow' } }, xAxis: { type: 'category', data: categories, name: xField }, yAxis: { type: 'value', name: yField }, series: [ { type: 'bar', name: '_base', data: baseData, stack: 'wf', itemStyle: { color: 'transparent' } }, { type: 'bar', name: 'Delta', data: deltaData, stack: 'wf', itemStyle: { color: (params: any) => colors[params.dataIndex] }, }, ], }; Object.assign(spec, option); delete spec.mark; delete spec.encoding; }, }; ================================================ FILE: src/lib/agents-chart/gofish/README.md ================================================ # GoFish Backend Compiles the core semantic layer into [GoFish Graphics](https://www.npmjs.com/package/gofish-graphics) (v0.0.22) render calls. Unlike the other three backends which produce serializable JSON specs, GoFish renders directly to the DOM via Solid.js using a fluent API. ## Output Format ```typescript { _gofish: { type, data, flow, mark, layers?, coord? }, // descriptor render: (container: HTMLElement) => void, // imperative render function _width: number, _height: number, _specDescription: string, // human-readable debug string } ``` A `GoFishSpec` object. The `render` function dynamically imports `gofish-graphics` at call time and executes the fluent API chain: `chart(data).flow(...).mark(...).render(el, opts)`. ## Assembly Pipeline | Phase | Step | Description | |-------|------|-------------| | **0** | `resolveSemantics` | Shared — resolve field types, aggregates, sort orders | | 0a | `declareLayoutMode` | Template layout declaration | | 0b | `convertTemporalData` | Shared — temporal parsing | | 0c | `filterOverflow` | Shared — category truncation | | **1** | `computeLayout` | Shared — step sizes, subplot dimensions | | **2** | Build `resolvedEncodings` → `template.instantiate` → optional `postProcess` → compute dimensions → wrap in `GoFishSpec` with `buildRenderFunction` | `GoFishSpec` | **Key differences from other backends:** - No `instantiate-spec.ts` — GoFish templates write the `_gofish` descriptor directly; the assembler wraps it in a render closure. - Has a `postProcess` template hook not present in other backends. - `buildRenderFunction` translates the JSON-like descriptor into live GoFish API calls at render time. ## File Structure ``` gofish/ assemble.ts – assembleGoFish(), buildRenderFunction(), buildFlowOp(), buildMark() index.ts – barrel exports templates/ index.ts – template registry (8 templates, 4 categories) scatter.ts – Scatter Plot (with optional data-driven color via rect) bar.ts – Bar, Grouped Bar, Stacked Bar line.ts – Line Chart (single + multi-series via layers) area.ts – Area Chart (single + stacked via layers) pie.ts – Pie Chart (clock() coordinate) scatterpie.ts – Scatter Pie (scatter-positioned mini pies) utils.ts – detectAxes(), extractCategories(), aggregateByCategory(), groupBy() ``` ## Template Definitions (8 templates) | Category | Charts | |----------|--------| | Scatter & Point | Scatter Plot | | Bar | Bar Chart, Grouped Bar, Stacked Bar | | Line & Area | Line Chart, Area Chart | | Part-to-Whole | Pie Chart, Scatter Pie Chart | ## GoFish API Patterns ### Flow Operators | Operator | Usage | Description | |----------|-------|-------------| | `spread(field, opts)` | Categorical axis | Distributes items along an axis by category | | `stack(field, opts)` | Stacking | Stacks items by category; `dir: "x"` or `"y"` | | `scatter(key, {x, y})` | Positional | Places items at (x, y) positions keyed by unique ID | | `group(field)` | Grouping | Groups data for multi-series marks (line, area) | ### Marks | Mark | Usage | Notes | |------|-------|-------| | `rect({h, w, fill})` | Bar, pie slices | Data-driven fill via field name | | `circle({r, fill})` | Scatter (no color) | `fill` accepts literal CSS colors only, NOT field names | | `scaffold({h, fill}).name("label")` | Line/area anchor | Creates invisible positioned points; `.name()` only works on configured marks (not bare `scaffold()`) | | `line()` / `area({opacity})` | Connect points | Applied after `scaffold` + `select` or `group` | ### Coordinate Transforms | Transform | Usage | |-----------|-------| | `clock()` | Polar coordinates for pie/rose charts. Passed to `chart(data, { coord: clock() })` | ### Rendering Paths in `buildRenderFunction` 1. **Layered charts** (multi-series line/area): Uses named marks + `gf.layer()` + `gf.select()` for cross-layer references. 2. **Simple charts** (bar, scatter, single-series line/area, pie): Direct `chart(data).flow().mark().render()`. 3. **Scatterpie**: Mark is a function `(data) => chart(data[0].collection, {coord: clock()}).flow(stack).mark(rect)`. ### Dimension Calculation - **Layered charts**: Use `subplotWidth`/`subplotHeight` from layout directly (gas-pressure continuous sizing). - **Non-layered discrete-axis charts**: Use `step × count` from layout. - **Padding**: 80px added to both width and height for axes/labels. ## Known Issues & Notes - **ESM-only**: `gofish-graphics` is an ESM-only package. Loaded via dynamic `import()` inside `buildRenderFunction` to avoid bundling issues with CommonJS consumers. - **Solid.js dependency**: GoFish renders to DOM via Solid.js — it does not produce a serializable spec. The `_gofish` descriptor is for debugging only. - **`scaffold()` bare vs configured**: `scaffold()` with no arguments returns a bare function whose `.name` is `Function.name` (a string, not callable). Always pass options: `scaffold({h: field}).name("label")`. - **`circle({fill})` limitation**: GoFish's `circle` mark only accepts literal CSS color strings for `fill`, not data field names. For data-driven scatter color, the template uses `rect({w:8, h:8, fill: field})` as small colored squares instead. - **No faceting support**: GoFish has no faceting mechanism. Column/row channels are accepted in template definitions but not implemented. - **Multi-series line/area**: Uses `scaffold({h, fill}).name("pts")` + `group(colorField)` + `line()`/`area()`. The assembler tries `mark.name(label)` first, falls back to `spec.as(label)`. - **Pie chart**: Uses `chart(data, { coord: clock() })` + `stack(catField, { dir: "x" })` + `rect({ w: valField, fill: catField })`. The `transform: { x: w/2, y: h/2 }` render option centers the chart. - **Scatter Pie**: Unique to GoFish — scatter-positioned mini pies. The template restructures flat data into `{_key, _x, _y, collection: [...]}` and the mark is a function returning a sub-chart. - **`--legacy-peer-deps`**: Required when installing `gofish-graphics` due to Solid.js peer dependency conflicts. ================================================ FILE: src/lib/agents-chart/gofish/assemble.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * GoFish chart assembly — Two-Stage Pipeline Coordinator. * * Reuses the **same core analysis pipeline** as all other backends: * Phase 0: resolveChannelSemantics → ChannelSemantics * Step 0a: declareLayoutMode → LayoutDeclaration * Step 0b: convertTemporalData → converted data * Step 0c: filterOverflow → filtered data, nominalCounts * Phase 1: computeLayout → LayoutResult * * Then diverges for Phase 2 (GoFish-specific): * template.instantiate → builds GoFish descriptor (_gofish) * assembler wraps into GoFishSpec with render(container) method * * ── Backend Translation Responsibilities ──────────────────────────── * GoFish renders directly to DOM via Solid.js. The assembler produces * a GoFishSpec object containing: * - _gofish: descriptor of flow operators, mark shape, data * - render(container): function that creates the GoFish chart * - _width / _height: computed canvas dimensions * - _warnings: any warnings from the pipeline * - _specDescription: human-readable description for debugging * * Key structural differences from other backends: * VL: { mark, encoding, data: {values}, width, height } * EC: { xAxis, yAxis, series: [{type, data}], tooltip, legend, grid } * CJS: { type, data: { labels, datasets[] }, options: { scales, plugins } } * GF: { _gofish: {type, data, flow, mark}, render: (el) => void } * * This module has NO React, Redux, or UI framework dependencies. */ import { ChartTemplateDef, ChartAssemblyInput, AssembleOptions, LayoutDeclaration, InstantiateContext, } from '../core/types'; import type { ChartWarning } from '../core/types'; import { gfGetTemplateDef } from './templates'; import { resolveChannelSemantics, convertTemporalData } from '../core/resolve-semantics'; import { computeZeroDecision } from '../core/semantic-types'; import { filterOverflow } from '../core/filter-overflow'; import { computeLayout, computeChannelBudgets } from '../core/compute-layout'; // --------------------------------------------------------------------------- // GoFish Spec Type // --------------------------------------------------------------------------- /** * Output of assembleGoFish — contains the GoFish descriptor and render function. */ export interface GoFishSpec { /** GoFish descriptor: data, flow operators, mark shape */ _gofish: { type: string; data: any[]; flow?: any[]; mark?: any; layers?: any[]; coord?: string; }; /** Render the GoFish chart into a DOM container */ render: (container: HTMLElement) => void; /** Computed width */ _width: number; /** Computed height */ _height: number; /** Warnings from assembly pipeline */ _warnings?: ChartWarning[]; /** Human-readable spec description for debugging */ _specDescription: string; /** Length of data used */ _dataLength: number; } // --------------------------------------------------------------------------- // Render function builder // --------------------------------------------------------------------------- /** * Build a render function that calls GoFish's fluent API to render a chart. * Dynamically imports gofish-graphics to avoid bundling issues. */ function buildRenderFunction( gfDesc: any, width: number, height: number, ): (container: HTMLElement) => void { return (container: HTMLElement) => { // Dynamic import of gofish-graphics (ESM-only, optional dependency) // @ts-ignore — gofish-graphics may not be installed import('gofish-graphics').then((gf: any) => { // Clear previous content container.innerHTML = ''; const renderOpts: any = { w: width, h: height, axes: true, }; if (gfDesc.type === 'todo') { // TODO chart type — show informational message container.innerHTML = `<div style="color:#666;padding:16px;font-size:13px;line-height:1.5;"> <strong>GoFish TODO:</strong><br/>${gfDesc.message || 'Not yet implemented.'} </div>`; return; } if (gfDesc.layers) { // Layer-based charts (line, area with multi-series) const layerSpecs: any[] = []; for (const layerDef of gfDesc.layers) { let spec: any; if (layerDef.select) { // Second layer: select from named marks spec = gf.chart(gf.select(layerDef.select)); } else { spec = gf.chart(gfDesc.data); } // Apply flow operators if (layerDef.flow && layerDef.flow.length > 0) { const flowOps = layerDef.flow.map((f: any) => buildFlowOp(gf, f)); spec = spec.flow(...flowOps); } // Apply mark if (layerDef.mark) { const mark = buildMark(gf, layerDef.mark); const layerName = layerDef.mark.name; if (layerName) { // Try naming via mark.name() (official docs pattern: // scaffold({h:"count"}).name("points")) // Falls back to spec.as() if mark.name isn't callable. try { const namedMark = mark.name(layerName); spec = spec.mark(namedMark); } catch { spec = spec.mark(mark); try { spec = spec.as(layerName); } catch { // Layer naming not available — select() may fail } } } else { spec = spec.mark(mark); } } layerSpecs.push(spec); } // Apply coordinate transform if specified if (gfDesc.coord === 'clock') { gf.layer({ coord: gf.clock() }, layerSpecs) .render(container, renderOpts); } else { gf.layer(layerSpecs).render(container, renderOpts); } } else { // Simple single-flow charts (bar, scatter, pie) // Pass coord directly to chart() when specified (e.g. clock() for pie) const chartOpts: any = {}; if (gfDesc.coord === 'clock') { chartOpts.coord = gf.clock(); } const hasChartOpts = Object.keys(chartOpts).length > 0; let spec = hasChartOpts ? gf.chart(gfDesc.data, chartOpts) : gf.chart(gfDesc.data); // Apply flow operators if (gfDesc.flow && gfDesc.flow.length > 0) { const flowOps = gfDesc.flow.map((f: any) => buildFlowOp(gf, f)); spec = spec.flow(...flowOps); } // Apply mark if (gfDesc.mark && gfDesc.mark.shape === 'scatterpie') { // Scatterpie: mark is a function that returns a sub-chart (pie per point) const { colorField, angleField, pieSize } = gfDesc.mark.options; spec = spec.mark((data: any) => { return gf.chart(data[0].collection, { coord: gf.clock() }) .flow(gf.stack(colorField, { dir: 'x', h: pieSize || 20 })) .mark(gf.rect({ w: angleField, fill: colorField })); }); } else if (gfDesc.mark) { spec = spec.mark(buildMark(gf, gfDesc.mark)); } // Render — add centering transform for polar charts if (gfDesc.coord === 'clock') { spec.render(container, { ...renderOpts, transform: { x: width / 2, y: height / 2 }, }); } else { spec.render(container, renderOpts); } } }).catch((err: any) => { container.innerHTML = `<div style="color:red;padding:8px;font-size:12px;"> GoFish render error: ${err.message} </div>`; }); }; } /** * Build a GoFish flow operator from descriptor. */ function buildFlowOp(gf: any, desc: any): any { switch (desc.op) { case 'spread': return gf.spread(desc.field, desc.options || {}); case 'stack': return gf.stack(desc.field, desc.options || {}); case 'scatter': return gf.scatter(desc.field, desc.options || {}); case 'group': return gf.group(desc.field); case 'derive': return gf.derive(desc.fn); default: throw new Error(`Unknown GoFish flow operator: ${desc.op}`); } } /** * Build a GoFish mark from descriptor. */ function buildMark(gf: any, desc: any): any { const opts = desc.options; const hasOpts = opts && Object.keys(opts).length > 0; const callMark = (fn: any) => hasOpts ? fn(opts) : fn(); switch (desc.shape) { case 'rect': return callMark(gf.rect); case 'circle': return callMark(gf.circle); case 'line': return callMark(gf.line); case 'area': return callMark(gf.area); case 'scaffold': return callMark(gf.scaffold); case 'ellipse': return callMark(gf.ellipse); case 'petal': return callMark(gf.petal); default: return callMark(gf.rect); } } /** * Build a human-readable description of the GoFish spec for debugging. */ function buildSpecDescription(gfDesc: any): string { const parts: string[] = [`chart(data[${gfDesc.data?.length ?? 0}])`]; if (gfDesc.layers) { for (const layer of gfDesc.layers) { const layerParts: string[] = []; if (layer.select) layerParts.push(`select("${layer.select}")`); if (layer.flow) { for (const f of layer.flow) { layerParts.push(`.${f.op}("${f.field}")`); } } if (layer.mark) { layerParts.push(`.mark(${layer.mark.shape}(${JSON.stringify(layer.mark.options)}))`); } parts.push(` layer: ${layerParts.join('')}`); } } else { if (gfDesc.flow) { for (const f of gfDesc.flow) { parts.push(`.flow(${f.op}("${f.field}", ${JSON.stringify(f.options || {})}))`); } } if (gfDesc.mark) { parts.push(`.mark(${gfDesc.mark.shape}(${JSON.stringify(gfDesc.mark.options || {})}))`); } } if (gfDesc.coord) { parts.push(`coord: ${gfDesc.coord}`); } return parts.join('\n'); } // --------------------------------------------------------------------------- // Public API // --------------------------------------------------------------------------- /** * Assemble a GoFish spec — a descriptor + render function. * * ```ts * const spec = assembleGoFish({ * data: { values: myRows }, * semantic_types: { weight: 'Quantity' }, * chart_spec: { chartType: 'Bar Chart', encodings: { x: { field: 'category' }, y: { field: 'value' } } }, * }); * spec.render(document.getElementById('chart')); * ``` * * @returns A GoFishSpec with render function, dimensions, and metadata */ export function assembleGoFish(input: ChartAssemblyInput): GoFishSpec { const chartType = input.chart_spec.chartType; const encodings = input.chart_spec.encodings; const data = input.data.values ?? []; const semanticTypes = input.semantic_types ?? {}; const canvasSize = input.chart_spec.canvasSize ?? { width: 400, height: 320 }; const chartProperties = input.chart_spec.chartProperties; const options = input.options ?? {}; const chartTemplate = gfGetTemplateDef(chartType) as ChartTemplateDef; if (!chartTemplate) { throw new Error(`Unknown GoFish chart type: ${chartType}. Use gfAllTemplateDefs to see available types.`); } const warnings: ChartWarning[] = []; // ═══════════════════════════════════════════════════════════════════════ // PHASE 0: Resolve Semantics (shared with all backends) // ═══════════════════════════════════════════════════════════════════════ const tplMark = chartTemplate.template?.mark; const templateMarkType = typeof tplMark === 'string' ? tplMark : tplMark?.type; // Convert temporal data once — feeds semantic resolution and all downstream stages const convertedData = convertTemporalData(data, semanticTypes); const channelSemantics = resolveChannelSemantics( encodings, data, semanticTypes, convertedData, ); // Finalize zero-baseline (requires template mark knowledge) const effectiveMarkType = templateMarkType || 'point'; for (const [channel, cs] of Object.entries(channelSemantics)) { if ((channel === 'x' || channel === 'y') && cs.type === 'quantitative') { const numericValues = data .map(r => r[cs.field]) .filter((v: any) => v != null && typeof v === 'number' && !isNaN(v)); cs.zero = computeZeroDecision( cs.semanticAnnotation.semanticType, channel, effectiveMarkType, numericValues, ); } } // ═══════════════════════════════════════════════════════════════════════ // STEP 0a: declareLayoutMode (shared hook) // ═══════════════════════════════════════════════════════════════════════ const declaration: LayoutDeclaration = chartTemplate.declareLayoutMode ? chartTemplate.declareLayoutMode(channelSemantics, data, chartProperties) : {}; const effectiveOptions: AssembleOptions = { ...options, ...(declaration.paramOverrides || {}), }; // ═══════════════════════════════════════════════════════════════════════ // STEP 0b: filterOverflow (shared) // ═══════════════════════════════════════════════════════════════════════ const allMarkTypes = new Set<string>(); if (templateMarkType) allMarkTypes.add(templateMarkType); // ── Channel budgets (shared, in layout module) ───────────────────── const budgets = computeChannelBudgets( channelSemantics, declaration, convertedData, canvasSize, effectiveOptions, ); const overflowResult = filterOverflow( channelSemantics, declaration, encodings, convertedData, budgets, allMarkTypes, ); const values = overflowResult.filteredData; warnings.push(...overflowResult.warnings); // ═══════════════════════════════════════════════════════════════════════ // PHASE 1: Compute Layout (shared — completely target-agnostic) // ═══════════════════════════════════════════════════════════════════════ const layoutResult = computeLayout( channelSemantics, declaration, values, canvasSize, effectiveOptions, ); layoutResult.truncations = overflowResult.truncations; // ═══════════════════════════════════════════════════════════════════════ // PHASE 2: Instantiate GoFish Spec (GF-specific) // ═══════════════════════════════════════════════════════════════════════ // Build resolved encodings for interface compatibility const resolvedEncodings: Record<string, any> = {}; for (const [channel, encoding] of Object.entries(encodings)) { const cs = channelSemantics[channel]; if (cs) { resolvedEncodings[channel] = { field: cs.field, type: cs.type, aggregate: encoding.aggregate, }; } } // Template instantiate const instantiateContext: InstantiateContext = { channelSemantics, layout: layoutResult, table: values, resolvedEncodings, encodings, chartProperties, canvasSize, semanticTypes, chartType, assembleOptions: effectiveOptions, }; const gfSpec: any = structuredClone(chartTemplate.template); chartTemplate.instantiate(gfSpec, instantiateContext); // Template-specific post-processing if (chartTemplate.postProcess) { chartTemplate.postProcess(gfSpec, instantiateContext); } // ═══════════════════════════════════════════════════════════════════════ // Compute dimensions // ═══════════════════════════════════════════════════════════════════════ const gfDescriptor = gfSpec._gofish; if (!gfDescriptor) { throw new Error(`GoFish template for "${chartType}" did not produce a _gofish descriptor`); } const PADDING = 80; // For layered charts (line, area) GoFish's spread() handles discrete // positioning internally — use the full subplot canvas, not step × count. const isLayered = !!gfDescriptor.layers; let plotWidth: number; let plotHeight: number; if (isLayered) { // Line / area charts: pass full canvas to GoFish plotWidth = layoutResult.subplotWidth || canvasSize.width; plotHeight = layoutResult.subplotHeight || canvasSize.height; } else { const xIsDiscrete = layoutResult.xNominalCount > 0 || layoutResult.xContinuousAsDiscrete > 0; const yIsDiscrete = layoutResult.yNominalCount > 0 || layoutResult.yContinuousAsDiscrete > 0; if (xIsDiscrete) { if (layoutResult.xStepUnit === 'group') { plotWidth = layoutResult.subplotWidth; } else { const xItemCount = layoutResult.xNominalCount || layoutResult.xContinuousAsDiscrete || 0; plotWidth = xItemCount > 0 ? layoutResult.xStep * xItemCount : (layoutResult.subplotWidth || canvasSize.width); } } else { plotWidth = layoutResult.subplotWidth || canvasSize.width; } if (yIsDiscrete) { if (layoutResult.yStepUnit === 'group') { plotHeight = layoutResult.subplotHeight; } else { const yItemCount = layoutResult.yNominalCount || layoutResult.yContinuousAsDiscrete || 0; plotHeight = yItemCount > 0 ? layoutResult.yStep * yItemCount : (layoutResult.subplotHeight || canvasSize.height); } } else { plotHeight = layoutResult.subplotHeight || canvasSize.height; } } const finalWidth = plotWidth + PADDING; const finalHeight = plotHeight + PADDING; // ═══════════════════════════════════════════════════════════════════════ // RESULT // ═══════════════════════════════════════════════════════════════════════ const result: GoFishSpec = { _gofish: gfDescriptor, render: buildRenderFunction(gfDescriptor, finalWidth - PADDING, finalHeight - PADDING), _width: finalWidth, _height: finalHeight, _specDescription: buildSpecDescription(gfDescriptor), _dataLength: values.length, }; if (warnings.length > 0) { result._warnings = warnings; } return result; } ================================================ FILE: src/lib/agents-chart/gofish/index.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * @module agents-chart/gofish * * GoFish backend for agents-chart. * * Compiles the core semantic layer into GoFish render calls. * GoFish uses a fluent API: chart(data).flow(...).mark(...).render(el, opts). * * Unlike VL/EC/CJS which produce JSON specs, GoFish renders directly to the * DOM via Solid.js. The assembler produces a GoFishSpec descriptor object * containing a `render(container)` function plus metadata for debugging. * * Architecture contrast with other backends: * VL: encoding-channel-based — { encoding: { x: { field, type }, y: ... } } * EC: series-based — { series: [{ type, data }], xAxis, yAxis } * CJS: dataset-based — { type, data: { labels, datasets[] }, options } * GF: flow-based — chart(data).flow(spread/stack/scatter).mark(rect/circle/line) * * Same core pipeline (Phase 0 + Phase 1), different Phase 2 output. */ // GF assembly function export { assembleGoFish, type GoFishSpec } from './assemble'; // GF template registry export { gfTemplateDefs, gfAllTemplateDefs, gfGetTemplateDef, gfGetTemplateChannels, } from './templates'; // GF recommendation & adaptation export { gfAdaptChart, gfRecommendEncodings } from './recommendation'; ================================================ FILE: src/lib/agents-chart/gofish/recommendation.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * GoFish recommendation & adaptation wrappers. */ import { adaptChannels, recommendChannels } from '../core/recommendation'; import { gfGetTemplateChannels } from './templates'; export function gfAdaptChart( sourceType: string, targetType: string, encodings: Record<string, string>, data?: any[], semanticTypes?: Record<string, string>, ): Record<string, string> { const targetChannels = gfGetTemplateChannels(targetType); return adaptChannels(sourceType, targetType, targetChannels, encodings, data, semanticTypes); } export function gfRecommendEncodings( chartType: string, data: any[], semanticTypes: Record<string, string>, ): Record<string, string> { const rec = recommendChannels(chartType, data, semanticTypes); const validChannels = gfGetTemplateChannels(chartType); const result: Record<string, string> = {}; for (const [ch, field] of Object.entries(rec)) { if (validChannels.includes(ch)) result[ch] = field; } return result; } ================================================ FILE: src/lib/agents-chart/gofish/templates/area.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * GoFish Area Chart template. * * Official GoFish area chart pattern: * layer([ * chart(data).flow(spread(x, {dir:"x", spacing})).mark(scaffold({h:y}).name("points")), * chart(select("points")).mark(area({opacity: 0.8})), * ]) * * Multi-series (stacked area) also needs group() — marked TODO. */ import { ChartTemplateDef } from '../../core/types'; import { detectAxes } from './utils'; export const gfAreaChartDef: ChartTemplateDef = { chart: 'Area Chart', template: { mark: 'area', encoding: {} }, channels: ['x', 'y', 'color', 'opacity', 'column', 'row'], markCognitiveChannel: 'area', declareLayoutMode: () => ({ paramOverrides: { continuousMarkCrossSection: { x: 100, y: 20, seriesCountAxis: 'auto' } }, }), instantiate: (spec, ctx) => { const { channelSemantics, table, layout, canvasSize } = ctx; const { categoryAxis, valueAxis } = detectAxes(channelSemantics); const colorField = channelSemantics.color?.field; const catField = channelSemantics[categoryAxis]?.field; const valField = channelSemantics[valueAxis]?.field; if (!catField || !valField) return; const isHorizontal = categoryAxis === 'y'; const spreadDir = isHorizontal ? 'y' : 'x'; const catCount = new Set(table.map((r: any) => r[catField])).size; const canvasW = isHorizontal ? (layout?.subplotHeight ?? canvasSize.height) : (layout?.subplotWidth ?? canvasSize.width); const spacing = Math.max(10, Math.round(canvasW / Math.max(1, catCount))); if (colorField) { // Stacked/layered area: spread + stack + scaffold({h, fill}).name + group + area // Pattern from official GoFish stacked-area-chart example. spec._gofish = { type: 'area-multi', data: table, layers: [ { flow: [ { op: 'spread', field: catField, options: { dir: spreadDir, spacing }, }, { op: 'stack', field: colorField, options: { dir: isHorizontal ? 'x' : 'y', label: false }, }, ], mark: { shape: 'scaffold', options: isHorizontal ? { w: valField, fill: colorField } : { h: valField, fill: colorField }, name: 'bars', }, }, { select: 'bars', flow: [{ op: 'group', field: colorField }], mark: { shape: 'area', options: { opacity: 0.8 } }, }, ], }; } else { // Single-series area: spread + scaffold + select + area spec._gofish = { type: 'area', data: table, layers: [ { flow: [{ op: 'spread', field: catField, options: { dir: spreadDir, spacing }, }], mark: { shape: 'scaffold', options: isHorizontal ? { w: valField } : { h: valField }, name: 'points', }, }, { select: 'points', flow: [], mark: { shape: 'area', options: { opacity: 0.8 } }, }, ], }; } delete spec.mark; delete spec.encoding; }, }; ================================================ FILE: src/lib/agents-chart/gofish/templates/bar.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * GoFish Bar Chart templates: Bar, Stacked Bar, Grouped Bar. * * GoFish approach: * chart(data).flow(spread(catField, { dir: "x" })).mark(rect({ h: valField })) * Stacked: add stack(colorField, { dir: "y" }) */ import { ChartTemplateDef } from '../../core/types'; import { detectBandedAxisFromSemantics, detectBandedAxisForceDiscrete, } from '../../vegalite/templates/utils'; import { detectAxes, aggregateByCategory, extractCategories, groupBy } from './utils'; // ─── Bar Chart ────────────────────────────────────────────────────────────── export const gfBarChartDef: ChartTemplateDef = { chart: 'Bar Chart', template: { mark: 'bar', encoding: {} }, channels: ['x', 'y', 'color', 'opacity', 'column', 'row'], markCognitiveChannel: 'length', declareLayoutMode: (cs, table) => { const result = detectBandedAxisFromSemantics(cs, table, { preferAxis: 'x' }); return { axisFlags: result ? { [result.axis]: { banded: true } } : { x: { banded: true } }, resolvedTypes: result?.resolvedTypes, }; }, instantiate: (spec, ctx) => { const { channelSemantics, table, layout } = ctx; const { categoryAxis, valueAxis } = detectAxes(channelSemantics); const catField = channelSemantics[categoryAxis]?.field; const valField = channelSemantics[valueAxis]?.field; if (!catField || !valField) return; const isHorizontal = categoryAxis === 'y'; // Compute bar width/height and spacing from layout const step = isHorizontal ? layout.yStep : layout.xStep; const barSize = step * (1 - layout.stepPadding); const spacing = step * layout.stepPadding; // Aggregate data: one bar per category const catCS = channelSemantics[categoryAxis]; const categories = extractCategories(table, catField, catCS?.ordinalSortOrder); const aggData = aggregateByCategory(table, catField, valField, categories); // Store GoFish render descriptor spec._gofish = { type: 'bar', data: aggData.map(d => ({ [catField]: d.category, [valField]: d.value })), flow: [{ op: 'spread', field: catField, options: { dir: isHorizontal ? 'y' : 'x', spacing: spacing, }, }], mark: { shape: 'rect', options: isHorizontal ? { w: valField, h: barSize } : { h: valField, w: barSize }, }, }; delete spec.mark; delete spec.encoding; }, }; // ─── Stacked Bar Chart ────────────────────────────────────────────────────── export const gfStackedBarChartDef: ChartTemplateDef = { chart: 'Stacked Bar Chart', template: { mark: 'bar', encoding: {} }, channels: ['x', 'y', 'color', 'column', 'row'], markCognitiveChannel: 'length', declareLayoutMode: (cs, table) => { const result = detectBandedAxisFromSemantics(cs, table, { preferAxis: 'x' }); return { axisFlags: result ? { [result.axis]: { banded: true } } : { x: { banded: true } }, resolvedTypes: result?.resolvedTypes, paramOverrides: { continuousMarkCrossSection: { x: 20, y: 20, seriesCountAxis: 'auto' } }, }; }, instantiate: (spec, ctx) => { const { channelSemantics, table, layout } = ctx; const { categoryAxis, valueAxis } = detectAxes(channelSemantics); const colorField = channelSemantics.color?.field; const catField = channelSemantics[categoryAxis]?.field; const valField = channelSemantics[valueAxis]?.field; if (!catField || !valField) return; const isHorizontal = categoryAxis === 'y'; const spreadDir = isHorizontal ? 'y' : 'x'; const stackDir = isHorizontal ? 'x' : 'y'; // Compute bar width/height and spacing from layout const step = isHorizontal ? layout.yStep : layout.xStep; const barSize = step * (1 - layout.stepPadding); const spacing = step * layout.stepPadding; const flow: any[] = [ { op: 'spread', field: catField, options: { dir: spreadDir, spacing: spacing, }, }, ]; if (colorField) { flow.push({ op: 'stack', field: colorField, options: { dir: stackDir, label: false }, }); } spec._gofish = { type: 'stacked-bar', data: table, flow, mark: { shape: 'rect', options: isHorizontal ? { w: valField, h: barSize, fill: colorField || undefined } : { h: valField, w: barSize, fill: colorField || undefined }, }, }; delete spec.mark; delete spec.encoding; }, }; // ─── Grouped Bar Chart ────────────────────────────────────────────────────── export const gfGroupedBarChartDef: ChartTemplateDef = { chart: 'Grouped Bar Chart', template: { mark: 'bar', encoding: {} }, channels: ['x', 'y', 'group', 'column', 'row'], markCognitiveChannel: 'length', declareLayoutMode: (cs, table) => { const result = detectBandedAxisForceDiscrete(cs, table, { preferAxis: 'x' }); const axis = result?.axis || 'x'; return { axisFlags: { [axis]: { banded: true } }, resolvedTypes: result?.resolvedTypes, }; }, instantiate: (spec, ctx) => { const { channelSemantics, table, layout } = ctx; const { categoryAxis, valueAxis } = detectAxes(channelSemantics); const groupField = channelSemantics.group?.field; const catField = channelSemantics[categoryAxis]?.field; const valField = channelSemantics[valueAxis]?.field; if (!catField || !valField) return; const isHorizontal = categoryAxis === 'y'; const spreadDir = isHorizontal ? 'y' : 'x'; // Compute bar width/height and spacing from layout const step = isHorizontal ? layout.yStep : layout.xStep; const usableStep = step * (1 - layout.stepPadding); const spacing = step * layout.stepPadding; // For grouped bars, divide bar size by number of groups (like ECharts does) let barSize = usableStep; if (groupField) { const groupCount = new Set(table.map((r: any) => r[groupField])).size; if (groupCount > 1) { barSize = Math.max(1, Math.floor(usableStep / groupCount)); } } const flow: any[] = [ { op: 'spread', field: catField, options: { dir: spreadDir, spacing: spacing, }, }, ]; // Group by group field using stack in same direction (side-by-side within each category) if (groupField) { flow.push({ op: 'stack', field: groupField, options: { dir: spreadDir, label: false }, }); } spec._gofish = { type: 'grouped-bar', data: table, flow, mark: { shape: 'rect', options: isHorizontal ? { w: valField, h: barSize, fill: groupField || undefined } : { h: valField, w: barSize, fill: groupField || undefined }, }, }; delete spec.mark; delete spec.encoding; }, }; ================================================ FILE: src/lib/agents-chart/gofish/templates/index.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * GoFish template registry. * * Mirrors the structure of other backend template registries. */ import { ChartTemplateDef } from '../../core/types'; import { gfScatterPlotDef } from './scatter'; import { gfBarChartDef, gfStackedBarChartDef, gfGroupedBarChartDef } from './bar'; import { gfLineChartDef } from './line'; import { gfAreaChartDef } from './area'; import { gfPieChartDef } from './pie'; import { gfScatterPieChartDef } from './scatterpie'; /** * GoFish chart template definitions, grouped by category. */ export const gfTemplateDefs: { [key: string]: ChartTemplateDef[] } = { 'Scatter & Point': [gfScatterPlotDef], 'Bar': [gfBarChartDef, gfGroupedBarChartDef, gfStackedBarChartDef], 'Line & Area': [gfLineChartDef, gfAreaChartDef], 'Part-to-Whole': [gfPieChartDef, gfScatterPieChartDef], }; /** * Flat list of all GoFish chart template definitions. */ export const gfAllTemplateDefs: ChartTemplateDef[] = Object.values(gfTemplateDefs).flat(); /** * Look up a GoFish chart template definition by chart type name. */ export function gfGetTemplateDef(chartType: string): ChartTemplateDef | undefined { return gfAllTemplateDefs.find(t => t.chart === chartType); } /** * Get the available channels for a GoFish chart type. */ export function gfGetTemplateChannels(chartType: string): string[] { return gfGetTemplateDef(chartType)?.channels || []; } ================================================ FILE: src/lib/agents-chart/gofish/templates/line.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * GoFish Line Chart template. * * Official GoFish line chart pattern: * layer([ * chart(data).flow(scatter(key, {x, y})).mark(scaffold().name("points")), * chart(select("points")).mark(line()), * ]) * * For categorical x-axis we use spread() instead of scatter(). * Multi-series (color) requires group() which needs layer naming — marked TODO. */ import { ChartTemplateDef } from '../../core/types'; import { detectAxes } from './utils'; export const gfLineChartDef: ChartTemplateDef = { chart: 'Line Chart', template: { mark: 'line', encoding: {} }, channels: ['x', 'y', 'color', 'opacity', 'column', 'row'], markCognitiveChannel: 'position', declareLayoutMode: () => ({ paramOverrides: { continuousMarkCrossSection: { x: 100, y: 20, seriesCountAxis: 'auto' } }, }), instantiate: (spec, ctx) => { const { channelSemantics, table, layout, canvasSize } = ctx; const { categoryAxis, valueAxis } = detectAxes(channelSemantics); const colorField = channelSemantics.color?.field; const catField = channelSemantics[categoryAxis]?.field; const valField = channelSemantics[valueAxis]?.field; if (!catField || !valField) return; const isHorizontal = categoryAxis === 'y'; const spreadDir = isHorizontal ? 'y' : 'x'; const catCount = new Set(table.map((r: any) => r[catField])).size; const canvasW = isHorizontal ? (layout?.subplotHeight ?? canvasSize.height) : (layout?.subplotWidth ?? canvasSize.width); const spacing = Math.max(10, Math.round(canvasW / Math.max(1, catCount))); if (colorField) { // Multi-series line: spread + scaffold({h, fill}).name + group + line // scaffold(options).name() works because configured marks have .name(). spec._gofish = { type: 'line-multi', data: table, layers: [ { flow: [{ op: 'spread', field: catField, options: { dir: spreadDir, spacing }, }], mark: { shape: 'scaffold', options: isHorizontal ? { w: valField, fill: colorField } : { h: valField, fill: colorField }, name: 'points', }, }, { select: 'points', flow: [{ op: 'group', field: colorField }], mark: { shape: 'line' }, }, ], }; } else { // Single-series line: spread + scaffold + select + line spec._gofish = { type: 'line', data: table, layers: [ { flow: [{ op: 'spread', field: catField, options: { dir: spreadDir, spacing }, }], mark: { shape: 'scaffold', options: isHorizontal ? { w: valField } : { h: valField }, name: 'points', }, }, { select: 'points', flow: [], mark: { shape: 'line' }, }, ], }; } delete spec.mark; delete spec.encoding; }, }; ================================================ FILE: src/lib/agents-chart/gofish/templates/pie.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * GoFish Pie Chart template. * * GoFish uses spread with polar coordinate transform for pie. * However the basic approach is: * chart(data).flow(stack(catField, { dir: "y" })) * .mark(rect({ h: valField, fill: catField })) * with a clock() coordinate transform on the layer. * * For simplicity in V1, we'll use spread + rect in polar space. */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { extractCategories, aggregateByCategory } from './utils'; export const gfPieChartDef: ChartTemplateDef = { chart: 'Pie Chart', template: { mark: 'arc', encoding: {} }, channels: ['size', 'color', 'column', 'row'], markCognitiveChannel: 'area', instantiate: (spec, ctx) => { const { channelSemantics, table } = ctx; const colorField = channelSemantics.color?.field; const sizeField = channelSemantics.size?.field; if (!colorField && !sizeField) return; const catField = colorField || sizeField!; const valField = sizeField || colorField!; const categories = extractCategories(table, catField, channelSemantics.color?.ordinalSortOrder); let pieData: any[]; if (sizeField && colorField && sizeField !== colorField) { // color = category, size = measure const aggData = aggregateByCategory(table, catField, valField, categories); pieData = aggData.map(d => ({ [catField]: d.category, [valField]: d.value })); } else if (colorField && !sizeField) { // Count occurrences per category const counts = new Map<string, number>(); for (const row of table) { const cat = String(row[catField] ?? ''); counts.set(cat, (counts.get(cat) ?? 0) + 1); } pieData = categories.map(cat => ({ [catField]: cat, _count: counts.get(cat) ?? 0, })); } else { pieData = table; } const measureField = (sizeField && colorField && sizeField !== colorField) ? valField : (sizeField || '_count'); spec._gofish = { type: 'pie', data: pieData, coord: 'clock', flow: [{ op: 'stack', field: catField, options: { dir: 'x' }, }], mark: { shape: 'rect', options: { w: measureField, fill: catField, }, }, }; delete spec.mark; delete spec.encoding; }, properties: [ { key: 'innerRadius', label: 'Donut', type: 'continuous', min: 0, max: 60, step: 5, defaultValue: 0 } as ChartPropertyDef, ], }; ================================================ FILE: src/lib/agents-chart/gofish/templates/scatter.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * GoFish Scatter Plot template. * * GoFish approach: * chart(data).flow(scatter(field, { x: xField, y: yField })).mark(circle({ r: N })) */ import { ChartTemplateDef } from '../../core/types'; export const gfScatterPlotDef: ChartTemplateDef = { chart: 'Scatter Plot', template: { mark: 'circle', encoding: {} }, channels: ['x', 'y', 'color', 'size', 'opacity', 'column', 'row'], markCognitiveChannel: 'position', instantiate: (spec, ctx) => { const { channelSemantics, table } = ctx; const xField = channelSemantics.x?.field; const yField = channelSemantics.y?.field; const colorField = channelSemantics.color?.field; if (!xField || !yField) return; // For GoFish scatter, we need a unique id per row. const dataWithId = table.map((row, i) => ({ ...row, _gf_id: `p${i}` })); // GoFish circle({fill}) only accepts literal CSS colors, not data fields. // For data-driven color, use rect({fill: field}) with small fixed size, // matching how stacked/grouped bar use rect({fill}) for data-driven fill. const markShape = colorField ? 'rect' : 'circle'; const markOptions: Record<string, any> = colorField ? { w: 8, h: 8, fill: colorField } : { r: 5 }; spec._gofish = { type: 'scatter', data: dataWithId, flow: [{ op: 'scatter', field: '_gf_id', options: { x: xField, y: yField }, }], mark: { shape: markShape, options: markOptions, }, }; delete spec.mark; delete spec.encoding; }, properties: [ { key: 'opacity', label: 'Opacity', type: 'continuous', min: 0.1, max: 1, step: 0.05, defaultValue: 1 }, ], }; ================================================ FILE: src/lib/agents-chart/gofish/templates/scatterpie.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * GoFish Scatter Pie template. * * Scatter-positioned pie charts. Each (x, y) location shows a small pie * chart whose slices are coloured by `color` and sized by `angle`. * * GoFish pattern: * chart(scatterData) * .flow(scatter("_key", { x: "_x", y: "_y" })) * .mark((data) => * chart(data[0].collection, { coord: clock() }) * .flow(stack(colorField, { dir: "x", h: pieSize })) * .mark(rect({ w: angleField, fill: colorField })) * ) * * Channels: x (position), y (position), color (category), angle (value). */ import { ChartTemplateDef } from '../../core/types'; export const gfScatterPieChartDef: ChartTemplateDef = { chart: 'Scatter Pie Chart', template: { mark: 'arc', encoding: {} }, channels: ['x', 'y', 'color', 'angle'], markCognitiveChannel: 'area', instantiate: (spec, ctx) => { const { channelSemantics, table } = ctx; const xField = channelSemantics.x?.field; const yField = channelSemantics.y?.field; const colorField = channelSemantics.color?.field; const angleField = channelSemantics.angle?.field || channelSemantics.size?.field; // fallback to size channel if (!xField || !yField || !colorField) return; // ----------------------------------------------------------------- // Restructure flat table into scatter-of-pies data. // // Input rows: { x: 10, y: 20, cat: "A", val: 5 }, ... // Output: // [{ _key:"10|20", _x:10, _y:20, // collection: [{cat:"A",val:5}, {cat:"B",val:3}] }, …] // ----------------------------------------------------------------- const positionMap = new Map<string, { _x: any; _y: any; collection: Record<string, any>[]; }>(); for (const row of table) { const xv = row[xField]; const yv = row[yField]; const key = `${xv}|${yv}`; if (!positionMap.has(key)) { positionMap.set(key, { _x: xv, _y: yv, collection: [] }); } const entry: Record<string, any> = { [colorField]: row[colorField] }; if (angleField) entry[angleField] = row[angleField]; positionMap.get(key)!.collection.push(entry); } const scatterData = Array.from(positionMap.entries()).map( ([key, val]) => ({ _key: key, _x: val._x, _y: val._y, collection: val.collection, }), ); // Determine measure field for slice width const measureField = angleField || '_count'; // If no angle field, add counts per category in each collection if (!angleField) { for (const pt of scatterData) { const counts = new Map<string, number>(); for (const item of pt.collection) { const cat = String(item[colorField] ?? ''); counts.set(cat, (counts.get(cat) ?? 0) + 1); } pt.collection = Array.from(counts.entries()).map( ([cat, cnt]) => ({ [colorField]: cat, _count: cnt }), ); } } // Pie radius — reasonable default relative to chart size const pieSize = 20; spec._gofish = { type: 'scatterpie', data: scatterData, flow: [{ op: 'scatter', field: '_key', options: { x: '_x', y: '_y' }, }], mark: { shape: 'scatterpie', options: { colorField, angleField: measureField, pieSize, }, }, }; delete spec.mark; delete spec.encoding; }, properties: [], }; ================================================ FILE: src/lib/agents-chart/gofish/templates/utils.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Shared helper functions for GoFish template hooks. * Pure logic — no UI dependencies. */ import type { ChannelSemantics } from '../../core/types'; // --------------------------------------------------------------------------- // Discrete-dimension helpers // --------------------------------------------------------------------------- const isDiscrete = (type: string | undefined) => type === 'nominal' || type === 'ordinal'; /** * Detect which axis is the category (banded) axis and which is the value axis. */ export function detectAxes( channelSemantics: Record<string, ChannelSemantics>, ): { categoryAxis: 'x' | 'y'; valueAxis: 'x' | 'y' } { const xCS = channelSemantics.x; const yCS = channelSemantics.y; if (xCS && isDiscrete(xCS.type)) { return { categoryAxis: 'x', valueAxis: 'y' }; } if (yCS && isDiscrete(yCS.type)) { return { categoryAxis: 'y', valueAxis: 'x' }; } return { categoryAxis: 'x', valueAxis: 'y' }; } /** * Extract unique category values from data for a given field, preserving order. * If `ordinalSortOrder` is provided, returns values sorted in that canonical order. */ export function extractCategories(data: any[], field: string, ordinalSortOrder?: string[]): string[] { const seen = new Set<string>(); const result: string[] = []; for (const row of data) { const val = row[field]; if (val != null) { const key = String(val); if (!seen.has(key)) { seen.add(key); result.push(key); } } } if (ordinalSortOrder && ordinalSortOrder.length > 0) { const orderMap = new Map(ordinalSortOrder.map((v, i) => [v, i])); result.sort((a, b) => { const ia = orderMap.get(a); const ib = orderMap.get(b); if (ia !== undefined && ib !== undefined) return ia - ib; if (ia !== undefined) return -1; if (ib !== undefined) return 1; return 0; }); } return result; } /** * Group data by a categorical field. * Returns a map: seriesName → rows[]. */ export function groupBy(data: any[], field: string): Map<string, any[]> { const groups = new Map<string, any[]>(); for (const row of data) { const key = String(row[field] ?? ''); if (!groups.has(key)) groups.set(key, []); groups.get(key)!.push(row); } return groups; } /** * Aggregate data by category field, summing the value field. * Returns [{category, value}, ...] with one row per unique category. */ export function aggregateByCategory( data: any[], catField: string, valField: string, categories: string[], ): { category: string; value: number }[] { const map = new Map<string, number>(); for (const row of data) { const key = String(row[catField] ?? ''); const val = Number(row[valField]) || 0; map.set(key, (map.get(key) ?? 0) + val); } return categories.map(cat => ({ category: cat, value: map.get(cat) ?? 0, })); } ================================================ FILE: src/lib/agents-chart/index.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * @module agents-chart * * Semantic-level chart assembly library. * * Given data, semantic types, encoding definitions, and a canvas size, * generates a chart specification. No React/Redux/UI dependencies. * * Architecture: * core/ — Target-agnostic: semantic types, layout, decisions, types * vegalite/ — Vega-Lite backend: assembly, templates, spec instantiation * echarts/ — ECharts backend: assembly, templates, spec instantiation * chartjs/ — Chart.js backend: assembly, templates, spec instantiation * gofish/ — GoFish backend: assembly, templates, imperative rendering * * Assembly functions: * assembleVegaLite(input) — Vega-Lite spec * assembleECharts(input) — ECharts option object * assembleChartjs(input) — Chart.js config object * assembleGoFish(input) — GoFish spec with render() function * * Template registries: * vlTemplateDefs / vlGetTemplateDef / vlGetTemplateChannels * ecTemplateDefs / ecGetTemplateDef / ecGetTemplateChannels * cjsTemplateDefs / cjsGetTemplateDef / cjsGetTemplateChannels * gfTemplateDefs / gfGetTemplateDef / gfGetTemplateChannels * * Usage: * ```ts * import { assembleVegaLite } from './lib/agents-chart'; * * const spec = assembleVegaLite({ * data: { values: myData }, * semantic_types: { weight: 'Quantity', mpg: 'Quantity', origin: 'Country' }, * chart_spec: { * chartType: 'Scatter Plot', * encodings: { x: { field: 'weight' }, y: { field: 'mpg' }, color: { field: 'origin' } }, * canvasSize: { width: 400, height: 300 }, * }, * }); * ``` */ // Core: types, semantic types, decisions, layout, overflow export * from './core'; // Vega-Lite backend: assembleVegaLite, templates, spec instantiation export * from './vegalite'; // ECharts backend: assembleECharts, templates, spec instantiation export * from './echarts'; // Chart.js backend: assembleChartjs, templates, spec instantiation export * from './chartjs'; // GoFish backend: assembleGoFish, templates, imperative rendering export * from './gofish'; ================================================ FILE: src/lib/agents-chart/test-data/area-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { Type } from '../../../data/types'; import { TestCase, makeField, makeEncodingItem } from './types'; import { seededRandom, genDates, genCategories, genOrdinalLabels, ORDINAL_PREFIXES } from './generators'; // ============================================================================ // Area Chart & Streamgraph Tests — Matrix-driven // // Each test is defined as a compact row in AREA_MATRIX / STREAMGRAPH_MATRIX. // A shared generator converts matrix entries into full TestCase objects. // // Matrix dimensions: // x axis type: Q (quantitative), T (temporal), O (ordinal) // y axis type: same // color channel: — | N (nominal, multi-series) | Q (gradient) // n: total data points // sparse: ~20% random dropout // // Ordinal (O) is used for axes — area charts require a meaningful // sequential order. Nominal (N) is used for unordered color groups. // Purely nominal axes are excluded (area fills imply continuity). // // Default test canvas: 300 × 300 px. // ============================================================================ type DimType = 'Q' | 'T' | 'N' | 'O'; interface AreaMatrixEntry { x: DimType; y: DimType; n: number; // total data points (0 → grid) color?: DimType; xCard?: number; yCard?: number; colorCard?: number; sparse?: boolean; desc?: string; extraTags?: string[]; } // ============================================================================ // AREA CHART MATRIX — one row per test case (23 tests) // // Note: O (ordinal) is used for categorical axes — area charts require // a meaningful sequential order. N (nominal) is used for color groups. // Purely nominal axis combinations are excluded because connecting // unordered categories with area fills is visually misleading. // ============================================================================ const AREA_MATRIX: AreaMatrixEntry[] = [ // ── T × Q (7 tests) — core stacked / layered area ─────────────── { x: 'T', y: 'Q', n: 30, desc: 'Simple time-series area — 30 dates' }, { x: 'T', y: 'Q', n: 96, color: 'N', colorCard: 4, desc: '4 stacked series × 24 dates' }, { x: 'T', y: 'Q', n: 480, color: 'N', colorCard: 8, desc: '8 series × 60 dates — large stacked' }, { x: 'T', y: 'Q', n: 1800, color: 'N', colorCard: 15, desc: '15 series × 120 dates — stress', extraTags: ['stress'] }, { x: 'T', y: 'Q', n: 120, color: 'N', colorCard: 3, desc: '3 layered/overlapping series' }, { x: 'T', y: 'Q', n: 180, color: 'N', colorCard: 3, sparse: true, desc: '3 series, ~20% missing values' }, { x: 'T', y: 'Q', n: 30, color: 'Q', desc: 'Continuous color gradient on area' }, // ── O × Q (4 tests) — ordered categories on x ─────────────────── // Area charts with ordinal x make sense when categories have an // inherent sequence (e.g. stages, ranked items, ordered groups). { x: 'O', y: 'Q', n: 5, xCard: 5, desc: 'Ordinal area — 5 ordered categories' }, { x: 'O', y: 'Q', n: 48, xCard: 12, color: 'N', colorCard: 4, desc: '12 ordinal × 4 stacked series' }, { x: 'O', y: 'Q', n: 30, xCard: 30, desc: '30 ordinal categories — label overflow', extraTags: ['overflow'] }, { x: 'O', y: 'Q', n: 5, xCard: 5, color: 'Q', desc: 'Ordinal + continuous color gradient' }, // ── Q × O (3 tests) — mirror ──────────────────────────────────── { x: 'Q', y: 'O', n: 5, yCard: 5, desc: 'Horizontal ordinal — 5 ordered cats on y' }, { x: 'Q', y: 'O', n: 48, yCard: 12, color: 'N', colorCard: 4, desc: 'Horizontal 12 ordinal × 4 series' }, { x: 'Q', y: 'O', n: 30, yCard: 30, desc: 'Horizontal 30 ordinal overflow', extraTags: ['overflow'] }, // ── Q × Q (3 tests) — quantitative both axes ──────────────────── { x: 'Q', y: 'Q', n: 30, desc: 'Quantitative x area — 30 pts' }, { x: 'Q', y: 'Q', n: 150, color: 'N', colorCard: 3, desc: '3 stacked curves × 50 pts' }, { x: 'Q', y: 'Q', n: 200, desc: 'Dense single-series area — 200 pts' }, // Excluded: T×T, Q×T — date-pair data doesn't suit area charts. // Area fills imply sequential progression; T×T/Q×T lack monotonic relationships. // Excluded: N×N, T×N, N×T — purely nominal axes don't suit area charts. // Area fills imply continuity/progression; nominal axes lack this. ]; // ============================================================================ // STREAMGRAPH MATRIX — one row per test case (6 tests) // // Streamgraphs are centre-stacked areas — always multi-series (color // required). Primarily T×Q but we exercise a few other combos. // ============================================================================ const STREAMGRAPH_MATRIX: AreaMatrixEntry[] = [ { x: 'T', y: 'Q', n: 200, color: 'N', colorCard: 5, desc: '5 genres × 40 dates — basic streamgraph' }, { x: 'T', y: 'Q', n: 800, color: 'N', colorCard: 10, desc: '10 industries × 80 dates — large' }, { x: 'T', y: 'Q', n: 3000, color: 'N', colorCard: 20, desc: '20 series × 150 dates — stress', extraTags: ['stress'] }, { x: 'T', y: 'Q', n: 200, color: 'N', colorCard: 5, sparse: true, desc: '5 series ~20% missing' }, { x: 'O', y: 'Q', n: 60, xCard: 12, color: 'N', colorCard: 5, desc: 'Ordinal streamgraph — 12 cats × 5 series' }, { x: 'Q', y: 'Q', n: 150, color: 'N', colorCard: 3, desc: 'Quant-x streamgraph — 3 series × 50 pts' }, ]; // ============================================================================ // Generator internals // ============================================================================ interface AreaCh { role: 'x' | 'y' | 'color'; dimType: DimType; fieldName: string; card?: number; levels?: string[]; dates?: string[]; } const AREA_NAMES: Record<string, Record<DimType, string>> = { x: { Q: 'X', T: 'Date', N: 'Series', O: 'Stage' }, y: { Q: 'Value', T: 'EndDate', N: 'Group', O: 'Step' }, color: { Q: 'ColorVal', T: 'Timestamp', N: 'Series', O: 'Level' }, }; const AREA_FALLBACKS: Record<DimType, string[]> = { Q: ['X', 'Value', 'Measure', 'Score'], T: ['Date', 'EndDate', 'StartDate', 'Timestamp'], N: ['Series', 'Group', 'Category', 'Type'], O: ['Stage', 'Step', 'Phase', 'Level', 'Round'], }; const AREA_CAT_POOLS = ['Category', 'Country', 'Department', 'Product', 'Company']; const AREA_T_STARTS = [2020, 2023, 2019, 2022]; function buildAreaChannels(entry: AreaMatrixEntry, nPerSeries: number): AreaCh[] { const used = new Set<string>(); const channels: AreaCh[] = []; let tIdx = 0; let cIdx = 0; let oIdx = 0; function pickName(dim: DimType, role: string): string { const primary = AREA_NAMES[role]?.[dim]; if (primary && !used.has(primary)) { used.add(primary); return primary; } for (const n of AREA_FALLBACKS[dim]) { if (!used.has(n)) { used.add(n); return n; } } return `${role}_field`; } const specs: { role: 'x' | 'y' | 'color'; dim: DimType; card?: number }[] = [ { role: 'x', dim: entry.x, card: entry.xCard }, { role: 'y', dim: entry.y, card: entry.yCard }, ]; if (entry.color) specs.push({ role: 'color', dim: entry.color, card: entry.colorCard }); for (const { role, dim, card } of specs) { const ch: AreaCh = { role, dimType: dim, fieldName: pickName(dim, role) }; if (dim === 'N') { const c = card || 3; ch.card = c; ch.levels = genCategories(AREA_CAT_POOLS[cIdx % AREA_CAT_POOLS.length], c); cIdx++; } if (dim === 'O') { const c = card || 5; ch.card = c; ch.levels = genOrdinalLabels(ORDINAL_PREFIXES[oIdx % ORDINAL_PREFIXES.length], c); oIdx++; } if (dim === 'T') { ch.dates = genDates(nPerSeries, AREA_T_STARTS[tIdx % AREA_T_STARTS.length]); tIdx++; } channels.push(ch); } return channels; } // --------------------------------------------------------------------------- // Data generation // --------------------------------------------------------------------------- /** Smooth random walk with upward drift — natural for cumulative / area metrics. */ function genAreaTrend(n: number, base: number, drift: number, volatility: number, rand: () => number): number[] { const values: number[] = [base]; let momentum = 0; for (let i = 1; i < n; i++) { momentum = 0.6 * momentum + (rand() - 0.45) * volatility + drift; values.push(Math.round(Math.max(0, values[i - 1] + momentum))); } return values; } function genAreaSeriesData( entry: AreaMatrixEntry, channels: AreaCh[], rand: () => number, ): Record<string, any>[] { const xCh = channels.find(c => c.role === 'x')!; const yCh = channels.find(c => c.role === 'y')!; const colorCh = channels.find(c => c.role === 'color'); const nSeries = (colorCh?.dimType === 'N' ? (entry.colorCard || 3) : 1); const nPerSeries = Math.max(1, Math.floor(entry.n / nSeries)); // Shared x-positions let xPositions: any[]; if (xCh.dimType === 'T') { xPositions = genDates(nPerSeries, 2020); } else if (xCh.dimType === 'O') { xPositions = xCh.levels!; } else { // Q xPositions = Array.from({ length: nPerSeries }, (_, i) => Math.round(i * 100 / Math.max(1, nPerSeries - 1) * 10) / 10); } const data: Record<string, any>[] = []; for (let s = 0; s < nSeries; s++) { const base = 50 + Math.round(rand() * 200); const drift = 0.5 + rand() * 2; const vol = 10 + rand() * 30; // Generate y-values let yValues: any[]; if (yCh.dimType === 'Q') { yValues = genAreaTrend(xPositions.length, base, drift, vol, rand); } else if (yCh.dimType === 'T') { yValues = genDates(xPositions.length, 2023 + s); } else { // O yValues = xPositions.map((_, i) => yCh.levels![i % yCh.levels!.length]); } for (let i = 0; i < xPositions.length; i++) { if (entry.sparse && rand() < 0.2) continue; const row: Record<string, any> = { [xCh.fieldName]: xPositions[i], [yCh.fieldName]: yValues[i], }; if (colorCh) { if (colorCh.dimType === 'N') { row[colorCh.fieldName] = colorCh.levels![s]; } else if (colorCh.dimType === 'Q') { row[colorCh.fieldName] = Math.round(rand() * 100) / 10; } } data.push(row); } } return data; } function genAreaGridData(channels: AreaCh[], rand: () => number): Record<string, any>[] { const xCh = channels.find(c => c.role === 'x')!; const yCh = channels.find(c => c.role === 'y')!; const colorCh = channels.find(c => c.role === 'color'); const data: Record<string, any>[] = []; for (const xVal of xCh.levels!) { for (const yVal of yCh.levels!) { if (rand() > 0.3) { const row: Record<string, any> = { [xCh.fieldName]: xVal, [yCh.fieldName]: yVal }; if (colorCh?.dimType === 'N') row[colorCh.fieldName] = colorCh.levels![Math.floor(rand() * colorCh.levels!.length)]; data.push(row); } } } return data; } function genAreaDatePairData(n: number, channels: AreaCh[], rand: () => number): Record<string, any>[] { const data: Record<string, any>[] = []; for (let i = 0; i < n; i++) { const row: Record<string, any> = {}; const startDay = Math.floor(rand() * 365); const duration = Math.floor(10 + rand() * 180); const start = new Date(2023, 0, 1); start.setDate(start.getDate() + startDay); const end = new Date(start); end.setDate(end.getDate() + duration); for (const ch of channels) { if (ch.dimType === 'T' && ch.role === 'x') row[ch.fieldName] = start.toISOString().slice(0, 10); else if (ch.dimType === 'T' && ch.role === 'y') row[ch.fieldName] = end.toISOString().slice(0, 10); else if (ch.dimType === 'N') row[ch.fieldName] = ch.levels![i % ch.levels!.length]; else if (ch.dimType === 'Q') row[ch.fieldName] = Math.round(rand() * 1000) / 10; } data.push(row); } return data; } // --------------------------------------------------------------------------- // Title & tags // --------------------------------------------------------------------------- function buildAreaTitle(entry: AreaMatrixEntry): string { const xLabel = entry.x === 'O' && entry.xCard ? `O(${entry.xCard})` : entry.x; const yLabel = entry.y === 'O' && entry.yCard ? `O(${entry.yCard})` : entry.y; const parts = [`${xLabel}×${yLabel}`]; if (entry.color) { parts.push(`+color(${entry.color === 'N' ? `N,${entry.colorCard || 3}` : entry.color})`); } if (entry.sparse) parts.push('sparse'); if (entry.n === 0) parts.push('grid'); else parts.push(`(${entry.n} pts)`); return parts.join(' '); } function buildAreaTags(entry: AreaMatrixEntry, dataLen: number): string[] { const tags: string[] = []; const dims = new Set<DimType>([entry.x, entry.y]); if (entry.color) dims.add(entry.color); if (dims.has('Q')) tags.push('quantitative'); if (dims.has('T')) tags.push('temporal'); if (dims.has('N')) tags.push('nominal'); if (dims.has('O')) tags.push('ordinal'); if (entry.color) tags.push('color'); if (entry.color === 'Q') tags.push('continuous-color'); if (entry.sparse) tags.push('sparse'); const n = dataLen; if (n <= 25) tags.push('small'); else if (n <= 100) tags.push('medium'); else { tags.push('large'); if (n > 500) tags.push('scaling'); } if (entry.extraTags) tags.push(...entry.extraTags); return [...new Set(tags)]; } // --------------------------------------------------------------------------- // Matrix entry → TestCase // --------------------------------------------------------------------------- function areaMatrixToTestCase( entry: AreaMatrixEntry, chartType: string, rand: () => number, ): TestCase { const nSeries = entry.colorCard || 1; const effectiveN = entry.n || (entry.xCard || 5) * (entry.yCard || 5); const nPerSeries = Math.max(1, Math.floor(effectiveN / nSeries)); const channels = buildAreaChannels(entry, nPerSeries); const isGrid = entry.x === 'O' && entry.y === 'O' && entry.n === 0; const isTT = entry.x === 'T' && entry.y === 'T'; let data: Record<string, any>[]; if (isGrid) { data = genAreaGridData(channels, rand); } else if (isTT) { data = genAreaDatePairData(entry.n, channels, rand); } else { data = genAreaSeriesData(entry, channels, rand); } const typeMap: Record<DimType, Type> = { Q: Type.Number, T: Type.Date, N: Type.String, O: Type.String }; const semMap: Record<DimType, string> = { Q: 'Quantity', T: 'Date', N: 'Category', O: 'Category' }; const fields = channels.map(ch => makeField(ch.fieldName)); const metadata: Record<string, { type: Type; semanticType: string; levels: any[] }> = {}; const encodingMap: Partial<Record<string, any>> = {}; for (const ch of channels) { metadata[ch.fieldName] = { type: typeMap[ch.dimType], semanticType: semMap[ch.dimType], levels: ch.levels || [], }; encodingMap[ch.role] = makeEncodingItem(ch.fieldName); } return { title: buildAreaTitle(entry), description: entry.desc || buildAreaTitle(entry), tags: buildAreaTags(entry, data.length), chartType, data, fields, metadata, encodingMap, }; } // ============================================================================ // Public exports // ============================================================================ export function genAreaTests(): TestCase[] { const rand = seededRandom(910); return AREA_MATRIX.map(entry => areaMatrixToTestCase(entry, 'Area Chart', rand)); } export function genStreamgraphTests(): TestCase[] { const rand = seededRandom(920); return STREAMGRAPH_MATRIX.map(entry => areaMatrixToTestCase(entry, 'Streamgraph', rand)); } ================================================ FILE: src/lib/agents-chart/test-data/bar-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { Type } from '../../../data/types'; import { TestCase, makeField, makeEncodingItem } from './types'; import { seededRandom, genDates, genCategories, genRandomNames } from './generators'; // ============================================================================ // Bar Chart Tests — Matrix-driven // // Three bar chart variants share a common matrix entry type and generator: // - Bar Chart: x, y, optional color // - Stacked Bar Chart: x, y, required color (stacking dimension) // - Grouped Bar Chart: x, y, required group (encoding key = 'group') // // Matrix dimensions: // x axis type: Q (quantitative), T (temporal), N (nominal) // y axis type: same // color/group: — | N (nominal) | Q (discrete numeric or continuous) // n: total data points (0 → N×N grid mode) // // Note: Bar charts don't distinguish nominal from ordinal visually — // all categorical channels use N (nominal). // // Default test canvas: 300 × 300 px. // ============================================================================ type DimType = 'Q' | 'T' | 'N'; interface BarMatrixEntry { x: DimType; y: DimType; n: number; // total data points (0 → C×C grid) color?: DimType; // third channel (mapped to 'color' or 'group') xCard?: number; yCard?: number; colorCard?: number; // cardinality; omit for continuous Q color desc?: string; extraTags?: string[]; } // ============================================================================ // THE MATRICES // ============================================================================ const BAR_MATRIX: BarMatrixEntry[] = [ // ── N × Q (6 tests) — classic vertical bars ───────────────────── { x: 'N', y: 'Q', n: 5, xCard: 5, desc: 'Basic bar — 5 categories' }, { x: 'N', y: 'Q', n: 20, xCard: 20, desc: '20 bars — label rotation' }, { x: 'N', y: 'Q', n: 30, xCard: 30, desc: '30 bars — thin bar handling' }, { x: 'N', y: 'Q', n: 100, xCard: 100, desc: '100 bars — discrete cutoff', extraTags: ['overflow', 'cutoff'] }, { x: 'N', y: 'Q', n: 15, xCard: 5, color: 'N', colorCard: 3, desc: '5 cats × 3 color groups' }, { x: 'N', y: 'Q', n: 100, xCard: 5, color: 'N', colorCard: 20, desc: '5 cats × 20 colors — saturation', extraTags: ['overflow'] }, // ── Q × N (3 tests) — horizontal bars ─────────────────────────── { x: 'Q', y: 'N', n: 10, yCard: 10, desc: 'Horizontal — 10 bars' }, { x: 'Q', y: 'N', n: 100, yCard: 100, desc: 'Horizontal — 100 bars cutoff', extraTags: ['overflow', 'cutoff'] }, { x: 'Q', y: 'N', n: 30, yCard: 10, color: 'N', colorCard: 3, desc: 'Horizontal + 3 color groups' }, // ── T × Q (3 tests) — temporal bars ───────────────────────────── { x: 'T', y: 'Q', n: 24, desc: 'Temporal bars — 24 dates' }, { x: 'T', y: 'Q', n: 100, desc: '100 dates — dynamic bar sizing', extraTags: ['overflow'] }, { x: 'T', y: 'Q', n: 72, color: 'N', colorCard: 3, desc: '24 dates × 3 cats — temporal + color' }, // ── Q × T (2 tests) — horizontal temporal ────────────────────── { x: 'Q', y: 'T', n: 18, desc: 'Horizontal temporal — 18 dates on y' }, { x: 'Q', y: 'T', n: 54, color: 'N', colorCard: 3, desc: 'Horizontal temporal + 3 colors' }, // ── Q × Q (2 tests) — continuous banded ───────────────────────── { x: 'Q', y: 'Q', n: 20, desc: 'Both quantitative — dynamic mark resizing' }, { x: 'Q', y: 'Q', n: 30, desc: 'Equally spaced 1..30 — continuous banded', extraTags: ['equally-spaced'] }, // ── Edge combos (3 tests) ─────────────────────────────────────── { x: 'N', y: 'N', n: 0, xCard: 5, yCard: 5, desc: 'Cat × cat bars (degenerate)', extraTags: ['edge-case'] }, { x: 'T', y: 'N', n: 25, yCard: 5, desc: 'Temporal x, categorical y' }, { x: 'N', y: 'T', n: 25, xCard: 5, desc: 'Categorical x, temporal y' }, ]; const STACKED_BAR_MATRIX: BarMatrixEntry[] = [ // ── N × Q + color (5 tests) ───────────────────────────────────── { x: 'N', y: 'Q', n: 12, xCard: 4, color: 'N', colorCard: 3, desc: 'Basic stack — 4 cats × 3 colors' }, { x: 'N', y: 'Q', n: 75, xCard: 15, color: 'N', colorCard: 5, desc: 'Large — 15 cats × 5 colors' }, { x: 'N', y: 'Q', n: 240, xCard: 80, color: 'N', colorCard: 3, desc: 'Very large — 80 cats × 3 (cutoff)', extraTags: ['overflow', 'cutoff'] }, { x: 'N', y: 'Q', n: 24, xCard: 6, color: 'Q', colorCard: 4, desc: 'Numeric color (1–4) — small' }, { x: 'N', y: 'Q', n: 150, xCard: 5, color: 'Q', colorCard: 30, desc: 'Numeric color (1–30) — large' }, // ── T × Q + color (2 tests) ───────────────────────────────────── { x: 'T', y: 'Q', n: 30, color: 'N', colorCard: 3, desc: 'Temporal stack — 10 dates × 3' }, { x: 'T', y: 'Q', n: 80, color: 'N', colorCard: 4, desc: '20 dates × 4 fuels — temporal stacked' }, // ── Q × Q + color (1 test) ────────────────────────────────────── { x: 'Q', y: 'Q', n: 30, color: 'N', colorCard: 3, desc: 'Both quant + 3 types — stacked' }, // ── Horizontal (2 tests) ──────────────────────────────────────── { x: 'Q', y: 'N', n: 24, yCard: 8, color: 'N', colorCard: 3, desc: 'Horizontal stack — 8 cats × 3' }, { x: 'Q', y: 'T', n: 45, color: 'N', colorCard: 3, desc: 'Horizontal temporal stack — 15 dates × 3' }, // ── Edge combos (2 tests) ─────────────────────────────────────── { x: 'N', y: 'N', n: 0, xCard: 5, yCard: 5, color: 'N', colorCard: 3, desc: 'Cat × cat stacked (degenerate)', extraTags: ['edge-case'] }, { x: 'T', y: 'N', n: 20, yCard: 5, color: 'N', colorCard: 4, desc: 'Temporal × cat stacked' }, ]; const GROUPED_BAR_MATRIX: BarMatrixEntry[] = [ // ── N × Q + group (4 tests) ───────────────────────────────────── { x: 'N', y: 'Q', n: 12, xCard: 4, color: 'N', colorCard: 3, desc: 'Basic grouped — 4 cats × 3 groups' }, { x: 'N', y: 'Q', n: 8, xCard: 8, desc: 'No group field — fallback to simple bar' }, { x: 'N', y: 'Q', n: 270, xCard: 90, color: 'N', colorCard: 3, desc: 'Very large — 90 cats × 3 (cutoff)', extraTags: ['overflow', 'cutoff'] }, { x: 'N', y: 'Q', n: 30, xCard: 6, color: 'Q', colorCard: 5, desc: 'Numeric group (1–5) — small' }, // ── T × Q + group (1 test) ────────────────────────────────────── { x: 'T', y: 'Q', n: 36, color: 'N', colorCard: 3, desc: 'Temporal grouped — 12 dates × 3' }, // ── Q × Q + group (1 test) ────────────────────────────────────── { x: 'Q', y: 'Q', n: 20, color: 'N', colorCard: 4, desc: 'Both quant + group — ensureNominalAxis' }, // ── Horizontal (2 tests) ──────────────────────────────────────── { x: 'Q', y: 'N', n: 24, yCard: 6, color: 'N', colorCard: 4, desc: 'Horizontal grouped — 6 × 4' }, { x: 'Q', y: 'T', n: 30, color: 'N', colorCard: 3, desc: 'Horizontal temporal grouped — 10 dates × 3' }, // ── Numeric & continuous group (2 tests) ──────────────────────── { x: 'N', y: 'Q', n: 400, xCard: 8, color: 'Q', colorCard: 50, desc: 'Numeric group (1–50) — large' }, { x: 'N', y: 'Q', n: 50, xCard: 5, color: 'Q', desc: 'Continuous float on group — gradient' }, // ── Edge combos (2 tests) ─────────────────────────────────────── { x: 'N', y: 'N', n: 0, xCard: 5, yCard: 5, color: 'N', colorCard: 3, desc: 'Cat × cat grouped (degenerate)', extraTags: ['edge-case'] }, { x: 'T', y: 'N', n: 20, yCard: 5, color: 'N', colorCard: 4, desc: 'Temporal × cat grouped' }, ]; // ============================================================================ // Generator internals // ============================================================================ interface BarCh { role: 'x' | 'y' | 'color'; dimType: DimType; fieldName: string; card?: number; levels?: any[]; dates?: string[]; } const BAR_NAMES: Record<string, Record<DimType, string>> = { x: { Q: 'X', T: 'Date', N: 'Category' }, y: { Q: 'Value', T: 'EndDate', N: 'Group' }, color: { Q: 'ColorVal', T: 'DateCol', N: 'Segment' }, }; const BAR_FALLBACKS: Record<DimType, string[]> = { Q: ['X', 'Value', 'Score', 'Amount', 'Measure'], T: ['Date', 'EndDate', 'Time', 'Timestamp'], N: ['Category', 'Group', 'Segment', 'Type', 'Level'], }; const BAR_CAT_POOLS = ['Product', 'Country', 'Department', 'Category', 'Company']; const BAR_T_STARTS = [2020, 2023, 2019, 2022]; function buildBarChannels(entry: BarMatrixEntry): BarCh[] { const used = new Set<string>(); const channels: BarCh[] = []; let tIdx = 0; let cIdx = 0; let cSeed = 500; function pickName(dim: DimType, role: string): string { const primary = BAR_NAMES[role]?.[dim]; if (primary && !used.has(primary)) { used.add(primary); return primary; } for (const n of BAR_FALLBACKS[dim]) { if (!used.has(n)) { used.add(n); return n; } } return `${role}_field`; } const effectiveN = entry.n || (entry.xCard || 5) * (entry.yCard || 5); const specs: { role: 'x' | 'y' | 'color'; dim: DimType; card?: number }[] = [ { role: 'x', dim: entry.x, card: entry.xCard }, { role: 'y', dim: entry.y, card: entry.yCard }, ]; if (entry.color) specs.push({ role: 'color', dim: entry.color, card: entry.colorCard }); for (const { role, dim, card } of specs) { const ch: BarCh = { role, dimType: dim, fieldName: pickName(dim, role) }; if (dim === 'N') { const c = card || (role === 'color' ? 3 : 5); ch.card = c; if (c > 30) { ch.levels = genRandomNames(c, cSeed); cSeed += 100; } else { ch.levels = genCategories(BAR_CAT_POOLS[cIdx % BAR_CAT_POOLS.length], c); } cIdx++; } if (dim === 'T') { ch.dates = genDates(effectiveN, BAR_T_STARTS[tIdx % BAR_T_STARTS.length]); tIdx++; } if (dim === 'Q' && role === 'color' && card) { // Discrete numeric values 1..card ch.card = card; ch.levels = Array.from({ length: card }, (_, i) => i + 1); } channels.push(ch); } return channels; } // --------------------------------------------------------------------------- // Data generation // --------------------------------------------------------------------------- function genBarYValue(yCh: BarCh, rand: () => number): any { if (yCh.dimType === 'Q') return Math.round(10 + rand() * 990); if (yCh.dimType === 'T') { const d = Math.floor(rand() * 365); const dt = new Date(2023, 0, 1); dt.setDate(dt.getDate() + d); return dt.toISOString().slice(0, 10); } // N return yCh.levels![Math.floor(rand() * yCh.levels!.length)]; } function genBarData( entry: BarMatrixEntry, channels: BarCh[], rand: () => number, ): Record<string, any>[] { const xCh = channels.find(c => c.role === 'x')!; const yCh = channels.find(c => c.role === 'y')!; const colorCh = channels.find(c => c.role === 'color'); // Grid mode for N×N if (entry.x === 'N' && entry.y === 'N' && entry.n === 0) { const data: Record<string, any>[] = []; for (const xVal of xCh.levels!) { for (const yVal of yCh.levels!) { if (rand() > 0.3) { const row: Record<string, any> = { [xCh.fieldName]: xVal, [yCh.fieldName]: yVal }; if (colorCh?.dimType === 'N') row[colorCh.fieldName] = colorCh.levels![Math.floor(rand() * colorCh.levels!.length)]; data.push(row); } } } return data; } // T×T date-pair mode if (entry.x === 'T' && entry.y === 'T') { const data: Record<string, any>[] = []; for (let i = 0; i < entry.n; i++) { const startDay = Math.floor(rand() * 365); const duration = Math.floor(10 + rand() * 180); const start = new Date(2023, 0, 1); start.setDate(start.getDate() + startDay); const end = new Date(start); end.setDate(end.getDate() + duration); const row: Record<string, any> = { [xCh.fieldName]: start.toISOString().slice(0, 10), [yCh.fieldName]: end.toISOString().slice(0, 10), }; if (colorCh?.dimType === 'N') row[colorCh.fieldName] = colorCh.levels![i % colorCh.levels!.length]; data.push(row); } return data; } // Standard mode const data: Record<string, any>[] = []; const isContinuousColor = colorCh && !colorCh.levels; // Determine x-positions let xPositions: any[]; if (xCh.dimType === 'N') { xPositions = xCh.levels!; } else if (xCh.dimType === 'T') { const nGroups = colorCh?.levels ? colorCh.levels.length : 1; const nDates = Math.max(1, Math.floor(entry.n / nGroups)); xPositions = genDates(nDates, 2020); } else { // Q const nGroups = colorCh?.levels ? colorCh.levels.length : 1; const nPts = Math.max(1, Math.floor(entry.n / nGroups)); xPositions = Array.from({ length: nPts }, (_, i) => i + 1); } if (isContinuousColor) { // Continuous color: multiple rows per x, each with a random color float const rowsPerX = Math.max(1, Math.floor(entry.n / xPositions.length)); for (const xVal of xPositions) { for (let r = 0; r < rowsPerX; r++) { const row: Record<string, any> = { [xCh.fieldName]: xVal }; row[yCh.fieldName] = genBarYValue(yCh, rand); row[colorCh!.fieldName] = Math.round(rand() * 4000) / 100; data.push(row); } } } else if (colorCh?.levels) { // Discrete color: one row per (x × color) for (const xVal of xPositions) { for (const cVal of colorCh.levels) { const row: Record<string, any> = { [xCh.fieldName]: xVal }; row[yCh.fieldName] = genBarYValue(yCh, rand); row[colorCh.fieldName] = cVal; data.push(row); } } } else { // No color for (const xVal of xPositions) { const row: Record<string, any> = { [xCh.fieldName]: xVal }; row[yCh.fieldName] = genBarYValue(yCh, rand); data.push(row); } } return data; } // --------------------------------------------------------------------------- // Title & tags // --------------------------------------------------------------------------- function buildBarTitle(entry: BarMatrixEntry): string { const xLabel = entry.x === 'N' && entry.xCard ? `N(${entry.xCard})` : entry.x; const yLabel = entry.y === 'N' && entry.yCard ? `N(${entry.yCard})` : entry.y; const parts = [`${xLabel}×${yLabel}`]; if (entry.color) { const cLabel = entry.color === 'N' ? `N,${entry.colorCard || 3}` : entry.colorCard ? `Q,${entry.colorCard}` : 'Q'; parts.push(`+color(${cLabel})`); } if (entry.n === 0) parts.push('grid'); else parts.push(`(${entry.n} pts)`); return parts.join(' '); } function buildBarTags(entry: BarMatrixEntry, dataLen: number): string[] { const tags: string[] = []; const dims = new Set<DimType>([entry.x, entry.y]); if (entry.color) dims.add(entry.color); if (dims.has('Q')) tags.push('quantitative'); if (dims.has('T')) tags.push('temporal'); if (dims.has('N')) tags.push('nominal'); if (entry.color) tags.push('color'); if (entry.color === 'Q') tags.push('numeric-color'); const n = dataLen; if (n <= 25) tags.push('small'); else if (n <= 100) tags.push('medium'); else tags.push('large'); if (entry.extraTags) tags.push(...entry.extraTags); return [...new Set(tags)]; } // --------------------------------------------------------------------------- // Matrix entry → TestCase // --------------------------------------------------------------------------- function barMatrixToTestCase( entry: BarMatrixEntry, chartType: string, thirdChannelKey: string, rand: () => number, ): TestCase { const channels = buildBarChannels(entry); const data = genBarData(entry, channels, rand); const typeMap: Record<DimType, Type> = { Q: Type.Number, T: Type.Date, N: Type.String }; const semMap: Record<DimType, string> = { Q: 'Quantity', T: 'Date', N: 'Category' }; const fields = channels.map(ch => makeField(ch.fieldName)); const metadata: Record<string, { type: Type; semanticType: string; levels: any[] }> = {}; const encodingMap: Partial<Record<string, any>> = {}; for (const ch of channels) { let semanticType = semMap[ch.dimType]; if (ch.dimType === 'Q' && ch.levels) semanticType = 'Rank'; // discrete numeric metadata[ch.fieldName] = { type: typeMap[ch.dimType], semanticType, levels: ch.levels || [], }; const encKey = ch.role === 'color' ? thirdChannelKey : ch.role; encodingMap[encKey] = makeEncodingItem(ch.fieldName); } return { title: buildBarTitle(entry), description: entry.desc || buildBarTitle(entry), tags: buildBarTags(entry, data.length), chartType, data, fields, metadata, encodingMap, }; } // ============================================================================ // Public exports // ============================================================================ export function genBarTests(): TestCase[] { const rand = seededRandom(100); return BAR_MATRIX.map(entry => barMatrixToTestCase(entry, 'Bar Chart', 'color', rand)); } export function genStackedBarTests(): TestCase[] { const rand = seededRandom(200); return STACKED_BAR_MATRIX.map(entry => barMatrixToTestCase(entry, 'Stacked Bar Chart', 'color', rand)); } export function genGroupedBarTests(): TestCase[] { const rand = seededRandom(300); return GROUPED_BAR_MATRIX.map(entry => barMatrixToTestCase(entry, 'Grouped Bar Chart', 'group', rand)); } ================================================ FILE: src/lib/agents-chart/test-data/chartjs-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Chart.js backend comparison tests. * * Runs the same test inputs through ALL THREE backends: * assembleVegaLite (Vega-Lite), assembleECharts (ECharts), assembleChartjs (Chart.js) * * Covers: Scatter Plot, Line Chart, Bar Chart, Stacked Bar Chart, * Grouped Bar Chart, Area Chart, Pie Chart, Histogram, Radar Chart */ import { Type } from '../../../data/types'; import { TestCase, makeField, makeEncodingItem } from './types'; import { seededRandom, genCategories, genMonths } from './generators'; // --------------------------------------------------------------------------- // Test data generators // --------------------------------------------------------------------------- function genScatterData(n: number, seed: number) { const rand = seededRandom(seed); return Array.from({ length: n }, () => ({ Weight: Math.round((40 + rand() * 60) * 10) / 10, Height: Math.round((150 + rand() * 50) * 10) / 10, })); } function genScatterColorData(n: number, seed: number) { const rand = seededRandom(seed); const categories = ['Alpha', 'Beta', 'Gamma']; return Array.from({ length: n }, (_, i) => ({ X: Math.round(rand() * 100 * 10) / 10, Y: Math.round(rand() * 100 * 10) / 10, Group: categories[i % categories.length], })); } function genBarData(seed: number) { const rand = seededRandom(seed); const products = ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries']; return products.map(p => ({ Product: p, Sales: Math.round(100 + rand() * 900), })); } function genLineData(seed: number) { const rand = seededRandom(seed); const months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']; return months.map(m => ({ Month: m, Revenue: Math.round(1000 + rand() * 5000), })); } function genMultiSeriesLineData(seed: number) { const rand = seededRandom(seed); const months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug']; const series = ['ProductA', 'ProductB', 'ProductC']; const data: any[] = []; for (const m of months) { for (const s of series) { data.push({ Month: m, Sales: Math.round(500 + rand() * 2000), Product: s, }); } } return data; } function genStackedBarData(seed: number) { const rand = seededRandom(seed); const quarters = ['Q1', 'Q2', 'Q3', 'Q4']; const regions = ['North', 'South', 'East', 'West']; const data: any[] = []; for (const q of quarters) { for (const r of regions) { data.push({ Quarter: q, Revenue: Math.round(200 + rand() * 800), Region: r, }); } } return data; } function genGroupedBarData(seed: number) { const rand = seededRandom(seed); const years = ['2022', '2023', '2024']; const departments = ['Sales', 'Engineering', 'Marketing']; const data: any[] = []; for (const y of years) { for (const d of departments) { data.push({ Year: y, Budget: Math.round(10000 + rand() * 50000), Department: d, }); } } return data; } function genAreaData(seed: number) { const rand = seededRandom(seed); const months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug']; const series = ['Web', 'Mobile', 'Desktop']; const data: any[] = []; for (const m of months) { for (const s of series) { data.push({ Month: m, Users: Math.round(500 + rand() * 3000), Platform: s, }); } } return data; } function genPieData(seed: number) { const rand = seededRandom(seed); const segments = ['Mobile', 'Desktop', 'Tablet', 'Other']; return segments.map(s => ({ Device: s, Visits: Math.round(100 + rand() * 1000), })); } function genHistogramData(n: number, seed: number) { const rand = seededRandom(seed); return Array.from({ length: n }, () => ({ Score: Math.round(rand() * 100), })); } function genRadarData(seed: number) { const rand = seededRandom(seed); const metrics = ['Speed', 'Power', 'Defense', 'Stamina', 'Accuracy', 'Agility']; const entities = ['Player A', 'Player B', 'Player C']; const data: any[] = []; for (const e of entities) { for (const m of metrics) { data.push({ Metric: m, Score: Math.round(30 + rand() * 70), Player: e, }); } } return data; } // --------------------------------------------------------------------------- // Test case builders // --------------------------------------------------------------------------- export function genChartJsScatterTests(): TestCase[] { const tests: TestCase[] = []; // 1. Basic scatter { const data = genScatterData(50, 42); tests.push({ title: 'CJS: Scatter — Basic Q×Q', description: '50 points, two quantitative axes.', tags: ['chartjs', 'scatter', 'quantitative'], chartType: 'Scatter Plot', data, fields: [makeField('Weight'), makeField('Height')], metadata: { Weight: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Height: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Weight'), y: makeEncodingItem('Height') }, }); } // 2. Scatter with color grouping { const data = genScatterColorData(90, 77); tests.push({ title: 'CJS: Scatter — Color Groups', description: '90 points, 3 groups. CJS: 3 datasets with different colors.', tags: ['chartjs', 'scatter', 'color', 'multi-series'], chartType: 'Scatter Plot', data, fields: [makeField('X'), makeField('Y'), makeField('Group')], metadata: { X: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Y: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Group: { type: Type.String, semanticType: 'Category', levels: ['Alpha', 'Beta', 'Gamma'] }, }, encodingMap: { x: makeEncodingItem('X'), y: makeEncodingItem('Y'), color: makeEncodingItem('Group') }, }); } // 3. Dense scatter { const data = genScatterData(500, 99); tests.push({ title: 'CJS: Scatter — Dense (500 pts)', description: 'Dense scatter plot. CJS adjusts pointRadius automatically.', tags: ['chartjs', 'scatter', 'dense'], chartType: 'Scatter Plot', data, fields: [makeField('Weight'), makeField('Height')], metadata: { Weight: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Height: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Weight'), y: makeEncodingItem('Height') }, }); } return tests; } export function genChartJsLineTests(): TestCase[] { const tests: TestCase[] = []; // 1. Single series line { const data = genLineData(200); tests.push({ title: 'CJS: Line — Single Series', description: 'Ordinal x-axis, single line.', tags: ['chartjs', 'line', 'single-series'], chartType: 'Line Chart', data, fields: [makeField('Month'), makeField('Revenue')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: ['Jan','Feb','Mar','Apr','May','Jun'] }, Revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Revenue') }, }); } // 2. Multi-series line { const data = genMultiSeriesLineData(300); tests.push({ title: 'CJS: Line — Multi-Series (3 products)', description: 'Color channel → multiple datasets.', tags: ['chartjs', 'line', 'multi-series', 'color'], chartType: 'Line Chart', data, fields: [makeField('Month'), makeField('Sales'), makeField('Product')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug'] }, Sales: { type: Type.Number, semanticType: 'Revenue', levels: [] }, Product: { type: Type.String, semanticType: 'Category', levels: ['ProductA','ProductB','ProductC'] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Sales'), color: makeEncodingItem('Product') }, }); } return tests; } export function genChartJsBarTests(): TestCase[] { const tests: TestCase[] = []; // 1. Simple bar { const data = genBarData(100); tests.push({ title: 'CJS: Bar — Basic', description: '5 products, single dataset.', tags: ['chartjs', 'bar', 'simple'], chartType: 'Bar Chart', data, fields: [makeField('Product'), makeField('Sales')], metadata: { Product: { type: Type.String, semanticType: 'Category', levels: ['Apples','Bananas','Cherries','Dates','Elderberries'] }, Sales: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { x: makeEncodingItem('Product'), y: makeEncodingItem('Sales') }, }); } // 2. Many categories { const rand = seededRandom(150); const cities = genCategories('City', 20); const data = cities.map(c => ({ City: c, Population: Math.round(10000 + rand() * 900000), })); tests.push({ title: 'CJS: Bar — 20 categories', description: 'Many categories — tests layout and label rotation.', tags: ['chartjs', 'bar', 'many-categories'], chartType: 'Bar Chart', data, fields: [makeField('City'), makeField('Population')], metadata: { City: { type: Type.String, semanticType: 'Category', levels: cities }, Population: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('City'), y: makeEncodingItem('Population') }, }); } return tests; } export function genChartJsStackedBarTests(): TestCase[] { const tests: TestCase[] = []; { const data = genStackedBarData(500); tests.push({ title: 'CJS: Stacked Bar — Regions × Quarters', description: 'Stacked bar chart with 4 quarters and 4 regions.', tags: ['chartjs', 'stacked-bar', 'color'], chartType: 'Stacked Bar Chart', data, fields: [makeField('Quarter'), makeField('Revenue'), makeField('Region')], metadata: { Quarter: { type: Type.String, semanticType: 'Category', levels: ['Q1','Q2','Q3','Q4'] }, Revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, Region: { type: Type.String, semanticType: 'Category', levels: ['North','South','East','West'] }, }, encodingMap: { x: makeEncodingItem('Quarter'), y: makeEncodingItem('Revenue'), color: makeEncodingItem('Region') }, }); } return tests; } export function genChartJsGroupedBarTests(): TestCase[] { const tests: TestCase[] = []; { const data = genGroupedBarData(600); tests.push({ title: 'CJS: Grouped Bar — 3 Years × 3 Departments', description: 'Grouped (side-by-side) bar chart.', tags: ['chartjs', 'grouped-bar', 'color'], chartType: 'Grouped Bar Chart', data, fields: [makeField('Year'), makeField('Budget'), makeField('Department')], metadata: { Year: { type: Type.String, semanticType: 'Category', levels: ['2022','2023','2024'] }, Budget: { type: Type.Number, semanticType: 'Revenue', levels: [] }, Department: { type: Type.String, semanticType: 'Category', levels: ['Sales','Engineering','Marketing'] }, }, encodingMap: { x: makeEncodingItem('Year'), y: makeEncodingItem('Budget'), group: makeEncodingItem('Department') }, }); } return tests; } export function genChartJsAreaTests(): TestCase[] { const tests: TestCase[] = []; { const data = genAreaData(700); tests.push({ title: 'CJS: Area — Stacked (3 Platforms)', description: 'Stacked area chart with fill.', tags: ['chartjs', 'area', 'stacked', 'color'], chartType: 'Area Chart', data, fields: [makeField('Month'), makeField('Users'), makeField('Platform')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug'] }, Users: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Platform: { type: Type.String, semanticType: 'Category', levels: ['Web','Mobile','Desktop'] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Users'), color: makeEncodingItem('Platform') }, }); } // Single series area { const data = genLineData(701); tests.push({ title: 'CJS: Area — Single Series', description: 'Single series area chart.', tags: ['chartjs', 'area', 'single-series'], chartType: 'Area Chart', data, fields: [makeField('Month'), makeField('Revenue')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: ['Jan','Feb','Mar','Apr','May','Jun'] }, Revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Revenue') }, }); } return tests; } export function genChartJsPieTests(): TestCase[] { const tests: TestCase[] = []; // 1. Basic pie { const data = genPieData(800); tests.push({ title: 'CJS: Pie — Device Breakdown', description: 'Pie chart: color=Device, size=Visits.', tags: ['chartjs', 'pie'], chartType: 'Pie Chart', data, fields: [makeField('Device'), makeField('Visits')], metadata: { Device: { type: Type.String, semanticType: 'Category', levels: ['Mobile','Desktop','Tablet','Other'] }, Visits: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { color: makeEncodingItem('Device'), size: makeEncodingItem('Visits') }, }); } // 2. Doughnut { const data = genPieData(801); tests.push({ title: 'CJS: Doughnut — Device Breakdown', description: 'Doughnut chart with innerRadius.', tags: ['chartjs', 'doughnut', 'pie'], chartType: 'Pie Chart', data, fields: [makeField('Device'), makeField('Visits')], metadata: { Device: { type: Type.String, semanticType: 'Category', levels: ['Mobile','Desktop','Tablet','Other'] }, Visits: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { color: makeEncodingItem('Device'), size: makeEncodingItem('Visits') }, chartProperties: { innerRadius: 40 }, }); } return tests; } export function genChartJsHistogramTests(): TestCase[] { const tests: TestCase[] = []; { const data = genHistogramData(200, 900); tests.push({ title: 'CJS: Histogram — Scores (200 pts)', description: 'Histogram with 10 bins.', tags: ['chartjs', 'histogram'], chartType: 'Histogram', data, fields: [makeField('Score')], metadata: { Score: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Score') }, }); } return tests; } export function genChartJsRadarTests(): TestCase[] { const tests: TestCase[] = []; { const data = genRadarData(1000); tests.push({ title: 'CJS: Radar — 3 Players × 6 Metrics', description: 'Radar chart with multiple groups.', tags: ['chartjs', 'radar', 'multi-group'], chartType: 'Radar Chart', data, fields: [makeField('Metric'), makeField('Score'), makeField('Player')], metadata: { Metric: { type: Type.String, semanticType: 'Category', levels: ['Speed','Power','Defense','Stamina','Accuracy','Agility'] }, Score: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Player: { type: Type.String, semanticType: 'Category', levels: ['Player A','Player B','Player C'] }, }, encodingMap: { x: makeEncodingItem('Metric'), y: makeEncodingItem('Score'), color: makeEncodingItem('Player') }, }); } return tests; } export function genChartJsStressTests(): TestCase[] { const tests: TestCase[] = []; // Large scatter { const data = genScatterData(1000, 1100); tests.push({ title: 'CJS: Stress — 1000pt Scatter', description: '1000-point scatter plot performance test.', tags: ['chartjs', 'stress', 'scatter'], chartType: 'Scatter Plot', data, fields: [makeField('Weight'), makeField('Height')], metadata: { Weight: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Height: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Weight'), y: makeEncodingItem('Height') }, }); } // Many categories bar { const rand = seededRandom(1200); const items = genCategories('Item', 50); const data = items.map(i => ({ Item: i, Value: Math.round(rand() * 1000), })); tests.push({ title: 'CJS: Stress — 50 Cat Bar', description: '50-category bar chart — tests overflow and label rotation.', tags: ['chartjs', 'stress', 'bar', 'overflow'], chartType: 'Bar Chart', data, fields: [makeField('Item'), makeField('Value')], metadata: { Item: { type: Type.String, semanticType: 'Category', levels: items }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Item'), y: makeEncodingItem('Value') }, }); } return tests; } // =========================================================================== // Rose Chart tests // =========================================================================== export function genChartJsRoseTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(1500); // 1. Basic rose — wind directions × speed { const directions = ['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW']; const data = directions.map(d => ({ Direction: d, Speed: Math.round(5 + rand() * 25) })); tests.push({ title: 'CJS: Rose — 8 Directions', description: 'Wind speed by compass direction. CJS: polarArea chart type.', tags: ['chartjs', 'rose', 'basic'], chartType: 'Rose Chart', data, fields: [makeField('Direction'), makeField('Speed')], metadata: { Direction: { type: Type.String, semanticType: 'Category', levels: directions }, Speed: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Direction'), y: makeEncodingItem('Speed') }, chartProperties: { alignment: 'center' }, }); } // 2. Stacked rose — directions × season { const directions = ['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW']; const seasons = ['Spring', 'Summer', 'Autumn', 'Winter']; const data: any[] = []; for (const d of directions) { for (const s of seasons) { data.push({ Direction: d, Speed: Math.round(3 + rand() * 20), Season: s }); } } tests.push({ title: 'CJS: Stacked Rose — 8 dirs × 4 seasons', description: 'Stacked wind rose by season.', tags: ['chartjs', 'rose', 'stacked'], chartType: 'Rose Chart', data, fields: [makeField('Direction'), makeField('Speed'), makeField('Season')], metadata: { Direction: { type: Type.String, semanticType: 'Category', levels: directions }, Speed: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Season: { type: Type.String, semanticType: 'Category', levels: seasons }, }, encodingMap: { x: makeEncodingItem('Direction'), y: makeEncodingItem('Speed'), color: makeEncodingItem('Season') }, chartProperties: { alignment: 'center' }, }); } // 3. Rose — 12 months { const months = genMonths(12); const data = months.map(m => ({ Month: m, Rainfall: Math.round(20 + rand() * 150) })); tests.push({ title: 'CJS: Rose — 12 Months Rainfall', description: 'Monthly rainfall as a rose chart.', tags: ['chartjs', 'rose', 'medium'], chartType: 'Rose Chart', data, fields: [makeField('Month'), makeField('Rainfall')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: months }, Rainfall: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Rainfall') }, }); } return tests; } ================================================ FILE: src/lib/agents-chart/test-data/date-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { Type } from '../../../data/types'; import { Channel, EncodingItem } from '../../../components/ComponentType'; import { TestCase, DateFormat, makeField, makeEncodingItem } from './types'; import { seededRandom } from './generators'; // --------------------------------------------------------------------------- // Shared helper: generate test cases from date format definitions × chart types // --------------------------------------------------------------------------- export function genDateTests(dateFormats: DateFormat[], seed: number): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(seed); const chartConfigs: { chartType: string; needsColor: boolean }[] = [ { chartType: 'Bar Chart', needsColor: false }, { chartType: 'Line Chart', needsColor: false }, { chartType: 'Scatter Plot', needsColor: false }, { chartType: 'Area Chart', needsColor: false }, { chartType: 'Stacked Bar Chart', needsColor: true }, ]; const colorCategories = ['Alpha', 'Beta', 'Gamma']; for (const fmt of dateFormats) { for (const cfg of chartConfigs) { const data: Record<string, any>[] = []; for (const v of fmt.values) { if (cfg.needsColor) { for (const cat of colorCategories) { data.push({ [fmt.fieldName]: v, Value: Math.round(50 + rand() * 500), Category: cat }); } } else { data.push({ [fmt.fieldName]: v, Value: Math.round(50 + rand() * 500) }); } } const fields = [makeField(fmt.fieldName), makeField('Value')]; const metadata: Record<string, { type: Type; semanticType: string; levels: any[] }> = { [fmt.fieldName]: { type: fmt.expectedType, semanticType: fmt.semanticType, levels: fmt.values }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }; const encodingMap: Partial<Record<Channel, EncodingItem>> = { x: makeEncodingItem(fmt.fieldName), y: makeEncodingItem('Value'), }; if (cfg.needsColor) { fields.push(makeField('Category')); metadata.Category = { type: Type.String, semanticType: 'Category', levels: colorCategories }; encodingMap.color = makeEncodingItem('Category'); } tests.push({ title: `${fmt.label} → ${cfg.chartType}`, description: `${fmt.description} (${fmt.values.length} values)`, tags: ['date-format', fmt.semanticType.toLowerCase(), cfg.chartType.toLowerCase().replace(/ /g, '-')], chartType: cfg.chartType, data, fields, metadata, encodingMap, }); } } return tests; } // --------------------------------------------------------------------------- // 1. Year — all representations // --------------------------------------------------------------------------- export function genDateYearTests(): TestCase[] { return genDateTests([ { label: 'Year (string)', description: 'Years as 4-digit strings: "2000", "2001", …', values: Array.from({ length: 12 }, (_, i) => String(2000 + i)), fieldName: 'Year', expectedType: Type.String, semanticType: 'Year', }, { label: 'Year (number)', description: 'Years as integers: 2000, 2001, …', values: Array.from({ length: 12 }, (_, i) => 2000 + i), fieldName: 'Year', expectedType: Type.Number, semanticType: 'Year', }, { label: 'Year (ISO date)', description: 'Years as ISO strings: "2000-01-01", "2001-01-01", …', values: Array.from({ length: 12 }, (_, i) => `${2000 + i}-01-01`), fieldName: 'Date', expectedType: Type.Date, semanticType: 'Date', }, { label: 'Year (UTC ms)', description: 'Years as UTC timestamps in ms: 946684800000, …', values: Array.from({ length: 12 }, (_, i) => new Date(2000 + i, 0, 1).getTime()), fieldName: 'Timestamp', expectedType: Type.Number, semanticType: 'Timestamp', }, { label: 'Year (2-digit string)', description: 'Two-digit years: "98", "99", "00", "01", …', values: Array.from({ length: 10 }, (_, i) => String((98 + i) % 100).padStart(2, '0')), fieldName: 'Year', expectedType: Type.String, semanticType: 'Year', }, { label: 'Fiscal Year (FY YYYY)', description: 'Fiscal years: "FY 2018", "FY 2019", …', values: Array.from({ length: 8 }, (_, i) => `FY ${2018 + i}`), fieldName: 'FiscalYear', expectedType: Type.String, semanticType: 'Year', }, ], 8801); } // --------------------------------------------------------------------------- // 2. Month only // --------------------------------------------------------------------------- export function genDateMonthTests(): TestCase[] { return genDateTests([ { label: 'Month (English)', description: 'Month short names: "Jan", "Feb", …', values: ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'], fieldName: 'Month', expectedType: Type.String, semanticType: 'Month', }, { label: 'Month (full English)', description: 'Full month names: "January", "February", …', values: ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'], fieldName: 'Month', expectedType: Type.String, semanticType: 'Month', }, { label: 'Month (number)', description: 'Months as integers: 1, 2, …, 12', values: Array.from({ length: 12 }, (_, i) => i + 1), fieldName: 'Month', expectedType: Type.Number, semanticType: 'Month', }, ], 8802); } // --------------------------------------------------------------------------- // 3. Year-Month // --------------------------------------------------------------------------- export function genDateYearMonthTests(): TestCase[] { return genDateTests([ { label: 'Year-Month (YYYY-MM, 1 year)', description: 'Year-month strings within one year: "2020-01", …, "2020-12"', values: Array.from({ length: 12 }, (_, i) => `2020-${String(i + 1).padStart(2, '0')}`), fieldName: 'Date', expectedType: Type.Date, semanticType: 'Date', }, { label: 'Year-Month (YYYY-MM, 3 years)', description: 'Year-month strings spanning 3 years: "2020-01", …, "2022-12"', values: (() => { const vals: string[] = []; for (let y = 2020; y <= 2022; y++) for (let m = 1; m <= 12; m++) vals.push(`${y}-${String(m).padStart(2, '0')}`); return vals; })(), fieldName: 'Date', expectedType: Type.Date, semanticType: 'Date', }, { label: 'Year-Month (Mon YYYY, 1 year)', description: 'Natural year-month within one year: "Jan 2020", …, "Dec 2020"', values: (() => { const months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']; return months.map(m => `${m} 2020`); })(), fieldName: 'Date', expectedType: Type.Date, semanticType: 'Date', }, { label: 'Year-Month (Mon YYYY, 5 years)', description: 'Natural year-month spanning 5 years: "Jan 2018", …, "Dec 2022"', values: (() => { const months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']; const vals: string[] = []; for (let y = 2018; y <= 2022; y++) for (const m of months) vals.push(`${m} ${y}`); return vals; })(), fieldName: 'Date', expectedType: Type.Date, semanticType: 'Date', }, { label: 'Quarter (Q# YYYY)', description: 'Quarter strings: "Q1 2020", "Q2 2020", …', values: (() => { const quarters: string[] = []; for (let y = 2018; y <= 2023; y++) { for (let q = 1; q <= 4; q++) quarters.push(`Q${q} ${y}`); } return quarters; })(), fieldName: 'Quarter', expectedType: Type.String, semanticType: 'Quarter', }, { label: 'Week (Wk ##)', description: 'Week labels: "Wk 01", "Wk 02", …, "Wk 24"', values: Array.from({ length: 24 }, (_, i) => `Wk ${String(i + 1).padStart(2, '0')}`), fieldName: 'Week', expectedType: Type.String, semanticType: 'Week', }, ], 8803); } // --------------------------------------------------------------------------- // 4. Decade // --------------------------------------------------------------------------- export function genDateDecadeTests(): TestCase[] { return genDateTests([ { label: 'Decade (XXXXs)', description: 'Decades as strings with "s": "1950s", "1960s", …', values: Array.from({ length: 8 }, (_, i) => `${1950 + i * 10}s`), fieldName: 'Decade', expectedType: Type.String, semanticType: 'Decade', }, { label: 'Decade (string)', description: 'Decades as plain strings: "1950", "1960", …', values: Array.from({ length: 8 }, (_, i) => String(1950 + i * 10)), fieldName: 'Decade', expectedType: Type.String, semanticType: 'Decade', }, { label: 'Decade (number)', description: 'Decades as integers: 1950, 1960, …', values: Array.from({ length: 8 }, (_, i) => 1950 + i * 10), fieldName: 'Decade', expectedType: Type.Number, semanticType: 'Decade', }, ], 8804); } // --------------------------------------------------------------------------- // 5. Date / DateTime // --------------------------------------------------------------------------- export function genDateDateTimeTests(): TestCase[] { return genDateTests([ { label: 'Date (ISO YYYY-MM-DD, 1 year)', description: 'ISO date strings within one year: "2020-01-15", "2020-02-15", …', values: Array.from({ length: 12 }, (_, i) => `2020-${String(i + 1).padStart(2, '0')}-15`), fieldName: 'Date', expectedType: Type.Date, semanticType: 'Date', }, { label: 'Date (ISO YYYY-MM-DD, 3 years)', description: 'ISO date strings spanning 3 years: "2020-01-15", …, "2022-12-15"', values: (() => { const vals: string[] = []; for (let y = 2020; y <= 2022; y++) for (let m = 1; m <= 12; m++) vals.push(`${y}-${String(m).padStart(2, '0')}-15`); return vals; })(), fieldName: 'Date', expectedType: Type.Date, semanticType: 'Date', }, { label: 'Date (Mon DD YYYY)', description: 'Natural date: "Jan 15 2020", "Feb 15 2020", …', values: (() => { const months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']; return months.map(m => `${m} 15 2020`); })(), fieldName: 'Date', expectedType: Type.Date, semanticType: 'Date', }, { label: 'Date (MM/DD/YYYY)', description: 'US date format: "01/15/2020", "02/15/2020", …', values: Array.from({ length: 12 }, (_, i) => `${String(i + 1).padStart(2, '0')}/15/2020`), fieldName: 'Date', expectedType: Type.Date, semanticType: 'Date', }, { label: 'Date (DD.MM.YYYY)', description: 'European date: "15.01.2020", "15.02.2020", …', values: Array.from({ length: 12 }, (_, i) => `15.${String(i + 1).padStart(2, '0')}.2020`), fieldName: 'Date', expectedType: Type.String, semanticType: 'Date', }, { label: 'DateTime (ISO 8601)', description: 'Full datetime: "2020-01-01T08:00:00", …', values: Array.from({ length: 10 }, (_, i) => `2020-01-${String(i + 1).padStart(2, '0')}T${String(8 + i).padStart(2, '0')}:00:00`), fieldName: 'DateTime', expectedType: Type.Date, semanticType: 'DateTime', }, ], 8805); } // --------------------------------------------------------------------------- // 6. Hours — within a day and across days // --------------------------------------------------------------------------- export function genDateHoursTests(): TestCase[] { return genDateTests([ { label: 'Hours (same day)', description: 'Hourly data within one day: "2020-01-15T00:00", …, "2020-01-15T23:00"', values: Array.from({ length: 24 }, (_, i) => `2020-01-15T${String(i).padStart(2, '0')}:00:00`), fieldName: 'DateTime', expectedType: Type.Date, semanticType: 'DateTime', }, { label: 'Hours (across days)', description: 'Hourly data across 3 days: "2020-01-01T00:00", …', values: Array.from({ length: 24 }, (_, i) => { const day = Math.floor(i / 8) + 1; const hour = i % 8 + 8; return `2020-01-${String(day).padStart(2, '0')}T${String(hour).padStart(2, '0')}:00:00`; }), fieldName: 'DateTime', expectedType: Type.Date, semanticType: 'DateTime', }, { label: 'Hours (UTC ms, same day)', description: 'Hourly timestamps in ms for one day', values: Array.from({ length: 24 }, (_, i) => new Date(2020, 0, 15, i).getTime()), fieldName: 'Timestamp', expectedType: Type.Number, semanticType: 'Timestamp', }, { label: 'Minutes (same hour)', description: 'Per-minute data within one hour: "2020-01-15T10:00", …, "2020-01-15T10:59"', values: Array.from({ length: 30 }, (_, i) => `2020-01-15T10:${String(i * 2).padStart(2, '0')}:00`), fieldName: 'DateTime', expectedType: Type.Date, semanticType: 'DateTime', }, ], 8806); } ================================================ FILE: src/lib/agents-chart/test-data/debug-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Debug Test Cases * * Regression tests derived from real evaluation failures. Each test * reproduces a case that scored poorly in the rec-agent evaluation. * Data is sampled (max 200 rows) from the original datasets. * * Auto-generated from inspect_output/ — do not edit manually. */ import { Type } from '../../../data/types'; import { TestCase, makeField, makeEncodingItem } from './types'; function gen_agenciesQ4RecGpt41(): TestCase { // Which counties have the highest number of law enforcement agencies, and how do these counts compare across states? // Score: 10/20 | The visualization partially answers which counties have more agencies but fails to compare across st return { title: 'agencies_q4_rec-gpt-4.1', description: 'Show number of agencies per county, faceted by state', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'county': 'LINN', 'state_abbr': 'OR', 'agency_count': 5 }, { 'county': 'EMANUEL', 'state_abbr': 'GA', 'agency_count': 7 }, { 'county': 'CHEROKEE, DEKALB', 'state_abbr': 'AL', 'agency_count': 1 }, { 'county': 'KENDALL', 'state_abbr': 'TX', 'agency_count': 6 }, { 'county': 'SHERMAN', 'state_abbr': 'KS', 'agency_count': 2 }, { 'county': 'BLACKFORD', 'state_abbr': 'IN', 'agency_count': 2 }, { 'county': 'BOND, MADISON', 'state_abbr': 'IL', 'agency_count': 1 }, { 'county': 'JEFF DAVIS', 'state_abbr': 'GA', 'agency_count': 2 }, { 'county': 'JEFFERSON', 'state_abbr': 'TX', 'agency_count': 8 }, { 'county': 'DIXIE', 'state_abbr': 'FL', 'agency_count': 7 }, { 'county': 'CHARLESTON, DORCHESTER', 'state_abbr': 'SC', 'agency_count': 2 }, { 'county': 'BOSQUE', 'state_abbr': 'TX', 'agency_count': 6 }, { 'county': 'WAYNE', 'state_abbr': 'WV', 'agency_count': 8 }, { 'county': 'UNION', 'state_abbr': 'NJ', 'agency_count': 26 }, { 'county': 'WASHINGTON', 'state_abbr': 'CO', 'agency_count': 1 }, { 'county': 'SANDUSKY', 'state_abbr': 'OH', 'agency_count': 6 }, { 'county': 'HARRISON', 'state_abbr': 'MO', 'agency_count': 2 }, { 'county': 'POPE', 'state_abbr': 'AR', 'agency_count': 7 }, { 'county': 'WHITE', 'state_abbr': 'AR', 'agency_count': 12 }, { 'county': 'BAY', 'state_abbr': 'FL', 'agency_count': 14 }, { 'county': 'WAYNE', 'state_abbr': 'IL', 'agency_count': 3 }, { 'county': 'HAMILTON', 'state_abbr': 'IN', 'agency_count': 8 }, { 'county': 'DICKEY', 'state_abbr': 'ND', 'agency_count': 3 }, { 'county': 'ASHLAND, HOLMES', 'state_abbr': 'OH', 'agency_count': 1 }, { 'county': 'FAYETTE, MARION', 'state_abbr': 'AL', 'agency_count': 1 }, { 'county': 'MINERAL', 'state_abbr': 'NV', 'agency_count': 1 }, { 'county': 'HENRY', 'state_abbr': 'IL', 'agency_count': 12 }, { 'county': 'HICKMAN', 'state_abbr': 'TN', 'agency_count': 2 }, { 'county': 'ERIE', 'state_abbr': 'PA', 'agency_count': 23 }, { 'county': 'PUTNAM', 'state_abbr': 'TN', 'agency_count': 6 }, { 'county': 'GLOUCESTER', 'state_abbr': 'NJ', 'agency_count': 28 }, { 'county': 'CEDAR', 'state_abbr': 'MO', 'agency_count': 2 }, { 'county': 'CLAY', 'state_abbr': 'IL', 'agency_count': 2 }, { 'county': 'MONTGOMERY', 'state_abbr': 'MS', 'agency_count': 2 }, { 'county': 'HIGHLAND', 'state_abbr': 'OH', 'agency_count': 6 }, { 'county': 'GREENWOOD', 'state_abbr': 'KS', 'agency_count': 1 }, { 'county': 'DUCHESNE', 'state_abbr': 'UT', 'agency_count': 2 }, { 'county': 'POLK', 'state_abbr': 'WI', 'agency_count': 6 }, { 'county': 'BALDWIN', 'state_abbr': 'AL', 'agency_count': 14 }, { 'county': 'COMANCHE', 'state_abbr': 'TX', 'agency_count': 3 }, { 'county': 'SALT LAKE', 'state_abbr': 'UT', 'agency_count': 19 }, { 'county': 'DES MOINES', 'state_abbr': 'IA', 'agency_count': 4 }, { 'county': 'GIBSON', 'state_abbr': 'TN', 'agency_count': 8 }, { 'county': 'LAFAYETTE, SALINE', 'state_abbr': 'MO', 'agency_count': 2 }, { 'county': 'CHARLES', 'state_abbr': 'MD', 'agency_count': 3 }, { 'county': 'GREELEY', 'state_abbr': 'KS', 'agency_count': 1 }, { 'county': 'HAWAII', 'state_abbr': 'HI', 'agency_count': 1 }, { 'county': 'EDWARDS', 'state_abbr': 'IL', 'agency_count': 3 }, { 'county': 'REEVES', 'state_abbr': 'TX', 'agency_count': 3 }, { 'county': 'CARROLL', 'state_abbr': 'MD', 'agency_count': 7 }, { 'county': 'DESOTO', 'state_abbr': 'FL', 'agency_count': 7 }, { 'county': 'ORANGE', 'state_abbr': 'FL', 'agency_count': 20 }, { 'county': 'MILLE LACS', 'state_abbr': 'MN', 'agency_count': 4 }, { 'county': 'INDIAN RIVER', 'state_abbr': 'FL', 'agency_count': 10 }, { 'county': 'DELTA', 'state_abbr': 'MI', 'agency_count': 4 }, { 'county': 'WINCHESTER CITY', 'state_abbr': 'VA', 'agency_count': 1 }, { 'county': 'FRANKLIN', 'state_abbr': 'ME', 'agency_count': 7 }, { 'county': 'CLERMONT, HAMILTON, WARREN', 'state_abbr': 'OH', 'agency_count': 1 }, { 'county': 'LYON', 'state_abbr': 'KS', 'agency_count': 4 }, { 'county': 'IRON', 'state_abbr': 'UT', 'agency_count': 7 }, { 'county': 'LITTLE RIVER', 'state_abbr': 'AR', 'agency_count': 2 }, { 'county': 'TRAVIS', 'state_abbr': 'TX', 'agency_count': 17 }, { 'county': 'BEAVERHEAD', 'state_abbr': 'MT', 'agency_count': 2 }, { 'county': 'MCPHERSON', 'state_abbr': 'NE', 'agency_count': 2 }, { 'county': 'WORTH', 'state_abbr': 'GA', 'agency_count': 4 }, { 'county': 'BROWN', 'state_abbr': 'MN', 'agency_count': 4 }, { 'county': 'YUMA', 'state_abbr': 'CO', 'agency_count': 3 }, { 'county': 'BERNALILLO', 'state_abbr': 'NM', 'agency_count': 3 }, { 'county': 'BOURBON', 'state_abbr': 'KY', 'agency_count': 3 }, { 'county': 'BUCKINGHAM', 'state_abbr': 'VA', 'agency_count': 2 }, { 'county': 'JOHNSTON', 'state_abbr': 'OK', 'agency_count': 2 }, { 'county': 'POTTAWATOMIE', 'state_abbr': 'OK', 'agency_count': 5 }, { 'county': 'MENOMINEE', 'state_abbr': 'WI', 'agency_count': 1 }, { 'county': 'SKAMANIA', 'state_abbr': 'WA', 'agency_count': 1 }, { 'county': 'WEXFORD', 'state_abbr': 'MI', 'agency_count': 4 }, { 'county': 'FULTON, HERKIMER', 'state_abbr': 'NY', 'agency_count': 1 }, { 'county': 'POWER', 'state_abbr': 'ID', 'agency_count': 2 }, { 'county': 'JEFFERSON', 'state_abbr': 'TN', 'agency_count': 5 }, { 'county': 'ARAPAHOE', 'state_abbr': 'CO', 'agency_count': 9 }, { 'county': 'SEARCY', 'state_abbr': 'AR', 'agency_count': 2 }, { 'county': 'ALLEGHENY, WASHINGTON', 'state_abbr': 'PA', 'agency_count': 1 }, { 'county': 'HANCOCK, HENDERSON', 'state_abbr': 'IL', 'agency_count': 1 }, { 'county': 'FRANKLIN', 'state_abbr': 'TX', 'agency_count': 2 }, { 'county': 'MCCRACKEN', 'state_abbr': 'KY', 'agency_count': 4 }, { 'county': 'BOULDER, WELD', 'state_abbr': 'CO', 'agency_count': 2 }, { 'county': 'KITTITAS', 'state_abbr': 'WA', 'agency_count': 5 }, { 'county': 'KOSCIUSKO', 'state_abbr': 'IN', 'agency_count': 8 }, { 'county': 'ST CROIX', 'state_abbr': 'WI', 'agency_count': 7 }, { 'county': 'OSCEOLA', 'state_abbr': 'FL', 'agency_count': 8 }, { 'county': 'CHOCTAW, WEBSTER', 'state_abbr': 'MS', 'agency_count': 1 }, { 'county': 'NOT SPECIFIED', 'state_abbr': 'OR', 'agency_count': 11 }, { 'county': 'MATHEWS', 'state_abbr': 'VA', 'agency_count': 2 }, { 'county': 'CLARE', 'state_abbr': 'MI', 'agency_count': 2 }, { 'county': 'HARDIN', 'state_abbr': 'IA', 'agency_count': 3 }, { 'county': 'LAPEER, SANILAC', 'state_abbr': 'MI', 'agency_count': 1 }, { 'county': 'NEWAYGO', 'state_abbr': 'MI', 'agency_count': 6 }, { 'county': 'EDGAR', 'state_abbr': 'IL', 'agency_count': 4 }, { 'county': 'WILLIAMSBURG', 'state_abbr': 'SC', 'agency_count': 10 }, { 'county': 'NEOSHO', 'state_abbr': 'KS', 'agency_count': 3 }, { 'county': 'RUTHERFORD', 'state_abbr': 'TN', 'agency_count': 6 }, { 'county': 'TURNER', 'state_abbr': 'SD', 'agency_count': 3 }, { 'county': 'NORTHAMPTON', 'state_abbr': 'PA', 'agency_count': 35 }, { 'county': 'ADAMS', 'state_abbr': 'CO', 'agency_count': 5 }, { 'county': 'NOBLE', 'state_abbr': 'OH', 'agency_count': 1 }, { 'county': 'MARION', 'state_abbr': 'OR', 'agency_count': 12 }, { 'county': 'GREENE', 'state_abbr': 'IA', 'agency_count': 2 }, { 'county': 'HARLAN', 'state_abbr': 'NE', 'agency_count': 2 }, { 'county': 'HUNT', 'state_abbr': 'TX', 'agency_count': 17 }, { 'county': 'BENTON', 'state_abbr': 'IN', 'agency_count': 2 }, { 'county': 'LEE', 'state_abbr': 'IA', 'agency_count': 3 }, { 'county': 'PARK', 'state_abbr': 'MT', 'agency_count': 2 }, { 'county': 'HOUSTON', 'state_abbr': 'MN', 'agency_count': 4 }, { 'county': 'GRAHAM', 'state_abbr': 'KS', 'agency_count': 2 }, { 'county': 'TILLAMOOK', 'state_abbr': 'OR', 'agency_count': 5 }, { 'county': 'ROBERTS', 'state_abbr': 'SD', 'agency_count': 2 }, { 'county': 'RIO ARRIBA', 'state_abbr': 'NM', 'agency_count': 1 }, { 'county': 'BROWN', 'state_abbr': 'IL', 'agency_count': 2 }, { 'county': 'CODINGTON', 'state_abbr': 'SD', 'agency_count': 2 }, { 'county': 'BEAUREGARD', 'state_abbr': 'LA', 'agency_count': 2 }, { 'county': 'MARTINSVILLE CITY', 'state_abbr': 'VA', 'agency_count': 1 }, { 'county': 'CHEROKEE, SMITH', 'state_abbr': 'TX', 'agency_count': 2 }, { 'county': 'MAVERICK', 'state_abbr': 'TX', 'agency_count': 2 }, { 'county': 'FRESNO', 'state_abbr': 'CA', 'agency_count': 28 }, { 'county': 'LA SALLE, LIVINGSTON', 'state_abbr': 'IL', 'agency_count': 1 }, { 'county': 'WYTHE', 'state_abbr': 'VA', 'agency_count': 4 }, { 'county': 'BAXTER', 'state_abbr': 'AR', 'agency_count': 6 }, { 'county': 'THROCKMORTON', 'state_abbr': 'TX', 'agency_count': 1 }, { 'county': 'EAST BATON ROUGE', 'state_abbr': 'LA', 'agency_count': 7 }, { 'county': 'NEW MADRID', 'state_abbr': 'MO', 'agency_count': 10 }, { 'county': 'SALINE', 'state_abbr': 'KS', 'agency_count': 3 }, { 'county': 'MODOC', 'state_abbr': 'CA', 'agency_count': 4 }, { 'county': 'LEE', 'state_abbr': 'IL', 'agency_count': 4 }, { 'county': 'CHENANGO', 'state_abbr': 'NY', 'agency_count': 9 }, { 'county': 'WASHBURN', 'state_abbr': 'WI', 'agency_count': 3 }, { 'county': 'MORGAN', 'state_abbr': 'TN', 'agency_count': 2 }, { 'county': 'TANGIPAHOA', 'state_abbr': 'LA', 'agency_count': 9 }, { 'county': 'PIATT', 'state_abbr': 'IL', 'agency_count': 4 }, { 'county': 'CLEARFIELD', 'state_abbr': 'PA', 'agency_count': 11 }, { 'county': 'EDGECOMBE, NASH, WILSON', 'state_abbr': 'NC', 'agency_count': 1 }, { 'county': 'LE SUEUR, SCOTT', 'state_abbr': 'MN', 'agency_count': 1 }, { 'county': 'JEFFERSON, WALWORTH', 'state_abbr': 'WI', 'agency_count': 1 }, { 'county': 'LAKE', 'state_abbr': 'OR', 'agency_count': 3 }, { 'county': 'MISSOULA', 'state_abbr': 'MT', 'agency_count': 3 }, { 'county': 'SCHLEY', 'state_abbr': 'GA', 'agency_count': 2 }, { 'county': 'MIAMI', 'state_abbr': 'KS', 'agency_count': 4 }, { 'county': 'FULTON', 'state_abbr': 'IN', 'agency_count': 2 }, { 'county': 'BASTROP', 'state_abbr': 'TX', 'agency_count': 5 }, { 'county': 'NYE', 'state_abbr': 'NV', 'agency_count': 1 }, { 'county': 'THOMAS', 'state_abbr': 'NE', 'agency_count': 2 }, { 'county': 'POTTAWATOMIE', 'state_abbr': 'KS', 'agency_count': 5 }, { 'county': 'LEON', 'state_abbr': 'TX', 'agency_count': 5 }, { 'county': 'ATHENS', 'state_abbr': 'OH', 'agency_count': 9 }, { 'county': 'CRAWFORD, FRANKLIN', 'state_abbr': 'MO', 'agency_count': 1 }, { 'county': 'SUMMERS', 'state_abbr': 'WV', 'agency_count': 5 }, { 'county': 'CLERMONT', 'state_abbr': 'OH', 'agency_count': 11 }, { 'county': 'PLATTE', 'state_abbr': 'MO', 'agency_count': 15 }, { 'county': 'ALGER', 'state_abbr': 'MI', 'agency_count': 3 }, { 'county': 'BOONE, WINNEBAGO', 'state_abbr': 'IL', 'agency_count': 2 }, { 'county': 'HART', 'state_abbr': 'GA', 'agency_count': 2 }, { 'county': 'PIERCE', 'state_abbr': 'ND', 'agency_count': 2 }, { 'county': 'PERSON', 'state_abbr': 'NC', 'agency_count': 2 }, { 'county': 'KENT, SUSSEX', 'state_abbr': 'DE', 'agency_count': 1 }, { 'county': 'SABINE', 'state_abbr': 'TX', 'agency_count': 4 }, { 'county': 'BENTON, WASHINGTON', 'state_abbr': 'AR', 'agency_count': 1 }, { 'county': 'KING, SNOHOMISH', 'state_abbr': 'WA', 'agency_count': 1 }, { 'county': 'CLAYTON', 'state_abbr': 'GA', 'agency_count': 10 }, { 'county': 'MONTGOMERY, TOOMBS', 'state_abbr': 'GA', 'agency_count': 1 }, { 'county': 'CRAIG', 'state_abbr': 'OK', 'agency_count': 2 }, { 'county': 'JACKSON', 'state_abbr': 'IA', 'agency_count': 4 }, { 'county': 'ELLIS, JOHNSON, TARRANT', 'state_abbr': 'TX', 'agency_count': 1 }, { 'county': 'LINCOLN', 'state_abbr': 'SD', 'agency_count': 5 }, { 'county': 'HOWARD', 'state_abbr': 'MO', 'agency_count': 2 }, { 'county': 'MORROW', 'state_abbr': 'OH', 'agency_count': 3 }, { 'county': 'KINGS', 'state_abbr': 'CA', 'agency_count': 7 }, { 'county': 'OLMSTED', 'state_abbr': 'MN', 'agency_count': 3 }, { 'county': 'CARLTON', 'state_abbr': 'MN', 'agency_count': 3 }, { 'county': 'MADISON', 'state_abbr': 'OH', 'agency_count': 3 }, { 'county': 'PRAIRIE', 'state_abbr': 'MT', 'agency_count': 1 }, { 'county': 'SHERIDAN', 'state_abbr': 'NE', 'agency_count': 3 }, { 'county': 'BLACKFORD, JAY', 'state_abbr': 'IN', 'agency_count': 1 }, { 'county': 'SAN MIGUEL', 'state_abbr': 'NM', 'agency_count': 3 }, { 'county': 'FERRY', 'state_abbr': 'WA', 'agency_count': 1 }, { 'county': 'TALLAPOOSA', 'state_abbr': 'AL', 'agency_count': 7 }, { 'county': 'LAWRENCE', 'state_abbr': 'SD', 'agency_count': 5 }, { 'county': 'CARTER, SULLIVAN, WASHINGTON', 'state_abbr': 'TN', 'agency_count': 1 }, { 'county': 'WALKER', 'state_abbr': 'GA', 'agency_count': 6 }, { 'county': 'BROWN', 'state_abbr': 'SD', 'agency_count': 3 }, { 'county': 'KENOSHA, WALWORTH', 'state_abbr': 'WI', 'agency_count': 1 }, { 'county': 'PAWNEE', 'state_abbr': 'NE', 'agency_count': 2 }, { 'county': 'FREESTONE', 'state_abbr': 'TX', 'agency_count': 4 }, { 'county': 'HARPER', 'state_abbr': 'KS', 'agency_count': 3 }, { 'county': 'CHILDRESS', 'state_abbr': 'TX', 'agency_count': 2 }, { 'county': 'JEFFERSON', 'state_abbr': 'OR', 'agency_count': 4 }, { 'county': 'BRACKEN', 'state_abbr': 'KY', 'agency_count': 3 }, { 'county': 'RANKIN', 'state_abbr': 'MS', 'agency_count': 8 }, { 'county': 'BLACK HAWK', 'state_abbr': 'IA', 'agency_count': 5 }, { 'county': 'FORREST, LAMAR', 'state_abbr': 'MS', 'agency_count': 1 }, { 'county': 'HOONAH-ANGOON', 'state_abbr': 'AK', 'agency_count': 1 }, { 'county': 'FAYETTE, HARDEMAN', 'state_abbr': 'TN', 'agency_count': 1 }, { 'county': 'PEPIN', 'state_abbr': 'WI', 'agency_count': 3 }, ], fields: [makeField('county'), makeField('state_abbr'), makeField('agency_count')], metadata: { 'county': { type: Type.String, semanticType: 'String', levels: ['ADAMS', 'ALGER', 'ALLEGHENY, WASHINGTON', 'ARAPAHOE', 'ASHLAND, HOLMES', 'ATHENS', 'BALDWIN', 'BASTROP', 'BAXTER', 'BAY', 'BEAUREGARD', 'BEAVERHEAD', 'BENTON', 'BENTON, WASHINGTON', 'BERNALILLO', 'BLACK HAWK', 'BLACKFORD', 'BLACKFORD, JAY', 'BOND, MADISON', 'BOONE, WINNEBAGO', 'BOSQUE', 'BOULDER, WELD', 'BOURBON', 'BRACKEN', 'BROWN', 'BUCKINGHAM', 'CARLTON', 'CARROLL', 'CARTER, SULLIVAN, WASHINGTON', 'CEDAR', 'CHARLES', 'CHARLESTON, DORCHESTER', 'CHENANGO', 'CHEROKEE, DEKALB', 'CHEROKEE, SMITH', 'CHILDRESS', 'CHOCTAW, WEBSTER', 'CLARE', 'CLAY', 'CLAYTON', 'CLEARFIELD', 'CLERMONT', 'CLERMONT, HAMILTON, WARREN', 'CODINGTON', 'COMANCHE', 'CRAIG', 'CRAWFORD, FRANKLIN', 'DELTA', 'DES MOINES', 'DESOTO'] }, 'state_abbr': { type: Type.String, semanticType: 'State', levels: ['AK', 'AL', 'AR', 'CA', 'CO', 'DE', 'FL', 'GA', 'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MD', 'ME', 'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NJ', 'NM', 'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'WA', 'WI', 'WV'] }, 'agency_count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('county'), y: makeEncodingItem('agency_count'), column: makeEncodingItem('state_abbr'), }, chartProperties: { cornerRadius: 3 }, }; } function gen_agenciesQ4RecGpt51(): TestCase { // Which counties have the highest number of law enforcement agencies, and how do these counts compare across states? // Score: 8/20 | The visualization partially addresses the question but is undermined by an apparent aggregation erro return { title: 'agencies_q4_rec-gpt-5.1', description: 'Compute **agency** counts by **county** and **state_abbr**', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'state_abbr': 'NE', 'state': 'Nebraska', 'county': 'BOYD', 'agency_count': 2, 'overall_rank': 57 }, { 'state_abbr': 'KS', 'state': 'Kansas', 'county': 'BUTLER', 'agency_count': 9, 'overall_rank': 50 }, { 'state_abbr': 'PA', 'state': 'Pennsylvania', 'county': 'ERIE', 'agency_count': 23, 'overall_rank': 36 }, { 'state_abbr': 'IA', 'state': 'Iowa', 'county': 'WORTH', 'agency_count': 1, 'overall_rank': 58 }, { 'state_abbr': 'MO', 'state': 'Missouri', 'county': 'AUDRAIN', 'agency_count': 5, 'overall_rank': 54 }, { 'state_abbr': 'WV', 'state': 'West Virginia', 'county': 'HAMPSHIRE', 'agency_count': 6, 'overall_rank': 53 }, { 'state_abbr': 'NY', 'state': 'New York', 'county': 'SCHOHARIE', 'agency_count': 6, 'overall_rank': 53 }, { 'state_abbr': 'MI', 'state': 'Michigan', 'county': 'ST JOSEPH', 'agency_count': 8, 'overall_rank': 51 }, { 'state_abbr': 'IA', 'state': 'Iowa', 'county': 'HANCOCK', 'agency_count': 1, 'overall_rank': 58 }, { 'state_abbr': 'TX', 'state': 'Texas', 'county': 'SAN PATRICIO', 'agency_count': 10, 'overall_rank': 49 }, { 'state_abbr': 'TX', 'state': 'Texas', 'county': 'DELTA', 'agency_count': 2, 'overall_rank': 57 }, { 'state_abbr': 'IA', 'state': 'Iowa', 'county': 'TAYLOR', 'agency_count': 1, 'overall_rank': 58 }, { 'state_abbr': 'VA', 'state': 'Virginia', 'county': 'NORTON CITY', 'agency_count': 1, 'overall_rank': 58 }, { 'state_abbr': 'GA', 'state': 'Georgia', 'county': 'HART', 'agency_count': 2, 'overall_rank': 57 }, { 'state_abbr': 'TX', 'state': 'Texas', 'county': 'ANGELINA', 'agency_count': 11, 'overall_rank': 48 }, { 'state_abbr': 'KY', 'state': 'Kentucky', 'county': 'DAVIESS', 'agency_count': 2, 'overall_rank': 57 }, { 'state_abbr': 'IA', 'state': 'Iowa', 'county': 'CERRO GORDO', 'agency_count': 3, 'overall_rank': 56 }, { 'state_abbr': 'FL', 'state': 'Florida', 'county': 'ORANGE', 'agency_count': 20, 'overall_rank': 39 }, { 'state_abbr': 'PA', 'state': 'Pennsylvania', 'county': 'BLAIR', 'agency_count': 21, 'overall_rank': 38 }, { 'state_abbr': 'MO', 'state': 'Missouri', 'county': 'DUNKLIN', 'agency_count': 10, 'overall_rank': 49 }, { 'state_abbr': 'NC', 'state': 'North Carolina', 'county': 'HARNETT', 'agency_count': 6, 'overall_rank': 53 }, { 'state_abbr': 'TN', 'state': 'Tennessee', 'county': 'SEVIER', 'agency_count': 6, 'overall_rank': 53 }, { 'state_abbr': 'TN', 'state': 'Tennessee', 'county': 'UNION', 'agency_count': 3, 'overall_rank': 56 }, { 'state_abbr': 'MA', 'state': 'Massachusetts', 'county': 'NOT SPECIFIED', 'agency_count': 2, 'overall_rank': 57 }, { 'state_abbr': 'OH', 'state': 'Ohio', 'county': 'TRUMBULL', 'agency_count': 22, 'overall_rank': 37 }, { 'state_abbr': 'IA', 'state': 'Iowa', 'county': 'PALO ALTO', 'agency_count': 2, 'overall_rank': 57 }, { 'state_abbr': 'AZ', 'state': 'Arizona', 'county': 'YUMA', 'agency_count': 6, 'overall_rank': 53 }, { 'state_abbr': 'AZ', 'state': 'Arizona', 'county': 'COCONINO, YAVAPAI', 'agency_count': 1, 'overall_rank': 58 }, { 'state_abbr': 'NV', 'state': 'Nevada', 'county': 'CHURCHILL', 'agency_count': 2, 'overall_rank': 57 }, { 'state_abbr': 'WI', 'state': 'Wisconsin', 'county': 'SHAWANO', 'agency_count': 2, 'overall_rank': 57 }, { 'state_abbr': 'GA', 'state': 'Georgia', 'county': 'HANCOCK', 'agency_count': 2, 'overall_rank': 57 }, { 'state_abbr': 'GA', 'state': 'Georgia', 'county': 'TAYLOR', 'agency_count': 3, 'overall_rank': 56 }, { 'state_abbr': 'ND', 'state': 'North Dakota', 'county': 'NOT SPECIFIED', 'agency_count': 6, 'overall_rank': 53 }, { 'state_abbr': 'LA', 'state': 'Louisiana', 'county': 'ST HELENA', 'agency_count': 3, 'overall_rank': 56 }, { 'state_abbr': 'KY', 'state': 'Kentucky', 'county': 'CARROLL', 'agency_count': 2, 'overall_rank': 57 }, { 'state_abbr': 'NC', 'state': 'North Carolina', 'county': 'IREDELL', 'agency_count': 5, 'overall_rank': 54 }, { 'state_abbr': 'NC', 'state': 'North Carolina', 'county': 'HARNETT, WAKE', 'agency_count': 1, 'overall_rank': 58 }, { 'state_abbr': 'TX', 'state': 'Texas', 'county': 'ELLIS, JOHNSON, TARRANT', 'agency_count': 1, 'overall_rank': 58 }, { 'state_abbr': 'MA', 'state': 'Massachusetts', 'county': 'ESSEX', 'agency_count': 42, 'overall_rank': 20 }, { 'state_abbr': 'KS', 'state': 'Kansas', 'county': 'HASKELL', 'agency_count': 1, 'overall_rank': 58 }, { 'state_abbr': 'NC', 'state': 'North Carolina', 'county': 'CLAY', 'agency_count': 1, 'overall_rank': 58 }, { 'state_abbr': 'CA', 'state': 'California', 'county': 'KINGS', 'agency_count': 7, 'overall_rank': 52 }, { 'state_abbr': 'WI', 'state': 'Wisconsin', 'county': 'BUFFALO', 'agency_count': 2, 'overall_rank': 57 }, { 'state_abbr': 'IA', 'state': 'Iowa', 'county': 'HARDIN', 'agency_count': 3, 'overall_rank': 56 }, { 'state_abbr': 'KY', 'state': 'Kentucky', 'county': 'OLDHAM', 'agency_count': 4, 'overall_rank': 55 }, { 'state_abbr': 'NC', 'state': 'North Carolina', 'county': 'HENDERSON', 'agency_count': 5, 'overall_rank': 54 }, { 'state_abbr': 'WV', 'state': 'West Virginia', 'county': 'WOOD', 'agency_count': 8, 'overall_rank': 51 }, { 'state_abbr': 'MO', 'state': 'Missouri', 'county': 'IRON', 'agency_count': 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'agency_count': 52, 'overall_rank': 14 }, { 'state_abbr': 'DE', 'state': 'Delaware', 'county': 'KENT, NEW CASTLE', 'agency_count': 1, 'overall_rank': 58 }, { 'state_abbr': 'TX', 'state': 'Texas', 'county': 'KENT', 'agency_count': 1, 'overall_rank': 58 }, ], fields: [makeField('state_abbr'), makeField('state'), makeField('county'), makeField('agency_count'), makeField('overall_rank')], metadata: { 'state_abbr': { type: Type.String, semanticType: 'State', levels: ['AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DE', 'FL', 'GA', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME', 'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NJ', 'NM', 'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'WA', 'WI', 'WV', 'WY'] }, 'state': { type: Type.String, semanticType: 'Category', levels: ['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', 'Colorado', 'Connecticut', 'Delaware', 'Florida', 'Georgia', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New Jersey', 'New Mexico', 'New York', 'North Carolina', 'North Dakota', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'South Carolina', 'South Dakota', 'Tennessee', 'Texas', 'Utah', 'Virginia', 'Washington', 'West Virginia', 'Wisconsin', 'Wyoming'] }, 'county': { type: Type.String, semanticType: 'String', levels: ['ADAIR', 'ADAMS, JEFFERSON', 'ALLEN', 'ANDERSON', 'ANGELINA', 'AUDRAIN', 'AUGUSTA', 'BASTROP', 'BAY, MIDLAND', 'BENTON', 'BLAIR', 'BORDEN', 'BOURBON, NICHOLAS', 'BOYD', 'BRAZORIA, FORT BEND, HARRIS', 'BRISTOL BAY', 'BROOME', 'BUENA VISTA', 'BUFFALO', 'BURNET, LLANO', 'BUTLER', 'CALCASIEU', 'CALDWELL', 'CAMDEN, MILLER', 'CAMPBELL', 'CARROLL', 'CASS', 'CASWELL', 'CATAHOULA', 'CATTARAUGUS', 'CECIL', 'CEDAR', 'CERRO GORDO', 'CHEROKEE, MUSKOGEE', 'CHEROKEE, SPARTANBURG', 'CHESTER', 'CHEYENNE', 'CHURCHILL', 'CLAY', 'CLEAR CREEK', 'CLEVELAND', 'CLINTON, ST CLAIR', 'COBB', 'COCONINO, YAVAPAI', 'COLEMAN', 'COTTON', 'DALLAS', 'DALLAS, ELLIS, TARRANT', 'DAVIE', 'DAVIESS'] }, 'agency_count': { type: Type.Number, semanticType: 'Count', levels: [] }, 'overall_rank': { type: Type.String, semanticType: 'Category', levels: ['14', '20', '27', '36', '37', '38', '39', '42', '43', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58'] }, }, encodingMap: { x: makeEncodingItem('county'), y: makeEncodingItem('agency_count'), color: makeEncodingItem('state_abbr'), }, chartProperties: { cornerRadius: 4 }, }; } function gen_apiLogosQ4RecGpt41(): TestCase { // What are the top 15 logo host domains (extracted from the url field) by number of APIs using them? // Score: 6/20 | The visualization has major data extraction and design issues: it appears to plot URL schemes rather return { title: 'api_logos_q4_rec-gpt-4.1', description: 'Show top 15 **logo host domains** by API count', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'host_domain': 'https', 'api_count': 2495 }, { 'host_domain': 'http', 'api_count': 29 }, { 'host_domain': '', 'api_count': 4 }, { 'host_domain': '..', 'api_count': 1 }, ], fields: [makeField('host_domain'), makeField('api_count')], metadata: { 'host_domain': { type: Type.String, semanticType: 'String', levels: ['', '..', 'http', 'https'] }, 'api_count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('host_domain'), y: makeEncodingItem('api_count'), }, chartProperties: { cornerRadius: 4 }, }; } function gen_apiLogosQ4RecGpt51(): TestCase { // What are the top 15 logo host domains (extracted from the url field) by number of APIs using them? // Score: 4/20 | The visualization suffers from major data extraction and scaling errors and does not correctly prese return { title: 'api_logos_q4_rec-gpt-5.1', description: 'Count APIs per **logo_host_domain** and show top 15', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'logo_host_domain': '..', 'api_count': 1 }, { 'logo_host_domain': 'http', 'api_count': 29 }, { 'logo_host_domain': 'https', 'api_count': 2495 }, ], fields: [makeField('logo_host_domain'), makeField('api_count')], metadata: { 'logo_host_domain': { type: Type.String, semanticType: 'String', levels: ['..', 'http', 'https'] }, 'api_count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('logo_host_domain'), y: makeEncodingItem('api_count'), }, chartProperties: { cornerRadius: 4 }, }; } function gen_apisguruApisQ3RecGpt41(): TestCase { // Among APIs with multiple versions in the catalog, which API names have the highest number of versions listed? // Score: 2/20 | The chart is essentially empty, with only axis labels visible. It does not display version counts by return { title: 'apisguru_apis_q3_rec-gpt-4.1', description: 'Show most versioned APIs by count of **version** per **name**', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ ], fields: [makeField('name'), makeField('version_count')], metadata: { 'name': { type: Type.String, semanticType: 'Name', levels: [] }, 'version_count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('name'), y: makeEncodingItem('version_count'), }, chartProperties: { cornerRadius: 4 }, }; } function gen_apisguruApisQ3RecGpt51(): TestCase { // Among APIs with multiple versions in the catalog, which API names have the highest number of versions listed? // Score: 0/20 | The visualization fails to display any meaningful data related to API version counts, so it does not return { title: 'apisguru_apis_q3_rec-gpt-5.1', description: 'Rank **name** by count of distinct **version** entries', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ ], fields: [makeField('name'), makeField('version_count')], metadata: { 'name': { type: Type.String, semanticType: 'Name', levels: [] }, 'version_count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('name'), y: makeEncodingItem('version_count'), }, chartProperties: { cornerRadius: 3 }, }; } function gen_careStateQ1RecGpt41(): TestCase { // How do average emergency department time measures vary across states for the most recent reporting period? // Score: 8/20 | The visualization uses the correct data domain (emergency department measures by state) but the stac return { title: 'care_state_q1_rec-gpt-4.1', description: 'Compare average emergency department time by **state** for latest period', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'state': 'GA', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'score': 69.0 }, { 'state': 'AR', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 203.0 }, { 'state': 'MT', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 166.0 }, { 'state': 'MN', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'score': 117.0 }, { 'state': 'ME', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'score': 198.0 }, { 'state': 'ID', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 227.0 }, { 'state': 'GA', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 160.0 }, { 'state': 'DE', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'score': 415.0 }, { 'state': 'SC', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 161.0 }, { 'state': 'AZ', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 205.0 }, { 'state': 'AR', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'score': 72.0 }, { 'state': 'FL', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 141.0 }, { 'state': 'MD', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 470.0 }, { 'state': 'MI', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 140.0 }, { 'state': 'WI', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 167.0 }, { 'state': 'AR', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'score': 167.0 }, { 'state': 'MA', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 216.0 }, { 'state': 'RI', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 336.0 }, { 'state': 'ME', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 174.0 }, { 'state': 'TN', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'score': 266.0 }, { 'state': 'MT', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 172.0 }, { 'state': 'AK', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'score': 182.0 }, { 'state': 'IN', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 111.0 }, { 'state': 'WV', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 328.0 }, { 'state': 'NM', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 296.0 }, { 'state': 'WI', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 211.0 }, { 'state': 'IN', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 125.0 }, { 'state': 'MD', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'score': 526.0 }, { 'state': 'NM', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 298.0 }, { 'state': 'WY', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 135.0 }, { 'state': 'FL', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 158.0 }, { 'state': 'OK', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 194.0 }, { 'state': 'FL', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'score': 241.0 }, { 'state': 'NY', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 335.0 }, { 'state': 'SC', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 170.0 }, { 'state': 'KS', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'score': 208.0 }, { 'state': 'NE', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 114.0 }, { 'state': 'IA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 290.0 }, { 'state': 'PA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'score': 235.0 }, { 'state': 'AR', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 114.0 }, { 'state': 'OH', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 156.0 }, { 'state': 'ME', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'score': 56.0 }, { 'state': 'MS', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 181.0 }, { 'state': 'CO', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'score': 70.0 }, { 'state': 'VT', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'score': 177.0 }, { 'state': 'TX', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 270.0 }, { 'state': 'LA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 168.0 }, { 'state': 'AZ', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 332.0 }, { 'state': 'MT', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 124.0 }, { 'state': 'IL', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 127.0 }, { 'state': 'RI', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 255.0 }, { 'state': 'NH', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'score': 209.0 }, { 'state': 'NE', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 211.0 }, { 'state': 'TN', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 198.0 }, { 'state': 'SC', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 275.0 }, { 'state': 'KY', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 178.0 }, { 'state': 'NC', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'score': 380.0 }, { 'state': 'FL', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 246.0 }, { 'state': 'NV', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 309.0 }, { 'state': 'AZ', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 200.0 }, { 'state': 'AR', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'score': 149.0 }, { 'state': 'NJ', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 296.0 }, { 'state': 'HI', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 157.0 }, { 'state': 'OR', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 153.0 }, { 'state': 'IL', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 173.0 }, { 'state': 'VT', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 145.0 }, { 'state': 'SC', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 256.0 }, { 'state': 'HI', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 202.0 }, { 'state': 'SD', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 113.0 }, { 'state': 'CA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 286.0 }, { 'state': 'LA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 110.0 }, { 'state': 'WY', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 129.0 }, { 'state': 'ME', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'score': 346.0 }, { 'state': 'NH', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'score': 210.0 }, { 'state': 'RI', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 354.0 }, { 'state': 'KY', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 214.0 }, { 'state': 'DC', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'score': 26.0 }, { 'state': 'KY', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 208.0 }, { 'state': 'KY', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 153.0 }, { 'state': 'GA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'score': 157.0 }, { 'state': 'NM', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'score': 183.0 }, { 'state': 'IA', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'score': 69.0 }, { 'state': 'NV', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 219.0 }, { 'state': 'NM', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 513.0 }, { 'state': 'NJ', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 145.0 }, { 'state': 'AZ', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 262.0 }, { 'state': 'NE', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'score': 155.0 }, { 'state': 'TX', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 257.0 }, { 'state': 'DE', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 283.0 }, { 'state': 'MS', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 184.0 }, { 'state': 'VT', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'score': 44.0 }, { 'state': 'HI', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'score': 67.0 }, { 'state': 'TX', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 230.0 }, { 'state': 'MI', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 164.0 }, { 'state': 'UT', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 131.0 }, { 'state': 'ID', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 152.0 }, { 'state': 'NH', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 172.0 }, { 'state': 'NV', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 155.0 }, { 'state': 'MT', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'score': 185.0 }, { 'state': 'GA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 288.0 }, { 'state': 'NM', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'score': 41.0 }, { 'state': 'IN', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'score': 195.0 }, { 'state': 'RI', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'score': 63.0 }, { 'state': 'OK', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 252.0 }, { 'state': 'OR', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 118.0 }, { 'state': 'AR', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 264.0 }, { 'state': 'HI', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 84.0 }, { 'state': 'PR', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 502.0 }, { 'state': 'AL', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'score': 192.0 }, { 'state': 'PA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'score': 289.0 }, { 'state': 'IN', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 116.0 }, { 'state': 'MA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 154.0 }, { 'state': 'ID', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 142.0 }, { 'state': 'AZ', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 130.0 }, { 'state': 'GA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 218.0 }, { 'state': 'NC', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 194.0 }, { 'state': 'WA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'score': 330.0 }, { 'state': 'SC', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 135.0 }, { 'state': 'RI', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 358.0 }, { 'state': 'NJ', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'score': 358.0 }, { 'state': 'MN', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 174.0 }, { 'state': 'LA', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'score': 66.0 }, { 'state': 'NJ', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 187.0 }, { 'state': 'ME', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 451.0 }, { 'state': 'CT', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 245.0 }, { 'state': 'WA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 131.0 }, { 'state': 'CT', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'score': 376.0 }, { 'state': 'IA', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 120.0 }, { 'state': 'OH', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 254.0 }, { 'state': 'MT', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 222.0 }, { 'state': 'MS', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'score': 211.0 }, { 'state': 'IA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 217.0 }, { 'state': 'OH', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 240.0 }, { 'state': 'NC', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'score': 71.0 }, { 'state': 'MD', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 431.0 }, { 'state': 'NC', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 312.0 }, { 'state': 'MA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 239.0 }, { 'state': 'VA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 169.0 }, { 'state': 'SD', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 112.0 }, { 'state': 'CT', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'score': 277.0 }, { 'state': 'MO', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 125.0 }, { 'state': 'MN', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 200.0 }, { 'state': 'CA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 206.0 }, { 'state': 'OK', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 105.0 }, { 'state': 'AR', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 206.0 }, { 'state': 'CO', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'score': 152.0 }, { 'state': 'DC', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 373.0 }, { 'state': 'VA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 266.0 }, { 'state': 'DC', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 352.0 }, { 'state': 'WI', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 171.0 }, { 'state': 'MD', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 154.0 }, { 'state': 'ND', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'score': 138.0 }, { 'state': 'WV', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 267.0 }, { 'state': 'LA', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 244.0 }, { 'state': 'LA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'score': 144.0 }, { 'state': 'VA', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 318.0 }, { 'state': 'MI', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'score': 178.0 }, { 'state': 'MS', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 169.0 }, { 'state': 'IA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 114.0 }, { 'state': 'MT', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'score': 178.0 }, { 'state': 'AK', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'score': 230.0 }, { 'state': 'UT', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'score': 182.0 }, { 'state': 'NY', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'score': 212.0 }, { 'state': 'CO', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 240.0 }, { 'state': 'TN', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 391.0 }, { 'state': 'VT', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 257.0 }, { 'state': 'TX', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 145.0 }, { 'state': 'NH', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'score': 61.0 }, { 'state': 'KS', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 164.0 }, { 'state': 'CO', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 161.0 }, { 'state': 'VA', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'score': 72.0 }, { 'state': 'MD', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 347.0 }, { 'state': 'DC', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'score': 359.0 }, { 'state': 'VT', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 244.0 }, { 'state': 'AK', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 157.0 }, { 'state': 'SC', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'score': 156.0 }, { 'state': 'NY', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'score': 326.0 }, { 'state': 'WA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 203.0 }, { 'state': 'MN', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 336.0 }, { 'state': 'DE', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 522.0 }, { 'state': 'MO', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 209.0 }, { 'state': 'CO', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 135.0 }, { 'state': 'NH', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 128.0 }, { 'state': 'IL', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 238.0 }, { 'state': 'SD', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'score': 164.0 }, { 'state': 'NY', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 128.0 }, { 'state': 'TN', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'score': 423.0 }, { 'state': 'FL', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 228.0 }, { 'state': 'OR', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'score': 197.0 }, { 'state': 'KY', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'score': 70.0 }, { 'state': 'DC', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'score': 446.0 }, { 'state': 'MS', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'score': 150.0 }, { 'state': 'MS', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 108.0 }, { 'state': 'AK', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 140.0 }, { 'state': 'ND', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'score': 167.0 }, { 'state': 'IN', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'score': 284.0 }, { 'state': 'WY', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'score': 194.0 }, { 'state': 'AL', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'score': 145.0 }, { 'state': 'OH', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'score': 134.0 }, { 'state': 'PA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'score': 201.0 }, ], fields: [makeField('state'), makeField('measure_name'), makeField('score')], metadata: { 'state': { type: Type.String, semanticType: 'State', levels: ['AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA', 'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME', 'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'PR', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'VT', 'WA', 'WI'] }, 'measure_name': { type: Type.String, semanticType: 'String', levels: ['Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better'] }, 'score': { type: Type.Number, semanticType: 'Number', levels: [] }, }, encodingMap: { x: makeEncodingItem('state'), y: makeEncodingItem('score'), color: makeEncodingItem('measure_name'), }, chartProperties: { cornerRadius: 4 }, }; } function gen_careStateQ1RecGpt51(): TestCase { // How do average emergency department time measures vary across states for the most recent reporting period? // Score: 9/20 | The visualization is loosely relevant and technically readable, but suffers from methodological and return { title: 'care_state_q1_rec-gpt-5.1', description: 'Compare average **score** for emergency department measures by **state**', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'state': 'NV', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 238.0 }, { 'state': 'CO', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'end_date': '2024-03-31', 'average_time_minutes': 248.0 }, { 'state': 'AL', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 119.0 }, { 'state': 'PA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 287.0 }, { 'state': 'IL', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 352.0 }, { 'state': 'ID', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 142.0 }, { 'state': 'HI', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 67.0 }, { 'state': 'DC', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 332.5 }, { 'state': 'PA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 289.0 }, { 'state': 'CO', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 123.0 }, { 'state': 'OH', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 163.0 }, { 'state': 'WY', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 184.0 }, { 'state': 'VT', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 244.0 }, { 'state': 'ND', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 138.0 }, { 'state': 'CA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 291.0 }, { 'state': 'NJ', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 187.0 }, { 'state': 'MI', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 282.0 }, { 'state': 'AL', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 244.0 }, { 'state': 'AL', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 192.0 }, { 'state': 'CA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'end_date': '2024-03-31', 'average_time_minutes': 286.0 }, { 'state': 'HI', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 110.0 }, { 'state': 'IA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 158.0 }, { 'state': 'MT', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 124.0 }, { 'state': 'NJ', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 176.0 }, { 'state': 'AL', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 168.0 }, { 'state': 'NE', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 155.0 }, { 'state': 'GA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 228.0 }, { 'state': 'OR', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 197.0 }, { 'state': 'NY', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 199.0 }, { 'state': 'OK', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 230.0 }, { 'state': 'WV', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 121.0 }, { 'state': 'ME', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 56.0 }, { 'state': 'HI', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 221.0 }, { 'state': 'MN', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 117.0 }, { 'state': 'NH', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 61.0 }, { 'state': 'IL', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 337.0 }, { 'state': 'TN', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 157.0 }, { 'state': 'VA', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 318.0 }, { 'state': 'AK', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 136.0 }, { 'state': 'RI', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 224.0 }, { 'state': 'SD', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'end_date': '2024-03-31', 'average_time_minutes': 176.0 }, { 'state': 'DE', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 415.0 }, { 'state': 'OK', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 289.0 }, { 'state': 'WV', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 146.0 }, { 'state': 'KY', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 70.0 }, { 'state': 'WA', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 243.0 }, { 'state': 'DE', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 250.5 }, { 'state': 'HI', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 207.0 }, { 'state': 'RI', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 277.5 }, { 'state': 'KY', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 208.0 }, { 'state': 'WV', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 267.0 }, { 'state': 'WI', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 211.0 }, { 'state': 'MA', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 68.0 }, { 'state': 'CO', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 133.0 }, { 'state': 'LA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'end_date': '2024-03-31', 'average_time_minutes': 335.0 }, { 'state': 'TX', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 69.0 }, { 'state': 'LA', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 244.0 }, { 'state': 'NJ', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 328.0 }, { 'state': 'IL', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 173.0 }, { 'state': 'SD', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 54.0 }, { 'state': 'AR', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 114.0 }, { 'state': 'PA', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 282.0 }, { 'state': 'MO', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 155.0 }, { 'state': 'ND', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 110.0 }, { 'state': 'CT', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 204.0 }, { 'state': 'MA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'end_date': '2024-03-31', 'average_time_minutes': 354.0 }, { 'state': 'CA', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 270.0 }, { 'state': 'ND', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 60.0 }, { 'state': 'IN', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 284.0 }, { 'state': 'TX', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 145.0 }, { 'state': 'NM', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 41.0 }, { 'state': 'NM', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 183.0 }, { 'state': 'MA', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 216.0 }, { 'state': 'NH', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 128.0 }, { 'state': 'GA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 116.0 }, { 'state': 'OK', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'end_date': '2024-03-31', 'average_time_minutes': 252.0 }, { 'state': 'AZ', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 206.0 }, { 'state': 'AR', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 149.0 }, { 'state': 'NY', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 212.0 }, { 'state': 'IA', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 201.0 }, { 'state': 'RI', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 63.0 }, { 'state': 'IN', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 116.0 }, { 'state': 'VA', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 166.0 }, { 'state': 'WI', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 230.0 }, { 'state': 'CO', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 148.0 }, { 'state': 'VA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 141.0 }, { 'state': 'VT', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 176.0 }, { 'state': 'MN', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 174.0 }, { 'state': 'NV', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 120.0 }, { 'state': 'MA', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 349.0 }, { 'state': 'DE', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'end_date': '2024-03-31', 'average_time_minutes': 522.0 }, { 'state': 'MA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 201.0 }, { 'state': 'LA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 200.0 }, { 'state': 'HI', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 179.5 }, { 'state': 'OH', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 252.0 }, { 'state': 'IL', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 320.0 }, { 'state': 'WA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 266.0 }, { 'state': 'OH', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 254.0 }, { 'state': 'NV', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 67.0 }, { 'state': 'AZ', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 247.0 }, { 'state': 'NJ', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 74.0 }, { 'state': 'SD', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 146.0 }, { 'state': 'FL', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 163.5 }, { 'state': 'NC', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 71.0 }, { 'state': 'UT', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 142.0 }, { 'state': 'WY', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 194.0 }, { 'state': 'SD', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 112.0 }, { 'state': 'MO', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 257.0 }, { 'state': 'WA', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 154.0 }, { 'state': 'IL', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 127.0 }, { 'state': 'WY', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 56.0 }, { 'state': 'OK', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 119.0 }, { 'state': 'NE', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 161.0 }, { 'state': 'WA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 166.0 }, { 'state': 'OH', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'end_date': '2024-03-31', 'average_time_minutes': 240.0 }, { 'state': 'KY', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 214.0 }, { 'state': 'AR', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 72.0 }, { 'state': 'IA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 188.5 }, { 'state': 'WI', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 78.0 }, { 'state': 'KS', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 172.0 }, { 'state': 'ME', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 174.0 }, { 'state': 'IL', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 228.5 }, { 'state': 'AZ', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 148.0 }, { 'state': 'HI', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 84.0 }, { 'state': 'NE', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'end_date': '2024-03-31', 'average_time_minutes': 211.0 }, { 'state': 'OR', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 260.0 }, { 'state': 'PA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'end_date': '2024-03-31', 'average_time_minutes': 308.0 }, { 'state': 'PA', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 188.0 }, { 'state': 'NY', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 128.0 }, { 'state': 'MT', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 127.0 }, { 'state': 'MD', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 347.0 }, { 'state': 'NV', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 219.0 }, { 'state': 'MN', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 70.0 }, { 'state': 'DC', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 359.0 }, { 'state': 'UT', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 212.0 }, { 'state': 'WV', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 59.0 }, { 'state': 'ID', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 227.0 }, { 'state': 'WI', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 194.0 }, { 'state': 'ND', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 147.0 }, { 'state': 'ID', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 68.0 }, { 'state': 'NH', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 357.0 }, { 'state': 'MI', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 178.0 }, { 'state': 'NH', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 210.0 }, { 'state': 'MD', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 67.0 }, { 'state': 'TX', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 119.0 }, { 'state': 'PR', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 705.0 }, { 'state': 'DC', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 361.0 }, { 'state': 'MT', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 185.0 }, { 'state': 'MS', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 208.0 }, { 'state': 'CA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 251.0 }, { 'state': 'AR', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 232.0 }, { 'state': 'CO', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 277.0 }, { 'state': 'DE', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 418.0 }, { 'state': 'TX', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 302.0 }, { 'state': 'VT', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 177.0 }, { 'state': 'WV', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 178.0 }, { 'state': 'NJ', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 145.0 }, { 'state': 'AZ', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 130.0 }, { 'state': 'MD', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 250.0 }, { 'state': 'MD', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 251.0 }, { 'state': 'SC', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 78.0 }, { 'state': 'MO', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 177.0 }, { 'state': 'NC', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 300.0 }, { 'state': 'ID', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 118.0 }, { 'state': 'NE', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 114.0 }, { 'state': 'AK', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 230.0 }, { 'state': 'CO', 'measure_name': 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better', 'end_date': '2024-03-31', 'average_time_minutes': 70.0 }, { 'state': 'UT', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 171.0 }, { 'state': 'SC', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'end_date': '2024-03-31', 'average_time_minutes': 275.0 }, { 'state': 'NV', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 236.0 }, { 'state': 'VT', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 324.0 }, { 'state': 'WY', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 258.0 }, { 'state': 'TX', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 257.0 }, { 'state': 'MI', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 270.0 }, { 'state': 'DE', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 170.0 }, { 'state': 'VT', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 576.0 }, { 'state': 'AK', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 196.0 }, { 'state': 'TX', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 176.0 }, { 'state': 'MT', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 172.0 }, { 'state': 'FL', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 209.0 }, { 'state': 'MT', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 178.0 }, { 'state': 'CO', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 269.0 }, { 'state': 'IN', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 220.0 }, { 'state': 'NM', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 296.0 }, { 'state': 'SC', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 135.0 }, { 'state': 'WI', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 137.0 }, { 'state': 'OH', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 306.0 }, { 'state': 'MA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 318.0 }, { 'state': 'DE', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 288.0 }, { 'state': 'ND', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 188.0 }, { 'state': 'NC', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 298.0 }, { 'state': 'AK', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 140.0 }, { 'state': 'NJ', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 179.0 }, { 'state': 'KY', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 153.0 }, { 'state': 'MS', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 211.0 }, { 'state': 'AL', 'measure_name': 'Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'end_date': '2024-03-31', 'average_time_minutes': 145.0 }, { 'state': 'NV', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'end_date': '2024-03-31', 'average_time_minutes': 309.0 }, { 'state': 'OK', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'end_date': '2024-03-31', 'average_time_minutes': 105.0 }, { 'state': 'KS', 'measure_name': 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'end_date': '2024-03-31', 'average_time_minutes': 154.0 }, { 'state': 'IA', 'measure_name': 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'end_date': '2024-03-31', 'average_time_minutes': 217.0 }, ], fields: [makeField('state'), makeField('measure_name'), makeField('end_date'), makeField('average_time_minutes')], metadata: { 'state': { type: Type.String, semanticType: 'State', levels: ['AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA', 'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME', 'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'PR', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'VT', 'WA', 'WI'] }, 'measure_name': { type: Type.String, semanticType: 'String', levels: ['Average (median) time patients spent in the emergency department before leaving from the visit A lower number of minutes is better', 'Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better', 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (high)', 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (low)', 'Average time patients spent in the emergency department before being sent home A lower number of minutes is better (moderate)', 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (high)', 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (low)', 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (moderate)', 'Average time patients spent in the emergency department before leaving from the visit - Psychiatric/Mental Health Patients. A lower number of minutes is better (very high)', 'Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival Higher percentages are better'] }, 'end_date': { type: Type.Date, semanticType: 'Date', levels: ['2024-03-31'] }, 'average_time_minutes': { type: Type.Number, semanticType: 'Number', levels: [] }, }, encodingMap: { x: makeEncodingItem('state'), y: makeEncodingItem('average_time_minutes'), column: makeEncodingItem('measure_name'), }, chartProperties: { cornerRadius: 2 }, }; } function gen_careStateQ3RecGpt41(): TestCase { // How have sepsis and septic shock bundle performance scores changed over time at the state level? // Score: 5/20 | The visualization is readable at a basic level but is incorrectly specified: the date axis is mispar return { title: 'care_state_q3_rec-gpt-4.1', description: 'Visualize change in **score** for sepsis/septic shock bundles by **state** and time', tags: ['debug', 'line-chart'], chartType: 'Line Chart', data: [ { 'state': 'RI', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 93.0 }, { 'state': 'DC', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 58.0 }, { 'state': 'AL', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 80.0 }, { 'state': 'VT', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 84.0 }, { 'state': 'KY', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 79.0 }, { 'state': 'IN', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 81.0 }, { 'state': 'IL', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 83.0 }, { 'state': 'DE', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 82.0 }, { 'state': 'VT', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 58.0 }, { 'state': 'CT', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 77.0 }, { 'state': 'TN', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 82.0 }, { 'state': 'WY', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 81.0 }, { 'state': 'NY', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 84.0 }, { 'state': 'CO', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 87.0 }, { 'state': 'OR', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 91.0 }, { 'state': 'NC', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 70.0 }, { 'state': 'AR', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 77.0 }, { 'state': 'AL', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 92.0 }, { 'state': 'CO', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 95.0 }, { 'state': 'ID', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 88.0 }, { 'state': 'IL', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 91.0 }, { 'state': 'NM', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 75.0 }, { 'state': 'PA', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 80.0 }, { 'state': 'WY', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 79.0 }, { 'state': 'OH', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 91.0 }, { 'state': 'IA', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 80.0 }, { 'state': 'SC', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 82.0 }, { 'state': 'TX', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 92.0 }, { 'state': 'WA', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 89.0 }, { 'state': 'MT', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 94.0 }, { 'state': 'IL', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 71.0 }, { 'state': 'ND', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 77.0 }, { 'state': 'OR', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 78.0 }, { 'state': 'KY', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 93.0 }, { 'state': 'AK', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 79.0 }, { 'state': 'GA', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 68.0 }, { 'state': 'WA', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 70.0 }, { 'state': 'ME', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 95.0 }, { 'state': 'TN', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 78.0 }, { 'state': 'FL', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 94.0 }, { 'state': 'WI', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 93.0 }, { 'state': 'ME', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 86.0 }, { 'state': 'WI', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 85.0 }, { 'state': 'WI', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 74.0 }, { 'state': 'MO', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 83.0 }, { 'state': 'CT', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 63.0 }, { 'state': 'MI', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 91.0 }, { 'state': 'MI', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 69.0 }, { 'state': 'VA', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 86.0 }, { 'state': 'KS', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 90.0 }, { 'state': 'AR', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 90.0 }, { 'state': 'NE', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 89.0 }, { 'state': 'NY', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 83.0 }, { 'state': 'DC', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 84.0 }, { 'state': 'MO', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 66.0 }, { 'state': 'CO', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 80.0 }, { 'state': 'OH', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 81.0 }, { 'state': 'LA', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 93.0 }, { 'state': 'MN', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 60.0 }, { 'state': 'OK', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 91.0 }, { 'state': 'IA', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 80.0 }, { 'state': 'CA', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 87.0 }, { 'state': 'PR', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 90.0 }, { 'state': 'IL', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 80.0 }, { 'state': 'LA', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 80.0 }, { 'state': 'PA', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 69.0 }, { 'state': 'VA', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 93.0 }, { 'state': 'CT', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 85.0 }, { 'state': 'WY', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 93.0 }, { 'state': 'SD', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 80.0 }, { 'state': 'NE', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 73.0 }, { 'state': 'MN', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 75.0 }, { 'state': 'GA', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 83.0 }, { 'state': 'MN', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 78.0 }, { 'state': 'MI', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 78.0 }, { 'state': 'ID', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 84.0 }, { 'state': 'KY', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 73.0 }, { 'state': 'CA', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 82.0 }, { 'state': 'GA', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 86.0 }, { 'state': 'WV', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 77.0 }, { 'state': 'NV', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 94.0 }, { 'state': 'WA', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 83.0 }, { 'state': 'IA', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 70.0 }, { 'state': 'DE', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 76.0 }, { 'state': 'NE', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 80.0 }, { 'state': 'MD', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 87.0 }, { 'state': 'RI', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 61.0 }, { 'state': 'NY', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 79.0 }, { 'state': 'WY', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 83.0 }, { 'state': 'SD', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 83.0 }, { 'state': 'NJ', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 93.0 }, { 'state': 'TX', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 81.0 }, { 'state': 'MO', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 77.0 }, { 'state': 'MO', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 92.0 }, { 'state': 'VT', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 80.0 }, { 'state': 'DC', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 88.0 }, { 'state': 'MT', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 86.0 }, { 'state': 'AL', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 70.0 }, { 'state': 'MS', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 91.0 }, { 'state': 'TN', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 64.0 }, { 'state': 'IA', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 93.0 }, { 'state': 'NJ', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 77.0 }, { 'state': 'VT', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 91.0 }, { 'state': 'CT', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 92.0 }, { 'state': 'LA', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 73.0 }, { 'state': 'RI', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 87.0 }, { 'state': 'SD', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 94.0 }, { 'state': 'MT', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 77.0 }, { 'state': 'MD', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 91.0 }, { 'state': 'IN', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 90.0 }, { 'state': 'UT', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 87.0 }, { 'state': 'MD', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 81.0 }, { 'state': 'OK', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 65.0 }, { 'state': 'NM', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 73.0 }, { 'state': 'DE', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 81.0 }, { 'state': 'OK', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 76.0 }, { 'state': 'PR', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 38.0 }, { 'state': 'NH', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 66.0 }, { 'state': 'NM', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 83.0 }, { 'state': 'NY', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 72.0 }, { 'state': 'UT', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 93.0 }, { 'state': 'ID', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 79.0 }, { 'state': 'NH', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 92.0 }, { 'state': 'PA', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 91.0 }, { 'state': 'HI', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 89.0 }, { 'state': 'NE', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 89.0 }, { 'state': 'OR', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 83.0 }, { 'state': 'KS', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 77.0 }, { 'state': 'ND', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 89.0 }, { 'state': 'OK', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 83.0 }, { 'state': 'VA', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 72.0 }, { 'state': 'AZ', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 75.0 }, { 'state': 'CA', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 93.0 }, { 'state': 'OH', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 77.0 }, { 'state': 'UT', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 83.0 }, { 'state': 'NC', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 93.0 }, { 'state': 'OR', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 68.0 }, { 'state': 'NJ', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 90.0 }, { 'state': 'NJ', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 84.0 }, { 'state': 'PR', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 28.0 }, { 'state': 'DE', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 57.0 }, { 'state': 'WI', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 89.0 }, { 'state': 'MA', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 68.0 }, { 'state': 'MT', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 87.0 }, { 'state': 'VA', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 81.0 }, { 'state': 'ND', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 75.0 }, { 'state': 'NV', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 81.0 }, { 'state': 'MS', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 71.0 }, { 'state': 'AR', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 82.0 }, { 'state': 'ND', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 57.0 }, { 'state': 'AK', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 65.0 }, { 'state': 'TX', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 70.0 }, { 'state': 'TX', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 87.0 }, { 'state': 'CA', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 74.0 }, { 'state': 'MI', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 85.0 }, { 'state': 'WV', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 91.0 }, { 'state': 'MA', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 78.0 }, { 'state': 'NV', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 72.0 }, { 'state': 'MA', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 81.0 }, { 'state': 'MS', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 85.0 }, { 'state': 'TN', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 91.0 }, { 'state': 'ID', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 66.0 }, { 'state': 'UT', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 70.0 }, { 'state': 'FL', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 83.0 }, { 'state': 'AZ', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 63.0 }, { 'state': 'AR', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 87.0 }, { 'state': 'SC', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 70.0 }, { 'state': 'KY', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 85.0 }, { 'state': 'KS', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 60.0 }, { 'state': 'NH', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 72.0 }, { 'state': 'WV', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 83.0 }, { 'state': 'ME', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 86.0 }, { 'state': 'MS', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 80.0 }, { 'state': 'KS', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 80.0 }, { 'state': 'NM', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 63.0 }, { 'state': 'PR', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 85.0 }, { 'state': 'HI', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 92.0 }, { 'state': 'HI', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 78.0 }, { 'state': 'SD', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 69.0 }, { 'state': 'DC', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 72.0 }, { 'state': 'IN', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 82.0 }, { 'state': 'AK', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 81.0 }, { 'state': 'SC', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 89.0 }, { 'state': 'HI', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 88.0 }, { 'state': 'AZ', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 88.0 }, { 'state': 'OH', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 71.0 }, { 'state': 'NH', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 79.0 }, { 'state': 'NC', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 79.0 }, { 'state': 'FL', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 73.0 }, { 'state': 'RI', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 78.0 }, { 'state': 'CO', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 90.0 }, { 'state': 'AL', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 86.0 }, { 'state': 'NC', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 84.0 }, { 'state': 'IN', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 66.0 }, { 'state': 'AZ', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 79.0 }, { 'state': 'GA', 'measure_id': 'SEV_SEP_3HR', 'start_date': '2023-04-01', 'score': 78.0 }, { 'state': 'NV', 'measure_id': 'SEP_SH_6HR', 'start_date': '2023-04-01', 'score': 88.0 }, { 'state': 'AK', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 89.0 }, { 'state': 'MD', 'measure_id': 'SEP_SH_3HR', 'start_date': '2023-04-01', 'score': 82.0 }, { 'state': 'SC', 'measure_id': 'SEV_SEP_6HR', 'start_date': '2023-04-01', 'score': 93.0 }, ], fields: [makeField('state'), makeField('measure_id'), makeField('start_date'), makeField('score')], metadata: { 'state': { type: Type.String, semanticType: 'State', levels: ['AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA', 'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME', 'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'PR', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'VT', 'WA', 'WI'] }, 'measure_id': { type: Type.String, semanticType: 'String', levels: ['SEP_SH_3HR', 'SEP_SH_6HR', 'SEV_SEP_3HR', 'SEV_SEP_6HR'] }, 'start_date': { type: Type.Date, semanticType: 'Date', levels: ['2023-04-01'] }, 'score': { type: Type.Number, semanticType: 'Score', levels: [] }, }, encodingMap: { x: makeEncodingItem('start_date'), y: makeEncodingItem('score'), color: makeEncodingItem('measure_id'), column: makeEncodingItem('state'), }, chartProperties: { interpolate: "linear" }, }; } function gen_careStateQ3RecGpt51(): TestCase { // How have sepsis and septic shock bundle performance scores changed over time at the state level? // Score: 8/20 | The visualization partially includes relevant measures but fails to show state-level changes over ti return { title: 'care_state_q3_rec-gpt-5.1', description: 'Track **Sepsis Care** bundle scores by **state** over time', tags: ['debug', 'line-chart'], chartType: 'Line Chart', data: [ { 'state': 'HI', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 88.0 }, { 'state': 'AR', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 87.0 }, { 'state': 'ND', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 54.0 }, { 'state': 'MS', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 63.0 }, { 'state': 'MI', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 91.0 }, { 'state': 'DC', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 49.0 }, { 'state': 'OR', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 78.0 }, { 'state': 'CO', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 80.0 }, { 'state': 'NY', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 79.0 }, { 'state': 'OR', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 91.0 }, { 'state': 'UT', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 87.0 }, { 'state': 'NC', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 93.0 }, { 'state': 'CA', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 87.0 }, { 'state': 'NH', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 66.0 }, { 'state': 'ME', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 86.0 }, { 'state': 'AL', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 80.0 }, { 'state': 'AL', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 86.0 }, { 'state': 'CA', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 82.0 }, { 'state': 'HI', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 71.0 }, { 'state': 'HI', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 92.0 }, { 'state': 'MS', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 91.0 }, { 'state': 'NH', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 92.0 }, { 'state': 'AL', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 70.0 }, { 'state': 'ND', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 77.0 }, { 'state': 'GA', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 57.0 }, { 'state': 'OK', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 76.0 }, { 'state': 'NV', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 72.0 }, { 'state': 'OH', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 91.0 }, { 'state': 'WI', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 85.0 }, { 'state': 'ME', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 86.0 }, { 'state': 'HI', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 78.0 }, { 'state': 'WY', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 68.0 }, { 'state': 'NH', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 57.0 }, { 'state': 'IL', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 71.0 }, { 'state': 'SC', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 89.0 }, { 'state': 'TX', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 87.0 }, { 'state': 'AK', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 65.0 }, { 'state': 'PA', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 91.0 }, { 'state': 'SC', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 70.0 }, { 'state': 'DE', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 48.0 }, { 'state': 'OH', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 77.0 }, { 'state': 'WI', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 69.0 }, { 'state': 'KY', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 85.0 }, { 'state': 'UT', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 70.0 }, { 'state': 'DC', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 84.0 }, { 'state': 'WA', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 70.0 }, { 'state': 'PR', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 16.0 }, { 'state': 'KY', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 73.0 }, { 'state': 'WV', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 83.0 }, { 'state': 'WA', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 83.0 }, { 'state': 'MA', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 81.0 }, { 'state': 'CA', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 93.0 }, { 'state': 'LA', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 73.0 }, { 'state': 'KY', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 79.0 }, { 'state': 'VT', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 80.0 }, { 'state': 'ID', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 84.0 }, { 'state': 'AR', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 77.0 }, { 'state': 'OR', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 68.0 }, { 'state': 'MN', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 75.0 }, { 'state': 'NC', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 84.0 }, { 'state': 'CT', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 63.0 }, { 'state': 'MA', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 68.0 }, { 'state': 'CA', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 67.0 }, { 'state': 'ND', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 57.0 }, { 'state': 'IN', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 59.0 }, { 'state': 'NM', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 48.0 }, { 'state': 'NJ', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 84.0 }, { 'state': 'LA', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 87.0 }, { 'state': 'NE', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 89.0 }, { 'state': 'FL', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 94.0 }, { 'state': 'OK', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 57.0 }, { 'state': 'AZ', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 79.0 }, { 'state': 'RI', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 78.0 }, { 'state': 'NV', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 94.0 }, { 'state': 'HI', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 89.0 }, { 'state': 'IL', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 91.0 }, { 'state': 'PR', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 90.0 }, { 'state': 'WA', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 60.0 }, { 'state': 'CO', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 73.0 }, { 'state': 'SC', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 93.0 }, { 'state': 'TN', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 64.0 }, { 'state': 'MN', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 60.0 }, { 'state': 'NM', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 75.0 }, { 'state': 'LA', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 80.0 }, { 'state': 'DE', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 57.0 }, { 'state': 'LA', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 93.0 }, { 'state': 'LA', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 64.0 }, { 'state': 'GA', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 78.0 }, { 'state': 'NY', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 72.0 }, { 'state': 'ID', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 79.0 }, { 'state': 'NV', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 65.0 }, { 'state': 'AZ', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 75.0 }, { 'state': 'NJ', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 69.0 }, { 'state': 'OH', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 81.0 }, { 'state': 'DE', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 76.0 }, { 'state': 'NC', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 70.0 }, { 'state': 'IA', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 80.0 }, { 'state': 'OH', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 59.0 }, { 'state': 'MN', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 78.0 }, { 'state': 'WV', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 60.0 }, { 'state': 'ID', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 88.0 }, { 'state': 'ND', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 75.0 }, { 'state': 'UT', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 93.0 }, { 'state': 'KS', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 80.0 }, { 'state': 'AR', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 90.0 }, { 'state': 'OR', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 60.0 }, { 'state': 'WA', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 89.0 }, { 'state': 'KS', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 59.0 }, { 'state': 'MD', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 87.0 }, { 'state': 'NY', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 58.0 }, { 'state': 'AZ', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 63.0 }, { 'state': 'GA', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 86.0 }, { 'state': 'NE', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 64.0 }, { 'state': 'NH', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 72.0 }, { 'state': 'NE', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 80.0 }, { 'state': 'MS', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 85.0 }, { 'state': 'MD', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 82.0 }, { 'state': 'RI', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 61.0 }, { 'state': 'DC', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 58.0 }, { 'state': 'RI', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 93.0 }, { 'state': 'WV', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 91.0 }, { 'state': 'IA', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 80.0 }, { 'state': 'RI', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 58.0 }, { 'state': 'WY', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 79.0 }, { 'state': 'ME', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 95.0 }, { 'state': 'WI', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 74.0 }, { 'state': 'PA', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 85.0 }, { 'state': 'NJ', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 90.0 }, { 'state': 'VA', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 72.0 }, { 'state': 'MT', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 71.0 }, { 'state': 'MS', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 71.0 }, { 'state': 'SD', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 65.0 }, { 'state': 'WY', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 83.0 }, { 'state': 'MI', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 59.0 }, { 'state': 'NV', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 88.0 }, { 'state': 'AZ', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 88.0 }, { 'state': 'NE', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 73.0 }, { 'state': 'VA', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 93.0 }, { 'state': 'VT', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 58.0 }, { 'state': 'TN', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 82.0 }, { 'state': 'PA', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 69.0 }, { 'state': 'IN', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 66.0 }, { 'state': 'NH', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 79.0 }, { 'state': 'PA', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 62.0 }, { 'state': 'FL', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 83.0 }, { 'state': 'NM', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 63.0 }, { 'state': 'OK', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 83.0 }, { 'state': 'NM', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 83.0 }, { 'state': 'CT', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 85.0 }, { 'state': 'IL', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 61.0 }, { 'state': 'TX', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 81.0 }, { 'state': 'MO', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 58.0 }, { 'state': 'MT', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 87.0 }, { 'state': 'WY', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 81.0 }, { 'state': 'TN', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 78.0 }, { 'state': 'UT', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 83.0 }, { 'state': 'PR', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 28.0 }, { 'state': 'CT', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 92.0 }, { 'state': 'MA', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 78.0 }, { 'state': 'KS', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 77.0 }, { 'state': 'RI', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 87.0 }, { 'state': 'ME', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 79.0 }, { 'state': 'DC', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 88.0 }, { 'state': 'SD', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 94.0 }, { 'state': 'MO', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 83.0 }, { 'state': 'SC', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 64.0 }, { 'state': 'AK', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 58.0 }, { 'state': 'VA', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 65.0 }, { 'state': 'CT', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 77.0 }, { 'state': 'IA', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 93.0 }, { 'state': 'WI', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 89.0 }, { 'state': 'WV', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 72.0 }, { 'state': 'AR', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 82.0 }, { 'state': 'MO', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 77.0 }, { 'state': 'DC', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 72.0 }, { 'state': 'KS', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 60.0 }, { 'state': 'TX', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 70.0 }, { 'state': 'IN', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 82.0 }, { 'state': 'OK', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 65.0 }, { 'state': 'MO', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 92.0 }, { 'state': 'FL', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 90.0 }, { 'state': 'MA', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 58.0 }, { 'state': 'NJ', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 93.0 }, { 'state': 'MI', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 85.0 }, { 'state': 'OH', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 71.0 }, { 'state': 'VA', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 81.0 }, { 'state': 'NM', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 73.0 }, { 'state': 'AK', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 79.0 }, { 'state': 'MO', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 66.0 }, { 'state': 'FL', 'period': '2023Q2', 'measure_name': 'Septic Shock 3-Hour Bundle', 'avg_score': 73.0 }, { 'state': 'TN', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 59.0 }, { 'state': 'CT', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 57.0 }, { 'state': 'WA', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 79.0 }, { 'state': 'MD', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 91.0 }, { 'state': 'AR', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 64.0 }, { 'state': 'ID', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 59.0 }, { 'state': 'IL', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 3-Hour Bundle', 'avg_score': 80.0 }, { 'state': 'GA', 'period': '2023Q2', 'measure_name': 'Septic Shock 6-Hour Bundle', 'avg_score': 83.0 }, { 'state': 'AK', 'period': '2023Q2', 'measure_name': 'Severe Sepsis 6-Hour Bundle', 'avg_score': 89.0 }, { 'state': 'MN', 'period': '2023Q2', 'measure_name': 'Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'avg_score': 57.0 }, ], fields: [makeField('state'), makeField('period'), makeField('measure_name'), makeField('avg_score')], metadata: { 'state': { type: Type.String, semanticType: 'State', levels: ['AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA', 'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME', 'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'PR', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'VT', 'WA', 'WI'] }, 'period': { type: Type.String, semanticType: 'YearQuarter', levels: ['2023Q2'] }, 'measure_name': { type: Type.String, semanticType: 'Category', levels: ['Percentage of patients who received appropriate care for severe sepsis and septic shock. Higher percentages are better', 'Septic Shock 3-Hour Bundle', 'Septic Shock 6-Hour Bundle', 'Severe Sepsis 3-Hour Bundle', 'Severe Sepsis 6-Hour Bundle'] }, 'avg_score': { type: Type.Number, semanticType: 'Number', levels: [] }, }, encodingMap: { x: makeEncodingItem('period'), y: makeEncodingItem('avg_score'), color: makeEncodingItem('measure_name'), column: makeEncodingItem('state'), }, chartProperties: { interpolate: "monotone" }, }; } function gen_colorRanksQ1RecGpt41(): TestCase { // How are color popularity ranks distributed across the full range from 1 to 949? // Score: 5/20 | The chart is misleading and unclear: it visually suggests a uniform count across nearly 950 ranks de return { title: 'color_ranks_q1_rec-gpt-4.1', description: 'Visualize count of colors at each **rank**', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'rank': 655, 'count': 1 }, { 'rank': 115, 'count': 1 }, { 'rank': 26, 'count': 1 }, { 'rank': 760, 'count': 1 }, { 'rank': 282, 'count': 1 }, { 'rank': 251, 'count': 1 }, { 'rank': 229, 'count': 1 }, { 'rank': 143, 'count': 1 }, { 'rank': 755, 'count': 1 }, { 'rank': 105, 'count': 1 }, { 'rank': 693, 'count': 1 }, { 'rank': 759, 'count': 1 }, { 'rank': 914, 'count': 1 }, { 'rank': 559, 'count': 1 }, { 'rank': 90, 'count': 1 }, { 'rank': 605, 'count': 1 }, { 'rank': 433, 'count': 1 }, { 'rank': 33, 'count': 1 }, { 'rank': 31, 'count': 1 }, { 'rank': 96, 'count': 1 }, { 'rank': 224, 'count': 1 }, { 'rank': 239, 'count': 1 }, { 'rank': 518, 'count': 1 }, { 'rank': 617, 'count': 1 }, { 'rank': 28, 'count': 1 }, { 'rank': 575, 'count': 1 }, { 'rank': 204, 'count': 1 }, { 'rank': 734, 'count': 1 }, { 'rank': 666, 'count': 1 }, { 'rank': 719, 'count': 1 }, { 'rank': 936, 'count': 1 }, { 'rank': 430, 'count': 1 }, { 'rank': 226, 'count': 1 }, { 'rank': 460, 'count': 1 }, { 'rank': 604, 'count': 1 }, { 'rank': 285, 'count': 1 }, { 'rank': 829, 'count': 1 }, { 'rank': 891, 'count': 1 }, { 'rank': 7, 'count': 1 }, { 'rank': 778, 'count': 1 }, { 'rank': 826, 'count': 1 }, { 'rank': 164, 'count': 1 }, { 'rank': 715, 'count': 1 }, { 'rank': 933, 'count': 1 }, { 'rank': 349, 'count': 1 }, { 'rank': 937, 'count': 1 }, { 'rank': 160, 'count': 1 }, { 'rank': 221, 'count': 1 }, { 'rank': 782, 'count': 1 }, { 'rank': 345, 'count': 1 }, { 'rank': 940, 'count': 1 }, { 'rank': 95, 'count': 1 }, { 'rank': 390, 'count': 1 }, { 'rank': 100, 'count': 1 }, { 'rank': 368, 'count': 1 }, { 'rank': 868, 'count': 1 }, { 'rank': 353, 'count': 1 }, { 'rank': 619, 'count': 1 }, { 'rank': 271, 'count': 1 }, { 'rank': 827, 'count': 1 }, { 'rank': 45, 'count': 1 }, { 'rank': 748, 'count': 1 }, { 'rank': 471, 'count': 1 }, { 'rank': 550, 'count': 1 }, { 'rank': 128, 'count': 1 }, { 'rank': 388, 'count': 1 }, { 'rank': 81, 'count': 1 }, { 'rank': 566, 'count': 1 }, { 'rank': 301, 'count': 1 }, { 'rank': 850, 'count': 1 }, { 'rank': 644, 'count': 1 }, { 'rank': 634, 'count': 1 }, { 'rank': 371, 'count': 1 }, { 'rank': 592, 'count': 1 }, { 'rank': 197, 'count': 1 }, { 'rank': 722, 'count': 1 }, { 'rank': 72, 'count': 1 }, { 'rank': 47, 'count': 1 }, { 'rank': 678, 'count': 1 }, { 'rank': 234, 'count': 1 }, { 'rank': 792, 'count': 1 }, { 'rank': 297, 'count': 1 }, { 'rank': 82, 'count': 1 }, { 'rank': 928, 'count': 1 }, { 'rank': 104, 'count': 1 }, { 'rank': 897, 'count': 1 }, { 'rank': 904, 'count': 1 }, { 'rank': 465, 'count': 1 }, { 'rank': 651, 'count': 1 }, { 'rank': 855, 'count': 1 }, { 'rank': 374, 'count': 1 }, { 'rank': 167, 'count': 1 }, { 'rank': 380, 'count': 1 }, { 'rank': 364, 'count': 1 }, { 'rank': 215, 'count': 1 }, { 'rank': 687, 'count': 1 }, { 'rank': 274, 'count': 1 }, { 'rank': 920, 'count': 1 }, { 'rank': 700, 'count': 1 }, { 'rank': 664, 'count': 1 }, { 'rank': 74, 'count': 1 }, { 'rank': 624, 'count': 1 }, { 'rank': 861, 'count': 1 }, { 'rank': 176, 'count': 1 }, { 'rank': 547, 'count': 1 }, { 'rank': 747, 'count': 1 }, { 'rank': 944, 'count': 1 }, { 'rank': 168, 'count': 1 }, { 'rank': 474, 'count': 1 }, { 'rank': 389, 'count': 1 }, { 'rank': 277, 'count': 1 }, { 'rank': 656, 'count': 1 }, { 'rank': 705, 'count': 1 }, { 'rank': 571, 'count': 1 }, { 'rank': 225, 'count': 1 }, { 'rank': 702, 'count': 1 }, { 'rank': 333, 'count': 1 }, { 'rank': 787, 'count': 1 }, { 'rank': 795, 'count': 1 }, { 'rank': 58, 'count': 1 }, { 'rank': 235, 'count': 1 }, { 'rank': 932, 'count': 1 }, { 'rank': 825, 'count': 1 }, { 'rank': 324, 'count': 1 }, { 'rank': 411, 'count': 1 }, { 'rank': 275, 'count': 1 }, { 'rank': 68, 'count': 1 }, { 'rank': 217, 'count': 1 }, { 'rank': 581, 'count': 1 }, { 'rank': 736, 'count': 1 }, { 'rank': 323, 'count': 1 }, { 'rank': 218, 'count': 1 }, { 'rank': 672, 'count': 1 }, { 'rank': 512, 'count': 1 }, { 'rank': 406, 'count': 1 }, { 'rank': 659, 'count': 1 }, { 'rank': 470, 'count': 1 }, { 'rank': 147, 'count': 1 }, { 'rank': 272, 'count': 1 }, { 'rank': 942, 'count': 1 }, { 'rank': 253, 'count': 1 }, { 'rank': 763, 'count': 1 }, { 'rank': 924, 'count': 1 }, { 'rank': 552, 'count': 1 }, { 'rank': 270, 'count': 1 }, { 'rank': 765, 'count': 1 }, { 'rank': 599, 'count': 1 }, { 'rank': 439, 'count': 1 }, { 'rank': 598, 'count': 1 }, { 'rank': 409, 'count': 1 }, { 'rank': 877, 'count': 1 }, { 'rank': 835, 'count': 1 }, { 'rank': 142, 'count': 1 }, { 'rank': 522, 'count': 1 }, { 'rank': 506, 'count': 1 }, { 'rank': 94, 'count': 1 }, { 'rank': 774, 'count': 1 }, { 'rank': 49, 'count': 1 }, { 'rank': 113, 'count': 1 }, { 'rank': 157, 'count': 1 }, { 'rank': 643, 'count': 1 }, { 'rank': 908, 'count': 1 }, { 'rank': 697, 'count': 1 }, { 'rank': 906, 'count': 1 }, { 'rank': 611, 'count': 1 }, { 'rank': 66, 'count': 1 }, { 'rank': 395, 'count': 1 }, { 'rank': 391, 'count': 1 }, { 'rank': 785, 'count': 1 }, { 'rank': 480, 'count': 1 }, { 'rank': 542, 'count': 1 }, { 'rank': 258, 'count': 1 }, { 'rank': 567, 'count': 1 }, { 'rank': 12, 'count': 1 }, { 'rank': 832, 'count': 1 }, { 'rank': 739, 'count': 1 }, { 'rank': 118, 'count': 1 }, { 'rank': 699, 'count': 1 }, { 'rank': 886, 'count': 1 }, { 'rank': 769, 'count': 1 }, { 'rank': 853, 'count': 1 }, { 'rank': 657, 'count': 1 }, { 'rank': 905, 'count': 1 }, { 'rank': 948, 'count': 1 }, { 'rank': 881, 'count': 1 }, { 'rank': 446, 'count': 1 }, { 'rank': 162, 'count': 1 }, { 'rank': 862, 'count': 1 }, { 'rank': 4, 'count': 1 }, { 'rank': 740, 'count': 1 }, { 'rank': 737, 'count': 1 }, { 'rank': 805, 'count': 1 }, { 'rank': 513, 'count': 1 }, { 'rank': 183, 'count': 1 }, { 'rank': 520, 'count': 1 }, { 'rank': 109, 'count': 1 }, { 'rank': 641, 'count': 1 }, { 'rank': 306, 'count': 1 }, { 'rank': 949, 'count': 1 }, { 'rank': 941, 'count': 1 }, ], fields: [makeField('rank'), makeField('count')], metadata: { 'rank': { type: Type.Number, semanticType: 'Rank', levels: [] }, 'count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('rank'), y: makeEncodingItem('count'), }, chartProperties: { cornerRadius: 3 }, }; } function gen_colorRanksQ5RecGpt41(): TestCase { // Across the full rank spectrum, how do the red, green, and blue channel intensities (from the hex codes) trend as rank go // Score: 6/20 | While the chart technically plots rank versus RGB intensities, major axis and overplotting issues ma return { title: 'color_ranks_q5_rec-gpt-4.1', description: 'Plot rank vs R, G, B intensity from hex codes', tags: ['debug', 'line-chart'], chartType: 'Line Chart', data: [ { 'rank': 722, 'channel': 'B', 'intensity': 89 }, { 'rank': 457, 'channel': 'R', 'intensity': 136 }, { 'rank': 103, 'channel': 'R', 'intensity': 237 }, { 'rank': 178, 'channel': 'G', 'intensity': 86 }, { 'rank': 55, 'channel': 'G', 'intensity': 155 }, { 'rank': 915, 'channel': 'R', 'intensity': 194 }, { 'rank': 572, 'channel': 'R', 'intensity': 80 }, { 'rank': 420, 'channel': 'R', 'intensity': 152 }, { 'rank': 874, 'channel': 'B', 'intensity': 34 }, { 'rank': 336, 'channel': 'B', 'intensity': 17 }, { 'rank': 357, 'channel': 'R', 'intensity': 102 }, { 'rank': 521, 'channel': 'B', 'intensity': 5 }, { 'rank': 780, 'channel': 'G', 'intensity': 5 }, { 'rank': 131, 'channel': 'R', 'intensity': 2 }, { 'rank': 123, 'channel': 'R', 'intensity': 127 }, { 'rank': 384, 'channel': 'R', 'intensity': 254 }, { 'rank': 896, 'channel': 'R', 'intensity': 173 }, { 'rank': 4, 'channel': 'G', 'intensity': 129 }, { 'rank': 172, 'channel': 'B', 'intensity': 98 }, { 'rank': 568, 'channel': 'B', 'intensity': 167 }, { 'rank': 109, 'channel': 'R', 'intensity': 214 }, { 'rank': 401, 'channel': 'B', 'intensity': 76 }, { 'rank': 815, 'channel': 'R', 'intensity': 157 }, { 'rank': 764, 'channel': 'B', 'intensity': 142 }, { 'rank': 335, 'channel': 'B', 'intensity': 8 }, { 'rank': 770, 'channel': 'G', 'intensity': 157 }, { 'rank': 903, 'channel': 'R', 'intensity': 255 }, { 'rank': 891, 'channel': 'G', 'intensity': 70 }, { 'rank': 516, 'channel': 'B', 'intensity': 103 }, { 'rank': 191, 'channel': 'G', 'intensity': 46 }, { 'rank': 27, 'channel': 'R', 'intensity': 53 }, { 'rank': 654, 'channel': 'R', 'intensity': 173 }, { 'rank': 783, 'channel': 'G', 'intensity': 2 }, { 'rank': 445, 'channel': 'G', 'intensity': 255 }, { 'rank': 190, 'channel': 'G', 'intensity': 97 }, { 'rank': 637, 'channel': 'R', 'intensity': 187 }, { 'rank': 882, 'channel': 'R', 'intensity': 208 }, { 'rank': 430, 'channel': 'G', 'intensity': 247 }, { 'rank': 419, 'channel': 'R', 'intensity': 138 }, { 'rank': 380, 'channel': 'R', 'intensity': 178 }, { 'rank': 608, 'channel': 'G', 'intensity': 152 }, { 'rank': 397, 'channel': 'R', 'intensity': 152 }, { 'rank': 522, 'channel': 'G', 'intensity': 248 }, { 'rank': 460, 'channel': 'G', 'intensity': 254 }, { 'rank': 575, 'channel': 'B', 'intensity': 122 }, { 'rank': 135, 'channel': 'G', 'intensity': 253 }, { 'rank': 178, 'channel': 'R', 'intensity': 169 }, { 'rank': 933, 'channel': 'G', 'intensity': 155 }, { 'rank': 299, 'channel': 'B', 'intensity': 204 }, { 'rank': 512, 'channel': 'R', 'intensity': 151 }, { 'rank': 602, 'channel': 'G', 'intensity': 183 }, { 'rank': 323, 'channel': 'R', 'intensity': 252 }, { 'rank': 364, 'channel': 'B', 'intensity': 16 }, { 'rank': 252, 'channel': 'G', 'intensity': 76 }, { 'rank': 677, 'channel': 'B', 'intensity': 204 }, { 'rank': 636, 'channel': 'B', 'intensity': 4 }, { 'rank': 533, 'channel': 'G', 'intensity': 155 }, { 'rank': 467, 'channel': 'B', 'intensity': 129 }, { 'rank': 788, 'channel': 'R', 'intensity': 143 }, { 'rank': 285, 'channel': 'R', 'intensity': 194 }, { 'rank': 188, 'channel': 'R', 'intensity': 10 }, { 'rank': 811, 'channel': 'B', 'intensity': 88 }, { 'rank': 934, 'channel': 'R', 'intensity': 239 }, { 'rank': 237, 'channel': 'G', 'intensity': 178 }, { 'rank': 327, 'channel': 'R', 'intensity': 126 }, { 'rank': 5, 'channel': 'G', 'intensity': 55 }, { 'rank': 414, 'channel': 'R', 'intensity': 247 }, { 'rank': 909, 'channel': 'G', 'intensity': 63 }, { 'rank': 706, 'channel': 'B', 'intensity': 52 }, { 'rank': 546, 'channel': 'G', 'intensity': 156 }, { 'rank': 667, 'channel': 'R', 'intensity': 245 }, { 'rank': 568, 'channel': 'G', 'intensity': 127 }, { 'rank': 507, 'channel': 'G', 'intensity': 111 }, { 'rank': 859, 'channel': 'R', 'intensity': 44 }, { 'rank': 848, 'channel': 'B', 'intensity': 78 }, { 'rank': 145, 'channel': 'G', 'intensity': 41 }, { 'rank': 902, 'channel': 'B', 'intensity': 163 }, { 'rank': 757, 'channel': 'B', 'intensity': 251 }, { 'rank': 293, 'channel': 'R', 'intensity': 1 }, { 'rank': 598, 'channel': 'B', 'intensity': 8 }, { 'rank': 703, 'channel': 'B', 'intensity': 230 }, { 'rank': 701, 'channel': 'R', 'intensity': 139 }, { 'rank': 290, 'channel': 'B', 'intensity': 1 }, { 'rank': 54, 'channel': 'G', 'intensity': 21 }, { 'rank': 670, 'channel': 'R', 'intensity': 51 }, { 'rank': 945, 'channel': 'G', 'intensity': 216 }, { 'rank': 606, 'channel': 'G', 'intensity': 79 }, { 'rank': 157, 'channel': 'G', 'intensity': 118 }, { 'rank': 724, 'channel': 'B', 'intensity': 138 }, { 'rank': 921, 'channel': 'B', 'intensity': 137 }, { 'rank': 384, 'channel': 'B', 'intensity': 8 }, { 'rank': 900, 'channel': 'R', 'intensity': 255 }, { 'rank': 907, 'channel': 'B', 'intensity': 165 }, { 'rank': 380, 'channel': 'G', 'intensity': 122 }, { 'rank': 230, 'channel': 'R', 'intensity': 156 }, { 'rank': 939, 'channel': 'R', 'intensity': 252 }, { 'rank': 132, 'channel': 'R', 'intensity': 214 }, { 'rank': 344, 'channel': 'G', 'intensity': 220 }, { 'rank': 695, 'channel': 'G', 'intensity': 47 }, { 'rank': 148, 'channel': 'G', 'intensity': 255 }, { 'rank': 272, 'channel': 'R', 'intensity': 33 }, { 'rank': 865, 'channel': 'R', 'intensity': 182 }, { 'rank': 426, 'channel': 'B', 'intensity': 4 }, { 'rank': 340, 'channel': 'G', 'intensity': 122 }, { 'rank': 871, 'channel': 'R', 'intensity': 253 }, { 'rank': 787, 'channel': 'B', 'intensity': 162 }, { 'rank': 147, 'channel': 'B', 'intensity': 255 }, { 'rank': 672, 'channel': 'G', 'intensity': 255 }, { 'rank': 736, 'channel': 'B', 'intensity': 49 }, { 'rank': 931, 'channel': 'G', 'intensity': 6 }, { 'rank': 586, 'channel': 'R', 'intensity': 36 }, { 'rank': 136, 'channel': 'G', 'intensity': 221 }, { 'rank': 62, 'channel': 'G', 'intensity': 255 }, { 'rank': 402, 'channel': 'B', 'intensity': 43 }, { 'rank': 310, 'channel': 'B', 'intensity': 254 }, { 'rank': 128, 'channel': 'G', 'intensity': 0 }, { 'rank': 497, 'channel': 'B', 'intensity': 127 }, { 'rank': 806, 'channel': 'G', 'intensity': 0 }, { 'rank': 493, 'channel': 'B', 'intensity': 57 }, { 'rank': 687, 'channel': 'G', 'intensity': 71 }, { 'rank': 534, 'channel': 'G', 'intensity': 78 }, { 'rank': 899, 'channel': 'R', 'intensity': 67 }, { 'rank': 567, 'channel': 'R', 'intensity': 239 }, { 'rank': 190, 'channel': 'B', 'intensity': 18 }, { 'rank': 124, 'channel': 'B', 'intensity': 194 }, { 'rank': 373, 'channel': 'R', 'intensity': 111 }, { 'rank': 193, 'channel': 'R', 'intensity': 255 }, { 'rank': 450, 'channel': 'R', 'intensity': 127 }, { 'rank': 627, 'channel': 'R', 'intensity': 1 }, { 'rank': 673, 'channel': 'B', 'intensity': 2 }, { 'rank': 656, 'channel': 'R', 'intensity': 41 }, { 'rank': 890, 'channel': 'B', 'intensity': 0 }, { 'rank': 781, 'channel': 'G', 'intensity': 229 }, { 'rank': 545, 'channel': 'B', 'intensity': 101 }, { 'rank': 261, 'channel': 'R', 'intensity': 183 }, { 'rank': 628, 'channel': 'G', 'intensity': 229 }, { 'rank': 615, 'channel': 'G', 'intensity': 255 }, { 'rank': 543, 'channel': 'B', 'intensity': 254 }, { 'rank': 20, 'channel': 'B', 'intensity': 239 }, { 'rank': 270, 'channel': 'B', 'intensity': 21 }, { 'rank': 81, 'channel': 'G', 'intensity': 255 }, { 'rank': 369, 'channel': 'B', 'intensity': 109 }, { 'rank': 48, 'channel': 'R', 'intensity': 255 }, { 'rank': 889, 'channel': 'B', 'intensity': 121 }, { 'rank': 470, 'channel': 'R', 'intensity': 254 }, { 'rank': 895, 'channel': 'B', 'intensity': 253 }, { 'rank': 302, 'channel': 'B', 'intensity': 3 }, { 'rank': 144, 'channel': 'G', 'intensity': 154 }, { 'rank': 728, 'channel': 'B', 'intensity': 5 }, { 'rank': 254, 'channel': 'G', 'intensity': 255 }, { 'rank': 832, 'channel': 'G', 'intensity': 159 }, { 'rank': 648, 'channel': 'R', 'intensity': 148 }, { 'rank': 910, 'channel': 'G', 'intensity': 126 }, { 'rank': 14, 'channel': 'R', 'intensity': 146 }, { 'rank': 130, 'channel': 'G', 'intensity': 59 }, { 'rank': 153, 'channel': 'B', 'intensity': 204 }, { 'rank': 732, 'channel': 'R', 'intensity': 65 }, { 'rank': 182, 'channel': 'B', 'intensity': 125 }, { 'rank': 436, 'channel': 'R', 'intensity': 118 }, { 'rank': 664, 'channel': 'B', 'intensity': 23 }, { 'rank': 274, 'channel': 'G', 'intensity': 101 }, { 'rank': 720, 'channel': 'B', 'intensity': 9 }, { 'rank': 597, 'channel': 'B', 'intensity': 16 }, { 'rank': 583, 'channel': 'G', 'intensity': 114 }, { 'rank': 662, 'channel': 'R', 'intensity': 1 }, { 'rank': 312, 'channel': 'B', 'intensity': 133 }, { 'rank': 275, 'channel': 'B', 'intensity': 111 }, { 'rank': 3, 'channel': 'R', 'intensity': 3 }, { 'rank': 556, 'channel': 'B', 'intensity': 2 }, { 'rank': 379, 'channel': 'G', 'intensity': 254 }, { 'rank': 104, 'channel': 'B', 'intensity': 8 }, { 'rank': 80, 'channel': 'R', 'intensity': 159 }, { 'rank': 459, 'channel': 'R', 'intensity': 190 }, { 'rank': 538, 'channel': 'G', 'intensity': 191 }, { 'rank': 311, 'channel': 'G', 'intensity': 254 }, { 'rank': 32, 'channel': 'G', 'intensity': 218 }, { 'rank': 238, 'channel': 'R', 'intensity': 213 }, { 'rank': 38, 'channel': 'G', 'intensity': 129 }, { 'rank': 351, 'channel': 'R', 'intensity': 158 }, { 'rank': 93, 'channel': 'B', 'intensity': 193 }, { 'rank': 284, 'channel': 'R', 'intensity': 116 }, { 'rank': 284, 'channel': 'B', 'intensity': 151 }, { 'rank': 516, 'channel': 'R', 'intensity': 83 }, { 'rank': 526, 'channel': 'R', 'intensity': 255 }, { 'rank': 805, 'channel': 'B', 'intensity': 111 }, { 'rank': 49, 'channel': 'B', 'intensity': 117 }, { 'rank': 354, 'channel': 'B', 'intensity': 23 }, { 'rank': 677, 'channel': 'R', 'intensity': 14 }, { 'rank': 137, 'channel': 'G', 'intensity': 30 }, { 'rank': 264, 'channel': 'B', 'intensity': 67 }, { 'rank': 587, 'channel': 'B', 'intensity': 231 }, { 'rank': 785, 'channel': 'G', 'intensity': 1 }, { 'rank': 868, 'channel': 'R', 'intensity': 133 }, { 'rank': 311, 'channel': 'B', 'intensity': 9 }, { 'rank': 928, 'channel': 'B', 'intensity': 199 }, { 'rank': 824, 'channel': 'R', 'intensity': 255 }, { 'rank': 328, 'channel': 'G', 'intensity': 238 }, { 'rank': 686, 'channel': 'G', 'intensity': 72 }, { 'rank': 854, 'channel': 'B', 'intensity': 73 }, { 'rank': 581, 'channel': 'G', 'intensity': 111 }, ], fields: [makeField('rank'), makeField('channel'), makeField('intensity')], metadata: { 'rank': { type: Type.Number, semanticType: 'Rank', levels: [] }, 'channel': { type: Type.String, semanticType: 'Type', levels: ['B', 'G', 'R'] }, 'intensity': { type: Type.Number, semanticType: 'Number', levels: [] }, }, encodingMap: { x: makeEncodingItem('rank'), y: makeEncodingItem('intensity'), color: makeEncodingItem('channel'), }, chartProperties: { interpolate: "linear" }, }; } function gen_colorRanksQ5RecGpt51(): TestCase { // Across the full rank spectrum, how do the red, green, and blue channel intensities (from the hex codes) trend as rank go // Score: 8/20 | The visualization uses the correct variables (RGB intensities vs. rank) but contains axis/data incon return { title: 'color_ranks_q5_rec-gpt-5.1', description: 'Analyze how **red**, **green**, **blue** intensities change over **rank**', tags: ['debug', 'line-chart'], chartType: 'Line Chart', data: [ { 'rank': 722, 'color': 'dark sand', 'hex': '#a88f59', 'channel': 'blue', 'intensity': 89 }, { 'rank': 457, 'color': 'poo brown', 'hex': '#885f01', 'channel': 'red', 'intensity': 136 }, { 'rank': 103, 'color': 'fuchsia', 'hex': '#ed0dd9', 'channel': 'red', 'intensity': 237 }, { 'rank': 178, 'color': 'sienna', 'hex': '#a9561e', 'channel': 'green', 'intensity': 86 }, { 'rank': 55, 'color': 'grass green', 'hex': '#3f9b0b', 'channel': 'green', 'intensity': 155 }, { 'rank': 915, 'color': 'olive yellow', 'hex': '#c2b709', 'channel': 'red', 'intensity': 194 }, { 'rank': 572, 'color': 'mid green', 'hex': '#50a747', 'channel': 'red', 'intensity': 80 }, { 'rank': 420, 'color': 'light sea green', 'hex': '#98f6b0', 'channel': 'red', 'intensity': 152 }, { 'rank': 874, 'color': 'sun yellow', 'hex': '#ffdf22', 'channel': 'blue', 'intensity': 34 }, { 'rank': 336, 'color': 'brown green', 'hex': '#706c11', 'channel': 'blue', 'intensity': 17 }, { 'rank': 357, 'color': 'military green', 'hex': '#667c3e', 'channel': 'red', 'intensity': 102 }, { 'rank': 521, 'color': 'orangish red', 'hex': '#f43605', 'channel': 'blue', 'intensity': 5 }, { 'rank': 780, 'color': 'plum purple', 'hex': '#4e0550', 'channel': 'green', 'intensity': 5 }, { 'rank': 131, 'color': 'midnight blue', 'hex': '#020035', 'channel': 'red', 'intensity': 2 }, { 'rank': 123, 'color': 'reddish brown', 'hex': '#7f2b0a', 'channel': 'red', 'intensity': 127 }, { 'rank': 384, 'color': 'amber', 'hex': '#feb308', 'channel': 'red', 'intensity': 254 }, { 'rank': 896, 'color': 'baby shit brown', 'hex': '#ad900d', 'channel': 'red', 'intensity': 173 }, { 'rank': 4, 'color': 'pink', 'hex': '#ff81c0', 'channel': 'green', 'intensity': 129 }, { 'rank': 172, 'color': 'deep pink', 'hex': '#cb0162', 'channel': 'blue', 'intensity': 98 }, { 'rank': 568, 'color': 'carnation pink', 'hex': '#ff7fa7', 'channel': 'blue', 'intensity': 167 }, { 'rank': 109, 'color': 'pinkish purple', 'hex': '#d648d7', 'channel': 'red', 'intensity': 214 }, { 'rank': 401, 'color': 'coffee', 'hex': '#a6814c', 'channel': 'blue', 'intensity': 76 }, { 'rank': 815, 'color': 'dark fuchsia', 'hex': '#9d0759', 'channel': 'red', 'intensity': 157 }, { 'rank': 764, 'color': 'metallic blue', 'hex': '#4f738e', 'channel': 'blue', 'intensity': 142 }, { 'rank': 335, 'color': 'dark maroon', 'hex': '#3c0008', 'channel': 'blue', 'intensity': 8 }, { 'rank': 770, 'color': 'flat green', 'hex': '#699d4c', 'channel': 'green', 'intensity': 157 }, { 'rank': 903, 'color': 'orange pink', 'hex': '#ff6f52', 'channel': 'red', 'intensity': 255 }, { 'rank': 891, 'color': 'darkish pink', 'hex': '#da467d', 'channel': 'green', 'intensity': 70 }, { 'rank': 516, 'color': 'gunmetal', 'hex': '#536267', 'channel': 'blue', 'intensity': 103 }, { 'rank': 191, 'color': 'red brown', 'hex': '#8b2e16', 'channel': 'green', 'intensity': 46 }, { 'rank': 27, 'color': 'dark purple', 'hex': '#35063e', 'channel': 'red', 'intensity': 53 }, { 'rank': 654, 'color': 'lemon green', 'hex': '#adf802', 'channel': 'red', 'intensity': 173 }, { 'rank': 783, 'color': 'carmine', 'hex': '#9d0216', 'channel': 'green', 'intensity': 2 }, { 'rank': 445, 'color': 'ivory', 'hex': '#ffffcb', 'channel': 'green', 'intensity': 255 }, { 'rank': 190, 'color': 'greenish brown', 'hex': '#696112', 'channel': 'green', 'intensity': 97 }, { 'rank': 637, 'color': 'yellowgreen', 'hex': '#bbf90f', 'channel': 'red', 'intensity': 187 }, { 'rank': 882, 'color': 'sickly yellow', 'hex': '#d0e429', 'channel': 'red', 'intensity': 208 }, { 'rank': 430, 'color': 'bright light blue', 'hex': '#26f7fd', 'channel': 'green', 'intensity': 247 }, { 'rank': 419, 'color': 'robin egg blue', 'hex': '#8af1fe', 'channel': 'red', 'intensity': 138 }, { 'rank': 380, 'color': 'golden brown', 'hex': '#b27a01', 'channel': 'red', 'intensity': 178 }, { 'rank': 608, 'color': 'baby poop green', 'hex': '#8f9805', 'channel': 'green', 'intensity': 152 }, { 'rank': 397, 'color': 'purpleish', 'hex': '#98568d', 'channel': 'red', 'intensity': 152 }, { 'rank': 522, 'color': 'greeny yellow', 'hex': '#c6f808', 'channel': 'green', 'intensity': 248 }, { 'rank': 460, 'color': 'bright lime green', 'hex': '#65fe08', 'channel': 'green', 'intensity': 254 }, { 'rank': 575, 'color': 'sandy', 'hex': '#f1da7a', 'channel': 'blue', 'intensity': 122 }, { 'rank': 135, 'color': 'greenish yellow', 'hex': '#cdfd02', 'channel': 'green', 'intensity': 253 }, { 'rank': 178, 'color': 'sienna', 'hex': '#a9561e', 'channel': 'red', 'intensity': 169 }, { 'rank': 933, 'color': 'pinkish tan', 'hex': '#d99b82', 'channel': 'green', 'intensity': 155 }, { 'rank': 299, 'color': 'true blue', 'hex': '#010fcc', 'channel': 'blue', 'intensity': 204 }, { 'rank': 512, 'color': 'warm grey', 'hex': '#978a84', 'channel': 'red', 'intensity': 151 }, { 'rank': 602, 'color': 'leafy green', 'hex': '#51b73b', 'channel': 'green', 'intensity': 183 }, { 'rank': 323, 'color': 'marigold', 'hex': '#fcc006', 'channel': 'red', 'intensity': 252 }, { 'rank': 364, 'color': 'dark gold', 'hex': '#b59410', 'channel': 'blue', 'intensity': 16 }, { 'rank': 252, 'color': 'dark mauve', 'hex': '#874c62', 'channel': 'green', 'intensity': 76 }, { 'rank': 677, 'color': 'water blue', 'hex': '#0e87cc', 'channel': 'blue', 'intensity': 204 }, { 'rank': 636, 'color': 'baby poo', 'hex': '#ab9004', 'channel': 'blue', 'intensity': 4 }, { 'rank': 533, 'color': 'ocher', 'hex': '#bf9b0c', 'channel': 'green', 'intensity': 155 }, { 'rank': 467, 'color': 'pale olive', 'hex': '#b9cc81', 'channel': 'blue', 'intensity': 129 }, { 'rank': 788, 'color': 'icky green', 'hex': '#8fae22', 'channel': 'red', 'intensity': 143 }, { 'rank': 285, 'color': 'puke yellow', 'hex': '#c2be0e', 'channel': 'red', 'intensity': 194 }, { 'rank': 188, 'color': 'pine green', 'hex': '#0a481e', 'channel': 'red', 'intensity': 10 }, { 'rank': 811, 'color': 'rich purple', 'hex': '#720058', 'channel': 'blue', 'intensity': 88 }, { 'rank': 934, 'color': 'macaroni and cheese', 'hex': '#efb435', 'channel': 'red', 'intensity': 239 }, { 'rank': 237, 'color': 'faded green', 'hex': '#7bb274', 'channel': 'green', 'intensity': 178 }, { 'rank': 327, 'color': 'light blue green', 'hex': '#7efbb3', 'channel': 'red', 'intensity': 126 }, { 'rank': 5, 'color': 'brown', 'hex': '#653700', 'channel': 'green', 'intensity': 55 }, { 'rank': 414, 'color': 'cherry red', 'hex': '#f7022a', 'channel': 'red', 'intensity': 247 }, { 'rank': 909, 'color': 'purply', 'hex': '#983fb2', 'channel': 'green', 'intensity': 63 }, { 'rank': 706, 'color': 'leaf', 'hex': '#71aa34', 'channel': 'blue', 'intensity': 52 }, { 'rank': 546, 'color': 'grassy green', 'hex': '#419c03', 'channel': 'green', 'intensity': 156 }, { 'rank': 667, 'color': 'hot magenta', 'hex': '#f504c9', 'channel': 'red', 'intensity': 245 }, { 'rank': 568, 'color': 'carnation pink', 'hex': '#ff7fa7', 'channel': 'green', 'intensity': 127 }, { 'rank': 507, 'color': 'soft purple', 'hex': '#a66fb5', 'channel': 'green', 'intensity': 111 }, { 'rank': 859, 'color': 'radioactive green', 'hex': '#2cfa1f', 'channel': 'red', 'intensity': 44 }, { 'rank': 848, 'color': 'camo', 'hex': '#7f8f4e', 'channel': 'blue', 'intensity': 78 }, { 'rank': 145, 'color': 'blue purple', 'hex': '#5729ce', 'channel': 'green', 'intensity': 41 }, { 'rank': 902, 'color': 'tea green', 'hex': '#bdf8a3', 'channel': 'blue', 'intensity': 163 }, { 'rank': 757, 'color': 'light light blue', 'hex': '#cafffb', 'channel': 'blue', 'intensity': 251 }, { 'rank': 293, 'color': 'bluegreen', 'hex': '#017a79', 'channel': 'red', 'intensity': 1 }, { 'rank': 598, 'color': 'frog green', 'hex': '#58bc08', 'channel': 'blue', 'intensity': 8 }, { 'rank': 703, 'color': 'lightish purple', 'hex': '#a552e6', 'channel': 'blue', 'intensity': 230 }, { 'rank': 701, 'color': 'lavender blue', 'hex': '#8b88f8', 'channel': 'red', 'intensity': 139 }, { 'rank': 290, 'color': 'pea soup', 'hex': '#929901', 'channel': 'blue', 'intensity': 1 }, { 'rank': 54, 'color': 'navy', 'hex': '#01153e', 'channel': 'green', 'intensity': 21 }, { 'rank': 670, 'color': 'cool green', 'hex': '#33b864', 'channel': 'red', 'intensity': 51 }, { 'rank': 945, 'color': 'fresh green', 'hex': '#69d84f', 'channel': 'green', 'intensity': 216 }, { 'rank': 606, 'color': 'heliotrope', 'hex': '#d94ff5', 'channel': 'green', 'intensity': 79 }, { 'rank': 157, 'color': 'olive drab', 'hex': '#6f7632', 'channel': 'green', 'intensity': 118 }, { 'rank': 724, 'color': 'putty', 'hex': '#beae8a', 'channel': 'blue', 'intensity': 138 }, { 'rank': 921, 'color': 'strong pink', 'hex': '#ff0789', 'channel': 'blue', 'intensity': 137 }, { 'rank': 384, 'color': 'amber', 'hex': '#feb308', 'channel': 'blue', 'intensity': 8 }, { 'rank': 900, 'color': 'egg shell', 'hex': '#fffcc4', 'channel': 'red', 'intensity': 255 }, { 'rank': 907, 'color': 'light pastel green', 'hex': '#b2fba5', 'channel': 'blue', 'intensity': 165 }, { 'rank': 380, 'color': 'golden brown', 'hex': '#b27a01', 'channel': 'green', 'intensity': 122 }, { 'rank': 230, 'color': 'brownish', 'hex': '#9c6d57', 'channel': 'red', 'intensity': 156 }, { 'rank': 939, 'color': 'yellowish tan', 'hex': '#fcfc81', 'channel': 'red', 'intensity': 252 }, { 'rank': 132, 'color': 'light violet', 'hex': '#d6b4fc', 'channel': 'red', 'intensity': 214 }, { 'rank': 344, 'color': 'dull yellow', 'hex': '#eedc5b', 'channel': 'green', 'intensity': 220 }, { 'rank': 695, 'color': 'lightish red', 'hex': '#fe2f4a', 'channel': 'green', 'intensity': 47 }, { 'rank': 148, 'color': 'off white', 'hex': '#ffffe4', 'channel': 'green', 'intensity': 255 }, { 'rank': 272, 'color': 'electric green', 'hex': '#21fc0d', 'channel': 'red', 'intensity': 33 }, { 'rank': 865, 'color': 'baby puke green', 'hex': '#b6c406', 'channel': 'red', 'intensity': 182 }, { 'rank': 426, 'color': 'ocre', 'hex': '#c69c04', 'channel': 'blue', 'intensity': 4 }, { 'rank': 340, 'color': 'lightish blue', 'hex': '#3d7afd', 'channel': 'green', 'intensity': 122 }, { 'rank': 871, 'color': 'butterscotch', 'hex': '#fdb147', 'channel': 'red', 'intensity': 253 }, { 'rank': 787, 'color': 'light khaki', 'hex': '#e6f2a2', 'channel': 'blue', 'intensity': 162 }, { 'rank': 147, 'color': 'electric blue', 'hex': '#0652ff', 'channel': 'blue', 'intensity': 255 }, { 'rank': 672, 'color': 'eggshell blue', 'hex': '#c4fff7', 'channel': 'green', 'intensity': 255 }, { 'rank': 736, 'color': 'dark', 'hex': '#1b2431', 'channel': 'blue', 'intensity': 49 }, { 'rank': 931, 'color': 'strong blue', 'hex': '#0c06f7', 'channel': 'green', 'intensity': 6 }, { 'rank': 586, 'color': 'tealish', 'hex': '#24bca8', 'channel': 'red', 'intensity': 36 }, { 'rank': 136, 'color': 'yellowish green', 'hex': '#b0dd16', 'channel': 'green', 'intensity': 221 }, { 'rank': 62, 'color': 'mint green', 'hex': '#8fff9f', 'channel': 'green', 'intensity': 255 }, { 'rank': 402, 'color': 'sepia', 'hex': '#985e2b', 'channel': 'blue', 'intensity': 43 }, { 'rank': 310, 'color': 'pale lavender', 'hex': '#eecffe', 'channel': 'blue', 'intensity': 254 }, { 'rank': 128, 'color': 'dark magenta', 'hex': '#960056', 'channel': 'green', 'intensity': 0 }, { 'rank': 497, 'color': 'light yellow green', 'hex': '#ccfd7f', 'channel': 'blue', 'intensity': 127 }, { 'rank': 806, 'color': 'vivid purple', 'hex': '#9900fa', 'channel': 'green', 'intensity': 0 }, { 'rank': 493, 'color': 'rouge', 'hex': '#ab1239', 'channel': 'blue', 'intensity': 57 }, { 'rank': 687, 'color': 'deep rose', 'hex': '#c74767', 'channel': 'green', 'intensity': 71 }, { 'rank': 534, 'color': 'milk chocolate', 'hex': '#7f4e1e', 'channel': 'green', 'intensity': 78 }, { 'rank': 899, 'color': 'eggplant purple', 'hex': '#430541', 'channel': 'red', 'intensity': 67 }, { 'rank': 567, 'color': 'tomato', 'hex': '#ef4026', 'channel': 'red', 'intensity': 239 }, { 'rank': 190, 'color': 'greenish brown', 'hex': '#696112', 'channel': 'blue', 'intensity': 18 }, { 'rank': 124, 'color': 'cream', 'hex': '#ffffc2', 'channel': 'blue', 'intensity': 194 }, { 'rank': 373, 'color': 'steel grey', 'hex': '#6f828a', 'channel': 'red', 'intensity': 111 }, { 'rank': 193, 'color': 'tangerine', 'hex': '#ff9408', 'channel': 'red', 'intensity': 255 }, { 'rank': 450, 'color': 'medium brown', 'hex': '#7f5112', 'channel': 'red', 'intensity': 127 }, { 'rank': 627, 'color': 'peacock blue', 'hex': '#016795', 'channel': 'red', 'intensity': 1 }, { 'rank': 673, 'color': 'barf green', 'hex': '#94ac02', 'channel': 'blue', 'intensity': 2 }, { 'rank': 656, 'color': 'dark grey blue', 'hex': '#29465b', 'channel': 'red', 'intensity': 41 }, { 'rank': 890, 'color': 'deep brown', 'hex': '#410200', 'channel': 'blue', 'intensity': 0 }, { 'rank': 781, 'color': 'pale peach', 'hex': '#ffe5ad', 'channel': 'green', 'intensity': 229 }, { 'rank': 545, 'color': 'pale lime green', 'hex': '#b1ff65', 'channel': 'blue', 'intensity': 101 }, { 'rank': 261, 'color': 'yellow brown', 'hex': '#b79400', 'channel': 'red', 'intensity': 183 }, { 'rank': 628, 'color': 'weird green', 'hex': '#3ae57f', 'channel': 'green', 'intensity': 229 }, { 'rank': 615, 'color': 'key lime', 'hex': '#aeff6e', 'channel': 'green', 'intensity': 255 }, { 'rank': 543, 'color': 'pale grey', 'hex': '#fdfdfe', 'channel': 'blue', 'intensity': 254 }, { 'rank': 20, 'color': 'lavender', 'hex': '#c79fef', 'channel': 'blue', 'intensity': 239 }, { 'rank': 270, 'color': 'golden yellow', 'hex': '#fec615', 'channel': 'blue', 'intensity': 21 }, { 'rank': 81, 'color': 'pastel green', 'hex': '#b0ff9d', 'channel': 'green', 'intensity': 255 }, { 'rank': 369, 'color': 'slate green', 'hex': '#658d6d', 'channel': 'blue', 'intensity': 109 }, { 'rank': 48, 'color': 'light pink', 'hex': '#ffd1df', 'channel': 'red', 'intensity': 255 }, { 'rank': 889, 'color': 'greenish beige', 'hex': '#c9d179', 'channel': 'blue', 'intensity': 121 }, { 'rank': 470, 'color': 'fire engine red', 'hex': '#fe0002', 'channel': 'red', 'intensity': 254 }, { 'rank': 895, 'color': 'liliac', 'hex': '#c48efd', 'channel': 'blue', 'intensity': 253 }, { 'rank': 302, 'color': 'dark olive green', 'hex': '#3c4d03', 'channel': 'blue', 'intensity': 3 }, { 'rank': 144, 'color': 'azure', 'hex': '#069af3', 'channel': 'green', 'intensity': 154 }, { 'rank': 728, 'color': 'reddy brown', 'hex': '#6e1005', 'channel': 'blue', 'intensity': 5 }, { 'rank': 254, 'color': 'ice blue', 'hex': '#d7fffe', 'channel': 'green', 'intensity': 255 }, { 'rank': 832, 'color': 'greyish teal', 'hex': '#719f91', 'channel': 'green', 'intensity': 159 }, { 'rank': 648, 'color': 'puke brown', 'hex': '#947706', 'channel': 'red', 'intensity': 148 }, { 'rank': 910, 'color': 'purpley grey', 'hex': '#947e94', 'channel': 'green', 'intensity': 126 }, { 'rank': 14, 'color': 'grey', 'hex': '#929591', 'channel': 'red', 'intensity': 146 }, { 'rank': 130, 'color': 'bluish purple', 'hex': '#703be7', 'channel': 'green', 'intensity': 59 }, { 'rank': 153, 'color': 'light turquoise', 'hex': '#7ef4cc', 'channel': 'blue', 'intensity': 204 }, { 'rank': 732, 'color': 'bright cyan', 'hex': '#41fdfe', 'channel': 'red', 'intensity': 65 }, { 'rank': 182, 'color': 'greyish green', 'hex': '#82a67d', 'channel': 'blue', 'intensity': 125 }, { 'rank': 436, 'color': 'dusty green', 'hex': '#76a973', 'channel': 'red', 'intensity': 118 }, { 'rank': 664, 'color': 'baby shit green', 'hex': '#889717', 'channel': 'blue', 'intensity': 23 }, { 'rank': 274, 'color': 'slate grey', 'hex': '#59656d', 'channel': 'green', 'intensity': 101 }, { 'rank': 720, 'color': 'saffron', 'hex': '#feb209', 'channel': 'blue', 'intensity': 9 }, { 'rank': 597, 'color': 'vivid green', 'hex': '#2fef10', 'channel': 'blue', 'intensity': 16 }, { 'rank': 583, 'color': 'pinkish orange', 'hex': '#ff724c', 'channel': 'green', 'intensity': 114 }, { 'rank': 662, 'color': 'dark aquamarine', 'hex': '#017371', 'channel': 'red', 'intensity': 1 }, { 'rank': 312, 'color': 'purple grey', 'hex': '#866f85', 'channel': 'blue', 'intensity': 133 }, { 'rank': 275, 'color': 'teal green', 'hex': '#25a36f', 'channel': 'blue', 'intensity': 111 }, { 'rank': 3, 'color': 'blue', 'hex': '#0343df', 'channel': 'red', 'intensity': 3 }, { 'rank': 556, 'color': 'chestnut', 'hex': '#742802', 'channel': 'blue', 'intensity': 2 }, { 'rank': 379, 'color': 'bright turquoise', 'hex': '#0ffef9', 'channel': 'green', 'intensity': 254 }, { 'rank': 104, 'color': 'hunter green', 'hex': '#0b4008', 'channel': 'blue', 'intensity': 8 }, { 'rank': 80, 'color': 'mint', 'hex': '#9ffeb0', 'channel': 'red', 'intensity': 159 }, { 'rank': 459, 'color': 'celadon', 'hex': '#befdb7', 'channel': 'red', 'intensity': 190 }, { 'rank': 538, 'color': 'greenish teal', 'hex': '#32bf84', 'channel': 'green', 'intensity': 191 }, { 'rank': 311, 'color': 'acid green', 'hex': '#8ffe09', 'channel': 'green', 'intensity': 254 }, { 'rank': 32, 'color': 'beige', 'hex': '#e6daa6', 'channel': 'green', 'intensity': 218 }, { 'rank': 238, 'color': 'dusty pink', 'hex': '#d58a94', 'channel': 'red', 'intensity': 213 }, { 'rank': 38, 'color': 'light brown', 'hex': '#ad8150', 'channel': 'green', 'intensity': 129 }, { 'rank': 351, 'color': 'medium purple', 'hex': '#9e43a2', 'channel': 'red', 'intensity': 158 }, { 'rank': 93, 'color': 'light teal', 'hex': '#90e4c1', 'channel': 'blue', 'intensity': 193 }, { 'rank': 284, 'color': 'bluish grey', 'hex': '#748b97', 'channel': 'red', 'intensity': 116 }, { 'rank': 284, 'color': 'bluish grey', 'hex': '#748b97', 'channel': 'blue', 'intensity': 151 }, { 'rank': 516, 'color': 'gunmetal', 'hex': '#536267', 'channel': 'red', 'intensity': 83 }, { 'rank': 526, 'color': 'butter yellow', 'hex': '#fffd74', 'channel': 'red', 'intensity': 255 }, { 'rank': 805, 'color': 'dark royal blue', 'hex': '#02066f', 'channel': 'blue', 'intensity': 111 }, { 'rank': 49, 'color': 'rose', 'hex': '#cf6275', 'channel': 'blue', 'intensity': 117 }, { 'rank': 354, 'color': 'pea soup green', 'hex': '#94a617', 'channel': 'blue', 'intensity': 23 }, { 'rank': 677, 'color': 'water blue', 'hex': '#0e87cc', 'channel': 'red', 'intensity': 14 }, { 'rank': 137, 'color': 'purplish blue', 'hex': '#601ef9', 'channel': 'green', 'intensity': 30 }, { 'rank': 264, 'color': 'jungle green', 'hex': '#048243', 'channel': 'blue', 'intensity': 67 }, { 'rank': 587, 'color': 'perrywinkle', 'hex': '#8f8ce7', 'channel': 'blue', 'intensity': 231 }, { 'rank': 785, 'color': 'dark hot pink', 'hex': '#d90166', 'channel': 'green', 'intensity': 1 }, { 'rank': 868, 'color': 'indian red', 'hex': '#850e04', 'channel': 'red', 'intensity': 133 }, { 'rank': 311, 'color': 'acid green', 'hex': '#8ffe09', 'channel': 'blue', 'intensity': 9 }, { 'rank': 928, 'color': 'blue blue', 'hex': '#2242c7', 'channel': 'blue', 'intensity': 199 }, { 'rank': 824, 'color': 'creme', 'hex': '#ffffb6', 'channel': 'red', 'intensity': 255 }, { 'rank': 328, 'color': 'yellowish', 'hex': '#faee66', 'channel': 'green', 'intensity': 238 }, { 'rank': 686, 'color': 'dusty red', 'hex': '#b9484e', 'channel': 'green', 'intensity': 72 }, { 'rank': 854, 'color': 'orangeish', 'hex': '#fd8d49', 'channel': 'blue', 'intensity': 73 }, { 'rank': 581, 'color': 'light urple', 'hex': '#b36ff6', 'channel': 'green', 'intensity': 111 }, ], fields: [makeField('rank'), makeField('color'), makeField('hex'), makeField('channel'), makeField('intensity')], metadata: { 'rank': { type: Type.Number, semanticType: 'Rank', levels: [] }, 'color': { type: Type.String, semanticType: 'Category', levels: ['acid green', 'amber', 'azure', 'baby poo', 'baby poop green', 'baby puke green', 'baby shit brown', 'baby shit green', 'barf green', 'beige', 'blue', 'blue blue', 'blue purple', 'bluegreen', 'bluish grey', 'bluish purple', 'bright cyan', 'bright light blue', 'bright lime green', 'bright turquoise', 'brown', 'brown green', 'brownish', 'butter yellow', 'butterscotch', 'camo', 'carmine', 'carnation pink', 'celadon', 'cherry red', 'chestnut', 'coffee', 'cool green', 'cream', 'creme', 'dark', 'dark aquamarine', 'dark fuchsia', 'dark gold', 'dark grey blue', 'dark hot pink', 'dark magenta', 'dark maroon', 'dark mauve', 'dark olive green', 'dark purple', 'dark royal blue', 'dark sand', 'darkish pink', 'deep brown'] }, 'hex': { type: Type.String, semanticType: 'Category', levels: ['#010fcc', '#01153e', '#016795', '#017371', '#017a79', '#020035', '#02066f', '#0343df', '#048243', '#0652ff', '#069af3', '#0a481e', '#0b4008', '#0c06f7', '#0e87cc', '#0ffef9', '#1b2431', '#21fc0d', '#2242c7', '#24bca8', '#25a36f', '#26f7fd', '#29465b', '#2cfa1f', '#2fef10', '#32bf84', '#33b864', '#35063e', '#3ae57f', '#3c0008', '#3c4d03', '#3d7afd', '#3f9b0b', '#410200', '#419c03', '#41fdfe', '#430541', '#4e0550', '#4f738e', '#50a747', '#51b73b', '#536267', '#5729ce', '#58bc08', '#59656d', '#601ef9', '#653700', '#658d6d', '#65fe08', '#667c3e'] }, 'channel': { type: Type.String, semanticType: 'String', levels: ['blue', 'green', 'red'] }, 'intensity': { type: Type.Number, semanticType: 'Number', levels: [] }, }, encodingMap: { x: makeEncodingItem('rank'), y: makeEncodingItem('intensity'), color: makeEncodingItem('channel'), }, chartProperties: { interpolate: "monotone" }, }; } function gen_countryListsQ1RecGpt41(): TestCase { // Which origin countries have the highest number of visa‑free destinations, and how do they rank globally? // Score: 3/20 | The visualization appears to be based on an incorrect visa_free_count aggregation, resulting in iden return { title: 'country_lists_q1_rec-gpt-4.1', description: 'show top countries ranked by visa-free destinations', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'country': 'Palestinian Territory', 'code': 'PS', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Hungary', 'code': 'HU', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Honduras', 'code': 'HN', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Haiti', 'code': 'HT', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Guyana', 'code': 'GY', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Guinea-Bissau', 'code': 'GW', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Guinea', 'code': 'GN', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Guatemala', 'code': 'GT', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Grenada', 'code': 'GD', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Greece', 'code': 'GR', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Ghana', 'code': 'GH', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Germany', 'code': 'DE', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Georgia', 'code': 'GE', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'The Gambia', 'code': 'GM', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Gabon', 'code': 'GA', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'France', 'code': 'FR', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Finland', 'code': 'FI', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Fiji', 'code': 'FJ', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Ethiopia', 'code': 'ET', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Estonia', 'code': 'EE', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Eritrea', 'code': 'ER', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Equatorial Guinea', 'code': 'GQ', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'El Salvador', 'code': 'SV', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Hong Kong (SAR China)', 'code': 'HK', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Iceland', 'code': 'IS', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Ecuador', 'code': 'EC', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'India', 'code': 'IN', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Libya', 'code': 'LY', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Liberia', 'code': 'LR', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Lesotho', 'code': 'LS', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Lebanon', 'code': 'LB', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Latvia', 'code': 'LV', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Laos', 'code': 'LA', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Kyrgyzstan', 'code': 'KG', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Kuwait', 'code': 'KW', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Kosovo', 'code': 'XK', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Kiribati', 'code': 'KI', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Kenya', 'code': 'KE', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Kazakhstan', 'code': 'KZ', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Jordan', 'code': 'JO', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Japan', 'code': 'JP', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Jamaica', 'code': 'JM', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Italy', 'code': 'IT', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Israel', 'code': 'IL', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Ireland', 'code': 'IE', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Iraq', 'code': 'IQ', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Iran', 'code': 'IR', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Indonesia', 'code': 'ID', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Egypt', 'code': 'EG', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Dominican Republic', 'code': 'DO', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Luxembourg', 'code': 'LU', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Brunei', 'code': 'BN', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Botswana', 'code': 'BW', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Bosnia and Herzegovina', 'code': 'BA', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Bolivia', 'code': 'BO', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Bhutan', 'code': 'BT', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Benin', 'code': 'BJ', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Belize', 'code': 'BZ', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Belgium', 'code': 'BE', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Belarus', 'code': 'BY', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Barbados', 'code': 'BB', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Bangladesh', 'code': 'BD', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Bahrain', 'code': 'BH', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Bahamas', 'code': 'BS', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Azerbaijan', 'code': 'AZ', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Austria', 'code': 'AT', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Australia', 'code': 'AU', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Armenia', 'code': 'AM', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Argentina', 'code': 'AR', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Antigua and Barbuda', 'code': 'AG', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Angola', 'code': 'AO', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Algeria', 'code': 'DZ', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Albania', 'code': 'AL', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Brazil', 'code': 'BR', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Bulgaria', 'code': 'BG', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Dominica', 'code': 'DM', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Burkina Faso', 'code': 'BF', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Djibouti', 'code': 'DJ', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Denmark', 'code': 'DK', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Czechia', 'code': 'CZ', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Cyprus', 'code': 'CY', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Cuba', 'code': 'CU', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Croatia', 'code': 'HR', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Cote d\'Ivoire', 'code': 'CI', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Costa Rica', 'code': 'CR', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Congo (Dem. Rep.)', 'code': 'CD', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Congo (Rep.)', 'code': 'CG', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Comoro Islands', 'code': 'KM', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Colombia', 'code': 'CO', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'China', 'code': 'CN', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Chile', 'code': 'CL', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Chad', 'code': 'TD', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Central African Republic', 'code': 'CF', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Cape Verde Islands', 'code': 'CV', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Canada', 'code': 'CA', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Cameroon', 'code': 'CM', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Cambodia', 'code': 'KH', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Burundi', 'code': 'BI', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Lithuania', 'code': 'LT', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Macao (SAR China)', 'code': 'MO', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Andorra', 'code': 'AD', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Togo', 'code': 'TG', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Thailand', 'code': 'TH', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Tanzania', 'code': 'TZ', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Tajikistan', 'code': 'TJ', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Taiwan (Chinese Taipei)', 'code': 'TW', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Syria', 'code': 'SY', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Switzerland', 'code': 'CH', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Sweden', 'code': 'SE', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'eSwatini', 'code': 'SZ', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Suriname', 'code': 'SR', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Sudan', 'code': 'SD', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'St. Vincent and the Grenadines', 'code': 'VC', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'St. Lucia', 'code': 'LC', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'St. Kitts and Nevis', 'code': 'KN', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Sri Lanka', 'code': 'LK', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Spain', 'code': 'ES', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'South Sudan', 'code': 'SS', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'South Korea', 'code': 'KR', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'South Africa', 'code': 'ZA', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Somalia', 'code': 'SO', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Solomon Islands', 'code': 'SB', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Slovenia', 'code': 'SI', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Timor-Leste', 'code': 'TL', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Tonga', 'code': 'TO', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Singapore', 'code': 'SG', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Trinidad and Tobago', 'code': 'TT', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Vatican City', 'code': 'VA', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'San Marino', 'code': 'SM', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Monaco', 'code': 'MC', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Liechtenstein', 'code': 'LI', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Zimbabwe', 'code': 'ZW', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Zambia', 'code': 'ZM', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Yemen', 'code': 'YE', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Vietnam', 'code': 'VN', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Venezuela', 'code': 'VE', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Vanuatu', 'code': 'VU', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Uzbekistan', 'code': 'UZ', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Uruguay', 'code': 'UY', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'United States', 'code': 'US', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'United Kingdom', 'code': 'GB', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'United Arab Emirates', 'code': 'AE', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Ukraine', 'code': 'UA', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Uganda', 'code': 'UG', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Tuvalu', 'code': 'TV', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Turkmenistan', 'code': 'TM', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Türkiye', 'code': 'TR', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Tunisia', 'code': 'TN', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Slovakia', 'code': 'SK', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Sierra Leone', 'code': 'SL', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'North Macedonia', 'code': 'MK', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Nicaragua', 'code': 'NI', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Netherlands', 'code': 'NL', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Nepal', 'code': 'NP', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Nauru', 'code': 'NR', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Namibia', 'code': null, 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Myanmar', 'code': 'MM', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Mozambique', 'code': 'MZ', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Morocco', 'code': 'MA', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Montenegro', 'code': 'ME', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Mongolia', 'code': 'MN', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Moldova', 'code': 'MD', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Micronesia', 'code': 'FM', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Mexico', 'code': 'MX', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Mauritius', 'code': 'MU', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Mauritania', 'code': 'MR', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Marshall Islands', 'code': 'MH', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Malta', 'code': 'MT', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Mali', 'code': 'ML', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Maldives', 'code': 'MV', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Malaysia', 'code': 'MY', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Malawi', 'code': 'MW', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Madagascar', 'code': 'MG', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'New Zealand', 'code': 'NZ', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Niger', 'code': 'NE', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Seychelles', 'code': 'SC', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Nigeria', 'code': 'NG', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Serbia', 'code': 'RS', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Senegal', 'code': 'SN', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Saudi Arabia', 'code': 'SA', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Sao Tome and Principe', 'code': 'ST', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Samoa', 'code': 'WS', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Rwanda', 'code': 'RW', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Russian Federation', 'code': 'RU', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Romania', 'code': 'RO', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Qatar', 'code': 'QA', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Portugal', 'code': 'PT', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Poland', 'code': 'PL', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Philippines', 'code': 'PH', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Peru', 'code': 'PE', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Paraguay', 'code': 'PY', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Papua New Guinea', 'code': 'PG', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Panama', 'code': 'PA', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Palau Islands', 'code': 'PW', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Pakistan', 'code': 'PK', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Oman', 'code': 'OM', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Norway', 'code': 'NO', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'North Korea', 'code': 'KP', 'visa_free_count': 1, 'rank': 1 }, { 'country': 'Afghanistan', 'code': 'AF', 'visa_free_count': 1, 'rank': 1 }, ], fields: [makeField('country'), makeField('code'), makeField('visa_free_count'), makeField('rank')], metadata: { 'country': { type: Type.String, semanticType: 'Country', levels: ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burundi', 'Cambodia', 'Cameroon', 'Canada', 'Cape Verde Islands', 'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia', 'Comoro Islands', 'Congo (Dem. Rep.)', 'Congo (Rep.)', 'Costa Rica', 'Cote d\'Ivoire', 'Croatia', 'Cuba', 'Cyprus', 'Czechia', 'Denmark', 'Djibouti', 'Dominica', 'Dominican Republic'] }, 'code': { type: Type.String, semanticType: 'Category', levels: ['AD', 'AE', 'AF', 'AG', 'AL', 'AM', 'AO', 'AR', 'AT', 'AU', 'AZ', 'BA', 'BB', 'BD', 'BE', 'BF', 'BG', 'BH', 'BI', 'BJ', 'BN', 'BO', 'BR', 'BS', 'BT', 'BW', 'BY', 'BZ', 'CA', 'CD', 'CF', 'CG', 'CH', 'CI', 'CL', 'CM', 'CN', 'CO', 'CR', 'CU', 'CV', 'CY', 'CZ', 'DE', 'DJ', 'DK', 'DM', 'DO', 'DZ', 'EC'] }, 'visa_free_count': { type: Type.Number, semanticType: 'Count', levels: [] }, 'rank': { type: Type.String, semanticType: 'Category', levels: ['1'] }, }, encodingMap: { x: makeEncodingItem('country'), y: makeEncodingItem('visa_free_count'), }, chartProperties: { cornerRadius: 5 }, }; } function gen_countryListsQ2RecGpt41(): TestCase { // For each origin country, what is the composition of its outbound travel access by visa type (visa required, visa on arri // Score: 7/20 | The visualization has the right structural idea but appears to be based on incorrect calculations, s return { title: 'country_lists_q2_rec-gpt-4.1', description: 'Show destination access composition for each origin country', tags: ['debug', 'grouped-bar-chart'], chartType: 'Grouped Bar Chart', data: [ { 'code': 'GW', 'country': 'Guinea-Bissau', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'TN', 'country': 'Tunisia', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'LI', 'country': 'Liechtenstein', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'DM', 'country': 'Dominica', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'WS', 'country': 'Samoa', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'SL', 'country': 'Sierra Leone', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'SO', 'country': 'Somalia', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'TZ', 'country': 'Tanzania', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'DO', 'country': 'Dominican Republic', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'TM', 'country': 'Turkmenistan', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'GM', 'country': 'The Gambia', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'DM', 'country': 'Dominica', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'BZ', 'country': 'Belize', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'KI', 'country': 'Kiribati', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'UA', 'country': 'Ukraine', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'IQ', 'country': 'Iraq', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'MD', 'country': 'Moldova', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'ZW', 'country': 'Zimbabwe', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'ZW', 'country': 'Zimbabwe', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'TV', 'country': 'Tuvalu', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'ZA', 'country': 'South Africa', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'SI', 'country': 'Slovenia', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'LR', 'country': 'Liberia', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'IN', 'country': 'India', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'LI', 'country': 'Liechtenstein', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'JO', 'country': 'Jordan', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'LK', 'country': 'Sri Lanka', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'GQ', 'country': 'Equatorial Guinea', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'GD', 'country': 'Grenada', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'ET', 'country': 'Ethiopia', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'DZ', 'country': 'Algeria', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'MN', 'country': 'Mongolia', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'SO', 'country': 'Somalia', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'MH', 'country': 'Marshall Islands', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'IQ', 'country': 'Iraq', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'WS', 'country': 'Samoa', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'CL', 'country': 'Chile', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'BA', 'country': 'Bosnia and Herzegovina', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'AD', 'country': 'Andorra', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'CY', 'country': 'Cyprus', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'CL', 'country': 'Chile', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'CH', 'country': 'Switzerland', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'FJ', 'country': 'Fiji', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'AO', 'country': 'Angola', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'PK', 'country': 'Pakistan', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'AU', 'country': 'Australia', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'SY', 'country': 'Syria', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'ZA', 'country': 'South Africa', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'CU', 'country': 'Cuba', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'PW', 'country': 'Palau Islands', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'AL', 'country': 'Albania', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'UG', 'country': 'Uganda', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'NL', 'country': 'Netherlands', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'TV', 'country': 'Tuvalu', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'NG', 'country': 'Nigeria', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'BF', 'country': 'Burkina Faso', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'OM', 'country': 'Oman', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'IN', 'country': 'India', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'SA', 'country': 'Saudi Arabia', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'CL', 'country': 'Chile', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'YE', 'country': 'Yemen', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'EC', 'country': 'Ecuador', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'MV', 'country': 'Maldives', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'KW', 'country': 'Kuwait', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'TG', 'country': 'Togo', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'NL', 'country': 'Netherlands', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'AE', 'country': 'United Arab Emirates', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'KZ', 'country': 'Kazakhstan', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'QA', 'country': 'Qatar', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'CA', 'country': 'Canada', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'HT', 'country': 'Haiti', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'HK', 'country': 'Hong Kong (SAR China)', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'BJ', 'country': 'Benin', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'BR', 'country': 'Brazil', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'NE', 'country': 'Niger', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'IL', 'country': 'Israel', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'KN', 'country': 'St. Kitts and Nevis', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'EE', 'country': 'Estonia', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'US', 'country': 'United States', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'VN', 'country': 'Vietnam', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'GH', 'country': 'Ghana', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'SB', 'country': 'Solomon Islands', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'CI', 'country': 'Cote d\'Ivoire', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'RO', 'country': 'Romania', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'AE', 'country': 'United Arab Emirates', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'BN', 'country': 'Brunei', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'AG', 'country': 'Antigua and Barbuda', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'BW', 'country': 'Botswana', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'TM', 'country': 'Turkmenistan', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'BS', 'country': 'Bahamas', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'AZ', 'country': 'Azerbaijan', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'MT', 'country': 'Malta', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'GW', 'country': 'Guinea-Bissau', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'CM', 'country': 'Cameroon', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'NE', 'country': 'Niger', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'SE', 'country': 'Sweden', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'NI', 'country': 'Nicaragua', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'KP', 'country': 'North Korea', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'SS', 'country': 'South Sudan', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'GE', 'country': 'Georgia', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'SA', 'country': 'Saudi Arabia', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'AR', 'country': 'Argentina', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'GA', 'country': 'Gabon', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'GT', 'country': 'Guatemala', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'US', 'country': 'United States', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'IS', 'country': 'Iceland', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'BT', 'country': 'Bhutan', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'SR', 'country': 'Suriname', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'KW', 'country': 'Kuwait', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'EC', 'country': 'Ecuador', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'AL', 'country': 'Albania', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'SE', 'country': 'Sweden', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'MV', 'country': 'Maldives', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'NL', 'country': 'Netherlands', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'ST', 'country': 'Sao Tome and Principe', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'GN', 'country': 'Guinea', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'FR', 'country': 'France', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'JO', 'country': 'Jordan', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'ZA', 'country': 'South Africa', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'FR', 'country': 'France', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'PG', 'country': 'Papua New Guinea', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'BI', 'country': 'Burundi', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'HR', 'country': 'Croatia', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'CI', 'country': 'Cote d\'Ivoire', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'VU', 'country': 'Vanuatu', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'SB', 'country': 'Solomon Islands', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'CV', 'country': 'Cape Verde Islands', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'AO', 'country': 'Angola', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'CN', 'country': 'China', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'PE', 'country': 'Peru', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'MZ', 'country': 'Mozambique', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'SA', 'country': 'Saudi Arabia', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'UY', 'country': 'Uruguay', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'KR', 'country': 'South Korea', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'JM', 'country': 'Jamaica', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'SV', 'country': 'El Salvador', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'PE', 'country': 'Peru', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'KR', 'country': 'South Korea', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'GR', 'country': 'Greece', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'LY', 'country': 'Libya', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'MM', 'country': 'Myanmar', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'GN', 'country': 'Guinea', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'ML', 'country': 'Mali', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'TJ', 'country': 'Tajikistan', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'SA', 'country': 'Saudi Arabia', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'AL', 'country': 'Albania', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'SL', 'country': 'Sierra Leone', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'DJ', 'country': 'Djibouti', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'AR', 'country': 'Argentina', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'XK', 'country': 'Kosovo', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'SN', 'country': 'Senegal', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'DJ', 'country': 'Djibouti', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'IE', 'country': 'Ireland', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'FM', 'country': 'Micronesia', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'IE', 'country': 'Ireland', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'MM', 'country': 'Myanmar', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'BY', 'country': 'Belarus', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'BN', 'country': 'Brunei', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'TZ', 'country': 'Tanzania', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'LS', 'country': 'Lesotho', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'LT', 'country': 'Lithuania', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'UG', 'country': 'Uganda', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'CZ', 'country': 'Czechia', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'VN', 'country': 'Vietnam', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'TN', 'country': 'Tunisia', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'SY', 'country': 'Syria', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'HT', 'country': 'Haiti', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'AT', 'country': 'Austria', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'KM', 'country': 'Comoro Islands', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'GA', 'country': 'Gabon', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'AT', 'country': 'Austria', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'ID', 'country': 'Indonesia', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'UY', 'country': 'Uruguay', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'NP', 'country': 'Nepal', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'NP', 'country': 'Nepal', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'CN', 'country': 'China', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'MY', 'country': 'Malaysia', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'KG', 'country': 'Kyrgyzstan', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'SC', 'country': 'Seychelles', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'KZ', 'country': 'Kazakhstan', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'VA', 'country': 'Vatican City', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'AT', 'country': 'Austria', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'SV', 'country': 'El Salvador', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'TT', 'country': 'Trinidad and Tobago', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'GA', 'country': 'Gabon', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'BD', 'country': 'Bangladesh', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'DK', 'country': 'Denmark', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'BA', 'country': 'Bosnia and Herzegovina', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'HR', 'country': 'Croatia', 'visa_type': 'Visa Online', 'count': 1, 'percentage': 20.0 }, { 'code': 'GN', 'country': 'Guinea', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'AT', 'country': 'Austria', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'AF', 'country': 'Afghanistan', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'BB', 'country': 'Barbados', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'MU', 'country': 'Mauritius', 'visa_type': 'Visa Required', 'count': 1, 'percentage': 20.0 }, { 'code': 'CH', 'country': 'Switzerland', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'BT', 'country': 'Bhutan', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'PS', 'country': 'Palestinian Territory', 'visa_type': 'Visa Free', 'count': 1, 'percentage': 20.0 }, { 'code': 'SV', 'country': 'El Salvador', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, { 'code': 'SV', 'country': 'El Salvador', 'visa_type': 'Visa on Arrival', 'count': 1, 'percentage': 20.0 }, { 'code': 'CV', 'country': 'Cape Verde Islands', 'visa_type': 'Electronic Travel Authorisation', 'count': 1, 'percentage': 20.0 }, ], fields: [makeField('code'), makeField('country'), makeField('visa_type'), makeField('count'), makeField('percentage')], metadata: { 'code': { type: Type.String, semanticType: 'Category', levels: ['AD', 'AE', 'AF', 'AG', 'AL', 'AO', 'AR', 'AT', 'AU', 'AZ', 'BA', 'BB', 'BD', 'BF', 'BI', 'BJ', 'BN', 'BR', 'BS', 'BT', 'BW', 'BY', 'BZ', 'CA', 'CH', 'CI', 'CL', 'CM', 'CN', 'CU', 'CV', 'CY', 'CZ', 'DJ', 'DK', 'DM', 'DO', 'DZ', 'EC', 'EE', 'ET', 'FJ', 'FM', 'FR', 'GA', 'GD', 'GE', 'GH', 'GM', 'GN'] }, 'country': { type: Type.String, semanticType: 'Country', levels: ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Antigua and Barbuda', 'Argentina', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bangladesh', 'Barbados', 'Belarus', 'Belize', 'Benin', 'Bhutan', 'Bosnia and Herzegovina', 'Botswana', 'Brazil', 'Brunei', 'Burkina Faso', 'Burundi', 'Cameroon', 'Canada', 'Cape Verde Islands', 'Chile', 'China', 'Comoro Islands', 'Cote d\'Ivoire', 'Croatia', 'Cuba', 'Cyprus', 'Czechia', 'Denmark', 'Djibouti', 'Dominica', 'Dominican Republic', 'Ecuador', 'El Salvador', 'Equatorial Guinea', 'Estonia', 'Ethiopia', 'Fiji', 'France', 'Gabon', 'Georgia', 'Ghana', 'Greece', 'Grenada'] }, 'visa_type': { type: Type.String, semanticType: 'Type', levels: ['Electronic Travel Authorisation', 'Visa Free', 'Visa Online', 'Visa Required', 'Visa on Arrival'] }, 'count': { type: Type.String, semanticType: 'Category', levels: ['1'] }, 'percentage': { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, encodingMap: { x: makeEncodingItem('country'), y: makeEncodingItem('percentage'), group: makeEncodingItem('visa_type'), }, chartProperties: { cornerRadius: 2 }, }; } function gen_countryListsQ2RecGpt51(): TestCase { // For each origin country, what is the composition of its outbound travel access by visa type (visa required, visa on arri // Score: 5/20 | The visualization uses an appropriate heatmap structure for summarizing visa-type access mixes by co return { title: 'country_lists_q2_rec-gpt-5.1', description: 'Summarize **country** outbound access mix by visa type shares', tags: ['debug', 'heatmap'], chartType: 'Heatmap', data: [ { 'code': 'GW', 'country': 'Guinea-Bissau', 'visa_type': 'Electronic travel authorisation', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'TN', 'country': 'Tunisia', 'visa_type': 'Electronic travel authorisation', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'LI', 'country': 'Liechtenstein', 'visa_type': 'Visa required', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'DM', 'country': 'Dominica', 'visa_type': 'Electronic travel authorisation', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'WS', 'country': 'Samoa', 'visa_type': 'Visa on arrival', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'SL', 'country': 'Sierra Leone', 'visa_type': 'Visa required', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'SO', 'country': 'Somalia', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'TZ', 'country': 'Tanzania', 'visa_type': 'Visa online', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'DO', 'country': 'Dominican Republic', 'visa_type': 'Electronic travel authorisation', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'TM', 'country': 'Turkmenistan', 'visa_type': 'Electronic travel authorisation', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'GM', 'country': 'The Gambia', 'visa_type': 'Visa online', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'DM', 'country': 'Dominica', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'BZ', 'country': 'Belize', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'KI', 'country': 'Kiribati', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'UA', 'country': 'Ukraine', 'visa_type': 'Electronic travel authorisation', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'IQ', 'country': 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'access_share': 0.2 }, { 'code': 'XK', 'country': 'Kosovo', 'visa_type': 'Visa on arrival', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'SN', 'country': 'Senegal', 'visa_type': 'Electronic travel authorisation', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'DJ', 'country': 'Djibouti', 'visa_type': 'Electronic travel authorisation', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'IE', 'country': 'Ireland', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'FM', 'country': 'Micronesia', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'IE', 'country': 'Ireland', 'visa_type': 'Visa online', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'MM', 'country': 'Myanmar', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'BY', 'country': 'Belarus', 'visa_type': 'Visa required', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'BN', 'country': 'Brunei', 'visa_type': 'Visa on arrival', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'TZ', 'country': 'Tanzania', 'visa_type': 'Visa on arrival', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'LS', 'country': 'Lesotho', 'visa_type': 'Visa on arrival', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'LT', 'country': 'Lithuania', 'visa_type': 'Visa required', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'UG', 'country': 'Uganda', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'CZ', 'country': 'Czechia', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'VN', 'country': 'Vietnam', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'TN', 'country': 'Tunisia', 'visa_type': 'Visa online', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'SY', 'country': 'Syria', 'visa_type': 'Visa on arrival', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'HT', 'country': 'Haiti', 'visa_type': 'Visa online', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'AT', 'country': 'Austria', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'KM', 'country': 'Comoro Islands', 'visa_type': 'Visa on arrival', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'GA', 'country': 'Gabon', 'visa_type': 'Visa on arrival', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'AT', 'country': 'Austria', 'visa_type': 'Visa on arrival', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'ID', 'country': 'Indonesia', 'visa_type': 'Visa required', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'UY', 'country': 'Uruguay', 'visa_type': 'Visa required', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'NP', 'country': 'Nepal', 'visa_type': 'Electronic travel authorisation', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'NP', 'country': 'Nepal', 'visa_type': 'Visa required', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'CN', 'country': 'China', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'MY', 'country': 'Malaysia', 'visa_type': 'Electronic travel authorisation', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'KG', 'country': 'Kyrgyzstan', 'visa_type': 'Visa on arrival', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'SC', 'country': 'Seychelles', 'visa_type': 'Visa online', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'KZ', 'country': 'Kazakhstan', 'visa_type': 'Visa on arrival', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'VA', 'country': 'Vatican City', 'visa_type': 'Visa on arrival', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'AT', 'country': 'Austria', 'visa_type': 'Electronic travel authorisation', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'SV', 'country': 'El Salvador', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'TT', 'country': 'Trinidad and Tobago', 'visa_type': 'Visa online', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'GA', 'country': 'Gabon', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'BD', 'country': 'Bangladesh', 'visa_type': 'Visa on arrival', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'DK', 'country': 'Denmark', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'BA', 'country': 'Bosnia and Herzegovina', 'visa_type': 'Electronic travel authorisation', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'HR', 'country': 'Croatia', 'visa_type': 'Visa online', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'GN', 'country': 'Guinea', 'visa_type': 'Visa on arrival', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'AT', 'country': 'Austria', 'visa_type': 'Visa required', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'AF', 'country': 'Afghanistan', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'BB', 'country': 'Barbados', 'visa_type': 'Visa on arrival', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'MU', 'country': 'Mauritius', 'visa_type': 'Visa required', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'CH', 'country': 'Switzerland', 'visa_type': 'Visa on arrival', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'BT', 'country': 'Bhutan', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'PS', 'country': 'Palestinian Territory', 'visa_type': 'Visa free', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'SV', 'country': 'El Salvador', 'visa_type': 'Electronic travel authorisation', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'SV', 'country': 'El Salvador', 'visa_type': 'Visa on arrival', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, { 'code': 'CV', 'country': 'Cape Verde Islands', 'visa_type': 'Electronic travel authorisation', 'access_count': 1, 'total_access': 5, 'access_share': 0.2 }, ], fields: [makeField('code'), makeField('country'), makeField('visa_type'), makeField('access_count'), makeField('total_access'), makeField('access_share')], metadata: { 'code': { type: Type.String, semanticType: 'Category', levels: ['AD', 'AE', 'AF', 'AG', 'AL', 'AO', 'AR', 'AT', 'AU', 'AZ', 'BA', 'BB', 'BD', 'BF', 'BI', 'BJ', 'BN', 'BR', 'BS', 'BT', 'BW', 'BY', 'BZ', 'CA', 'CH', 'CI', 'CL', 'CM', 'CN', 'CU', 'CV', 'CY', 'CZ', 'DJ', 'DK', 'DM', 'DO', 'DZ', 'EC', 'EE', 'ET', 'FJ', 'FM', 'FR', 'GA', 'GD', 'GE', 'GH', 'GM', 'GN'] }, 'country': { type: Type.String, semanticType: 'String', levels: ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Antigua and Barbuda', 'Argentina', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bangladesh', 'Barbados', 'Belarus', 'Belize', 'Benin', 'Bhutan', 'Bosnia and Herzegovina', 'Botswana', 'Brazil', 'Brunei', 'Burkina Faso', 'Burundi', 'Cameroon', 'Canada', 'Cape Verde Islands', 'Chile', 'China', 'Comoro Islands', 'Cote d\'Ivoire', 'Croatia', 'Cuba', 'Cyprus', 'Czechia', 'Denmark', 'Djibouti', 'Dominica', 'Dominican Republic', 'Ecuador', 'El Salvador', 'Equatorial Guinea', 'Estonia', 'Ethiopia', 'Fiji', 'France', 'Gabon', 'Georgia', 'Ghana', 'Greece', 'Grenada'] }, 'visa_type': { type: Type.String, semanticType: 'Status', levels: ['Electronic travel authorisation', 'Visa free', 'Visa on arrival', 'Visa online', 'Visa required'] }, 'access_count': { type: Type.String, semanticType: 'Category', levels: ['1'] }, 'total_access': { type: Type.String, semanticType: 'Category', levels: ['5'] }, 'access_share': { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, encodingMap: { x: makeEncodingItem('country'), y: makeEncodingItem('visa_type'), color: makeEncodingItem('access_share'), }, chartProperties: { colorScheme: "blues" }, }; } function gen_countryListsQ3RecGpt41(): TestCase { // Is there a relationship between how many countries a passport can enter visa‑free and how many require a visa for that p // Score: 6/20 | While the chart technically plots the requested variables, incorrect scaling and total overplotting return { title: 'country_lists_q3_rec-gpt-4.1', description: 'Visualize relationship between **visa_free_access_count** and **visa_required_count**', tags: ['debug', 'scatter-plot'], chartType: 'Scatter Plot', data: [ { 'code': 'PS', 'country': 'Palestinian Territory', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'AD', 'country': 'Andorra', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'VA', 'country': 'Vatican City', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'SM', 'country': 'San Marino', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'MC', 'country': 'Monaco', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'LI', 'country': 'Liechtenstein', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'ZW', 'country': 'Zimbabwe', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'ZM', 'country': 'Zambia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'YE', 'country': 'Yemen', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'VN', 'country': 'Vietnam', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'VE', 'country': 'Venezuela', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'VU', 'country': 'Vanuatu', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'UZ', 'country': 'Uzbekistan', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'UY', 'country': 'Uruguay', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'US', 'country': 'United States', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'GB', 'country': 'United Kingdom', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'AE', 'country': 'United Arab Emirates', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'UA', 'country': 'Ukraine', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'UG', 'country': 'Uganda', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'TV', 'country': 'Tuvalu', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'TM', 'country': 'Turkmenistan', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'TR', 'country': 'Türkiye', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'TN', 'country': 'Tunisia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'TT', 'country': 'Trinidad and Tobago', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'TO', 'country': 'Tonga', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'TG', 'country': 'Togo', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'TL', 'country': 'Timor-Leste', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'TH', 'country': 'Thailand', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'TZ', 'country': 'Tanzania', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'TJ', 'country': 'Tajikistan', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'TW', 'country': 'Taiwan (Chinese Taipei)', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'SY', 'country': 'Syria', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'CH', 'country': 'Switzerland', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'SE', 'country': 'Sweden', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'SZ', 'country': 'eSwatini', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'SR', 'country': 'Suriname', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'SD', 'country': 'Sudan', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'VC', 'country': 'St. Vincent and the Grenadines', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'LC', 'country': 'St. Lucia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'KN', 'country': 'St. Kitts and Nevis', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'LK', 'country': 'Sri Lanka', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'ES', 'country': 'Spain', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'SS', 'country': 'South Sudan', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'KR', 'country': 'South Korea', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'ZA', 'country': 'South Africa', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'SO', 'country': 'Somalia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'SB', 'country': 'Solomon Islands', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'SI', 'country': 'Slovenia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'SK', 'country': 'Slovakia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'SG', 'country': 'Singapore', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'SL', 'country': 'Sierra Leone', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'SC', 'country': 'Seychelles', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'RS', 'country': 'Serbia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'SN', 'country': 'Senegal', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'SA', 'country': 'Saudi Arabia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'ST', 'country': 'Sao Tome and Principe', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'WS', 'country': 'Samoa', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'RW', 'country': 'Rwanda', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'RU', 'country': 'Russian Federation', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'RO', 'country': 'Romania', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'QA', 'country': 'Qatar', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'PT', 'country': 'Portugal', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'PL', 'country': 'Poland', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'PH', 'country': 'Philippines', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'PE', 'country': 'Peru', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'PY', 'country': 'Paraguay', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'PG', 'country': 'Papua New Guinea', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'PA', 'country': 'Panama', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'PW', 'country': 'Palau Islands', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'PK', 'country': 'Pakistan', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'OM', 'country': 'Oman', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'NO', 'country': 'Norway', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'KP', 'country': 'North Korea', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'NG', 'country': 'Nigeria', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'NE', 'country': 'Niger', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'NI', 'country': 'Nicaragua', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'NZ', 'country': 'New Zealand', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'NL', 'country': 'Netherlands', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'NP', 'country': 'Nepal', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'NR', 'country': 'Nauru', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': null, 'country': 'Namibia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'MM', 'country': 'Myanmar', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'MZ', 'country': 'Mozambique', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'MA', 'country': 'Morocco', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'ME', 'country': 'Montenegro', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'MN', 'country': 'Mongolia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'MD', 'country': 'Moldova', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'FM', 'country': 'Micronesia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'MX', 'country': 'Mexico', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'MU', 'country': 'Mauritius', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'MR', 'country': 'Mauritania', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'MH', 'country': 'Marshall Islands', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'MT', 'country': 'Malta', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'ML', 'country': 'Mali', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'MV', 'country': 'Maldives', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'MY', 'country': 'Malaysia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'MW', 'country': 'Malawi', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'MG', 'country': 'Madagascar', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'MK', 'country': 'North Macedonia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'MO', 'country': 'Macao (SAR China)', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'LU', 'country': 'Luxembourg', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'LT', 'country': 'Lithuania', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'LY', 'country': 'Libya', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'LR', 'country': 'Liberia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'LS', 'country': 'Lesotho', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'LB', 'country': 'Lebanon', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'LV', 'country': 'Latvia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'LA', 'country': 'Laos', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'KG', 'country': 'Kyrgyzstan', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'KW', 'country': 'Kuwait', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'XK', 'country': 'Kosovo', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'KI', 'country': 'Kiribati', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'KE', 'country': 'Kenya', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'KZ', 'country': 'Kazakhstan', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'JO', 'country': 'Jordan', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'JP', 'country': 'Japan', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'JM', 'country': 'Jamaica', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'IT', 'country': 'Italy', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'IL', 'country': 'Israel', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'IE', 'country': 'Ireland', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'IQ', 'country': 'Iraq', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'IR', 'country': 'Iran', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'ID', 'country': 'Indonesia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'IN', 'country': 'India', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'IS', 'country': 'Iceland', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'HU', 'country': 'Hungary', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'HK', 'country': 'Hong Kong (SAR China)', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'HN', 'country': 'Honduras', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'HT', 'country': 'Haiti', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'GY', 'country': 'Guyana', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'GW', 'country': 'Guinea-Bissau', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'GN', 'country': 'Guinea', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'GT', 'country': 'Guatemala', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'GD', 'country': 'Grenada', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'GR', 'country': 'Greece', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'GH', 'country': 'Ghana', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'DE', 'country': 'Germany', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'GE', 'country': 'Georgia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'GM', 'country': 'The Gambia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'GA', 'country': 'Gabon', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'FR', 'country': 'France', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'FI', 'country': 'Finland', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'FJ', 'country': 'Fiji', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'ET', 'country': 'Ethiopia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'EE', 'country': 'Estonia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'ER', 'country': 'Eritrea', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'GQ', 'country': 'Equatorial Guinea', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'SV', 'country': 'El Salvador', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'EG', 'country': 'Egypt', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'EC', 'country': 'Ecuador', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'DO', 'country': 'Dominican Republic', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'DM', 'country': 'Dominica', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'DJ', 'country': 'Djibouti', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'DK', 'country': 'Denmark', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'CZ', 'country': 'Czechia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'CY', 'country': 'Cyprus', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'CU', 'country': 'Cuba', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'HR', 'country': 'Croatia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'CI', 'country': 'Cote d\'Ivoire', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'CR', 'country': 'Costa Rica', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'CD', 'country': 'Congo (Dem. Rep.)', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'CG', 'country': 'Congo (Rep.)', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'KM', 'country': 'Comoro Islands', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'CO', 'country': 'Colombia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'CN', 'country': 'China', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'CL', 'country': 'Chile', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'TD', 'country': 'Chad', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'CF', 'country': 'Central African Republic', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'CV', 'country': 'Cape Verde Islands', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'CA', 'country': 'Canada', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'CM', 'country': 'Cameroon', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'KH', 'country': 'Cambodia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'BI', 'country': 'Burundi', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'BF', 'country': 'Burkina Faso', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'BG', 'country': 'Bulgaria', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'BN', 'country': 'Brunei', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'BR', 'country': 'Brazil', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'BW', 'country': 'Botswana', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'BA', 'country': 'Bosnia and Herzegovina', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'BO', 'country': 'Bolivia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'BT', 'country': 'Bhutan', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'BJ', 'country': 'Benin', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'BZ', 'country': 'Belize', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'BE', 'country': 'Belgium', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'BY', 'country': 'Belarus', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'BB', 'country': 'Barbados', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'BD', 'country': 'Bangladesh', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'BH', 'country': 'Bahrain', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'BS', 'country': 'Bahamas', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'AZ', 'country': 'Azerbaijan', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'AT', 'country': 'Austria', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'AU', 'country': 'Australia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'AM', 'country': 'Armenia', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'AR', 'country': 'Argentina', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'AG', 'country': 'Antigua and Barbuda', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'AO', 'country': 'Angola', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'DZ', 'country': 'Algeria', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'AL', 'country': 'Albania', 'visa_free_access_count': 1, 'visa_required_count': 1 }, { 'code': 'AF', 'country': 'Afghanistan', 'visa_free_access_count': 1, 'visa_required_count': 1 }, ], fields: [makeField('code'), makeField('country'), makeField('visa_free_access_count'), makeField('visa_required_count')], metadata: { 'code': { type: Type.String, semanticType: 'Category', levels: ['AD', 'AE', 'AF', 'AG', 'AL', 'AM', 'AO', 'AR', 'AT', 'AU', 'AZ', 'BA', 'BB', 'BD', 'BE', 'BF', 'BG', 'BH', 'BI', 'BJ', 'BN', 'BO', 'BR', 'BS', 'BT', 'BW', 'BY', 'BZ', 'CA', 'CD', 'CF', 'CG', 'CH', 'CI', 'CL', 'CM', 'CN', 'CO', 'CR', 'CU', 'CV', 'CY', 'CZ', 'DE', 'DJ', 'DK', 'DM', 'DO', 'DZ', 'EC'] }, 'country': { type: Type.String, semanticType: 'Country', levels: ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burundi', 'Cambodia', 'Cameroon', 'Canada', 'Cape Verde Islands', 'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia', 'Comoro Islands', 'Congo (Dem. Rep.)', 'Congo (Rep.)', 'Costa Rica', 'Cote d\'Ivoire', 'Croatia', 'Cuba', 'Cyprus', 'Czechia', 'Denmark', 'Djibouti', 'Dominica', 'Dominican Republic'] }, 'visa_free_access_count': { type: Type.Number, semanticType: 'Count', levels: [] }, 'visa_required_count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('visa_free_access_count'), y: makeEncodingItem('visa_required_count'), color: makeEncodingItem('country'), }, chartProperties: { opacity: 0.7 }, }; } function gen_countryListsQ4RecGpt41(): TestCase { // How is the number of visa‑free destinations distributed across all passports (are most passports highly restricted or br // Score: 10/20 | The visualization is visually clear but based on an incorrectly computed visa_free_access_count, col return { title: 'country_lists_q4_rec-gpt-4.1', description: 'Show the distribution of **visa_free_access_count** across all passports', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'visa_free_access_count': 1, 'count': 199 }, ], fields: [makeField('visa_free_access_count'), makeField('count')], metadata: { 'visa_free_access_count': { type: Type.Number, semanticType: 'Number', levels: [] }, 'count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('visa_free_access_count'), y: makeEncodingItem('count'), }, chartProperties: { cornerRadius: 1 }, }; } function gen_endangeredStatusQ1RecGpt41(): TestCase { // What is the distribution of languages across the different endangered status categories? // Score: 10/20 | Conceptually relevant bar chart showing distribution by endangered status, but a serious aggregation return { title: 'endangered_status_q1_rec-gpt-4.1', description: 'Show count of languages per **status_label**', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'status_label': 'extinct', 'count': 1341 }, { 'status_label': 'moribund', 'count': 461 }, { 'status_label': 'nearly extinct', 'count': 353 }, { 'status_label': 'not endangered', 'count': 2791 }, { 'status_label': 'shifting', 'count': 1933 }, { 'status_label': 'threatened', 'count': 1688 }, ], fields: [makeField('status_label'), makeField('count')], metadata: { 'status_label': { type: Type.String, semanticType: 'Status', levels: ['extinct', 'moribund', 'nearly extinct', 'not endangered', 'shifting', 'threatened'] }, 'count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('status_label'), y: makeEncodingItem('count'), }, chartProperties: { cornerRadius: 4 }, }; } function gen_familiesQ1RecGpt51(): TestCase { // How is the number of languages per family distributed across all families? // Score: 4/20 | The chart is poorly suited to answering the question about the distribution of languages per family. return { title: 'families_q1_rec-gpt-5.1', description: 'Summarize distribution of languages per **family**', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'language_count': 1, 'family_count': 4832 }, ], fields: [makeField('language_count'), makeField('family_count')], metadata: { 'language_count': { type: Type.Number, semanticType: 'Number', levels: [] }, 'family_count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('language_count'), y: makeEncodingItem('family_count'), }, chartProperties: { cornerRadius: 2 }, }; } function gen_familiesQ2RecGpt41(): TestCase { // Which language families have the highest number of languages, and how do the top 15 families compare? // Score: 7/20 | The visualization is technically legible but based on a flawed aggregation that assigns all families return { title: 'families_q2_rec-gpt-4.1', description: 'Show top 15 families by language count', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'family': '!Ui', 'language_count': 1 }, { 'family': 'Oroch-Udihe', 'language_count': 1 }, { 'family': 'Orkon-West Ambrym', 'language_count': 1 }, { 'family': 'Oriya-Gauda-Kamrupa', 'language_count': 1 }, { 'family': 'Oriente Quechua', 'language_count': 1 }, { 'family': 'Oregon Athabaskan', 'language_count': 1 }, { 'family': 'Opon-Yukpan', 'language_count': 1 }, { 'family': 'Opata-Eudeve', 'language_count': 1 }, { 'family': 'Oninic', 'language_count': 1 }, { 'family': 'Onin-based pidgin', 'language_count': 1 }, { 'family': 'Ongamo-Maa', 'language_count': 1 }, { 'family': 'Ong-Ta\'oih', 'language_count': 1 }, { 'family': 'One', 'language_count': 1 }, { 'family': 'Omosan', 'language_count': 1 }, { 'family': 'Omo-Tana', 'language_count': 1 }, ], fields: [makeField('family'), makeField('language_count')], metadata: { 'family': { type: Type.String, semanticType: 'Category', levels: ['!Ui', 'Omo-Tana', 'Omosan', 'One', 'Ong-Ta\'oih', 'Ongamo-Maa', 'Onin-based pidgin', 'Oninic', 'Opata-Eudeve', 'Opon-Yukpan', 'Oregon Athabaskan', 'Oriente Quechua', 'Oriya-Gauda-Kamrupa', 'Orkon-West Ambrym', 'Oroch-Udihe'] }, 'language_count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('family'), y: makeEncodingItem('language_count'), }, chartProperties: { cornerRadius: 3 }, }; } function gen_familiesQ2RecGpt51(): TestCase { // Which language families have the highest number of languages, and how do the top 15 families compare? // Score: 7/20 | The chart is a basic bar plot of language counts per family but fails to correctly aggregate and ran return { title: 'families_q2_rec-gpt-5.1', description: 'Rank **family** values by language **count**', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'family': '!Ui', 'language_count': 1 }, { 'family': 'Oroch-Udihe', 'language_count': 1 }, { 'family': 'Orkon-West Ambrym', 'language_count': 1 }, { 'family': 'Oriya-Gauda-Kamrupa', 'language_count': 1 }, { 'family': 'Oriente Quechua', 'language_count': 1 }, { 'family': 'Oregon Athabaskan', 'language_count': 1 }, { 'family': 'Opon-Yukpan', 'language_count': 1 }, { 'family': 'Opata-Eudeve', 'language_count': 1 }, { 'family': 'Oninic', 'language_count': 1 }, { 'family': 'Onin-based pidgin', 'language_count': 1 }, { 'family': 'Ongamo-Maa', 'language_count': 1 }, { 'family': 'Ong-Ta\'oih', 'language_count': 1 }, { 'family': 'One', 'language_count': 1 }, { 'family': 'Omosan', 'language_count': 1 }, { 'family': 'Omo-Tana', 'language_count': 1 }, ], fields: [makeField('family'), makeField('language_count')], metadata: { 'family': { type: Type.String, semanticType: 'Category', levels: ['!Ui', 'Omo-Tana', 'Omosan', 'One', 'Ong-Ta\'oih', 'Ongamo-Maa', 'Onin-based pidgin', 'Oninic', 'Opata-Eudeve', 'Opon-Yukpan', 'Oregon Athabaskan', 'Oriente Quechua', 'Oriya-Gauda-Kamrupa', 'Orkon-West Ambrym', 'Oroch-Udihe'] }, 'language_count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('family'), y: makeEncodingItem('language_count'), }, chartProperties: { cornerRadius: 4 }, }; } function gen_familiesQ3RecGpt51(): TestCase { // What proportion of all languages is accounted for by the top 10 largest families compared with all other families combin // Score: 10/20 | The chart is conceptually relevant and somewhat readable, but it contains a major error in the compu return { title: 'families_q3_rec-gpt-5.1', description: 'Compare share of languages in **family** top 10 vs others', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'category': 'Other families', 'language_count': 4822, 'proportion': 0.9979304636 }, { 'category': 'Top 10 families', 'language_count': 10, 'proportion': 0.0020695364 }, ], fields: [makeField('category'), makeField('language_count'), makeField('proportion')], metadata: { 'category': { type: Type.String, semanticType: 'String', levels: ['Other families', 'Top 10 families'] }, 'language_count': { type: Type.String, semanticType: 'Category', levels: ['10', '4822'] }, 'proportion': { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, encodingMap: { x: makeEncodingItem('category'), y: makeEncodingItem('proportion'), }, chartProperties: { cornerRadius: 4 }, }; } function gen_familiesQ4RecGpt41(): TestCase { // How does the count of languages differ between families with only one language and families with multiple languages? // Score: 9/20 | The chart accurately plots a count for single-language families but fails to show the multiple-langu return { title: 'families_q4_rec-gpt-4.1', description: 'Compare counts of families with only 1 vs multiple languages', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'family_language_count_group': 'Single language', 'count_of_families': 4832 }, ], fields: [makeField('family_language_count_group'), makeField('count_of_families')], metadata: { 'family_language_count_group': { type: Type.String, semanticType: 'Status', levels: ['Single language'] }, 'count_of_families': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('family_language_count_group'), y: makeEncodingItem('count_of_families'), }, chartProperties: { cornerRadius: 6 }, }; } function gen_familiesQ4RecGpt51(): TestCase { // How does the count of languages differ between families with only one language and families with multiple languages? // Score: 7/20 | The chart is incomplete for the task: it only displays total languages for single-language families, return { title: 'families_q4_rec-gpt-5.1', description: 'Compare total **language** counts for single- vs multi-language **family** groups', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'family': null, 'language_count_per_family': null, 'family_type': 'Single-language families', 'total_languages': 4832, 'family_count': 4832 }, ], fields: [makeField('family'), makeField('language_count_per_family'), makeField('family_type'), makeField('total_languages'), makeField('family_count')], metadata: { 'family': { type: Type.String, semanticType: 'Category', levels: [] }, 'language_count_per_family': { type: Type.String, semanticType: 'Category', levels: [] }, 'family_type': { type: Type.String, semanticType: 'Category', levels: ['Single-language families'] }, 'total_languages': { type: Type.Number, semanticType: 'Count', levels: [] }, 'family_count': { type: Type.String, semanticType: 'Category', levels: ['4832'] }, }, encodingMap: { x: makeEncodingItem('family_type'), y: makeEncodingItem('total_languages'), color: makeEncodingItem('family_type'), }, chartProperties: { cornerRadius: 4 }, }; } function gen_familiesQ5RecGpt41(): TestCase { // Is there a relationship between whether a family name is compound (contains a hyphen) and the number of languages in tha // Score: 5/20 | The chart attempts to compare language counts for compound vs simple family names but appears to hav return { title: 'families_q5_rec-gpt-4.1', description: 'Compare count of languages for compound vs simple family names', tags: ['debug', 'boxplot'], chartType: 'Boxplot', data: [ { 'is_compound': true, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': false, 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}, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': true, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, { 'is_compound': false, 'language_count': 1 }, ], fields: [makeField('is_compound'), makeField('language_count')], metadata: { 'is_compound': { type: Type.String, semanticType: 'Boolean', levels: ['False', 'True'] }, 'language_count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('is_compound'), y: makeEncodingItem('language_count'), }, }; } function gen_fpiCodesQ5RecGpt51(): TestCase { // For agencies that have multiple program_code ranges (e.g., 000-xxx, 005-xxx, 422-xxx), how do the counts of programs com // Score: 2/20 | The visualization is effectively empty: while axis titles and a legend refer to the correct variable return { title: 'fpi_codes_q5_rec-gpt-5.1', description: 'Compare **program_code** prefix range counts within each **agency_name**', tags: ['debug', 'grouped-bar-chart'], chartType: 'Grouped Bar Chart', data: [ ], fields: [makeField('agency_name'), makeField('program_range'), makeField('program_count')], metadata: { 'agency_name': { type: Type.String, semanticType: 'Category', levels: [] }, 'program_range': { type: Type.String, semanticType: 'Status', levels: [] }, 'program_count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('agency_name'), y: makeEncodingItem('program_count'), group: makeEncodingItem('program_range'), }, chartProperties: { cornerRadius: 2 }, }; } function gen_modelDatQ1RecGpt51(): TestCase { // For a given study (single doi) and model_number, how do point estimates of the outcome vary across comparison groups (re // Score: 10/20 | The chart partially meets the task by displaying point estimates across comparison groups, but it fa return { title: 'model_dat_q1_rec-gpt-5.1', description: 'Filter one **doi**, **model_number**; compute group point estimates and CI widths', tags: ['debug', 'lollipop-chart'], chartType: 'Lollipop Chart', data: [ { 'doi': '10.1097/AOG.0000000000003552', 'model_number': 1, 'outcome': 'Sterilization at the time of cesarean delivery', 'group_name': 'Black or African American', 'group_type': 'compare', 'point': 44.5, 'lower': -99.0, 'upper': -99.0, 'ci_width': null }, { 'doi': '10.1097/AOG.0000000000003552', 'model_number': 1, 'outcome': 'Sterilization at the time of cesarean delivery', 'group_name': 'White', 'group_type': 'compare', 'point': 34.4, 'lower': -99.0, 'upper': -99.0, 'ci_width': null }, { 'doi': '10.1097/AOG.0000000000003552', 'model_number': 1, 'outcome': 'Sterilization at the time of cesarean delivery', 'group_name': 'Hispanic', 'group_type': 'compare', 'point': 16.4, 'lower': -99.0, 'upper': -99.0, 'ci_width': null }, { 'doi': '10.1097/AOG.0000000000003552', 'model_number': 1, 'outcome': 'Sterilization at the time of cesarean delivery', 'group_name': 'Asian', 'group_type': 'compare', 'point': 1.6, 'lower': -99.0, 'upper': -99.0, 'ci_width': null }, { 'doi': '10.1097/AOG.0000000000003552', 'model_number': 1, 'outcome': 'Sterilization at the time of cesarean delivery', 'group_name': 'Other', 'group_type': 'compare', 'point': 3.1, 'lower': -99.0, 'upper': -99.0, 'ci_width': null }, ], fields: [makeField('doi'), makeField('model_number'), makeField('outcome'), makeField('group_name'), makeField('group_type'), makeField('point'), makeField('lower'), makeField('upper'), makeField('ci_width')], metadata: { 'doi': { type: Type.String, semanticType: 'Category', levels: ['10.1097/AOG.0000000000003552'] }, 'model_number': { type: Type.String, semanticType: 'Category', levels: ['1'] }, 'outcome': { type: Type.String, semanticType: 'Category', levels: ['Sterilization at the time of cesarean delivery'] }, 'group_name': { type: Type.String, semanticType: 'String', levels: ['Asian', 'Black or African American', 'Hispanic', 'Other', 'White'] }, 'group_type': { type: Type.String, semanticType: 'Status', levels: ['compare'] }, 'point': { type: Type.Number, semanticType: 'Number', levels: [] }, 'lower': { type: Type.String, semanticType: 'Category', levels: ['-99.0'] }, 'upper': { type: Type.String, semanticType: 'Category', levels: ['-99.0'] }, 'ci_width': { type: Type.Number, semanticType: 'Number', levels: [] }, }, encodingMap: { x: makeEncodingItem('group_name'), y: makeEncodingItem('point'), color: makeEncodingItem('group_type'), size: makeEncodingItem('ci_width'), }, }; } function gen_modelDatQ2RecGpt41(): TestCase { // Among models that are stratified versus not stratified, how does the distribution of point estimates differ for a select // Score: 7/20 | The visualization shows a single boxplot of point estimates for stratified models, but omits non-str return { title: 'model_dat_q2_rec-gpt-4.1', description: 'Compare distributions of **point** by **stratified** for selected outcome/measure', tags: ['debug', 'boxplot'], chartType: 'Boxplot', data: [ { 'stratified': 'Yes', 'point': 44.5 }, { 'stratified': 'Yes', 'point': 34.4 }, { 'stratified': 'Yes', 'point': 16.4 }, { 'stratified': 'Yes', 'point': 1.6 }, { 'stratified': 'Yes', 'point': 3.1 }, ], fields: [makeField('stratified'), makeField('point')], metadata: { 'stratified': { type: Type.String, semanticType: 'Boolean', levels: ['Yes'] }, 'point': { type: Type.Number, semanticType: 'Number', levels: [] }, }, encodingMap: { x: makeEncodingItem('stratified'), y: makeEncodingItem('point'), }, }; } function gen_modelDatQ2RecGpt51(): TestCase { // Among models that are stratified versus not stratified, how does the distribution of point estimates differ for a select // Score: 10/20 | The chart partially addresses the task by plotting point estimates against stratified status, but it return { title: 'model_dat_q2_rec-gpt-5.1', description: 'Compare **point** estimate distributions by **stratified** status', tags: ['debug', 'boxplot'], chartType: 'Boxplot', data: [ { 'outcome': '2 year survival ovarian cancer', 'measure': 'HR', 'stratified': 'Yes', 'point': 0.9 }, { 'outcome': 'Human papillomavirus vaccination rate', 'measure': 'RR', 'stratified': 'No', 'point': 1.35 }, { 'outcome': 'D Haplotype frequency', 'measure': 'Percent', 'stratified': 'No', 'point': 12.0 }, { 'outcome': 'survival', 'measure': 'median years', 'stratified': 'Yes', 'point': 8.42 }, { 'outcome': 'cervical cancer', 'measure': 'RR', 'stratified': 'Yes', 'point': 2.9 }, { 'outcome': 'complicated vaginal delivery', 'measure': 'RR', 'stratified': 'Yes', 'point': 1.36 }, { 'outcome': 'Self-reported barrier to infertility care: referral source', 'measure': 'OR', 'stratified': 'No', 'point': 1.6 }, { 'outcome': 'Ectopic Pregnancy', 'measure': 'RR', 'stratified': 'Yes', 'point': 1.54 }, { 'outcome': 'overall mortality: nonendometrioid carcinoma', 'measure': 'HR', 'stratified': 'No', 'point': 1.39 }, { 'outcome': 'Number of abortions done in residency rotation', 'measure': 'Percent', 'stratified': 'Yes', 'point': 40.8 }, { 'outcome': 'clinical loss', 'measure': 'RR', 'stratified': 'No', 'point': 1.76 }, { 'outcome': 'Total cases 1975-2017', 'measure': 'count', 'stratified': 'Yes', 'point': 10548.0 }, { 'outcome': 'adherence to national comprehensive cancer network treatment guidelines in treatment recieved', 'measure': '% of adherent cases', 'stratified': 'No', 'point': 4.6 }, { 'outcome': 'diagnosed with invasive cervical cancer', 'measure': 'Percent', 'stratified': 'Yes', 'point': 77.1 }, { 'outcome': 'Maternal In-Hospital Death', 'measure': 'Incidence', 'stratified': 'Yes', 'point': 13.2 }, { 'outcome': 'preterm infant delivery < 34 weeks', 'measure': 'OR', 'stratified': 'No', 'point': 1.61 }, { 'outcome': 'Presence of Haplotype DE', 'measure': 'Percent', 'stratified': 'No', 'point': 100.0 }, { 'outcome': 'Presence of Haplotype I', 'measure': 'Percent', 'stratified': 'No', 'point': 67.0 }, { 'outcome': 'provider has talked with patient about vaccine (no)', 'measure': 'Percent', 'stratified': 'No', 'point': 36.4 }, { 'outcome': 'All cause mortality', 'measure': 'HR', 'stratified': 'Yes', 'point': 1.26 }, { 'outcome': 'Points of worry and concern in seeking infertility treatment: having a miscarriage', 'measure': 'OR', 'stratified': 'No', 'point': 12.7 }, { 'outcome': 'klabunde-modified charlson comorbidity score of 2', 'measure': 'Prevalence', 'stratified': 'Yes', 'point': 12.4 }, { 'outcome': 'Any medication to treat opioid use disorder in the year after delivery', 'measure': 'HR', 'stratified': 'No', 'point': 1.53 }, { 'outcome': 'J Haplotype frequency', 'measure': 'Percent', 'stratified': 'No', 'point': 4.0 }, { 'outcome': 'access to centers with high observed/expected ratios', 'measure': 'OR', 'stratified': 'No', 'point': 1.21 }, { 'outcome': 'All-cause mortality', 'measure': 'HR', 'stratified': 'Yes', 'point': 1.4 }, { 'outcome': 'Microbiota in endometrial tumors - Tepidimonas genus', 'measure': 'Percent', 'stratified': 'No', 'point': 0.9 }, { 'outcome': 'Clinical in vitro fertilization outcomes within state insurance mandate status group, 2014-2019: Live Birth Among Embryo Transfers', 'measure': 'OR', 'stratified': 'Yes', 'point': 0.88 }, { 'outcome': 'radiotherapy', 'measure': 'Percent', 'stratified': 'Yes', 'point': 76.6 }, { 'outcome': 'correlation coefficients between state-specific maternal demographic and maternal mortality in the united states, 2005-2014', 'measure': 'Simple Pearson\'s Correlation Coefficient', 'stratified': 'No', 'point': -0.016 }, { 'outcome': 'association of race/ethnicity with question responses regarding contraceptive preferences and attitudes - preferences (extremely important): does not contain hormones', 'measure': 'OR', 'stratified': 'No', 'point': 1.72 }, { 'outcome': 'All cause mortality', 'measure': 'HR', 'stratified': 'Yes', 'point': 1.31 }, { 'outcome': 'abnormal pap test by pap test alone or cotesting', 'measure': 'Prevalence', 'stratified': 'Yes', 'point': 14.13 }, { 'outcome': 'cardiovascular severe maternal morbidity at delivery', 'measure': 'OR', 'stratified': 'No', 'point': 1.76 }, { 'outcome': 'Total Endometrial cancer', 'measure': 'Incidence', 'stratified': 'No', 'point': 85.57 }, { 'outcome': 'Pregnancy-related mortality', 'measure': 'Incidence', 'stratified': 'Yes', 'point': 25.3 }, { 'outcome': 'Cervical cancer trial accrual', 'measure': 'Absolute difference', 'stratified': 'No', 'point': 0.7 }, { 'outcome': 'MED12 mutations in leiomyoma', 'measure': 'Percent', 'stratified': 'No', 'point': 75.5 }, { 'outcome': 'Permanent Contraception Fulfillment at Hospital Discharge', 'measure': 'OR', 'stratified': 'No', 'point': 1.18 }, { 'outcome': 'Hysterectomy', 'measure': 'OR', 'stratified': 'Yes', 'point': 1.08 }, { 'outcome': 'age-standardized five-year net survival', 'measure': 'Percent', 'stratified': 'Yes', 'point': 46.8 }, { 'outcome': 'preterm infant delivery < 34 weeks', 'measure': 'OR', 'stratified': 'No', 'point': 2.02 }, { 'outcome': 'Primary cesarean delivery', 'measure': 'RR', 'stratified': 'Yes', 'point': 1.27 }, { 'outcome': 'Total Endometrial cancer', 'measure': 'Incidence', 'stratified': 'No', 'point': 86.34 }, { 'outcome': 'Insurance coverage', 'measure': 'Percent', 'stratified': 'Yes', 'point': 2.3 }, { 'outcome': 'Death', 'measure': 'HR', 'stratified': 'No', 'point': 1.2 }, { 'outcome': 'complication occurrence', 'measure': 'OR', 'stratified': 'No', 'point': 1.37 }, { 'outcome': 'endometrial cancer 5-year survival', 'measure': 'Percent', 'stratified': 'Yes', 'point': 25.5 }, { 'outcome': 'Number of abortions done in residency rotation', 'measure': 'Percent', 'stratified': 'Yes', 'point': 46.8 }, { 'outcome': 'provider has talked with patient about vaccine (no)', 'measure': 'Percent', 'stratified': 'No', 'point': 40.9 }, { 'outcome': 'assisted reproduction technology outcomes', 'measure': 'OR', 'stratified': 'Yes', 'point': 1.04 }, { 'outcome': '5-year overall survival', 'measure': 'Percent', 'stratified': 'No', 'point': 85.0 }, { 'outcome': 'endometrial cancer five-year survival', 'measure': 'Percent', 'stratified': 'No', 'point': 80.3 }, { 'outcome': 'Primary cesarean delivery', 'measure': 'RR', 'stratified': 'Yes', 'point': 1.15 }, { 'outcome': 'Laparoscopic', 'measure': 'OR', 'stratified': 'No', 'point': 0.81 }, { 'outcome': 'cesarean delivery', 'measure': 'OR', 'stratified': 'No', 'point': 0.92 }, { 'outcome': 'Permanent Contraception Fulfillment at 365 Days Postpartum', 'measure': 'OR', 'stratified': 'No', 'point': 0.68 }, { 'outcome': 'Seeking treatment for urinary incontinence in visits 7, 8, or 9', 'measure': 'OR', 'stratified': 'Yes', 'point': 1.85 }, { 'outcome': 'endometrial cancer mortality: high-grade endometrioid carcinoma', 'measure': 'HR', 'stratified': 'No', 'point': 1.03 }, { 'outcome': 'debris', 'measure': '%', 'stratified': 'No', 'point': 2.6 }, { 'outcome': 'pregnancy-related mortality', 'measure': 'rate per 100,000 live births', 'stratified': 'Yes', 'point': 7.2 }, { 'outcome': '1 h or more between complete dilation and initiation of pushing', 'measure': 'OR', 'stratified': 'No', 'point': 1.23 }, { 'outcome': 'Minimally-invasive surgical approach', 'measure': 'OR', 'stratified': 'No', 'point': 0.6 }, { 'outcome': 'observed infant death with anomalies', 'measure': 'Prevalence', 'stratified': 'Yes', 'point': 204.9 }, { 'outcome': 'severe maternal morbidity per 10,000 delivery hospitalizations', 'measure': 'Incidence', 'stratified': 'No', 'point': 231.1 }, { 'outcome': 'Primary Cesarean Delivery', 'measure': 'RR', 'stratified': 'Yes', 'point': 1.17 }, { 'outcome': 'Composite perioperative complication', 'measure': 'OR', 'stratified': 'Yes', 'point': 0.76 }, { 'outcome': 'Surgical procedure for endometriosis - Laparoscopic excision or fulguration of pelvic lesions', 'measure': 'Percent', 'stratified': 'No', 'point': 19.7 }, { 'outcome': 'high density lipoprotein cholesterol levels (<= 50 mg/dL)', 'measure': 'Prevalence', 'stratified': 'Yes', 'point': 42.9 }, { 'outcome': 'receiving national comprehensive cancer network guideline–adherent treatment for gynecologic cancer', 'measure': '%', 'stratified': 'No', 'point': 79.1 }, { 'outcome': 'Excessive Gestational Weight Gain', 'measure': 'OR', 'stratified': 'Yes', 'point': 0.85 }, { 'outcome': 'overall survival', 'measure': 'HR', 'stratified': 'No', 'point': 1.0 }, { 'outcome': 'any method of contraceptive use', 'measure': 'OR', 'stratified': 'Yes', 'point': 0.76 }, { 'outcome': 'Seeking treatment for urinary incontinence in visits 7, 8, or 9', 'measure': 'OR', 'stratified': 'Yes', 'point': 0.58 }, { 'outcome': 'Indication for oocyte cryopreservation - Fertility preservation (not otherwise specified) or no reason given', 'measure': 'Percent', 'stratified': 'No', 'point': 24.3 }, { 'outcome': 'Self-reported barrier to infertility care: ability to bear children rests on', 'measure': 'OR', 'stratified': 'No', 'point': 3.1 }, { 'outcome': 'Maternal morbidity composite- Venous thromboembolism', 'measure': 'Percent', 'stratified': 'No', 'point': 0.1 }, { 'outcome': 'Primary Cesarean Delivery', 'measure': 'RR', 'stratified': 'Yes', 'point': 1.56 }, { 'outcome': 'estimated difference in mortality', 'measure': 'Absolute difference', 'stratified': 'Yes', 'point': 42.1 }, { 'outcome': 'Points of worry and concern in seeking infertility treatment: having an ectopic pregnancy', 'measure': 'OR', 'stratified': 'No', 'point': 11.3 }, { 'outcome': 'Intention to provide abortion', 'measure': 'OR', 'stratified': 'No', 'point': 1.69 }, { 'outcome': 'assisted reproduction technology outcomes', 'measure': 'OR', 'stratified': 'Yes', 'point': 1.47 }, { 'outcome': 'Total Endometrial cancer', 'measure': 'Incidence', 'stratified': 'No', 'point': 100.1 }, { 'outcome': 'contacting a papanicolaou test referral before 1 month of follow-up', 'measure': 'OR', 'stratified': 'No', 'point': 2.04 }, { 'outcome': 'Opportunistic Salpingectomy by Adoption: Hysterectomy Procedures', 'measure': 'RR', 'stratified': 'Yes', 'point': 0.93 }, { 'outcome': 'BMI cut-off value sensitivity', 'measure': 'Percent', 'stratified': 'Yes', 'point': 81.3 }, { 'outcome': 'Peritoneal excision', 'measure': 'OR', 'stratified': 'Yes', 'point': 0.77 }, { 'outcome': 'in hospital mortality', 'measure': 'RR', 'stratified': 'Yes', 'point': 0.94 }, { 'outcome': 'Primary cesarean delivery', 'measure': 'RR', 'stratified': 'Yes', 'point': 0.96 }, { 'outcome': 'Vulvar cancer mortality', 'measure': 'HR', 'stratified': 'Yes', 'point': 0.66 }, { 'outcome': 'Fertility preferences have changed due to the pandemic', 'measure': 'OR', 'stratified': 'No', 'point': 1.1 }, { 'outcome': 'Increased Hazard of Death Within 5 Years Among Patients with Advanced Stage Disease', 'measure': 'HR', 'stratified': 'Yes', 'point': 0.8 }, { 'outcome': 'systemic therapy', 'measure': 'Percent', 'stratified': 'Yes', 'point': 23.3 }, { 'outcome': 'Live birth', 'measure': 'RR', 'stratified': 'Yes', 'point': 1.02 }, { 'outcome': 'Clinical in vitro fertilization outcomes within state insurance mandate status group, 2014-2019: Cumulative Live Birth', 'measure': 'OR', 'stratified': 'Yes', 'point': 0.93 }, { 'outcome': 'death from other causes', 'measure': 'HR', 'stratified': 'No', 'point': 1.06 }, { 'outcome': 'hysterectomy with bilateral oophorectomy', 'measure': 'Incidence', 'stratified': 'Yes', 'point': 18.6 }, { 'outcome': 'Opportunistic Salpingectomy by Adoption: Hysterectomy Procedures', 'measure': 'RR', 'stratified': 'Yes', 'point': 0.93 }, { 'outcome': 'does not enroll in hospice', 'measure': 'OR', 'stratified': 'No', 'point': 1.44 }, { 'outcome': 'low-birth-weight among singletons conceived with assisted reproductive technology', 'measure': 'RR', 'stratified': 'Yes', 'point': 1.26 }, { 'outcome': 'endometrial cancer mortality: all histologic subtypes', 'measure': 'HR', 'stratified': 'No', 'point': 1.22 }, { 'outcome': 'live birth rate', 'measure': 'RR', 'stratified': 'No', 'point': 0.74 }, { 'outcome': 'receiving treatment at a High volume hospital', 'measure': 'OR', 'stratified': 'No', 'point': 0.85 }, { 'outcome': 'pregnancy-related mortality ratio', 'measure': 'number of pregnancy-related deaths per 100,000 live births', 'stratified': 'Yes', 'point': 16.1 }, { 'outcome': 'assisted reproduction technology outcomes', 'measure': 'OR', 'stratified': 'Yes', 'point': 1.21 }, { 'outcome': 'distant cervical cancer', 'measure': 'RR', 'stratified': 'Yes', 'point': 1.6 }, { 'outcome': '2 year survival cervical cancer', 'measure': 'HR', 'stratified': 'Yes', 'point': 0.91 }, { 'outcome': 'all-cause death', 'measure': 'HR', 'stratified': 'Yes', 'point': 0.68 }, { 'outcome': 'association among race and ethnicity and receipt of oxycodone tablet equivalents', 'measure': 'Predicted mean value', 'stratified': 'Yes', 'point': 5.9 }, { 'outcome': 'Ovarian cancer mortality', 'measure': 'HR', 'stratified': 'Yes', 'point': 1.29 }, { 'outcome': 'Clinical pregnancy', 'measure': 'RR', 'stratified': 'Yes', 'point': 0.97 }, { 'outcome': 'endometrial cancer 2-year survival', 'measure': 'Percent', 'stratified': 'Yes', 'point': 51.0 }, { 'outcome': 'individual complication occurrence - sepsis', 'measure': 'Percent', 'stratified': 'No', 'point': 0.1 }, { 'outcome': 'Cesarean indication- Labor Dystocia', 'measure': 'RR', 'stratified': 'No', 'point': 0.93 }, { 'outcome': '5 year disease specific survival', 'measure': 'Percent', 'stratified': 'No', 'point': 47.3 }, { 'outcome': 'Self-reported barrier to infertility care: ability to bear children rests on', 'measure': 'OR', 'stratified': 'No', 'point': 0.3 }, { 'outcome': 'Gestational diabetes', 'measure': 'average annual percent change', 'stratified': 'Yes', 'point': 4.3 }, { 'outcome': 'Absence of Haplotype DE', 'measure': 'Percent', 'stratified': 'No', 'point': 42.0 }, { 'outcome': 'Uninsurance at Birth', 'measure': 'OR', 'stratified': 'Yes', 'point': 2.08 }, { 'outcome': 'endometrial cancer 2-year survival', 'measure': 'Percent', 'stratified': 'Yes', 'point': 97.4 }, { 'outcome': 'no surgery', 'measure': 'OR', 'stratified': 'Yes', 'point': 2.04 }, { 'outcome': 'Increased Hazard of Death Within 5 Years Among Patients with Advanced Stage Disease who underwent surgical treatment', 'measure': 'HR', 'stratified': 'Yes', 'point': 1.27 }, { 'outcome': 'contraceptive use: any method vs. no method', 'measure': 'OR', 'stratified': 'No', 'point': 0.65 }, { 'outcome': 'Ectopic pregnancy rate', 'measure': 'Percent', 'stratified': 'Yes', 'point': 1.19 }, { 'outcome': 'insurance status by race-ethnicity', 'measure': 'weighted proportions', 'stratified': 'Yes', 'point': 65.8 }, { 'outcome': 'readmission risk among women with endometrial cancer', 'measure': 'OR', 'stratified': 'No', 'point': 1.13 }, { 'outcome': 'endometrial cancer 2-year survival', 'measure': 'Percent', 'stratified': 'Yes', 'point': 96.5 }, { 'outcome': 'Ectopic pregnancy rate', 'measure': 'Percent', 'stratified': 'Yes', 'point': 1.9 }, { 'outcome': 'Clinical in vitro fertilization outcomes within state insurance mandate status group, 2014-2019: Cycle Cancellation', 'measure': 'OR', 'stratified': 'Yes', 'point': 1.09 }, { 'outcome': 'death from endometrial cancer', 'measure': 'Prevalence', 'stratified': 'Yes', 'point': 25.1 }, { 'outcome': 'grade: unknown', 'measure': 'Percent', 'stratified': 'Yes', 'point': 8.2 }, { 'outcome': 'preterm birth', 'measure': 'OR', 'stratified': 'Yes', 'point': 1.45 }, { 'outcome': 'funneling', 'measure': '%', 'stratified': 'No', 'point': 0.5 }, { 'outcome': 'Vaginal delivery', 'measure': 'OR', 'stratified': 'No', 'point': 0.8 }, { 'outcome': 'cesarean delivery', 'measure': 'OR', 'stratified': 'No', 'point': 0.95 }, { 'outcome': 'puerperal infection', 'measure': 'RR', 'stratified': 'Yes', 'point': 2.85 }, { 'outcome': 'survival', 'measure': 'median years', 'stratified': 'Yes', 'point': 3.25 }, { 'outcome': 'access to centers with high observed/expected ratios', 'measure': 'OR', 'stratified': 'No', 'point': 0.91 }, { 'outcome': 'odds of cesarean delivery by maternal race and ethnicity in the multiparous cohort', 'measure': 'OR', 'stratified': 'No', 'point': 1.51 }, { 'outcome': 'Have ever heard of IUDS', 'measure': 'OR', 'stratified': 'No', 'point': 0.14 }, { 'outcome': 'age of diagnosis', 'measure': 'median', 'stratified': 'Yes', 'point': 69.0 }, { 'outcome': 'average annual decline in ectopic pregnancy mortality ratio during 1980–2007', 'measure': '%', 'stratified': 'No', 'point': 3.5 }, { 'outcome': 'risk for severe morbidity (including transfusion)', 'measure': 'RR', 'stratified': 'No', 'point': 1.14 }, { 'outcome': 'associated number of severe maternal morbidity cases', 'measure': 'Product of the excess SMM rate and the number of discharges in the racial and ethnic category', 'stratified': 'No', 'point': 93344.0 }, { 'outcome': 'stillbirth', 'measure': 'rate per 10,000 ongoing pregnancies', 'stratified': 'Yes', 'point': 6.2 }, { 'outcome': 'glucose (>= 100 mg/dL)', 'measure': 'Prevalence', 'stratified': 'Yes', 'point': 9.0 }, { 'outcome': 'All cause mortality', 'measure': 'HR', 'stratified': 'Yes', 'point': 1.39 }, { 'outcome': 'Ectopic Pregnancy', 'measure': 'RR', 'stratified': 'Yes', 'point': 1.04 }, { 'outcome': 'willingness to participate before and after educational intervention', 'measure': 'Percentage of sample willing to participate in clinical trials', 'stratified': 'No', 'point': 33.0 }, { 'outcome': 'medical comorbidities of white and black women undergoing any hysterectomy and laparoscopic hysterectomy', 'measure': 'Comorbidity %', 'stratified': 'Yes', 'point': 25.9 }, { 'outcome': 'no vaccination history', 'measure': 'Percent', 'stratified': 'No', 'point': 55.2 }, { 'outcome': 'surgery refusal', 'measure': 'OR', 'stratified': 'No', 'point': 1.16 }, { 'outcome': 'endometrial cancer survival', 'measure': 'HR', 'stratified': 'Yes', 'point': 1.92 }, { 'outcome': 'Association between patient characteristics and missed opportunities', 'measure': 'OR', 'stratified': 'No', 'point': 1.08 }, { 'outcome': 'Death from other causes', 'measure': 'HR', 'stratified': 'No', 'point': 0.95 }, { 'outcome': 'Composite perioperative complication', 'measure': 'OR', 'stratified': 'No', 'point': 1.09 }, { 'outcome': 'Lymphadenectomy', 'measure': 'OR', 'stratified': 'No', 'point': 0.81 }, { 'outcome': '5-min Apgar score less than 5', 'measure': 'RR', 'stratified': 'No', 'point': 1.35 }, { 'outcome': 'Live birth', 'measure': 'RR', 'stratified': 'No', 'point': 0.97 }, { 'outcome': 'preterm infant delivery < 34 weeks', 'measure': 'OR', 'stratified': 'No', 'point': 1.88 }, { 'outcome': 'Agreement with the statement regarding the effect of stress on the ability to bear children', 'measure': 'Mean point estimate', 'stratified': 'No', 'point': 52.8 }, { 'outcome': 'ovarian cancer treatment', 'measure': 'OR', 'stratified': 'No', 'point': 0.48 }, { 'outcome': 'stage: distant', 'measure': 'Percent', 'stratified': 'Yes', 'point': 2.8 }, { 'outcome': 'stage: regional', 'measure': 'Percent', 'stratified': 'No', 'point': 13.6 }, { 'outcome': 'Agreement with the statement regarding the effect of timing/age on the ability to bear children', 'measure': 'Mean point estimate', 'stratified': 'No', 'point': 30.7 }, { 'outcome': 'bilateral oophorectomy alone in 2012', 'measure': 'rate per 10,000 women', 'stratified': 'No', 'point': 1.1 }, { 'outcome': 'u.s. women\'s use of long-acting reversible contraception methods', 'measure': 'OR', 'stratified': 'Yes', 'point': 1.4 }, { 'outcome': 'obstetric thrombosis or embolism', 'measure': 'RR', 'stratified': 'Yes', 'point': 0.86 }, { 'outcome': 'severe maternal morbidity without blood transfusion per 10,000 delivery', 'measure': 'Incidence', 'stratified': 'No', 'point': 50.2 }, { 'outcome': 'Cervical cancer trial accural', 'measure': 'Absolute difference', 'stratified': 'No', 'point': -8.5 }, { 'outcome': 'Live birth', 'measure': 'RR', 'stratified': 'No', 'point': 1.05 }, { 'outcome': 'readmission risk among women with endometrial cancer', 'measure': 'OR', 'stratified': 'Yes', 'point': 0.96 }, { 'outcome': 'Health Professional Shortage Area; Medically Underserved Areas/Populations', 'measure': 'Percent', 'stratified': 'No', 'point': 37.5 }, { 'outcome': 'Clinical pregnancy', 'measure': 'RR', 'stratified': 'No', 'point': 0.81 }, { 'outcome': 'pregnancy-related mortality', 'measure': 'rate per 100,000 live births', 'stratified': 'Yes', 'point': 10.7 }, { 'outcome': 'survival', 'measure': 'median years', 'stratified': 'Yes', 'point': 4.75 }, { 'outcome': 'Increased Hazard of Death Within 5 Years Among Patients with Advanced Stage Disease', 'measure': 'HR', 'stratified': 'Yes', 'point': 1.07 }, { 'outcome': 'individual complication occurrence - sepsis', 'measure': 'Percent', 'stratified': 'No', 'point': 0.0 }, { 'outcome': '2 year survival vulvar cancer', 'measure': 'HR', 'stratified': 'Yes', 'point': 1.18 }, { 'outcome': 'Primary cesarean delivery', 'measure': 'RR', 'stratified': 'Yes', 'point': 1.19 }, { 'outcome': 'Difference of human papillomavirus vaccination rates before and after the mandate', 'measure': 'Percent difference', 'stratified': 'No', 'point': 5.1 }, { 'outcome': 'Primary Cesarean Delivery', 'measure': 'RR', 'stratified': 'Yes', 'point': 1.21 }, { 'outcome': 'travel distance over time based on sociodemographic characteristics', 'measure': 'travel distance in miles', 'stratified': 'Yes', 'point': 2.6 }, { 'outcome': 'patients receiving ovarian cancer related Surgical procedures', 'measure': 'Percent', 'stratified': 'Yes', 'point': 10.4 }, { 'outcome': 'maternal in-hospital mortality', 'measure': 'RR', 'stratified': 'No', 'point': 1.44 }, { 'outcome': 'Somatic testing performed', 'measure': 'OR', 'stratified': 'No', 'point': 1.48 }, { 'outcome': 'hospitalization for COVID-19', 'measure': 'OR', 'stratified': 'No', 'point': 2.95 }, { 'outcome': 'readmission risk among women with endometrial cancer', 'measure': 'OR', 'stratified': 'No', 'point': 0.76 }, { 'outcome': 'open compared with minimally invasive surgery', 'measure': 'OR', 'stratified': 'No', 'point': 2.39 }, { 'outcome': 'survival rate', 'measure': 'Prevalence', 'stratified': 'Yes', 'point': 97.2 }, { 'outcome': '30-Day Major Postoperative Complications', 'measure': 'RR', 'stratified': 'No', 'point': 0.71 }, { 'outcome': 'Mean cervical cancer survival', 'measure': 'Months', 'stratified': 'No', 'point': 85.7 }, { 'outcome': 'brachytherapy use', 'measure': 'OR', 'stratified': 'No', 'point': 0.87 }, { 'outcome': 'Duration of Radiation Therapy', 'measure': 'Percent', 'stratified': 'Yes', 'point': 57.0 }, { 'outcome': 'Laparotomy', 'measure': 'OR', 'stratified': 'No', 'point': 1.85 }, { 'outcome': 'Primary Cesarean Delivery', 'measure': 'RR', 'stratified': 'Yes', 'point': 0.92 }, { 'outcome': '2 year survival ovarian cancer', 'measure': 'HR', 'stratified': 'Yes', 'point': 0.98 }, { 'outcome': 'bilateral oophorectomy alone', 'measure': 'Incidence', 'stratified': 'Yes', 'point': 0.7 }, { 'outcome': 'Disease-specific mortality', 'measure': 'HR', 'stratified': 'Yes', 'point': 1.4 }, { 'outcome': 'baseline BMI', 'measure': 'Percent', 'stratified': 'Yes', 'point': 58.0 }, ], fields: [makeField('outcome'), makeField('measure'), makeField('stratified'), makeField('point')], metadata: { 'outcome': { type: Type.String, semanticType: 'String', levels: ['1 h or more between complete dilation and initiation of pushing', '2 year survival cervical cancer', '2 year survival ovarian cancer', '2 year survival vulvar cancer', '30-Day Major Postoperative Complications', '5 year disease specific survival', '5-min Apgar score less than 5', '5-year overall survival', 'Absence of Haplotype DE', 'Agreement with the statement regarding the effect of stress on the ability to bear children', 'Agreement with the statement regarding the effect of timing/age on the ability to bear children', 'All cause mortality', 'All-cause mortality', 'Any medication to treat opioid use disorder in the year after delivery', 'Association between patient characteristics and missed opportunities', 'BMI cut-off value sensitivity', 'Cervical cancer trial accrual', 'Cervical cancer trial accural', 'Cesarean indication- Labor Dystocia', 'Clinical in vitro fertilization outcomes within state insurance mandate status group, 2014-2019: Cumulative Live Birth', 'Clinical in vitro fertilization outcomes within state insurance mandate status group, 2014-2019: Cycle Cancellation', 'Clinical in vitro fertilization outcomes within state insurance mandate status group, 2014-2019: Live Birth Among Embryo Transfers', 'Clinical pregnancy', 'Composite perioperative complication', 'D Haplotype frequency', 'Death', 'Death from other causes', 'Difference of human papillomavirus vaccination rates before and after the mandate', 'Disease-specific mortality', 'Duration of Radiation Therapy', 'Ectopic Pregnancy', 'Ectopic pregnancy rate', 'Excessive Gestational Weight Gain', 'Fertility preferences have changed due to the pandemic', 'Gestational diabetes', 'Have ever heard of IUDS', 'Health Professional Shortage Area; Medically Underserved Areas/Populations', 'Human papillomavirus vaccination rate', 'Hysterectomy', 'Increased Hazard of Death Within 5 Years Among Patients with Advanced Stage Disease', 'Increased Hazard of Death Within 5 Years Among Patients with Advanced Stage Disease who underwent surgical treatment', 'Indication for oocyte cryopreservation - Fertility preservation (not otherwise specified) or no reason given', 'Insurance coverage', 'Intention to provide abortion', 'J Haplotype frequency', 'Laparoscopic', 'Laparotomy', 'Live birth', 'Lymphadenectomy', 'MED12 mutations in leiomyoma'] }, 'measure': { type: Type.String, semanticType: 'String', levels: ['%', '% of adherent cases', 'Absolute difference', 'Comorbidity %', 'HR', 'Incidence', 'Mean point estimate', 'Months', 'OR', 'Percent', 'Percent difference', 'Percentage of sample willing to participate in clinical trials', 'Predicted mean value', 'Prevalence', 'Product of the excess SMM rate and the number of discharges in the racial and ethnic category', 'RR', 'Simple Pearson\'s Correlation Coefficient', 'average annual percent change', 'count', 'median', 'median years', 'number of pregnancy-related deaths per 100,000 live births', 'rate per 10,000 ongoing pregnancies', 'rate per 10,000 women', 'rate per 100,000 live births', 'travel distance in miles', 'weighted proportions'] }, 'stratified': { type: Type.String, semanticType: 'Status', levels: ['No', 'Yes'] }, 'point': { type: Type.Number, semanticType: 'Number', levels: [] }, }, encodingMap: { x: makeEncodingItem('stratified'), y: makeEncodingItem('point'), color: makeEncodingItem('stratified'), }, }; } function gen_modelDatQ4RecGpt41(): TestCase { // How do point estimates and their confidence intervals differ between subanalysis groups (subgrp) and the main analysis ( // Score: 7/20 | The visualization is technically rendered but conceptually misaligned with the task: it omits confid return { title: 'model_dat_q4_rec-gpt-4.1', description: 'Compare **point** and confidence interval by **subgrp** vs main', tags: ['debug', 'grouped-bar-chart'], chartType: 'Grouped Bar Chart', data: [ { 'outcome': 'overall survival', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 2.83, 'lower': 1.4, 'upper': 5.69 }, { 'outcome': 'Composite maternal adverse outcome without transfusion', 'compare': 'Hispanic', 'subanalysis': 'Yes', 'subgrp': 'Primary Cesarean Deliveries', 'point': 0.95, 'lower': 0.85, 'upper': 1.07 }, { 'outcome': 'All-cause mortality', 'compare': 'African American', 'subanalysis': 'No', 'subgrp': null, 'point': 1.03, 'lower': 0.9, 'upper': 1.17 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 1.15, 'lower': 0.92, 'upper': 1.43 }, { 'outcome': 'Ovarian cancer trial accrual', 'compare': 'Uninsured', 'subanalysis': 'Yes', 'subgrp': 'Black', 'point': 1.8, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'No adjuvant treatment', 'compare': 'Hispanic', 'subanalysis': 'No', 'subgrp': null, 'point': 1.16, 'lower': 0.95, 'upper': 1.41 }, { 'outcome': 'Death', 'compare': 'Hispanic', 'subanalysis': 'No', 'subgrp': null, 'point': 0.93, 'lower': 0.67, 'upper': 1.29 }, { 'outcome': 'Composite maternal adverse outcome', 'compare': 'Hispanic', 'subanalysis': 'No', 'subgrp': null, 'point': 1.0, 'lower': 0.96, 'upper': 1.04 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Asian', 'subanalysis': 'No', 'subgrp': null, 'point': 0.99, 'lower': 0.99, 'upper': 1.0 }, { 'outcome': 'clinical pregnancy', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 0.94, 'lower': 0.85, 'upper': 1.04 }, { 'outcome': 'Clear understanding that diagnostic testing was optional', 'compare': 'Latina (English speaking)', 'subanalysis': 'Yes', 'subgrp': 'Race, ethnicity and language', 'point': 0.28, 'lower': 0.13, 'upper': 0.61 }, { 'outcome': 'live birth', 'compare': 'Asian', 'subanalysis': 'No', 'subgrp': null, 'point': 0.98, 'lower': 0.9, 'upper': 1.07 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': 'multiparous', 'point': 2.1, 'lower': 1.71, 'upper': 2.58 }, { 'outcome': 'All-cause mortality', 'compare': 'African American', 'subanalysis': 'Yes', 'subgrp': '<60 y', 'point': 1.96, 'lower': 1.35, 'upper': 2.85 }, { 'outcome': 'All-cause mortality', 'compare': 'African American', 'subanalysis': 'Yes', 'subgrp': 'All ages', 'point': 0.83, 'lower': 0.64, 'upper': 1.08 }, { 'outcome': 'Clear understanding that screening was optional', 'compare': 'African American or Black', 'subanalysis': 'Yes', 'subgrp': 'Race, ethnicity and language', 'point': 0.5, 'lower': 0.28, 'upper': 0.9 }, { 'outcome': 'Mortality differences', 'compare': 'us born hispanics', 'subanalysis': 'No', 'subgrp': null, 'point': 1.63, 'lower': 1.49, 'upper': 1.78 }, { 'outcome': 'Other and mixed Endometrial cancer', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': '1997-2001', 'point': 0.96, 'lower': 0.64, 'upper': 1.28 }, { 'outcome': 'Vulvar cancer mortality', 'compare': 'African American', 'subanalysis': 'Yes', 'subgrp': '>=60 y', 'point': 0.54, 'lower': 0.41, 'upper': 0.73 }, { 'outcome': 'live birth', 'compare': 'Hispanic', 'subanalysis': 'Yes', 'subgrp': 'embryos tested with preimplantation genetic testing', 'point': 1.01, 'lower': 0.81, 'upper': 1.26 }, { 'outcome': 'All-cause mortality', 'compare': 'African American', 'subanalysis': 'Yes', 'subgrp': '<60 y', 'point': 1.11, 'lower': 0.69, 'upper': 1.79 }, { 'outcome': 'composite maternal adverse outcome', 'compare': 'Non-Hispanic black', 'subanalysis': 'No', 'subgrp': null, 'point': 1.33, 'lower': 1.26, 'upper': 1.41 }, { 'outcome': 'Mortality differences', 'compare': 'Foreign born hispanics', 'subanalysis': 'Yes', 'subgrp': '2006-2010', 'point': 2.05, 'lower': 1.76, 'upper': 2.38 }, { 'outcome': 'overall survival based on the cox proportional hazard model', 'compare': 'Other', 'subanalysis': 'No', 'subgrp': null, 'point': 0.82, 'lower': 0.74, 'upper': 0.92 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Hispanic', 'subanalysis': 'No', 'subgrp': null, 'point': 1.87, 'lower': 1.76, 'upper': 1.99 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': 'multiparous', 'point': 1.67, 'lower': 1.37, 'upper': 2.02 }, { 'outcome': 'Mortality differences', 'compare': 'us born hispanics', 'subanalysis': 'No', 'subgrp': null, 'point': 1.57, 'lower': 1.43, 'upper': 1.71 }, { 'outcome': 'Total Endometrial cancer', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': '1997-2001', 'point': 87.51, 'lower': 74.0, 'upper': 101.0 }, { 'outcome': 'live birth', 'compare': 'Asian', 'subanalysis': 'No', 'subgrp': null, 'point': 0.97, 'lower': 0.9, 'upper': 1.05 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 1.27, 'lower': 1.14, 'upper': 1.41 }, { 'outcome': '30-Day Major Postoperative Complications', 'compare': 'Non-Hispanic Black', 'subanalysis': 'Yes', 'subgrp': 'Healthy sample', 'point': 1.78, 'lower': 1.17, 'upper': 2.69 }, { 'outcome': 'High Risk Endometrial cancer', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': '2012-2014', 'point': 50.35, 'lower': 44.23, 'upper': 56.46 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Hispanic', 'subanalysis': 'Yes', 'subgrp': 'Treatment', 'point': 1.05, 'lower': 0.72, 'upper': 1.52 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'multiparous', 'point': 0.96, 'lower': 0.79, 'upper': 1.16 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'Diagnosis', 'point': 1.63, 'lower': 1.27, 'upper': 2.11 }, { 'outcome': 'High Risk Endometrial cancer', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 51.34, 'lower': 47.3, 'upper': 55.4 }, { 'outcome': 'Mortality differences', 'compare': 'us born hispanics', 'subanalysis': 'Yes', 'subgrp': '2006-2010', 'point': 1.16, 'lower': 0.99, 'upper': 1.36 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'multiparous', 'point': 1.5, 'lower': 1.2, 'upper': 1.87 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Hispanic', 'subanalysis': 'No', 'subgrp': null, 'point': 1.26, 'lower': 1.18, 'upper': 1.42 }, { 'outcome': 'Clear understanding that diagnostic testing was optional', 'compare': 'other (Asian, Pacific Islander, Native American, Mixed, other)', 'subanalysis': 'No', 'subgrp': null, 'point': 0.5, 'lower': 0.25, 'upper': 0.99 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': 'nulliparous', 'point': 0.95, 'lower': 0.84, 'upper': 1.07 }, { 'outcome': 'Clear understanding that screening was optional', 'compare': 'Latina (Spanish speaking)', 'subanalysis': 'Yes', 'subgrp': 'Race, ethnicity and language', 'point': 0.8, 'lower': 0.51, 'upper': 1.27 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'No', 'subgrp': null, 'point': 1.16, 'lower': 1.03, 'upper': 1.3 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'multiparous', 'point': 1.43, 'lower': 0.89, 'upper': 2.3 }, { 'outcome': 'low birthweight', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 1.64, 'lower': 1.38, 'upper': 1.96 }, { 'outcome': 'Presence of fibroid', 'compare': 'White', 'subanalysis': 'No', 'subgrp': null, 'point': 6.9, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'All-cause mortality', 'compare': 'African American', 'subanalysis': 'Yes', 'subgrp': '<60 y', 'point': 1.91, 'lower': 1.26, 'upper': 2.91 }, { 'outcome': 'Uterine cancer trial accural', 'compare': 'Insured-Medicaid', 'subanalysis': 'Yes', 'subgrp': 'Black', 'point': 8.2, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'clinical pregnancy', 'compare': 'Asian', 'subanalysis': 'No', 'subgrp': null, 'point': 31.4, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'Correct recall of whether screening was done', 'compare': 'African American or Black', 'subanalysis': 'Yes', 'subgrp': 'Race, ethnicity and language', 'point': 0.29, 'lower': 0.12, 'upper': 0.68 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'No', 'subgrp': null, 'point': 1.15, 'lower': 1.05, 'upper': 1.26 }, { 'outcome': 'Cervical cancer trial accural', 'compare': 'Privately insured', 'subanalysis': 'Yes', 'subgrp': 'Black', 'point': -22.2, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'clinical pregnancy', 'compare': 'Hispanic', 'subanalysis': 'Yes', 'subgrp': 'embryos tested with preimplantation genetic testing', 'point': 1.01, 'lower': 0.84, 'upper': 1.2 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 1.0, 'lower': 0.87, 'upper': 1.15 }, { 'outcome': 'overall mortality', 'compare': 'Hispanic', 'subanalysis': 'Yes', 'subgrp': 'women with high-grade endometrioid carcinoma', 'point': 0.355, 'lower': 0.318, 'upper': 0.396 }, { 'outcome': 'multiple pregnancy', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'embryos tested with preimplantation genetic testing', 'point': 0.52, 'lower': 0.13, 'upper': 2.07 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'No', 'subgrp': null, 'point': 1.61, 'lower': 1.51, 'upper': 1.71 }, { 'outcome': 'endometrial cancer mortality', 'compare': 'Hispanic', 'subanalysis': 'Yes', 'subgrp': 'women with high-grade endometrioid carcinoma', 'point': 0.246, 'lower': 0.212, 'upper': 0.28 }, { 'outcome': 'Mortality differences', 'compare': 'Birthplace unknown hispanics', 'subanalysis': 'No', 'subgrp': null, 'point': 0.73, 'lower': 0.66, 'upper': 0.81 }, { 'outcome': 'Carcinosarcoma Endometrial cancer', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': '1997-2001', 'point': 4.21, 'lower': 3.36, 'upper': 5.05 }, { 'outcome': 'All-cause mortality', 'compare': 'African American', 'subanalysis': 'Yes', 'subgrp': '>=60 y', 'point': 0.82, 'lower': 0.56, 'upper': 1.21 }, { 'outcome': 'Other and mixed Endometrial cancer', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': '1997-2001', 'point': 3.49, 'lower': -6.13, 'upper': 13.1 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 1.07, 'lower': 0.79, 'upper': 1.44 }, { 'outcome': 'Cervical cancer trial accural', 'compare': 'Uninsured', 'subanalysis': 'No', 'subgrp': null, 'point': 6.9, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 2.8, 'lower': 2.63, 'upper': 3.0 }, { 'outcome': 'Composite maternal adverse outcome', 'compare': 'Hispanic', 'subanalysis': 'No', 'subgrp': null, 'point': 1.06, 'lower': 1.02, 'upper': 1.1 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Asian', 'subanalysis': 'No', 'subgrp': null, 'point': 1.25, 'lower': 1.15, 'upper': 1.35 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 17.3, 'lower': 16.2, 'upper': 18.4 }, { 'outcome': 'Total Endometrial cancer', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': '1997-2001', 'point': 101.79, 'lower': 96.1, 'upper': 107.4 }, { 'outcome': 'overall mortality', 'compare': 'non-Hispanic Black', 'subanalysis': 'Yes', 'subgrp': 'women with nonendometrioid carcinoma', 'point': 0.63, 'lower': 0.6, 'upper': 0.659 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'No', 'subgrp': null, 'point': 1.47, 'lower': 1.36, 'upper': 1.58 }, { 'outcome': 'High Risk Endometrial cancer', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': '2002-2006', 'point': 27.83, 'lower': 25.58, 'upper': 30.07 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'No', 'subgrp': null, 'point': 1.77, 'lower': 1.45, 'upper': 2.17 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'No', 'subgrp': null, 'point': 0.3, 'lower': 0.28, 'upper': 0.32 }, { 'outcome': 'Mortality differences', 'compare': 'Foreign born hispanics', 'subanalysis': 'No', 'subgrp': null, 'point': 1.61, 'lower': 1.46, 'upper': 1.76 }, { 'outcome': 'Presence of fibroid', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': 'Pedunculated', 'point': 0.0, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'Carcinosarcoma Endometrial cancer', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': '2007-2011', 'point': 3.53, 'lower': 3.18, 'upper': 3.89 }, { 'outcome': 'low birthweight', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'modified intent to treat', 'point': 2.04, 'lower': 1.23, 'upper': 3.41 }, { 'outcome': 'overall survival', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 1.14, 'lower': 1.05, 'upper': 1.24 }, { 'outcome': 'Low Risk Endometrial cancer', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': '1997-2001', 'point': 78.71, 'lower': 73.24, 'upper': 84.18 }, { 'outcome': 'clinical loss', 'compare': 'Hispanic', 'subanalysis': 'No', 'subgrp': null, 'point': 0.74, 'lower': 0.49, 'upper': 1.12 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Hispanic', 'subanalysis': 'No', 'subgrp': null, 'point': 11.0, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'High Risk Endometrial cancer', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': '2007-2011', 'point': 24.71, 'lower': 23.08, 'upper': 26.35 }, { 'outcome': 'Uterine cancer trial accural', 'compare': 'Privately insured', 'subanalysis': 'Yes', 'subgrp': 'Black', 'point': -15.3, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'No', 'subgrp': null, 'point': 1.08, 'lower': 0.98, 'upper': 1.19 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 1.08, 'lower': 0.96, 'upper': 1.21 }, { 'outcome': 'composite maternal adverse outcome', 'compare': 'Hispanic', 'subanalysis': 'Yes', 'subgrp': 'Gestational Age 24-36 wk', 'point': 0.75, 'lower': 0.63, 'upper': 0.89 }, { 'outcome': 'Mortality differences', 'compare': 'us born hispanics', 'subanalysis': 'No', 'subgrp': null, 'point': 1.81, 'lower': 1.65, 'upper': 1.98 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'multiparous', 'point': 1.36, 'lower': 1.02, 'upper': 1.81 }, { 'outcome': 'Antenatal depression screen completion', 'compare': 'Black Individuals', 'subanalysis': 'No', 'subgrp': null, 'point': 2.45, 'lower': 1.87, 'upper': 3.21 }, { 'outcome': 'Other and mixed Endometrial cancer', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': '2002-2006', 'point': 4.08, 'lower': 2.29, 'upper': 5.87 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Hispanic', 'subanalysis': 'Yes', 'subgrp': 'Treatment', 'point': 1.16, 'lower': 0.81, 'upper': 1.66 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'nulliparous', 'point': 1.32, 'lower': 1.2, 'upper': 1.44 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': 'nulliparous', 'point': 1.15, 'lower': 1.03, 'upper': 1.27 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 1.23, 'lower': 1.17, 'upper': 1.29 }, { 'outcome': 'Carcinosarcoma Endometrial cancer', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': '2002-2006', 'point': 10.03, 'lower': 6.15, 'upper': 13.92 }, { 'outcome': 'Mortality differences', 'compare': 'us born hispanics', 'subanalysis': 'Yes', 'subgrp': '2006-2010', 'point': 2.09, 'lower': 1.78, 'upper': 2.45 }, { 'outcome': 'endometrial cancer five-year survival', 'compare': 'Birthplace-unknown Hispanics', 'subanalysis': 'Yes', 'subgrp': '2006-2010', 'point': 92.3, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'nulliparous', 'point': 1.1, 'lower': 0.62, 'upper': 1.96 }, { 'outcome': 'Mortality differences', 'compare': 'us born hispanics', 'subanalysis': 'Yes', 'subgrp': '2006-2010', 'point': 1.57, 'lower': 1.34, 'upper': 1.84 }, { 'outcome': 'Vulvar cancer mortality', 'compare': 'African American', 'subanalysis': 'Yes', 'subgrp': '>=60 y', 'point': 0.32, 'lower': 0.18, 'upper': 0.57 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': 'nulliparous', 'point': 0.96, 'lower': 0.9, 'upper': 1.04 }, { 'outcome': 'Uterine cancer trial accural', 'compare': 'Insured-Medicaid', 'subanalysis': 'Yes', 'subgrp': 'White', 'point': -2.5, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'Disease-specific death', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'Surgically treated cases', 'point': 2.15, 'lower': 1.39, 'upper': 3.32 }, { 'outcome': 'Presence of fibroid', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': 'Intramural', 'point': 50.0, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Hispanic', 'subanalysis': 'No', 'subgrp': null, 'point': 2.74, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'Ovarian cancer trial accrual', 'compare': 'Unknown', 'subanalysis': 'Yes', 'subgrp': 'White', 'point': 0.0, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'excluding blood transfusions', 'point': 1.99, 'lower': 1.17, 'upper': 3.36 }, { 'outcome': 'clinical pregnancy', 'compare': 'Asian', 'subanalysis': 'No', 'subgrp': null, 'point': 1.0, 'lower': 0.93, 'upper': 1.06 }, { 'outcome': 'Presence of fibroid', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'Pedunculated', 'point': 9.1, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'endometrial cancer mortality', 'compare': 'non-Hispanic Black', 'subanalysis': 'Yes', 'subgrp': 'women with nonendometrioid carcinoma', 'point': 0.471, 'lower': 0.442, 'upper': 0.496 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': 'multiparous', 'point': 0.97, 'lower': 0.8, 'upper': 1.17 }, { 'outcome': 'endometrial cancer mortality', 'compare': 'non-Hispanic Black', 'subanalysis': 'Yes', 'subgrp': 'women with high-grade endometrioid carcinoma', 'point': 0.33, 'lower': 0.295, 'upper': 0.365 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': 'nulliparous', 'point': 1.13, 'lower': 1.07, 'upper': 1.2 }, { 'outcome': 'peripartum hysterectomy', 'compare': 'Unknown', 'subanalysis': 'Yes', 'subgrp': 'low-risk patients', 'point': 1.36, 'lower': 0.79, 'upper': 2.34 }, { 'outcome': 'overall mortality', 'compare': 'Hispanic', 'subanalysis': 'No', 'subgrp': null, 'point': 0.429, 'lower': 0.399, 'upper': 0.459 }, { 'outcome': 'Death', 'compare': 'Hispanic', 'subanalysis': 'No', 'subgrp': null, 'point': 1.2, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'cervical insufficiency', 'compare': 'American Indian/Alaskan Native', 'subanalysis': 'No', 'subgrp': null, 'point': 1.16, 'lower': 0.79, 'upper': 1.7 }, { 'outcome': 'Vulvar cancer mortality', 'compare': 'African American', 'subanalysis': 'Yes', 'subgrp': '>=60 y', 'point': 0.59, 'lower': 0.36, 'upper': 0.96 }, { 'outcome': 'Clear understanding that diagnostic testing was optional', 'compare': 'other (Asian, Pacific Islander, Native American, Mixed, other)', 'subanalysis': 'Yes', 'subgrp': 'Race, ethnicity and language', 'point': 0.35, 'lower': 0.19, 'upper': 0.66 }, { 'outcome': 'All-cause mortality', 'compare': 'African American', 'subanalysis': 'Yes', 'subgrp': 'All ages', 'point': 0.82, 'lower': 0.62, 'upper': 1.07 }, { 'outcome': 'Composite maternal adverse outcome without transfusion', 'compare': 'Hispanic', 'subanalysis': 'No', 'subgrp': null, 'point': 0.99, 'lower': 0.91, 'upper': 1.08 }, { 'outcome': 'Endometrial G3 cancer', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': '2002-2006', 'point': 18.2, 'lower': 16.69, 'upper': 19.71 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'Treatment', 'point': 1.53, 'lower': 1.04, 'upper': 2.26 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 25.8, 'lower': 23.5, 'upper': 28.0 }, { 'outcome': 'Carcinosarcoma Endometrial cancer', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 11.9, 'lower': 10.5, 'upper': 13.3 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': 'nulliparous', 'point': 1.28, 'lower': 1.09, 'upper': 1.49 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': 'nulliparous', 'point': 1.17, 'lower': 1.1, 'upper': 1.25 }, { 'outcome': 'Uterine cancer trial accural', 'compare': 'Unknown', 'subanalysis': 'Yes', 'subgrp': 'Black', 'point': 2.4, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'clinical loss', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 1.59, 'lower': 1.28, 'upper': 1.99 }, { 'outcome': 'Overall death', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'Surgically treated cases', 'point': 1.39, 'lower': 1.11, 'upper': 1.75 }, { 'outcome': 'clinical pregnancy', 'compare': 'Hispanic', 'subanalysis': 'Yes', 'subgrp': 'embryos tested with preimplantation genetic testing', 'point': 0.99, 'lower': 0.81, 'upper': 1.2 }, { 'outcome': 'All-cause mortality', 'compare': 'African American', 'subanalysis': 'No', 'subgrp': null, 'point': 1.3, 'lower': 1.1, 'upper': 1.5 }, { 'outcome': 'Composite maternal adverse outcome without transfusion', 'compare': 'Non-Hispanic Black', 'subanalysis': 'Yes', 'subgrp': 'Primary Cesarean Deliveries', 'point': 1.05, 'lower': 0.93, 'upper': 1.19 }, { 'outcome': 'preterm birth', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'modified intent to treat', 'point': 1.37, 'lower': 0.84, 'upper': 2.26 }, { 'outcome': 'spontaneous preterm birth', 'compare': 'Other', 'subanalysis': 'No', 'subgrp': null, 'point': 6.1, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 16.9, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'spontaneous preterm birth', 'compare': 'Other', 'subanalysis': 'Yes', 'subgrp': null, 'point': 0.72, 'lower': 0.22, 'upper': 2.38 }, { 'outcome': 'preterm birth', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 1.53, 'lower': 1.29, 'upper': 1.82 }, { 'outcome': 'Serous Endometrial cancer', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 14.04, 'lower': 12.3, 'upper': 15.8 }, { 'outcome': 'High Risk Endometrial cancer', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': '2002-2006', 'point': 45.36, 'lower': 36.79, 'upper': 53.93 }, { 'outcome': 'overall survival based on the cox proportional hazard model', 'compare': 'Hispanic', 'subanalysis': 'No', 'subgrp': null, 'point': 0.68, 'lower': 0.62, 'upper': 0.74 }, { 'outcome': '30-Day Major Postoperative Complications', 'compare': 'Hispanic', 'subanalysis': 'Yes', 'subgrp': 'Healthy sample', 'point': 0.72, 'lower': 0.42, 'upper': 1.22 }, { 'outcome': 'low birthweight', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'modified intent to treat', 'point': 1.74, 'lower': 0.97, 'upper': 3.13 }, { 'outcome': 'Vulvar cancer mortality', 'compare': 'African American', 'subanalysis': 'Yes', 'subgrp': 'All ages', 'point': 0.65, 'lower': 0.43, 'upper': 0.97 }, { 'outcome': 'Mortality differences', 'compare': 'Birthplace unknown hispanics', 'subanalysis': 'Yes', 'subgrp': '2000-2005', 'point': 0.71, 'lower': 0.62, 'upper': 0.81 }, { 'outcome': 'Vulvar cancer mortality', 'compare': 'African American', 'subanalysis': 'Yes', 'subgrp': 'All ages', 'point': 0.67, 'lower': 0.53, 'upper': 0.84 }, { 'outcome': 'Composite maternal adverse outcome', 'compare': 'Non-Hispanic Black', 'subanalysis': 'No', 'subgrp': null, 'point': 0.85, 'lower': 0.8, 'upper': 0.89 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'nulliparous', 'point': 1.21, 'lower': 1.06, 'upper': 1.37 }, { 'outcome': 'endometrial cancer five-year survival', 'compare': 'Foreign born Hispanics', 'subanalysis': 'Yes', 'subgrp': '2006-2010', 'point': 76.9, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 1.67, 'lower': 1.3, 'upper': 2.15 }, { 'outcome': 'live birth', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 16.9, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'No', 'subgrp': null, 'point': 1.08, 'lower': 0.98, 'upper': 1.18 }, { 'outcome': 'High Risk Endometrial cancer', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': '1997-2001', 'point': 31.44, 'lower': 27.99, 'upper': 34.88 }, { 'outcome': 'clinical pregnancy', 'compare': 'Asian', 'subanalysis': 'No', 'subgrp': null, 'point': 1.02, 'lower': 0.95, 'upper': 1.1 }, { 'outcome': 'clinical loss', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'embryos tested with preimplantation genetic testing', 'point': 1.76, 'lower': 1.0, 'upper': 3.07 }, { 'outcome': '30-Day Major Postoperative Complications', 'compare': 'Hispanic', 'subanalysis': 'No', 'subgrp': null, 'point': 0.71, 'lower': 0.47, 'upper': 1.09 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'nulliparous', 'point': 0.93, 'lower': 0.8, 'upper': 1.08 }, { 'outcome': 'Vulvar cancer mortality', 'compare': 'African American', 'subanalysis': 'Yes', 'subgrp': 'All ages', 'point': 0.77, 'lower': 0.49, 'upper': 1.22 }, { 'outcome': 'All-cause mortality', 'compare': 'African American', 'subanalysis': 'No', 'subgrp': null, 'point': 1.55, 'lower': 1.18, 'upper': 2.03 }, { 'outcome': 'endometrial cancer mortality', 'compare': 'Hispanic', 'subanalysis': 'Yes', 'subgrp': 'women with nonendometrioid carcinoma', 'point': 0.405, 'lower': 0.364, 'upper': 0.446 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'No', 'subgrp': null, 'point': 1.32, 'lower': 1.24, 'upper': 1.4 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'nulliparous', 'point': 1.17, 'lower': 1.06, 'upper': 1.29 }, { 'outcome': 'Ovarian cancer trial accrual', 'compare': 'Privately insured', 'subanalysis': 'No', 'subgrp': null, 'point': 9.7, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'Antenatal depression screen completion', 'compare': 'Black Individuals', 'subanalysis': 'No', 'subgrp': null, 'point': 1.24, 'lower': 0.9, 'upper': 1.7 }, { 'outcome': 'Ovarian cancer trial accrual', 'compare': 'Privately insured', 'subanalysis': 'Yes', 'subgrp': 'White', 'point': 14.0, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'preterm birth', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'modified intent to treat', 'point': 1.05, 'lower': 0.62, 'upper': 1.79 }, { 'outcome': 'Clear understanding that diagnostic testing was optional', 'compare': 'African American or Black', 'subanalysis': 'Yes', 'subgrp': 'Race, ethnicity and language', 'point': 0.2, 'lower': 0.11, 'upper': 0.36 }, { 'outcome': 'Vulvar cancer mortality', 'compare': 'African American', 'subanalysis': 'Yes', 'subgrp': '<60 y', 'point': 0.72, 'lower': 0.26, 'upper': 1.99 }, { 'outcome': 'Carcinosarcoma Endometrial cancer', 'compare': 'White', 'subanalysis': 'No', 'subgrp': null, 'point': 3.51, 'lower': 3.3, 'upper': 3.7 }, { 'outcome': 'clinical loss', 'compare': 'Hispanic', 'subanalysis': 'No', 'subgrp': null, 'point': 1.03, 'lower': 0.77, 'upper': 1.38 }, { 'outcome': 'Correct recall of whether screening was done', 'compare': 'African American or Black', 'subanalysis': 'No', 'subgrp': null, 'point': 0.66, 'lower': 0.23, 'upper': 1.89 }, { 'outcome': 'Correct recall of whether screening was done', 'compare': 'Latina (Spanish speaking)', 'subanalysis': 'Yes', 'subgrp': 'Race, ethnicity and language', 'point': 0.58, 'lower': 0.27, 'upper': 1.23 }, { 'outcome': 'Vulvar cancer mortality', 'compare': 'African American', 'subanalysis': 'No', 'subgrp': null, 'point': 0.65, 'lower': 0.43, 'upper': 0.97 }, { 'outcome': 'Low Risk Endometrial cancer', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 39.08, 'lower': 36.3, 'upper': 41.8 }, { 'outcome': 'multiple pregnancy', 'compare': 'Asian', 'subanalysis': 'No', 'subgrp': null, 'point': 21.8, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'clinical loss', 'compare': 'Asian', 'subanalysis': 'Yes', 'subgrp': 'embryos tested with preimplantation genetic testing', 'point': 0.98, 'lower': 0.64, 'upper': 1.31 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': 'multiparous', 'point': 1.19, 'lower': 1.02, 'upper': 1.38 }, { 'outcome': 'peripartum hysterectomy', 'compare': 'Unknown', 'subanalysis': 'No', 'subgrp': null, 'point': 0.97, 'lower': 0.88, 'upper': 1.07 }, { 'outcome': 'Mortality differences', 'compare': 'Foreign born hispanics', 'subanalysis': 'Yes', 'subgrp': '2006-2010', 'point': 1.61, 'lower': 1.38, 'upper': 1.87 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'nulliparous', 'point': 1.0, 'lower': 0.85, 'upper': 1.18 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 3.02, 'lower': 2.88, 'upper': 3.17 }, { 'outcome': 'composite maternal adverse outcome', 'compare': 'Non-Hispanic black', 'subanalysis': 'Yes', 'subgrp': 'Gestational Age 24-36 wk', 'point': 1.19, 'lower': 1.04, 'upper': 1.36 }, { 'outcome': 'Serous Endometrial cancer', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': '2007-2011', 'point': 12.82, 'lower': 9.03, 'upper': 16.61 }, { 'outcome': 'cervical insufficiency', 'compare': 'Asian/Pacific Islander', 'subanalysis': 'Yes', 'subgrp': 'multiparous women', 'point': 0.97, 'lower': 0.81, 'upper': 1.16 }, { 'outcome': 'Total Endometrial cancer', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': '2012-2014', 'point': 101.75, 'lower': 85.8, 'upper': 117.1 }, { 'outcome': 'Composite neonatal adverse outcome', 'compare': 'Non-Hispanic Black', 'subanalysis': 'No', 'subgrp': null, 'point': 1.12, 'lower': 1.1, 'upper': 1.15 }, { 'outcome': 'Ovarian cancer trial accrual', 'compare': 'Unknown', 'subanalysis': 'Yes', 'subgrp': 'Black', 'point': 1.2, 'lower': -99.0, 'upper': -99.0 }, { 'outcome': 'No adjuvant treatment', 'compare': 'non-Hispanic Black', 'subanalysis': 'No', 'subgrp': null, 'point': 0.96, 'lower': 0.84, 'upper': 1.11 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Hispanic', 'subanalysis': 'Yes', 'subgrp': 'Diagnosis', 'point': 1.17, 'lower': 0.91, 'upper': 1.49 }, { 'outcome': 'Primary cesarean delivery', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': 'multiparous', 'point': 3.23, 'lower': 2.52, 'upper': 4.13 }, { 'outcome': 'All-cause mortality', 'compare': 'African American', 'subanalysis': 'No', 'subgrp': null, 'point': 0.9, 'lower': 0.71, 'upper': 1.15 }, { 'outcome': 'Other and mixed Endometrial cancer', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': '2007-2011', 'point': 6.99, 'lower': 5.28, 'upper': 8.7 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Black', 'subanalysis': 'No', 'subgrp': null, 'point': 1.76, 'lower': 1.69, 'upper': 1.84 }, { 'outcome': '30-Day Major Postoperative Complications', 'compare': 'Non-Hispanic Black', 'subanalysis': 'Yes', 'subgrp': 'Healthy sample', 'point': 1.64, 'lower': 1.08, 'upper': 2.5 }, { 'outcome': 'Carcinosarcoma Endometrial cancer', 'compare': 'White', 'subanalysis': 'Yes', 'subgrp': '2002-2006', 'point': 3.49, 'lower': 3.0, 'upper': 3.97 }, { 'outcome': 'Antenatal depression screen completion', 'compare': 'Black Individuals', 'subanalysis': 'Yes', 'subgrp': 'those with a positive screen for depression', 'point': 0.7, 'lower': 0.3, 'upper': 1.62 }, { 'outcome': 'multiple pregnancy', 'compare': 'Hispanic', 'subanalysis': 'Yes', 'subgrp': 'embryos tested with preimplantation genetic testing', 'point': 1.9, 'lower': 1.04, 'upper': 3.46 }, { 'outcome': 'Carcinosarcoma Endometrial cancer', 'compare': 'Black', 'subanalysis': 'Yes', 'subgrp': '2012-2014', 'point': 12.6, 'lower': 10.51, 'upper': 14.69 }, { 'outcome': 'multiple pregnancy', 'compare': 'Hispanic', 'subanalysis': 'Yes', 'subgrp': 'embryos tested with preimplantation genetic testing', 'point': 1.88, 'lower': 1.04, 'upper': 3.38 }, ], fields: [makeField('outcome'), makeField('compare'), makeField('subanalysis'), makeField('subgrp'), makeField('point'), makeField('lower'), makeField('upper')], metadata: { 'outcome': { type: Type.String, semanticType: 'Category', levels: ['30-Day Major Postoperative Complications', 'All-cause mortality', 'Antenatal depression screen completion', 'Carcinosarcoma Endometrial cancer', 'Cervical cancer trial accural', 'Clear understanding that diagnostic testing was optional', 'Clear understanding that screening was optional', 'Composite maternal adverse outcome', 'Composite maternal adverse outcome without transfusion', 'Composite neonatal adverse outcome', 'Correct recall of whether screening was done', 'Death', 'Disease-specific death', 'Endometrial G3 cancer', 'High Risk Endometrial cancer', 'Low Risk Endometrial cancer', 'Mortality differences', 'No adjuvant treatment', 'Other and mixed Endometrial cancer', 'Ovarian cancer trial accrual', 'Overall death', 'Presence of fibroid', 'Primary cesarean delivery', 'Serous Endometrial cancer', 'Total Endometrial cancer', 'Uterine cancer trial accural', 'Vulvar cancer mortality', 'cervical insufficiency', 'clinical loss', 'clinical pregnancy', 'composite maternal adverse outcome', 'endometrial cancer five-year survival', 'endometrial cancer mortality', 'live birth', 'low birthweight', 'multiple pregnancy', 'overall mortality', 'overall survival', 'overall survival based on the cox proportional hazard model', 'peripartum hysterectomy', 'preterm birth', 'severe maternal morbidity', 'spontaneous preterm birth'] }, 'compare': { type: Type.String, semanticType: 'Category', levels: ['African American', 'African American or Black', 'American Indian/Alaskan Native', 'Asian', 'Asian/Pacific Islander', 'Birthplace unknown hispanics', 'Birthplace-unknown Hispanics', 'Black', 'Black Individuals', 'Foreign born Hispanics', 'Foreign born hispanics', 'Hispanic', 'Insured-Medicaid', 'Latina (English speaking)', 'Latina (Spanish speaking)', 'Non-Hispanic Black', 'Non-Hispanic black', 'Other', 'Privately insured', 'Uninsured', 'Unknown', 'White', 'non-Hispanic Black', 'other (Asian, Pacific Islander, Native American, Mixed, other)', 'us born hispanics'] }, 'subanalysis': { type: Type.String, semanticType: 'Status', levels: ['No', 'Yes'] }, 'subgrp': { type: Type.String, semanticType: 'Category', levels: ['1997-2001', '2000-2005', '2002-2006', '2006-2010', '2007-2011', '2012-2014', '<60 y', '>=60 y', 'All ages', 'Black', 'Diagnosis', 'Gestational Age 24-36 wk', 'Healthy sample', 'Intramural', 'Pedunculated', 'Primary Cesarean Deliveries', 'Race, ethnicity and language', 'Surgically treated cases', 'Treatment', 'White', 'embryos tested with preimplantation genetic testing', 'excluding blood transfusions', 'low-risk patients', 'modified intent to treat', 'multiparous', 'multiparous women', 'nulliparous', 'those with a positive screen for depression', 'women with high-grade endometrioid carcinoma', 'women with nonendometrioid carcinoma'] }, 'point': { type: Type.Number, semanticType: 'Number', levels: [] }, 'lower': { type: Type.String, semanticType: 'Category', levels: ['-6.13', '-99.0', '0.11', '0.12', '0.13', '0.18', '0.19', '0.212', '0.22', '0.23', '0.25', '0.26', '0.27', '0.28', '0.295', '0.3', '0.318', '0.36', '0.364', '0.399', '0.41', '0.42', '0.43', '0.442', '0.47', '0.49', '0.51', '0.53', '0.56', '0.6', '0.62', '0.63', '0.64', '0.66', '0.67', '0.69', '0.71', '0.72', '0.74', '0.77', '0.79', '0.8', '0.81', '0.84', '0.85', '0.87', '0.88', '0.89', '0.9', '0.91'] }, 'upper': { type: Type.String, semanticType: 'Category', levels: ['-99.0', '0.28', '0.32', '0.36', '0.365', '0.396', '0.446', '0.459', '0.496', '0.57', '0.61', '0.659', '0.66', '0.68', '0.73', '0.74', '0.81', '0.84', '0.89', '0.9', '0.92', '0.96', '0.97', '0.99', '1.0', '1.04', '1.05', '1.06', '1.07', '1.08', '1.09', '1.1', '1.11', '1.12', '1.15', '1.16', '1.17', '1.18', '1.19', '1.2', '1.21', '1.22', '1.23', '1.24', '1.25', '1.26', '1.27', '1.28', '1.29', '1.3'] }, }, encodingMap: { x: makeEncodingItem('compare'), y: makeEncodingItem('point'), group: makeEncodingItem('subanalysis'), column: makeEncodingItem('outcome'), }, chartProperties: { cornerRadius: 5 }, }; } function gen_modelDatQ5RecGpt41(): TestCase { // For models reporting percent-type measures, what is the composition of point estimates across race/ethnicity-related com // Score: 10/20 | The visualization is partially relevant and technically sound but conceptually misaligned with the r return { title: 'model_dat_q5_rec-gpt-4.1', description: 'Show distribution of **point** by **compare** for selected **outcome** (percent-type measures)', tags: ['debug', 'boxplot'], chartType: 'Boxplot', data: [ { 'outcome': 'individual complication occurrence - reoperation', 'compare': 'Black', 'point': 0.4 }, { 'outcome': 'Presence of fibroid', 'compare': 'White', 'point': 50.0 }, { 'outcome': 'Eligible, not enrolled among patients with a trial available', 'compare': 'White', 'point': 40.0 }, { 'outcome': 'endometrial cancer 5-year survival', 'compare': 'Non-Hispanic: Black', 'point': 55.7 }, { 'outcome': 'endometrial cancer 2-year survival difference', 'compare': 'Non-Hispanic: Black', 'point': -10.6 }, { 'outcome': 'endometrial cancer 2-year survival', 'compare': 'Non-Hispanic: Black', 'point': 97.0 }, { 'outcome': 'Need for 2nd line chemotherapy', 'compare': 'African American', 'point': 26.3 }, { 'outcome': 'vaccination in same month as abnormal pap test result', 'compare': 'hispanic', 'point': 0.0 }, { 'outcome': 'Gestational diabetes', 'compare': 'American Indian', 'point': 7.7 }, { 'outcome': 'Indication for oocyte cryopreservation - diminished ovarian reserve', 'compare': 'Hispanic', 'point': 27.0 }, { 'outcome': 'Heard of HPV Vaccine (Yes)', 'compare': 'african american', 'point': 76.0 }, { 'outcome': 'Microbiota in endometrial tumors - Firmicutes phylum', 'compare': 'Obese white', 'point': 0.44 }, { 'outcome': 'cervical length cutoff > 30 mm', 'compare': 'Black', 'point': 9.0 }, { 'outcome': 'Not eligible among patients with a trial available', 'compare': 'Hawaiian/PI', 'point': 0.0 }, { 'outcome': 'Not eligible among patients with a trial available', 'compare': 'White', 'point': 14.0 }, { 'outcome': 'heard of HPV Vaccine (No)', 'compare': 'non-hispanic', 'point': 12.5 }, { 'outcome': 'endometrial cancer 2-year survival difference', 'compare': 'Non-Hispanic: Black', 'point': -3.0 }, { 'outcome': 'endometrial cancer 2-year survival', 'compare': 'Non-Hispanic: White', 'point': 86.6 }, { 'outcome': 'No complications', 'compare': 'non-Hispanic Black', 'point': 50.0 }, { 'outcome': 'Microbiota in endometrial tumors - Dietzia genus', 'compare': 'Obese black', 'point': 0.78 }, { 'outcome': 'Eligible, not enrolled among patients with a trial available', 'compare': 'Native American', 'point': 12.5 }, { 'outcome': 'age-standardized five-year net survival', 'compare': 'Black', 'point': 56.7 }, { 'outcome': 'Seasonal influenza vaccination', 'compare': 'White, non-Hispanic', 'point': 41.0 }, { 'outcome': 'Cesarean birth - Labor dystocia', 'compare': 'Hispanic', 'point': 9.2 }, { 'outcome': 'cervical length cutoff < 25 mm', 'compare': 'White', 'point': 1.0 }, { 'outcome': 'endometrial cancer 2-year survival', 'compare': 'Non-Hispanic: Black', 'point': 96.5 }, { 'outcome': 'willingness to participate before and after educational intervention', 'compare': 'Other', 'point': 66.0 }, { 'outcome': 'Microbiota in endometrial tumors - Cyanobacteria phylum', 'compare': 'Obese black', 'point': 1.9 }, { 'outcome': 'endometrial cancer 5-year survival', 'compare': 'Non-Hispanic: Black', 'point': 58.7 }, { 'outcome': 'germline testing performed', 'compare': 'Other/unknown', 'point': 41.18 }, { 'outcome': 'Acute renal failure or insufficiency', 'compare': 'Black', 'point': 4.1 }, { 'outcome': 'cervical length cutoff > 30 mm', 'compare': 'Hispanic', 'point': 4.5 }, { 'outcome': 'histology: endometrioid', 'compare': 'White', 'point': 76.4 }, { 'outcome': 'Pulmonary', 'compare': 'Black', 'point': 4.1 }, { 'outcome': 'Ectopic pregnancy rate', 'compare': 'American Indian/Alaskan Native', 'point': 3.11 }, { 'outcome': 'baseline BMI', 'compare': 'Latina/Hispanic', 'point': 26.7 }, { 'outcome': 'Heard of HPV Vaccine (Yes)', 'compare': 'non-hispanic', 'point': 87.5 }, { 'outcome': 'grade: moderately differentiated', 'compare': 'White', 'point': 28.2 }, { 'outcome': 'vaccination before abnormal pap test result', 'compare': 'african american', 'point': 20.0 }, { 'outcome': 'grade: well differentiated', 'compare': 'Black', 'point': 55.6 }, { 'outcome': 'stage: regional', 'compare': 'White', 'point': 12.7 }, { 'outcome': 'Microbiota in endometrial tumors - Geobacillus genus', 'compare': 'Obese black', 'point': 0.03 }, { 'outcome': 'endometrial cancer 5-year survival', 'compare': 'Non-Hispanic: Black', 'point': 87.4 }, { 'outcome': '5 year disease specific survival', 'compare': 'white', 'point': 76.9 }, { 'outcome': 'hispanic-white disparity in severe maternal morbidity', 'compare': 'Hispanic', 'point': 1.26 }, { 'outcome': 'Surgical procedure for endometriosis - Hysterectomy', 'compare': 'Black', 'point': 48.7 }, { 'outcome': 'Duration of Radiation Therapy', 'compare': 'Non-Hispanic Black', 'point': 55.0 }, { 'outcome': 'stage: unknown', 'compare': 'Black', 'point': 8.7 }, { 'outcome': 'diagnosed with invasive cervical cancer', 'compare': 'other', 'point': 0.5 }, { 'outcome': 'Indication for oocyte cryopreservation - Fertility perservation, medical reason', 'compare': 'Asian/Pacific Islander', 'point': 0.9 }, { 'outcome': 'endometrial cancer 5-year survival', 'compare': 'Non-Hispanic: White', 'point': 65.5 }, { 'outcome': 'laparoscopic myomectomy approach', 'compare': 'Other', 'point': 4.5 }, { 'outcome': 'Educational Themed Content', 'compare': 'White', 'point': 20.4 }, { 'outcome': 'grade: undifferentiated', 'compare': 'White', 'point': 0.8 }, { 'outcome': 'cancer-directed surgery', 'compare': 'White', 'point': 19.1 }, { 'outcome': 'Amount heard about 2009 H1N1 vaccine', 'compare': 'White, non-Hispanic', 'point': 35.0 }, { 'outcome': 'preterm birth', 'compare': 'white', 'point': 62.4 }, { 'outcome': '5 year disease specific survival', 'compare': 'asian', 'point': 64.4 }, { 'outcome': 'Neonatal outcomes- Small for gestational age', 'compare': 'Hispanic', 'point': 8.5 }, { 'outcome': 'endometrial cancer 2-year survival difference', 'compare': 'Non-Hispanic: Black', 'point': -0.5 }, { 'outcome': 'endometrial cancer 5-year survival difference', 'compare': 'Non-Hispanic: Black', 'point': -17.1 }, { 'outcome': 'diagnosed with invasive cervical cancer', 'compare': 'black', 'point': 16.3 }, { 'outcome': 'vaccination before abnormal pap test result', 'compare': 'non-hispanic', 'point': 55.6 }, { 'outcome': 'five-year overall survival associated with treatment adherence status', 'compare': 'Surgery/Chemotherapy No/No', 'point': 3.9 }, { 'outcome': 'individual complication occurrence - UTI', 'compare': 'Asian', 'point': 0.03 }, { 'outcome': 'histology: non-endometrioid', 'compare': 'Black', 'point': 50.0 }, { 'outcome': 'Insurance coverage', 'compare': 'Medicare/other govt', 'point': 19.3 }, { 'outcome': 'grade: well differentiated', 'compare': 'White', 'point': 43.8 }, { 'outcome': 'stage: local', 'compare': 'Black', 'point': 77.8 }, { 'outcome': 'Ectopic pregnancy rate', 'compare': 'American Indian/Alaskan Native', 'point': 1.21 }, { 'outcome': 'cannabis users advised not to breastfeed', 'compare': 'Non-Hispanic Mixed', 'point': 52.3 }, { 'outcome': 'endometrial cancer 5-year survival', 'compare': 'Non-Hispanic: White', 'point': 91.5 }, { 'outcome': 'Cesarean birth - Non-reassuring fetal status', 'compare': 'Hispanic', 'point': 11.1 }, { 'outcome': 'preterm birth', 'compare': 'native', 'point': 63.9 }, { 'outcome': 'Microbiota in endometrial tumors - Prevotella genus', 'compare': 'Obese white', 'point': 0.16 }, { 'outcome': 'individual complication occurrence - UTI', 'compare': 'Hispanic', 'point': 0.22 }, { 'outcome': 'HDL', 'compare': 'PCOS Black', 'point': 45.5 }, { 'outcome': 'Surgical procedure for endometriosis - Myomectomy', 'compare': 'Asian', 'point': 5.0 }, { 'outcome': 'Insurance coverage', 'compare': 'Other', 'point': 0.1 }, { 'outcome': 'prevalence of human papillomavirus vaccine series completion', 'compare': 'Hispanic', 'point': 38.9 }, { 'outcome': 'grade: well differentiated', 'compare': 'Black', 'point': 48.9 }, { 'outcome': 'endometrial cancer 5-year survival', 'compare': 'Non-Hispanic: White', 'point': 91.9 }, { 'outcome': 'individual complication occurrence - reoperation', 'compare': 'Hispanic', 'point': 0.4 }, { 'outcome': 'mature oocytes', 'compare': 'Asian/Pacific Islander', 'point': 78.9 }, { 'outcome': 'endometrial cancer 2-year survival', 'compare': 'Non-Hispanic: Black', 'point': 96.4 }, { 'outcome': 'BMI cut-off value specificity', 'compare': 'White', 'point': 93.6 }, { 'outcome': '5 year disease specific survival', 'compare': 'white', 'point': 38.1 }, { 'outcome': 'endometrial cancer 2-year survival', 'compare': 'Non-Hispanic: Black', 'point': 83.5 }, { 'outcome': 'endometrial cancer five-year survival', 'compare': 'Birthplace-unknown Hispanics', 'point': 92.3 }, { 'outcome': 'grade: unknown', 'compare': 'Black', 'point': 15.7 }, { 'outcome': 'endometrial cancer 5-year survival', 'compare': 'Non-Hispanic: White', 'point': 91.3 }, { 'outcome': 'preterm birth', 'compare': 'asian', 'point': 3.9 }, { 'outcome': 'Ectopic pregnancy rate', 'compare': 'White', 'point': 2.29 }, { 'outcome': 'age-standardized five-year net survival', 'compare': 'Black', 'point': 46.8 }, { 'outcome': 'endometrial cancer five-year survival', 'compare': 'U.S. born Hispanics', 'point': 78.8 }, { 'outcome': 'Ectopic pregnancy rate', 'compare': 'Asian', 'point': 1.99 }, { 'outcome': 'Maternal morbidity composite- Transfusion any other products', 'compare': 'Hispanic', 'point': 0.4 }, { 'outcome': 'hysterectomy with oophorectomy', 'compare': 'non-Hispanic White', 'point': 57.74 }, { 'outcome': 'stage: unknown', 'compare': 'White', 'point': 6.5 }, { 'outcome': 'individual complication occurrence - sepsis', 'compare': 'Asian', 'point': 0.0 }, { 'outcome': 'severe maternal morbidity', 'compare': 'Hispanic', 'point': 2.74 }, { 'outcome': 'national comprehensive cancer network nonadherent care', 'compare': 'Other', 'point': 1.3 }, { 'outcome': 'endometrial cancer 5-year survival', 'compare': 'Non-Hispanic: White', 'point': 91.0 }, { 'outcome': 'endometrial cancer 2-year survival', 'compare': 'Non-Hispanic: Black', 'point': 79.6 }, { 'outcome': 'Minimally Invasive Myomectomy for Uterine Leiomyomas', 'compare': 'Non-Black', 'point': 1.14 }, { 'outcome': 'endometrial cancer 2-year survival difference', 'compare': 'Non-Hispanic: Black', 'point': -2.0 }, { 'outcome': 'initial National Comprehensive Cancer Network distress score = 0', 'compare': 'Non-Hispanic White', 'point': 19.1 }, { 'outcome': 'clinical pregnancy', 'compare': 'Asian', 'point': 31.4 }, { 'outcome': 'systemic therapy', 'compare': 'Black', 'point': 57.7 }, { 'outcome': 'grade: poorly differentiated', 'compare': 'Black', 'point': 20.0 }, { 'outcome': 'grade: undifferentiated', 'compare': 'Black', 'point': 2.8 }, { 'outcome': 'Rural', 'compare': 'Other or unknown', 'point': 12.2 }, { 'outcome': 'Adjuvant treatment received', 'compare': 'non-Hispanic Black', 'point': 72.0 }, { 'outcome': 'Cervical funneling', 'compare': 'Hispanic', 'point': 1.8 }, { 'outcome': 'provider has talked with patient about vaccine (no)', 'compare': 'white', 'point': 21.9 }, { 'outcome': '5 year disease specific survival', 'compare': 'white', 'point': 9.4 }, { 'outcome': 'Cumulative pregnancy', 'compare': 'white female', 'point': 53.6 }, { 'outcome': 'Heart failure or MI', 'compare': 'White', 'point': 0.0 }, { 'outcome': 'laparoscopic myomectomy approach', 'compare': 'Hispanic', 'point': 3.0 }, { 'outcome': 'Insurance coverage', 'compare': 'Medicare/other govt', 'point': 39.7 }, { 'outcome': 'Gestational diabetes', 'compare': 'Hispanic', 'point': 4.1 }, { 'outcome': 'Microbiota in endometrial tumors - Bradyrhizobium genus', 'compare': 'Obese white', 'point': 0.35 }, { 'outcome': 'preterm birth', 'compare': 'asian', 'point': 12.0 }, { 'outcome': 'stage: regional', 'compare': 'White', 'point': 10.0 }, { 'outcome': 'grade: poorly differentiated', 'compare': 'Black', 'point': 18.5 }, { 'outcome': 'Microbiota in endometrial tumors - Bradyrhizobium genus', 'compare': 'Obese black', 'point': 0.02 }, { 'outcome': 'preventable pregnancy-related death', 'compare': 'Non-Hispanic other', 'point': 100.0 }, { 'outcome': 'Surgical procedure for endometriosis - Oophorectomy', 'compare': 'Native American', 'point': 5.3 }, { 'outcome': 'endometrial cancer 2-year survival difference', 'compare': 'Non-Hispanic: Black', 'point': 1.2 }, { 'outcome': 'Indication for oocyte cryopreservation - Other', 'compare': 'Non-Hispanic White', 'point': 0.9 }, { 'outcome': 'F Haplotype frequency', 'compare': 'African-American', 'point': 8.0 }, { 'outcome': 'Number of abortions done in residency rotation', 'compare': 'Black', 'point': 3.2 }, { 'outcome': 'Surgical procedure for endometriosis - Hysterectomy', 'compare': 'Asian', 'point': 32.9 }, { 'outcome': 'individual complication occurrence - reoperation', 'compare': 'Other', 'point': 0.5 }, { 'outcome': 'endometrial cancer 5-year survival', 'compare': 'Non-Hispanic: Black', 'point': 58.5 }, { 'outcome': 'live birth', 'compare': 'Hispanic', 'point': 28.5 }, { 'outcome': 'Acute renal failure or insufficiency', 'compare': 'White', 'point': 96.9 }, { 'outcome': 'five-year overall survival associated with treatment adherence status', 'compare': 'Surgery/Chemotherapy Yes/No', 'point': 40.3 }, { 'outcome': 'Nonachievement of sterilization at time of cesarean delivery', 'compare': 'White', 'point': 26.0 }, { 'outcome': 'endometrial cancer 5-year survival', 'compare': 'Non-Hispanic: Black', 'point': 84.8 }, { 'outcome': 'Intraoperative complication', 'compare': 'non-Hispanic Black', 'point': 8.0 }, { 'outcome': 'BMI', 'compare': 'PCOS White', 'point': 51.7 }, { 'outcome': 'Adjuvant treatment received', 'compare': 'non-Hispanic White', 'point': 54.0 }, { 'outcome': 'recurrence free survival', 'compare': 'Non-African American', 'point': 91.0 }, { 'outcome': 'c.130-c.131 missense mutations G>C', 'compare': 'White', 'point': 18.8 }, { 'outcome': 'endometrial cancer 5-year survival', 'compare': 'Non-Hispanic: Black', 'point': 20.5 }, { 'outcome': 'individual complication occurrence - reoperation', 'compare': 'White', 'point': 0.6 }, { 'outcome': 'grade: undifferentiated', 'compare': 'Black', 'point': 0.7 }, { 'outcome': 'Insurance coverage', 'compare': 'Other', 'point': 0.0 }, { 'outcome': 'Minimally Invasive Myomectomy for Uterine Leiomyomas', 'compare': 'Black', 'point': 1.15 }, { 'outcome': 'Surgical procedure for endometriosis - Salpingectomy or tubal ligation', 'compare': 'Black', 'point': 1.1 }, { 'outcome': 'Sterilization at the time of cesarean delivery', 'compare': 'White', 'point': 34.4 }, { 'outcome': 'Microbiota in endometrial tumors - Candidatus Rhodoluna genus', 'compare': 'Obese black', 'point': 6.53 }, { 'outcome': 'BMI cut-off value sensitivity', 'compare': 'Hispanic', 'point': 83.2 }, { 'outcome': 'Cervical length < 25 mm', 'compare': 'White', 'point': 1.0 }, { 'outcome': 'Presence of Haplotype H', 'compare': 'African-American', 'point': 46.0 }, { 'outcome': 'Presence of fibroid', 'compare': 'Black', 'point': 72.7 }, { 'outcome': 'grade: unknown', 'compare': 'Black', 'point': 16.7 }, { 'outcome': 'endometrial cancer 5-year survival', 'compare': 'Non-Hispanic: Black', 'point': 20.8 }, { 'outcome': 'endometrial cancer 2-year survival difference', 'compare': 'Non-Hispanic: Black', 'point': -3.2 }, { 'outcome': 'preterm birth', 'compare': 'black', 'point': 5.5 }, { 'outcome': 'endometrial cancer 2-year survival', 'compare': 'Non-Hispanic: Black', 'point': 66.9 }, { 'outcome': 'no vaccination history', 'compare': 'non-hispanic', 'point': 55.2 }, { 'outcome': 'overall projected decline in ectopic pregnancy mortality ratio between 2003–2007 and 2013–2017', 'compare': 'African American', 'point': 24.9 }, { 'outcome': 'preterm birth', 'compare': 'native', 'point': 13.4 }, { 'outcome': 'Surgical procedure for endometriosis - Myomectomy', 'compare': 'White', 'point': 1.8 }, { 'outcome': 'Duration of Radiation Therapy', 'compare': 'Other', 'point': 60.0 }, { 'outcome': 'Reason patient did not receive radiation therapy for treatment of cervical cancer', 'compare': 'Other', 'point': 81.0 }, { 'outcome': 'Maternal morbidity composite- Venous thromboembolism', 'compare': 'Non-Hispanic Black', 'point': 0.1 }, { 'outcome': 'severe maternal morbidity not preventable, improvement needed', 'compare': 'Non-Hispanic Black', 'point': 28.0 }, { 'outcome': 'Indication for oocyte cryopreservation - gonadotoxic therapy', 'compare': 'Asian/Pacific Islander', 'point': 3.4 }, { 'outcome': 'Metabolic syndrome', 'compare': 'PCOS Black', 'point': 42.1 }, { 'outcome': 'Surgical procedure for endometriosis - Oophorectomy', 'compare': 'Black', 'point': 6.7 }, { 'outcome': 'Microbiota in endometrial tumors - Paenibacillus genus', 'compare': 'Obese black', 'point': 2.24 }, { 'outcome': 'cervical length cutoff > 30 mm', 'compare': 'Other', 'point': 4.5 }, { 'outcome': 'Ectopic pregnancy rate', 'compare': 'Black', 'point': 3.12 }, { 'outcome': 'Myomectomy', 'compare': 'Whites', 'point': 31.0 }, { 'outcome': 'endometrial cancer five-year survival', 'compare': 'Birthplace-unknown Hispanics', 'point': 91.6 }, { 'outcome': 'Pregestational diabetes', 'compare': 'American Indian', 'point': 3.1 }, { 'outcome': 'grade: undifferentiated', 'compare': 'White', 'point': 0.8 }, { 'outcome': 'five-year survival', 'compare': 'African American', 'point': 0.84 }, { 'outcome': 'Surgical procedure for endometriosis - Laparoscopic excision or fulguration of pelvic lesions', 'compare': 'Native American', 'point': 19.7 }, { 'outcome': 'change in willingness to participate', 'compare': 'Asian', 'point': 57.0 }, { 'outcome': 'Number of abortions done in residency rotation', 'compare': 'Other', 'point': 32.1 }, { 'outcome': 'endometrial cancer 2-year survival difference', 'compare': 'Non-Hispanic: Black', 'point': -1.7 }, { 'outcome': 'endometrial cancer 2-year survival difference', 'compare': 'Non-Hispanic: Black', 'point': -0.4 }, { 'outcome': 'preterm birth', 'compare': 'native', 'point': 12.7 }, { 'outcome': 'five year survival rate', 'compare': 'White', 'point': 77.1 }, { 'outcome': 'Presence of Haplotype J', 'compare': 'African-American', 'point': 64.0 }, { 'outcome': 'Microbiota in endometrial tumors - Bacteroidetes phylum', 'compare': 'Obese black', 'point': 22.38 }, { 'outcome': 'Indication for oocyte cryopreservation - Other', 'compare': 'Non-Hispanic Black', 'point': 1.3 }, { 'outcome': 'endometrial cancer 2-year survival difference', 'compare': 'Non-Hispanic: Black', 'point': -9.5 }, { 'outcome': 'Microbiota in endometrial tumors - Bacteroidetes phylum', 'compare': 'Obese white', 'point': 3.09 }, { 'outcome': 'Somatic testing performed', 'compare': 'Black', 'point': 27.5 }, { 'outcome': 'preterm birth', 'compare': 'asian', 'point': 55.9 }, { 'outcome': 'individual complication occurrence - transfusion', 'compare': 'Other', 'point': 4.7 }, { 'outcome': 'preterm birth', 'compare': 'asian', 'point': 1.3 }, { 'outcome': 'endometrial cancer 2-year survival', 'compare': 'Non-Hispanic: White', 'point': 87.0 }, { 'outcome': 'preterm birth', 'compare': 'black', 'point': 21.5 }, { 'outcome': 'Not eligible among patients with a trial available', 'compare': 'Native American', 'point': 12.5 }, ], fields: [makeField('outcome'), makeField('compare'), makeField('point')], metadata: { 'outcome': { type: Type.String, semanticType: 'String', levels: ['5 year disease specific survival', 'Acute renal failure or insufficiency', 'Adjuvant treatment received', 'Amount heard about 2009 H1N1 vaccine', 'BMI', 'BMI cut-off value sensitivity', 'BMI cut-off value specificity', 'Cervical funneling', 'Cervical length < 25 mm', 'Cesarean birth - Labor dystocia', 'Cesarean birth - Non-reassuring fetal status', 'Cumulative pregnancy', 'Duration of Radiation Therapy', 'Ectopic pregnancy rate', 'Educational Themed Content', 'Eligible, not enrolled among patients with a trial available', 'F Haplotype frequency', 'Gestational diabetes', 'HDL', 'Heard of HPV Vaccine (Yes)', 'Heart failure or MI', 'Indication for oocyte cryopreservation - Fertility perservation, medical reason', 'Indication for oocyte cryopreservation - Other', 'Indication for oocyte cryopreservation - diminished ovarian reserve', 'Indication for oocyte cryopreservation - gonadotoxic therapy', 'Insurance coverage', 'Intraoperative complication', 'Maternal morbidity composite- Transfusion any other products', 'Maternal morbidity composite- Venous thromboembolism', 'Metabolic syndrome', 'Microbiota in endometrial tumors - Bacteroidetes phylum', 'Microbiota in endometrial tumors - Bradyrhizobium genus', 'Microbiota in endometrial tumors - Candidatus Rhodoluna genus', 'Microbiota in endometrial tumors - Cyanobacteria phylum', 'Microbiota in endometrial tumors - Dietzia genus', 'Microbiota in endometrial tumors - Firmicutes phylum', 'Microbiota in endometrial tumors - Geobacillus genus', 'Microbiota in endometrial tumors - Paenibacillus genus', 'Microbiota in endometrial tumors - Prevotella genus', 'Minimally Invasive Myomectomy for Uterine Leiomyomas', 'Myomectomy', 'Need for 2nd line chemotherapy', 'Neonatal outcomes- Small for gestational age', 'No complications', 'Nonachievement of sterilization at time of cesarean delivery', 'Not eligible among patients with a trial available', 'Number of abortions done in residency rotation', 'Pregestational diabetes', 'Presence of Haplotype H', 'Presence of Haplotype J'] }, 'compare': { type: Type.String, semanticType: 'Category', levels: ['African American', 'African-American', 'American Indian', 'American Indian/Alaskan Native', 'Asian', 'Asian/Pacific Islander', 'Birthplace-unknown Hispanics', 'Black', 'Hawaiian/PI', 'Hispanic', 'Latina/Hispanic', 'Medicare/other govt', 'Native American', 'Non-African American', 'Non-Black', 'Non-Hispanic Black', 'Non-Hispanic Mixed', 'Non-Hispanic White', 'Non-Hispanic other', 'Non-Hispanic: Black', 'Non-Hispanic: White', 'Obese black', 'Obese white', 'Other', 'Other or unknown', 'Other/unknown', 'PCOS Black', 'PCOS White', 'Surgery/Chemotherapy No/No', 'Surgery/Chemotherapy Yes/No', 'U.S. born Hispanics', 'White', 'White, non-Hispanic', 'Whites', 'african american', 'asian', 'black', 'hispanic', 'native', 'non-Hispanic Black', 'non-Hispanic White', 'non-hispanic', 'other', 'white', 'white female'] }, 'point': { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, encodingMap: { x: makeEncodingItem('compare'), y: makeEncodingItem('point'), column: makeEncodingItem('outcome'), }, }; } function gen_ombCodesQ4RecGpt41(): TestCase { // How do treasury codes map to CGAC codes, and are there treasury codes that correspond to multiple CGAC codes or vice ver // Score: 2.5/5 | The visualization is conceptually relevant but suffers from likely data/axis issues, overcrowded lab return { title: 'omb_codes_q4_rec-gpt-4.1', description: 'Summarize mapping from **treasury_code** to **cgac_code**', tags: ['debug', 'heatmap'], chartType: 'Heatmap', data: [ { 'treasury_code': '0', 'cgac_code': 0.0, 'count': 3 }, { 'treasury_code': '0*', 'cgac_code': 'NaN', 'count': 11 }, { 'treasury_code': '1', 'cgac_code': 1.0, 'count': 1 }, { 'treasury_code': '10', 'cgac_code': 10.0, 'count': 9 }, { 'treasury_code': '11', 'cgac_code': 11.0, 'count': 23 }, { 'treasury_code': '12', 'cgac_code': 12.0, 'count': 28 }, { 'treasury_code': '13', 'cgac_code': 13.0, 'count': 13 }, { 'treasury_code': '14', 'cgac_code': 14.0, 'count': 18 }, { 'treasury_code': '15', 'cgac_code': 15.0, 'count': 13 }, { 'treasury_code': '16', 'cgac_code': 16.0, 'count': 13 }, { 'treasury_code': '17', 'cgac_code': 'NaN', 'count': 1 }, { 'treasury_code': '18', 'cgac_code': 18.0, 'count': 1 }, { 'treasury_code': '19', 'cgac_code': 19.0, 'count': 4 }, { 'treasury_code': '2', 'cgac_code': 2.0, 'count': 1 }, { 'treasury_code': '20', 'cgac_code': 20.0, 'count': 14 }, { 'treasury_code': '21', 'cgac_code': 21.0, 'count': 1 }, { 'treasury_code': '21', 'cgac_code': 'NaN', 'count': 1 }, { 'treasury_code': '23', 'cgac_code': 23.0, 'count': 1 }, { 'treasury_code': '24', 'cgac_code': 24.0, 'count': 1 }, { 'treasury_code': '25', 'cgac_code': 25.0, 'count': 1 }, { 'treasury_code': '26', 'cgac_code': 26.0, 'count': 1 }, { 'treasury_code': '27', 'cgac_code': 27.0, 'count': 1 }, { 'treasury_code': '28', 'cgac_code': 28.0, 'count': 1 }, { 'treasury_code': '29', 'cgac_code': 29.0, 'count': 1 }, { 'treasury_code': '3', 'cgac_code': 3.0, 'count': 1 }, { 'treasury_code': '31', 'cgac_code': 31.0, 'count': 1 }, { 'treasury_code': '33', 'cgac_code': 33.0, 'count': 1 }, { 'treasury_code': '34', 'cgac_code': 34.0, 'count': 1 }, { 'treasury_code': '36', 'cgac_code': 36.0, 'count': 4 }, { 'treasury_code': '39', 'cgac_code': 912.0, 'count': 1 }, { 'treasury_code': '39', 'cgac_code': 913.0, 'count': 1 }, { 'treasury_code': '39', 'cgac_code': 914.0, 'count': 1 }, { 'treasury_code': '39', 'cgac_code': 915.0, 'count': 1 }, { 'treasury_code': '39', 'cgac_code': 920.0, 'count': 1 }, { 'treasury_code': '4', 'cgac_code': 4.0, 'count': 1 }, { 'treasury_code': '41', 'cgac_code': 389.0, 'count': 1 }, { 'treasury_code': '45', 'cgac_code': 45.0, 'count': 1 }, { 'treasury_code': '46', 'cgac_code': 309.0, 'count': 1 }, { 'treasury_code': '47', 'cgac_code': 47.0, 'count': 4 }, { 'treasury_code': '48', 'cgac_code': 376.0, 'count': 1 }, { 'treasury_code': '48', 'cgac_code': 377.0, 'count': 1 }, { 'treasury_code': '48', 'cgac_code': 431.0, 'count': 1 }, { 'treasury_code': '48', 'cgac_code': 435.0, 'count': 1 }, { 'treasury_code': '48', 'cgac_code': 453.0, 'count': 1 }, { 'treasury_code': '48', 'cgac_code': 479.0, 'count': 1 }, { 'treasury_code': '48', 'cgac_code': 575.0, 'count': 1 }, { 'treasury_code': '48', 'cgac_code': 584.0, 'count': 1 }, { 'treasury_code': '49', 'cgac_code': 49.0, 'count': 1 }, { 'treasury_code': '5', 'cgac_code': 5.0, 'count': 1 }, { 'treasury_code': '50', 'cgac_code': 50.0, 'count': 1 }, { 'treasury_code': '51', 'cgac_code': 51.0, 'count': 4 }, { 'treasury_code': '54', 'cgac_code': 54.0, 'count': 1 }, { 'treasury_code': '56', 'cgac_code': 56.0, 'count': 1 }, { 'treasury_code': '57', 'cgac_code': 'NaN', 'count': 1 }, { 'treasury_code': '59', 'cgac_code': 417.0, 'count': 3 }, { 'treasury_code': '60', 'cgac_code': 60.0, 'count': 1 }, { 'treasury_code': '61', 'cgac_code': 61.0, 'count': 1 }, { 'treasury_code': '62', 'cgac_code': 62.0, 'count': 1 }, { 'treasury_code': '63', 'cgac_code': 420.0, 'count': 1 }, { 'treasury_code': '64', 'cgac_code': 455.0, 'count': 1 }, { 'treasury_code': '65', 'cgac_code': 65.0, 'count': 1 }, { 'treasury_code': '68', 'cgac_code': 68.0, 'count': 1 }, { 'treasury_code': '69', 'cgac_code': 69.0, 'count': 13 }, { 'treasury_code': '70', 'cgac_code': 70.0, 'count': 14 }, { 'treasury_code': '71', 'cgac_code': 71.0, 'count': 1 }, { 'treasury_code': '72', 'cgac_code': 72.0, 'count': 2 }, { 'treasury_code': '72', 'cgac_code': 'NaN', 'count': 1 }, { 'treasury_code': '73', 'cgac_code': 73.0, 'count': 1 }, { 'treasury_code': '74', 'cgac_code': 74.0, 'count': 1 }, { 'treasury_code': '75', 'cgac_code': 75.0, 'count': 14 }, { 'treasury_code': '76', 'cgac_code': 465.0, 'count': 1 }, { 'treasury_code': '78', 'cgac_code': 352.0, 'count': 2 }, { 'treasury_code': '8', 'cgac_code': 8.0, 'count': 1 }, { 'treasury_code': '80', 'cgac_code': 80.0, 'count': 1 }, { 'treasury_code': '82', 'cgac_code': 82.0, 'count': 1 }, { 'treasury_code': '83', 'cgac_code': 83.0, 'count': 1 }, { 'treasury_code': '84', 'cgac_code': 84.0, 'count': 1 }, { 'treasury_code': '84', 'cgac_code': 'NaN', 'count': 1 }, { 'treasury_code': '86', 'cgac_code': 86.0, 'count': 10 }, { 'treasury_code': '88', 'cgac_code': 88.0, 'count': 1 }, { 'treasury_code': '89', 'cgac_code': 89.0, 'count': 6 }, { 'treasury_code': '9', 'cgac_code': 9.0, 'count': 3 }, { 'treasury_code': '90', 'cgac_code': 90.0, 'count': 1 }, { 'treasury_code': '91', 'cgac_code': 91.0, 'count': 11 }, { 'treasury_code': '93', 'cgac_code': 93.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 302.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 306.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 310.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 313.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 323.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 326.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 338.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 339.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 345.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 347.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 349.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 360.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 362.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 368.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 372.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 373.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 381.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 382.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 387.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 394.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 413.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 421.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 424.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 432.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 434.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 456.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 458.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 467.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 471.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 485.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 486.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 487.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 510.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 511.0, 'count': 2 }, { 'treasury_code': '95', 'cgac_code': 512.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 513.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 514.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 517.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 519.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 524.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 525.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 534.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 535.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 537.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 539.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 542.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 573.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 579.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 580.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 581.0, 'count': 1 }, { 'treasury_code': '95', 'cgac_code': 'NaN', 'count': 11 }, { 'treasury_code': '96', 'cgac_code': 96.0, 'count': 1 }, { 'treasury_code': '97', 'cgac_code': 17.0, 'count': 1 }, { 'treasury_code': '97', 'cgac_code': 97.0, 'count': 3 }, { 'treasury_code': '97', 'cgac_code': 'NaN', 'count': 1 }, ], fields: [makeField('treasury_code'), makeField('cgac_code'), makeField('count')], metadata: { 'treasury_code': { type: Type.String, semanticType: 'String', levels: ['0', '0*', '1', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '2', '20', '21', '23', '24', '25', '26', '27', '28', '29', '3', '31', '33', '34', '36', '39', '4', '41', '45', '46', '47', '48', '49', '5', '50', '51', '54', '56', '57', '59', '60', '61', '62', '63', '64', '65', '68'] }, 'cgac_code': { type: Type.Number, semanticType: 'Number', levels: [] }, 'count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('treasury_code'), y: makeEncodingItem('cgac_code'), color: makeEncodingItem('count'), }, chartProperties: { colorScheme: "blueorange" }, }; } function gen_ombCodesQ5RecGpt41(): TestCase { // For each agency, how do the assigned treasury codes vary across its bureaus, and which agencies show the greatest divers // Score: 10/20 | The visualization partially answers which agencies have more distinct treasury codes but does not ef return { title: 'omb_codes_q5_rec-gpt-4.1', description: 'Show top agencies by diversity of **treasury_code** across bureaus', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'agency_name': 'Legislative Branch', 'treasury_code_count': 9 }, { 'agency_name': 'Other Defense Civil Programs', 'treasury_code_count': 5 }, { 'agency_name': 'Department of Defense - Military Programs', 'treasury_code_count': 5 }, { 'agency_name': 'International Assistance Programs', 'treasury_code_count': 4 }, { 'agency_name': 'Department of State', 'treasury_code_count': 2 }, { 'agency_name': 'District of Columbia', 'treasury_code_count': 2 }, { 'agency_name': 'National Infrastructure Bank', 'treasury_code_count': 1 }, { 'agency_name': 'National Credit Union Administration', 'treasury_code_count': 1 }, { 'agency_name': 'National Endowment for the Arts', 'treasury_code_count': 1 }, { 'agency_name': 'National Endowment for the Humanities', 'treasury_code_count': 1 }, { 'agency_name': 'National Mediation Board', 'treasury_code_count': 1 }, { 'agency_name': 'National Labor Relations Board', 'treasury_code_count': 1 }, { 'agency_name': 'National Capital Planning Commission', 'treasury_code_count': 1 }, { 'agency_name': 'National Railroad Passenger Corporation Office of Inspector General', 'treasury_code_count': 1 }, { 'agency_name': 'National Science Foundation', 'treasury_code_count': 1 }, { 'agency_name': 'National Transportation Safety Board', 'treasury_code_count': 1 }, { 'agency_name': 'Neighborhood Reinvestment Corporation', 'treasury_code_count': 1 }, { 'agency_name': 'National Council on Disability', 'treasury_code_count': 1 }, { 'agency_name': 'Access Board', 'treasury_code_count': 1 }, { 'agency_name': 'National Archives and Records Administration', 'treasury_code_count': 1 }, { 'agency_name': 'Northern Border Regional Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Morris K. Udall and Stewart L. Udall Foundation', 'treasury_code_count': 1 }, { 'agency_name': 'Military Compensation and Retirement Modernization Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Merit Systems Protection Board', 'treasury_code_count': 1 }, { 'agency_name': 'Marine Mammal Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Legal Services Corporation', 'treasury_code_count': 1 }, { 'agency_name': 'Judicial Branch', 'treasury_code_count': 1 }, { 'agency_name': 'Japan-United States Friendship Commission', 'treasury_code_count': 1 }, { 'agency_name': 'James Madison Memorial Fellowship Foundation', 'treasury_code_count': 1 }, { 'agency_name': 'International Trade Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Intelligence Community Management Account', 'treasury_code_count': 1 }, { 'agency_name': 'Institute of Museum and Library Services', 'treasury_code_count': 1 }, { 'agency_name': 'Institute of American Indian and Alaska Native Culture and Arts Development', 'treasury_code_count': 1 }, { 'agency_name': 'Indian Law and Order Commission', 'treasury_code_count': 1 }, { 'agency_name': 'National Aeronautics and Space Administration', 'treasury_code_count': 1 }, { 'agency_name': 'Nuclear Regulatory Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Harry S Truman Scholarship Foundation', 'treasury_code_count': 1 }, { 'agency_name': 'Nuclear Waste Technical Review Board', 'treasury_code_count': 1 }, { 'agency_name': 'United States Interagency Council on Homelessness', 'treasury_code_count': 1 }, { 'agency_name': 'United States Institute of Peace', 'treasury_code_count': 1 }, { 'agency_name': 'United States Holocaust Memorial Museum', 'treasury_code_count': 1 }, { 'agency_name': 'United States Enrichment Corporation Fund', 'treasury_code_count': 1 }, { 'agency_name': 'United States Court of Appeals for Veterans Claims', 'treasury_code_count': 1 }, { 'agency_name': 'United Mine Workers of America Benefit Funds', 'treasury_code_count': 1 }, { 'agency_name': 'Tennessee Valley Authority', 'treasury_code_count': 1 }, { 'agency_name': 'State Justice Institute', 'treasury_code_count': 1 }, { 'agency_name': 'Standard Setting Body', 'treasury_code_count': 1 }, { 'agency_name': 'Social Security Administration', 'treasury_code_count': 1 }, { 'agency_name': 'Smithsonian Institution', 'treasury_code_count': 1 }, { 'agency_name': 'Small Business Administration', 'treasury_code_count': 1 }, { 'agency_name': 'Securities and Exchange Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Securities Investor Protection Corporation', 'treasury_code_count': 1 }, { 'agency_name': 'Recovery Act Accountability and Transparency Board', 'treasury_code_count': 1 }, { 'agency_name': 'Railroad Retirement Board', 'treasury_code_count': 1 }, { 'agency_name': 'Public Defender Service for the District of Columbia', 'treasury_code_count': 1 }, { 'agency_name': 'Public Company Accounting Oversight Board', 'treasury_code_count': 1 }, { 'agency_name': 'Privacy and Civil Liberties Oversight Board', 'treasury_code_count': 1 }, { 'agency_name': 'Presidio Trust', 'treasury_code_count': 1 }, { 'agency_name': 'Postal Service', 'treasury_code_count': 1 }, { 'agency_name': 'Patient-Centered Outcomes Research Trust Fund', 'treasury_code_count': 1 }, { 'agency_name': 'Other Commissions and Boards', 'treasury_code_count': 1 }, { 'agency_name': 'Office of the Federal Coordinator for Alaska Natural Gas Transportation Projects', 'treasury_code_count': 1 }, { 'agency_name': 'Office of Special Counsel', 'treasury_code_count': 1 }, { 'agency_name': 'Office of Personnel Management', 'treasury_code_count': 1 }, { 'agency_name': 'Office of Navajo and Hopi Indian Relocation', 'treasury_code_count': 1 }, { 'agency_name': 'Office of Government Ethics', 'treasury_code_count': 1 }, { 'agency_name': 'Occupational Safety and Health Review Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Independent Payment Advisory Board', 'treasury_code_count': 1 }, { 'agency_name': 'General Services Administration', 'treasury_code_count': 1 }, { 'agency_name': 'Gulf Coast Ecosystem Restoration Council', 'treasury_code_count': 1 }, { 'agency_name': 'Department of Housing and Urban Development', 'treasury_code_count': 1 }, { 'agency_name': 'Department of Health and Human Services', 'treasury_code_count': 1 }, { 'agency_name': 'Department of Energy', 'treasury_code_count': 1 }, { 'agency_name': 'Department of Education', 'treasury_code_count': 1 }, { 'agency_name': 'Department of Commerce', 'treasury_code_count': 1 }, { 'agency_name': 'Department of Agriculture', 'treasury_code_count': 1 }, { 'agency_name': 'Denali Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Delta Regional Authority', 'treasury_code_count': 1 }, { 'agency_name': 'Defense Nuclear Facilities Safety Board', 'treasury_code_count': 1 }, { 'agency_name': 'Court Services and Offender Supervision Agency for the District of Columbia', 'treasury_code_count': 1 }, { 'agency_name': 'Council of the Inspectors General on Integrity and Efficiency', 'treasury_code_count': 1 }, { 'agency_name': 'Corps of Engineers - Civil Works', 'treasury_code_count': 1 }, { 'agency_name': 'Corporation for Travel Promotion', 'treasury_code_count': 1 }, { 'agency_name': 'Corporation for Public Broadcasting', 'treasury_code_count': 1 }, { 'agency_name': 'Corporation for National and Community Service', 'treasury_code_count': 1 }, { 'agency_name': 'Consumer Product Safety Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Commodity Futures Trading Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Committee for Purchase from People who are Blind or Severely Disabled, activities', 'treasury_code_count': 1 }, { 'agency_name': 'Commission on Civil Rights', 'treasury_code_count': 1 }, { 'agency_name': 'Commission of Fine Arts', 'treasury_code_count': 1 }, { 'agency_name': 'Civilian Property Realignment Board', 'treasury_code_count': 1 }, { 'agency_name': 'Christopher Columbus Fellowship Foundation', 'treasury_code_count': 1 }, { 'agency_name': 'Chemical Safety and Hazard Investigation Board', 'treasury_code_count': 1 }, { 'agency_name': 'Central Intelligence Agency', 'treasury_code_count': 1 }, { 'agency_name': 'Bureau of Consumer Financial Protection', 'treasury_code_count': 1 }, { 'agency_name': 'Broadcasting Board of Governors', 'treasury_code_count': 1 }, { 'agency_name': 'Barry Goldwater Scholarship and Excellence in Education Foundation', 'treasury_code_count': 1 }, { 'agency_name': 'Appalachian Regional Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Affordable Housing Program', 'treasury_code_count': 1 }, { 'agency_name': 'Advisory Council on Historic Preservation', 'treasury_code_count': 1 }, { 'agency_name': 'Department of Homeland Security', 'treasury_code_count': 1 }, { 'agency_name': 'Department of Justice', 'treasury_code_count': 1 }, { 'agency_name': 'Administrative Conference of the United States', 'treasury_code_count': 1 }, { 'agency_name': 'Department of Labor', 'treasury_code_count': 1 }, { 'agency_name': 'Financing Vehicles and the Board of Governors of the Federal Reserve', 'treasury_code_count': 1 }, { 'agency_name': 'Federal Trade Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Federal Retirement Thrift Investment Board', 'treasury_code_count': 1 }, { 'agency_name': 'Federal National Mortgage Association', 'treasury_code_count': 1 }, { 'agency_name': 'Federal Mine Safety and Health Review Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Federal Mediation and Conciliation Service', 'treasury_code_count': 1 }, { 'agency_name': 'Federal Maritime Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Federal Labor Relations Authority', 'treasury_code_count': 1 }, { 'agency_name': 'Federal Housing Finance Agency', 'treasury_code_count': 1 }, { 'agency_name': 'Federal Home Loan Mortgage Corporation', 'treasury_code_count': 1 }, { 'agency_name': 'Federal Home Loan Bank System', 'treasury_code_count': 1 }, { 'agency_name': 'Federal Financial Institutions Examination Council', 'treasury_code_count': 1 }, { 'agency_name': 'Federal Election Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Federal Drug Control Programs', 'treasury_code_count': 1 }, { 'agency_name': 'Federal Deposit Insurance Corporation', 'treasury_code_count': 1 }, { 'agency_name': 'Federal Communications Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Farm Credit System Insurance Corporation', 'treasury_code_count': 1 }, { 'agency_name': 'Farm Credit System', 'treasury_code_count': 1 }, { 'agency_name': 'Farm Credit Administration', 'treasury_code_count': 1 }, { 'agency_name': 'Export-Import Bank of the United States', 'treasury_code_count': 1 }, { 'agency_name': 'Executive Office of the President', 'treasury_code_count': 1 }, { 'agency_name': 'Equal Employment Opportunity Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Environmental Protection Agency', 'treasury_code_count': 1 }, { 'agency_name': 'Electric Reliability Organization', 'treasury_code_count': 1 }, { 'agency_name': 'Election Assistance Commission', 'treasury_code_count': 1 }, { 'agency_name': 'Department of the Treasury', 'treasury_code_count': 1 }, { 'agency_name': 'Department of the Interior', 'treasury_code_count': 1 }, { 'agency_name': 'Department of Veterans Affairs', 'treasury_code_count': 1 }, { 'agency_name': 'Department of Transportation', 'treasury_code_count': 1 }, { 'agency_name': 'Vietnam Education Foundation', 'treasury_code_count': 1 }, ], fields: [makeField('agency_name'), makeField('treasury_code_count')], metadata: { 'agency_name': { type: Type.String, semanticType: 'Entity names', levels: ['Access Board', 'Administrative Conference of the United States', 'Advisory Council on Historic Preservation', 'Affordable Housing Program', 'Appalachian Regional Commission', 'Barry Goldwater Scholarship and Excellence in Education Foundation', 'Broadcasting Board of Governors', 'Bureau of Consumer Financial Protection', 'Central Intelligence Agency', 'Chemical Safety and Hazard Investigation Board', 'Christopher Columbus Fellowship Foundation', 'Civilian Property Realignment Board', 'Commission of Fine Arts', 'Commission on Civil Rights', 'Committee for Purchase from People who are Blind or Severely Disabled, activities', 'Commodity Futures Trading Commission', 'Consumer Product Safety Commission', 'Corporation for National and Community Service', 'Corporation for Public Broadcasting', 'Corporation for Travel Promotion', 'Corps of Engineers - Civil Works', 'Council of the Inspectors General on Integrity and Efficiency', 'Court Services and Offender Supervision Agency for the District of Columbia', 'Defense Nuclear Facilities Safety Board', 'Delta Regional Authority', 'Denali Commission', 'Department of Agriculture', 'Department of Commerce', 'Department of Defense - Military Programs', 'Department of Education', 'Department of Energy', 'Department of Health and Human Services', 'Department of Homeland Security', 'Department of Housing and Urban Development', 'Department of Justice', 'Department of Labor', 'Department of State', 'Department of Transportation', 'Department of Veterans Affairs', 'Department of the Interior', 'Department of the Treasury', 'District of Columbia', 'Election Assistance Commission', 'Electric Reliability Organization', 'Environmental Protection Agency', 'Equal Employment Opportunity Commission', 'Executive Office of the President', 'Export-Import Bank of the United States', 'Farm Credit Administration', 'Farm Credit System'] }, 'treasury_code_count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('agency_name'), y: makeEncodingItem('treasury_code_count'), }, chartProperties: { cornerRadius: 2 }, }; } function gen_penguinsRawQ1RecGpt41(): TestCase { // How do average body mass and flipper length differ across penguin species on each island? // Score: 10/20 | The visualization is partially relevant and readable but fails to incorporate the island dimension r return { title: 'penguins_raw_q1_rec-gpt-4.1', description: 'Compare average **Body Mass (g)** and **Flipper Length (mm)** by **Species** for each **Island**', tags: ['debug', 'grouped-bar-chart'], chartType: 'Grouped Bar Chart', data: [ { 'Species': 'Adelie Penguin (Pygoscelis adeliae)', 'Island': 'Biscoe', 'mean_body_mass_g': 3709.6590909091, 'mean_flipper_length_mm': 188.7954545455 }, { 'Species': 'Adelie Penguin (Pygoscelis adeliae)', 'Island': 'Dream', 'mean_body_mass_g': 3688.3928571429, 'mean_flipper_length_mm': 189.7321428571 }, { 'Species': 'Adelie Penguin (Pygoscelis adeliae)', 'Island': 'Torgersen', 'mean_body_mass_g': 3706.3725490196, 'mean_flipper_length_mm': 191.1960784314 }, { 'Species': 'Chinstrap penguin (Pygoscelis antarctica)', 'Island': 'Dream', 'mean_body_mass_g': 3733.0882352941, 'mean_flipper_length_mm': 195.8235294118 }, { 'Species': 'Gentoo penguin (Pygoscelis papua)', 'Island': 'Biscoe', 'mean_body_mass_g': 5076.0162601626, 'mean_flipper_length_mm': 217.1869918699 }, ], fields: [makeField('Species'), makeField('Island'), makeField('mean_body_mass_g'), makeField('mean_flipper_length_mm')], metadata: { 'Species': { type: Type.String, semanticType: 'Category', levels: ['Adelie Penguin (Pygoscelis adeliae)', 'Chinstrap penguin (Pygoscelis antarctica)', 'Gentoo penguin (Pygoscelis papua)'] }, 'Island': { type: Type.String, semanticType: 'Island', levels: ['Biscoe', 'Dream', 'Torgersen'] }, 'mean_body_mass_g': { type: Type.Number, semanticType: 'Amount', levels: [] }, 'mean_flipper_length_mm': { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Species'), y: makeEncodingItem('mean_body_mass_g'), group: makeEncodingItem('mean_flipper_length_mm'), column: makeEncodingItem('Island'), }, chartProperties: { cornerRadius: 5 }, }; } function gen_penguinsRawQ1RecGpt51(): TestCase { // How do average body mass and flipper length differ across penguin species on each island? // Score: 10/20 | The chart is a readable grouped bar plot by island and species but fails to clearly or correctly rep return { title: 'penguins_raw_q1_rec-gpt-5.1', description: 'Summarize average **Body Mass (g)** and **Flipper Length (mm)** by species and island', tags: ['debug', 'grouped-bar-chart'], chartType: 'Grouped Bar Chart', data: [ { 'Island': 'Biscoe', 'Species': 'Adelie Penguin (Pygoscelis adeliae)', 'metric': 'Average Body Mass (g)', 'average_value': 3709.6590909091 }, { 'Island': 'Biscoe', 'Species': 'Gentoo penguin (Pygoscelis papua)', 'metric': 'Average Body Mass (g)', 'average_value': 5076.0162601626 }, { 'Island': 'Dream', 'Species': 'Adelie Penguin (Pygoscelis adeliae)', 'metric': 'Average Body Mass (g)', 'average_value': 3688.3928571429 }, { 'Island': 'Dream', 'Species': 'Chinstrap penguin (Pygoscelis antarctica)', 'metric': 'Average Body Mass (g)', 'average_value': 3733.0882352941 }, { 'Island': 'Torgersen', 'Species': 'Adelie Penguin (Pygoscelis adeliae)', 'metric': 'Average Body Mass (g)', 'average_value': 3706.3725490196 }, { 'Island': 'Biscoe', 'Species': 'Adelie Penguin (Pygoscelis adeliae)', 'metric': 'Average Flipper Length (mm)', 'average_value': 188.7954545455 }, { 'Island': 'Biscoe', 'Species': 'Gentoo penguin (Pygoscelis papua)', 'metric': 'Average Flipper Length (mm)', 'average_value': 217.1869918699 }, { 'Island': 'Dream', 'Species': 'Adelie Penguin (Pygoscelis adeliae)', 'metric': 'Average Flipper Length (mm)', 'average_value': 189.7321428571 }, { 'Island': 'Dream', 'Species': 'Chinstrap penguin (Pygoscelis antarctica)', 'metric': 'Average Flipper Length (mm)', 'average_value': 195.8235294118 }, { 'Island': 'Torgersen', 'Species': 'Adelie Penguin (Pygoscelis adeliae)', 'metric': 'Average Flipper Length (mm)', 'average_value': 191.1960784314 }, ], fields: [makeField('Island'), makeField('Species'), makeField('metric'), makeField('average_value')], metadata: { 'Island': { type: Type.String, semanticType: 'Region', levels: ['Biscoe', 'Dream', 'Torgersen'] }, 'Species': { type: Type.String, semanticType: 'Category', levels: ['Adelie Penguin (Pygoscelis adeliae)', 'Chinstrap penguin (Pygoscelis antarctica)', 'Gentoo penguin (Pygoscelis papua)'] }, 'metric': { type: Type.String, semanticType: 'Category', levels: ['Average Body Mass (g)', 'Average Flipper Length (mm)'] }, 'average_value': { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Island'), y: makeEncodingItem('average_value'), group: makeEncodingItem('Species'), column: makeEncodingItem('metric'), }, chartProperties: { cornerRadius: 3 }, }; } function gen_pixarFilmsQ3RecGpt41(): TestCase { // What is the distribution of Pixar film runtimes, and are there any noticeable clusters or outliers? // Score: 0/20 | The chart is essentially empty, showing only axis labels without any plotted data. As a result, it d return { title: 'pixar_films_q3_rec-gpt-4.1', description: 'Show distribution of **run_time** for Pixar films', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'run_time': '81-85', 'Count': 1 }, { 'run_time': '86-90', 'Count': 0 }, { 'run_time': '91-95', 'Count': 7 }, { 'run_time': '96-100', 'Count': 5 }, { 'run_time': '101-105', 'Count': 6 }, { 'run_time': '106-110', 'Count': 1 }, { 'run_time': '111-115', 'Count': 1 }, { 'run_time': '116-120', 'Count': 2 }, { 'run_time': '121-125', 'Count': 0 }, { 'run_time': '126-130', 'Count': 0 }, { 'run_time': '131-135', 'Count': 0 }, { 'run_time': '136-140', 'Count': 0 }, { 'run_time': '141-145', 'Count': 0 }, { 'run_time': '146-150', 'Count': 1 }, { 'run_time': '151-155', 'Count': 1 }, ], fields: [makeField('run_time'), makeField('Count')], metadata: { 'run_time': { type: Type.Number, semanticType: 'Quantity', levels: [] }, 'Count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('run_time'), y: makeEncodingItem('Count'), }, chartProperties: { cornerRadius: 3 }, }; } function gen_rankByYearQ3RecGpt41(): TestCase { // What is the distribution of visa-free access counts across all countries in the most recent year, and does it show clear // Score: 2/20 | The chart appears to have failed to render the data, leaving only empty axes. It does not communicat return { title: 'rank_by_year_q3_rec-gpt-4.1', description: 'Show distribution of **visa_free_count** for countries in most recent **year**', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'visa_free_count': '(24.999, 30.0]', 'Count': 3 }, { 'visa_free_count': '(30.0, 35.0]', 'Count': 3 }, { 'visa_free_count': '(35.0, 40.0]', 'Count': 6 }, { 'visa_free_count': '(40.0, 45.0]', 'Count': 9 }, { 'visa_free_count': '(45.0, 50.0]', 'Count': 9 }, { 'visa_free_count': '(50.0, 55.0]', 'Count': 11 }, { 'visa_free_count': '(55.0, 60.0]', 'Count': 13 }, { 'visa_free_count': '(60.0, 65.0]', 'Count': 9 }, { 'visa_free_count': '(65.0, 70.0]', 'Count': 8 }, { 'visa_free_count': '(70.0, 75.0]', 'Count': 8 }, { 'visa_free_count': '(75.0, 80.0]', 'Count': 5 }, { 'visa_free_count': '(80.0, 85.0]', 'Count': 5 }, { 'visa_free_count': '(85.0, 90.0]', 'Count': 7 }, { 'visa_free_count': '(90.0, 95.0]', 'Count': 5 }, { 'visa_free_count': '(95.0, 100.0]', 'Count': 1 }, { 'visa_free_count': '(100.0, 105.0]', 'Count': 2 }, { 'visa_free_count': '(105.0, 110.0]', 'Count': 0 }, { 'visa_free_count': '(110.0, 115.0]', 'Count': 3 }, { 'visa_free_count': '(115.0, 120.0]', 'Count': 1 }, { 'visa_free_count': '(120.0, 125.0]', 'Count': 7 }, { 'visa_free_count': '(125.0, 130.0]', 'Count': 7 }, { 'visa_free_count': '(130.0, 135.0]', 'Count': 5 }, { 'visa_free_count': '(135.0, 140.0]', 'Count': 2 }, { 'visa_free_count': '(140.0, 145.0]', 'Count': 3 }, { 'visa_free_count': '(145.0, 150.0]', 'Count': 8 }, { 'visa_free_count': '(150.0, 155.0]', 'Count': 3 }, { 'visa_free_count': '(155.0, 160.0]', 'Count': 5 }, { 'visa_free_count': '(160.0, 165.0]', 'Count': 2 }, { 'visa_free_count': '(165.0, 170.0]', 'Count': 5 }, { 'visa_free_count': '(170.0, 175.0]', 'Count': 1 }, { 'visa_free_count': '(175.0, 180.0]', 'Count': 5 }, { 'visa_free_count': '(180.0, 185.0]', 'Count': 17 }, { 'visa_free_count': '(185.0, 190.0]', 'Count': 20 }, { 'visa_free_count': '(190.0, 195.0]', 'Count': 1 }, ], fields: [makeField('visa_free_count'), makeField('Count')], metadata: { 'visa_free_count': { type: Type.Number, semanticType: 'Number', levels: [] }, 'Count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('visa_free_count'), y: makeEncodingItem('Count'), }, chartProperties: { cornerRadius: 4 }, }; } function gen_simpsonsCharactersQ1RecGpt41(): TestCase { // What is the overall distribution of character genders in the dataset? // Score: 2/20 | The visualization is essentially empty and does not show the distribution of character genders. Alth return { title: 'simpsons_characters_q1_rec-gpt-4.1', description: 'Show the count of characters for each **gender**', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'character_count': 'Unknown', 'count': 6399 }, { 'character_count': 'm', 'count': 252 }, { 'character_count': 'f', 'count': 71 }, ], fields: [makeField('character_count'), makeField('count')], metadata: { 'character_count': { type: Type.Number, semanticType: 'Count', levels: [] }, 'count': { type: Type.String, semanticType: 'Category', levels: ['252', '6399', '71'] }, }, encodingMap: { x: makeEncodingItem('gender'), y: makeEncodingItem('character_count'), }, chartProperties: { cornerRadius: 5 }, }; } function gen_simpsonsLocationsQ2RecGpt41(): TestCase { // How is the number of locations distributed across the alphabet based on the first letter of the normalized_name? // Score: 9/20 | The visualization conceptually matches the task by showing counts of locations by first letter, but return { title: 'simpsons_locations_q2_rec-gpt-4.1', description: 'Show count of locations by first letter of **normalized_name**', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'first_letter': '1', 'location_count': 7 }, { 'first_letter': '2', 'location_count': 1 }, { 'first_letter': '3', 'location_count': 3 }, { 'first_letter': '4', 'location_count': 3 }, { 'first_letter': '5', 'location_count': 3 }, { 'first_letter': '9', 'location_count': 1 }, { 'first_letter': 'A', 'location_count': 207 }, { 'first_letter': 'B', 'location_count': 299 }, { 'first_letter': 'C', 'location_count': 409 }, { 'first_letter': 'D', 'location_count': 199 }, { 'first_letter': 'E', 'location_count': 92 }, { 'first_letter': 'F', 'location_count': 203 }, { 'first_letter': 'G', 'location_count': 151 }, { 'first_letter': 'H', 'location_count': 196 }, { 'first_letter': 'I', 'location_count': 101 }, { 'first_letter': 'J', 'location_count': 50 }, { 'first_letter': 'K', 'location_count': 77 }, { 'first_letter': 'L', 'location_count': 168 }, { 'first_letter': 'M', 'location_count': 302 }, { 'first_letter': 'N', 'location_count': 107 }, { 'first_letter': 'O', 'location_count': 108 }, { 'first_letter': 'P', 'location_count': 226 }, { 'first_letter': 'Q', 'location_count': 15 }, { 'first_letter': 'R', 'location_count': 201 }, { 'first_letter': 'S', 'location_count': 835 }, { 'first_letter': 'T', 'location_count': 235 }, { 'first_letter': 'U', 'location_count': 43 }, { 'first_letter': 'V', 'location_count': 64 }, { 'first_letter': 'W', 'location_count': 124 }, { 'first_letter': 'X', 'location_count': 1 }, { 'first_letter': 'Y', 'location_count': 19 }, { 'first_letter': 'Z', 'location_count': 9 }, ], fields: [makeField('first_letter'), makeField('location_count')], metadata: { 'first_letter': { type: Type.String, semanticType: 'String', levels: ['1', '2', '3', '4', '5', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'] }, 'location_count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('first_letter'), y: makeEncodingItem('location_count'), }, chartProperties: { cornerRadius: 4 }, }; } function gen_simpsonsLocationsQ2RecGpt51(): TestCase { // How is the number of locations distributed across the alphabet based on the first letter of the normalized_name? // Score: 1/20 | The figure is essentially empty apart from axis labels and a line, so it fails to show the distribut return { title: 'simpsons_locations_q2_rec-gpt-5.1', description: 'Count **normalized_name** locations by first letter of the alphabet', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'location_count': 'A', 'count': 207 }, { 'location_count': 'B', 'count': 299 }, { 'location_count': 'C', 'count': 409 }, { 'location_count': 'D', 'count': 199 }, { 'location_count': 'E', 'count': 92 }, { 'location_count': 'F', 'count': 203 }, { 'location_count': 'G', 'count': 151 }, { 'location_count': 'H', 'count': 196 }, { 'location_count': 'I', 'count': 101 }, { 'location_count': 'J', 'count': 50 }, { 'location_count': 'K', 'count': 77 }, { 'location_count': 'L', 'count': 168 }, { 'location_count': 'M', 'count': 302 }, { 'location_count': 'N', 'count': 107 }, { 'location_count': 'O', 'count': 108 }, { 'location_count': 'P', 'count': 226 }, { 'location_count': 'Q', 'count': 15 }, { 'location_count': 'R', 'count': 201 }, { 'location_count': 'S', 'count': 835 }, { 'location_count': 'T', 'count': 235 }, { 'location_count': 'U', 'count': 43 }, { 'location_count': 'V', 'count': 64 }, { 'location_count': 'W', 'count': 124 }, { 'location_count': 'X', 'count': 1 }, { 'location_count': 'Y', 'count': 19 }, { 'location_count': 'Z', 'count': 9 }, ], fields: [makeField('location_count'), makeField('count')], metadata: { 'location_count': { type: Type.Number, semanticType: 'Count', levels: [] }, 'count': { type: Type.String, semanticType: 'Category', levels: ['1', '101', '107', '108', '124', '15', '151', '168', '19', '196', '199', '201', '203', '207', '226', '235', '299', '302', '409', '43', '50', '64', '77', '835', '9', '92'] }, }, encodingMap: { x: makeEncodingItem('first_letter'), y: makeEncodingItem('location_count'), }, chartProperties: { cornerRadius: 3 }, }; } function gen_simpsonsLocationsQ5RecGpt41(): TestCase { // How is the set of locations composed when grouping normalized_name values into high-level place types (e.g., names conta // Score: 10/20 | The chart is conceptually relevant to the task, grouping locations into inferred place types, but ap return { title: 'simpsons_locations_q5_rec-gpt-4.1', description: 'Count locations grouped by inferred place type from **normalized_name**', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'place_type': 'Other', 'count': 3277 }, { 'place_type': 'House', 'count': 197 }, { 'place_type': 'Office', 'count': 147 }, { 'place_type': 'Street', 'count': 84 }, { 'place_type': 'Apartment', 'count': 71 }, { 'place_type': 'School', 'count': 68 }, { 'place_type': 'Store', 'count': 67 }, { 'place_type': 'Hotel', 'count': 43 }, { 'place_type': 'Station', 'count': 38 }, { 'place_type': 'Park', 'count': 37 }, { 'place_type': 'Hospital', 'count': 33 }, { 'place_type': 'Theater', 'count': 32 }, { 'place_type': 'Club', 'count': 31 }, { 'place_type': 'Airport', 'count': 30 }, { 'place_type': 'Home', 'count': 29 }, { 'place_type': 'Restaurant', 'count': 28 }, { 'place_type': 'Museum', 'count': 27 }, { 'place_type': 'Bar', 'count': 22 }, { 'place_type': 'Church', 'count': 20 }, { 'place_type': 'Factory', 'count': 20 }, { 'place_type': 'Arena', 'count': 17 }, { 'place_type': 'Mall', 'count': 16 }, { 'place_type': 'Bank', 'count': 14 }, { 'place_type': 'Auditorium', 'count': 14 }, { 'place_type': 'Court', 'count': 13 }, { 'place_type': 'Farm', 'count': 12 }, { 'place_type': 'Library', 'count': 11 }, { 'place_type': 'Bridge', 'count': 11 }, { 'place_type': 'Lab', 'count': 10 }, { 'place_type': 'Casino', 'count': 8 }, { 'place_type': 'Gym', 'count': 8 }, { 'place_type': 'Clinic', 'count': 6 }, { 'place_type': 'Cafe', 'count': 6 }, { 'place_type': 'Zoo', 'count': 5 }, { 'place_type': 'Diner', 'count': 4 }, { 'place_type': 'Arcade', 'count': 3 }, ], fields: [makeField('place_type'), makeField('count')], metadata: { 'place_type': { type: Type.String, semanticType: 'Category', levels: ['Airport', 'Apartment', 'Arcade', 'Arena', 'Auditorium', 'Bank', 'Bar', 'Bridge', 'Cafe', 'Casino', 'Church', 'Clinic', 'Club', 'Court', 'Diner', 'Factory', 'Farm', 'Gym', 'Home', 'Hospital', 'Hotel', 'House', 'Lab', 'Library', 'Mall', 'Museum', 'Office', 'Other', 'Park', 'Restaurant', 'School', 'Station', 'Store', 'Street', 'Theater', 'Zoo'] }, 'count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('place_type'), y: makeEncodingItem('count'), }, chartProperties: { cornerRadius: 4 }, }; } function gen_topicsQ1RecGpt41(): TestCase { // What is the distribution of songs (or lines) across the different lyrical topics? // Score: 10/20 | The chart technically shows a count per lyrical topic, but likely counts topics rather than songs/li return { title: 'topics_q1_rec-gpt-4.1', description: 'Show count of songs per **lyrical_topics**', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'lyrical_topics': 'Addiction', 'song_count': 1 }, { 'lyrical_topics': 'Jobs', 'song_count': 1 }, { 'lyrical_topics': 'Prostitution', 'song_count': 1 }, { 'lyrical_topics': 'Poverty', 'song_count': 1 }, { 'lyrical_topics': 'Positivity', 'song_count': 1 }, { 'lyrical_topics': 'Police', 'song_count': 1 }, { 'lyrical_topics': 'Playing Music', 'song_count': 1 }, { 'lyrical_topics': 'Partying', 'song_count': 1 }, { 'lyrical_topics': 'Pain', 'song_count': 1 }, { 'lyrical_topics': 'Paranoia', 'song_count': 1 }, { 'lyrical_topics': 'Nudity', 'song_count': 1 }, { 'lyrical_topics': 'New York', 'song_count': 1 }, { 'lyrical_topics': 'Murder', 'song_count': 1 }, { 'lyrical_topics': 'Materialism', 'song_count': 1 }, { 'lyrical_topics': 'Masturbation', 'song_count': 1 }, { 'lyrical_topics': 'Marriage', 'song_count': 1 }, { 'lyrical_topics': 'Magic', 'song_count': 1 }, { 'lyrical_topics': 'Lust/Sex', 'song_count': 1 }, { 'lyrical_topics': 'Love', 'song_count': 1 }, { 'lyrical_topics': 'Lost Innocence', 'song_count': 1 }, { 'lyrical_topics': 'Lost Love', 'song_count': 1 }, { 'lyrical_topics': 'Longing for Love', 'song_count': 1 }, { 'lyrical_topics': 'Lonesomeness', 'song_count': 1 }, { 'lyrical_topics': 'Racism', 'song_count': 1 }, { 'lyrical_topics': 'Rain', 'song_count': 1 }, { 'lyrical_topics': 'Redemption', 'song_count': 1 }, { 'lyrical_topics': 'Sin', 'song_count': 1 }, { 'lyrical_topics': 'War', 'song_count': 1 }, { 'lyrical_topics': 'Violence', 'song_count': 1 }, { 'lyrical_topics': 'Utopia/Perfect Places', 'song_count': 1 }, { 'lyrical_topics': 'Time', 'song_count': 1 }, { 'lyrical_topics': 'Thrift Shopping', 'song_count': 1 }, { 'lyrical_topics': 'Survival', 'song_count': 1 }, { 'lyrical_topics': 'Surfing/Sailing', 'song_count': 1 }, { 'lyrical_topics': 'Superstition', 'song_count': 1 }, { 'lyrical_topics': 'Summer', 'song_count': 1 }, { 'lyrical_topics': 'Shyness', 'song_count': 1 }, { 'lyrical_topics': 'Regret', 'song_count': 1 }, { 'lyrical_topics': 'Self-Acceptance', 'song_count': 1 }, { 'lyrical_topics': 'Secrets', 'song_count': 1 }, { 'lyrical_topics': 'School', 'song_count': 1 }, { 'lyrical_topics': 'Sadness', 'song_count': 1 }, { 'lyrical_topics': 'Revenge', 'song_count': 1 }, { 'lyrical_topics': 'Restraint', 'song_count': 1 }, { 'lyrical_topics': 'Restlessness', 'song_count': 1 }, { 'lyrical_topics': 'Resilience', 'song_count': 1 }, { 'lyrical_topics': 'Respect', 'song_count': 1 }, { 'lyrical_topics': 'Life', 'song_count': 1 }, { 'lyrical_topics': 'Jewelry', 'song_count': 1 }, { 'lyrical_topics': 'Anger', 'song_count': 1 }, { 'lyrical_topics': 'Insomnia', 'song_count': 1 }, { 'lyrical_topics': 'Empowerment', 'song_count': 1 }, { 'lyrical_topics': 'Dystopia', 'song_count': 1 }, { 'lyrical_topics': 'Driving', 'song_count': 1 }, { 'lyrical_topics': 'Dreaming', 'song_count': 1 }, { 'lyrical_topics': 'Doubt', 'song_count': 1 }, { 'lyrical_topics': 'Death', 'song_count': 1 }, { 'lyrical_topics': 'Dancing', 'song_count': 1 }, { 'lyrical_topics': 'Coexistance', 'song_count': 1 }, { 'lyrical_topics': 'Childhood', 'song_count': 1 }, { 'lyrical_topics': 'Child Birth', 'song_count': 1 }, { 'lyrical_topics': 'Christmas', 'song_count': 1 }, { 'lyrical_topics': 'Candy', 'song_count': 1 }, { 'lyrical_topics': 'Calming Down', 'song_count': 1 }, { 'lyrical_topics': 'Bragging', 'song_count': 1 }, { 'lyrical_topics': 'Better Times', 'song_count': 1 }, { 'lyrical_topics': 'Batman', 'song_count': 1 }, { 'lyrical_topics': 'Bands', 'song_count': 1 }, { 'lyrical_topics': 'Bad Relationships', 'song_count': 1 }, { 'lyrical_topics': 'Bad Behavior', 'song_count': 1 }, { 'lyrical_topics': 'Badassery', 'song_count': 1 }, { 'lyrical_topics': 'Appreciation', 'song_count': 1 }, { 'lyrical_topics': 'Endings/Goodbyes', 'song_count': 1 }, { 'lyrical_topics': 'Enlightement', 'song_count': 1 }, { 'lyrical_topics': 'Escape', 'song_count': 1 }, { 'lyrical_topics': 'God', 'song_count': 1 }, { 'lyrical_topics': 'Illigitimacy', 'song_count': 1 }, { 'lyrical_topics': 'Infidelity', 'song_count': 1 }, { 'lyrical_topics': 'Hypocrisy', 'song_count': 1 }, { 'lyrical_topics': 'Hope', 'song_count': 1 }, { 'lyrical_topics': 'Homelessness', 'song_count': 1 }, { 'lyrical_topics': 'Home', 'song_count': 1 }, { 'lyrical_topics': 'Happiness', 'song_count': 1 }, { 'lyrical_topics': 'Growing Up', 'song_count': 1 }, { 'lyrical_topics': 'Good vs. Evil', 'song_count': 1 }, { 'lyrical_topics': 'Ghosts', 'song_count': 1 }, { 'lyrical_topics': 'Failure', 'song_count': 1 }, { 'lyrical_topics': 'Getting Older', 'song_count': 1 }, { 'lyrical_topics': 'Getting Back Together', 'song_count': 1 }, { 'lyrical_topics': 'Flying', 'song_count': 1 }, { 'lyrical_topics': 'Friendship', 'song_count': 1 }, { 'lyrical_topics': 'Forbidden Love', 'song_count': 1 }, { 'lyrical_topics': 'Fighting', 'song_count': 1 }, { 'lyrical_topics': 'Family', 'song_count': 1 }, { 'lyrical_topics': 'Fame', 'song_count': 1 }, { 'lyrical_topics': 'Faithfulness', 'song_count': 1 }, { 'lyrical_topics': 'Work', 'song_count': 1 }, ], fields: [makeField('lyrical_topics'), makeField('song_count')], metadata: { 'lyrical_topics': { type: Type.String, semanticType: 'Category', levels: ['Addiction', 'Anger', 'Appreciation', 'Bad Behavior', 'Bad Relationships', 'Badassery', 'Bands', 'Batman', 'Better Times', 'Bragging', 'Calming Down', 'Candy', 'Child Birth', 'Childhood', 'Christmas', 'Coexistance', 'Dancing', 'Death', 'Doubt', 'Dreaming', 'Driving', 'Dystopia', 'Empowerment', 'Endings/Goodbyes', 'Enlightement', 'Escape', 'Failure', 'Faithfulness', 'Fame', 'Family', 'Fighting', 'Flying', 'Forbidden Love', 'Friendship', 'Getting Back Together', 'Getting Older', 'Ghosts', 'God', 'Good vs. Evil', 'Growing Up', 'Happiness', 'Home', 'Homelessness', 'Hope', 'Hypocrisy', 'Illigitimacy', 'Infidelity', 'Insomnia', 'Jewelry', 'Jobs'] }, 'song_count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('lyrical_topics'), y: makeEncodingItem('song_count'), }, chartProperties: { cornerRadius: 4 }, }; } function gen_topicsQ2RecGpt51(): TestCase { // Which lyrical topics are most common versus least common, and how do their counts compare? // Score: 7/20 | The visualization mechanically plots a count for each lyrical topic but appears to just count unique return { title: 'topics_q2_rec-gpt-5.1', description: 'Count how often each **lyrical_topics** category appears', tags: ['debug', 'lollipop-chart'], chartType: 'Lollipop Chart', data: [ { 'lyrical_topics': 'Addiction', 'topic_count': 1 }, { 'lyrical_topics': 'Anger', 'topic_count': 1 }, { 'lyrical_topics': 'Appreciation', 'topic_count': 1 }, { 'lyrical_topics': 'Badassery', 'topic_count': 1 }, { 'lyrical_topics': 'Bad Behavior', 'topic_count': 1 }, { 'lyrical_topics': 'Bad Relationships', 'topic_count': 1 }, { 'lyrical_topics': 'Bands', 'topic_count': 1 }, { 'lyrical_topics': 'Batman', 'topic_count': 1 }, { 'lyrical_topics': 'Better Times', 'topic_count': 1 }, { 'lyrical_topics': 'Bragging', 'topic_count': 1 }, { 'lyrical_topics': 'Calming Down', 'topic_count': 1 }, { 'lyrical_topics': 'Candy', 'topic_count': 1 }, { 'lyrical_topics': 'Christmas', 'topic_count': 1 }, { 'lyrical_topics': 'Child Birth', 'topic_count': 1 }, { 'lyrical_topics': 'Childhood', 'topic_count': 1 }, { 'lyrical_topics': 'Coexistance', 'topic_count': 1 }, { 'lyrical_topics': 'Dancing', 'topic_count': 1 }, { 'lyrical_topics': 'Death', 'topic_count': 1 }, { 'lyrical_topics': 'Doubt', 'topic_count': 1 }, { 'lyrical_topics': 'Dreaming', 'topic_count': 1 }, { 'lyrical_topics': 'Driving', 'topic_count': 1 }, { 'lyrical_topics': 'Dystopia', 'topic_count': 1 }, { 'lyrical_topics': 'Empowerment', 'topic_count': 1 }, { 'lyrical_topics': 'Endings/Goodbyes', 'topic_count': 1 }, { 'lyrical_topics': 'Enlightement', 'topic_count': 1 }, { 'lyrical_topics': 'Escape', 'topic_count': 1 }, { 'lyrical_topics': 'Failure', 'topic_count': 1 }, { 'lyrical_topics': 'Faithfulness', 'topic_count': 1 }, { 'lyrical_topics': 'Fame', 'topic_count': 1 }, { 'lyrical_topics': 'Family', 'topic_count': 1 }, { 'lyrical_topics': 'Fighting', 'topic_count': 1 }, { 'lyrical_topics': 'Forbidden Love', 'topic_count': 1 }, { 'lyrical_topics': 'Friendship', 'topic_count': 1 }, { 'lyrical_topics': 'Flying', 'topic_count': 1 }, { 'lyrical_topics': 'Getting Back Together', 'topic_count': 1 }, { 'lyrical_topics': 'Getting Older', 'topic_count': 1 }, { 'lyrical_topics': 'Ghosts', 'topic_count': 1 }, { 'lyrical_topics': 'God', 'topic_count': 1 }, { 'lyrical_topics': 'Good vs. Evil', 'topic_count': 1 }, { 'lyrical_topics': 'Growing Up', 'topic_count': 1 }, { 'lyrical_topics': 'Happiness', 'topic_count': 1 }, { 'lyrical_topics': 'Home', 'topic_count': 1 }, { 'lyrical_topics': 'Homelessness', 'topic_count': 1 }, { 'lyrical_topics': 'Hope', 'topic_count': 1 }, { 'lyrical_topics': 'Hypocrisy', 'topic_count': 1 }, { 'lyrical_topics': 'Infidelity', 'topic_count': 1 }, { 'lyrical_topics': 'Illigitimacy', 'topic_count': 1 }, { 'lyrical_topics': 'Insomnia', 'topic_count': 1 }, { 'lyrical_topics': 'Jewelry', 'topic_count': 1 }, { 'lyrical_topics': 'Jobs', 'topic_count': 1 }, { 'lyrical_topics': 'Life', 'topic_count': 1 }, { 'lyrical_topics': 'Lonesomeness', 'topic_count': 1 }, { 'lyrical_topics': 'Longing for Love', 'topic_count': 1 }, { 'lyrical_topics': 'Lost Love', 'topic_count': 1 }, { 'lyrical_topics': 'Lost Innocence', 'topic_count': 1 }, { 'lyrical_topics': 'Love', 'topic_count': 1 }, { 'lyrical_topics': 'Lust/Sex', 'topic_count': 1 }, { 'lyrical_topics': 'Magic', 'topic_count': 1 }, { 'lyrical_topics': 'Marriage', 'topic_count': 1 }, { 'lyrical_topics': 'Masturbation', 'topic_count': 1 }, { 'lyrical_topics': 'Materialism', 'topic_count': 1 }, { 'lyrical_topics': 'Murder', 'topic_count': 1 }, { 'lyrical_topics': 'New York', 'topic_count': 1 }, { 'lyrical_topics': 'Nudity', 'topic_count': 1 }, { 'lyrical_topics': 'Paranoia', 'topic_count': 1 }, { 'lyrical_topics': 'Pain', 'topic_count': 1 }, { 'lyrical_topics': 'Partying', 'topic_count': 1 }, { 'lyrical_topics': 'Playing Music', 'topic_count': 1 }, { 'lyrical_topics': 'Police', 'topic_count': 1 }, { 'lyrical_topics': 'Positivity', 'topic_count': 1 }, { 'lyrical_topics': 'Poverty', 'topic_count': 1 }, { 'lyrical_topics': 'Prostitution', 'topic_count': 1 }, { 'lyrical_topics': 'Racism', 'topic_count': 1 }, { 'lyrical_topics': 'Rain', 'topic_count': 1 }, { 'lyrical_topics': 'Redemption', 'topic_count': 1 }, { 'lyrical_topics': 'Regret', 'topic_count': 1 }, { 'lyrical_topics': 'Respect', 'topic_count': 1 }, { 'lyrical_topics': 'Resilience', 'topic_count': 1 }, { 'lyrical_topics': 'Restlessness', 'topic_count': 1 }, { 'lyrical_topics': 'Restraint', 'topic_count': 1 }, { 'lyrical_topics': 'Revenge', 'topic_count': 1 }, { 'lyrical_topics': 'Sadness', 'topic_count': 1 }, { 'lyrical_topics': 'School', 'topic_count': 1 }, { 'lyrical_topics': 'Secrets', 'topic_count': 1 }, { 'lyrical_topics': 'Self-Acceptance', 'topic_count': 1 }, { 'lyrical_topics': 'Shyness', 'topic_count': 1 }, { 'lyrical_topics': 'Sin', 'topic_count': 1 }, { 'lyrical_topics': 'Summer', 'topic_count': 1 }, { 'lyrical_topics': 'Superstition', 'topic_count': 1 }, { 'lyrical_topics': 'Surfing/Sailing', 'topic_count': 1 }, { 'lyrical_topics': 'Survival', 'topic_count': 1 }, { 'lyrical_topics': 'Thrift Shopping', 'topic_count': 1 }, { 'lyrical_topics': 'Time', 'topic_count': 1 }, { 'lyrical_topics': 'Utopia/Perfect Places', 'topic_count': 1 }, { 'lyrical_topics': 'Violence', 'topic_count': 1 }, { 'lyrical_topics': 'War', 'topic_count': 1 }, { 'lyrical_topics': 'Work', 'topic_count': 1 }, ], fields: [makeField('lyrical_topics'), makeField('topic_count')], metadata: { 'lyrical_topics': { type: Type.String, semanticType: 'Category', levels: ['Addiction', 'Anger', 'Appreciation', 'Bad Behavior', 'Bad Relationships', 'Badassery', 'Bands', 'Batman', 'Better Times', 'Bragging', 'Calming Down', 'Candy', 'Child Birth', 'Childhood', 'Christmas', 'Coexistance', 'Dancing', 'Death', 'Doubt', 'Dreaming', 'Driving', 'Dystopia', 'Empowerment', 'Endings/Goodbyes', 'Enlightement', 'Escape', 'Failure', 'Faithfulness', 'Fame', 'Family', 'Fighting', 'Flying', 'Forbidden Love', 'Friendship', 'Getting Back Together', 'Getting Older', 'Ghosts', 'God', 'Good vs. Evil', 'Growing Up', 'Happiness', 'Home', 'Homelessness', 'Hope', 'Hypocrisy', 'Illigitimacy', 'Infidelity', 'Insomnia', 'Jewelry', 'Jobs'] }, 'topic_count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('topic_count'), y: makeEncodingItem('lyrical_topics'), }, }; } function gen_user2025Q1RecGpt41(): TestCase { // How is the number of scheduled sessions distributed across the conference days? // Score: 9/20 | The visualization partially addresses the question of how sessions are distributed across conference return { title: 'user2025_q1_rec-gpt-4.1', description: 'Show count of scheduled **session** per **date**', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'date': '2025-08-01', 'session_count': 31 }, { 'date': '2025-08-08', 'session_count': 22 }, { 'date': '2025-08-09', 'session_count': 40 }, { 'date': '2025-08-10', 'session_count': 35 }, ], fields: [makeField('date'), makeField('session_count')], metadata: { 'date': { type: Type.Date, semanticType: 'Date', levels: ['2025-08-01', '2025-08-08', '2025-08-09', '2025-08-10'] }, 'session_count': { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('date'), y: makeEncodingItem('session_count'), }, chartProperties: { cornerRadius: 4 }, }; } function gen_user2025Q5RecGpt51(): TestCase { // How is the composition of virtual vs. in‑person sessions changing over the conference dates, and what proportion of each // Score: 10/20 | The visualization provides a basic comparison of virtual vs in-person sessions across dates but lack return { title: 'user2025_q5_rec-gpt-5.1', description: 'Summarize **session** modality mix and recording rates by **date**', tags: ['debug', 'line-chart'], chartType: 'Line Chart', data: [ { 'date': '2025-08-01', 'modality': 'Virtual', 'total_sessions': 31, 'recorded_sessions': 29, 'sessions_per_date': 31, 'metric': 'Share of sessions by modality', 'value': 1.0 }, { 'date': '2025-08-08', 'modality': 'In-person', 'total_sessions': 22, 'recorded_sessions': 14, 'sessions_per_date': 22, 'metric': 'Share of sessions by modality', 'value': 1.0 }, { 'date': '2025-08-09', 'modality': 'In-person', 'total_sessions': 40, 'recorded_sessions': 40, 'sessions_per_date': 40, 'metric': 'Share of sessions by modality', 'value': 1.0 }, { 'date': '2025-08-10', 'modality': 'In-person', 'total_sessions': 35, 'recorded_sessions': 34, 'sessions_per_date': 35, 'metric': 'Share of sessions by modality', 'value': 1.0 }, { 'date': '2025-08-01', 'modality': 'Virtual', 'total_sessions': 31, 'recorded_sessions': 29, 'sessions_per_date': 31, 'metric': 'Share recorded within modality', 'value': 0.935483871 }, { 'date': '2025-08-08', 'modality': 'In-person', 'total_sessions': 22, 'recorded_sessions': 14, 'sessions_per_date': 22, 'metric': 'Share recorded within modality', 'value': 0.6363636364 }, { 'date': '2025-08-09', 'modality': 'In-person', 'total_sessions': 40, 'recorded_sessions': 40, 'sessions_per_date': 40, 'metric': 'Share recorded within modality', 'value': 1.0 }, { 'date': '2025-08-10', 'modality': 'In-person', 'total_sessions': 35, 'recorded_sessions': 34, 'sessions_per_date': 35, 'metric': 'Share recorded within modality', 'value': 0.9714285714 }, ], fields: [makeField('date'), makeField('modality'), makeField('total_sessions'), makeField('recorded_sessions'), makeField('sessions_per_date'), makeField('metric'), makeField('value')], metadata: { 'date': { type: Type.Date, semanticType: 'Date', levels: ['2025-08-01', '2025-08-08', '2025-08-09', '2025-08-10'] }, 'modality': { type: Type.String, semanticType: 'Category', levels: ['In-person', 'Virtual'] }, 'total_sessions': { type: Type.String, semanticType: 'Category', levels: ['22', '31', '35', '40'] }, 'recorded_sessions': { type: Type.String, semanticType: 'Category', levels: ['14', '29', '34', '40'] }, 'sessions_per_date': { type: Type.String, semanticType: 'Category', levels: ['22', '31', '35', '40'] }, 'metric': { type: Type.String, semanticType: 'String', levels: ['Share of sessions by modality', 'Share recorded within modality'] }, 'value': { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, encodingMap: { x: makeEncodingItem('date'), y: makeEncodingItem('value'), color: makeEncodingItem('modality'), column: makeEncodingItem('metric'), }, chartProperties: { interpolate: "monotone" }, }; } function gen_vesuviusQ4RecGpt41(): TestCase { // Which years have the highest average duration_magnitude_md, and how do they rank from highest to lowest? // Score: 10/20 | Visually clean bar chart of average duration_magnitude_md per year, but it misrepresents the dataset return { title: 'vesuvius_q4_rec-gpt-4.1', description: 'Show average **duration_magnitude_md** per **year**, ranked highest to lowest', tags: ['debug', 'bar-chart'], chartType: 'Bar Chart', data: [ { 'year': 2011, 'avg_duration_magnitude_md': 1.2, 'rank': 1 }, { 'year': 2012, 'avg_duration_magnitude_md': 0.7, 'rank': 2 }, { 'year': 2023, 'avg_duration_magnitude_md': 0.3005106888, 'rank': 3 }, { 'year': 2022, 'avg_duration_magnitude_md': 0.245601907, 'rank': 4 }, { 'year': 2020, 'avg_duration_magnitude_md': 0.2355498721, 'rank': 5 }, { 'year': 2017, 'avg_duration_magnitude_md': 0.2258205689, 'rank': 6 }, { 'year': 2014, 'avg_duration_magnitude_md': 0.1986091794, 'rank': 7 }, { 'year': 2013, 'avg_duration_magnitude_md': 0.1971326165, 'rank': 8 }, { 'year': 2021, 'avg_duration_magnitude_md': 0.1837883284, 'rank': 9 }, { 'year': 2018, 'avg_duration_magnitude_md': 0.1801710731, 'rank': 10 }, { 'year': 2016, 'avg_duration_magnitude_md': 0.1779725086, 'rank': 11 }, { 'year': 2024, 'avg_duration_magnitude_md': 0.1388629283, 'rank': 12 }, { 'year': 2015, 'avg_duration_magnitude_md': 0.0563169165, 'rank': 13 }, { 'year': 2019, 'avg_duration_magnitude_md': 0.0494635373, 'rank': 14 }, ], fields: [makeField('year'), makeField('avg_duration_magnitude_md'), makeField('rank')], metadata: { 'year': { type: Type.Number, semanticType: 'Year', levels: [] }, 'avg_duration_magnitude_md': { type: Type.Number, semanticType: 'Number', levels: [] }, 'rank': { type: Type.Number, semanticType: 'Rank', levels: [] }, }, encodingMap: { x: makeEncodingItem('year'), y: makeEncodingItem('avg_duration_magnitude_md'), }, chartProperties: { cornerRadius: 4 }, }; } function gen_vesuviusQ5RecGpt41(): TestCase { // How are earthquake epicenters spatially distributed around Mount Vesuvius, and how does depth_km vary across latitude an // Score: 1/20 | The visualization is essentially just a depth colorbar with a single dot and lacks the necessary spa return { title: 'vesuvius_q5_rec-gpt-4.1', description: 'Map earthquake epicenters and show **depth_km** variation', tags: ['debug', 'scatter-plot'], chartType: 'Scatter Plot', data: [ { 'latitude': 40.821831, 'longitude': 14.4305, 'depth_km': 0.23 }, { 'latitude': 40.817169, 'longitude': 14.427, 'depth_km': 0.07 }, { 'latitude': 40.818668, 'longitude': 14.43267, 'depth_km': 0.4 }, { 'latitude': 40.817329, 'longitude': 14.42833, 'depth_km': 0.3 }, { 'latitude': 40.821831, 'longitude': 14.42683, 'depth_km': 0.37 }, { 'latitude': 40.824329, 'longitude': 14.42383, 'depth_km': 0.66 }, { 'latitude': 40.8195, 'longitude': 14.42783, 'depth_km': 0.21 }, { 'latitude': 40.817669, 'longitude': 14.43267, 'depth_km': 0.16 }, { 'latitude': 40.822201, 'longitude': 14.4287, 'depth_km': 0.78 }, { 'latitude': 40.818668, 'longitude': 14.42517, 'depth_km': 0.24 }, { 'latitude': 40.819, 'longitude': 14.42383, 'depth_km': 0.07 }, { 'latitude': 40.817001, 'longitude': 14.4305, 'depth_km': 0.47 }, { 'latitude': 40.823669, 'longitude': 14.43017, 'depth_km': 0.23 }, { 'latitude': 40.82267, 'longitude': 14.4345, 'depth_km': 0.43 }, { 'latitude': 40.822, 'longitude': 14.42, 'depth_km': 0.22 }, { 'latitude': 40.820332, 'longitude': 14.41967, 'depth_km': 0.84 }, { 'latitude': 40.823669, 'longitude': 14.42433, 'depth_km': 1.71 }, { 'latitude': 40.8223, 'longitude': 14.4315, 'depth_km': 0.28 }, { 'latitude': 40.820839, 'longitude': 14.424, 'depth_km': 0.09 }, { 'latitude': 40.823167, 'longitude': 14.422333, 'depth_km': 0.17 }, { 'latitude': 40.82333, 'longitude': 14.42617, 'depth_km': 0.11 }, { 'latitude': 40.817001, 'longitude': 14.43367, 'depth_km': 0.05 }, { 'latitude': 40.819672, 'longitude': 14.4345, 'depth_km': 0.15 }, { 'latitude': 40.818501, 'longitude': 14.428, 'depth_km': 0.13 }, { 'latitude': 40.824329, 'longitude': 14.42533, 'depth_km': 0.93 }, { 'latitude': 40.820839, 'longitude': 14.43633, 'depth_km': 0.64 }, { 'latitude': 40.817829, 'longitude': 14.42467, 'depth_km': 0.2 }, { 'latitude': 40.81633, 'longitude': 14.42917, 'depth_km': 0.21 }, { 'latitude': 40.822498, 'longitude': 14.42883, 'depth_km': 0.17 }, { 'latitude': 40.82, 'longitude': 14.42217, 'depth_km': 0.22 }, { 'latitude': 40.821671, 'longitude': 14.4255, 'depth_km': 0.06 }, { 'latitude': 40.821301, 'longitude': 14.4317, 'depth_km': 0.98 }, { 'latitude': 40.819328, 'longitude': 14.42833, 'depth_km': 0.11 }, { 'latitude': 40.822701, 'longitude': 14.4298, 'depth_km': 0.11 }, { 'latitude': 40.823502, 'longitude': 14.42333, 'depth_km': 0.2 }, { 'latitude': 40.820332, 'longitude': 14.431, 'depth_km': 0.22 }, { 'latitude': 40.81633, 'longitude': 14.42667, 'depth_km': 0.07 }, { 'latitude': 40.819667, 'longitude': 14.433, 'depth_km': 0.18 }, { 'latitude': 40.80917, 'longitude': 14.42333, 'depth_km': 0.78 }, { 'latitude': 40.8237, 'longitude': 14.4335, 'depth_km': 0.27 }, { 'latitude': 40.816669, 'longitude': 14.43117, 'depth_km': 0.32 }, { 'latitude': 40.818668, 'longitude': 14.4275, 'depth_km': 0.34 }, { 'latitude': 40.823299, 'longitude': 14.4295, 'depth_km': 1.56 }, { 'latitude': 40.821171, 'longitude': 14.43033, 'depth_km': 0.1 }, { 'latitude': 40.820171, 'longitude': 14.43017, 'depth_km': 0.02 }, { 'latitude': 40.821999, 'longitude': 14.43483, 'depth_km': 0.8 }, { 'latitude': 40.819, 'longitude': 14.4275, 'depth_km': 0.17 }, { 'latitude': 40.819, 'longitude': 14.43183, 'depth_km': 0.23 }, { 'latitude': 40.819, 'longitude': 14.42567, 'depth_km': 0.05 }, { 'latitude': 40.818668, 'longitude': 14.43217, 'depth_km': 0.09 }, { 'latitude': 40.824669, 'longitude': 14.4295, 'depth_km': 0.07 }, { 'latitude': 40.8232, 'longitude': 14.429, 'depth_km': 1.48 }, { 'latitude': 40.819672, 'longitude': 14.4315, 'depth_km': 0.17 }, { 'latitude': 40.8235, 'longitude': 14.426167, 'depth_km': 0.32 }, { 'latitude': 40.821499, 'longitude': 14.4278, 'depth_km': 0.16 }, { 'latitude': 40.819672, 'longitude': 14.42883, 'depth_km': 0.21 }, { 'latitude': 40.820499, 'longitude': 14.4322, 'depth_km': 0.06 }, { 'latitude': 40.820202, 'longitude': 14.4247, 'depth_km': 0.14 }, { 'latitude': 40.820671, 'longitude': 14.43033, 'depth_km': 0.28 }, { 'latitude': 40.820671, 'longitude': 14.43383, 'depth_km': 0.08 }, { 'latitude': 40.8265, 'longitude': 14.42683, 'depth_km': 0.54 }, { 'latitude': 40.819328, 'longitude': 14.4325, 'depth_km': 0.06 }, { 'latitude': 40.82217, 'longitude': 14.431, 'depth_km': 0.34 }, { 'latitude': 40.820171, 'longitude': 14.432, 'depth_km': 0.25 }, { 'latitude': 40.825833, 'longitude': 14.421167, 'depth_km': 0.25 }, { 'latitude': 40.821499, 'longitude': 14.4283, 'depth_km': 0.24 }, { 'latitude': 40.821831, 'longitude': 14.42917, 'depth_km': 0.35 }, { 'latitude': 40.824329, 'longitude': 14.4245, 'depth_km': 0.27 }, { 'latitude': 40.819328, 'longitude': 14.42733, 'depth_km': 0.22 }, { 'latitude': 40.8195, 'longitude': 14.42167, 'depth_km': 0.17 }, { 'latitude': 40.822498, 'longitude': 14.43067, 'depth_km': 0.25 }, { 'latitude': 40.814499, 'longitude': 14.43117, 'depth_km': 0.08 }, { 'latitude': 40.820499, 'longitude': 14.42817, 'depth_km': 0.15 }, { 'latitude': 40.818802, 'longitude': 14.4257, 'depth_km': 0.11 }, { 'latitude': 40.819832, 'longitude': 14.43117, 'depth_km': 0.12 }, { 'latitude': 40.81683, 'longitude': 14.4225, 'depth_km': 0.08 }, { 'latitude': 40.817829, 'longitude': 14.42617, 'depth_km': 0.15 }, { 'latitude': 40.818668, 'longitude': 14.43117, 'depth_km': 0.32 }, { 'latitude': 40.817329, 'longitude': 14.4275, 'depth_km': 0.15 }, { 'latitude': 40.827167, 'longitude': 14.4205, 'depth_km': 0.19 }, { 'latitude': 40.823502, 'longitude': 14.4295, 'depth_km': 0.21 }, { 'latitude': 40.819667, 'longitude': 14.427, 'depth_km': 0.15 }, { 'latitude': 40.819832, 'longitude': 14.429, 'depth_km': 0.16 }, { 'latitude': 40.819832, 'longitude': 14.433, 'depth_km': 0.19 }, { 'latitude': 40.82217, 'longitude': 14.42883, 'depth_km': 0.18 }, { 'latitude': 40.820332, 'longitude': 14.42433, 'depth_km': 1.42 }, { 'latitude': 40.821331, 'longitude': 14.43233, 'depth_km': 0.27 }, { 'latitude': 40.82, 'longitude': 14.4213, 'depth_km': 0.27 }, { 'latitude': 40.821999, 'longitude': 14.4268, 'depth_km': 0.14 }, { 'latitude': 40.8228, 'longitude': 14.4258, 'depth_km': 0.21 }, { 'latitude': 40.819328, 'longitude': 14.42467, 'depth_km': 0.25 }, { 'latitude': 40.815331, 'longitude': 14.4275, 'depth_km': 0.2 }, { 'latitude': 40.820833, 'longitude': 14.432167, 'depth_km': 0.06 }, { 'latitude': 40.8175, 'longitude': 14.430333, 'depth_km': 0.28 }, { 'latitude': 40.817169, 'longitude': 14.43217, 'depth_km': 0.28 }, { 'latitude': 40.820671, 'longitude': 14.43033, 'depth_km': 0.1 }, { 'latitude': 40.817001, 'longitude': 14.42517, 'depth_km': 0.25 }, { 'latitude': 40.80917, 'longitude': 14.427, 'depth_km': 0.57 }, { 'latitude': 40.818668, 'longitude': 14.4365, 'depth_km': 0.64 }, { 'latitude': 40.818001, 'longitude': 14.4283, 'depth_km': 0.18 }, { 'latitude': 40.816669, 'longitude': 14.41967, 'depth_km': 0.34 }, { 'latitude': 40.823299, 'longitude': 14.423, 'depth_km': 0.49 }, { 'latitude': 40.820702, 'longitude': 14.4277, 'depth_km': 0.14 }, { 'latitude': 40.818833, 'longitude': 14.426667, 'depth_km': 0.11 }, { 'latitude': 40.820839, 'longitude': 14.4325, 'depth_km': 0.2 }, { 'latitude': 40.825329, 'longitude': 14.412, 'depth_km': 0.05 }, { 'latitude': 40.815331, 'longitude': 14.42483, 'depth_km': 0.22 }, { 'latitude': 40.819328, 'longitude': 14.4325, 'depth_km': 0.27 }, { 'latitude': 40.820171, 'longitude': 14.426, 'depth_km': 0.25 }, { 'latitude': 40.822498, 'longitude': 14.43033, 'depth_km': 0.46 }, { 'latitude': 40.823002, 'longitude': 14.4335, 'depth_km': 0.29 }, { 'latitude': 40.819698, 'longitude': 14.4205, 'depth_km': 1.08 }, { 'latitude': 40.820999, 'longitude': 14.433, 'depth_km': 0.37 }, { 'latitude': 40.824833, 'longitude': 14.425333, 'depth_km': 0.3 }, { 'latitude': 40.825161, 'longitude': 14.42883, 'depth_km': 1.83 }, { 'latitude': 40.824329, 'longitude': 14.42867, 'depth_km': 0.48 }, { 'latitude': 40.8205, 'longitude': 14.4215, 'depth_km': 0.18 }, { 'latitude': 40.81617, 'longitude': 14.42767, 'depth_km': 0.34 }, { 'latitude': 40.822833, 'longitude': 14.422167, 'depth_km': 0.27 }, { 'latitude': 40.825329, 'longitude': 14.424, 'depth_km': 0.21 }, { 'latitude': 40.821331, 'longitude': 14.42467, 'depth_km': 0.2 }, { 'latitude': 40.82333, 'longitude': 14.428, 'depth_km': 2.58 }, { 'latitude': 40.8218, 'longitude': 14.4262, 'depth_km': 0.14 }, { 'latitude': 40.82, 'longitude': 14.42983, 'depth_km': 0.57 }, { 'latitude': 40.823002, 'longitude': 14.42317, 'depth_km': 1.49 }, { 'latitude': 40.819832, 'longitude': 14.42633, 'depth_km': 0.37 }, { 'latitude': 40.820167, 'longitude': 14.420167, 'depth_km': 0.39 }, { 'latitude': 40.821331, 'longitude': 14.42283, 'depth_km': 0.27 }, { 'latitude': 40.822498, 'longitude': 14.4305, 'depth_km': 0.59 }, { 'latitude': 40.8195, 'longitude': 14.4263, 'depth_km': 0.18 }, { 'latitude': 40.820999, 'longitude': 14.424, 'depth_km': 1.87 }, { 'latitude': 40.817829, 'longitude': 14.42717, 'depth_km': 0.36 }, { 'latitude': 40.824501, 'longitude': 14.429, 'depth_km': 0.22 }, { 'latitude': 40.821999, 'longitude': 14.42967, 'depth_km': 0.46 }, { 'latitude': 40.818501, 'longitude': 14.43367, 'depth_km': 0.15 }, { 'latitude': 40.823669, 'longitude': 14.4255, 'depth_km': 0.06 }, { 'latitude': 40.820667, 'longitude': 14.4315, 'depth_km': 0.4 }, { 'latitude': 40.817829, 'longitude': 14.4325, 'depth_km': 0.34 }, { 'latitude': 40.82217, 'longitude': 14.42883, 'depth_km': 0.56 }, { 'latitude': 40.817169, 'longitude': 14.444, 'depth_km': 0.32 }, { 'latitude': 40.82, 'longitude': 14.431333, 'depth_km': 0.18 }, { 'latitude': 40.821171, 'longitude': 14.423, 'depth_km': 0.14 }, { 'latitude': 40.809502, 'longitude': 14.41983, 'depth_km': 1.15 }, { 'latitude': 40.822498, 'longitude': 14.4232, 'depth_km': 0.22 }, { 'latitude': 40.818001, 'longitude': 14.4275, 'depth_km': 0.25 }, { 'latitude': 40.822498, 'longitude': 14.42533, 'depth_km': 1.69 }, { 'latitude': 40.821671, 'longitude': 14.42567, 'depth_km': 0.15 }, { 'latitude': 40.819698, 'longitude': 14.4283, 'depth_km': 0.18 }, { 'latitude': 40.820702, 'longitude': 14.4225, 'depth_km': 0.2 }, { 'latitude': 40.820301, 'longitude': 14.4247, 'depth_km': 0.12 }, { 'latitude': 40.824167, 'longitude': 14.426, 'depth_km': 0.4 }, { 'latitude': 40.823833, 'longitude': 14.420833, 'depth_km': 0.2 }, { 'latitude': 40.82217, 'longitude': 14.42933, 'depth_km': 0.11 }, { 'latitude': 40.819672, 'longitude': 14.4325, 'depth_km': 0.23 }, { 'latitude': 40.819832, 'longitude': 14.43167, 'depth_km': 0.1 }, { 'latitude': 40.820999, 'longitude': 14.4305, 'depth_km': 0.15 }, { 'latitude': 40.820839, 'longitude': 14.42567, 'depth_km': 0.19 }, { 'latitude': 40.818501, 'longitude': 14.43367, 'depth_km': 0.2 }, { 'latitude': 40.82283, 'longitude': 14.4275, 'depth_km': 0.73 }, { 'latitude': 40.807331, 'longitude': 14.42017, 'depth_km': 0.66 }, { 'latitude': 40.82283, 'longitude': 14.42417, 'depth_km': 1.73 }, { 'latitude': 40.818161, 'longitude': 14.4245, 'depth_km': 0.23 }, { 'latitude': 40.824501, 'longitude': 14.41783, 'depth_km': 0.07 }, { 'latitude': 40.820171, 'longitude': 14.42733, 'depth_km': 0.11 }, { 'latitude': 40.8265, 'longitude': 14.42783, 'depth_km': 0.42 }, { 'latitude': 40.82, 'longitude': 14.4315, 'depth_km': 0.46 }, { 'latitude': 40.818329, 'longitude': 14.42783, 'depth_km': 1.1 }, { 'latitude': 40.815331, 'longitude': 14.425, 'depth_km': 0.35 }, { 'latitude': 40.827167, 'longitude': 14.423333, 'depth_km': 0.31 }, { 'latitude': 40.81583, 'longitude': 14.435, 'depth_km': 0.18 }, { 'latitude': 40.823002, 'longitude': 14.43217, 'depth_km': 0.14 }, { 'latitude': 40.822167, 'longitude': 14.425667, 'depth_km': 0.56 }, { 'latitude': 40.820671, 'longitude': 14.43517, 'depth_km': 0.15 }, { 'latitude': 40.821831, 'longitude': 14.42817, 'depth_km': 0.3 }, { 'latitude': 40.822833, 'longitude': 14.423833, 'depth_km': 0.48 }, { 'latitude': 40.818829, 'longitude': 14.431, 'depth_km': 0.02 }, { 'latitude': 40.822833, 'longitude': 14.421833, 'depth_km': 0.14 }, { 'latitude': 40.823299, 'longitude': 14.4295, 'depth_km': 0.55 }, { 'latitude': 40.827332, 'longitude': 14.42383, 'depth_km': 0.18 }, { 'latitude': 40.819, 'longitude': 14.432, 'depth_km': 0.21 }, { 'latitude': 40.818001, 'longitude': 14.42267, 'depth_km': 1.65 }, { 'latitude': 40.820202, 'longitude': 14.4267, 'depth_km': 0.12 }, { 'latitude': 40.822201, 'longitude': 14.4238, 'depth_km': 0.07 }, { 'latitude': 40.825298, 'longitude': 14.4293, 'depth_km': 0.2 }, { 'latitude': 40.824299, 'longitude': 14.4297, 'depth_km': 0.25 }, { 'latitude': 40.814333, 'longitude': 14.432333, 'depth_km': 0.53 }, { 'latitude': 40.824329, 'longitude': 14.4275, 'depth_km': 0.08 }, { 'latitude': 40.824501, 'longitude': 14.42783, 'depth_km': 0.13 }, { 'latitude': 40.82283, 'longitude': 14.42667, 'depth_km': 0.06 }, { 'latitude': 40.821999, 'longitude': 14.4228, 'depth_km': 0.12 }, { 'latitude': 40.816002, 'longitude': 14.42917, 'depth_km': 1.16 }, { 'latitude': 40.817829, 'longitude': 14.42917, 'depth_km': 0.2 }, { 'latitude': 40.819328, 'longitude': 14.4315, 'depth_km': 0.09 }, { 'latitude': 40.817501, 'longitude': 14.4255, 'depth_km': 0.1 }, { 'latitude': 40.827332, 'longitude': 14.43367, 'depth_km': 0.09 }, { 'latitude': 40.816333, 'longitude': 14.433, 'depth_km': 1.55 }, { 'latitude': 40.813999, 'longitude': 14.42067, 'depth_km': 0.24 }, { 'latitude': 40.818501, 'longitude': 14.4255, 'depth_km': 0.16 }, { 'latitude': 40.820839, 'longitude': 14.428, 'depth_km': 0.3 }, { 'latitude': 40.8195, 'longitude': 14.429833, 'depth_km': 0.09 }, ], fields: [makeField('latitude'), makeField('longitude'), makeField('depth_km')], metadata: { 'latitude': { type: Type.Number, semanticType: 'Latitude', levels: [] }, 'longitude': { type: Type.Number, semanticType: 'Longitude', levels: [] }, 'depth_km': { type: Type.Number, semanticType: 'Depth', levels: [] }, }, encodingMap: { longitude: makeEncodingItem('longitude'), latitude: makeEncodingItem('latitude'), color: makeEncodingItem('depth_km'), }, chartProperties: { opacity: 0.7 }, }; } function gen_vesuviusQ5RecGpt51(): TestCase { // How are earthquake epicenters spatially distributed around Mount Vesuvius, and how does depth_km vary across latitude an // Score: 10/20 | The visualization technically maps epicenter locations and depth but uses an inverted, world-scale m return { title: 'vesuvius_q5_rec-gpt-5.1', description: 'Map **latitude**, **longitude** and color by **depth_km**', tags: ['debug', 'world-map'], chartType: 'World Map', data: [ { 'latitude': 40.821831, 'longitude': 14.4305, 'depth_km': 0.23 }, { 'latitude': 40.817169, 'longitude': 14.427, 'depth_km': 0.07 }, { 'latitude': 40.818668, 'longitude': 14.43267, 'depth_km': 0.4 }, { 'latitude': 40.817329, 'longitude': 14.42833, 'depth_km': 0.3 }, { 'latitude': 40.821831, 'longitude': 14.42683, 'depth_km': 0.37 }, { 'latitude': 40.824329, 'longitude': 14.42383, 'depth_km': 0.66 }, { 'latitude': 40.8195, 'longitude': 14.42783, 'depth_km': 0.21 }, { 'latitude': 40.817669, 'longitude': 14.43267, 'depth_km': 0.16 }, { 'latitude': 40.822201, 'longitude': 14.4287, 'depth_km': 0.78 }, { 'latitude': 40.818668, 'longitude': 14.42517, 'depth_km': 0.24 }, { 'latitude': 40.819, 'longitude': 14.42383, 'depth_km': 0.07 }, { 'latitude': 40.817001, 'longitude': 14.4305, 'depth_km': 0.47 }, { 'latitude': 40.823669, 'longitude': 14.43017, 'depth_km': 0.23 }, { 'latitude': 40.82267, 'longitude': 14.4345, 'depth_km': 0.43 }, { 'latitude': 40.822, 'longitude': 14.42, 'depth_km': 0.22 }, { 'latitude': 40.820332, 'longitude': 14.41967, 'depth_km': 0.84 }, { 'latitude': 40.823669, 'longitude': 14.42433, 'depth_km': 1.71 }, { 'latitude': 40.8223, 'longitude': 14.4315, 'depth_km': 0.28 }, { 'latitude': 40.820839, 'longitude': 14.424, 'depth_km': 0.09 }, { 'latitude': 40.823167, 'longitude': 14.422333, 'depth_km': 0.17 }, { 'latitude': 40.82333, 'longitude': 14.42617, 'depth_km': 0.11 }, { 'latitude': 40.817001, 'longitude': 14.43367, 'depth_km': 0.05 }, { 'latitude': 40.819672, 'longitude': 14.4345, 'depth_km': 0.15 }, { 'latitude': 40.818501, 'longitude': 14.428, 'depth_km': 0.13 }, { 'latitude': 40.824329, 'longitude': 14.42533, 'depth_km': 0.93 }, { 'latitude': 40.820839, 'longitude': 14.43633, 'depth_km': 0.64 }, { 'latitude': 40.817829, 'longitude': 14.42467, 'depth_km': 0.2 }, { 'latitude': 40.81633, 'longitude': 14.42917, 'depth_km': 0.21 }, { 'latitude': 40.822498, 'longitude': 14.42883, 'depth_km': 0.17 }, { 'latitude': 40.82, 'longitude': 14.42217, 'depth_km': 0.22 }, { 'latitude': 40.821671, 'longitude': 14.4255, 'depth_km': 0.06 }, { 'latitude': 40.821301, 'longitude': 14.4317, 'depth_km': 0.98 }, { 'latitude': 40.819328, 'longitude': 14.42833, 'depth_km': 0.11 }, { 'latitude': 40.822701, 'longitude': 14.4298, 'depth_km': 0.11 }, { 'latitude': 40.823502, 'longitude': 14.42333, 'depth_km': 0.2 }, { 'latitude': 40.820332, 'longitude': 14.431, 'depth_km': 0.22 }, { 'latitude': 40.81633, 'longitude': 14.42667, 'depth_km': 0.07 }, { 'latitude': 40.819667, 'longitude': 14.433, 'depth_km': 0.18 }, { 'latitude': 40.80917, 'longitude': 14.42333, 'depth_km': 0.78 }, { 'latitude': 40.8237, 'longitude': 14.4335, 'depth_km': 0.27 }, { 'latitude': 40.816669, 'longitude': 14.43117, 'depth_km': 0.32 }, { 'latitude': 40.818668, 'longitude': 14.4275, 'depth_km': 0.34 }, { 'latitude': 40.823299, 'longitude': 14.4295, 'depth_km': 1.56 }, { 'latitude': 40.821171, 'longitude': 14.43033, 'depth_km': 0.1 }, { 'latitude': 40.820171, 'longitude': 14.43017, 'depth_km': 0.02 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'depth_km': 0.12 }, { 'latitude': 40.81683, 'longitude': 14.4225, 'depth_km': 0.08 }, { 'latitude': 40.817829, 'longitude': 14.42617, 'depth_km': 0.15 }, { 'latitude': 40.818668, 'longitude': 14.43117, 'depth_km': 0.32 }, { 'latitude': 40.817329, 'longitude': 14.4275, 'depth_km': 0.15 }, { 'latitude': 40.827167, 'longitude': 14.4205, 'depth_km': 0.19 }, { 'latitude': 40.823502, 'longitude': 14.4295, 'depth_km': 0.21 }, { 'latitude': 40.819667, 'longitude': 14.427, 'depth_km': 0.15 }, { 'latitude': 40.819832, 'longitude': 14.429, 'depth_km': 0.16 }, { 'latitude': 40.819832, 'longitude': 14.433, 'depth_km': 0.19 }, { 'latitude': 40.82217, 'longitude': 14.42883, 'depth_km': 0.18 }, { 'latitude': 40.820332, 'longitude': 14.42433, 'depth_km': 1.42 }, { 'latitude': 40.821331, 'longitude': 14.43233, 'depth_km': 0.27 }, { 'latitude': 40.82, 'longitude': 14.4213, 'depth_km': 0.27 }, { 'latitude': 40.821999, 'longitude': 14.4268, 'depth_km': 0.14 }, { 'latitude': 40.8228, 'longitude': 14.4258, 'depth_km': 0.21 }, { 'latitude': 40.819328, 'longitude': 14.42467, 'depth_km': 0.25 }, { 'latitude': 40.815331, 'longitude': 14.4275, 'depth_km': 0.2 }, { 'latitude': 40.820833, 'longitude': 14.432167, 'depth_km': 0.06 }, { 'latitude': 40.8175, 'longitude': 14.430333, 'depth_km': 0.28 }, { 'latitude': 40.817169, 'longitude': 14.43217, 'depth_km': 0.28 }, { 'latitude': 40.820671, 'longitude': 14.43033, 'depth_km': 0.1 }, { 'latitude': 40.817001, 'longitude': 14.42517, 'depth_km': 0.25 }, { 'latitude': 40.80917, 'longitude': 14.427, 'depth_km': 0.57 }, { 'latitude': 40.818668, 'longitude': 14.4365, 'depth_km': 0.64 }, { 'latitude': 40.818001, 'longitude': 14.4283, 'depth_km': 0.18 }, { 'latitude': 40.816669, 'longitude': 14.41967, 'depth_km': 0.34 }, { 'latitude': 40.823299, 'longitude': 14.423, 'depth_km': 0.49 }, { 'latitude': 40.820702, 'longitude': 14.4277, 'depth_km': 0.14 }, { 'latitude': 40.818833, 'longitude': 14.426667, 'depth_km': 0.11 }, { 'latitude': 40.820839, 'longitude': 14.4325, 'depth_km': 0.2 }, { 'latitude': 40.825329, 'longitude': 14.412, 'depth_km': 0.05 }, { 'latitude': 40.815331, 'longitude': 14.42483, 'depth_km': 0.22 }, { 'latitude': 40.819328, 'longitude': 14.4325, 'depth_km': 0.27 }, { 'latitude': 40.820171, 'longitude': 14.426, 'depth_km': 0.25 }, { 'latitude': 40.822498, 'longitude': 14.43033, 'depth_km': 0.46 }, { 'latitude': 40.823002, 'longitude': 14.4335, 'depth_km': 0.29 }, { 'latitude': 40.819698, 'longitude': 14.4205, 'depth_km': 1.08 }, { 'latitude': 40.820999, 'longitude': 14.433, 'depth_km': 0.37 }, { 'latitude': 40.824833, 'longitude': 14.425333, 'depth_km': 0.3 }, { 'latitude': 40.825161, 'longitude': 14.42883, 'depth_km': 1.83 }, { 'latitude': 40.824329, 'longitude': 14.42867, 'depth_km': 0.48 }, { 'latitude': 40.8205, 'longitude': 14.4215, 'depth_km': 0.18 }, { 'latitude': 40.81617, 'longitude': 14.42767, 'depth_km': 0.34 }, { 'latitude': 40.822833, 'longitude': 14.422167, 'depth_km': 0.27 }, { 'latitude': 40.825329, 'longitude': 14.424, 'depth_km': 0.21 }, { 'latitude': 40.821331, 'longitude': 14.42467, 'depth_km': 0.2 }, { 'latitude': 40.82333, 'longitude': 14.428, 'depth_km': 2.58 }, { 'latitude': 40.8218, 'longitude': 14.4262, 'depth_km': 0.14 }, { 'latitude': 40.82, 'longitude': 14.42983, 'depth_km': 0.57 }, { 'latitude': 40.823002, 'longitude': 14.42317, 'depth_km': 1.49 }, { 'latitude': 40.819832, 'longitude': 14.42633, 'depth_km': 0.37 }, { 'latitude': 40.820167, 'longitude': 14.420167, 'depth_km': 0.39 }, { 'latitude': 40.821331, 'longitude': 14.42283, 'depth_km': 0.27 }, { 'latitude': 40.822498, 'longitude': 14.4305, 'depth_km': 0.59 }, { 'latitude': 40.8195, 'longitude': 14.4263, 'depth_km': 0.18 }, { 'latitude': 40.820999, 'longitude': 14.424, 'depth_km': 1.87 }, { 'latitude': 40.817829, 'longitude': 14.42717, 'depth_km': 0.36 }, { 'latitude': 40.824501, 'longitude': 14.429, 'depth_km': 0.22 }, { 'latitude': 40.821999, 'longitude': 14.42967, 'depth_km': 0.46 }, { 'latitude': 40.818501, 'longitude': 14.43367, 'depth_km': 0.15 }, { 'latitude': 40.823669, 'longitude': 14.4255, 'depth_km': 0.06 }, { 'latitude': 40.820667, 'longitude': 14.4315, 'depth_km': 0.4 }, { 'latitude': 40.817829, 'longitude': 14.4325, 'depth_km': 0.34 }, { 'latitude': 40.82217, 'longitude': 14.42883, 'depth_km': 0.56 }, { 'latitude': 40.817169, 'longitude': 14.444, 'depth_km': 0.32 }, { 'latitude': 40.82, 'longitude': 14.431333, 'depth_km': 0.18 }, { 'latitude': 40.821171, 'longitude': 14.423, 'depth_km': 0.14 }, { 'latitude': 40.809502, 'longitude': 14.41983, 'depth_km': 1.15 }, { 'latitude': 40.822498, 'longitude': 14.4232, 'depth_km': 0.22 }, { 'latitude': 40.818001, 'longitude': 14.4275, 'depth_km': 0.25 }, { 'latitude': 40.822498, 'longitude': 14.42533, 'depth_km': 1.69 }, { 'latitude': 40.821671, 'longitude': 14.42567, 'depth_km': 0.15 }, { 'latitude': 40.819698, 'longitude': 14.4283, 'depth_km': 0.18 }, { 'latitude': 40.820702, 'longitude': 14.4225, 'depth_km': 0.2 }, { 'latitude': 40.820301, 'longitude': 14.4247, 'depth_km': 0.12 }, { 'latitude': 40.824167, 'longitude': 14.426, 'depth_km': 0.4 }, { 'latitude': 40.823833, 'longitude': 14.420833, 'depth_km': 0.2 }, { 'latitude': 40.82217, 'longitude': 14.42933, 'depth_km': 0.11 }, { 'latitude': 40.819672, 'longitude': 14.4325, 'depth_km': 0.23 }, { 'latitude': 40.819832, 'longitude': 14.43167, 'depth_km': 0.1 }, { 'latitude': 40.820999, 'longitude': 14.4305, 'depth_km': 0.15 }, { 'latitude': 40.820839, 'longitude': 14.42567, 'depth_km': 0.19 }, { 'latitude': 40.818501, 'longitude': 14.43367, 'depth_km': 0.2 }, { 'latitude': 40.82283, 'longitude': 14.4275, 'depth_km': 0.73 }, { 'latitude': 40.807331, 'longitude': 14.42017, 'depth_km': 0.66 }, { 'latitude': 40.82283, 'longitude': 14.42417, 'depth_km': 1.73 }, { 'latitude': 40.818161, 'longitude': 14.4245, 'depth_km': 0.23 }, { 'latitude': 40.824501, 'longitude': 14.41783, 'depth_km': 0.07 }, { 'latitude': 40.820171, 'longitude': 14.42733, 'depth_km': 0.11 }, { 'latitude': 40.8265, 'longitude': 14.42783, 'depth_km': 0.42 }, { 'latitude': 40.82, 'longitude': 14.4315, 'depth_km': 0.46 }, { 'latitude': 40.818329, 'longitude': 14.42783, 'depth_km': 1.1 }, { 'latitude': 40.815331, 'longitude': 14.425, 'depth_km': 0.35 }, { 'latitude': 40.827167, 'longitude': 14.423333, 'depth_km': 0.31 }, { 'latitude': 40.81583, 'longitude': 14.435, 'depth_km': 0.18 }, { 'latitude': 40.823002, 'longitude': 14.43217, 'depth_km': 0.14 }, { 'latitude': 40.822167, 'longitude': 14.425667, 'depth_km': 0.56 }, { 'latitude': 40.820671, 'longitude': 14.43517, 'depth_km': 0.15 }, { 'latitude': 40.821831, 'longitude': 14.42817, 'depth_km': 0.3 }, { 'latitude': 40.822833, 'longitude': 14.423833, 'depth_km': 0.48 }, { 'latitude': 40.818829, 'longitude': 14.431, 'depth_km': 0.02 }, { 'latitude': 40.822833, 'longitude': 14.421833, 'depth_km': 0.14 }, { 'latitude': 40.823299, 'longitude': 14.4295, 'depth_km': 0.55 }, { 'latitude': 40.827332, 'longitude': 14.42383, 'depth_km': 0.18 }, { 'latitude': 40.819, 'longitude': 14.432, 'depth_km': 0.21 }, { 'latitude': 40.818001, 'longitude': 14.42267, 'depth_km': 1.65 }, { 'latitude': 40.820202, 'longitude': 14.4267, 'depth_km': 0.12 }, { 'latitude': 40.822201, 'longitude': 14.4238, 'depth_km': 0.07 }, { 'latitude': 40.825298, 'longitude': 14.4293, 'depth_km': 0.2 }, { 'latitude': 40.824299, 'longitude': 14.4297, 'depth_km': 0.25 }, { 'latitude': 40.814333, 'longitude': 14.432333, 'depth_km': 0.53 }, { 'latitude': 40.824329, 'longitude': 14.4275, 'depth_km': 0.08 }, { 'latitude': 40.824501, 'longitude': 14.42783, 'depth_km': 0.13 }, { 'latitude': 40.82283, 'longitude': 14.42667, 'depth_km': 0.06 }, { 'latitude': 40.821999, 'longitude': 14.4228, 'depth_km': 0.12 }, { 'latitude': 40.816002, 'longitude': 14.42917, 'depth_km': 1.16 }, { 'latitude': 40.817829, 'longitude': 14.42917, 'depth_km': 0.2 }, { 'latitude': 40.819328, 'longitude': 14.4315, 'depth_km': 0.09 }, { 'latitude': 40.817501, 'longitude': 14.4255, 'depth_km': 0.1 }, { 'latitude': 40.827332, 'longitude': 14.43367, 'depth_km': 0.09 }, { 'latitude': 40.816333, 'longitude': 14.433, 'depth_km': 1.55 }, { 'latitude': 40.813999, 'longitude': 14.42067, 'depth_km': 0.24 }, { 'latitude': 40.818501, 'longitude': 14.4255, 'depth_km': 0.16 }, { 'latitude': 40.820839, 'longitude': 14.428, 'depth_km': 0.3 }, { 'latitude': 40.8195, 'longitude': 14.429833, 'depth_km': 0.09 }, ], fields: [makeField('latitude'), makeField('longitude'), makeField('depth_km')], metadata: { 'latitude': { type: Type.Number, semanticType: 'Latitude', levels: [] }, 'longitude': { type: Type.Number, semanticType: 'Longitude', levels: [] }, 'depth_km': { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { longitude: makeEncodingItem('longitude'), latitude: makeEncodingItem('latitude'), color: makeEncodingItem('depth_km'), }, chartProperties: { projection: "mercator", projectionCenter: [14.43, 40.82] }, }; } // ============================================================================ // Export // ============================================================================ export function genDebugTests(): TestCase[] { return [ gen_agenciesQ4RecGpt41(), gen_agenciesQ4RecGpt51(), gen_apiLogosQ4RecGpt41(), gen_apiLogosQ4RecGpt51(), gen_apisguruApisQ3RecGpt41(), gen_apisguruApisQ3RecGpt51(), gen_careStateQ1RecGpt41(), gen_careStateQ1RecGpt51(), gen_careStateQ3RecGpt41(), gen_careStateQ3RecGpt51(), gen_colorRanksQ1RecGpt41(), gen_colorRanksQ5RecGpt41(), gen_colorRanksQ5RecGpt51(), gen_countryListsQ1RecGpt41(), gen_countryListsQ2RecGpt41(), gen_countryListsQ2RecGpt51(), gen_countryListsQ3RecGpt41(), gen_countryListsQ4RecGpt41(), gen_endangeredStatusQ1RecGpt41(), gen_familiesQ1RecGpt51(), gen_familiesQ2RecGpt41(), gen_familiesQ2RecGpt51(), gen_familiesQ3RecGpt51(), gen_familiesQ4RecGpt41(), gen_familiesQ4RecGpt51(), gen_familiesQ5RecGpt41(), gen_fpiCodesQ5RecGpt51(), gen_modelDatQ1RecGpt51(), gen_modelDatQ2RecGpt41(), gen_modelDatQ2RecGpt51(), gen_modelDatQ4RecGpt41(), gen_modelDatQ5RecGpt41(), gen_ombCodesQ4RecGpt41(), gen_ombCodesQ5RecGpt41(), gen_penguinsRawQ1RecGpt41(), gen_penguinsRawQ1RecGpt51(), gen_pixarFilmsQ3RecGpt41(), gen_rankByYearQ3RecGpt41(), gen_simpsonsCharactersQ1RecGpt41(), gen_simpsonsLocationsQ2RecGpt41(), gen_simpsonsLocationsQ2RecGpt51(), gen_simpsonsLocationsQ5RecGpt41(), gen_topicsQ1RecGpt41(), gen_topicsQ2RecGpt51(), gen_user2025Q1RecGpt41(), gen_user2025Q5RecGpt51(), gen_vesuviusQ4RecGpt41(), gen_vesuviusQ5RecGpt41(), gen_vesuviusQ5RecGpt51(), ]; } ================================================ FILE: src/lib/agents-chart/test-data/discrete-axis-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Discrete-axis sizing tests. * * For each major chart type, creates test cases at three cardinality levels * (20, 60, 120) designed for a 400×300 canvas. This systematically exercises * the assembler's elastic stretch, overflow filtering, label rotation, and * step-based sizing across discrete X and discrete Y orientations. * * Cardinality rationale (400×300 canvas, ~20px default step): * - 20 items: fits comfortably, no compression needed * - 60 items: triggers elastic stretch, label rotation * - 120 items: triggers overflow filtering (too many to show) */ import { TestCase, makeField, makeEncodingItem, buildMetadata } from './types'; import { seededRandom } from './generators'; // --------------------------------------------------------------------------- // Helpers // --------------------------------------------------------------------------- function cats(prefix: string, n: number): string[] { const pad = String(n).length; return Array.from({ length: n }, (_, i) => `${prefix}${String(i + 1).padStart(pad, '0')}`); } function randVal(rand: () => number, min = 10, max = 500): number { return Math.round(min + rand() * (max - min)); } const SIZES = [ { n: 20, label: '20' }, { n: 60, label: '60' }, { n: 120, label: '120' }, ] as const; // --------------------------------------------------------------------------- // Bar Chart // --------------------------------------------------------------------------- function genBarSizing(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(100); for (const { n, label } of SIZES) { // Discrete X const xCats = cats('Cat', n); const xData = xCats.map(c => ({ Category: c, Value: randVal(rand) })); tests.push({ title: `Bar ▸ X ×${label}`, description: `Bar chart with ${n} categories on X axis`, tags: ['bar', 'discrete-x', `n${label}`], chartType: 'Bar Chart', data: xData, fields: [makeField('Category'), makeField('Value')], metadata: buildMetadata(xData), encodingMap: { x: makeEncodingItem('Category'), y: makeEncodingItem('Value') }, }); // Discrete Y const yCats = cats('Item', n); const yData = yCats.map(c => ({ Item: c, Revenue: randVal(rand) })); tests.push({ title: `Bar ▸ Y ×${label}`, description: `Horizontal bar with ${n} categories on Y axis`, tags: ['bar', 'discrete-y', `n${label}`], chartType: 'Bar Chart', data: yData, fields: [makeField('Item'), makeField('Revenue')], metadata: buildMetadata(yData), encodingMap: { y: makeEncodingItem('Item'), x: makeEncodingItem('Revenue') }, }); } return tests; } // --------------------------------------------------------------------------- // Stacked Bar Chart // --------------------------------------------------------------------------- function genStackedBarSizing(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(200); const segments = cats('Seg', 3); for (const { n, label } of SIZES) { // Discrete X { const categories = cats('City', n); const data: Record<string, any>[] = []; for (const c of categories) for (const s of segments) data.push({ City: c, Segment: s, Amount: randVal(rand) }); tests.push({ title: `Stacked Bar ▸ X ×${label}`, description: `Stacked bar with ${n} x-categories × 3 segments`, tags: ['stacked-bar', 'discrete-x', `n${label}`], chartType: 'Stacked Bar Chart', data, fields: [makeField('City'), makeField('Segment'), makeField('Amount')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('City'), y: makeEncodingItem('Amount'), color: makeEncodingItem('Segment') }, }); } // Discrete Y { const categories = cats('Dept', n); const data: Record<string, any>[] = []; for (const c of categories) for (const s of segments) data.push({ Dept: c, Segment: s, Budget: randVal(rand) }); tests.push({ title: `Stacked Bar ▸ Y ×${label}`, description: `Horizontal stacked bar with ${n} y-categories × 3 segments`, tags: ['stacked-bar', 'discrete-y', `n${label}`], chartType: 'Stacked Bar Chart', data, fields: [makeField('Dept'), makeField('Segment'), makeField('Budget')], metadata: buildMetadata(data), encodingMap: { y: makeEncodingItem('Dept'), x: makeEncodingItem('Budget'), color: makeEncodingItem('Segment') }, }); } } return tests; } // --------------------------------------------------------------------------- // Grouped Bar Chart // --------------------------------------------------------------------------- function genGroupedBarSizing(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(300); const groups = cats('Grp', 3); for (const { n, label } of SIZES) { // Discrete X { const categories = cats('Cat', n); const data: Record<string, any>[] = []; for (const c of categories) for (const g of groups) data.push({ Category: c, Group: g, Value: randVal(rand) }); tests.push({ title: `Grouped Bar ▸ X ×${label}`, description: `Grouped bar with ${n} x-categories × 3 groups`, tags: ['grouped-bar', 'discrete-x', `n${label}`], chartType: 'Grouped Bar Chart', data, fields: [makeField('Category'), makeField('Group'), makeField('Value')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Category'), y: makeEncodingItem('Value'), group: makeEncodingItem('Group') }, }); } // Discrete Y { const categories = cats('Region', n); const data: Record<string, any>[] = []; for (const c of categories) for (const g of groups) data.push({ Region: c, Group: g, Sales: randVal(rand) }); tests.push({ title: `Grouped Bar ▸ Y ×${label}`, description: `Horizontal grouped bar with ${n} y-categories × 3 groups`, tags: ['grouped-bar', 'discrete-y', `n${label}`], chartType: 'Grouped Bar Chart', data, fields: [makeField('Region'), makeField('Group'), makeField('Sales')], metadata: buildMetadata(data), encodingMap: { y: makeEncodingItem('Region'), x: makeEncodingItem('Sales'), group: makeEncodingItem('Group') }, }); } } return tests; } // --------------------------------------------------------------------------- // Scatter Plot // --------------------------------------------------------------------------- function genScatterSizing(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(400); for (const { n, label } of SIZES) { // Discrete X { const categories = cats('Species', n); const pointsPer = 5; const data: Record<string, any>[] = []; for (const c of categories) for (let i = 0; i < pointsPer; i++) data.push({ Species: c, Weight: randVal(rand, 10, 200) }); tests.push({ title: `Scatter ▸ X ×${label}`, description: `Scatter with ${n} discrete categories on X`, tags: ['scatter', 'discrete-x', `n${label}`], chartType: 'Scatter Plot', data, fields: [makeField('Species'), makeField('Weight')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Species'), y: makeEncodingItem('Weight') }, }); } // Discrete Y { const categories = cats('Lab', n); const pointsPer = 5; const data: Record<string, any>[] = []; for (const c of categories) for (let i = 0; i < pointsPer; i++) data.push({ Lab: c, Score: randVal(rand, 0, 100) }); tests.push({ title: `Scatter ▸ Y ×${label}`, description: `Scatter with ${n} discrete categories on Y`, tags: ['scatter', 'discrete-y', `n${label}`], chartType: 'Scatter Plot', data, fields: [makeField('Lab'), makeField('Score')], metadata: buildMetadata(data), encodingMap: { y: makeEncodingItem('Lab'), x: makeEncodingItem('Score') }, }); } } return tests; } // --------------------------------------------------------------------------- // Line Chart // --------------------------------------------------------------------------- function genLineSizing(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(500); for (const { n, label } of SIZES) { // Discrete X (ordinal — numbered steps imply sequence) { const categories = cats('Step', n); const data = categories.map(c => ({ Step: c, Value: randVal(rand) })); tests.push({ title: `Line ▸ X ×${label}`, description: `Line chart with ${n} ordinal steps on X`, tags: ['line', 'discrete-x', `n${label}`], chartType: 'Line Chart', data, fields: [makeField('Step'), makeField('Value')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Step'), y: makeEncodingItem('Value') }, }); } } return tests; } // --------------------------------------------------------------------------- // Area Chart // --------------------------------------------------------------------------- function genAreaSizing(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(600); const series = cats('S', 3); for (const { n, label } of SIZES) { // Discrete X (ordinal — numbered periods imply sequence) { const categories = cats('Period', n); const data: Record<string, any>[] = []; for (const c of categories) for (const s of series) data.push({ Period: c, Series: s, Value: randVal(rand, 5, 200) }); tests.push({ title: `Area ▸ X ×${label}`, description: `Stacked area with ${n} ordinal periods × 3 series`, tags: ['area', 'discrete-x', `n${label}`], chartType: 'Area Chart', data, fields: [makeField('Period'), makeField('Series'), makeField('Value')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Period'), y: makeEncodingItem('Value'), color: makeEncodingItem('Series') }, }); } } return tests; } // --------------------------------------------------------------------------- // Lollipop Chart // --------------------------------------------------------------------------- function genLollipopSizing(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(700); for (const { n, label } of SIZES) { // Discrete X { const categories = cats('Item', n); const data = categories.map(c => ({ Item: c, Value: randVal(rand) })); tests.push({ title: `Lollipop ▸ X ×${label}`, description: `Lollipop with ${n} categories on X`, tags: ['lollipop', 'discrete-x', `n${label}`], chartType: 'Lollipop Chart', data, fields: [makeField('Item'), makeField('Value')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Item'), y: makeEncodingItem('Value') }, }); } // Discrete Y { const categories = cats('Country', n); const data = categories.map(c => ({ Country: c, GDP: randVal(rand, 100, 5000) })); tests.push({ title: `Lollipop ▸ Y ×${label}`, description: `Horizontal lollipop with ${n} categories on Y`, tags: ['lollipop', 'discrete-y', `n${label}`], chartType: 'Lollipop Chart', data, fields: [makeField('Country'), makeField('GDP')], metadata: buildMetadata(data), encodingMap: { y: makeEncodingItem('Country'), x: makeEncodingItem('GDP') }, }); } } return tests; } // --------------------------------------------------------------------------- // Heatmap (always discrete X + Y) // --------------------------------------------------------------------------- function genHeatmapSizing(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(800); for (const { n, label } of SIZES) { const yN = Math.max(5, Math.round(n * 0.6)); const xCats = cats('Col', n); const yCats = cats('Row', yN); const data: Record<string, any>[] = []; for (const x of xCats) for (const y of yCats) data.push({ Col: x, Row: y, Value: randVal(rand, 0, 100) }); tests.push({ title: `Heatmap ▸ XY ×${label}×${yN}`, description: `Heatmap with ${n} columns × ${yN} rows`, tags: ['heatmap', 'discrete-xy', `n${label}`], chartType: 'Heatmap', data, fields: [makeField('Col'), makeField('Row'), makeField('Value')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Col'), y: makeEncodingItem('Row'), color: makeEncodingItem('Value') }, }); } return tests; } // --------------------------------------------------------------------------- // Boxplot // --------------------------------------------------------------------------- function genBoxplotSizing(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(900); const pointsPer = 15; for (const { n, label } of SIZES) { // Discrete X { const categories = cats('Group', n); const data: Record<string, any>[] = []; for (const c of categories) for (let i = 0; i < pointsPer; i++) data.push({ Group: c, Value: randVal(rand, 0, 200) }); tests.push({ title: `Boxplot ▸ X ×${label}`, description: `Boxplot with ${n} groups on X`, tags: ['boxplot', 'discrete-x', `n${label}`], chartType: 'Boxplot', data, fields: [makeField('Group'), makeField('Value')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Group'), y: makeEncodingItem('Value') }, }); } // Discrete Y { const categories = cats('Cat', n); const data: Record<string, any>[] = []; for (const c of categories) for (let i = 0; i < pointsPer; i++) data.push({ Category: c, Score: randVal(rand, 0, 100) }); tests.push({ title: `Boxplot ▸ Y ×${label}`, description: `Horizontal boxplot with ${n} groups on Y`, tags: ['boxplot', 'discrete-y', `n${label}`], chartType: 'Boxplot', data, fields: [makeField('Category'), makeField('Score')], metadata: buildMetadata(data), encodingMap: { y: makeEncodingItem('Category'), x: makeEncodingItem('Score') }, }); } } return tests; } // --------------------------------------------------------------------------- // Strip Plot // --------------------------------------------------------------------------- function genStripSizing(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(1000); const pointsPer = 10; for (const { n, label } of SIZES) { // Discrete X { const categories = cats('Species', n); const data: Record<string, any>[] = []; for (const c of categories) for (let i = 0; i < pointsPer; i++) data.push({ Species: c, Length: randVal(rand, 10, 80) }); tests.push({ title: `Strip ▸ X ×${label}`, description: `Strip plot with ${n} categories on X`, tags: ['strip', 'discrete-x', `n${label}`], chartType: 'Strip Plot', data, fields: [makeField('Species'), makeField('Length')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Species'), y: makeEncodingItem('Length') }, }); } // Discrete Y { const categories = cats('Lab', n); const data: Record<string, any>[] = []; for (const c of categories) for (let i = 0; i < pointsPer; i++) data.push({ Lab: c, Result: randVal(rand, 0, 100) }); tests.push({ title: `Strip ▸ Y ×${label}`, description: `Horizontal strip with ${n} categories on Y`, tags: ['strip', 'discrete-y', `n${label}`], chartType: 'Strip Plot', data, fields: [makeField('Lab'), makeField('Result')], metadata: buildMetadata(data), encodingMap: { y: makeEncodingItem('Lab'), x: makeEncodingItem('Result') }, }); } } return tests; } // --------------------------------------------------------------------------- // Bump Chart // --------------------------------------------------------------------------- function genBumpSizing(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(1100); const teams = cats('Team', 5); for (const { n, label } of SIZES) { const steps = cats('Week', n); const data: Record<string, any>[] = []; for (const s of steps) for (const t of teams) data.push({ Week: s, Team: t, Rank: Math.floor(rand() * 5) + 1 }); tests.push({ title: `Bump ▸ X ×${label}`, description: `Bump chart with ${n} time steps × 5 teams`, tags: ['bump', 'discrete-x', `n${label}`], chartType: 'Bump Chart', data, fields: [makeField('Week'), makeField('Team'), makeField('Rank')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Week'), y: makeEncodingItem('Rank'), color: makeEncodingItem('Team') }, }); } return tests; } // --------------------------------------------------------------------------- // Pyramid Chart // --------------------------------------------------------------------------- function genPyramidSizing(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(1200); const genders = ['Male', 'Female']; for (const { n, label } of SIZES) { const ageBands = cats('Age', n); const data: Record<string, any>[] = []; for (const a of ageBands) for (const g of genders) data.push({ AgeGroup: a, Gender: g, Population: randVal(rand, 1000, 50000) }); tests.push({ title: `Pyramid ▸ Y ×${label}`, description: `Pyramid chart with ${n} age groups on Y`, tags: ['pyramid', 'discrete-y', `n${label}`], chartType: 'Pyramid Chart', data, fields: [makeField('AgeGroup'), makeField('Gender'), makeField('Population')], metadata: buildMetadata(data), encodingMap: { y: makeEncodingItem('AgeGroup'), x: makeEncodingItem('Population'), color: makeEncodingItem('Gender') }, }); } return tests; } // --------------------------------------------------------------------------- // Waterfall Chart // --------------------------------------------------------------------------- function genWaterfallSizing(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(1300); for (const { n, label } of SIZES) { const steps = cats('Step', n); const data = steps.map((s, i) => ({ Step: s, Amount: i === 0 ? randVal(rand, 500, 1000) : i === n - 1 ? randVal(rand, 200, 800) : (rand() > 0.5 ? 1 : -1) * randVal(rand, 10, 150), Type: i === 0 ? 'start' : i === n - 1 ? 'end' : 'delta', })); tests.push({ title: `Waterfall ▸ X ×${label}`, description: `Waterfall with ${n} steps on X`, tags: ['waterfall', 'discrete-x', `n${label}`], chartType: 'Waterfall Chart', data, fields: [makeField('Step'), makeField('Amount'), makeField('Type')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Step'), y: makeEncodingItem('Amount'), color: makeEncodingItem('Type') }, }); } return tests; } // --------------------------------------------------------------------------- // Ranged Dot Plot // --------------------------------------------------------------------------- function genRangedDotSizing(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(1400); const phases = ['Before', 'After']; for (const { n, label } of SIZES) { const categories = cats('Metric', n); const data: Record<string, any>[] = []; for (const c of categories) for (const p of phases) data.push({ Metric: c, Phase: p, Score: randVal(rand, 20, 90) }); tests.push({ title: `Ranged Dot ▸ Y ×${label}`, description: `Ranged dot plot with ${n} categories on Y`, tags: ['ranged-dot', 'discrete-y', `n${label}`], chartType: 'Ranged Dot Plot', data, fields: [makeField('Metric'), makeField('Phase'), makeField('Score')], metadata: buildMetadata(data), encodingMap: { y: makeEncodingItem('Metric'), x: makeEncodingItem('Score'), color: makeEncodingItem('Phase') }, }); } return tests; } // --------------------------------------------------------------------------- // Pie Chart (discrete color) // --------------------------------------------------------------------------- function genPieSizing(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(1500); for (const { n, label } of SIZES) { const categories = cats('Slice', n); const data = categories.map(c => ({ Slice: c, Share: randVal(rand, 1, 100) })); tests.push({ title: `Pie ▸ color ×${label}`, description: `Pie chart with ${n} slices`, tags: ['pie', 'discrete-color', `n${label}`], chartType: 'Pie Chart', data, fields: [makeField('Slice'), makeField('Share')], metadata: buildMetadata(data), encodingMap: { color: makeEncodingItem('Slice'), size: makeEncodingItem('Share') }, }); } return tests; } // --------------------------------------------------------------------------- // Radar Chart // --------------------------------------------------------------------------- function genRadarSizing(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(1600); const entities = cats('E', 3); for (const { n, label } of SIZES) { const axes = cats('Axis', n); const data: Record<string, any>[] = []; for (const e of entities) for (const a of axes) data.push({ Metric: a, Entity: e, Score: randVal(rand, 10, 100) }); tests.push({ title: `Radar ▸ ×${label} axes`, description: `Radar chart with ${n} metric axes × 3 entities`, tags: ['radar', 'discrete-x', `n${label}`], chartType: 'Radar Chart', data, fields: [makeField('Metric'), makeField('Entity'), makeField('Score')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Metric'), y: makeEncodingItem('Score'), color: makeEncodingItem('Entity') }, }); } return tests; } // --------------------------------------------------------------------------- // Combined export // --------------------------------------------------------------------------- export function genDiscreteAxisTests(): TestCase[] { return [ ...genBarSizing(), ...genStackedBarSizing(), ...genGroupedBarSizing(), ...genScatterSizing(), ...genLineSizing(), ...genAreaSizing(), ...genLollipopSizing(), ...genHeatmapSizing(), ...genBoxplotSizing(), ...genStripSizing(), ...genBumpSizing(), ...genPyramidSizing(), ...genWaterfallSizing(), ...genRangedDotSizing(), ...genPieSizing(), ...genRadarSizing(), ]; } ================================================ FILE: src/lib/agents-chart/test-data/distribution-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { Type } from '../../../data/types'; import { TestCase, makeField, makeEncodingItem, buildMetadata } from './types'; import { seededRandom, genCategories, genMonths } from './generators'; // ------ Histogram ------ export function genHistogramTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(400); // 1. Small normal distribution { const data = Array.from({ length: 100 }, () => { // Box-Muller transform const u1 = rand(), u2 = rand(); return { Value: Math.round((Math.sqrt(-2 * Math.log(u1)) * Math.cos(2 * Math.PI * u2)) * 15 + 50) }; }); tests.push({ title: 'Normal distribution (100 points)', description: 'Gaussian data — basic histogram', tags: ['quantitative', 'medium'], chartType: 'Histogram', data, fields: [makeField('Value')], metadata: { Value: { type: Type.Number, semanticType: 'Quantity', levels: [] } }, encodingMap: { x: makeEncodingItem('Value') }, }); } // 2. With color split { const groups = ['Male', 'Female']; const data: any[] = []; for (let i = 0; i < 200; i++) { const g = groups[i % 2]; const offset = g === 'Male' ? 170 : 160; const u1 = rand(), u2 = rand(); data.push({ Height: Math.round((Math.sqrt(-2 * Math.log(u1)) * Math.cos(2 * Math.PI * u2)) * 8 + offset), Gender: g, }); } tests.push({ title: 'Histogram + Color (gender split)', description: '200 points, two groups', tags: ['quantitative', 'nominal', 'color', 'medium'], chartType: 'Histogram', data, fields: [makeField('Height'), makeField('Gender')], metadata: { Height: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Gender: { type: Type.String, semanticType: 'Category', levels: groups }, }, encodingMap: { x: makeEncodingItem('Height'), color: makeEncodingItem('Gender') }, }); } // 3. Large dataset { const data = Array.from({ length: 1000 }, () => ({ Income: Math.round(20000 + rand() * 180000), })); tests.push({ title: 'Large histogram (1000 points)', description: 'Income distribution, large dataset', tags: ['quantitative', 'large'], chartType: 'Histogram', data, fields: [makeField('Income')], metadata: { Income: { type: Type.Number, semanticType: 'Amount', levels: [] } }, encodingMap: { x: makeEncodingItem('Income') }, }); } return tests; } // ------ Boxplot ------ export function genBoxplotTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(700); // 1. Nominal × Quant { const groups = genCategories('Category', 5); const data: any[] = []; for (const g of groups) for (let i = 0; i < 30; i++) { data.push({ Group: g, Value: Math.round(rand() * 100) }); } tests.push({ title: 'Nominal × Quant (5 groups)', description: '5 categories × 30 observations each', tags: ['nominal', 'quantitative', 'medium'], chartType: 'Boxplot', data, fields: [makeField('Group'), makeField('Value')], metadata: { Group: { type: Type.String, semanticType: 'Category', levels: groups }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Group'), y: makeEncodingItem('Value') }, }); } // 2. Two quant axes (ensureNominalAxis test) { const data: any[] = []; for (let level = 1; level <= 5; level++) for (let i = 0; i < 20; i++) { data.push({ Level: level, Score: Math.round(level * 10 + rand() * 40) }); } tests.push({ title: 'Quant × Quant (ensureNominalAxis)', description: 'Both axes quant — lower cardinality should convert to nominal', tags: ['quantitative', 'medium', 'dtype-conversion'], chartType: 'Boxplot', data, fields: [makeField('Level'), makeField('Score')], metadata: { Level: { type: Type.Number, semanticType: 'Rank', levels: [1, 2, 3, 4, 5] }, Score: { type: Type.Number, semanticType: 'Score', levels: [] }, }, encodingMap: { x: makeEncodingItem('Level'), y: makeEncodingItem('Score') }, }); } // 3. Large boxplot { const depts = genCategories('Department', 12); const data: any[] = []; for (const d of depts) for (let i = 0; i < 50; i++) { data.push({ Department: d, Salary: Math.round(30000 + rand() * 120000) }); } tests.push({ title: 'Nominal × Quant (large, 12 groups)', description: '12 departments × 50 observations', tags: ['nominal', 'quantitative', 'large'], chartType: 'Boxplot', data, fields: [makeField('Department'), makeField('Salary')], metadata: { Department: { type: Type.String, semanticType: 'Department', levels: depts }, Salary: { type: Type.Number, semanticType: 'Amount', levels: [] }, }, encodingMap: { x: makeEncodingItem('Department'), y: makeEncodingItem('Salary') }, }); } // 4. Nominal × Quant + Color (small, 4 groups × 2 colors) { const groups = genCategories('Category', 4); const genders = ['Male', 'Female']; const data: any[] = []; for (const g of groups) for (const s of genders) for (let i = 0; i < 25; i++) { data.push({ Group: g, Gender: s, Score: Math.round(20 + rand() * 80) }); } tests.push({ title: 'Nominal × Quant + Color (4 groups × 2)', description: '4 categories split by gender — colored boxplot', tags: ['nominal', 'quantitative', 'color', 'small'], chartType: 'Boxplot', data, fields: [makeField('Group'), makeField('Score'), makeField('Gender')], metadata: { Group: { type: Type.String, semanticType: 'Category', levels: groups }, Score: { type: Type.Number, semanticType: 'Score', levels: [] }, Gender: { type: Type.String, semanticType: 'Category', levels: genders }, }, encodingMap: { x: makeEncodingItem('Group'), y: makeEncodingItem('Score'), color: makeEncodingItem('Gender') }, }); } // 5. Nominal × Quant + Color (medium, 6 groups × 4 colors) { const countries = genCategories('Country', 6); const quarters = ['Q1', 'Q2', 'Q3', 'Q4']; const data: any[] = []; for (const c of countries) for (const q of quarters) for (let i = 0; i < 20; i++) { data.push({ Country: c, Quarter: q, Revenue: Math.round(500 + rand() * 5000) }); } tests.push({ title: 'Nominal × Quant + Color (6 groups × 4)', description: '6 countries × 4 quarters — tests boxplot color grouping', tags: ['nominal', 'quantitative', 'color', 'medium'], chartType: 'Boxplot', data, fields: [makeField('Country'), makeField('Revenue'), makeField('Quarter')], metadata: { Country: { type: Type.String, semanticType: 'Country', levels: countries }, Revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, Quarter: { type: Type.String, semanticType: 'Category', levels: quarters }, }, encodingMap: { x: makeEncodingItem('Country'), y: makeEncodingItem('Revenue'), color: makeEncodingItem('Quarter') }, }); } // 6. Large boxplot + many colors (8 departments × 5 levels) { const depts = genCategories('Department', 8); const levels = ['Intern', 'Junior', 'Mid', 'Senior', 'Lead']; const data: any[] = []; for (const d of depts) for (const l of levels) for (let i = 0; i < 15; i++) { data.push({ Department: d, Level: l, Compensation: Math.round(25000 + rand() * 175000) }); } tests.push({ title: 'Nominal × Quant + Color (large, 8 × 5)', description: '8 departments × 5 levels — many colored boxes', tags: ['nominal', 'quantitative', 'color', 'large'], chartType: 'Boxplot', data, fields: [makeField('Department'), makeField('Compensation'), makeField('Level')], metadata: { Department: { type: Type.String, semanticType: 'Department', levels: depts }, Compensation: { type: Type.Number, semanticType: 'Amount', levels: [] }, Level: { type: Type.String, semanticType: 'Category', levels: levels }, }, encodingMap: { x: makeEncodingItem('Department'), y: makeEncodingItem('Compensation'), color: makeEncodingItem('Level') }, }); } return tests; } // ------ Density Plot ------ export function genDensityTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(940); // 1. Simple density (one distribution) { const data = Array.from({ length: 200 }, () => ({ Score: Math.round(50 + (rand() + rand() + rand() - 1.5) * 30), // roughly normal })); tests.push({ title: 'Single Distribution (200 pts)', description: 'Approximately normal distribution of scores', tags: ['quantitative', 'small'], chartType: 'Density Plot', data, fields: [makeField('Score')], metadata: { Score: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Score') }, }); } // 2. Grouped density with color { const groups = ['Control', 'Treatment A', 'Treatment B']; const data: any[] = []; for (const g of groups) { const offset = g === 'Control' ? 0 : g === 'Treatment A' ? 10 : 20; for (let i = 0; i < 150; i++) { data.push({ Value: Math.round(50 + offset + (rand() + rand() + rand() - 1.5) * 20), Group: g, }); } } tests.push({ title: 'Grouped Density (3 groups, 450 pts)', description: 'Three overlapping distributions colored by group', tags: ['quantitative', 'color', 'medium'], chartType: 'Density Plot', data, fields: [makeField('Value'), makeField('Group')], metadata: { Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Group: { type: Type.String, semanticType: 'Category', levels: groups }, }, encodingMap: { x: makeEncodingItem('Value'), color: makeEncodingItem('Group') }, }); } // 3. Bimodal distribution { const data: any[] = []; for (let i = 0; i < 300; i++) { const peak = rand() > 0.4 ? 30 : 70; data.push({ Measurement: Math.round(peak + (rand() - 0.5) * 20) }); } tests.push({ title: 'Bimodal Distribution (300 pts)', description: 'Two peaks — tests bandwidth sensitivity', tags: ['quantitative', 'medium'], chartType: 'Density Plot', data, fields: [makeField('Measurement')], metadata: { Measurement: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Measurement') }, }); } // 4. Color + Column facet { const sites = ['Lab A', 'Lab B']; const data: any[] = []; for (const site of sites) { const offset = site === 'Lab A' ? 0 : 15; for (let i = 0; i < 200; i++) { data.push({ Reading: Math.round(40 + offset + (rand() + rand() + rand() - 1.5) * 25), Batch: rand() > 0.5 ? 'Morning' : 'Evening', Site: site, }); } } tests.push({ title: 'Color + Column Facet (2 sites)', description: 'Density by batch, faceted by site', tags: ['quantitative', 'color', 'facet', 'medium'], chartType: 'Density Plot', data, fields: [makeField('Reading'), makeField('Batch'), makeField('Site')], metadata: { Reading: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Batch: { type: Type.String, semanticType: 'Category', levels: ['Morning', 'Evening'] }, Site: { type: Type.String, semanticType: 'Category', levels: sites }, }, encodingMap: { x: makeEncodingItem('Reading'), color: makeEncodingItem('Batch'), column: makeEncodingItem('Site'), }, }); } return tests; } // ------ Strip Plot (Jitter) ------ export function genStripPlotTests(): TestCase[] { const tests: TestCase[] = []; // 1. Basic categorical x, numeric y { const species = ['Setosa', 'Versicolor', 'Virginica']; const rand = seededRandom(77); const data: any[] = []; for (const sp of species) { const base = sp === 'Setosa' ? 1.5 : sp === 'Versicolor' ? 4.3 : 5.5; for (let i = 0; i < 20; i++) { data.push({ Species: sp, PetalLength: Math.round((base + (rand() - 0.5) * 2) * 10) / 10 }); } } tests.push({ title: 'Iris Petal Length (3 species, 60 pts)', description: 'Categorical x, quantitative y with jitter', tags: ['jitter', 'nominal', 'small'], chartType: 'Strip Plot', data, fields: [makeField('Species'), makeField('PetalLength')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Species'), y: makeEncodingItem('PetalLength') }, }); } // 2. With color encoding { const groups = ['Control', 'Treatment A', 'Treatment B']; const genders = ['M', 'F']; const rand = seededRandom(88); const data: any[] = []; for (const g of groups) { const base = g === 'Control' ? 50 : g === 'Treatment A' ? 65 : 80; for (const sex of genders) { for (let i = 0; i < 10; i++) { data.push({ Group: g, Gender: sex, Score: Math.round(base + (rand() - 0.4) * 30), }); } } } tests.push({ title: 'Clinical Trial Scores (color = Gender)', description: 'Strip plot with color grouping', tags: ['jitter', 'nominal', 'color'], chartType: 'Strip Plot', data, fields: [makeField('Group'), makeField('Score'), makeField('Gender')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Group'), y: makeEncodingItem('Score'), color: makeEncodingItem('Gender'), }, }); } // 3. No jitter (jitterWidth = 0) { const data = [ { Category: 'A', Value: 10 }, { Category: 'A', Value: 15 }, { Category: 'A', Value: 12 }, { Category: 'A', Value: 18 }, { Category: 'B', Value: 25 }, { Category: 'B', Value: 30 }, { Category: 'B', Value: 22 }, { Category: 'B', Value: 28 }, ]; tests.push({ title: 'No Jitter (aligned strip)', description: 'jitterWidth=0 produces a clean strip', tags: ['jitter', 'config'], chartType: 'Strip Plot', data, fields: [makeField('Category'), makeField('Value')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Category'), y: makeEncodingItem('Value') }, chartProperties: { jitterWidth: 0 }, }); } return tests; } ================================================ FILE: src/lib/agents-chart/test-data/echarts-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ECharts backend comparison tests. * * Runs the same test inputs through BOTH assembleVegaLite (Vega-Lite) and * assembleECharts (ECharts) to verify: * 1. Both produce valid output from the same inputs * 2. The structural differences are as expected (encoding-based vs series-based) * 3. Core analysis phases (semantics, layout, overflow) produce identical results * * Covers: Scatter Plot, Line Chart, Bar Chart, Stacked Bar Chart, Grouped Bar Chart */ import { Type } from '../../../data/types'; import { TestCase, makeField, makeEncodingItem } from './types'; import { seededRandom, genCategories, genDates, genMonths } from './generators'; // --------------------------------------------------------------------------- // Test data generators — shared across VL and EC // --------------------------------------------------------------------------- function genScatterData(n: number, seed: number) { const rand = seededRandom(seed); return Array.from({ length: n }, () => ({ Weight: Math.round((40 + rand() * 60) * 10) / 10, Height: Math.round((150 + rand() * 50) * 10) / 10, })); } function genScatterColorData(n: number, seed: number) { const rand = seededRandom(seed); const categories = ['Alpha', 'Beta', 'Gamma']; return Array.from({ length: n }, (_, i) => ({ X: Math.round(rand() * 100 * 10) / 10, Y: Math.round(rand() * 100 * 10) / 10, Group: categories[i % categories.length], })); } function genBarData(seed: number) { const rand = seededRandom(seed); const products = ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries']; return products.map(p => ({ Product: p, Sales: Math.round(100 + rand() * 900), })); } function genLineData(seed: number) { const rand = seededRandom(seed); const months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']; return months.map(m => ({ Month: m, Revenue: Math.round(1000 + rand() * 5000), })); } function genMultiSeriesLineData(seed: number) { const rand = seededRandom(seed); const months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug']; const series = ['ProductA', 'ProductB', 'ProductC']; const data: any[] = []; for (const m of months) { for (const s of series) { data.push({ Month: m, Sales: Math.round(500 + rand() * 2000), Product: s, }); } } return data; } function genStackedBarData(seed: number) { const rand = seededRandom(seed); const quarters = ['Q1', 'Q2', 'Q3', 'Q4']; const regions = ['North', 'South', 'East', 'West']; const data: any[] = []; for (const q of quarters) { for (const r of regions) { data.push({ Quarter: q, Revenue: Math.round(200 + rand() * 800), Region: r, }); } } return data; } function genGroupedBarData(seed: number) { const rand = seededRandom(seed); const years = ['2022', '2023', '2024']; const departments = ['Sales', 'Engineering', 'Marketing']; const data: any[] = []; for (const y of years) { for (const d of departments) { data.push({ Year: y, Budget: Math.round(10000 + rand() * 50000), Department: d, }); } } return data; } // --------------------------------------------------------------------------- // Test case builders // --------------------------------------------------------------------------- export function genEChartsScatterTests(): TestCase[] { const tests: TestCase[] = []; // 1. Basic scatter — quant × quant { const data = genScatterData(50, 42); tests.push({ title: 'EC: Scatter — Basic Q×Q', description: '50 points, two quantitative axes. Compare VL encoding-based vs EC series-based.', tags: ['echarts', 'scatter', 'quantitative'], chartType: 'Scatter Plot', data, fields: [makeField('Weight'), makeField('Height')], metadata: { Weight: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Height: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Weight'), y: makeEncodingItem('Height') }, }); } // 2. Scatter with color grouping { const data = genScatterColorData(90, 77); tests.push({ title: 'EC: Scatter — Color Groups', description: '90 points, 3 groups. VL: one encoding.color; EC: 3 separate series.', tags: ['echarts', 'scatter', 'color', 'multi-series'], chartType: 'Scatter Plot', data, fields: [makeField('X'), makeField('Y'), makeField('Group')], metadata: { X: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Y: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Group: { type: Type.String, semanticType: 'Category', levels: ['Alpha', 'Beta', 'Gamma'] }, }, encodingMap: { x: makeEncodingItem('X'), y: makeEncodingItem('Y'), color: makeEncodingItem('Group') }, }); } // 3. Dense scatter — tests point sizing { const data = genScatterData(500, 99); tests.push({ title: 'EC: Scatter — Dense (500 pts)', description: 'Dense scatter plot. VL uses applyPointSizeScaling; EC controls itemStyle.', tags: ['echarts', 'scatter', 'dense'], chartType: 'Scatter Plot', data, fields: [makeField('Weight'), makeField('Height')], metadata: { Weight: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Height: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Weight'), y: makeEncodingItem('Height') }, }); } return tests; } export function genEChartsLineTests(): TestCase[] { const tests: TestCase[] = []; // 1. Single series line { const data = genLineData(200); tests.push({ title: 'EC: Line — Single Series', description: 'Ordinal x-axis, single line. VL: mark=line; EC: series type=line.', tags: ['echarts', 'line', 'single-series'], chartType: 'Line Chart', data, fields: [makeField('Month'), makeField('Revenue')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: ['Jan','Feb','Mar','Apr','May','Jun'] }, Revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Revenue') }, }); } // 2. Multi-series line (the key difference test) { const data = genMultiSeriesLineData(300); tests.push({ title: 'EC: Line — Multi-Series (3 products)', description: 'Color channel → multiple lines. VL: single spec with color encoding; EC: 3 explicit series with category-aligned data.', tags: ['echarts', 'line', 'multi-series', 'color'], chartType: 'Line Chart', data, fields: [makeField('Month'), makeField('Sales'), makeField('Product')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug'] }, Sales: { type: Type.Number, semanticType: 'Revenue', levels: [] }, Product: { type: Type.String, semanticType: 'Category', levels: ['ProductA','ProductB','ProductC'] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Sales'), color: makeEncodingItem('Product') }, }); } // 3. Multi-series with many categories { const rand = seededRandom(400); const months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']; const series = genCategories('Region', 8); const data: any[] = []; for (const m of months) { for (const s of series) { data.push({ Month: m, Value: Math.round(rand() * 1000), Region: s }); } } tests.push({ title: 'EC: Line — 8 Series × 12 Months', description: 'High series count. VL: one color encoding; EC: 8 separate series objects.', tags: ['echarts', 'line', 'multi-series', 'medium'], chartType: 'Line Chart', data, fields: [makeField('Month'), makeField('Value'), makeField('Region')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: months }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Region: { type: Type.String, semanticType: 'Category', levels: series }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Value'), color: makeEncodingItem('Region') }, }); } return tests; } export function genEChartsBarTests(): TestCase[] { const tests: TestCase[] = []; // 1. Simple bar { const data = genBarData(500); tests.push({ title: 'EC: Bar — Simple (5 bars)', description: 'Nominal x, quantitative y. VL: mark=bar + encoding; EC: series type=bar + xAxis.data.', tags: ['echarts', 'bar', 'simple'], chartType: 'Bar Chart', data, fields: [makeField('Product'), makeField('Sales')], metadata: { Product: { type: Type.String, semanticType: 'Product', levels: ['Apples','Bananas','Cherries','Dates','Elderberries'] }, Sales: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { x: makeEncodingItem('Product'), y: makeEncodingItem('Sales') }, }); } // 2. Many bars (tests overflow and label rotation) { const rand = seededRandom(501); const cats = genCategories('Item', 25); const data = cats.map(c => ({ Item: c, Count: Math.round(10 + rand() * 90) })); tests.push({ title: 'EC: Bar — Many Categories (25)', description: 'Tests label handling. VL: labelAngle in axis config; EC: axisLabel.rotate.', tags: ['echarts', 'bar', 'medium', 'overflow'], chartType: 'Bar Chart', data, fields: [makeField('Item'), makeField('Count')], metadata: { Item: { type: Type.String, semanticType: 'Category', levels: cats }, Count: { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('Item'), y: makeEncodingItem('Count') }, }); } return tests; } export function genEChartsStackedBarTests(): TestCase[] { const tests: TestCase[] = []; // 1. Basic stacked bar { const data = genStackedBarData(600); tests.push({ title: 'EC: Stacked Bar — 4Q × 4 Regions', description: 'VL: color channel auto-stacks; EC: series[].stack="total" explicit.', tags: ['echarts', 'stacked-bar', 'color'], chartType: 'Stacked Bar Chart', data, fields: [makeField('Quarter'), makeField('Revenue'), makeField('Region')], metadata: { Quarter: { type: Type.String, semanticType: 'Category', levels: ['Q1','Q2','Q3','Q4'] }, Revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, Region: { type: Type.String, semanticType: 'Category', levels: ['North','South','East','West'] }, }, encodingMap: { x: makeEncodingItem('Quarter'), y: makeEncodingItem('Revenue'), color: makeEncodingItem('Region') }, }); } // 2. Stacked bar with many stacks { const rand = seededRandom(601); const cats = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri']; const types = genCategories('TaskType', 6); const data: any[] = []; for (const c of cats) { for (const t of types) { data.push({ Day: c, Hours: Math.round(1 + rand() * 8), TaskType: t }); } } tests.push({ title: 'EC: Stacked Bar — 5 Days × 6 Types', description: 'More stack segments. Tests legend sizing in both backends.', tags: ['echarts', 'stacked-bar', 'medium'], chartType: 'Stacked Bar Chart', data, fields: [makeField('Day'), makeField('Hours'), makeField('TaskType')], metadata: { Day: { type: Type.String, semanticType: 'Category', levels: cats }, Hours: { type: Type.Number, semanticType: 'Duration', levels: [] }, TaskType: { type: Type.String, semanticType: 'Category', levels: types }, }, encodingMap: { x: makeEncodingItem('Day'), y: makeEncodingItem('Hours'), color: makeEncodingItem('TaskType') }, }); } return tests; } export function genEChartsGroupedBarTests(): TestCase[] { const tests: TestCase[] = []; // 1. Basic grouped bar { const data = genGroupedBarData(700); tests.push({ title: 'EC: Grouped Bar — 3 Years × 3 Depts', description: 'VL: group channel → xOffset; EC: multiple series side-by-side (barGap).', tags: ['echarts', 'grouped-bar', 'group'], chartType: 'Grouped Bar Chart', data, fields: [makeField('Year'), makeField('Budget'), makeField('Department')], metadata: { Year: { type: Type.String, semanticType: 'Year', levels: ['2022','2023','2024'] }, Budget: { type: Type.Number, semanticType: 'Amount', levels: [] }, Department: { type: Type.String, semanticType: 'Category', levels: ['Sales','Engineering','Marketing'] }, }, encodingMap: { x: makeEncodingItem('Year'), y: makeEncodingItem('Budget'), group: makeEncodingItem('Department'), }, }); } // 2. Grouped bar with more groups { const rand = seededRandom(701); const categories = ['A', 'B', 'C', 'D']; const groups = genCategories('Method', 5); const data: any[] = []; for (const c of categories) { for (const g of groups) { data.push({ Category: c, Score: Math.round(rand() * 100), Method: g }); } } tests.push({ title: 'EC: Grouped Bar — 4 Categories × 5 Methods', description: 'More groups per category. Tests bar width calculation in both backends.', tags: ['echarts', 'grouped-bar', 'medium'], chartType: 'Grouped Bar Chart', data, fields: [makeField('Category'), makeField('Score'), makeField('Method')], metadata: { Category: { type: Type.String, semanticType: 'Category', levels: categories }, Score: { type: Type.Number, semanticType: 'Score', levels: [] }, Method: { type: Type.String, semanticType: 'Category', levels: groups }, }, encodingMap: { x: makeEncodingItem('Category'), y: makeEncodingItem('Score'), group: makeEncodingItem('Method'), }, }); } return tests; } // --------------------------------------------------------------------------- // Stress / overflow test cases // --------------------------------------------------------------------------- export function genEChartsStressTests(): TestCase[] { const tests: TestCase[] = []; // 1. Grouped bar — many categories (20 products × 3 groups) { const rand = seededRandom(900); const products = genCategories('Product', 20); const channels = ['Online', 'Retail', 'Wholesale']; const data: any[] = []; for (const p of products) { for (const c of channels) { data.push({ Product: p, Sales: Math.round(100 + rand() * 9000), Channel: c }); } } tests.push({ title: 'EC Stress: Grouped Bar — 20 Categories × 3 Groups', description: 'Many x-axis categories with grouping. Tests horizontal overflow and label crowding.', tags: ['echarts', 'grouped-bar', 'stress', 'overflow'], chartType: 'Grouped Bar Chart', data, fields: [makeField('Product'), makeField('Sales'), makeField('Channel')], metadata: { Product: { type: Type.String, semanticType: 'Category', levels: products }, Sales: { type: Type.Number, semanticType: 'Revenue', levels: [] }, Channel: { type: Type.String, semanticType: 'Category', levels: channels }, }, encodingMap: { x: makeEncodingItem('Product'), y: makeEncodingItem('Sales'), group: makeEncodingItem('Channel'), }, }); } // 2. Grouped bar — many groups (4 quarters × 10 regions) { const rand = seededRandom(901); const quarters = ['Q1', 'Q2', 'Q3', 'Q4']; const regions = genCategories('Region', 10); const data: any[] = []; for (const q of quarters) { for (const r of regions) { data.push({ Quarter: q, Revenue: Math.round(500 + rand() * 5000), Region: r }); } } tests.push({ title: 'EC Stress: Grouped Bar — 4 Quarters × 10 Groups', description: 'Few categories but many groups per category. Tests bar width when bands are subdivided heavily.', tags: ['echarts', 'grouped-bar', 'stress', 'many-groups'], chartType: 'Grouped Bar Chart', data, fields: [makeField('Quarter'), makeField('Revenue'), makeField('Region')], metadata: { Quarter: { type: Type.String, semanticType: 'Category', levels: quarters }, Revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, Region: { type: Type.String, semanticType: 'Category', levels: regions }, }, encodingMap: { x: makeEncodingItem('Quarter'), y: makeEncodingItem('Revenue'), group: makeEncodingItem('Region'), }, }); } // 3. Grouped bar — many categories AND many groups (12 months × 6 types) { const rand = seededRandom(902); const months = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']; const types = genCategories('Type', 6); const data: any[] = []; for (const m of months) { for (const t of types) { data.push({ Month: m, Count: Math.round(10 + rand() * 200), Type: t }); } } tests.push({ title: 'EC Stress: Grouped Bar — 12 Months × 6 Types', description: 'Both many categories and many groups. Extreme horizontal stretch scenario.', tags: ['echarts', 'grouped-bar', 'stress', 'extreme'], chartType: 'Grouped Bar Chart', data, fields: [makeField('Month'), makeField('Count'), makeField('Type')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: months }, Count: { type: Type.Number, semanticType: 'Count', levels: [] }, Type: { type: Type.String, semanticType: 'Category', levels: types }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Count'), group: makeEncodingItem('Type'), }, }); } // 4. Line chart — many x-values (60 days) causing horizontal stretch { const rand = seededRandom(903); const days: string[] = []; for (let i = 1; i <= 60; i++) { const month = Math.ceil(i / 30).toString().padStart(2, '0'); const dayOfMonth = ((i - 1) % 30 + 1).toString().padStart(2, '0'); days.push(`2024-${month}-${dayOfMonth}`); } const data = days.map(d => ({ Date: d, Value: Math.round(rand() * 500) })); tests.push({ title: 'EC Stress: Line — 60 Daily Points', description: 'Many x-axis values on a single line. Tests horizontal stretch and label rotation/density.', tags: ['echarts', 'line', 'stress', 'stretch'], chartType: 'Line Chart', data, fields: [makeField('Date'), makeField('Value')], metadata: { Date: { type: Type.String, semanticType: 'Temporal', levels: days }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Date'), y: makeEncodingItem('Value') }, }); } // 5. Multi-series line — many x-values with multiple series (30 weeks × 5 products) { const rand = seededRandom(904); const weeks: string[] = []; for (let i = 1; i <= 30; i++) weeks.push(`W${i}`); const products = genCategories('Prod', 5); const data: any[] = []; for (const w of weeks) { for (const p of products) { data.push({ Week: w, Sales: Math.round(50 + rand() * 500), Product: p }); } } tests.push({ title: 'EC Stress: Line — 30 Weeks × 5 Products', description: 'Multi-series line with many x-values. Tests legend + horizontal stretch together.', tags: ['echarts', 'line', 'stress', 'multi-series', 'stretch'], chartType: 'Line Chart', data, fields: [makeField('Week'), makeField('Sales'), makeField('Product')], metadata: { Week: { type: Type.String, semanticType: 'Category', levels: weeks }, Sales: { type: Type.Number, semanticType: 'Revenue', levels: [] }, Product: { type: Type.String, semanticType: 'Category', levels: products }, }, encodingMap: { x: makeEncodingItem('Week'), y: makeEncodingItem('Sales'), color: makeEncodingItem('Product') }, }); } // 6. Stacked bar — many categories (15 cities × 4 segments) { const rand = seededRandom(905); const cities = genCategories('City', 15); const segments = ['Residential', 'Commercial', 'Industrial', 'Government']; const data: any[] = []; for (const c of cities) { for (const s of segments) { data.push({ City: c, Spending: Math.round(1000 + rand() * 20000), Segment: s }); } } tests.push({ title: 'EC Stress: Stacked Bar — 15 Cities × 4 Segments', description: 'Many categories with stacking. Tests whether stacked bars maintain adequate width with many x-values.', tags: ['echarts', 'stacked-bar', 'stress', 'overflow'], chartType: 'Stacked Bar Chart', data, fields: [makeField('City'), makeField('Spending'), makeField('Segment')], metadata: { City: { type: Type.String, semanticType: 'Category', levels: cities }, Spending: { type: Type.Number, semanticType: 'Amount', levels: [] }, Segment: { type: Type.String, semanticType: 'Category', levels: segments }, }, encodingMap: { x: makeEncodingItem('City'), y: makeEncodingItem('Spending'), color: makeEncodingItem('Segment') }, }); } return tests; } // =========================================================================== // Area Chart tests // =========================================================================== export function genEChartsAreaTests(): TestCase[] { const tests: TestCase[] = []; // 1. Single-series area { const rand = seededRandom(1000); const months = ['Jan','Feb','Mar','Apr','May','Jun']; const data = months.map(m => ({ Month: m, Revenue: Math.round(100 + rand() * 900) })); tests.push({ title: 'EC: Area — Single Series', description: 'Single area chart. VL: mark=area; EC: line series + areaStyle.', tags: ['echarts', 'area', 'single-series'], chartType: 'Area Chart', data, fields: [makeField('Month'), makeField('Revenue')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: months }, Revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Revenue') }, }); } // 2. Stacked multi-series area { const rand = seededRandom(1001); const months = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug']; const products = ['Desktop', 'Mobile', 'Tablet']; const data: any[] = []; for (const m of months) { for (const p of products) { data.push({ Month: m, Sales: Math.round(50 + rand() * 500), Product: p }); } } tests.push({ title: 'EC: Area — Stacked 3 Products', description: 'Stacked area. VL: y.stack; EC: series[].stack + areaStyle.', tags: ['echarts', 'area', 'stacked', 'multi-series'], chartType: 'Area Chart', data, fields: [makeField('Month'), makeField('Sales'), makeField('Product')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: months }, Sales: { type: Type.Number, semanticType: 'Revenue', levels: [] }, Product: { type: Type.String, semanticType: 'Category', levels: products }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Sales'), color: makeEncodingItem('Product') }, }); } return tests; } // =========================================================================== // Pie Chart tests // =========================================================================== export function genEChartsPieTests(): TestCase[] { const tests: TestCase[] = []; // 1. Basic pie { const data = [ { Category: 'Electronics', Revenue: 4500 }, { Category: 'Clothing', Revenue: 3200 }, { Category: 'Food', Revenue: 2800 }, { Category: 'Books', Revenue: 1500 }, { Category: 'Sports', Revenue: 900 }, ]; tests.push({ title: 'EC: Pie — 5 Slices', description: 'Basic pie chart. VL: mark=arc + theta; EC: series type=pie.', tags: ['echarts', 'pie', 'basic'], chartType: 'Pie Chart', data, fields: [makeField('Category'), makeField('Revenue')], metadata: { Category: { type: Type.String, semanticType: 'Category', levels: ['Electronics','Clothing','Food','Books','Sports'] }, Revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { color: makeEncodingItem('Category'), size: makeEncodingItem('Revenue') }, }); } // 2. Pie with many slices { const rand = seededRandom(1010); const categories = genCategories('Item', 10); const data = categories.map(c => ({ Item: c, Count: Math.round(10 + rand() * 90) })); tests.push({ title: 'EC: Pie — 10 Slices', description: 'Many-slice pie. Tests label overlap and legend sizing.', tags: ['echarts', 'pie', 'medium'], chartType: 'Pie Chart', data, fields: [makeField('Item'), makeField('Count')], metadata: { Item: { type: Type.String, semanticType: 'Category', levels: categories }, Count: { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { color: makeEncodingItem('Item'), size: makeEncodingItem('Count') }, }); } return tests; } // =========================================================================== // Heatmap tests // =========================================================================== export function genEChartsHeatmapTests(): TestCase[] { const tests: TestCase[] = []; // 1. Basic heatmap { const rand = seededRandom(1020); const days = ['Mon','Tue','Wed','Thu','Fri']; const hours = ['9am','10am','11am','12pm','1pm','2pm','3pm','4pm','5pm']; const data: any[] = []; for (const d of days) { for (const h of hours) { data.push({ Day: d, Hour: h, Activity: Math.round(rand() * 100) }); } } tests.push({ title: 'EC: Heatmap — 5 Days × 9 Hours', description: 'Categorical x+y with quantitative color. VL: mark=rect + color scale; EC: heatmap + visualMap.', tags: ['echarts', 'heatmap', 'basic'], chartType: 'Heatmap', data, fields: [makeField('Day'), makeField('Hour'), makeField('Activity')], metadata: { Day: { type: Type.String, semanticType: 'Category', levels: days }, Hour: { type: Type.String, semanticType: 'Category', levels: hours }, Activity: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Day'), y: makeEncodingItem('Hour'), color: makeEncodingItem('Activity') }, }); } // 2. Larger heatmap { const rand = seededRandom(1021); const months = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']; const regions = genCategories('Region', 8); const data: any[] = []; for (const m of months) { for (const r of regions) { data.push({ Month: m, Region: r, Sales: Math.round(rand() * 10000) }); } } tests.push({ title: 'EC: Heatmap — 12 Months × 8 Regions', description: 'Larger heatmap. Tests color gradient and label density.', tags: ['echarts', 'heatmap', 'medium'], chartType: 'Heatmap', data, fields: [makeField('Month'), makeField('Region'), makeField('Sales')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: months }, Region: { type: Type.String, semanticType: 'Category', levels: regions }, Sales: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Region'), color: makeEncodingItem('Sales') }, }); } return tests; } // =========================================================================== // Histogram tests // =========================================================================== export function genEChartsHistogramTests(): TestCase[] { const tests: TestCase[] = []; // 1. Simple histogram { const rand = seededRandom(1030); const data = Array.from({ length: 200 }, () => ({ Score: Math.round(rand() * 100), })); tests.push({ title: 'EC: Histogram — 200 Values', description: 'Single-variable histogram. VL: encoding.x.bin=true; EC: client-side binning.', tags: ['echarts', 'histogram', 'basic'], chartType: 'Histogram', data, fields: [makeField('Score')], metadata: { Score: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Score') }, }); } // 2. Histogram with color grouping { const rand = seededRandom(1031); const groups = ['Male', 'Female']; const data: any[] = []; for (const g of groups) { const offset = g === 'Male' ? 10 : -5; for (let i = 0; i < 150; i++) { data.push({ Height: Math.round(155 + offset + rand() * 40), Gender: g, }); } } tests.push({ title: 'EC: Histogram — Stacked by Gender', description: 'Stacked histogram with color grouping.', tags: ['echarts', 'histogram', 'stacked', 'color'], chartType: 'Histogram', data, fields: [makeField('Height'), makeField('Gender')], metadata: { Height: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Gender: { type: Type.String, semanticType: 'Category', levels: groups }, }, encodingMap: { x: makeEncodingItem('Height'), color: makeEncodingItem('Gender') }, }); } return tests; } // =========================================================================== // Boxplot tests // =========================================================================== export function genEChartsBoxplotTests(): TestCase[] { const tests: TestCase[] = []; // 1. Basic boxplot — 4 categories { const rand = seededRandom(1040); const categories = ['Spring', 'Summer', 'Autumn', 'Winter']; const data: any[] = []; for (const c of categories) { const base = c === 'Summer' ? 28 : c === 'Winter' ? 5 : 15; for (let i = 0; i < 40; i++) { data.push({ Season: c, Temperature: Math.round((base + (rand() - 0.5) * 20) * 10) / 10, }); } } tests.push({ title: 'EC: Boxplot — 4 Seasons', description: 'Box-and-whisker per season. VL: mark=boxplot auto-quartiles; EC: client-side quartile computation.', tags: ['echarts', 'boxplot', 'basic'], chartType: 'Boxplot', data, fields: [makeField('Season'), makeField('Temperature')], metadata: { Season: { type: Type.String, semanticType: 'Category', levels: categories }, Temperature: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Season'), y: makeEncodingItem('Temperature') }, }); } // 2. Boxplot with many categories { const rand = seededRandom(1041); const cities = genCategories('City', 8); const data: any[] = []; for (const c of cities) { for (let i = 0; i < 30; i++) { data.push({ City: c, Salary: Math.round(30000 + rand() * 70000), }); } } tests.push({ title: 'EC: Boxplot — 8 Cities', description: 'More categories with salary distributions. Tests box width scaling.', tags: ['echarts', 'boxplot', 'medium'], chartType: 'Boxplot', data, fields: [makeField('City'), makeField('Salary')], metadata: { City: { type: Type.String, semanticType: 'Category', levels: cities }, Salary: { type: Type.Number, semanticType: 'Amount', levels: [] }, }, encodingMap: { x: makeEncodingItem('City'), y: makeEncodingItem('Salary') }, }); } return tests; } // =========================================================================== // Radar Chart tests // =========================================================================== export function genEChartsRadarTests(): TestCase[] { const tests: TestCase[] = []; // 1. Single-group radar { const data = [ { Metric: 'Speed', Value: 80 }, { Metric: 'Strength', Value: 70 }, { Metric: 'Defense', Value: 90 }, { Metric: 'Agility', Value: 65 }, { Metric: 'Intelligence', Value: 85 }, ]; tests.push({ title: 'EC: Radar — Single Polygon', description: 'Single-group radar. VL: manual trig + layered marks; EC: native radar series.', tags: ['echarts', 'radar', 'single'], chartType: 'Radar Chart', data, fields: [makeField('Metric'), makeField('Value')], metadata: { Metric: { type: Type.String, semanticType: 'Category', levels: ['Speed','Strength','Defense','Agility','Intelligence'] }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Metric'), y: makeEncodingItem('Value') }, }); } // 2. Multi-group radar { const metrics = ['Attack', 'Defense', 'Speed', 'HP', 'Special', 'Accuracy']; const groups = ['Warrior', 'Mage', 'Rogue']; const rand = seededRandom(1050); const data: any[] = []; for (const g of groups) { for (const m of metrics) { data.push({ Skill: m, Score: Math.round(30 + rand() * 70), Class: g }); } } tests.push({ title: 'EC: Radar — 3 Groups × 6 Axes', description: 'Multi-group radar comparison. EC excels here — native polar layout vs VL manual trig.', tags: ['echarts', 'radar', 'multi-group'], chartType: 'Radar Chart', data, fields: [makeField('Skill'), makeField('Score'), makeField('Class')], metadata: { Skill: { type: Type.String, semanticType: 'Category', levels: metrics }, Score: { type: Type.Number, semanticType: 'Score', levels: [] }, Class: { type: Type.String, semanticType: 'Category', levels: groups }, }, encodingMap: { x: makeEncodingItem('Skill'), y: makeEncodingItem('Score'), color: makeEncodingItem('Class') }, }); } // 3. Radar with many axes { const metrics = ['Metric1','Metric2','Metric3','Metric4','Metric5','Metric6','Metric7','Metric8','Metric9','Metric10']; const rand = seededRandom(1051); const data = metrics.map(m => ({ Metric: m, Value: Math.round(20 + rand() * 80) })); tests.push({ title: 'EC: Radar — 10 Axes', description: 'Dense radar with many axes. Tests label crowding on spokes.', tags: ['echarts', 'radar', 'dense'], chartType: 'Radar Chart', data, fields: [makeField('Metric'), makeField('Value')], metadata: { Metric: { type: Type.String, semanticType: 'Category', levels: metrics }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Metric'), y: makeEncodingItem('Value') }, }); } return tests; } // --------------------------------------------------------------------------- // Candlestick Chart // --------------------------------------------------------------------------- export function genEChartsCandlestickTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(1100); function genOHLC(days: number, startPrice: number) { const data: any[] = []; let price = startPrice; const baseDate = new Date('2024-01-02'); for (let i = 0; i < days; i++) { const date = new Date(baseDate); date.setDate(baseDate.getDate() + i); const change = (rand() - 0.48) * 4; const open = Math.round(price * 100) / 100; const close = Math.round((price + change) * 100) / 100; const high = Math.round((Math.max(open, close) + rand() * 2) * 100) / 100; const low = Math.round((Math.min(open, close) - rand() * 2) * 100) / 100; data.push({ Date: date.toISOString().slice(0, 10), Open: open, High: high, Low: low, Close: close, }); price = close; } return data; } // 1. 30-day OHLC { const data = genOHLC(30, 150); tests.push({ title: 'EC: Candlestick — 30-day OHLC', description: 'One month stock data. EC: native candlestick series; VL: layered rule+bar.', tags: ['echarts', 'candlestick', 'small'], chartType: 'Candlestick Chart', data, fields: [makeField('Date'), makeField('Open'), makeField('High'), makeField('Low'), makeField('Close')], metadata: { Date: { type: Type.String, semanticType: 'Date', levels: [] }, Open: { type: Type.Number, semanticType: 'Quantity', levels: [] }, High: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Low: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Close: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Date'), open: makeEncodingItem('Open'), high: makeEncodingItem('High'), low: makeEncodingItem('Low'), close: makeEncodingItem('Close'), }, }); } // 2. 90-day dense { const data = genOHLC(90, 50); tests.push({ title: 'EC: Candlestick — 90-day Dense', description: 'Three months — tests candle width auto-sizing and dataZoom.', tags: ['echarts', 'candlestick', 'medium'], chartType: 'Candlestick Chart', data, fields: [makeField('Date'), makeField('Open'), makeField('High'), makeField('Low'), makeField('Close')], metadata: { Date: { type: Type.String, semanticType: 'Date', levels: [] }, Open: { type: Type.Number, semanticType: 'Quantity', levels: [] }, High: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Low: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Close: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Date'), open: makeEncodingItem('Open'), high: makeEncodingItem('High'), low: makeEncodingItem('Low'), close: makeEncodingItem('Close'), }, }); } return tests; } // --------------------------------------------------------------------------- // Streamgraph // --------------------------------------------------------------------------- export function genEChartsStreamgraphTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(1150); const genFlow = (n: number, base: number, volatility: number): number[] => { const values: number[] = [base]; let momentum = 0; for (let i = 1; i < n; i++) { momentum = 0.6 * momentum + (rand() - 0.5) * volatility; values.push(Math.round(Math.max(10, values[i - 1] + momentum))); } return values; }; // 1. Basic streamgraph — 5 series { const dates = genDates(40, 2020); const genres = ['Rock', 'Pop', 'Jazz', 'Electronic', 'Classical']; const data: any[] = []; for (const g of genres) { const base = 100 + Math.round(rand() * 200); const series = genFlow(40, base, 30); for (let i = 0; i < dates.length; i++) { data.push({ Date: dates[i], Genre: g, Listeners: series[i] }); } } tests.push({ title: 'EC: Streamgraph — 5 Series', description: '40 dates × 5 genres. EC: stacked area with baseline offset; VL: area + y.stack=center.', tags: ['echarts', 'streamgraph', 'medium'], chartType: 'Streamgraph', data, fields: [makeField('Date'), makeField('Listeners'), makeField('Genre')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Listeners: { type: Type.Number, semanticType: 'Quantity', levels: genres }, Genre: { type: Type.String, semanticType: 'Category', levels: genres }, }, encodingMap: { x: makeEncodingItem('Date'), y: makeEncodingItem('Listeners'), color: makeEncodingItem('Genre') }, }); } // 2. Dense streamgraph — 8 series { const dates = genDates(60, 2018); const categories = genCategories('Sector', 8); const data: any[] = []; for (const cat of categories) { const base = 150 + Math.round(rand() * 300); const series = genFlow(60, base, 35); for (let i = 0; i < dates.length; i++) { data.push({ Date: dates[i], Sector: cat, Revenue: series[i] }); } } tests.push({ title: 'EC: Streamgraph — 8 Series Dense', description: '60 dates × 8 sectors — dense center-stacked flow.', tags: ['echarts', 'streamgraph', 'large'], chartType: 'Streamgraph', data, fields: [makeField('Date'), makeField('Revenue'), makeField('Sector')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Revenue: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Sector: { type: Type.String, semanticType: 'Category', levels: categories }, }, encodingMap: { x: makeEncodingItem('Date'), y: makeEncodingItem('Revenue'), color: makeEncodingItem('Sector') }, }); } return tests; } // ============================================================================ // ECharts Facet Tests // ============================================================================ /** * Helper: build a facet test for dual VL+EC rendering. */ function buildEChartsFacetTest(opts: { title: string; description: string; tags: string[]; chartType: string; colCount?: number; rowCount?: number; xCategories?: string[]; scatter?: boolean; seed: number; }): TestCase { const { title, description, tags, chartType, colCount, rowCount, xCategories, scatter, seed } = opts; const rand = seededRandom(seed); const colVals = colCount ? genCategories('Region', colCount) : undefined; const rowVals = rowCount ? genCategories('Zone', rowCount) : undefined; const data: any[] = []; const facets: { col?: string; row?: string }[] = []; if (colVals && rowVals) { for (const c of colVals) for (const r of rowVals) facets.push({ col: c, row: r }); } else if (colVals) { for (const c of colVals) facets.push({ col: c }); } else if (rowVals) { for (const r of rowVals) facets.push({ row: r }); } for (const facet of facets) { if (scatter) { for (let i = 0; i < 15; i++) { data.push({ X: Math.round(10 + rand() * 90), Y: Math.round(10 + rand() * 90), ...(facet.col != null ? { Col: facet.col } : {}), ...(facet.row != null ? { Row: facet.row } : {}), }); } } else { for (const cat of xCategories!) { data.push({ Category: cat, Value: Math.round(50 + rand() * 500), ...(facet.col != null ? { Col: facet.col } : {}), ...(facet.row != null ? { Row: facet.row } : {}), }); } } } const encodingMap: Partial<Record<string, any>> = {}; const fields: any[] = []; const metadata: Record<string, any> = {}; if (scatter) { encodingMap.x = makeEncodingItem('X'); encodingMap.y = makeEncodingItem('Y'); fields.push(makeField('X'), makeField('Y')); metadata['X'] = { type: Type.Number, semanticType: 'Value', levels: [] }; metadata['Y'] = { type: Type.Number, semanticType: 'Value', levels: [] }; } else { encodingMap.x = makeEncodingItem('Category'); encodingMap.y = makeEncodingItem('Value'); fields.push(makeField('Category'), makeField('Value')); metadata['Category'] = { type: Type.String, semanticType: 'Category', levels: xCategories }; metadata['Value'] = { type: Type.Number, semanticType: 'Revenue', levels: [] }; } if (colVals) { encodingMap.column = makeEncodingItem('Col'); fields.push(makeField('Col')); metadata['Col'] = { type: Type.String, semanticType: 'Category', levels: colVals }; } if (rowVals) { encodingMap.row = makeEncodingItem('Row'); fields.push(makeField('Row')); metadata['Row'] = { type: Type.String, semanticType: 'Category', levels: rowVals }; } return { title, description, tags, chartType, data, fields, metadata, encodingMap } as TestCase; } /** Small facet counts — columns, rows, col×row */ export function genEChartsFacetSmallTests(): TestCase[] { const cats = ['A', 'B', 'C', 'D']; return [ buildEChartsFacetTest({ title: 'EC Facet: 2 Columns — Bar', description: '2 column facets, 4 bars each.', tags: ['echarts', 'facet', 'column', 'small'], chartType: 'Bar Chart', colCount: 2, xCategories: cats, seed: 1300, }), buildEChartsFacetTest({ title: 'EC Facet: 3 Columns — Scatter', description: '3 column facets with scatter plots.', tags: ['echarts', 'facet', 'column', 'small'], chartType: 'Scatter Plot', colCount: 3, scatter: true, seed: 1301, }), buildEChartsFacetTest({ title: 'EC Facet: 2 Rows — Bar', description: '2 row facets, 4 bars each.', tags: ['echarts', 'facet', 'row', 'small'], chartType: 'Bar Chart', rowCount: 2, xCategories: cats, seed: 1302, }), buildEChartsFacetTest({ title: 'EC Facet: 3 Rows — Scatter', description: '3 row facets with scatter plots.', tags: ['echarts', 'facet', 'row', 'small'], chartType: 'Scatter Plot', rowCount: 3, scatter: true, seed: 1303, }), buildEChartsFacetTest({ title: 'EC Facet: 2×2 Col×Row — Bar', description: '2 columns × 2 rows = 4 panels.', tags: ['echarts', 'facet', 'colrow', 'small'], chartType: 'Bar Chart', colCount: 2, rowCount: 2, xCategories: cats, seed: 1304, }), buildEChartsFacetTest({ title: 'EC Facet: 2×3 Col×Row — Scatter', description: '2 columns × 3 rows = 6 panels.', tags: ['echarts', 'facet', 'colrow', 'small'], chartType: 'Scatter Plot', colCount: 2, rowCount: 3, scatter: true, seed: 1305, }), ]; } /** Larger column counts that require horizontal wrapping */ export function genEChartsFacetWrapTests(): TestCase[] { const cats = ['A', 'B', 'C']; return [ buildEChartsFacetTest({ title: 'EC Facet: 6 Columns — Bar (wrap)', description: '6 column facets × 3 bars. Tests horizontal wrapping.', tags: ['echarts', 'facet', 'column', 'wrap'], chartType: 'Bar Chart', colCount: 6, xCategories: cats, seed: 1310, }), buildEChartsFacetTest({ title: 'EC Facet: 8 Columns — Scatter (wrap)', description: '8 column facets with scatter plots.', tags: ['echarts', 'facet', 'column', 'wrap'], chartType: 'Scatter Plot', colCount: 8, scatter: true, seed: 1311, }), buildEChartsFacetTest({ title: 'EC Facet: 10 Columns — Bar (heavy wrap)', description: '10 column facets. Extreme horizontal wrap.', tags: ['echarts', 'facet', 'column', 'wrap', 'heavy'], chartType: 'Bar Chart', colCount: 10, xCategories: cats, seed: 1312, }), ]; } /** Large col×row grids requiring clipping */ export function genEChartsFacetClipTests(): TestCase[] { const cats = ['A', 'B', 'C']; return [ buildEChartsFacetTest({ title: 'EC Facet: 4×3 Col×Row — Bar (12 panels)', description: '4 columns × 3 rows = 12 panels.', tags: ['echarts', 'facet', 'colrow', 'clip'], chartType: 'Bar Chart', colCount: 4, rowCount: 3, xCategories: cats, seed: 1320, }), buildEChartsFacetTest({ title: 'EC Facet: 5×4 Col×Row — Scatter (20 panels)', description: '5 columns × 4 rows = 20 panels.', tags: ['echarts', 'facet', 'colrow', 'clip'], chartType: 'Scatter Plot', colCount: 5, rowCount: 4, scatter: true, seed: 1321, }), buildEChartsFacetTest({ title: 'EC Facet: 6×5 Col×Row — Bar (30 panels)', description: '6 columns × 5 rows = 30 panels. Extreme grid.', tags: ['echarts', 'facet', 'colrow', 'clip', 'heavy'], chartType: 'Bar Chart', colCount: 6, rowCount: 5, xCategories: cats, seed: 1322, }), buildEChartsFacetTest({ title: 'EC Facet: 8 Rows — Scatter (vertical clip)', description: '8 row facets. Tests vertical overflow.', tags: ['echarts', 'facet', 'row', 'clip'], chartType: 'Scatter Plot', rowCount: 8, scatter: true, seed: 1323, }), ]; } // =========================================================================== // Rose Chart tests // =========================================================================== export function genEChartsRoseTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(1400); // 1. Basic rose — wind directions × speed { const directions = ['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW']; const data = directions.map(d => ({ Direction: d, Speed: Math.round(5 + rand() * 25) })); tests.push({ title: 'EC: Rose — 8 Directions', description: 'Wind speed by compass direction. VL: arc+theta+radius; EC: series type=bar (polar).', tags: ['echarts', 'rose', 'basic'], chartType: 'Rose Chart', data, fields: [makeField('Direction'), makeField('Speed')], metadata: { Direction: { type: Type.String, semanticType: 'Category', levels: directions }, Speed: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Direction'), y: makeEncodingItem('Speed') }, chartProperties: { alignment: 'center' }, }); } // 2. Stacked rose — directions × season { const directions = ['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW']; const seasons = ['Spring', 'Summer', 'Autumn', 'Winter']; const data: any[] = []; for (const d of directions) { for (const s of seasons) { data.push({ Direction: d, Speed: Math.round(3 + rand() * 20), Season: s }); } } tests.push({ title: 'EC: Stacked Rose — 8 dirs × 4 seasons', description: 'Stacked wind rose by season. Tests polar stacked bar rendering.', tags: ['echarts', 'rose', 'stacked'], chartType: 'Rose Chart', data, fields: [makeField('Direction'), makeField('Speed'), makeField('Season')], metadata: { Direction: { type: Type.String, semanticType: 'Category', levels: directions }, Speed: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Season: { type: Type.String, semanticType: 'Category', levels: seasons }, }, encodingMap: { x: makeEncodingItem('Direction'), y: makeEncodingItem('Speed'), color: makeEncodingItem('Season') }, chartProperties: { alignment: 'center' }, }); } // 3. Rose — 12 months { const months = genMonths(12); const data = months.map(m => ({ Month: m, Rainfall: Math.round(20 + rand() * 150) })); tests.push({ title: 'EC: Rose — 12 Months Rainfall', description: 'Monthly rainfall as a rose chart. Tests many-category angular layout.', tags: ['echarts', 'rose', 'medium'], chartType: 'Rose Chart', data, fields: [makeField('Month'), makeField('Rainfall')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: months }, Rainfall: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Rainfall') }, }); } return tests; } // =========================================================================== // Gauge Chart tests (ECharts-only) // =========================================================================== export function genEChartsGaugeTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(1500); // 1. Basic gauge — single KPI value { const data = [{ Score: 72.5 }]; tests.push({ title: 'EC: Gauge — Single KPI', description: 'Single-value gauge chart. ECharts-only — no VL equivalent.', tags: ['echarts', 'gauge', 'basic'], chartType: 'Gauge Chart', data, fields: [makeField('Score')], metadata: { Score: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { size: makeEncodingItem('Score') }, chartProperties: { max: 100 }, }); } // 2. Multiple pointers — multi-KPI gauge { const data = [ { Metric: 'CPU', Usage: 65 }, { Metric: 'Memory', Usage: 82 }, { Metric: 'Disk', Usage: 43 }, ]; tests.push({ title: 'EC: Gauge — Multi-Pointer (3 KPIs)', description: 'Three pointers on a single gauge for CPU/Memory/Disk.', tags: ['echarts', 'gauge', 'multi'], chartType: 'Gauge Chart', data, fields: [makeField('Metric'), makeField('Usage')], metadata: { Metric: { type: Type.String, semanticType: 'Category', levels: ['CPU', 'Memory', 'Disk'] }, Usage: { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, encodingMap: { size: makeEncodingItem('Usage'), column: makeEncodingItem('Metric') }, chartProperties: { max: 100 }, }); } // 3. Aggregate gauge — average of many values { const data = Array.from({ length: 50 }, () => ({ Temperature: Math.round((18 + rand() * 15) * 10) / 10, })); tests.push({ title: 'EC: Gauge — Aggregated (50 rows avg)', description: 'Gauge showing average of 50 temperature readings.', tags: ['echarts', 'gauge', 'aggregate'], chartType: 'Gauge Chart', data, fields: [makeField('Temperature')], metadata: { Temperature: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { size: makeEncodingItem('Temperature') }, chartProperties: { min: 0, max: 50 }, }); } return tests; } // =========================================================================== // Funnel Chart tests (ECharts-only) // =========================================================================== export function genEChartsFunnelTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(1600); // 1. Basic sales funnel { const stages = ['Visits', 'Signups', 'Trials', 'Purchases', 'Renewals']; const data = stages.map((s, i) => ({ Stage: s, Count: Math.round(10000 / Math.pow(2, i) + rand() * 500), })); tests.push({ title: 'EC: Funnel — Sales Pipeline', description: 'Classic conversion funnel. ECharts-only — no VL equivalent.', tags: ['echarts', 'funnel', 'basic'], chartType: 'Funnel Chart', data, fields: [makeField('Stage'), makeField('Count')], metadata: { Stage: { type: Type.String, semanticType: 'Category', levels: stages }, Count: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { y: makeEncodingItem('Stage'), size: makeEncodingItem('Count') }, }); } // 2. Recruitment funnel — ascending { const steps = ['Applied', 'Screened', 'Interviewed', 'Offered', 'Hired']; const data = steps.map((s, i) => ({ Step: s, Candidates: Math.round(500 / Math.pow(1.8, i) + rand() * 30), })); tests.push({ title: 'EC: Funnel — Recruitment (ascending)', description: 'Hiring funnel sorted ascending (narrowest at top).', tags: ['echarts', 'funnel', 'ascending'], chartType: 'Funnel Chart', data, fields: [makeField('Step'), makeField('Candidates')], metadata: { Step: { type: Type.String, semanticType: 'Category', levels: steps }, Candidates: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { y: makeEncodingItem('Step'), size: makeEncodingItem('Candidates') }, chartProperties: { sort: 'ascending' }, }); } // 3. Many stages { const steps = ['Awareness', 'Interest', 'Consideration', 'Intent', 'Evaluation', 'Trial', 'Purchase', 'Loyalty']; const data = steps.map((s, i) => ({ Phase: s, Users: Math.round(50000 / Math.pow(1.5, i) + rand() * 1000), })); tests.push({ title: 'EC: Funnel — 8-Stage Marketing', description: 'Marketing funnel with 8 stages. Tests label fitting.', tags: ['echarts', 'funnel', 'many-stages'], chartType: 'Funnel Chart', data, fields: [makeField('Phase'), makeField('Users')], metadata: { Phase: { type: Type.String, semanticType: 'Category', levels: steps }, Users: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { y: makeEncodingItem('Phase'), size: makeEncodingItem('Users') }, }); } return tests; } // =========================================================================== // Treemap tests (ECharts-only) // =========================================================================== export function genEChartsTreemapTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(1700); // 1. Flat treemap — market sectors { const sectors = ['Technology', 'Healthcare', 'Finance', 'Energy', 'Consumer', 'Industrials']; const data = sectors.map(s => ({ Sector: s, MarketCap: Math.round(500 + rand() * 4500), })); tests.push({ title: 'EC: Treemap — Market Sectors', description: 'Flat treemap of market cap by sector. ECharts-only — no VL equivalent.', tags: ['echarts', 'treemap', 'flat'], chartType: 'Treemap', data, fields: [makeField('Sector'), makeField('MarketCap')], metadata: { Sector: { type: Type.String, semanticType: 'Category', levels: sectors }, MarketCap: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { color: makeEncodingItem('Sector'), size: makeEncodingItem('MarketCap') }, }); } // 2. Hierarchical treemap — regions → countries { const hierarchy: Record<string, string[]> = { 'Americas': ['USA', 'Canada', 'Brazil', 'Mexico'], 'Europe': ['UK', 'Germany', 'France', 'Italy'], 'Asia': ['China', 'Japan', 'India', 'Korea'], }; const data: any[] = []; for (const [region, countries] of Object.entries(hierarchy)) { for (const country of countries) { data.push({ Region: region, Country: country, Revenue: Math.round(100 + rand() * 2000), }); } } tests.push({ title: 'EC: Treemap — Regions × Countries', description: 'Two-level treemap: 3 regions → 4 countries each.', tags: ['echarts', 'treemap', 'hierarchical'], chartType: 'Treemap', data, fields: [makeField('Region'), makeField('Country'), makeField('Revenue')], metadata: { Region: { type: Type.String, semanticType: 'Category', levels: Object.keys(hierarchy) }, Country: { type: Type.String, semanticType: 'Country', levels: [] }, Revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { color: makeEncodingItem('Region'), detail: makeEncodingItem('Country'), size: makeEncodingItem('Revenue'), }, }); } // 3. Large flat treemap — 15 categories { const categories = genCategories('Item', 15); const data = categories.map(c => ({ Item: c, Size: Math.round(50 + rand() * 500), })); tests.push({ title: 'EC: Treemap — 15 Categories', description: 'Large flat treemap with 15 items. Tests label fitting and color cycling.', tags: ['echarts', 'treemap', 'large'], chartType: 'Treemap', data, fields: [makeField('Item'), makeField('Size')], metadata: { Item: { type: Type.String, semanticType: 'Category', levels: categories }, Size: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { color: makeEncodingItem('Item'), size: makeEncodingItem('Size') }, }); } return tests; } // =========================================================================== // Sunburst Chart tests (ECharts-only) // =========================================================================== export function genEChartsSunburstTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(1800); // 1. Flat sunburst — budget categories { const categories = ['Housing', 'Food', 'Transport', 'Entertainment', 'Savings', 'Healthcare']; const data = categories.map(c => ({ Category: c, Amount: Math.round(200 + rand() * 2000), })); tests.push({ title: 'EC: Sunburst — Budget Categories', description: 'Single-ring sunburst of budget allocation. ECharts-only — no VL equivalent.', tags: ['echarts', 'sunburst', 'flat'], chartType: 'Sunburst Chart', data, fields: [makeField('Category'), makeField('Amount')], metadata: { Category: { type: Type.String, semanticType: 'Category', levels: categories }, Amount: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { color: makeEncodingItem('Category'), size: makeEncodingItem('Amount') }, }); } // 2. Two-ring sunburst — departments → teams { const hierarchy: Record<string, string[]> = { 'Engineering': ['Frontend', 'Backend', 'Infra', 'QA'], 'Product': ['Design', 'PM', 'Research'], 'Operations': ['HR', 'Finance', 'Legal'], }; const data: any[] = []; for (const [dept, teams] of Object.entries(hierarchy)) { for (const team of teams) { data.push({ Department: dept, Team: team, Headcount: Math.round(5 + rand() * 50), }); } } tests.push({ title: 'EC: Sunburst — Departments × Teams', description: 'Two-ring sunburst: 3 departments → 3–4 teams each.', tags: ['echarts', 'sunburst', 'hierarchical'], chartType: 'Sunburst Chart', data, fields: [makeField('Department'), makeField('Team'), makeField('Headcount')], metadata: { Department: { type: Type.String, semanticType: 'Category', levels: Object.keys(hierarchy) }, Team: { type: Type.String, semanticType: 'Category', levels: [] }, Headcount: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { color: makeEncodingItem('Department'), detail: makeEncodingItem('Team'), size: makeEncodingItem('Headcount'), }, }); } // 3. Sunburst with many items { const continents: Record<string, string[]> = { 'North America': ['USA', 'Canada', 'Mexico'], 'Europe': ['UK', 'France', 'Germany', 'Spain', 'Italy'], 'Asia': ['China', 'Japan', 'India', 'Korea', 'Thailand'], 'South America': ['Brazil', 'Argentina', 'Chile'], }; const data: any[] = []; for (const [cont, countries] of Object.entries(continents)) { for (const country of countries) { data.push({ Continent: cont, Country: country, Population: Math.round(10 + rand() * 1400), }); } } tests.push({ title: 'EC: Sunburst — 4 Continents × Countries', description: 'Two-ring sunburst with 16 countries across 4 continents.', tags: ['echarts', 'sunburst', 'large'], chartType: 'Sunburst Chart', data, fields: [makeField('Continent'), makeField('Country'), makeField('Population')], metadata: { Continent: { type: Type.String, semanticType: 'Category', levels: Object.keys(continents) }, Country: { type: Type.String, semanticType: 'Country', levels: [] }, Population: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { color: makeEncodingItem('Continent'), detail: makeEncodingItem('Country'), size: makeEncodingItem('Population'), }, }); } return tests; } // =========================================================================== // Sankey Diagram tests (ECharts-only) // =========================================================================== export function genEChartsSankeyTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(1900); // 1. Simple energy flow { const data = [ { Source: 'Coal', Target: 'Electricity', Value: 250 }, { Source: 'Gas', Target: 'Electricity', Value: 180 }, { Source: 'Gas', Target: 'Heating', Value: 120 }, { Source: 'Oil', Target: 'Transport', Value: 300 }, { Source: 'Oil', Target: 'Industry', Value: 80 }, { Source: 'Electricity', Target: 'Residential', Value: 200 }, { Source: 'Electricity', Target: 'Industry', Value: 230 }, { Source: 'Heating', Target: 'Residential', Value: 120 }, ]; tests.push({ title: 'EC: Sankey — Energy Flow', description: 'Energy source → use Sankey diagram. ECharts-only — no VL equivalent.', tags: ['echarts', 'sankey', 'basic'], chartType: 'Sankey Diagram', data, fields: [makeField('Source'), makeField('Target'), makeField('Value')], metadata: { Source: { type: Type.String, semanticType: 'Category', levels: [] }, Target: { type: Type.String, semanticType: 'Category', levels: [] }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Source'), y: makeEncodingItem('Target'), size: makeEncodingItem('Value'), }, }); } // 2. Website user flow { const data = [ { From: 'Home', To: 'Products', Users: 450 }, { From: 'Home', To: 'About', Users: 120 }, { From: 'Home', To: 'Blog', Users: 200 }, { From: 'Products', To: 'Cart', Users: 180 }, { From: 'Products', To: 'Details', Users: 270 }, { From: 'Details', To: 'Cart', Users: 150 }, { From: 'Cart', To: 'Checkout', Users: 200 }, { From: 'Cart', To: 'Home', Users: 50 }, { From: 'Blog', To: 'Products', Users: 80 }, ]; tests.push({ title: 'EC: Sankey — Website User Flow', description: 'Page-to-page navigation flow with link width proportional to user count.', tags: ['echarts', 'sankey', 'user-flow'], chartType: 'Sankey Diagram', data, fields: [makeField('From'), makeField('To'), makeField('Users')], metadata: { From: { type: Type.String, semanticType: 'Category', levels: [] }, To: { type: Type.String, semanticType: 'Category', levels: [] }, Users: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('From'), y: makeEncodingItem('To'), size: makeEncodingItem('Users'), }, }); } // 3. Dense Sankey — budget allocation { const sources = ['Federal', 'State', 'Municipal']; const intermediates = ['Education', 'Healthcare', 'Defense', 'Infrastructure']; const destinations = ['Salaries', 'Equipment', 'Contracts', 'Research']; const data: any[] = []; for (const src of sources) { for (const mid of intermediates) { data.push({ Source: src, Target: mid, Amount: Math.round(50 + rand() * 500) }); } } for (const mid of intermediates) { for (const dst of destinations) { data.push({ Source: mid, Target: dst, Amount: Math.round(30 + rand() * 300) }); } } tests.push({ title: 'EC: Sankey — Budget Flow (3-layer)', description: '3 sources → 4 intermediates → 4 destinations. Tests dense multi-layer layout.', tags: ['echarts', 'sankey', 'dense'], chartType: 'Sankey Diagram', data, fields: [makeField('Source'), makeField('Target'), makeField('Amount')], metadata: { Source: { type: Type.String, semanticType: 'Category', levels: [] }, Target: { type: Type.String, semanticType: 'Category', levels: [] }, Amount: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Source'), y: makeEncodingItem('Target'), size: makeEncodingItem('Amount'), }, }); } return tests; } // =========================================================================== // Stress tests for ECharts-only chart types // =========================================================================== export function genEChartsUniqueStressTests(): TestCase[] { const tests: TestCase[] = []; // ── Gauge stress ───────────────────────────────────────────────────── // 1. Gauge — many KPIs (6 pointers side-by-side) { const rand = seededRandom(2000); const metrics = ['CPU', 'Memory', 'Disk', 'Network', 'GPU', 'IO']; const data = metrics.map(m => ({ Metric: m, Usage: Math.round(10 + rand() * 90), })); tests.push({ title: 'EC Stress: Gauge — 6 KPIs', description: '6 separate gauge dials side-by-side. Tests layout spacing with many gauges.', tags: ['echarts', 'gauge', 'stress'], chartType: 'Gauge Chart', data, fields: [makeField('Metric'), makeField('Usage')], metadata: { Metric: { type: Type.String, semanticType: 'Category', levels: metrics }, Usage: { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, encodingMap: { size: makeEncodingItem('Usage'), column: makeEncodingItem('Metric') }, chartProperties: { max: 100 }, }); } // 2. Gauge — aggregated from many rows { const rand = seededRandom(2001); const data = Array.from({ length: 200 }, () => ({ Latency: Math.round((5 + rand() * 500) * 10) / 10, })); tests.push({ title: 'EC Stress: Gauge — Aggregated (200 rows)', description: 'Single gauge averaging 200 latency readings.', tags: ['echarts', 'gauge', 'stress', 'aggregate'], chartType: 'Gauge Chart', data, fields: [makeField('Latency')], metadata: { Latency: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { size: makeEncodingItem('Latency') }, chartProperties: { min: 0, max: 500 }, }); } // ── Funnel stress ──────────────────────────────────────────────────── // 3. Funnel — 12 stages { const rand = seededRandom(2010); const stages = [ 'Impression', 'View', 'Click', 'Visit', 'Browse', 'Add to Cart', 'Checkout Start', 'Address', 'Payment', 'Review', 'Confirm', 'Purchase', ]; const data = stages.map((s, i) => ({ Stage: s, Users: Math.round(100000 / Math.pow(1.4, i) + rand() * 2000), })); tests.push({ title: 'EC Stress: Funnel — 12 Stages', description: '12-stage e-commerce funnel. Tests label fitting in narrow trapezoids.', tags: ['echarts', 'funnel', 'stress', 'many-stages'], chartType: 'Funnel Chart', data, fields: [makeField('Stage'), makeField('Users')], metadata: { Stage: { type: Type.String, semanticType: 'Category', levels: stages }, Users: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { y: makeEncodingItem('Stage'), size: makeEncodingItem('Users') }, }); } // 4. Funnel — extreme value ratio (top 100× bottom) { const stages = ['Awareness', 'Interest', 'Desire', 'Action', 'Retention']; const values = [100000, 25000, 5000, 800, 120]; const data = stages.map((s, i) => ({ Phase: s, Count: values[i] })); tests.push({ title: 'EC Stress: Funnel — Extreme Ratio (833:1)', description: 'Top stage is ~833× larger than bottom. Tests rendering of very thin tail stages.', tags: ['echarts', 'funnel', 'stress', 'extreme-ratio'], chartType: 'Funnel Chart', data, fields: [makeField('Phase'), makeField('Count')], metadata: { Phase: { type: Type.String, semanticType: 'Category', levels: stages }, Count: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { y: makeEncodingItem('Phase'), size: makeEncodingItem('Count') }, }); } // ── Treemap stress ─────────────────────────────────────────────────── // 5. Treemap — 30 flat categories { const rand = seededRandom(2020); const categories = genCategories('Stock', 30); const data = categories.map(c => ({ Stock: c, MarketCap: Math.round(100 + rand() * 5000), })); tests.push({ title: 'EC Stress: Treemap — 30 Items (Flat)', description: '30-item flat treemap. Tests label density and color cycling.', tags: ['echarts', 'treemap', 'stress', 'flat-large'], chartType: 'Treemap', data, fields: [makeField('Stock'), makeField('MarketCap')], metadata: { Stock: { type: Type.String, semanticType: 'Category', levels: categories }, MarketCap: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { color: makeEncodingItem('Stock'), size: makeEncodingItem('MarketCap') }, }); } // 6. Treemap — deep hierarchy (6 regions × 8 products) { const rand = seededRandom(2021); const regions = ['North America', 'Europe', 'Asia Pacific', 'Latin America', 'Middle East', 'Africa']; const products = ['Software', 'Hardware', 'Services', 'Cloud', 'Security', 'Analytics', 'Mobile', 'AI']; const data: any[] = []; for (const r of regions) { for (const p of products) { data.push({ Region: r, Product: p, Revenue: Math.round(50 + rand() * 3000), }); } } tests.push({ title: 'EC Stress: Treemap — 6 Regions × 8 Products (48 leaves)', description: '48-leaf hierarchical treemap. Tests nested labels and color saturation.', tags: ['echarts', 'treemap', 'stress', 'hierarchical-large'], chartType: 'Treemap', data, fields: [makeField('Region'), makeField('Product'), makeField('Revenue')], metadata: { Region: { type: Type.String, semanticType: 'Category', levels: regions }, Product: { type: Type.String, semanticType: 'Category', levels: products }, Revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { color: makeEncodingItem('Region'), detail: makeEncodingItem('Product'), size: makeEncodingItem('Revenue'), }, }); } // 7. Treemap — extreme value skew (one item dominates) { const rand = seededRandom(2022); const items = ['Dominant', 'Small A', 'Small B', 'Small C', 'Tiny D', 'Tiny E', 'Tiny F', 'Tiny G']; const values = [50000, 800, 600, 400, 100, 80, 50, 30]; const data = items.map((item, i) => ({ Category: item, Value: values[i] + Math.round(rand() * 50) })); tests.push({ title: 'EC Stress: Treemap — Extreme Skew', description: 'One category dominates (~96% of total). Tests visibility of tiny rectangles.', tags: ['echarts', 'treemap', 'stress', 'skew'], chartType: 'Treemap', data, fields: [makeField('Category'), makeField('Value')], metadata: { Category: { type: Type.String, semanticType: 'Category', levels: items }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { color: makeEncodingItem('Category'), size: makeEncodingItem('Value') }, }); } // ── Sunburst stress ────────────────────────────────────────────────── // 8. Sunburst — 5 continents × 6 countries (30 outer slices) { const rand = seededRandom(2030); const hierarchy: Record<string, string[]> = { 'North America': ['USA', 'Canada', 'Mexico', 'Cuba', 'Jamaica', 'Panama'], 'Europe': ['UK', 'France', 'Germany', 'Spain', 'Italy', 'Netherlands'], 'Asia': ['China', 'Japan', 'India', 'Korea', 'Thailand', 'Vietnam'], 'Africa': ['Nigeria', 'Egypt', 'South Africa', 'Kenya', 'Ghana', 'Morocco'], 'Oceania': ['Australia', 'New Zealand', 'Fiji', 'Samoa', 'Tonga', 'Vanuatu'], }; const data: any[] = []; for (const [continent, countries] of Object.entries(hierarchy)) { for (const country of countries) { data.push({ Continent: continent, Country: country, GDP: Math.round(10 + rand() * 20000), }); } } tests.push({ title: 'EC Stress: Sunburst — 5 × 6 (30 outer slices)', description: '30 countries across 5 continents. Tests outer-ring label crowding.', tags: ['echarts', 'sunburst', 'stress', 'large'], chartType: 'Sunburst Chart', data, fields: [makeField('Continent'), makeField('Country'), makeField('GDP')], metadata: { Continent: { type: Type.String, semanticType: 'Category', levels: Object.keys(hierarchy) }, Country: { type: Type.String, semanticType: 'Country', levels: [] }, GDP: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { color: makeEncodingItem('Continent'), detail: makeEncodingItem('Country'), size: makeEncodingItem('GDP'), }, }); } // 9. Sunburst — flat with 20 slices { const rand = seededRandom(2031); const categories = genCategories('Expense', 20); const data = categories.map(c => ({ Expense: c, Amount: Math.round(100 + rand() * 5000), })); tests.push({ title: 'EC Stress: Sunburst — 20 Flat Slices', description: 'Single-ring sunburst with 20 categories. Tests label overlap on thin slices.', tags: ['echarts', 'sunburst', 'stress', 'flat-large'], chartType: 'Sunburst Chart', data, fields: [makeField('Expense'), makeField('Amount')], metadata: { Expense: { type: Type.String, semanticType: 'Category', levels: categories }, Amount: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { color: makeEncodingItem('Expense'), size: makeEncodingItem('Amount') }, }); } // ── Sankey stress ──────────────────────────────────────────────────── // 10. Sankey — 4-layer deep (5 → 6 → 6 → 4 = 21 nodes, ~70 links) { const rand = seededRandom(2040); const layer1 = ['Source A', 'Source B', 'Source C', 'Source D', 'Source E']; const layer2 = genCategories('Process', 6); const layer3 = genCategories('Output', 6); const layer4 = ['Final X', 'Final Y', 'Final Z', 'Final W']; const data: any[] = []; for (const s of layer1) { for (const p of layer2) { if (rand() > 0.4) { data.push({ Source: s, Target: p, Flow: Math.round(20 + rand() * 300) }); } } } for (const p of layer2) { for (const o of layer3) { if (rand() > 0.35) { data.push({ Source: p, Target: o, Flow: Math.round(10 + rand() * 200) }); } } } for (const o of layer3) { for (const f of layer4) { if (rand() > 0.3) { data.push({ Source: o, Target: f, Flow: Math.round(5 + rand() * 150) }); } } } tests.push({ title: 'EC Stress: Sankey — 4-Layer Deep (~70 links)', description: '5 sources → 6 processes → 6 outputs → 4 finals. Tests multi-layer routing.', tags: ['echarts', 'sankey', 'stress', 'deep'], chartType: 'Sankey Diagram', data, fields: [makeField('Source'), makeField('Target'), makeField('Flow')], metadata: { Source: { type: Type.String, semanticType: 'Category', levels: [] }, Target: { type: Type.String, semanticType: 'Category', levels: [] }, Flow: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Source'), y: makeEncodingItem('Target'), size: makeEncodingItem('Flow'), }, }); } // 11. Sankey — wide fan-out (2 sources → 15 targets) { const rand = seededRandom(2041); const sources = ['Revenue', 'Funding']; const targets = genCategories('Dept', 15); const data: any[] = []; for (const s of sources) { for (const t of targets) { data.push({ From: s, To: t, Budget: Math.round(100 + rand() * 5000) }); } } tests.push({ title: 'EC Stress: Sankey — Wide Fan-Out (2 → 15)', description: '2 sources distributing to 15 departments. Tests node stacking with many targets.', tags: ['echarts', 'sankey', 'stress', 'fan-out'], chartType: 'Sankey Diagram', data, fields: [makeField('From'), makeField('To'), makeField('Budget')], metadata: { From: { type: Type.String, semanticType: 'Category', levels: sources }, To: { type: Type.String, semanticType: 'Category', levels: targets }, Budget: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('From'), y: makeEncodingItem('To'), size: makeEncodingItem('Budget'), }, }); } // 12. Sankey — dense mesh (8 × 8 = 64 links) { const rand = seededRandom(2042); const left = genCategories('Origin', 8); const right = genCategories('Dest', 8); const data: any[] = []; for (const l of left) { for (const r of right) { data.push({ Origin: l, Dest: r, Volume: Math.round(10 + rand() * 500) }); } } tests.push({ title: 'EC Stress: Sankey — Dense Mesh (8 × 8 = 64 links)', description: 'Fully connected 8→8 network. Tests link crossing and visual clarity.', tags: ['echarts', 'sankey', 'stress', 'mesh'], chartType: 'Sankey Diagram', data, fields: [makeField('Origin'), makeField('Dest'), makeField('Volume')], metadata: { Origin: { type: Type.String, semanticType: 'Category', levels: left }, Dest: { type: Type.String, semanticType: 'Category', levels: right }, Volume: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Origin'), y: makeEncodingItem('Dest'), size: makeEncodingItem('Volume'), }, }); } return tests; } ================================================ FILE: src/lib/agents-chart/test-data/facet-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { Type } from '../../../data/types'; import { Channel, EncodingItem } from '../../../components/ComponentType'; import { TestCase, makeField, makeEncodingItem } from './types'; import { seededRandom, genCategories } from './generators'; /** Facet cardinality sizes */ export const FACET_SIZES = { S: 2, M: 4, L: 8, XL: 12 } as const; /** Discrete axis cardinality sizes */ export const DISCRETE_SIZES = { S: 4, M: 8, L: 20, XL: 50 } as const; /** * Generate facet test cases for a given facet mode (column, row, or column+row). * For each combination of facetSize × axisType: * - Continuous × Continuous (scatter in each facet) * - Continuous × Discrete-S/M/L/XL (bar in each facet) */ export function genFacetTests( mode: 'column' | 'row' | 'column+row', ): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(mode === 'column' ? 500 : mode === 'row' ? 600 : 700); const facetSizeEntries = Object.entries(FACET_SIZES) as [string, number][]; const discreteSizeEntries = Object.entries(DISCRETE_SIZES) as [string, number][]; for (const [facetLabel, facetCount] of facetSizeEntries) { // For column+row mode, split facetCount across columns and rows let colCount: number, rowCount: number; if (mode === 'column+row') { colCount = Math.max(2, Math.ceil(Math.sqrt(facetCount))); rowCount = Math.max(2, Math.ceil(facetCount / colCount)); } else { colCount = facetCount; rowCount = facetCount; } const facetDesc = mode === 'column+row' ? `${colCount} cols × ${rowCount} rows` : `${facetCount} facets`; // --- 1. Continuous × Continuous (scatter) --- { const facetVals = mode === 'column+row' ? null // handled separately : genCategories('Region', mode === 'column' ? colCount : rowCount); const colVals = mode === 'column+row' ? genCategories('Region', colCount) : undefined; const rowVals = mode === 'column+row' ? genCategories('Zone', rowCount) : undefined; const data: any[] = []; const pointsPerFacet = 20; if (mode === 'column+row') { for (const c of colVals!) for (const r of rowVals!) { for (let i = 0; i < pointsPerFacet; i++) { data.push({ X: Math.round(10 + rand() * 90), Y: Math.round(10 + rand() * 90), Col: c, Row: r, }); } } } else { for (const f of facetVals!) { for (let i = 0; i < pointsPerFacet; i++) { data.push({ X: Math.round(10 + rand() * 90), Y: Math.round(10 + rand() * 90), Facet: f, }); } } } const encodingMap: Partial<Record<Channel, EncodingItem>> = { x: makeEncodingItem('X'), y: makeEncodingItem('Y'), }; const fields = [makeField('X'), makeField('Y')]; const metadata: Record<string, any> = { X: { type: Type.Number, semanticType: 'Value', levels: [] }, Y: { type: Type.Number, semanticType: 'Value', levels: [] }, }; if (mode === 'column+row') { encodingMap.column = makeEncodingItem('Col'); encodingMap.row = makeEncodingItem('Row'); fields.push(makeField('Col'), makeField('Row')); metadata['Col'] = { type: Type.String, semanticType: 'Category', levels: colVals }; metadata['Row'] = { type: Type.String, semanticType: 'Category', levels: rowVals }; } else if (mode === 'column') { encodingMap.column = makeEncodingItem('Facet'); fields.push(makeField('Facet')); metadata['Facet'] = { type: Type.String, semanticType: 'Category', levels: facetVals }; } else { encodingMap.row = makeEncodingItem('Facet'); fields.push(makeField('Facet')); metadata['Facet'] = { type: Type.String, semanticType: 'Category', levels: facetVals }; } tests.push({ title: `Cont × Cont — facet ${facetLabel} (${facetDesc})`, description: `Scatter plot with ${facetDesc}`, tags: ['quantitative', 'facet', facetLabel.toLowerCase()], chartType: 'Scatter Plot', data, fields, metadata, encodingMap, }); } // --- 2. Continuous × Discrete (bar chart) --- for (const [discLabel, discCount] of discreteSizeEntries) { const categories = genCategories('Item', discCount); const facetVals = mode === 'column+row' ? null : genCategories('Region', mode === 'column' ? colCount : rowCount); const colVals = mode === 'column+row' ? genCategories('Region', colCount) : undefined; const rowVals = mode === 'column+row' ? genCategories('Zone', rowCount) : undefined; const data: any[] = []; if (mode === 'column+row') { for (const c of colVals!) for (const r of rowVals!) { for (const cat of categories) { data.push({ Category: cat, Value: Math.round(50 + rand() * 500), Col: c, Row: r, }); } } } else { for (const f of facetVals!) { for (const cat of categories) { data.push({ Category: cat, Value: Math.round(50 + rand() * 500), Facet: f, }); } } } const encodingMap: Partial<Record<Channel, EncodingItem>> = { x: makeEncodingItem('Category'), y: makeEncodingItem('Value'), }; const fields = [makeField('Category'), makeField('Value')]; const metadata: Record<string, any> = { Category: { type: Type.String, semanticType: 'Category', levels: categories }, Value: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }; if (mode === 'column+row') { encodingMap.column = makeEncodingItem('Col'); encodingMap.row = makeEncodingItem('Row'); fields.push(makeField('Col'), makeField('Row')); metadata['Col'] = { type: Type.String, semanticType: 'Category', levels: colVals }; metadata['Row'] = { type: Type.String, semanticType: 'Category', levels: rowVals }; } else if (mode === 'column') { encodingMap.column = makeEncodingItem('Facet'); fields.push(makeField('Facet')); metadata['Facet'] = { type: Type.String, semanticType: 'Category', levels: facetVals }; } else { encodingMap.row = makeEncodingItem('Facet'); fields.push(makeField('Facet')); metadata['Facet'] = { type: Type.String, semanticType: 'Category', levels: facetVals }; } tests.push({ title: `Cont × Disc-${discLabel} — facet ${facetLabel} (${facetDesc})`, description: `Bar chart: ${discCount} categories × ${facetDesc}`, tags: ['nominal', 'quantitative', 'facet', facetLabel.toLowerCase(), `disc-${discLabel.toLowerCase()}`], chartType: 'Bar Chart', data, fields, metadata, encodingMap, }); } } return tests; } export function genFacetColumnTests(): TestCase[] { return genFacetTests('column'); } export function genFacetRowTests(): TestCase[] { return genFacetTests('row'); } export function genFacetColRowTests(): TestCase[] { return genFacetTests('column+row'); } // ============================================================================ // Targeted Facet Tests // ============================================================================ /** * Helper: build a facet test case from parameters. */ function buildFacetTest(opts: { title: string; description: string; tags: string[]; chartType: string; colCount?: number; rowCount?: number; xCategories: string[]; yIsContinuous: boolean; seed: number; }): TestCase { const { title, description, tags, chartType, colCount, rowCount, xCategories, yIsContinuous, seed } = opts; const rand = seededRandom(seed); const colVals = colCount ? genCategories('Region', colCount) : undefined; const rowVals = rowCount ? genCategories('Zone', rowCount) : undefined; const data: Record<string, any>[] = []; const facets: { col?: string; row?: string }[] = []; if (colVals && rowVals) { for (const c of colVals) for (const r of rowVals) facets.push({ col: c, row: r }); } else if (colVals) { for (const c of colVals) facets.push({ col: c }); } else if (rowVals) { for (const r of rowVals) facets.push({ row: r }); } for (const facet of facets) { if (yIsContinuous) { // Scatter: continuous × continuous for (let i = 0; i < 15; i++) { data.push({ X: Math.round(10 + rand() * 90), Y: Math.round(10 + rand() * 90), ...(facet.col != null ? { Col: facet.col } : {}), ...(facet.row != null ? { Row: facet.row } : {}), }); } } else { // Bar: discrete × continuous for (const cat of xCategories) { data.push({ Category: cat, Value: Math.round(50 + rand() * 500), ...(facet.col != null ? { Col: facet.col } : {}), ...(facet.row != null ? { Row: facet.row } : {}), }); } } } const encodingMap: Partial<Record<Channel, EncodingItem>> = {}; const fields: ReturnType<typeof makeField>[] = []; const metadata: Record<string, any> = {}; if (yIsContinuous) { encodingMap.x = makeEncodingItem('X'); encodingMap.y = makeEncodingItem('Y'); fields.push(makeField('X'), makeField('Y')); metadata['X'] = { type: Type.Number, semanticType: 'Value', levels: [] }; metadata['Y'] = { type: Type.Number, semanticType: 'Value', levels: [] }; } else { encodingMap.x = makeEncodingItem('Category'); encodingMap.y = makeEncodingItem('Value'); fields.push(makeField('Category'), makeField('Value')); metadata['Category'] = { type: Type.String, semanticType: 'Category', levels: xCategories }; metadata['Value'] = { type: Type.Number, semanticType: 'Revenue', levels: [] }; } if (colVals) { encodingMap.column = makeEncodingItem('Col'); fields.push(makeField('Col')); metadata['Col'] = { type: Type.String, semanticType: 'Category', levels: colVals }; } if (rowVals) { encodingMap.row = makeEncodingItem('Row'); fields.push(makeField('Row')); metadata['Row'] = { type: Type.String, semanticType: 'Category', levels: rowVals }; } return { title, description, tags, chartType, data, fields, metadata, encodingMap }; } /** * 1. Small facet counts — columns only, rows only, and col×row. * Should render comfortably without wrapping or clipping. */ export function genFacetSmallTests(): TestCase[] { const cats = ['A', 'B', 'C', 'D']; return [ // 2 columns, bar buildFacetTest({ title: '2 Columns — Bar', description: '2 column facets, 4 bars each. Should fit side-by-side easily.', tags: ['facet', 'column', 'small', 'bar'], chartType: 'Bar Chart', colCount: 2, xCategories: cats, yIsContinuous: false, seed: 1200, }), // 3 columns, scatter buildFacetTest({ title: '3 Columns — Scatter', description: '3 column facets with scatter plots.', tags: ['facet', 'column', 'small', 'scatter'], chartType: 'Scatter Plot', colCount: 3, xCategories: [], yIsContinuous: true, seed: 1201, }), // 2 rows, bar buildFacetTest({ title: '2 Rows — Bar', description: '2 row facets, 4 bars each. Should stack vertically.', tags: ['facet', 'row', 'small', 'bar'], chartType: 'Bar Chart', rowCount: 2, xCategories: cats, yIsContinuous: false, seed: 1202, }), // 3 rows, scatter buildFacetTest({ title: '3 Rows — Scatter', description: '3 row facets with scatter plots.', tags: ['facet', 'row', 'small', 'scatter'], chartType: 'Scatter Plot', rowCount: 3, xCategories: [], yIsContinuous: true, seed: 1203, }), // 2×2 col×row, bar buildFacetTest({ title: '2×2 Col×Row — Bar', description: '2 columns × 2 rows = 4 facet panels (bar chart).', tags: ['facet', 'colrow', 'small', 'bar'], chartType: 'Bar Chart', colCount: 2, rowCount: 2, xCategories: cats, yIsContinuous: false, seed: 1204, }), // 2×3 col×row, scatter buildFacetTest({ title: '2×3 Col×Row — Scatter', description: '2 columns × 3 rows = 6 facet panels (scatter).', tags: ['facet', 'colrow', 'small', 'scatter'], chartType: 'Scatter Plot', colCount: 2, rowCount: 3, xCategories: [], yIsContinuous: true, seed: 1205, }), ]; } /** * 2. Larger column counts that require horizontal wrapping. * 6-8 columns should exceed the default ~400px subplot width. */ export function genFacetWrapTests(): TestCase[] { const cats = ['A', 'B', 'C']; return [ // 6 columns, bar buildFacetTest({ title: '6 Columns — Bar (needs wrap)', description: '6 column facets × 3 bars. Should require horizontal wrapping or scrolling.', tags: ['facet', 'column', 'wrap', 'bar'], chartType: 'Bar Chart', colCount: 6, xCategories: cats, yIsContinuous: false, seed: 1210, }), // 8 columns, scatter buildFacetTest({ title: '8 Columns — Scatter (needs wrap)', description: '8 column facets with scatter plots. Tests horizontal overflow.', tags: ['facet', 'column', 'wrap', 'scatter'], chartType: 'Scatter Plot', colCount: 8, xCategories: [], yIsContinuous: true, seed: 1211, }), // 10 columns, bar with more categories buildFacetTest({ title: '10 Columns — Bar (heavy wrap)', description: '10 column facets × 3 bars each. Extreme horizontal wrap test.', tags: ['facet', 'column', 'wrap', 'heavy', 'bar'], chartType: 'Bar Chart', colCount: 10, xCategories: cats, yIsContinuous: false, seed: 1212, }), ]; } /** * 3. Large col×row grids that require clipping/scrolling. * Many panels stress the layout engine. */ export function genFacetClipTests(): TestCase[] { const cats = ['A', 'B', 'C']; return [ // 4×3 = 12 panels buildFacetTest({ title: '4×3 Col×Row — Bar (12 panels)', description: '4 columns × 3 rows = 12 facet panels. Tests dense grid layout.', tags: ['facet', 'colrow', 'clip', 'bar'], chartType: 'Bar Chart', colCount: 4, rowCount: 3, xCategories: cats, yIsContinuous: false, seed: 1220, }), // 5×4 = 20 panels, scatter buildFacetTest({ title: '5×4 Col×Row — Scatter (20 panels)', description: '5 columns × 4 rows = 20 facet panels. Heavy grid requiring clip.', tags: ['facet', 'colrow', 'clip', 'scatter'], chartType: 'Scatter Plot', colCount: 5, rowCount: 4, xCategories: [], yIsContinuous: true, seed: 1221, }), // 6×5 = 30 panels buildFacetTest({ title: '6×5 Col×Row — Bar (30 panels)', description: '6 columns × 5 rows = 30 facet panels. Extreme grid test.', tags: ['facet', 'colrow', 'clip', 'heavy', 'bar'], chartType: 'Bar Chart', colCount: 6, rowCount: 5, xCategories: cats, yIsContinuous: false, seed: 1222, }), // 8 rows, scatter — vertical clip buildFacetTest({ title: '8 Rows — Scatter (vertical clip)', description: '8 row facets with scatter plots. Tests vertical overflow.', tags: ['facet', 'row', 'clip', 'scatter'], chartType: 'Scatter Plot', rowCount: 8, xCategories: [], yIsContinuous: true, seed: 1223, }), ]; } // ============================================================================ // Overflowed Facet Tests // ============================================================================ /** * Helper: build a facet overflow test with many column facets * and a banded (discrete) x-axis with `xCount` values. */ function buildOverflowFacetTest(opts: { title: string; description: string; tags: string[]; chartType: string; colCount?: number; rowCount?: number; /** Number of banded/discrete x values per facet panel */ xBandedCount?: number; /** If true, use continuous x × y (scatter) instead of discrete x */ continuousXY?: boolean; seed: number; }): TestCase { const { title, description, tags, chartType, colCount, rowCount, xBandedCount, continuousXY, seed } = opts; const rand = seededRandom(seed); const colVals = colCount ? genCategories('Region', colCount) : undefined; const rowVals = rowCount ? genCategories('Zone', rowCount) : undefined; const data: Record<string, any>[] = []; const facets: { col?: string; row?: string }[] = []; if (colVals && rowVals) { for (const c of colVals) for (const r of rowVals) facets.push({ col: c, row: r }); } else if (colVals) { for (const c of colVals) facets.push({ col: c }); } else if (rowVals) { for (const r of rowVals) facets.push({ row: r }); } const xCategories = xBandedCount ? genCategories('Item', xBandedCount) : []; for (const facet of facets) { if (continuousXY) { for (let i = 0; i < 20; i++) { data.push({ X: Math.round(10 + rand() * 90), Y: Math.round(10 + rand() * 90), ...(facet.col != null ? { Col: facet.col } : {}), ...(facet.row != null ? { Row: facet.row } : {}), }); } } else { for (const cat of xCategories) { data.push({ Category: cat, Value: Math.round(50 + rand() * 500), ...(facet.col != null ? { Col: facet.col } : {}), ...(facet.row != null ? { Row: facet.row } : {}), }); } } } const encodingMap: Partial<Record<Channel, EncodingItem>> = {}; const fields: ReturnType<typeof makeField>[] = []; const metadata: Record<string, any> = {}; if (continuousXY) { encodingMap.x = makeEncodingItem('X'); encodingMap.y = makeEncodingItem('Y'); fields.push(makeField('X'), makeField('Y')); metadata['X'] = { type: Type.Number, semanticType: 'Value', levels: [] }; metadata['Y'] = { type: Type.Number, semanticType: 'Value', levels: [] }; } else { encodingMap.x = makeEncodingItem('Category'); encodingMap.y = makeEncodingItem('Value'); fields.push(makeField('Category'), makeField('Value')); metadata['Category'] = { type: Type.String, semanticType: 'Category', levels: xCategories }; metadata['Value'] = { type: Type.Number, semanticType: 'Revenue', levels: [] }; } if (colVals) { encodingMap.column = makeEncodingItem('Col'); fields.push(makeField('Col')); metadata['Col'] = { type: Type.String, semanticType: 'Category', levels: colVals }; } if (rowVals) { encodingMap.row = makeEncodingItem('Row'); fields.push(makeField('Row')); metadata['Row'] = { type: Type.String, semanticType: 'Category', levels: rowVals }; } return { title, description, tags, chartType, data, fields, metadata, encodingMap }; } /** * Overflowed Column facets — enough column facet values that the layout * must clip/wrap, combined with discrete (banded) or continuous axes. * * Tests that computeFacetGrid correctly caps and wraps column-only facets. */ export function genFacetOverflowedColTests(): TestCase[] { return [ // 20 columns with 30 discrete x values each — banded axis makes // each subplot wide, so far fewer columns fit than with continuous. buildOverflowFacetTest({ title: '20 Cols × 30 Discrete — Bar (banded overflow)', description: '20 column facets, 30 bars each. Banded x-axis forces wide subplots — heavy overflow + wrap.', tags: ['facet', 'column', 'overflow', 'banded', 'bar'], chartType: 'Bar Chart', colCount: 20, xBandedCount: 30, seed: 1300, }), // 20 columns with continuous x × y — smaller subplots fit more columns. buildOverflowFacetTest({ title: '20 Cols — Scatter (continuous overflow)', description: '20 column facets with scatter plots. Continuous axes allow more columns before overflow.', tags: ['facet', 'column', 'overflow', 'continuous', 'scatter'], chartType: 'Scatter Plot', colCount: 20, continuousXY: true, seed: 1301, }), ]; } /** * Overflowed Column + Row facets — both dimensions exceed comfortable * capacity, requiring independent capping on each axis. * * With canvas 400×300 and minSubplotSize 60: * - 20 bars → minSubplotWidth = max(60, 20×6) = 120 → maxFacetCols = floor(600/120) = 5 * - continuous y → minSubplotHeight = 60 → maxFacetRows = floor(450/60) = 7 * So 8 cols clips to 5, 10 rows clips to 7. */ export function genFacetOverflowedColRowTests(): TestCase[] { return [ // 8 cols × 10 rows, 20 bars each → clips to ~5×7. buildOverflowFacetTest({ title: '8×10 Col×Row × 20 Bars (overflow both)', description: '8 columns × 10 rows, 20 bars each. Both dimensions overflow: cols clip to ~5, rows to ~7.', tags: ['facet', 'colrow', 'overflow', 'bar'], chartType: 'Bar Chart', colCount: 8, rowCount: 10, xBandedCount: 20, seed: 1310, }), // 15 cols × 12 rows, scatter → clips to ~10×7 (continuous needs only 60px). buildOverflowFacetTest({ title: '15×12 Col×Row — Scatter (extreme overflow)', description: '15 columns × 12 rows = 180 panels (scatter). Both dimensions far exceed budget.', tags: ['facet', 'colrow', 'overflow', 'extreme', 'scatter'], chartType: 'Scatter Plot', colCount: 15, rowCount: 12, continuousXY: true, seed: 1311, }), ]; } /** * Overflowed Row facets — enough row facet values that the layout * must clip vertically. */ export function genFacetOverflowedRowTests(): TestCase[] { return [ // 15 rows with 10 bars each. buildOverflowFacetTest({ title: '15 Rows — Bar (row overflow)', description: '15 row facets, 10 bars each. Vertical overflow requiring row clipping.', tags: ['facet', 'row', 'overflow', 'bar'], chartType: 'Bar Chart', rowCount: 15, xBandedCount: 10, seed: 1320, }), // 12 rows, scatter — vertical overflow. buildOverflowFacetTest({ title: '12 Rows — Scatter (row overflow)', description: '12 row facets with scatter plots. Tests vertical clipping.', tags: ['facet', 'row', 'overflow', 'scatter'], chartType: 'Scatter Plot', rowCount: 12, continuousXY: true, seed: 1321, }), ]; } ================================================ FILE: src/lib/agents-chart/test-data/gas-pressure-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Gas pressure model test cases (docs/design-stretch-model.md §2). * * 3 × 3 matrix of scatter plots varying: * Density: sparse (50 pts) × dense (500 pts) × very dense (3000 pts) * Distribution: uniform × single cluster × two clusters * * All use Scatter Plot with quantitative X/Y so the gas pressure model * (not the spring model) drives canvas sizing. */ import { Type } from '../../../data/types'; import { TestCase, makeField, makeEncodingItem } from './types'; import { seededRandom } from './generators'; // --------------------------------------------------------------------------- // Data distribution generators // --------------------------------------------------------------------------- /** Uniform random in [0, 100] × [0, 100] */ function genUniform(n: number, seed: number): { x: number; y: number }[] { const rand = seededRandom(seed); return Array.from({ length: n }, () => ({ x: Math.round(rand() * 1000) / 10, y: Math.round(rand() * 1000) / 10, })); } /** 70% of points in a tight cluster at (70, 70), rest spread uniformly */ function genSingleCluster(n: number, seed: number): { x: number; y: number }[] { const rand = seededRandom(seed); const clusterN = Math.round(n * 0.7); const points: { x: number; y: number }[] = []; // Dense cluster centered at (70, 70), σ ≈ 5 for (let i = 0; i < clusterN; i++) { // Box-Muller approximation using seeded rand const u1 = rand() || 0.001; const u2 = rand(); const z0 = Math.sqrt(-2 * Math.log(u1)) * Math.cos(2 * Math.PI * u2); const z1 = Math.sqrt(-2 * Math.log(u1)) * Math.sin(2 * Math.PI * u2); points.push({ x: Math.round((70 + z0 * 5) * 10) / 10, y: Math.round((70 + z1 * 5) * 10) / 10, }); } // Sparse background for (let i = clusterN; i < n; i++) { points.push({ x: Math.round(rand() * 1000) / 10, y: Math.round(rand() * 1000) / 10, }); } return points; } /** Two clusters: 40% at (25, 25) σ≈5, 40% at (75, 75) σ≈5, 20% uniform */ function genTwoClusters(n: number, seed: number): { x: number; y: number }[] { const rand = seededRandom(seed); const c1N = Math.round(n * 0.4); const c2N = Math.round(n * 0.4); const points: { x: number; y: number }[] = []; const addCluster = (cx: number, cy: number, count: number) => { for (let i = 0; i < count; i++) { const u1 = rand() || 0.001; const u2 = rand(); const z0 = Math.sqrt(-2 * Math.log(u1)) * Math.cos(2 * Math.PI * u2); const z1 = Math.sqrt(-2 * Math.log(u1)) * Math.sin(2 * Math.PI * u2); points.push({ x: Math.round((cx + z0 * 5) * 10) / 10, y: Math.round((cy + z1 * 5) * 10) / 10, }); } }; addCluster(25, 25, c1N); addCluster(75, 75, c2N); // Sparse background for (let i = c1N + c2N; i < n; i++) { points.push({ x: Math.round(rand() * 1000) / 10, y: Math.round(rand() * 1000) / 10, }); } return points; } // --------------------------------------------------------------------------- // Test case builder // --------------------------------------------------------------------------- const DENSITIES = [ { label: 'Sparse', n: 50, tag: 'sparse' }, { label: 'Dense', n: 500, tag: 'dense' }, { label: 'Very Dense', n: 3000, tag: 'very-dense' }, ] as const; const DISTRIBUTIONS = [ { label: 'Uniform', gen: genUniform, tag: 'uniform' }, { label: 'Single Cluster', gen: genSingleCluster, tag: 'cluster-1' }, { label: 'Two Clusters', gen: genTwoClusters, tag: 'cluster-2' }, ] as const; function buildTestCase( densityLabel: string, distLabel: string, n: number, data: { x: number; y: number }[], tags: string[], ): TestCase { const rows = data.map(p => ({ X: p.x, Y: p.y })); return { title: `${densityLabel} × ${distLabel} (N=${n})`, description: `${n} scatter points, ${distLabel.toLowerCase()} distribution. Tests gas pressure model §2.`, tags: ['gas-pressure', 'scatter', ...tags], chartType: 'Scatter Plot', data: rows, fields: [makeField('X'), makeField('Y')], metadata: { X: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Y: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('X'), y: makeEncodingItem('Y'), }, }; } // --------------------------------------------------------------------------- // Asymmetric density generators (X and Y have different spreads) // --------------------------------------------------------------------------- /** Wide X range [0,100], narrow Y range [45,55] — horizontal band */ function genWideXNarrowY(n: number, seed: number): { x: number; y: number }[] { const rand = seededRandom(seed); return Array.from({ length: n }, () => ({ x: Math.round(rand() * 1000) / 10, y: Math.round((45 + rand() * 10) * 10) / 10, })); } /** Narrow X range [45,55], wide Y range [0,100] — vertical band */ function genNarrowXWideY(n: number, seed: number): { x: number; y: number }[] { const rand = seededRandom(seed); return Array.from({ length: n }, () => ({ x: Math.round((45 + rand() * 10) * 10) / 10, y: Math.round(rand() * 1000) / 10, })); } /** Wide X [0,100], Y concentrated in two narrow bands [10-15] and [85-90] */ function genWideXBandedY(n: number, seed: number): { x: number; y: number }[] { const rand = seededRandom(seed); return Array.from({ length: n }, () => { const band = rand() < 0.5 ? 10 : 85; return { x: Math.round(rand() * 1000) / 10, y: Math.round((band + rand() * 5) * 10) / 10, }; }); } /** Diagonal stripe: Y ≈ X ± 3 — points cluster along the diagonal */ function genDiagonalStripe(n: number, seed: number): { x: number; y: number }[] { const rand = seededRandom(seed); return Array.from({ length: n }, () => { const base = rand() * 100; const u1 = rand() || 0.001; const u2 = rand(); const noise = Math.sqrt(-2 * Math.log(u1)) * Math.cos(2 * Math.PI * u2) * 3; return { x: Math.round(base * 10) / 10, y: Math.round((base + noise) * 10) / 10, }; }); } /** X uniform [0,100], Y exponential (most points near 0, tail to ~50) */ function genUniformXExponentialY(n: number, seed: number): { x: number; y: number }[] { const rand = seededRandom(seed); return Array.from({ length: n }, () => ({ x: Math.round(rand() * 1000) / 10, y: Math.round(-Math.log(rand() || 0.001) * 10 * 10) / 10, })); } const ASYMMETRIC_CASES = [ { label: 'Wide X, Narrow Y', gen: genWideXNarrowY, tag: 'wide-x-narrow-y', desc: 'X spans full range, Y compressed to 10% — horizontal band' }, { label: 'Narrow X, Wide Y', gen: genNarrowXWideY, tag: 'narrow-x-wide-y', desc: 'X compressed to 10%, Y spans full range — vertical band' }, { label: 'Wide X, Banded Y', gen: genWideXBandedY, tag: 'wide-x-banded-y', desc: 'X uniform, Y in two narrow bands — two horizontal stripes' }, { label: 'Diagonal Stripe', gen: genDiagonalStripe, tag: 'diagonal', desc: 'Points along Y≈X diagonal with σ≈3 noise — linear cluster' }, { label: 'Uniform X, Exp Y', gen: genUniformXExponentialY, tag: 'uniform-x-exp-y', desc: 'X uniform, Y exponential — bottom-heavy skew' }, ] as const; // --------------------------------------------------------------------------- // Public generator // --------------------------------------------------------------------------- /** * Generate the 3×3 gas-pressure test matrix. * Row = density (sparse / dense / very dense) * Column = distribution (uniform / single cluster / two clusters) */ export function genGasPressureTests(): TestCase[] { const tests: TestCase[] = []; let seed = 1000; // --- Symmetric density tests (3×3 matrix) --- for (const density of DENSITIES) { for (const dist of DISTRIBUTIONS) { const points = dist.gen(density.n, seed++); tests.push(buildTestCase( density.label, dist.label, density.n, points, [density.tag, dist.tag], )); } } // --- Asymmetric X/Y density tests --- // Uses dense (500) and very dense (3000) to show the effect clearly for (const asym of ASYMMETRIC_CASES) { for (const density of [DENSITIES[1], DENSITIES[2]]) { const points = asym.gen(density.n, seed++); tests.push({ ...buildTestCase( density.label, asym.label, density.n, points, [density.tag, asym.tag, 'asymmetric'], ), description: `${density.n} points, ${asym.desc}.`, }); } } // --- Per-axis stretch tests: stretch X but not Y --- // Case 1: Many evenly-spaced X values, few distinct Y values. // 1000 points on 200 unique X positions × 5 Y rows → X is dense, Y is sparse. { const r = seededRandom(seed++); const yLevels = [10, 30, 50, 70, 90]; const n = 1000; const points = Array.from({ length: n }, () => ({ x: Math.round(r() * 1000) / 10, // 0–100, ~200 unique y: yLevels[Math.floor(r() * yLevels.length)], // only 5 values })); tests.push({ ...buildTestCase('Dense', 'Stretch X Only (rows)', n, points, ['dense', 'stretch-x', 'per-axis']), description: '1000 points on ~200 unique X positions but only 5 Y rows. X should stretch, Y should not.', }); } // Case 2: Dense horizontal cluster at center, full Y range. // 800 points with X clustered in [40,60] (σ≈3) but Y uniform [0,100]. // X is over-packed in a narrow horizontal band → stretch X. // Y is well-spread → no Y stretch needed. { const r = seededRandom(seed++); const n = 800; const points = Array.from({ length: n }, () => { const u1 = r() || 0.001; const u2 = r(); const zx = Math.sqrt(-2 * Math.log(u1)) * Math.cos(2 * Math.PI * u2); return { x: Math.round((50 + zx * 3) * 10) / 10, // tight around 50, σ≈3 y: Math.round(r() * 1000) / 10, // uniform 0–100 }; }); tests.push({ ...buildTestCase('Dense', 'Stretch X Only (cluster)', n, points, ['dense', 'stretch-x', 'per-axis', 'cluster-x']), description: '800 points with X clustered at 50±3 but Y uniform [0,100]. X is over-packed, Y is fine.', }); } // --- maintainContinuousAxisRatio tests --- // Same data as "Stretch X Only (rows)" but with the ratio lock on: // both axes should stretch together using the larger factor. { const r = seededRandom(seed++); const yLevels = [10, 30, 50, 70, 90]; const n = 1000; const points = Array.from({ length: n }, () => ({ x: Math.round(r() * 1000) / 10, y: yLevels[Math.floor(r() * yLevels.length)], })); tests.push({ ...buildTestCase('Dense', 'Ratio Lock ON (rows)', n, points, ['dense', 'ratio-lock', 'per-axis']), description: '1000 points, 200 X positions × 5 Y rows, maintainContinuousAxisRatio=true. Both axes stretch equally.', assembleOptions: { maintainContinuousAxisRatio: true }, }); } // Same data without ratio lock for comparison { const r = seededRandom(seed++); const yLevels = [10, 30, 50, 70, 90]; const n = 1000; const points = Array.from({ length: n }, () => ({ x: Math.round(r() * 1000) / 10, y: yLevels[Math.floor(r() * yLevels.length)], })); tests.push({ ...buildTestCase('Dense', 'Ratio Lock OFF (rows)', n, points, ['dense', 'no-ratio-lock', 'per-axis']), description: '1000 points, 200 X positions × 5 Y rows, default independent stretch. X stretches, Y does not.', }); } return tests; } ================================================ FILE: src/lib/agents-chart/test-data/generators.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Synthetic data generators for chart gallery test cases. * Pure utility functions — no React/UI dependencies. */ // ============================================================================ // Synthetic Data Generators // ============================================================================ /** Seeded random for reproducibility */ export function seededRandom(seed: number) { return () => { seed = (seed * 16807 + 0) % 2147483647; return (seed - 1) / 2147483646; }; } /** Generate an array of sequential dates */ export function genDates(n: number, startYear = 2018): string[] { const dates: string[] = []; const start = new Date(startYear, 0, 1); for (let i = 0; i < n; i++) { const d = new Date(start); d.setDate(start.getDate() + Math.floor(i * (365 * 3 / n))); dates.push(d.toISOString().slice(0, 10)); } return dates; } /** Generate month names */ export function genMonths(n: number): string[] { const months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']; return months.slice(0, Math.min(n, 12)); } /** Generate year values */ export function genYears(n: number, start = 2000): number[] { return Array.from({ length: n }, (_, i) => start + i); } /** Generate natural-looking date strings like "Jun 12 1998" */ export function genNaturalDates(n: number, startYear = 1998): string[] { const monthNames = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']; const dates: string[] = []; const start = new Date(startYear, 0, 1); for (let i = 0; i < n; i++) { const d = new Date(start); d.setDate(start.getDate() + Math.floor(i * (365 * 5 / n))); dates.push(`${monthNames[d.getMonth()]} ${String(d.getDate()).padStart(2, '0')} ${d.getFullYear()}`); } return dates; } /** Generate ordinal labels with inherent sequence: "Stage 1", "Stage 2", etc. */ export function genOrdinalLabels(prefix: string, n: number): string[] { return Array.from({ length: n }, (_, i) => `${prefix} ${i + 1}`); } /** Ordinal label prefixes — rotated for variety across channels */ export const ORDINAL_PREFIXES = ['Stage', 'Step', 'Phase', 'Level', 'Round']; /** Generate category names by semantic type */ export function genCategories(semanticType: string, n: number): string[] { const pools: Record<string, string[]> = { Country: ['USA', 'China', 'Japan', 'Germany', 'UK', 'France', 'India', 'Brazil', 'Canada', 'Australia', 'South Korea', 'Mexico', 'Italy', 'Spain', 'Russia', 'Netherlands', 'Sweden', 'Norway', 'Denmark', 'Finland', 'Switzerland', 'Belgium', 'Austria', 'Poland', 'Portugal', 'Turkey', 'Argentina', 'Chile', 'Colombia', 'Peru'], Company: ['Apple', 'Google', 'Microsoft', 'Amazon', 'Meta', 'Tesla', 'Netflix', 'Adobe', 'Intel', 'Nvidia', 'Samsung', 'IBM', 'Oracle', 'SAP', 'Salesforce', 'Uber', 'Lyft', 'Spotify', 'Snap', 'Twitter', 'Palantir', 'Shopify', 'Square', 'Zoom', 'Slack', 'Twilio', 'Datadog', 'Snowflake', 'Confluent', 'MongoDB'], Product: ['Laptop', 'Phone', 'Tablet', 'Desktop', 'Monitor', 'Keyboard', 'Mouse', 'Headphones', 'Speaker', 'Camera', 'TV', 'Router', 'Printer', 'Scanner', 'SSD', 'HDD', 'RAM', 'GPU', 'CPU', 'Motherboard'], Category: ['Electronics', 'Clothing', 'Food', 'Books', 'Sports', 'Home', 'Garden', 'Auto', 'Health', 'Beauty', 'Toys', 'Music', 'Movies', 'Software', 'Games', 'Office', 'Pet', 'Baby', 'Tools', 'Crafts'], Department: ['Engineering', 'Sales', 'Marketing', 'HR', 'Finance', 'Legal', 'Operations', 'Support', 'Design', 'Research', 'QA', 'DevOps', 'Security', 'Analytics', 'Product'], Status: ['Active', 'Inactive', 'Pending', 'Completed', 'Failed'], Name: ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve', 'Frank', 'Grace', 'Hank', 'Ivy', 'Jack', 'Kate', 'Leo', 'Mona', 'Nick', 'Olivia', 'Pat', 'Quinn', 'Ray', 'Sara', 'Tom', 'Uma', 'Vic', 'Wendy', 'Xander', 'Yara', 'Zoe', 'Aaron', 'Beth', 'Carl', 'Dana'], Director: ['Steven Spielberg', 'James Cameron', 'Chris Columbus', 'George Lucas', 'Peter Jackson', 'Robert Zemeckis', 'Michael Bay', 'Roland Emmerich', 'Gore Verbinski', 'Tim Burton', 'Andrew Adamson', 'Sam Raimi', 'Ron Howard', 'Christopher Nolan', 'M. Night Shyamalan', 'David Yates', 'John Lasseter', 'Carlos Saldanha', 'Andy Wachowski', 'Ridley Scott'], MovieTitle: ['The Dark Knight', 'Spider-Man', 'Avatar', 'Titanic', 'Jurassic Park', 'Star Wars', 'The Matrix', 'Inception', 'Interstellar', 'Gladiator', 'The Avengers', 'Iron Man', 'Frozen', 'Toy Story', 'Finding Nemo', 'Shrek', 'Cars', 'Up', 'WALL-E', 'Coco', 'Moana', 'Ratatouille', 'Inside Out', 'Big Hero 6', 'Brave', 'Tangled', 'Zootopia', 'The Lion King', 'Aladdin', 'Beauty and the Beast'], }; const pool = pools[semanticType] || pools.Category; return pool.slice(0, Math.min(n, pool.length)); } /** Generate n random unique names (first + last) for very large discrete tests */ export function genRandomNames(n: number, seed = 777): string[] { const firsts = ['James', 'Mary', 'John', 'Patricia', 'Robert', 'Jennifer', 'Michael', 'Linda', 'William', 'Elizabeth', 'David', 'Barbara', 'Richard', 'Susan', 'Joseph', 'Jessica', 'Thomas', 'Sarah', 'Charles', 'Karen', 'Christopher', 'Lisa', 'Daniel', 'Nancy', 'Matthew', 'Betty', 'Anthony', 'Margaret', 'Mark', 'Sandra', 'Steven', 'Ashley', 'Paul', 'Dorothy', 'Andrew', 'Kimberly', 'Joshua', 'Emily', 'Kenneth', 'Donna']; const lasts = ['Smith', 'Johnson', 'Williams', 'Brown', 'Jones', 'Garcia', 'Miller', 'Davis', 'Rodriguez', 'Martinez', 'Hernandez', 'Lopez', 'Gonzalez', 'Wilson', 'Anderson', 'Thomas', 'Taylor', 'Moore', 'Jackson', 'Martin', 'Lee', 'Perez', 'Thompson', 'White', 'Harris', 'Sanchez', 'Clark', 'Ramirez', 'Lewis', 'Robinson', 'Walker', 'Young', 'Allen', 'King', 'Wright', 'Scott', 'Torres', 'Nguyen', 'Hill', 'Flores']; const rand = seededRandom(seed); const names = new Set<string>(); while (names.size < n) { const f = firsts[Math.floor(rand() * firsts.length)]; const l = lasts[Math.floor(rand() * lasts.length)]; names.add(`${f} ${l}`); } return [...names]; } /** Generate random numeric measure values */ export function genMeasure(n: number, min = 10, max = 1000, integers = false): number[] { return Array.from({ length: n }, () => { const v = min + Math.random() * (max - min); return integers ? Math.round(v) : Math.round(v * 100) / 100; }); } ================================================ FILE: src/lib/agents-chart/test-data/gofish-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * GoFish backend comparison tests. * * Runs the same test inputs through ALL FOUR backends: * assembleVegaLite, assembleECharts, assembleChartjs, assembleGoFish * * Covers: Scatter Plot, Line Chart, Bar Chart, Stacked Bar Chart, * Grouped Bar Chart, Area Chart, Pie Chart */ import { Type } from '../../../data/types'; import { TestCase, makeField, makeEncodingItem } from './types'; import { seededRandom, genCategories } from './generators'; // --------------------------------------------------------------------------- // Test data generators // --------------------------------------------------------------------------- function genScatterData(n: number, seed: number) { const rand = seededRandom(seed); return Array.from({ length: n }, () => ({ Weight: Math.round((40 + rand() * 60) * 10) / 10, Height: Math.round((150 + rand() * 50) * 10) / 10, })); } function genScatterColorData(n: number, seed: number) { const rand = seededRandom(seed); const categories = ['Alpha', 'Beta', 'Gamma']; return Array.from({ length: n }, (_, i) => ({ X: Math.round(rand() * 100 * 10) / 10, Y: Math.round(rand() * 100 * 10) / 10, Group: categories[i % categories.length], })); } function genBarData(seed: number) { const rand = seededRandom(seed); const products = ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries']; return products.map(p => ({ Product: p, Sales: Math.round(100 + rand() * 900), })); } function genLineData(seed: number) { const rand = seededRandom(seed); const months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']; return months.map(m => ({ Month: m, Revenue: Math.round(1000 + rand() * 5000), })); } function genMultiSeriesLineData(seed: number) { const rand = seededRandom(seed); const months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug']; const series = ['ProductA', 'ProductB', 'ProductC']; const data: any[] = []; for (const m of months) { for (const s of series) { data.push({ Month: m, Sales: Math.round(500 + rand() * 2000), Product: s, }); } } return data; } function genStackedBarData(seed: number) { const rand = seededRandom(seed); const quarters = ['Q1', 'Q2', 'Q3', 'Q4']; const regions = ['North', 'South', 'East', 'West']; const data: any[] = []; for (const q of quarters) { for (const r of regions) { data.push({ Quarter: q, Revenue: Math.round(200 + rand() * 800), Region: r, }); } } return data; } function genGroupedBarData(seed: number) { const rand = seededRandom(seed); const years = ['2022', '2023', '2024']; const departments = ['Sales', 'Engineering', 'Marketing']; const data: any[] = []; for (const y of years) { for (const d of departments) { data.push({ Year: y, Budget: Math.round(500 + rand() * 2000), Department: d, }); } } return data; } function genAreaData(seed: number) { const rand = seededRandom(seed); const months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']; return months.map(m => ({ Month: m, Value: Math.round(100 + rand() * 500), })); } function genStackedAreaData(seed: number) { const rand = seededRandom(seed); const months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug']; const categories = ['Desktop', 'Mobile', 'Tablet']; const data: any[] = []; for (const m of months) { for (const c of categories) { data.push({ Month: m, Users: Math.round(200 + rand() * 800), Platform: c, }); } } return data; } function genPieData(seed: number) { const rand = seededRandom(seed); const categories = ['Electronics', 'Clothing', 'Food', 'Books', 'Toys']; return categories.map(c => ({ Category: c, Revenue: Math.round(100 + rand() * 900), })); } function genScatterPieData(seed: number) { const rand = seededRandom(seed); const locations = [ { City: 'NYC', Lon: -74.0, Lat: 40.7 }, { City: 'LA', Lon: -118.2, Lat: 34.1 }, { City: 'Chicago', Lon: -87.6, Lat: 41.9 }, { City: 'Houston', Lon: -95.4, Lat: 29.8 }, { City: 'Phoenix', Lon: -112.1, Lat: 33.4 }, ]; const species = ['Dogs', 'Cats', 'Birds']; const data: any[] = []; for (const loc of locations) { for (const sp of species) { data.push({ Longitude: loc.Lon, Latitude: loc.Lat, Species: sp, Count: Math.round(50 + rand() * 500), }); } } return data; } // --------------------------------------------------------------------------- // Test case builders // --------------------------------------------------------------------------- export function genGoFishScatterTests(): TestCase[] { const tests: TestCase[] = []; // 1. Basic scatter { const data = genScatterData(50, 42); tests.push({ title: 'GF: Scatter — Basic Q×Q', description: '50 points, two quantitative axes.', tags: ['gofish', 'scatter', 'quantitative'], chartType: 'Scatter Plot', data, fields: [makeField('Weight'), makeField('Height')], metadata: { Weight: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Height: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Weight'), y: makeEncodingItem('Height') }, }); } // 2. Scatter with color grouping { const data = genScatterColorData(60, 77); tests.push({ title: 'GF: Scatter — Color Groups', description: '60 points, 3 groups with color encoding.', tags: ['gofish', 'scatter', 'color', 'multi-series'], chartType: 'Scatter Plot', data, fields: [makeField('X'), makeField('Y'), makeField('Group')], metadata: { X: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Y: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Group: { type: Type.String, semanticType: 'Category', levels: ['Alpha', 'Beta', 'Gamma'] }, }, encodingMap: { x: makeEncodingItem('X'), y: makeEncodingItem('Y'), color: makeEncodingItem('Group') }, }); } return tests; } export function genGoFishLineTests(): TestCase[] { const tests: TestCase[] = []; // 1. Single series line { const data = genLineData(200); tests.push({ title: 'GF: Line — Single Series', description: 'Ordinal x-axis, single line.', tags: ['gofish', 'line', 'single-series'], chartType: 'Line Chart', data, fields: [makeField('Month'), makeField('Revenue')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'] }, Revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Revenue') }, }); } // 2. Multi-series line { const data = genMultiSeriesLineData(300); tests.push({ title: 'GF: Line — Multi-Series (3 products)', description: 'Color channel → multiple lines via layer + select.', tags: ['gofish', 'line', 'multi-series', 'color'], chartType: 'Line Chart', data, fields: [makeField('Month'), makeField('Sales'), makeField('Product')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug'] }, Sales: { type: Type.Number, semanticType: 'Revenue', levels: [] }, Product: { type: Type.String, semanticType: 'Category', levels: ['ProductA', 'ProductB', 'ProductC'] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Sales'), color: makeEncodingItem('Product') }, }); } return tests; } export function genGoFishBarTests(): TestCase[] { const tests: TestCase[] = []; // 1. Simple bar { const data = genBarData(100); tests.push({ title: 'GF: Bar — Basic', description: '5 products, single color.', tags: ['gofish', 'bar', 'simple'], chartType: 'Bar Chart', data, fields: [makeField('Product'), makeField('Sales')], metadata: { Product: { type: Type.String, semanticType: 'Category', levels: ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'] }, Sales: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { x: makeEncodingItem('Product'), y: makeEncodingItem('Sales') }, }); } // 2. Many categories { const rand = seededRandom(150); const cities = genCategories('City', 12); const data = cities.map(c => ({ City: c, Population: Math.round(10000 + rand() * 900000), })); tests.push({ title: 'GF: Bar — 12 categories', description: 'Many categories — tests GoFish layout.', tags: ['gofish', 'bar', 'many-categories'], chartType: 'Bar Chart', data, fields: [makeField('City'), makeField('Population')], metadata: { City: { type: Type.String, semanticType: 'Category', levels: cities }, Population: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('City'), y: makeEncodingItem('Population') }, }); } return tests; } export function genGoFishStackedBarTests(): TestCase[] { const tests: TestCase[] = []; { const data = genStackedBarData(500); tests.push({ title: 'GF: Stacked Bar — Regions × Quarters', description: 'Stacked bar chart with 4 quarters and 4 regions.', tags: ['gofish', 'stacked-bar', 'color'], chartType: 'Stacked Bar Chart', data, fields: [makeField('Quarter'), makeField('Revenue'), makeField('Region')], metadata: { Quarter: { type: Type.String, semanticType: 'Category', levels: ['Q1', 'Q2', 'Q3', 'Q4'] }, Revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, Region: { type: Type.String, semanticType: 'Category', levels: ['North', 'South', 'East', 'West'] }, }, encodingMap: { x: makeEncodingItem('Quarter'), y: makeEncodingItem('Revenue'), color: makeEncodingItem('Region') }, }); } return tests; } export function genGoFishGroupedBarTests(): TestCase[] { const tests: TestCase[] = []; { const data = genGroupedBarData(600); tests.push({ title: 'GF: Grouped Bar — 3 Years × 3 Departments', description: 'Grouped (side-by-side) bar chart.', tags: ['gofish', 'grouped-bar', 'group'], chartType: 'Grouped Bar Chart', data, fields: [makeField('Year'), makeField('Budget'), makeField('Department')], metadata: { Year: { type: Type.String, semanticType: 'Year', levels: ['2022', '2023', '2024'] }, Budget: { type: Type.Number, semanticType: 'Amount', levels: [] }, Department: { type: Type.String, semanticType: 'Category', levels: ['Sales', 'Engineering', 'Marketing'] }, }, encodingMap: { x: makeEncodingItem('Year'), y: makeEncodingItem('Budget'), group: makeEncodingItem('Department') }, }); } return tests; } export function genGoFishAreaTests(): TestCase[] { const tests: TestCase[] = []; { const data = genAreaData(700); tests.push({ title: 'GF: Area — Monthly Values', description: 'Single area chart over months.', tags: ['gofish', 'area', 'single-series'], chartType: 'Area Chart', data, fields: [makeField('Month'), makeField('Value')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'] }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Value') }, }); } return tests; } export function genGoFishStackedAreaTests(): TestCase[] { const tests: TestCase[] = []; { const data = genStackedAreaData(750); tests.push({ title: 'GF: Stacked Area — Platforms × Months', description: 'Stacked area chart with 3 platforms over 8 months (TODO: multi-series).', tags: ['gofish', 'stacked-area', 'color', 'multi-series'], chartType: 'Area Chart', data, fields: [makeField('Month'), makeField('Users'), makeField('Platform')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug'] }, Users: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Platform: { type: Type.String, semanticType: 'Category', levels: ['Desktop', 'Mobile', 'Tablet'] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Users'), color: makeEncodingItem('Platform') }, }); } return tests; } export function genGoFishPieTests(): TestCase[] { const tests: TestCase[] = []; { const data = genPieData(800); tests.push({ title: 'GF: Pie — Revenue by Category', description: 'Pie chart with 5 categories.', tags: ['gofish', 'pie', 'part-to-whole'], chartType: 'Pie Chart', data, fields: [makeField('Category'), makeField('Revenue')], metadata: { Category: { type: Type.String, semanticType: 'Category', levels: ['Electronics', 'Clothing', 'Food', 'Books', 'Toys'] }, Revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { color: makeEncodingItem('Category'), size: makeEncodingItem('Revenue') }, }); } return tests; } export function genGoFishScatterPieTests(): TestCase[] { const tests: TestCase[] = []; { const data = genScatterPieData(850); tests.push({ title: 'GF: Scatter Pie — Species by City', description: '5 cities × 3 species, pie at each (x, y) location.', tags: ['gofish', 'scatterpie', 'color', 'angle'], chartType: 'Scatter Pie Chart', data, fields: [makeField('Longitude'), makeField('Latitude'), makeField('Species'), makeField('Count')], metadata: { Longitude: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Latitude: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Species: { type: Type.String, semanticType: 'Category', levels: ['Dogs', 'Cats', 'Birds'] }, Count: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Longitude'), y: makeEncodingItem('Latitude'), color: makeEncodingItem('Species'), angle: makeEncodingItem('Count'), }, }); } return tests; } export function genGoFishStressTests(): TestCase[] { const tests: TestCase[] = []; // Dense scatter { const data = genScatterData(200, 999); tests.push({ title: 'GF: Stress — Dense Scatter (200 pts)', description: 'Dense scatter plot to test GoFish rendering.', tags: ['gofish', 'scatter', 'dense', 'stress'], chartType: 'Scatter Plot', data, fields: [makeField('Weight'), makeField('Height')], metadata: { Weight: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Height: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Weight'), y: makeEncodingItem('Height') }, }); } // Large bar chart { const rand = seededRandom(1000); const items = genCategories('Item', 25); const data = items.map(item => ({ Item: item, Sales: Math.round(50 + rand() * 500), })); tests.push({ title: 'GF: Stress — 25-category Bar', description: 'Tests GoFish auto-spacing with many categories.', tags: ['gofish', 'bar', 'many-categories', 'stress'], chartType: 'Bar Chart', data, fields: [makeField('Item'), makeField('Sales')], metadata: { Item: { type: Type.String, semanticType: 'Category', levels: items }, Sales: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { x: makeEncodingItem('Item'), y: makeEncodingItem('Sales') }, }); } return tests; } ================================================ FILE: src/lib/agents-chart/test-data/index.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Barrel export for chart test-data generators. * * Re-exports every generator plus the shared types, * and exposes the master TEST_GENERATORS map and GALLERY_SECTIONS config. */ // Shared types & helpers export type { TestCase, DateFormat, GallerySection } from './types'; export { makeField, makeEncodingItem, inferType, buildMetadata } from './types'; // Utilities export { seededRandom, genDates, genMonths, genYears, genNaturalDates, genCategories, genRandomNames, genMeasure } from './generators'; // Chart-type generators export { genScatterTests, genRegressionTests } from './scatter-tests'; export { genBarTests, genStackedBarTests, genGroupedBarTests } from './bar-tests'; export { genHistogramTests, genBoxplotTests, genDensityTests, genStripPlotTests } from './distribution-tests'; export { genLineTests } from './line-tests'; export { genDottedLineTests, genBumpChartTests } from './line-area-tests'; export { genAreaTests, genStreamgraphTests } from './area-tests'; export { genHeatmapTests, genPieTests, genRangedDotPlotTests, genLollipopTests, genCustomTests, genWaterfallTests, genCandlestickTests, genRadarTests, genPyramidTests, genRoseTests, } from './specialized-tests'; export { FACET_SIZES, DISCRETE_SIZES, genFacetColumnTests, genFacetRowTests, genFacetColRowTests, genFacetSmallTests, genFacetWrapTests, genFacetClipTests, genFacetOverflowedColTests, genFacetOverflowedColRowTests, genFacetOverflowedRowTests } from './facet-tests'; export { genOverflowTests, genElasticityTests } from './stress-tests'; export { genGasPressureTests } from './gas-pressure-tests'; export { genLineAreaStretchTests } from './line-area-stretch-tests'; export { genEChartsScatterTests, genEChartsLineTests, genEChartsBarTests, genEChartsStackedBarTests, genEChartsGroupedBarTests, genEChartsStressTests, genEChartsAreaTests, genEChartsPieTests, genEChartsHeatmapTests, genEChartsHistogramTests, genEChartsBoxplotTests, genEChartsRadarTests, genEChartsCandlestickTests, genEChartsStreamgraphTests, genEChartsFacetSmallTests, genEChartsFacetWrapTests, genEChartsFacetClipTests, genEChartsRoseTests, genEChartsGaugeTests, genEChartsFunnelTests, genEChartsTreemapTests, genEChartsSunburstTests, genEChartsSankeyTests, genEChartsUniqueStressTests } from './echarts-tests'; export { genChartJsScatterTests, genChartJsLineTests, genChartJsBarTests, genChartJsStackedBarTests, genChartJsGroupedBarTests, genChartJsAreaTests, genChartJsPieTests, genChartJsHistogramTests, genChartJsRadarTests, genChartJsStressTests, genChartJsRoseTests } from './chartjs-tests'; export { genGoFishScatterTests, genGoFishLineTests, genGoFishBarTests, genGoFishStackedBarTests, genGoFishGroupedBarTests, genGoFishAreaTests, genGoFishStackedAreaTests, genGoFishPieTests, genGoFishScatterPieTests, genGoFishStressTests } from './gofish-tests'; export { genDiscreteAxisTests } from './discrete-axis-tests'; export { genDateTests, genDateYearTests, genDateMonthTests, genDateYearMonthTests, genDateDecadeTests, genDateDateTimeTests, genDateHoursTests } from './date-tests'; export { genSemanticContextTests, genSnapToBoundTests } from './semantic-tests'; export { genDebugTests } from './debug-tests'; // --------------------------------------------------------------------------- // Master map & gallery sections // --------------------------------------------------------------------------- import { TestCase, GallerySection } from './types'; import { genScatterTests, genRegressionTests } from './scatter-tests'; import { genBarTests, genStackedBarTests, genGroupedBarTests } from './bar-tests'; import { genHistogramTests, genBoxplotTests, genDensityTests, genStripPlotTests } from './distribution-tests'; import { genLineTests } from './line-tests'; import { genDottedLineTests, genBumpChartTests } from './line-area-tests'; import { genAreaTests, genStreamgraphTests } from './area-tests'; import { genHeatmapTests, genPieTests, genRangedDotPlotTests, genLollipopTests, genCustomTests, genWaterfallTests, genCandlestickTests, genRadarTests, genPyramidTests, genRoseTests, } from './specialized-tests'; import { genFacetColumnTests, genFacetRowTests, genFacetColRowTests, genFacetSmallTests, genFacetWrapTests, genFacetClipTests, genFacetOverflowedColTests, genFacetOverflowedColRowTests, genFacetOverflowedRowTests } from './facet-tests'; import { genOverflowTests, genElasticityTests } from './stress-tests'; import { genGasPressureTests } from './gas-pressure-tests'; import { genLineAreaStretchTests } from './line-area-stretch-tests'; import { genDiscreteAxisTests } from './discrete-axis-tests'; import { genDateYearTests, genDateMonthTests, genDateYearMonthTests, genDateDecadeTests, genDateDateTimeTests, genDateHoursTests } from './date-tests'; import { genSemanticContextTests, genSnapToBoundTests } from './semantic-tests'; import { genDebugTests } from './debug-tests'; import { genEChartsScatterTests, genEChartsLineTests, genEChartsBarTests, genEChartsStackedBarTests, genEChartsGroupedBarTests, genEChartsStressTests, genEChartsAreaTests, genEChartsPieTests, genEChartsHeatmapTests, genEChartsHistogramTests, genEChartsBoxplotTests, genEChartsRadarTests, genEChartsCandlestickTests, genEChartsStreamgraphTests, genEChartsFacetSmallTests, genEChartsFacetWrapTests, genEChartsFacetClipTests, genEChartsRoseTests, genEChartsGaugeTests, genEChartsFunnelTests, genEChartsTreemapTests, genEChartsSunburstTests, genEChartsSankeyTests, genEChartsUniqueStressTests } from './echarts-tests'; import { genChartJsScatterTests, genChartJsLineTests, genChartJsBarTests, genChartJsStackedBarTests, genChartJsGroupedBarTests, genChartJsAreaTests, genChartJsPieTests, genChartJsHistogramTests, genChartJsRadarTests, genChartJsStressTests, genChartJsRoseTests } from './chartjs-tests'; import { genGoFishScatterTests, genGoFishLineTests, genGoFishBarTests, genGoFishStackedBarTests, genGoFishGroupedBarTests, genGoFishAreaTests, genGoFishStackedAreaTests, genGoFishPieTests, genGoFishScatterPieTests, genGoFishStressTests } from './gofish-tests'; /** All test generators mapped by chart group */ export const TEST_GENERATORS: Record<string, () => TestCase[]> = { 'Scatter Plot': genScatterTests, 'Regression': genRegressionTests, 'Bar Chart': genBarTests, 'Stacked Bar Chart': genStackedBarTests, 'Grouped Bar Chart': genGroupedBarTests, 'Histogram': genHistogramTests, 'Heatmap': genHeatmapTests, 'Line Chart': genLineTests, 'Dotted Line Chart': genDottedLineTests, 'Bump Chart': genBumpChartTests, 'Boxplot': genBoxplotTests, 'Pie Chart': genPieTests, 'Ranged Dot Plot': genRangedDotPlotTests, 'Area Chart': genAreaTests, 'Streamgraph': genStreamgraphTests, 'Lollipop Chart': genLollipopTests, 'Density Plot': genDensityTests, 'Candlestick Chart': genCandlestickTests, 'Waterfall Chart': genWaterfallTests, 'Strip Plot': genStripPlotTests, 'Radar Chart': genRadarTests, 'Pyramid Chart': genPyramidTests, 'Rose Chart': genRoseTests, 'Custom Charts': genCustomTests, 'Facet: Columns': genFacetColumnTests, 'Facet: Rows': genFacetRowTests, 'Facet: Cols+Rows': genFacetColRowTests, 'Facet: Small': genFacetSmallTests, 'Facet: Wrap': genFacetWrapTests, 'Facet: Clip': genFacetClipTests, 'Facet: Overflowed Col': genFacetOverflowedColTests, 'Facet: Overflowed Col+Row': genFacetOverflowedColRowTests, 'Facet: Overflowed Row': genFacetOverflowedRowTests, 'Overflow': genOverflowTests, 'Elasticity & Stretch': genElasticityTests, 'Dates: Year': genDateYearTests, 'Dates: Month': genDateMonthTests, 'Dates: Year-Month': genDateYearMonthTests, 'Dates: Decade': genDateDecadeTests, 'Dates: Date/DateTime': genDateDateTimeTests, 'Dates: Hours': genDateHoursTests, 'Discrete Axis Sizing': genDiscreteAxisTests, 'Gas Pressure (§2)': genGasPressureTests, 'Line/Area Stretch': genLineAreaStretchTests, 'Semantic Context': genSemanticContextTests, 'Snap-to-Bound': genSnapToBoundTests, 'Debug Cases': genDebugTests, 'ECharts: Scatter': genEChartsScatterTests, 'ECharts: Line': genEChartsLineTests, 'ECharts: Bar': genEChartsBarTests, 'ECharts: Stacked Bar': genEChartsStackedBarTests, 'ECharts: Grouped Bar': genEChartsGroupedBarTests, 'ECharts: Area': genEChartsAreaTests, 'ECharts: Pie': genEChartsPieTests, 'ECharts: Heatmap': genEChartsHeatmapTests, 'ECharts: Histogram': genEChartsHistogramTests, 'ECharts: Boxplot': genEChartsBoxplotTests, 'ECharts: Radar': genEChartsRadarTests, 'ECharts: Candlestick': genEChartsCandlestickTests, 'ECharts: Streamgraph': genEChartsStreamgraphTests, 'ECharts: Facet Small': genEChartsFacetSmallTests, 'ECharts: Facet Wrap': genEChartsFacetWrapTests, 'ECharts: Facet Clip': genEChartsFacetClipTests, 'ECharts: Rose': genEChartsRoseTests, 'ECharts: Stress Tests': genEChartsStressTests, 'ECharts: Gauge': genEChartsGaugeTests, 'ECharts: Funnel': genEChartsFunnelTests, 'ECharts: Treemap': genEChartsTreemapTests, 'ECharts: Sunburst': genEChartsSunburstTests, 'ECharts: Sankey': genEChartsSankeyTests, 'ECharts: Unique Stress': genEChartsUniqueStressTests, 'Chart.js: Scatter': genChartJsScatterTests, 'Chart.js: Line': genChartJsLineTests, 'Chart.js: Bar': genChartJsBarTests, 'Chart.js: Stacked Bar': genChartJsStackedBarTests, 'Chart.js: Grouped Bar': genChartJsGroupedBarTests, 'Chart.js: Area': genChartJsAreaTests, 'Chart.js: Pie': genChartJsPieTests, 'Chart.js: Histogram': genChartJsHistogramTests, 'Chart.js: Radar': genChartJsRadarTests, 'Chart.js: Rose': genChartJsRoseTests, 'Chart.js: Stress Tests': genChartJsStressTests, 'GoFish Basic': () => [ ...genGoFishScatterTests(), ...genGoFishLineTests(), ...genGoFishBarTests(), ...genGoFishStackedBarTests(), ...genGoFishGroupedBarTests(), ...genGoFishAreaTests(), ...genGoFishStackedAreaTests(), ...genGoFishPieTests(), ...genGoFishScatterPieTests(), ...genGoFishStressTests(), ], }; /** Gallery organised into three sections */ export const GALLERY_SECTIONS: GallerySection[] = [ { label: 'Semantic Context', description: 'Demonstrates how semantic type annotations improve chart output: formatting, domain constraints, axis reversal, scale type, and interpolation', entries: ['Semantic Context', 'Snap-to-Bound'], }, { label: 'Debug Cases', description: 'Regression tests from evaluation failures: log+bin+zeros, temporal+bin, single-point line', entries: ['Debug Cases'], }, { label: 'VegaLite', description: 'Demos for every supported chart type', entries: [ 'Scatter Plot', 'Regression', 'Bar Chart', 'Stacked Bar Chart', 'Grouped Bar Chart', 'Histogram', 'Heatmap', 'Line Chart', 'Dotted Line Chart', 'Boxplot', 'Pie Chart', 'Ranged Dot Plot', 'Area Chart', 'Streamgraph', 'Lollipop Chart', 'Density Plot', 'Bump Chart', 'Candlestick Chart', 'Waterfall Chart', 'Strip Plot', 'Radar Chart', 'Pyramid Chart', 'Rose Chart', 'Custom Charts', ], }, { label: 'Facets', description: 'Faceting modes and feature combinations', entries: ['Facet: Columns', 'Facet: Rows', 'Facet: Cols+Rows', 'Facet: Small', 'Facet: Wrap', 'Facet: Clip', 'Facet: Overflowed Col', 'Facet: Overflowed Col+Row', 'Facet: Overflowed Row'], }, { label: 'Stress Tests', description: 'Overflow, elasticity, and temporal format stress tests', entries: [ 'Overflow', 'Elasticity & Stretch', 'Discrete Axis Sizing', 'Gas Pressure (§2)', 'Line/Area Stretch', 'Dates: Year', 'Dates: Month', 'Dates: Year-Month', 'Dates: Decade', 'Dates: Date/DateTime', 'Dates: Hours', ], }, { label: 'ECharts Backend', description: 'Same inputs through ECharts backend — compare series-based output vs VL encoding-based output', entries: [ // VegaLite 同款（同一批 test case 用 VL+EC 双端渲染） 'Scatter Plot', 'Regression', 'Bar Chart', 'Stacked Bar Chart', 'Grouped Bar Chart', 'Histogram', 'Heatmap', 'Line Chart', 'Dotted Line Chart', 'Boxplot', 'Pie Chart', 'Ranged Dot Plot', 'Area Chart', 'Streamgraph', 'Lollipop Chart', 'Density Plot', 'Bump Chart', 'Candlestick Chart', 'Waterfall Chart', 'Strip Plot', 'Radar Chart', 'Pyramid Chart', 'Rose Chart', // ECharts 专属（Facet、Gauge、Funnel、Treemap、Sunburst、Sankey、Stress） // 'ECharts: Scatter', // 同 VegaLite 同款 Scatter Plot // 'ECharts: Line', // 同 VegaLite 同款 Line Chart // 'ECharts: Bar', // 同 VegaLite 同款 Bar Chart // 'ECharts: Stacked Bar', // 同 VegaLite 同款 Stacked Bar Chart // 'ECharts: Grouped Bar', // 同 VegaLite 同款 Grouped Bar Chart // 'ECharts: Area', // 同 VegaLite 同款 Area Chart // 'ECharts: Pie', // 同 VegaLite 同款 Pie Chart // 'ECharts: Heatmap', // 同 VegaLite 同款 Heatmap // 'ECharts: Histogram', // 同 VegaLite 同款 Histogram // 'ECharts: Boxplot', // 同 VegaLite 同款 Boxplot // 'ECharts: Radar', // 同 VegaLite 同款 Radar Chart // 'ECharts: Candlestick', // 同 VegaLite 同款 Candlestick Chart // 'ECharts: Streamgraph', // 同 VegaLite 同款 Streamgraph // 'ECharts: Rose', // 同 VegaLite 同款 Rose Chart 'ECharts: Facet Small', 'ECharts: Facet Wrap', 'ECharts: Facet Clip', 'ECharts: Gauge', 'ECharts: Funnel', 'ECharts: Treemap', 'ECharts: Sunburst', 'ECharts: Sankey', 'ECharts: Unique Stress', 'ECharts: Stress Tests', ], }, { label: 'Chart.js Backend', description: 'Same inputs through Chart.js backend — compare dataset-based output vs VL/EC output', entries: [ 'Chart.js: Scatter', 'Chart.js: Line', 'Chart.js: Bar', 'Chart.js: Stacked Bar', 'Chart.js: Grouped Bar', 'Chart.js: Area', 'Chart.js: Pie', 'Chart.js: Histogram', 'Chart.js: Radar', 'Chart.js: Rose', 'Chart.js: Stress Tests', ], }, { label: 'GoFish Basic', description: 'All GoFish chart examples on one page', entries: [ 'GoFish Basic', ], }, ]; ================================================ FILE: src/lib/agents-chart/test-data/line-area-stretch-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Line & Area chart stretch stress tests. * * Covers 5 scenarios × 2 chart types + boundary cases + vertical (flipped) variants. * * Scenarios: * A. Many X positions, few series (200 dates × 3 series = 600 pts) * B. Few X positions, many series (12 dates × 20 series = 240 pts) * C. Many X positions, many series (200 dates × 20 series = 4000 pts) * D. Moderate X, 40 series (100 dates × 40 series = 4000 pts) * E. Moderate X, 60 series (100 dates × 60 series = 6000 pts) * * Current line/area params: { x: 100, y: 20, seriesCountAxis: 'auto' } * Default elasticity: 0.3, maxStretch: 1.5 * * Expected stretch (base canvas 400×300): * * Scenario A (200×3): * X: uniqueX=200, σ1d=√100=10 → pressure=200×10/400=5.0 → 5.0^0.3=1.62 → capped 1.5 * Y: nSeries=3, σ_y=20 → pressure=3×20/300=0.20 → <1, no stretch * → width=600, height=300 ✓ X-only stretch * * Scenario B (12×20): * X: uniqueX=12, σ1d=10 → pressure=12×10/400=0.30 → <1, no stretch * Y: nSeries=20, σ_y=20 → pressure=20×20/300=1.33 → 1.33^0.3=1.09 * → width=400, height=328 ✓ Mild Y stretch * * Scenario C (200×20): * X: pressure=5.0 → capped 1.5 * Y: nSeries=20 → pressure=1.33 → 1.09 * → width=600, height=328 ✓ X dominant * * Scenario D (100×40): * X: uniqueX=100, σ1d=10 → pressure=2.5 → 2.5^0.3=1.32 * Y: nSeries=40, σ_y=20 → pressure=2.67 → 2.67^0.3=1.35 * → width=528, height=406 ✓ Both axes stretch similarly * * Scenario E (100×60): * X: pressure=2.5 → 1.32 * Y: nSeries=60, σ_y=20 → pressure=4.0 → 4.0^0.3=1.52 → capped 1.5 * → width=528, height=450 ✓ Y hits cap */ import { Type } from '../../../data/types'; import { TestCase, makeField, makeEncodingItem } from './types'; import { seededRandom, genDates } from './generators'; // --------------------------------------------------------------------------- // Smooth random walk generator (shared across tests) // --------------------------------------------------------------------------- function makeRandWalk(rand: () => number) { return (n: number, base: number, volatility: number): number[] => { const values: number[] = [base]; let momentum = 0; for (let i = 1; i < n; i++) { momentum = 0.65 * momentum + (rand() - 0.5) * volatility; values.push(Math.round(Math.max(0, values[i - 1] + momentum))); } return values; }; } // --------------------------------------------------------------------------- // Series name pools (realistic) // --------------------------------------------------------------------------- const SERIES_3 = ['Revenue', 'Costs', 'Profit']; const SERIES_20 = [ 'Automotive', 'Banking', 'Construction', 'Defense', 'Energy', 'Fashion', 'Gaming', 'Healthcare', 'Insurance', 'Jewelry', 'Logistics', 'Manufacturing', 'Networking', 'Oil & Gas', 'Pharma', 'Real Estate', 'Retail', 'Software', 'Telecom', 'Utilities', ]; const SERIES_40 = [ ...SERIES_20, 'Agriculture', 'Aerospace', 'Biotech', 'Chemicals', 'Consulting', 'Education', 'Entertainment', 'Fintech', 'Forestry', 'Hospitality', 'Legal', 'Media', 'Mining', 'Packaging', 'Publishing', 'Semiconductors', 'Shipping', 'Sports', 'Textiles', 'Waste Mgmt', ]; const SERIES_60 = [ ...SERIES_40, 'Advertising', 'Architecture', 'Brewing', 'Ceramics', 'Dairy', 'E-commerce', 'Fisheries', 'Furniture', 'Genomics', 'HVAC', 'Irrigation', 'Journalism', 'Knitwear', 'Lighting', 'Marine', 'Nutrition', 'Optics', 'Plumbing', 'Quarrying', 'Robotics', ]; // --------------------------------------------------------------------------- // Generator // --------------------------------------------------------------------------- export function genLineAreaStretchTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(8800); const walk = makeRandWalk(rand); // Helper to build a multi-series dataset const buildData = (nDates: number, seriesNames: string[], startYear: number) => { const dates = genDates(nDates, startYear); const data: any[] = []; for (const s of seriesNames) { const base = 50 + Math.round(rand() * 300); const vals = walk(nDates, base, 15); for (let i = 0; i < dates.length; i++) { data.push({ Date: dates[i], Series: s, Value: vals[i] }); } } return { dates, data }; }; // ----------------------------------------------------------------------- // Scenario A: Many X (200 dates) × Few series (3) // ----------------------------------------------------------------------- { const { data } = buildData(200, SERIES_3, 2015); const makeCase = (chartType: string, tag: string): TestCase => ({ title: `${tag}: 200 dates × 3 series (600 pts)`, description: 'Many time points, few series — X should stretch to max, Y mild', tags: ['temporal', 'quantitative', 'color', 'stretch-test'], chartType, data, fields: [makeField('Date'), makeField('Value'), makeField('Series')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Series: { type: Type.String, semanticType: 'Category', levels: SERIES_3 }, }, encodingMap: { x: makeEncodingItem('Date'), y: makeEncodingItem('Value'), color: makeEncodingItem('Series') }, }); tests.push(makeCase('Line Chart', 'Line A')); tests.push(makeCase('Area Chart', 'Area A')); } // ----------------------------------------------------------------------- // Scenario B: Few X (12 dates) × Many series (20) // ----------------------------------------------------------------------- { const { data } = buildData(12, SERIES_20, 2020); const makeCase = (chartType: string, tag: string): TestCase => ({ title: `${tag}: 12 dates × 20 series (240 pts)`, description: 'Few time points, many series — should barely stretch', tags: ['temporal', 'quantitative', 'color', 'stretch-test'], chartType, data, fields: [makeField('Date'), makeField('Value'), makeField('Series')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Series: { type: Type.String, semanticType: 'Category', levels: SERIES_20 }, }, encodingMap: { x: makeEncodingItem('Date'), y: makeEncodingItem('Value'), color: makeEncodingItem('Series') }, }); tests.push(makeCase('Line Chart', 'Line B')); tests.push(makeCase('Area Chart', 'Area B')); } // ----------------------------------------------------------------------- // Scenario C: Many X (200 dates) × Many series (20) // ----------------------------------------------------------------------- { const { data } = buildData(200, SERIES_20, 2010); const makeCase = (chartType: string, tag: string): TestCase => ({ title: `${tag}: 200 dates × 20 series (4000 pts)`, description: 'Dense spaghetti — X should max out, Y moderate', tags: ['temporal', 'quantitative', 'color', 'stretch-test'], chartType, data, fields: [makeField('Date'), makeField('Value'), makeField('Series')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Series: { type: Type.String, semanticType: 'Category', levels: SERIES_20 }, }, encodingMap: { x: makeEncodingItem('Date'), y: makeEncodingItem('Value'), color: makeEncodingItem('Series') }, }); tests.push(makeCase('Line Chart', 'Line C')); tests.push(makeCase('Area Chart', 'Area C')); } // ----------------------------------------------------------------------- // Scenario D: Many X (100 dates) × 40 series // X: uniqueX=100, σ1d=√100=10 → pressure=100×10/400=2.5 → 2.5^0.3=1.32 // Y: nSeries=40, σ_y=20 → pressure=40×20/300=2.67 → 2.67^0.3=1.35 // ----------------------------------------------------------------------- { const { data } = buildData(100, SERIES_40, 2018); const makeCase = (chartType: string, tag: string): TestCase => ({ title: `${tag}: 100 dates × 40 series (4000 pts)`, description: '40 overlapping series — Y should stretch noticeably', tags: ['temporal', 'quantitative', 'color', 'stretch-test'], chartType, data, fields: [makeField('Date'), makeField('Value'), makeField('Series')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Series: { type: Type.String, semanticType: 'Category', levels: SERIES_40 }, }, encodingMap: { x: makeEncodingItem('Date'), y: makeEncodingItem('Value'), color: makeEncodingItem('Series') }, }); tests.push(makeCase('Line Chart', 'Line D')); tests.push(makeCase('Area Chart', 'Area D')); } // ----------------------------------------------------------------------- // Scenario E: Many X (100 dates) × 60 series // X: uniqueX=100, σ1d=10 → pressure=2.5 → 2.5^0.3=1.32 // Y: nSeries=60, σ_y=20 → pressure=60×20/300=4.0 → 4.0^0.3=1.52 → capped 1.5 // ----------------------------------------------------------------------- { const { data } = buildData(100, SERIES_60, 2016); const makeCase = (chartType: string, tag: string): TestCase => ({ title: `${tag}: 100 dates × 60 series (6000 pts)`, description: '60 series — Y should hit maxStretch cap', tags: ['temporal', 'quantitative', 'color', 'stretch-test'], chartType, data, fields: [makeField('Date'), makeField('Value'), makeField('Series')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Series: { type: Type.String, semanticType: 'Category', levels: SERIES_60 }, }, encodingMap: { x: makeEncodingItem('Date'), y: makeEncodingItem('Value'), color: makeEncodingItem('Series') }, }); tests.push(makeCase('Line Chart', 'Line E')); tests.push(makeCase('Area Chart', 'Area E')); } // ----------------------------------------------------------------------- // Boundary: Very few X (5 dates) × 2 series — should not stretch at all // ----------------------------------------------------------------------- { const { data } = buildData(5, ['Actual', 'Forecast'], 2024); tests.push({ title: 'Line boundary: 5 dates × 2 series (no stretch)', description: 'Minimal data — no stretch expected', tags: ['temporal', 'quantitative', 'color', 'stretch-test'], chartType: 'Line Chart', data, fields: [makeField('Date'), makeField('Value'), makeField('Series')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Series: { type: Type.String, semanticType: 'Category', levels: ['Actual', 'Forecast'] }, }, encodingMap: { x: makeEncodingItem('Date'), y: makeEncodingItem('Value'), color: makeEncodingItem('Series') }, }); } // ----------------------------------------------------------------------- // Boundary: 50 dates × 1 series — single line, moderate X stretch // ----------------------------------------------------------------------- { const dates = genDates(50, 2022); const vals = walk(50, 200, 20); const data = dates.map((d, i) => ({ Date: d, Value: vals[i] })); tests.push({ title: 'Line boundary: 50 dates × 1 series (single line)', description: 'Single series — X stretch only, no Y stretch', tags: ['temporal', 'quantitative', 'stretch-test'], chartType: 'Line Chart', data, fields: [makeField('Date'), makeField('Value')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Date'), y: makeEncodingItem('Value') }, }); } // ----------------------------------------------------------------------- // Boundary: 100 dates × 8 series — the screenshot case // ----------------------------------------------------------------------- { const { data } = buildData(100, ['Auto', 'Books', 'Clothing', 'Electronics', 'Food', 'Garden', 'Home', 'Sports'], 2015); tests.push({ title: 'Line reference: 100 dates × 8 series (800 pts)', description: 'The original screenshot case — should stretch X clearly more than Y', tags: ['temporal', 'quantitative', 'color', 'stretch-test'], chartType: 'Line Chart', data, fields: [makeField('Date'), makeField('Value'), makeField('Series')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Series: { type: Type.String, semanticType: 'Category', levels: ['Auto', 'Books', 'Clothing', 'Electronics', 'Food', 'Garden', 'Home', 'Sports'] }, }, encodingMap: { x: makeEncodingItem('Date'), y: makeEncodingItem('Value'), color: makeEncodingItem('Series') }, }); } // ----------------------------------------------------------------------- // Vertical (axes-flipped): Y=temporal, X=quantitative // Tests that seriesCountAxis:'auto' correctly resolves when flipped. // In 2D path, auto → Y for standard; when flipped the positional axis // is Y (dates) and the series overlap is on X (values). // ----------------------------------------------------------------------- // Vertical Line: 100 dates × 8 series { const { data } = buildData(100, ['Auto', 'Books', 'Clothing', 'Electronics', 'Food', 'Garden', 'Home', 'Sports'], 2017); tests.push({ title: 'Vertical Line: 100 dates × 8 series', description: 'Axes flipped — Y=dates, X=values. Series overlap on X axis.', tags: ['temporal', 'quantitative', 'color', 'stretch-test', 'vertical'], chartType: 'Line Chart', data, fields: [makeField('Date'), makeField('Value'), makeField('Series')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Series: { type: Type.String, semanticType: 'Category', levels: ['Auto', 'Books', 'Clothing', 'Electronics', 'Food', 'Garden', 'Home', 'Sports'] }, }, encodingMap: { y: makeEncodingItem('Date'), x: makeEncodingItem('Value'), color: makeEncodingItem('Series') }, }); } // Vertical Line: 200 dates × 20 series (dense spaghetti, flipped) { const { data } = buildData(200, SERIES_20, 2012); tests.push({ title: 'Vertical Line: 200 dates × 20 series', description: 'Dense vertical spaghetti — Y should stretch (positional dates), X mild (series)', tags: ['temporal', 'quantitative', 'color', 'stretch-test', 'vertical'], chartType: 'Line Chart', data, fields: [makeField('Date'), makeField('Value'), makeField('Series')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Series: { type: Type.String, semanticType: 'Category', levels: SERIES_20 }, }, encodingMap: { y: makeEncodingItem('Date'), x: makeEncodingItem('Value'), color: makeEncodingItem('Series') }, }); } // Vertical Area: 100 dates × 40 series (stacked, flipped) { const { data } = buildData(100, SERIES_40, 2019); tests.push({ title: 'Vertical Area: 100 dates × 40 series', description: 'Vertical stacked area with 40 series — both axes should stretch', tags: ['temporal', 'quantitative', 'color', 'stretch-test', 'vertical'], chartType: 'Area Chart', data, fields: [makeField('Date'), makeField('Value'), makeField('Series')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Series: { type: Type.String, semanticType: 'Category', levels: SERIES_40 }, }, encodingMap: { y: makeEncodingItem('Date'), x: makeEncodingItem('Value'), color: makeEncodingItem('Series') }, }); } // Vertical Line: 12 dates × 60 series (extreme, flipped) { const { data } = buildData(12, SERIES_60, 2024); tests.push({ title: 'Vertical Line: 12 dates × 60 series', description: 'Extreme series count vertical — X (series axis) should hit cap', tags: ['temporal', 'quantitative', 'color', 'stretch-test', 'vertical'], chartType: 'Line Chart', data, fields: [makeField('Date'), makeField('Value'), makeField('Series')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Series: { type: Type.String, semanticType: 'Category', levels: SERIES_60 }, }, encodingMap: { y: makeEncodingItem('Date'), x: makeEncodingItem('Value'), color: makeEncodingItem('Series') }, }); } return tests; } ================================================ FILE: src/lib/agents-chart/test-data/line-area-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { Type } from '../../../data/types'; import { TestCase, makeField, makeEncodingItem } from './types'; import { seededRandom, genDates, genYears, genMonths, genCategories } from './generators'; // Line Chart tests have been moved to line-tests.ts (matrix-driven). // Area Chart & Streamgraph tests have been moved to area-tests.ts (matrix-driven). // ------ Dotted Line Chart ------ export function genDottedLineTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(650); { const months = genMonths(12); const data = months.map(m => ({ Month: m, Temperature: Math.round(10 + rand() * 25) })); tests.push({ title: 'Ordinal × Quant (dotted line)', description: 'Monthly temperatures with point markers', tags: ['ordinal', 'quantitative', 'small'], chartType: 'Dotted Line Chart', data, fields: [makeField('Month'), makeField('Temperature')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: months }, Temperature: { type: Type.Number, semanticType: 'Temperature', levels: [] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Temperature') }, }); } return tests; } // ------ Bump Chart ------ export function genBumpChartTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(700); // 1. Classic bump chart: rank over rounds (intended use) { const teams = ['Team A', 'Team B', 'Team C', 'Team D', 'Team E']; const rounds = ['Round 1', 'Round 2', 'Round 3', 'Round 4', 'Round 5', 'Round 6']; const data: any[] = []; for (const r of rounds) { const shuffled = [...teams].sort(() => rand() - 0.5); shuffled.forEach((t, i) => { data.push({ Round: r, Team: t, Rank: i + 1 }); }); } tests.push({ title: 'Ordinal × Rank + Color (classic bump)', description: '6 rounds × 5 teams — rank changes over rounds', tags: ['ordinal', 'quantitative', 'color', 'small'], chartType: 'Bump Chart', data, fields: [makeField('Round'), makeField('Rank'), makeField('Team')], metadata: { Round: { type: Type.String, semanticType: 'Category', levels: rounds }, Rank: { type: Type.Number, semanticType: 'Rank', levels: [] }, Team: { type: Type.String, semanticType: 'Team', levels: teams }, }, encodingMap: { x: makeEncodingItem('Round'), y: makeEncodingItem('Rank'), color: makeEncodingItem('Team') }, }); } // 2. Temporal bump: rank over years (intended use) { const years = genYears(10, 2015); const countries = ['USA', 'China', 'Germany', 'Japan', 'India']; const data: any[] = []; for (const y of years) { const shuffled = [...countries].sort(() => rand() - 0.5); shuffled.forEach((c, i) => { data.push({ Year: y, Country: c, Rank: i + 1 }); }); } tests.push({ title: 'Temporal × Rank + Color (yearly ranking)', description: '10 years × 5 countries — GDP ranking over time', tags: ['temporal', 'quantitative', 'color', 'medium'], chartType: 'Bump Chart', data, fields: [makeField('Year'), makeField('Country'), makeField('Rank')], metadata: { Year: { type: Type.Number, semanticType: 'Year', levels: years }, Rank: { type: Type.Number, semanticType: 'Rank', levels: [] }, Country: { type: Type.String, semanticType: 'Country', levels: countries }, }, encodingMap: { x: makeEncodingItem('Year'), y: makeEncodingItem('Rank'), color: makeEncodingItem('Country') }, }); } // 3. Many series (potential clutter — stress test) { const months = genMonths(12); const players = genCategories('Player', 12); const data: any[] = []; for (const m of months) { const shuffled = [...players].sort(() => rand() - 0.5); shuffled.forEach((p, i) => { data.push({ Month: m, Player: p, Rank: i + 1 }); }); } tests.push({ title: 'Ordinal × Rank + Color (many series, 144 pts)', description: '12 months × 12 players — crowded bump chart', tags: ['ordinal', 'quantitative', 'color', 'large'], chartType: 'Bump Chart', data, fields: [makeField('Month'), makeField('Rank'), makeField('Player')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: months }, Rank: { type: Type.Number, semanticType: 'Rank', levels: [] }, Player: { type: Type.String, semanticType: 'Category', levels: players }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Rank'), color: makeEncodingItem('Player') }, }); } // 4. Score instead of rank — no "Rank" semantic type (edge case) { const dates = genDates(8, 2024); const brands = ['Brand A', 'Brand B', 'Brand C']; const data: any[] = []; for (const d of dates) for (const b of brands) { data.push({ Date: d, Brand: b, Score: Math.round(50 + rand() * 50) }); } tests.push({ title: 'Temporal × Quant + Color (score, no rank semantic)', description: '8 dates × 3 brands — score values without Rank semantic type', tags: ['temporal', 'quantitative', 'color', 'small'], chartType: 'Bump Chart', data, fields: [makeField('Date'), makeField('Score'), makeField('Brand')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Score: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Brand: { type: Type.String, semanticType: 'Category', levels: brands }, }, encodingMap: { x: makeEncodingItem('Date'), y: makeEncodingItem('Score'), color: makeEncodingItem('Brand') }, }); } // 5. No color — single series (degenerate case) { const rounds = ['Q1', 'Q2', 'Q3', 'Q4']; const data = rounds.map((r, i) => ({ Quarter: r, Rank: Math.ceil(1 + rand() * 5) })); tests.push({ title: 'Ordinal × Rank (single series, no color)', description: '4 quarters — bump chart without color encoding', tags: ['ordinal', 'quantitative', 'small'], chartType: 'Bump Chart', data, fields: [makeField('Quarter'), makeField('Rank')], metadata: { Quarter: { type: Type.String, semanticType: 'Category', levels: rounds }, Rank: { type: Type.Number, semanticType: 'Rank', levels: [] }, }, encodingMap: { x: makeEncodingItem('Quarter'), y: makeEncodingItem('Rank') }, }); } // 6. Two items only (minimal case) { const years = genYears(5, 2020); const items = ['Alpha', 'Beta']; const data: any[] = []; for (const y of years) { const flip = rand() > 0.5; data.push({ Year: y, Item: items[0], Rank: flip ? 1 : 2 }); data.push({ Year: y, Item: items[1], Rank: flip ? 2 : 1 }); } tests.push({ title: 'Temporal × Rank + Color (2 items only)', description: '5 years × 2 items — minimal bump chart', tags: ['temporal', 'quantitative', 'color', 'small'], chartType: 'Bump Chart', data, fields: [makeField('Year'), makeField('Rank'), makeField('Item')], metadata: { Year: { type: Type.Number, semanticType: 'Year', levels: years }, Rank: { type: Type.Number, semanticType: 'Rank', levels: [] }, Item: { type: Type.String, semanticType: 'Category', levels: items }, }, encodingMap: { x: makeEncodingItem('Year'), y: makeEncodingItem('Rank'), color: makeEncodingItem('Item') }, }); } return tests; } ================================================ FILE: src/lib/agents-chart/test-data/line-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { Type } from '../../../data/types'; import { TestCase, makeField, makeEncodingItem } from './types'; import { seededRandom, genDates, genCategories, genOrdinalLabels, ORDINAL_PREFIXES } from './generators'; // ============================================================================ // Line Chart Tests — Matrix-driven // // Each test is defined as a compact row in LINE_MATRIX. A generator // function converts matrix entries into full TestCase objects. // // Matrix dimensions: // x axis type: Q (quantitative), T (temporal), O (ordinal) // y axis type: same // color channel: — | N (nominal, multi-series) | Q (gradient) // n: total data points // sparse: ~20% random dropout // // Ordinal (O) is used for axes — line charts require a meaningful // sequential order. Nominal (N) is used for unordered color groups. // Purely nominal axes are excluded (lines imply sequence). // // Default test canvas: 300 × 300 px. // ============================================================================ type DimType = 'Q' | 'T' | 'N' | 'O'; interface LineMatrixEntry { x: DimType; y: DimType; n: number; // total data points color?: DimType; xCard?: number; yCard?: number; colorCard?: number; sparse?: boolean; desc?: string; extraTags?: string[]; } // ============================================================================ // THE MATRIX — one row per test case (23 tests) // // Note: O (ordinal) is used for categorical axes — line charts require // a meaningful sequential order. N (nominal) is used for color groups. // Purely nominal axis combinations are excluded because connecting // unordered categories with lines is visually misleading. // ============================================================================ const LINE_MATRIX: LineMatrixEntry[] = [ // ── T × Q (6 tests) — core time series ────────────────────────── { x: 'T', y: 'Q', n: 30, desc: 'Simple time series — 30 dates' }, { x: 'T', y: 'Q', n: 200, color: 'N', colorCard: 4, desc: '4 series × 50 dates — smooth random walks' }, { x: 'T', y: 'Q', n: 800, color: 'N', colorCard: 8, desc: '8 series × 100 dates — crowded' }, { x: 'T', y: 'Q', n: 4000, color: 'N', colorCard: 20, desc: '20 series spaghetti — stress', extraTags: ['stress'] }, { x: 'T', y: 'Q', n: 180, color: 'N', colorCard: 3, sparse: true, desc: '3 series × 60 dates, ~20% missing' }, { x: 'T', y: 'Q', n: 30, color: 'Q', desc: 'Continuous color gradient on time series' }, // ── O × Q (4 tests) — ordered categories on x ─────────────────── // Line charts with ordinal x make sense when categories have an // inherent sequence (e.g. stages, ranked items, ordered groups). { x: 'O', y: 'Q', n: 5, xCard: 5, desc: 'Ordinal line — 5 ordered categories' }, { x: 'O', y: 'Q', n: 48, xCard: 12, color: 'N', colorCard: 4, desc: '12 ordinal × 4 series' }, { x: 'O', y: 'Q', n: 30, xCard: 30, desc: '30 ordinal categories — label overflow', extraTags: ['overflow'] }, { x: 'O', y: 'Q', n: 5, xCard: 5, color: 'Q', desc: 'Ordinal + continuous color gradient' }, // ── Q × O (3 tests) — mirror ──────────────────────────────────── { x: 'Q', y: 'O', n: 5, yCard: 5, desc: 'Horizontal ordinal — 5 ordered cats on y' }, { x: 'Q', y: 'O', n: 48, yCard: 12, color: 'N', colorCard: 4, desc: 'Horizontal 12 ordinal × 4 series' }, { x: 'Q', y: 'O', n: 30, yCard: 30, desc: 'Horizontal 30 ordinal overflow', extraTags: ['overflow'] }, // ── Q × Q (3 tests) — quantitative x ──────────────────────────── { x: 'Q', y: 'Q', n: 30, desc: 'Quantitative x line — 30 pts' }, { x: 'Q', y: 'Q', n: 150, color: 'N', colorCard: 3, desc: '3 parametric curves × 50 pts' }, { x: 'Q', y: 'Q', n: 200, desc: 'Dense single curve — 200 pts' }, // ── T × T ──────────────────────────────────────────────────────── // Excluded: T×T date-pair data (start vs end date) doesn't suit line // charts — each row is an independent event, not a sequential series. // Lines connect points in data order producing random zig-zags. // T×T pairs are better served by scatter plots or dumbbell charts. // Excluded: N×N, T×N, N×T — purely nominal axes don't suit line charts. // Lines imply sequence/progression; connecting unordered categories is misleading. ]; // ============================================================================ // Generator internals // ============================================================================ interface LineCh { role: 'x' | 'y' | 'color'; dimType: DimType; fieldName: string; card?: number; levels?: string[]; dates?: string[]; } const LINE_NAMES: Record<string, Record<DimType, string>> = { x: { Q: 'X', T: 'Date', N: 'Series', O: 'Stage' }, y: { Q: 'Value', T: 'EndDate', N: 'Group', O: 'Step' }, color: { Q: 'ColorVal', T: 'Timestamp', N: 'Series', O: 'Level' }, }; const LINE_FALLBACKS: Record<DimType, string[]> = { Q: ['X', 'Value', 'Measure', 'Score'], T: ['Date', 'EndDate', 'StartDate', 'Timestamp'], N: ['Series', 'Group', 'Category', 'Type'], O: ['Stage', 'Step', 'Phase', 'Level', 'Round'], }; const LINE_CAT_POOLS = ['Category', 'Country', 'Department', 'Product', 'Company']; const LINE_T_STARTS = [2020, 2023, 2019, 2022]; function buildLineChannels(entry: LineMatrixEntry, nPerSeries: number): LineCh[] { const used = new Set<string>(); const channels: LineCh[] = []; let tIdx = 0; let cIdx = 0; let oIdx = 0; function pickName(dim: DimType, role: string): string { const primary = LINE_NAMES[role]?.[dim]; if (primary && !used.has(primary)) { used.add(primary); return primary; } for (const n of LINE_FALLBACKS[dim]) { if (!used.has(n)) { used.add(n); return n; } } return `${role}_field`; } const specs: { role: 'x' | 'y' | 'color'; dim: DimType; card?: number }[] = [ { role: 'x', dim: entry.x, card: entry.xCard }, { role: 'y', dim: entry.y, card: entry.yCard }, ]; if (entry.color) specs.push({ role: 'color', dim: entry.color, card: entry.colorCard }); for (const { role, dim, card } of specs) { const ch: LineCh = { role, dimType: dim, fieldName: pickName(dim, role) }; if (dim === 'N') { const c = card || 3; ch.card = c; ch.levels = genCategories(LINE_CAT_POOLS[cIdx % LINE_CAT_POOLS.length], c); cIdx++; } if (dim === 'O') { const c = card || 5; ch.card = c; ch.levels = genOrdinalLabels(ORDINAL_PREFIXES[oIdx % ORDINAL_PREFIXES.length], c); oIdx++; } if (dim === 'T') { ch.dates = genDates(nPerSeries, LINE_T_STARTS[tIdx % LINE_T_STARTS.length]); tIdx++; } channels.push(ch); } return channels; } // --------------------------------------------------------------------------- // Data generation // --------------------------------------------------------------------------- /** Smooth random-walk series (momentum + noise). */ function genLineWalk(n: number, base: number, volatility: number, rand: () => number): number[] { const v: number[] = [base]; let m = 0; for (let i = 1; i < n; i++) { m = 0.7 * m + (rand() - 0.5) * volatility; v.push(Math.round(Math.max(0, v[i - 1] + m))); } return v; } function genLineSeriesData( entry: LineMatrixEntry, channels: LineCh[], rand: () => number, ): Record<string, any>[] { const xCh = channels.find(c => c.role === 'x')!; const yCh = channels.find(c => c.role === 'y')!; const colorCh = channels.find(c => c.role === 'color'); const nSeries = (colorCh?.dimType === 'N' ? (entry.colorCard || 3) : 1); const nPerSeries = Math.max(1, Math.floor(entry.n / nSeries)); // Shared x-positions let xPositions: any[]; if (xCh.dimType === 'T') { xPositions = genDates(nPerSeries, 2020); } else if (xCh.dimType === 'O') { xPositions = xCh.levels!; } else { // Q xPositions = Array.from({ length: nPerSeries }, (_, i) => Math.round(i * 100 / Math.max(1, nPerSeries - 1) * 10) / 10); } const data: Record<string, any>[] = []; for (let s = 0; s < nSeries; s++) { const base = 50 + Math.round(rand() * 200); const vol = 10 + rand() * 30; // Generate y-values let yValues: any[]; if (yCh.dimType === 'Q') { yValues = genLineWalk(xPositions.length, base, vol, rand); } else if (yCh.dimType === 'T') { yValues = genDates(xPositions.length, 2023 + s); } else { // O yValues = xPositions.map((_, i) => yCh.levels![i % yCh.levels!.length]); } for (let i = 0; i < xPositions.length; i++) { if (entry.sparse && rand() < 0.2) continue; const row: Record<string, any> = { [xCh.fieldName]: xPositions[i], [yCh.fieldName]: yValues[i], }; if (colorCh) { if (colorCh.dimType === 'N') { row[colorCh.fieldName] = colorCh.levels![s]; } else if (colorCh.dimType === 'Q') { row[colorCh.fieldName] = Math.round(rand() * 100) / 10; } } data.push(row); } } return data; } function genLineGridData(channels: LineCh[], rand: () => number): Record<string, any>[] { const xCh = channels.find(c => c.role === 'x')!; const yCh = channels.find(c => c.role === 'y')!; const colorCh = channels.find(c => c.role === 'color'); const data: Record<string, any>[] = []; for (const xVal of xCh.levels!) { for (const yVal of yCh.levels!) { if (rand() > 0.3) { const row: Record<string, any> = { [xCh.fieldName]: xVal, [yCh.fieldName]: yVal }; if (colorCh?.dimType === 'N') row[colorCh.fieldName] = colorCh.levels![Math.floor(rand() * colorCh.levels!.length)]; data.push(row); } } } return data; } // --------------------------------------------------------------------------- // Title & tags // --------------------------------------------------------------------------- function buildLineTitle(entry: LineMatrixEntry): string { const xLabel = entry.x === 'O' && entry.xCard ? `O(${entry.xCard})` : entry.x; const yLabel = entry.y === 'O' && entry.yCard ? `O(${entry.yCard})` : entry.y; const parts = [`${xLabel}×${yLabel}`]; if (entry.color) { parts.push(`+color(${entry.color === 'N' ? `N,${entry.colorCard || 3}` : entry.color})`); } if (entry.sparse) parts.push('sparse'); if (entry.n === 0) parts.push('grid'); else parts.push(`(${entry.n} pts)`); return parts.join(' '); } function buildLineTags(entry: LineMatrixEntry, dataLen: number): string[] { const tags: string[] = []; const dims = new Set<DimType>([entry.x, entry.y]); if (entry.color) dims.add(entry.color); if (dims.has('Q')) tags.push('quantitative'); if (dims.has('T')) tags.push('temporal'); if (dims.has('N')) tags.push('nominal'); if (dims.has('O')) tags.push('ordinal'); if (entry.color) tags.push('color'); if (entry.color === 'Q') tags.push('continuous-color'); if (entry.sparse) tags.push('sparse'); const n = dataLen; if (n <= 25) tags.push('small'); else if (n <= 100) tags.push('medium'); else { tags.push('large'); if (n > 500) tags.push('scaling'); } if (entry.extraTags) tags.push(...entry.extraTags); return [...new Set(tags)]; } // --------------------------------------------------------------------------- // Matrix entry → TestCase // --------------------------------------------------------------------------- function lineMatrixToTestCase(entry: LineMatrixEntry, rand: () => number): TestCase { const nSeries = entry.colorCard || 1; const effectiveN = entry.n || (entry.xCard || 5) * (entry.yCard || 5); const nPerSeries = Math.max(1, Math.floor(effectiveN / nSeries)); const channels = buildLineChannels(entry, nPerSeries); const isGrid = entry.x === 'O' && entry.y === 'O' && entry.n === 0; let data: Record<string, any>[]; if (isGrid) { data = genLineGridData(channels, rand); } else { data = genLineSeriesData(entry, channels, rand); } const typeMap: Record<DimType, Type> = { Q: Type.Number, T: Type.Date, N: Type.String, O: Type.String }; const semMap: Record<DimType, string> = { Q: 'Quantity', T: 'Date', N: 'Category', O: 'Category' }; const fields = channels.map(ch => makeField(ch.fieldName)); const metadata: Record<string, { type: Type; semanticType: string; levels: any[] }> = {}; const encodingMap: Partial<Record<string, any>> = {}; for (const ch of channels) { metadata[ch.fieldName] = { type: typeMap[ch.dimType], semanticType: semMap[ch.dimType], levels: ch.levels || [], }; encodingMap[ch.role] = makeEncodingItem(ch.fieldName); } return { title: buildLineTitle(entry), description: entry.desc || buildLineTitle(entry), tags: buildLineTags(entry, data.length), chartType: 'Line Chart', data, fields, metadata, encodingMap, }; } // ============================================================================ // Forecast test cases — demonstrate strokeDash for actual vs forecast // ============================================================================ function genForecastTestSingleSeries(rand: () => number): TestCase { // Single series: 12 months actual + 4 months forecast (continuous dates) const allDates = genDates(16, 2024); // 16 continuous dates const actualDates = allDates.slice(0, 12); const forecastDates = allDates.slice(12); const data: Record<string, any>[] = []; let val = 100; // Actual data for (const d of actualDates) { val = Math.round(val + (rand() - 0.4) * 15); data.push({ Date: d, Revenue: val, Type: 'actual' }); } // Duplicate last actual point as first forecast point (for line connection) const lastActual = data[data.length - 1]; data.push({ Date: lastActual.Date, Revenue: lastActual.Revenue, Type: 'forecast' }); // Forecast data (trending upward) for (const d of forecastDates) { val = Math.round(val + rand() * 12 + 3); data.push({ Date: d, Revenue: val, Type: 'forecast' }); } return { title: 'Forecast — single series, actual vs forecast', description: 'Single time series with actual (solid) vs forecast (dashed) using strokeDash', tags: ['temporal', 'forecast', 'strokeDash', 'medium'], chartType: 'Line Chart', data, fields: [makeField('Date'), makeField('Revenue'), makeField('Type')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Revenue: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Type: { type: Type.String, semanticType: 'Category', levels: ['actual', 'forecast'] }, }, encodingMap: { x: makeEncodingItem('Date'), y: makeEncodingItem('Revenue'), strokeDash: makeEncodingItem('Type'), }, }; } function genForecastTestMultiSeries(rand: () => number): TestCase { // 3 product series: 10 actual + 3 forecast (continuous dates) const products = ['Widget A', 'Widget B', 'Widget C']; const allDates = genDates(13, 2024); // 13 continuous dates const actualDates = allDates.slice(0, 10); const forecastDates = allDates.slice(10); const data: Record<string, any>[] = []; for (const product of products) { let val = 50 + Math.round(rand() * 100); const vol = 5 + rand() * 15; // Actual data for (const d of actualDates) { val = Math.round(Math.max(10, val + (rand() - 0.45) * vol)); data.push({ Date: d, Sales: val, Product: product, Type: 'actual' }); } // Duplicate last actual point as first forecast (for line connection) const lastActual = data[data.length - 1]; data.push({ Date: lastActual.Date, Sales: lastActual.Sales, Product: product, Type: 'forecast' }); // Forecast data for (const d of forecastDates) { val = Math.round(val + rand() * 10 + 2); data.push({ Date: d, Sales: val, Product: product, Type: 'forecast' }); } } return { title: 'Forecast — 3 series, color + strokeDash', description: '3 product series with color for series grouping and strokeDash for actual vs forecast', tags: ['temporal', 'nominal', 'forecast', 'strokeDash', 'color', 'medium'], chartType: 'Line Chart', data, fields: [makeField('Date'), makeField('Sales'), makeField('Product'), makeField('Type')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Sales: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Product: { type: Type.String, semanticType: 'Category', levels: products }, Type: { type: Type.String, semanticType: 'Category', levels: ['actual', 'forecast'] }, }, encodingMap: { x: makeEncodingItem('Date'), y: makeEncodingItem('Sales'), color: makeEncodingItem('Product'), strokeDash: makeEncodingItem('Type'), }, }; } // ============================================================================ // Public export // ============================================================================ export function genLineTests(): TestCase[] { const rand = seededRandom(600); const matrixTests = LINE_MATRIX.map(entry => lineMatrixToTestCase(entry, rand)); // Forecast tests const forecastRand = seededRandom(700); const forecastTests = [ genForecastTestSingleSeries(forecastRand), genForecastTestMultiSeries(forecastRand), ]; return [...matrixTests, ...forecastTests]; } ================================================ FILE: src/lib/agents-chart/test-data/scatter-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { Type } from '../../../data/types'; import { TestCase, makeField, makeEncodingItem } from './types'; import { seededRandom, genDates, genCategories, genRandomNames } from './generators'; // ============================================================================ // Scatter Plot Tests — Matrix-driven // // Each test is defined as a compact row in SCATTER_MATRIX. A generator // function converts matrix entries into full TestCase objects. // // Matrix dimensions: // Axis types: Q (quantitative), T (temporal), N (nominal) // xy combos: 9 = [Q,T,N] × [Q,T,N] // 3rd channel: none | color(Q/T/N) | size(Q/N) | both // Density: small (≤25 pts), medium (26–100), large (>100) // // Note: Scatter plots don't distinguish nominal from ordinal visually — // all categorical channels use N (nominal). The one exception is size='N' // which generates ranked labels (Low/Medium/High) for ordinal size levels. // // Default test canvas: 300 × 300 px. // ============================================================================ type DimType = 'Q' | 'T' | 'N'; /** A single row in the scatter test matrix. */ interface MatrixEntry { x: DimType; y: DimType; n: number; // data points (0 → derive from xCard × yCard for C×C grid) color?: DimType; size?: DimType; xCard?: number; // C cardinality for x yCard?: number; // C cardinality for y colorCard?: number; // C cardinality for color sizeCard?: number; // C/ordinal cardinality for size hugeRange?: boolean; // size field 1K–1B desc?: string; // override auto-generated description extraTags?: string[]; } // ============================================================================ // THE MATRIX — one row per test case (25 tests) // ============================================================================ const SCATTER_MATRIX: MatrixEntry[] = [ // ── Q × Q (15 tests) ───────────────────────────────────────────── // xy only { x: 'Q', y: 'Q', n: 20, desc: 'Baseline — two quantitative axes, no extra encodings' }, // + color { x: 'Q', y: 'Q', n: 20, color: 'N', colorCard: 3 }, { x: 'Q', y: 'Q', n: 20, color: 'Q', desc: 'Continuous color — expect gradient legend' }, { x: 'Q', y: 'Q', n: 30, color: 'T', desc: 'Temporal color gradient — shows progression over time' }, // + size { x: 'Q', y: 'Q', n: 50, size: 'Q' }, { x: 'Q', y: 'Q', n: 20, size: 'N', sizeCard: 4, desc: 'Ordinal size — 4 discrete priority levels' }, // + size + color { x: 'Q', y: 'Q', n: 15, size: 'Q', color: 'N', colorCard: 3, desc: 'Gapminder-style: bubbles + 3 color groups' }, { x: 'Q', y: 'Q', n: 30, size: 'Q', color: 'Q', desc: 'Both size and color are continuous — 4D encoding' }, { x: 'Q', y: 'Q', n: 20, size: 'Q', color: 'N', colorCard: 20, hugeRange: true, desc: 'Size 1K–1B — tests sqrt scale discrimination' }, // density / scaling { x: 'Q', y: 'Q', n: 100, desc: 'Moderate density — point-size reduction' }, { x: 'Q', y: 'Q', n: 500, desc: 'High density — aggressive point-size reduction' }, { x: 'Q', y: 'Q', n: 200, color: 'N', colorCard: 20, desc: 'Dense scatter with 20 nominal color groups' }, { x: 'Q', y: 'Q', n: 100, color: 'N', colorCard: 50, desc: '50 colors — tests legend overflow', extraTags: ['overflow'] }, // bubble scaling { x: 'Q', y: 'Q', n: 10, size: 'Q', desc: 'Sparse — large bubbles expected', extraTags: ['scaling'] }, { x: 'Q', y: 'Q', n: 200, size: 'Q', desc: 'Dense — bubbles should shrink significantly', extraTags: ['scaling'] }, // ── N × Q (4 tests) ────────────────────────────────────────────── { x: 'N', y: 'Q', n: 25, xCard: 5, color: 'Q', desc: 'Strip + continuous color gradient' }, { x: 'N', y: 'Q', n: 25, xCard: 5, size: 'Q', desc: 'Bubble strip — size encodes a measure' }, { x: 'N', y: 'Q', n: 30, xCard: 2, desc: 'Binary category strip (e.g., Yes/No)', extraTags: ['edge-case'] }, { x: 'N', y: 'Q', n: 60, xCard: 60, desc: '60 categories — heavy discrete-axis overflow', extraTags: ['overflow'] }, // ── Q × N (3 tests) — mirrors N×Q with flipped orientation ────── { x: 'Q', y: 'N', n: 25, yCard: 5, color: 'Q', desc: 'Horizontal strip + continuous color' }, { x: 'Q', y: 'N', n: 25, yCard: 5, size: 'Q', desc: 'Horizontal bubble strip' }, { x: 'Q', y: 'N', n: 60, yCard: 60, desc: 'Horizontal 60-cat overflow on y', extraTags: ['overflow'] }, // ── N × N (3 tests) ────────────────────────────────────────────── { x: 'N', y: 'N', n: 0, xCard: 5, yCard: 6, size: 'Q', desc: 'Bubble grid — partial grid occupancy' }, { x: 'N', y: 'N', n: 0, xCard: 5, yCard: 4, color: 'Q', desc: 'Heatmap-like scatter — continuous color on grid' }, { x: 'N', y: 'N', n: 0, xCard: 15, yCard: 12, size: 'Q', desc: 'Large grid — high-cardinality overflow on both axes', extraTags: ['overflow', 'scaling'] }, ]; // ============================================================================ // Generator internals // ============================================================================ interface ChannelInfo { role: 'x' | 'y' | 'color' | 'size'; dimType: DimType; fieldName: string; card?: number; levels?: string[]; dates?: string[]; } /** Preferred field names per (role, dimType). */ const PREFERRED_NAMES: Record<string, Record<DimType, string>> = { x: { Q: 'X', T: 'Date', N: 'Category' }, y: { Q: 'Y', T: 'EndDate', N: 'Group' }, color: { Q: 'ColorVal', T: 'Timestamp', N: 'Segment' }, size: { Q: 'Size', T: 'Period', N: 'Level' }, }; /** Fallback pools when the preferred name is already taken. */ const FALLBACK_NAMES: Record<DimType, string[]> = { Q: ['X', 'Y', 'Measure', 'Value', 'Score'], T: ['Date', 'EndDate', 'StartDate', 'Timestamp'], N: ['Category', 'Group', 'Segment', 'Level', 'Type'], }; /** Semantic-type pool so multiple C channels get distinct category sets. */ const CAT_SEMANTICS = ['Category', 'Country', 'Department', 'Product', 'Company']; /** Start years for temporal channels (staggered to avoid overlap in T×T). */ const T_START_YEARS = [2020, 2023, 2019, 2022]; // --------------------------------------------------------------------------- // Channel & data generation // --------------------------------------------------------------------------- function buildChannels(entry: MatrixEntry): ChannelInfo[] { const used = new Set<string>(); const channels: ChannelInfo[] = []; let tIdx = 0; // temporal channel counter let cIdx = 0; // categorical channel counter let cSeed = 500; function pickName(dim: DimType, role: string): string { const primary = PREFERRED_NAMES[role]?.[dim]; if (primary && !used.has(primary)) { used.add(primary); return primary; } for (const n of FALLBACK_NAMES[dim]) { if (!used.has(n)) { used.add(n); return n; } } return `${role}_field`; } const specs: { role: 'x' | 'y' | 'color' | 'size'; dim: DimType; card?: number }[] = [ { role: 'x', dim: entry.x, card: entry.xCard }, { role: 'y', dim: entry.y, card: entry.yCard }, ]; if (entry.color) specs.push({ role: 'color', dim: entry.color, card: entry.colorCard }); if (entry.size) specs.push({ role: 'size', dim: entry.size, card: entry.sizeCard }); const effectiveN = entry.n || (entry.xCard || 5) * (entry.yCard || 5); for (const { role, dim, card } of specs) { const ch: ChannelInfo = { role, dimType: dim, fieldName: pickName(dim, role) }; if (dim === 'N') { const c = card || (role === 'size' ? 4 : role === 'color' ? 3 : 5); ch.card = c; if (role === 'size') { ch.levels = ['Low', 'Medium', 'High', 'Critical', 'Extreme'].slice(0, c); } else if (c > 30) { ch.levels = genRandomNames(c, cSeed); cSeed += 100; } else { ch.levels = genCategories(CAT_SEMANTICS[cIdx % CAT_SEMANTICS.length], c); } cIdx++; } if (dim === 'T') { ch.dates = genDates(effectiveN, T_START_YEARS[tIdx % T_START_YEARS.length]); tIdx++; } channels.push(ch); } return channels; } /** Generate a single field value for row `i`. */ function genValue( ch: ChannelInfo, i: number, entry: MatrixEntry, rand: () => number, ): any { switch (ch.dimType) { case 'Q': { if (entry.hugeRange && ch.role === 'size') return Math.round(1e3 + rand() * 1e9); return Math.round(rand() * 1000) / 10; } case 'T': return ch.dates![i % ch.dates!.length]; case 'N': return ch.levels![i % ch.levels!.length]; } } /** Generate data for C×C grid mode (cross-product, ~70 % occupancy). */ function genGridData( channels: ChannelInfo[], rand: () => number, ): Record<string, any>[] { const xCh = channels.find(c => c.role === 'x')!; const yCh = channels.find(c => c.role === 'y')!; const extras = channels.filter(c => c.role !== 'x' && c.role !== 'y'); const data: Record<string, any>[] = []; for (const xVal of xCh.levels!) { for (const yVal of yCh.levels!) { if (rand() > 0.3) { const row: Record<string, any> = { [xCh.fieldName]: xVal, [yCh.fieldName]: yVal, }; for (const ch of extras) { if (ch.dimType === 'Q') row[ch.fieldName] = Math.round(100 + rand() * 900); else if (ch.dimType === 'N') row[ch.fieldName] = ch.levels![Math.floor(rand() * ch.levels!.length)]; } data.push(row); } } } return data; } // --------------------------------------------------------------------------- // Title & tags // --------------------------------------------------------------------------- function buildTitle(entry: MatrixEntry): string { const xLabel = entry.x === 'N' && entry.xCard ? `N(${entry.xCard})` : entry.x; const yLabel = entry.y === 'N' && entry.yCard ? `N(${entry.yCard})` : entry.y; const parts = [`${xLabel}×${yLabel}`]; const extras: string[] = []; if (entry.color) { extras.push(`color(${entry.color === 'N' ? `N,${entry.colorCard || 3}` : entry.color})`); } if (entry.size) { extras.push(`size(${entry.size === 'N' ? `N,${entry.sizeCard || 4}` : entry.size})`); } if (extras.length) parts.push('+' + extras.join('+')); if (entry.hugeRange) parts.push('hugeRange'); if (entry.n === 0) parts.push('grid'); else parts.push(`(${entry.n} ${entry.n === 1 ? 'pt' : 'pts'})`); return parts.join(' '); } function buildTags(entry: MatrixEntry, dataLen: number): string[] { const tags: string[] = []; // Dimension types present const dims = new Set<DimType>([entry.x, entry.y]); if (entry.color) dims.add(entry.color); if (entry.size) dims.add(entry.size); if (dims.has('Q')) tags.push('quantitative'); if (dims.has('T')) tags.push('temporal'); if (dims.has('N')) tags.push('nominal'); // Channel presence if (entry.color) tags.push('color'); if (entry.size) tags.push('size'); if (entry.color === 'Q' || entry.color === 'T') tags.push('continuous-color'); // Scale const n = dataLen; if (n <= 25) tags.push('small'); else if (n <= 100) tags.push('medium'); else { tags.push('large'); tags.push('scaling'); } if (entry.extraTags) tags.push(...entry.extraTags); return [...new Set(tags)]; } // --------------------------------------------------------------------------- // Matrix entry → TestCase // --------------------------------------------------------------------------- function matrixToTestCase(entry: MatrixEntry, rand: () => number): TestCase { const channels = buildChannels(entry); const isGrid = entry.x === 'N' && entry.y === 'N' && entry.n === 0; // Generate data let data: Record<string, any>[]; if (isGrid) { data = genGridData(channels, rand); } else { data = Array.from({ length: entry.n }, (_, i) => { const row: Record<string, any> = {}; for (const ch of channels) row[ch.fieldName] = genValue(ch, i, entry, rand); return row; }); } // Build fields, metadata, encodingMap const fields = channels.map(ch => makeField(ch.fieldName)); const typeMap: Record<DimType, Type> = { Q: Type.Number, T: Type.Date, N: Type.String }; const semMap: Record<DimType, string> = { Q: 'Quantity', T: 'Date', N: 'Category' }; const metadata: Record<string, { type: Type; semanticType: string; levels: any[] }> = {}; const encodingMap: Partial<Record<string, any>> = {}; for (const ch of channels) { let semanticType = semMap[ch.dimType]; if (ch.role === 'size' && ch.dimType === 'N') semanticType = 'Rank'; metadata[ch.fieldName] = { type: typeMap[ch.dimType], semanticType, levels: ch.levels || [], }; encodingMap[ch.role] = makeEncodingItem(ch.fieldName); } return { title: buildTitle(entry), description: entry.desc || buildTitle(entry), tags: buildTags(entry, data.length), chartType: 'Scatter Plot', data, fields, metadata, encodingMap, }; } // ============================================================================ // Public exports // ============================================================================ export function genScatterTests(): TestCase[] { const rand = seededRandom(42); return SCATTER_MATRIX.map(entry => matrixToTestCase(entry, rand)); } // ============================================================================ // Regression Tests (not matrix-driven) // ============================================================================ export function genRegressionTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(55); // 1. Basic regression (quant × quant) { const n = 40; const data = Array.from({ length: n }, () => { const x = 10 + rand() * 80; return { Hours: Math.round(x * 10) / 10, Score: Math.round(20 + x * 0.8 + (rand() - 0.5) * 30) }; }); tests.push({ title: 'Basic regression (40 pts)', description: 'Hours vs Score — simple linear trend', tags: ['quantitative', 'small'], chartType: 'Regression', data, fields: [makeField('Hours'), makeField('Score')], metadata: { Hours: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Score: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Hours'), y: makeEncodingItem('Score') }, }); } // 2. Regression with color (grouped lines) { const groups = ['Male', 'Female']; const data: any[] = []; for (const g of groups) { const offset = g === 'Male' ? 5 : -5; for (let i = 0; i < 30; i++) { const x = 10 + rand() * 80; data.push({ Experience: Math.round(x * 10) / 10, Salary: Math.round(30 + x * 0.6 + offset + (rand() - 0.5) * 20), Gender: g, }); } } tests.push({ title: 'Regression + Color (2 groups)', description: '60 points — separate regression per gender', tags: ['quantitative', 'color', 'medium'], chartType: 'Regression', data, fields: [makeField('Experience'), makeField('Salary'), makeField('Gender')], metadata: { Experience: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Salary: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Gender: { type: Type.String, semanticType: 'Category', levels: groups }, }, encodingMap: { x: makeEncodingItem('Experience'), y: makeEncodingItem('Salary'), color: makeEncodingItem('Gender') }, }); } // 3. Regression with 4 color groups { const regions = ['North', 'South', 'East', 'West']; const data: any[] = []; const offsets: Record<string, number> = { North: 10, South: -5, East: 3, West: -8 }; const slopes: Record<string, number> = { North: 0.7, South: 0.5, East: 0.9, West: 0.4 }; for (const r of regions) { for (let i = 0; i < 25; i++) { const x = 5 + rand() * 90; data.push({ Advertising: Math.round(x * 10) / 10, Revenue: Math.round(offsets[r] + x * slopes[r] + (rand() - 0.5) * 15), Region: r, }); } } tests.push({ title: 'Regression + Color (4 groups)', description: '100 points — separate regression per region with distinct slopes', tags: ['quantitative', 'color', 'medium'], chartType: 'Regression', data, fields: [makeField('Advertising'), makeField('Revenue'), makeField('Region')], metadata: { Advertising: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Revenue: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Region: { type: Type.String, semanticType: 'Category', levels: regions }, }, encodingMap: { x: makeEncodingItem('Advertising'), y: makeEncodingItem('Revenue'), color: makeEncodingItem('Region') }, }); } // 4. Regression with 6 color groups (many categories) { const subjects = ['Math', 'Science', 'English', 'History', 'Art', 'Music']; const data: any[] = []; const baseIntercepts = [15, 25, 20, 10, 30, 5]; const baseSlopes = [0.8, 0.6, 0.5, 0.7, 0.3, 0.9]; for (let si = 0; si < subjects.length; si++) { for (let i = 0; i < 20; i++) { const x = 10 + rand() * 80; data.push({ StudyHours: Math.round(x * 10) / 10, ExamScore: Math.round(baseIntercepts[si] + x * baseSlopes[si] + (rand() - 0.5) * 18), Subject: subjects[si], }); } } tests.push({ title: 'Regression + Color (6 groups)', description: '120 points — separate regression per subject', tags: ['quantitative', 'color', 'large'], chartType: 'Regression', data, fields: [makeField('StudyHours'), makeField('ExamScore'), makeField('Subject')], metadata: { StudyHours: { type: Type.Number, semanticType: 'Quantity', levels: [] }, ExamScore: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Subject: { type: Type.String, semanticType: 'Category', levels: subjects }, }, encodingMap: { x: makeEncodingItem('StudyHours'), y: makeEncodingItem('ExamScore'), color: makeEncodingItem('Subject') }, }); } // 5. Regression with 3 color groups + size encoding { const tiers = ['Budget', 'Mid-Range', 'Premium']; const data: any[] = []; const tierOffsets = [5, 20, 40]; const tierSlopes = [0.3, 0.5, 0.8]; for (let ti = 0; ti < tiers.length; ti++) { for (let i = 0; i < 30; i++) { const x = 10 + rand() * 85; const weight = Math.round(50 + rand() * 150); data.push({ EngineSize: Math.round(x * 10) / 10, Horsepower: Math.round(tierOffsets[ti] + x * tierSlopes[ti] + (rand() - 0.5) * 12), Tier: tiers[ti], Weight: weight, }); } } tests.push({ title: 'Regression + Color (3 groups) + Size', description: '90 points — regression per tier with weight as size', tags: ['quantitative', 'color', 'size', 'medium'], chartType: 'Regression', data, fields: [makeField('EngineSize'), makeField('Horsepower'), makeField('Tier'), makeField('Weight')], metadata: { EngineSize: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Horsepower: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Tier: { type: Type.String, semanticType: 'Category', levels: tiers }, Weight: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('EngineSize'), y: makeEncodingItem('Horsepower'), color: makeEncodingItem('Tier'), size: makeEncodingItem('Weight'), }, }); } // 6. Logarithmic regression { const n = 50; const data = Array.from({ length: n }, () => { const x = 1 + rand() * 99; // avoid log(0) return { Investment: Math.round(x * 10) / 10, Returns: Math.round(10 * Math.log(x) + 5 + (rand() - 0.5) * 8) }; }); tests.push({ title: 'Logarithmic regression (50 pts)', description: 'Investment vs Returns — log trend', tags: ['quantitative', 'small', 'log'], chartType: 'Regression', data, fields: [makeField('Investment'), makeField('Returns')], metadata: { Investment: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Returns: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Investment'), y: makeEncodingItem('Returns') }, chartProperties: { regressionMethod: 'log' }, }); } // 7. Quadratic regression { const n = 60; const data = Array.from({ length: n }, () => { const x = -10 + rand() * 20; const y = 0.5 * x * x - 2 * x + 3 + (rand() - 0.5) * 6; return { Position: Math.round(x * 10) / 10, Height: Math.round(y * 10) / 10 }; }); tests.push({ title: 'Quadratic regression (60 pts)', description: 'Parabolic data — quad fit', tags: ['quantitative', 'medium', 'quad'], chartType: 'Regression', data, fields: [makeField('Position'), makeField('Height')], metadata: { Position: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Height: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Position'), y: makeEncodingItem('Height') }, chartProperties: { regressionMethod: 'quad' }, }); } // 8. Polynomial regression (order 4) with color { const groups = ['Group A', 'Group B', 'Group C']; const data: any[] = []; for (const g of groups) { const shift = groups.indexOf(g) * 3; for (let i = 0; i < 30; i++) { const x = -5 + rand() * 10; const y = shift + 0.05 * Math.pow(x, 3) - 0.3 * x * x + x + 5 + (rand() - 0.5) * 4; data.push({ Input: Math.round(x * 10) / 10, Output: Math.round(y * 10) / 10, Category: g, }); } } tests.push({ title: 'Poly regression (3 groups, order 4)', description: '90 points — cubic-ish data with poly(4) fit per group', tags: ['quantitative', 'color', 'medium', 'poly'], chartType: 'Regression', data, fields: [makeField('Input'), makeField('Output'), makeField('Category')], metadata: { Input: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Output: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Category: { type: Type.String, semanticType: 'Category', levels: groups }, }, encodingMap: { x: makeEncodingItem('Input'), y: makeEncodingItem('Output'), color: makeEncodingItem('Category') }, chartProperties: { regressionMethod: 'poly', polyOrder: 4 }, }); } // 9. Exponential regression { const n = 45; const data = Array.from({ length: n }, () => { const x = rand() * 5; const y = 2 * Math.exp(0.4 * x) + (rand() - 0.5) * 3; return { Time: Math.round(x * 10) / 10, Growth: Math.round(Math.max(0.1, y) * 10) / 10 }; }); tests.push({ title: 'Exponential regression (45 pts)', description: 'Time vs Growth — exponential trend', tags: ['quantitative', 'small', 'exp'], chartType: 'Regression', data, fields: [makeField('Time'), makeField('Growth')], metadata: { Time: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Growth: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Time'), y: makeEncodingItem('Growth') }, chartProperties: { regressionMethod: 'exp' }, }); } // 10. Power regression with 2 color groups { const classes = ['Species A', 'Species B']; const data: any[] = []; for (const cls of classes) { const scale = cls === 'Species A' ? 1.5 : 0.8; const exp = cls === 'Species A' ? 0.6 : 0.8; for (let i = 0; i < 30; i++) { const x = 1 + rand() * 50; const y = scale * Math.pow(x, exp) + (rand() - 0.5) * 3; data.push({ BodyMass: Math.round(x * 10) / 10, MetabolicRate: Math.round(Math.max(0.1, y) * 10) / 10, Species: cls, }); } } tests.push({ title: 'Power regression + Color (2 groups)', description: '60 points — allometric scaling per species', tags: ['quantitative', 'color', 'medium', 'pow'], chartType: 'Regression', data, fields: [makeField('BodyMass'), makeField('MetabolicRate'), makeField('Species')], metadata: { BodyMass: { type: Type.Number, semanticType: 'Quantity', levels: [] }, MetabolicRate: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Species: { type: Type.String, semanticType: 'Category', levels: classes }, }, encodingMap: { x: makeEncodingItem('BodyMass'), y: makeEncodingItem('MetabolicRate'), color: makeEncodingItem('Species') }, chartProperties: { regressionMethod: 'pow' }, }); } return tests; } ================================================ FILE: src/lib/agents-chart/test-data/semantic-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Semantic Context Test Cases * * Each test demonstrates how semantic type annotations improve chart output * via the field-context pipeline (vlApplyFieldContext). * * Every test includes a description explaining what happens WITHOUT the * semantic annotation so developers can see the importance of each property. */ import { Type } from '../../../data/types'; import type { SemanticAnnotation } from '../core/field-semantics'; import { TestCase, makeField, makeEncodingItem } from './types'; // ============================================================================ // Shared helper data // ============================================================================ const CITIES = ['Seattle', 'Austin', 'Boston', 'Denver', 'Miami']; const MONTHS = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']; const TEAMS = ['Alpha', 'Beta', 'Gamma', 'Delta']; function seeded(seed: number) { let s = seed; return () => { s = (s * 16807 + 0) % 2147483647; return s / 2147483647; }; } // ============================================================================ // 1. Revenue formatting → axis.format / axis.labelExpr // Semantic type: "Revenue" → currency format ($1.2M) // // WITHOUT this annotation: // Axis labels show raw numbers: 1200000, 2300000, 3500000 // No currency symbol, no abbreviation — hard to read large values. // // WITH "Revenue": // Axis labels show: $1.2M, $2.3M, $3.5M // Currency prefix ($) + SI abbreviation via format + labelExpr. // ============================================================================ function genRevenueFormatTest(): TestCase { const rand = seeded(42); const data = CITIES.map(city => ({ city, revenue: Math.round(500000 + rand() * 4500000), })); return { title: 'Revenue Formatting ($)', description: 'Semantic type "Revenue" adds currency prefix ($) and SI abbreviation to axis labels. ' + 'WITHOUT this: axis would show raw numbers like 1200000.', tags: ['semantic', 'format', 'currency', 'revenue'], chartType: 'Bar Chart', data, fields: [makeField('city'), makeField('revenue')], metadata: { city: { type: Type.String, semanticType: 'City', levels: CITIES }, revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { x: makeEncodingItem('city'), y: makeEncodingItem('revenue'), }, }; } // ============================================================================ // 2. Percentage formatting + nice=false // Semantic type: "Percentage" → ".1%" format, nice: false // // WITHOUT this annotation: // Values 0.72, 0.85 shown as plain decimals on axis. // Axis range may extend beyond 1.0 (e.g., to 1.2) due to nice rounding. // // WITH "Percentage": // Axis labels show "72%", "85%". // nice=false prevents axis from extending past 100%. // ============================================================================ function genPercentageFormatTest(): TestCase { const rand = seeded(99); const categories = ['Marketing', 'Sales', 'Engineering', 'Support', 'Design', 'HR']; const data = categories.map(dept => ({ department: dept, completion_rate: Math.round((0.40 + rand() * 0.58) * 100) / 100, })); return { title: 'Percentage Formatting (%)', description: 'Semantic type "Percentage" formats axis as "72%" instead of 0.72, ' + 'and sets nice=false so axis doesn\'t extend past 100%. ' + 'WITHOUT this: axis shows decimal fractions and may overshoot to 1.2.', tags: ['semantic', 'format', 'percentage', 'nice'], chartType: 'Bar Chart', data, fields: [makeField('department'), makeField('completion_rate')], metadata: { department: { type: Type.String, semanticType: 'Category', levels: categories }, completion_rate: { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, semanticAnnotations: { completion_rate: { semanticType: 'Percentage', intrinsicDomain: [0, 1] }, }, encodingMap: { x: makeEncodingItem('department'), y: makeEncodingItem('completion_rate'), }, }; } // ============================================================================ // 3. Domain constraint + Tick constraint (Rating [1,5]) // Semantic type: { semanticType: "Rating", intrinsicDomain: [1, 5] } // → scale.domain = [1, 5] // → axis.values = [1, 2, 3, 4, 5] (integer ticks only) // // WITHOUT this annotation: // Axis auto-scales to data range (e.g., 2.3–4.7) — partial range. // Tick marks at fractional values (2.5, 3.0, 3.5) — meaningless for ratings. // // WITH "Rating" + intrinsicDomain: // Full 1–5 range always visible; only integers 1,2,3,4,5 on axis. // ============================================================================ function genRatingDomainTest(): TestCase { const rand = seeded(77); const products = ['Widget A', 'Widget B', 'Widget C', 'Widget D', 'Widget E', 'Widget F', 'Widget G', 'Widget H']; const data = products.map(p => ({ product: p, rating: Math.round((2.0 + rand() * 2.8) * 10) / 10, reviews: Math.round(10 + rand() * 490), })); return { title: 'Rating Domain [1–5] & Integer Ticks', description: 'Enriched annotation { semanticType: "Rating", intrinsicDomain: [1,5] } ' + 'pins the axis to 1–5 with integer-only ticks. ' + 'WITHOUT this: axis auto-scales to data range (e.g., 2.3–4.7) ' + 'and shows fractional tick marks like 2.5, 3.5.', tags: ['semantic', 'domain', 'ticks', 'rating'], chartType: 'Scatter Plot', data, fields: [makeField('product'), makeField('rating'), makeField('reviews')], metadata: { product: { type: Type.String, semanticType: 'Category', levels: products }, rating: { type: Type.Number, semanticType: 'Rating', levels: [] }, reviews: { type: Type.Number, semanticType: 'Count', levels: [] }, }, // Enriched annotation for rating — provides intrinsic domain semanticAnnotations: { rating: { semanticType: 'Rating', intrinsicDomain: [1, 5] }, }, encodingMap: { x: makeEncodingItem('reviews'), y: makeEncodingItem('rating'), }, }; } // ============================================================================ // 4. Reversed axis (Rank) // Semantic type: "Rank" → scale.reverse = true // // WITHOUT this annotation: // Rank 1 appears at the BOTTOM of the y-axis (lowest numeric value). // Visually misleading — the "best" team appears lowest on the chart. // // WITH "Rank": // Rank 1 at TOP of y-axis. Visual position matches intuitive ranking. // ============================================================================ function genRankReversedTest(): TestCase { const rand = seeded(55); const data: Record<string, any>[] = []; for (const team of TEAMS) { for (let m = 0; m < 6; m++) { data.push({ month: MONTHS[m], team, rank: Math.ceil(rand() * TEAMS.length), }); } } return { title: 'Rank Reversed Axis', description: 'Semantic type "Rank" reverses the y-axis so rank 1 (best) appears at the TOP. ' + 'WITHOUT this: rank 1 sits at the bottom — first place looks like last place.', tags: ['semantic', 'reversed', 'rank'], chartType: 'Line Chart', data, fields: [makeField('month'), makeField('team'), makeField('rank')], metadata: { month: { type: Type.String, semanticType: 'Month', levels: MONTHS.slice(0, 6) }, team: { type: Type.String, semanticType: 'Category', levels: TEAMS }, rank: { type: Type.Number, semanticType: 'Rank', levels: [] }, }, encodingMap: { x: makeEncodingItem('month'), y: makeEncodingItem('rank'), color: makeEncodingItem('team'), }, }; } // ============================================================================ // 5. Scale type — log scale for Population // Semantic type: "Population" → scale.type = "log" // Only triggers when data spans ≥ 4 orders of magnitude (10,000×) // AND the semantic type is in the allow-list (Population, GDP, etc.) // // WITHOUT this annotation: // Linear scale compresses small settlements into an invisible band // while megacities dominate. Impossible to compare across 4+ orders. // // WITH "Population": // Log scale spreads values evenly — hamlet (500) and megacity (10M) // are both clearly visible and comparable. // ============================================================================ function genPopulationLogScaleTest(): TestCase { // 12 places spanning ~20,000× (500 to 10M) to exceed the 10,000× threshold const places = [ { place: 'Hamlet A', population: 500, area_km2: 1 }, { place: 'Hamlet B', population: 1200, area_km2: 3 }, { place: 'Village A', population: 3500, area_km2: 10 }, { place: 'Village B', population: 8000, area_km2: 18 }, { place: 'Small Town', population: 22000, area_km2: 40 }, { place: 'Town', population: 65000, area_km2: 80 }, { place: 'Small City', population: 180000, area_km2: 150 }, { place: 'City', population: 520000, area_km2: 300 }, { place: 'Large City', population: 1400000, area_km2: 550 }, { place: 'Metro A', population: 3200000, area_km2: 900 }, { place: 'Metro B', population: 5800000, area_km2: 1400 }, { place: 'Megacity', population: 10000000, area_km2: 2500 }, ]; return { title: 'Population Log Scale (≥10,000× span)', description: 'Semantic type "Population" applies log scale when data spans ≥ 4 orders ' + 'of magnitude (500 to 10M = 20,000×). Only triggers for allow-listed types. ' + 'WITHOUT this: linear scale compresses small values into an invisible band.', tags: ['semantic', 'scaleType', 'log', 'population'], chartType: 'Scatter Plot', data: places, fields: [makeField('place'), makeField('population'), makeField('area_km2')], metadata: { place: { type: Type.String, semanticType: 'Category', levels: places.map(p => p.place) }, population: { type: Type.Number, semanticType: 'Population', levels: [] }, area_km2: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('area_km2'), y: makeEncodingItem('population'), }, }; } // ============================================================================ // 6. Interpolation — monotone for Temperature // Semantic type: "Temperature" → mark.interpolate = "monotone" // Also adds "°C" suffix via format → labelExpr. // // WITHOUT this annotation: // Default linear interpolation creates jagged sawtooth lines between // data points. No unit suffix on axis. // // WITH "Temperature": // Smooth monotone interpolation preserves local extrema without // overshooting (no false dips below min or above max between points). // Axis labels show "15°C", "22°C" etc. // ============================================================================ function genTemperatureInterpolationTest(): TestCase { const data: Record<string, any>[] = []; // Two cities with different seasonal patterns const seattleTemps = [-1, 2, 6, 10, 14, 18, 22, 21, 17, 11, 5, 1]; const miamiTemps = [20, 21, 23, 25, 27, 29, 30, 30, 29, 27, 24, 21]; for (let i = 0; i < 12; i++) { data.push({ month: MONTHS[i], city: 'Seattle', temperature: seattleTemps[i] }); data.push({ month: MONTHS[i], city: 'Miami', temperature: miamiTemps[i] }); } return { title: 'Temperature Interpolation (monotone + °C)', description: 'Semantic type "Temperature" applies monotone interpolation for smooth curves ' + 'and adds "°C" suffix to axis labels. ' + 'WITHOUT this: jagged linear segments between points, no unit on axis.', tags: ['semantic', 'interpolation', 'temperature', 'format'], chartType: 'Line Chart', data, fields: [makeField('month'), makeField('city'), makeField('temperature')], metadata: { month: { type: Type.String, semanticType: 'Month', levels: MONTHS }, city: { type: Type.String, semanticType: 'City', levels: ['Seattle', 'Miami'] }, temperature: { type: Type.Number, semanticType: 'Temperature', levels: [] }, }, // Enriched annotation for temperature — unit drives suffix semanticAnnotations: { temperature: { semanticType: 'Temperature', unit: '°C' }, }, encodingMap: { x: makeEncodingItem('month'), y: makeEncodingItem('temperature'), color: makeEncodingItem('city'), }, }; } // ============================================================================ // 7. Combined: Revenue + Percentage side by side // Shows that different semantic types on y-axis produce different formatting // even with the same chart template. // // WITHOUT annotations: // Both charts show plain numeric axis (0.72 and 3500000). // // WITH annotations: // Revenue chart: "$3.5M" Percentage chart: "72%" // ============================================================================ function genRevenueVsPercentTest(): TestCase { const rand = seeded(31); const data = CITIES.map(city => ({ city, revenue: Math.round(1000000 + rand() * 4000000), growth_rate: Math.round((0.02 + rand() * 0.28) * 1000) / 1000, })); return { title: 'Revenue vs Growth Rate (dual semantics)', description: 'Two numeric columns with different semantics on the same data. ' + 'Revenue gets "$" prefix + SI abbreviation; growth_rate gets "%" format. ' + 'WITHOUT annotations: both axes show plain numbers.', tags: ['semantic', 'format', 'currency', 'percentage', 'dual'], chartType: 'Scatter Plot', data, fields: [makeField('city'), makeField('revenue'), makeField('growth_rate')], metadata: { city: { type: Type.String, semanticType: 'City', levels: CITIES }, revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, growth_rate: { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, semanticAnnotations: { growth_rate: { semanticType: 'Percentage', intrinsicDomain: [0, 1] }, }, encodingMap: { x: makeEncodingItem('revenue'), y: makeEncodingItem('growth_rate'), color: makeEncodingItem('city'), }, }; } // ============================================================================ // 8. Score domain [0, 100] with tick constraint // Enriched annotation: { semanticType: "Score", intrinsicDomain: [0, 100] } // → scale.domain = [0, 100], axis.tickMinStep = 1 // // WITHOUT this: // Axis may show 65–92 (auto-scaled to data) with arbitrary tick spacing. // // WITH "Score" + intrinsicDomain: // Full 0–100 range, giving context that 85 is "85 out of 100". // ============================================================================ function genScoreDomainTest(): TestCase { const rand = seeded(63); const students = ['Alice', 'Bob', 'Carol', 'Dave', 'Eve', 'Frank', 'Grace', 'Hank']; const data = students.map(s => ({ student: s, exam_score: Math.round(55 + rand() * 40), study_hours: Math.round(2 + rand() * 18), })); return { title: 'Exam Score Domain [0–100]', description: 'Enriched annotation { semanticType: "Score", intrinsicDomain: [0, 100] } ' + 'pins the axis to 0–100, giving full context. ' + 'WITHOUT this: axis auto-scales to data range (e.g., 58–94), ' + 'losing the "out of 100" context.', tags: ['semantic', 'domain', 'score'], chartType: 'Bar Chart', data, fields: [makeField('student'), makeField('exam_score'), makeField('study_hours')], metadata: { student: { type: Type.String, semanticType: 'Category', levels: students }, exam_score: { type: Type.Number, semanticType: 'Score', levels: [] }, study_hours:{ type: Type.Number, semanticType: 'Quantity', levels: [] }, }, semanticAnnotations: { exam_score: { semanticType: 'Score', intrinsicDomain: [0, 100] }, }, encodingMap: { x: makeEncodingItem('student'), y: makeEncodingItem('exam_score'), }, }; } // ============================================================================ // 15. Rating bar chart — domain [1,5] should NOT extend to zero // Semantic type: { semanticType: "Rating", intrinsicDomain: [1, 5] } // // The key design question: should a bar chart of ratings include zero? // Most default tools naively force zero on all bar charts. But Rating // is a bounded scale [1, 5] — zero is outside the scale and wastes 20% // of visual space while compressing the meaningful 1-5 differences. // // The semantic domain constraint [1, 5] is authoritative and should // clear any auto-computed zero:true from the zero-baseline heuristic. // // WITHOUT semantic domain: bar starts at 0, compressing the 1-5 range // WITH semantic domain [1,5]: bars span the full [1,5] range // ============================================================================ function genRatingBarDomainTest(): TestCase { const rand = seeded(501); const restaurants = ['Sakura Sushi', 'Bella Pasta', 'Taco Loco', 'Zen Noodle', 'Burger Joint', 'Le Bistro']; const data = restaurants.map(r => ({ restaurant: r, avg_rating: Math.round((2.5 + rand() * 2.3) * 10) / 10, })); return { title: 'Rating Bar — Domain [1,5] vs Zero', description: 'Bar chart of ratings with intrinsicDomain [1,5]. The semantic domain ' + 'should prevent zero-extension — bars span [1,5] not [0,5]. ' + 'WITHOUT this: bar chart forces zero, wasting 20% of space on impossible values.', tags: ['semantic', 'domain', 'zero', 'rating', 'bar'], chartType: 'Bar Chart', data, fields: [makeField('restaurant'), makeField('avg_rating')], metadata: { restaurant: { type: Type.String, semanticType: 'Category', levels: restaurants }, avg_rating: { type: Type.Number, semanticType: 'Rating', levels: [] }, }, semanticAnnotations: { avg_rating: { semanticType: 'Rating', intrinsicDomain: [1, 5] }, }, encodingMap: { x: makeEncodingItem('restaurant'), y: makeEncodingItem('avg_rating'), }, }; } // ============================================================================ // 16. Revenue bar chart — zero baseline IS meaningful // Semantic type: "Revenue" (zero-meaningful class) // // Revenue is an additive measure where 0 = no revenue. Bar length from // zero communicates proportional magnitude. Including zero is correct. // // WITHOUT semantic type: VL may or may not include zero by default // WITH "Revenue": zero:true ensures bars start at 0 // ============================================================================ function genRevenueBarZeroTest(): TestCase { const rand = seeded(502); const products = ['Widget A', 'Widget B', 'Widget C', 'Widget D', 'Widget E']; const data = products.map(p => ({ product: p, revenue: Math.round(80000 + rand() * 320000), })); return { title: 'Revenue Bar — Zero Baseline', description: 'Revenue is zero-meaningful: $0 = no revenue. Bar charts must start at zero ' + 'to preserve proportional bar length. ' + 'WITHOUT this: data-fit might show bars starting at $80K, exaggerating differences.', tags: ['semantic', 'zero', 'revenue', 'bar'], chartType: 'Bar Chart', data, fields: [makeField('product'), makeField('revenue')], metadata: { product: { type: Type.String, semanticType: 'Category', levels: products }, revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { x: makeEncodingItem('product'), y: makeEncodingItem('revenue'), }, }; } // ============================================================================ // 17. Temperature scatter — zero is arbitrary, should NOT include zero // Semantic type: "Temperature" (zero-arbitrary class) // // 0°F / 0°C are arbitrary scale points, not physical absence. // Including zero on a scatter plot of 60–95°F summer temps wastes the // bottom 60% of the canvas. // // WITHOUT semantic type: VL default may or may not include zero // WITH "Temperature": zero:false, VL nice-rounds to clean bounds // ============================================================================ function genTemperatureScatterZeroTest(): TestCase { const rand = seeded(503); const days = Array.from({ length: 20 }, (_, i) => `Day ${i + 1}`); const data = days.map(d => ({ day: d, high_temp: Math.round(65 + rand() * 30), humidity: Math.round(30 + rand() * 50), })); return { title: 'Temperature Scatter — No Zero', description: 'Temperature zero (0°F/°C) is an arbitrary scale point. For data in 65–95°F, ' + 'including zero wastes 60% of the canvas. zero:false lets VL data-fit with ' + 'nice tick rounding. ' + 'WITHOUT this: chart might waste space including the irrelevant zero.', tags: ['semantic', 'zero', 'temperature', 'scatter'], chartType: 'Scatter Plot', data, fields: [makeField('day'), makeField('high_temp'), makeField('humidity')], metadata: { day: { type: Type.String, semanticType: 'Category', levels: days }, high_temp: { type: Type.Number, semanticType: 'Temperature', levels: [] }, humidity: { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, semanticAnnotations: { humidity: { semanticType: 'Percentage', intrinsicDomain: [0, 100] }, }, encodingMap: { x: makeEncodingItem('humidity'), y: makeEncodingItem('high_temp'), }, }; } // ============================================================================ // 18. Percentage line — snap-to-bound heuristic // Semantic type: "Percentage" → intrinsicDomain [0, 100] // // Uses snap-to-bound: if data approaches the theoretical endpoint // (≥90% of max), the axis snaps to it. Data at 40–85% is below the // snap threshold → axis data-fits. Data at 60–97% would snap to 100%. // The zero-baseline contextual logic independently decides whether // to include 0 (bar = yes, line = depends on proximity). // ============================================================================ function genPercentageLineDomainTest(): TestCase { const rand = seeded(504); const weeks = Array.from({ length: 10 }, (_, i) => `Week ${i + 1}`); const data = weeks.map(w => ({ week: w, completion_rate: Math.round(40 + rand() * 45), })); return { title: 'Percentage Line — Snap-to-Bound', description: 'Completion rates (40–85%) with intrinsicDomain [0, 100]. Data max (85%) is ' + 'below the 90% snap threshold, so the axis data-fits rather than forcing 100%. ' + 'If data reached 97%, the axis would snap to 100% (stopping at 97% looks arbitrary).', tags: ['semantic', 'domain', 'zero', 'percentage', 'line'], chartType: 'Line Chart', data, fields: [makeField('week'), makeField('completion_rate')], metadata: { week: { type: Type.String, semanticType: 'Category', levels: weeks }, completion_rate: { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, semanticAnnotations: { completion_rate: { semanticType: 'Percentage', intrinsicDomain: [0, 100] }, }, encodingMap: { x: makeEncodingItem('week'), y: makeEncodingItem('completion_rate'), }, }; } // ============================================================================ // Public generator // ============================================================================ export function genSemanticContextTests(): TestCase[] { return [ genRevenueFormatTest(), genPercentageFormatTest(), genRatingDomainTest(), genRankReversedTest(), genPopulationLogScaleTest(), genTemperatureInterpolationTest(), genRevenueVsPercentTest(), genScoreDomainTest(), genProfitSignedCurrencyTest(), genWeightUnitSuffixTest(), genCountStepInterpolationTest(), genLatitudeDomainClampTest(), genPercentageChangeSignedTest(), genMonthCanonicalOrderTest(), genRatingBarDomainTest(), genRevenueBarZeroTest(), genTemperatureScatterZeroTest(), genPercentageLineDomainTest(), genDayOfWeekOrderTest(), genYearFormatTest(), genSentimentDivergingColorTest(), genRevenueSequentialColorTest(), genCorrelationDivergingTest(), genNiceFalseScoreTest(), // --- New tests (25–39) --- genDurationUnitSuffixTest(), genQuarterCanonicalOrderTest(), genCostCurrencyFormatTest(), genDirectionCompassOrderTest(), genAgeGroupOrdinalTest(), genBooleanColorTest(), genLongitudeDomainClampTest(), genIndexOrdinalTest(), genPercentageWholeNumberTest(), genScoreColorDivergingTest(), genPriceEurCurrencyTest(), genYearOrdinalDisambiguationTest(), genProfitColorDivergingTest(), genCountIntegerFormatTest(), genUnregisteredTypeFallbackTest(), ]; } // ============================================================================ // 9. Profit — signed currency format (+$120K / -$50K) // Semantic type: "Profit" → signed-currency format class // // WITHOUT this annotation: // Axis shows raw numbers: 120000, -50000. No sign prefix on positive // values, no currency symbol. Hard to tell gains from losses at a glance. // // WITH "Profit": // Axis labels show: +$120K, -$50K. Sign always visible, currency prefix. // ============================================================================ function genProfitSignedCurrencyTest(): TestCase { const rand = seeded(111); const quarters = ['Q1-2024', 'Q2-2024', 'Q3-2024', 'Q4-2024', 'Q1-2025', 'Q2-2025', 'Q3-2025', 'Q4-2025']; const data = quarters.map(q => ({ quarter: q, profit: Math.round((-200000 + rand() * 600000) / 1000) * 1000, })); return { title: 'Profit Signed Currency (+$/-$)', description: 'Semantic type "Profit" uses signed-currency format: "+$120K" / "-$50K". ' + 'Sign is always visible on positive values, making gains vs losses obvious. ' + 'WITHOUT this: raw numbers without sign prefix or currency symbol.', tags: ['semantic', 'format', 'signed-currency', 'profit'], chartType: 'Bar Chart', data, fields: [makeField('quarter'), makeField('profit')], metadata: { quarter: { type: Type.String, semanticType: 'Category', levels: quarters }, profit: { type: Type.Number, semanticType: 'Profit', levels: [] }, }, encodingMap: { x: makeEncodingItem('quarter'), y: makeEncodingItem('profit'), }, }; } // ============================================================================ // 10. Weight with unit suffix (kg) // Semantic type: "Weight" + unit: "kg" → "72.5 kg" on axis // // WITHOUT this annotation: // Axis shows bare numbers: 72.5, 85.0. No indication of unit. // User has to look at column name to figure out what these numbers mean. // // WITH "Weight" + unit: // Axis labels show "72.5 kg", "85.0 kg". Unit is part of the label. // ============================================================================ function genWeightUnitSuffixTest(): TestCase { const rand = seeded(202); const athletes = ['Chen', 'Maria', 'James', 'Yuki', 'Priya', 'Lars', 'Fatima', 'Carlos', 'Anna', 'Kwame']; const data = athletes.map(name => ({ athlete: name, weight_kg: Math.round((55 + rand() * 50) * 10) / 10, height_cm: Math.round(155 + rand() * 40), })); return { title: 'Weight Unit Suffix (kg)', description: 'Enriched annotation { semanticType: "Weight", unit: "kg" } appends " kg" ' + 'to axis labels. WITHOUT this: bare numbers with no unit context.', tags: ['semantic', 'format', 'unit-suffix', 'weight'], chartType: 'Scatter Plot', data, fields: [makeField('athlete'), makeField('weight_kg'), makeField('height_cm')], metadata: { athlete: { type: Type.String, semanticType: 'Category', levels: athletes }, weight_kg: { type: Type.Number, semanticType: 'Weight', levels: [] }, height_cm: { type: Type.Number, semanticType: 'Distance', levels: [] }, }, semanticAnnotations: { weight_kg: { semanticType: 'Weight', unit: 'kg' }, height_cm: { semanticType: 'Distance', unit: 'cm' }, }, encodingMap: { x: makeEncodingItem('height_cm'), y: makeEncodingItem('weight_kg'), }, }; } // ============================================================================ // 11. Count — step-after interpolation // Semantic type: "Count" → interpolation = "step-after" // // WITHOUT this annotation: // Line chart connects counts with straight diagonal lines, implying // gradual continuous change between data points. // // WITH "Count": // Step-after interpolation shows flat steps — count stays constant // until the next event, then jumps. Accurate for discrete events. // ============================================================================ function genCountStepInterpolationTest(): TestCase { const data: Record<string, any>[] = []; const weeks = ['Wk1', 'Wk2', 'Wk3', 'Wk4', 'Wk5', 'Wk6', 'Wk7', 'Wk8', 'Wk9', 'Wk10', 'Wk11', 'Wk12']; const bugCounts = [3, 7, 12, 8, 15, 22, 18, 11, 9, 14, 6, 4]; const featureDone = [1, 2, 4, 6, 7, 8, 10, 13, 15, 16, 18, 20]; for (let i = 0; i < 12; i++) { data.push({ week: weeks[i], metric: 'Bugs Filed', count: bugCounts[i] }); data.push({ week: weeks[i], metric: 'Features Completed', count: featureDone[i] }); } return { title: 'Count Step-After Interpolation', description: 'Semantic type "Count" uses step-after interpolation — count stays ' + 'constant then jumps at each data point (discrete events). ' + 'WITHOUT this: diagonal lines imply gradual change between weeks.', tags: ['semantic', 'interpolation', 'count', 'step'], chartType: 'Line Chart', data, fields: [makeField('week'), makeField('metric'), makeField('count')], metadata: { week: { type: Type.String, semanticType: 'Category', levels: weeks }, metric: { type: Type.String, semanticType: 'Category', levels: ['Bugs Filed', 'Features Completed'] }, count: { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('week'), y: makeEncodingItem('count'), color: makeEncodingItem('metric'), }, }; } // ============================================================================ // 12. Latitude — hard domain [-90, 90] with clamp // Semantic type: "Latitude" → scale.domain = [-90, 90], clamp = true // // WITHOUT this annotation: // Axis auto-scales to data (e.g., 30–60). No indication these are // geographic degrees. Axis could "nice-round" to 25–65. // // WITH "Latitude": // Full -90 to 90 range. Clamp prevents out-of-range rendering. // Shows data in geographic context. // ============================================================================ function genLatitudeDomainClampTest(): TestCase { const cities = [ { city: 'Singapore', latitude: 1.35, gdp_per_capita: 65000 }, { city: 'Mumbai', latitude: 19.08, gdp_per_capita: 7200 }, { city: 'Cairo', latitude: 30.04, gdp_per_capita: 3900 }, { city: 'Istanbul', latitude: 41.01, gdp_per_capita: 9500 }, { city: 'Paris', latitude: 48.86, gdp_per_capita: 42000 }, { city: 'London', latitude: 51.51, gdp_per_capita: 46000 }, { city: 'Moscow', latitude: 55.76, gdp_per_capita: 11300 }, { city: 'Helsinki', latitude: 60.17, gdp_per_capita: 49000 }, { city: 'Reykjavik', latitude: 64.13, gdp_per_capita: 52000 }, { city: 'Tromsø', latitude: 69.65, gdp_per_capita: 65000 }, ]; return { title: 'Latitude Hard Domain [-90°, 90°]', description: 'Semantic type "Latitude" pins axis to [-90, 90] with clamp=true ' + '(hard physical domain). Shows where cities fall in global context. ' + 'WITHOUT this: axis auto-scales to data range (1–70), losing context.', tags: ['semantic', 'domain', 'clamp', 'latitude', 'geographic'], chartType: 'Scatter Plot', data: cities, fields: [makeField('city'), makeField('latitude'), makeField('gdp_per_capita')], metadata: { city: { type: Type.String, semanticType: 'City', levels: cities.map(c => c.city) }, latitude: { type: Type.Number, semanticType: 'Latitude', levels: [] }, gdp_per_capita: { type: Type.Number, semanticType: 'Amount', levels: [] }, }, encodingMap: { x: makeEncodingItem('gdp_per_capita'), y: makeEncodingItem('latitude'), }, }; } // ============================================================================ // 13. Percentage Change — signed percent (+12.3% / -5.1%) // Semantic type: "PercentageChange" → signed-percent format // // WITHOUT this annotation: // Axis shows 0.12, -0.05 — no sign on positive, no % symbol. // Hard to interpret at a glance. // // WITH "PercentageChange": // Axis shows "+12.3%", "-5.1%". Sign always visible, % suffix. // ============================================================================ function genPercentageChangeSignedTest(): TestCase { const rand = seeded(333); const stocks = ['AAPL', 'GOOG', 'MSFT', 'AMZN', 'META', 'TSLA', 'NVDA', 'AMD', 'NFLX', 'DIS']; const data = stocks.map(s => ({ stock: s, ytd_change: Math.round((-0.30 + rand() * 0.80) * 1000) / 1000, market_cap_B: Math.round(50 + rand() * 2950), })); return { title: 'Percentage Change (signed ±%)', description: 'Semantic type "PercentageChange" uses signed-percent format: "+12.3%" / "-5.1%". ' + 'Sign is always visible, making gains and losses instantly clear. ' + 'WITHOUT this: raw decimals like 0.12 or -0.05 with no % symbol.', tags: ['semantic', 'format', 'signed-percent', 'percentage-change'], chartType: 'Bar Chart', data, fields: [makeField('stock'), makeField('ytd_change'), makeField('market_cap_B')], metadata: { stock: { type: Type.String, semanticType: 'Category', levels: stocks }, ytd_change: { type: Type.Number, semanticType: 'PercentageChange', levels: [] }, market_cap_B: { type: Type.Number, semanticType: 'Amount', levels: [] }, }, semanticAnnotations: { ytd_change: { semanticType: 'PercentageChange', intrinsicDomain: [-1, 1] }, }, encodingMap: { x: makeEncodingItem('stock'), y: makeEncodingItem('ytd_change'), }, }; } // ============================================================================ // 14. Month canonical ordering // Semantic type: "Month" → ordinal sort order preserved // // WITHOUT this annotation: // Months sorted alphabetically: Apr, Aug, Dec, Feb, Jan, Jul... // Completely wrong for temporal data. // // WITH "Month": // Canonical order: Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec. // ============================================================================ function genMonthCanonicalOrderTest(): TestCase { const rand = seeded(444); // Deliberately out of order in data to show sorting const shuffledMonths = ['Mar', 'Nov', 'Jul', 'Jan', 'Sep', 'May', 'Apr', 'Dec', 'Feb', 'Oct', 'Jun', 'Aug']; const data = shuffledMonths.map(m => ({ month: m, sales: Math.round(10000 + rand() * 40000), })); return { title: 'Month Canonical Ordering', description: 'Semantic type "Month" preserves calendar order (Jan→Dec) regardless ' + 'of data insertion order. ' + 'WITHOUT this: months sort alphabetically (Apr, Aug, Dec, Feb...).', tags: ['semantic', 'ordering', 'month', 'canonical'], chartType: 'Bar Chart', data, fields: [makeField('month'), makeField('sales')], metadata: { month: { type: Type.String, semanticType: 'Month', levels: shuffledMonths }, sales: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { x: makeEncodingItem('month'), y: makeEncodingItem('sales'), }, }; } // ============================================================================ // 19. Day-of-Week canonical ordering // Semantic type: "Day" → canonical Mon–Sun order // // WITHOUT this annotation: // Day names sorted alphabetically: Fri, Mon, Sat, Sun, Thu, Tue, Wed. // This destroys the weekly cycle. // // WITH "Day": // Canonical weekday order: Mon, Tue, Wed, Thu, Fri, Sat, Sun // (or data-matching variant). ordinalSortOrder provides the domain. // ============================================================================ function genDayOfWeekOrderTest(): TestCase { const rand = seeded(555); // Deliberately shuffled const shuffledDays = ['Thu', 'Mon', 'Sat', 'Wed', 'Fri', 'Tue', 'Sun']; const data = shuffledDays.map(d => ({ day: d, visitors: Math.round(200 + rand() * 800), })); return { title: 'Day-of-Week Canonical Order', description: 'Semantic type "Day" preserves weekday order (Mon→Sun) regardless ' + 'of data encounter order. ' + 'WITHOUT this: days sort alphabetically (Fri, Mon, Sat...).', tags: ['semantic', 'ordering', 'day', 'canonical'], chartType: 'Bar Chart', data, fields: [makeField('day'), makeField('visitors')], metadata: { day: { type: Type.String, semanticType: 'Day', levels: shuffledDays }, visitors: { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('day'), y: makeEncodingItem('visitors'), }, }; } // ============================================================================ // 20. Year integer format — no comma separator // Semantic type: "Year" → format "d" (2024 not "2,024") // // WITHOUT this annotation: // Numeric years treated as generic numbers → formatted with thousands // separator: "2,024" instead of "2024". Looks like a decimal value. // // WITH "Year": // Format class = 'integer', and the pipeline uses bare "d" format // for years (without comma grouping). Axis reads: 2020, 2021, 2022... // ============================================================================ function genYearFormatTest(): TestCase { const years = [2018, 2019, 2020, 2021, 2022, 2023, 2024]; const rand = seeded(666); const data = years.map(y => ({ year: y, gdp_trillion: Math.round((18 + rand() * 8) * 100) / 100, })); return { title: 'Year Format (no comma)', description: 'Semantic type "Year" uses integer format without comma grouping: ' + '"2024" not "2,024". ' + 'WITHOUT this: years shown as "2,024" with thousands separator.', tags: ['semantic', 'format', 'year', 'integer'], chartType: 'Line Chart', data, fields: [makeField('year'), makeField('gdp_trillion')], metadata: { year: { type: Type.Number, semanticType: 'Year', levels: years }, gdp_trillion: { type: Type.Number, semanticType: 'Amount', levels: [] }, }, encodingMap: { x: makeEncodingItem('year'), y: makeEncodingItem('gdp_trillion'), }, }; } // ============================================================================ // 21. Sentiment — diverging color scheme // Semantic type: "Sentiment" on color channel → diverging color scheme // with midpoint at 0 // // WITHOUT this annotation: // Color mapped to a generic sequential palette (e.g., viridis). // Positive and negative sentiment look like arbitrary gradients, // not intuitively split around a neutral center. // // WITH "Sentiment": // Diverging color scheme (e.g., redblue) centered on 0. // Negative = one hue, positive = another, 0 = neutral white/grey. // scale.domainMid = 0 ensures the midpoint is visually centered. // ============================================================================ function genSentimentDivergingColorTest(): TestCase { const rand = seeded(777); const products = ['Widget A', 'Widget B', 'Gadget X', 'Gadget Y', 'Tool M', 'Tool N', 'Part P', 'Part Q']; const data = products.map(p => ({ product: p, reviews: Math.round(50 + rand() * 450), sentiment: Math.round((-0.8 + rand() * 1.6) * 100) / 100, })); return { title: 'Sentiment Diverging Color', description: 'Semantic type "Sentiment" on the color channel triggers a diverging ' + 'color scheme (redblue) with domainMid = 0. Negative sentiment gets ' + 'one hue, positive gets another, and zero is neutral. ' + 'WITHOUT this: generic sequential palette with no semantic midpoint.', tags: ['semantic', 'colorScheme', 'diverging', 'sentiment'], chartType: 'Bar Chart', data, fields: [makeField('product'), makeField('reviews'), makeField('sentiment')], metadata: { product: { type: Type.String, semanticType: 'Category', levels: products }, reviews: { type: Type.Number, semanticType: 'Count', levels: [] }, sentiment: { type: Type.Number, semanticType: 'Sentiment', levels: [] }, }, encodingMap: { x: makeEncodingItem('product'), y: makeEncodingItem('reviews'), color: makeEncodingItem('sentiment'), }, }; } // ============================================================================ // 22. Revenue sequential color — quantitative color uses sequential scheme // Semantic type: "Revenue" on color channel → sequential gold-green scheme // // WITHOUT this annotation: // Default sequential palette (viridis). Works but doesn't convey // financial context. // // WITH "Revenue": // Financial-themed sequential scheme (goldgreen) automatically chosen // for quantitative financial data. Warmer = higher revenue. // ============================================================================ function genRevenueSequentialColorTest(): TestCase { const rand = seeded(888); const regions = ['North', 'South', 'East', 'West', 'Central']; const data: Record<string, any>[] = []; for (const region of regions) { for (const q of ['Q1', 'Q2', 'Q3', 'Q4']) { data.push({ region, quarter: q, revenue: Math.round(100000 + rand() * 900000), }); } } return { title: 'Revenue Sequential Color', description: 'Semantic type "Revenue" on the color channel triggers a financial-themed ' + 'sequential scheme (goldgreen). Higher revenue → warmer color. ' + 'WITHOUT this: default viridis or generic palette with no financial connotation.', tags: ['semantic', 'colorScheme', 'sequential', 'revenue'], chartType: 'Bar Chart', data, fields: [makeField('region'), makeField('quarter'), makeField('revenue')], metadata: { region: { type: Type.String, semanticType: 'Category', levels: regions }, quarter: { type: Type.String, semanticType: 'Quarter', levels: ['Q1', 'Q2', 'Q3', 'Q4'] }, revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { x: makeEncodingItem('region'), y: makeEncodingItem('revenue'), color: makeEncodingItem('revenue'), }, }; } // ============================================================================ // 23. Correlation — diverging color + bounded domain [-1, 1] // Semantic type: "Correlation" → domain [-1, 1], diverging color, // clamped scale, signed-decimal format // // WITHOUT this annotation: // Color scale auto-fits to data range (e.g., [0.12, 0.89]). // No diverging scheme, no fixed [-1,1] bounds, no midpoint centering. // // WITH "Correlation": // Domain clamped to [-1, 1], diverging color with midpoint at 0, // signed-decimal format (+0.85 / -0.42). Full semantic context. // ============================================================================ function genCorrelationDivergingTest(): TestCase { const rand = seeded(999); const vars = ['GDP', 'Unemployment', 'Inflation', 'Education', 'Health']; const data: Record<string, any>[] = []; for (let i = 0; i < vars.length; i++) { for (let j = 0; j < vars.length; j++) { const corr = i === j ? 1.0 : Math.round((-0.9 + rand() * 1.8) * 100) / 100; data.push({ var_x: vars[i], var_y: vars[j], correlation: corr, }); } } return { title: 'Correlation Diverging + Domain', description: 'Semantic type "Correlation" on color sets a diverging scheme with ' + 'midpoint 0 and domain [-1, 1]. Positive correlations appear in one hue, ' + 'negative in another. Domain is fixed to the mathematical bounds. ' + 'WITHOUT this: auto-fitted color domain, sequential palette, no midpoint.', tags: ['semantic', 'colorScheme', 'diverging', 'domain', 'correlation'], chartType: 'Heatmap', data, fields: [makeField('var_x'), makeField('var_y'), makeField('correlation')], metadata: { var_x: { type: Type.String, semanticType: 'Category', levels: vars }, var_y: { type: Type.String, semanticType: 'Category', levels: vars }, correlation: { type: Type.Number, semanticType: 'Correlation', levels: [] }, }, semanticAnnotations: { correlation: { semanticType: 'Correlation', intrinsicDomain: [-1, 1] }, }, encodingMap: { x: makeEncodingItem('var_x'), y: makeEncodingItem('var_y'), color: makeEncodingItem('correlation'), }, }; } // ============================================================================ // 24. Nice = false — bounded domain without nice rounding // Semantic type: "Score" with domain [0, 100] → nice = false // // WITHOUT nice = false: // VL's "nice" rounding extends domain [0, 100] to [0, 120] or similar — // wasting space and implying scores > 100 exist. // // WITH nice = false (from bounded domainShape): // Axis endpoints stick exactly to [0, 100]. No extension beyond the // intrinsic bounds of the measurement scale. // ============================================================================ function genNiceFalseScoreTest(): TestCase { const rand = seeded(1010); const students = ['Alice', 'Bob', 'Carol', 'Dave', 'Eve', 'Frank', 'Grace', 'Heidi', 'Ivan', 'Judy']; const data = students.map(s => ({ student: s, math_score: Math.round(45 + rand() * 55), science_score: Math.round(30 + rand() * 70), })); return { title: 'Nice = false (Score [0-100])', description: 'Score\'s bounded domainShape sets nice = false so VL doesn\'t ' + 'extend the axis beyond [0, 100]. Ticks stop at the intrinsic bounds. ' + 'WITHOUT nice = false: VL extends to [0, 120] — implying impossible scores.', tags: ['semantic', 'nice', 'domain', 'score', 'bounded'], chartType: 'Scatter Plot', data, fields: [makeField('student'), makeField('math_score'), makeField('science_score')], metadata: { student: { type: Type.String, semanticType: 'Category', levels: students }, math_score: { type: Type.Number, semanticType: 'Score', levels: [] }, science_score: { type: Type.Number, semanticType: 'Score', levels: [] }, }, semanticAnnotations: { math_score: { semanticType: 'Score', intrinsicDomain: [0, 100] }, science_score: { semanticType: 'Score', intrinsicDomain: [0, 100] }, }, encodingMap: { x: makeEncodingItem('math_score'), y: makeEncodingItem('science_score'), }, }; } // ============================================================================ // 25. Duration with unit suffix — additive measure, "min" suffix // Semantic type: "Duration" + unit: "min" → "42 min" on axis // // Duration is an additive measure (durations sum to a total), open domain, // meaningful zero (0 = no time elapsed), and uses unit-suffix format. // // WITHOUT this annotation: // Axis shows bare numbers: 42, 85. No time context. // Zero might not be included in axis. // // WITH "Duration" + unit: // Axis labels show "42 min", "85 min". Zero baseline included. // ============================================================================ function genDurationUnitSuffixTest(): TestCase { const rand = seeded(2501); const tasks = ['Setup', 'Build', 'Test', 'Review', 'Deploy', 'Cleanup', 'Backup', 'Sync']; const data = tasks.map(t => ({ task: t, duration_min: Math.round(5 + rand() * 115), })); return { title: 'Duration Unit Suffix (min)', description: 'Semantic type "Duration" with unit "min" appends " min" to axis labels. ' + 'Duration is additive (total makes sense) with meaningful zero. ' + 'WITHOUT this: bare numbers with no time unit context.', tags: ['semantic', 'format', 'unit-suffix', 'duration', 'zero'], chartType: 'Bar Chart', data, fields: [makeField('task'), makeField('duration_min')], metadata: { task: { type: Type.String, semanticType: 'Category', levels: tasks }, duration_min: { type: Type.Number, semanticType: 'Duration', levels: [] }, }, semanticAnnotations: { duration_min: { semanticType: 'Duration', unit: 'min' }, }, encodingMap: { x: makeEncodingItem('task'), y: makeEncodingItem('duration_min'), }, }; } // ============================================================================ // 26. Quarter canonical order + cyclic behavior // Semantic type: "Quarter" → ordinal sort Q1, Q2, Q3, Q4 // // Quarter is cyclic (Q4 wraps to Q1) and uses ordinal encoding. // The canonical order ensures quarters appear in fiscal sequence. // // WITHOUT this annotation: // Quarters sorted alphabetically: Q1, Q2, Q3, Q4 happens to work // but only by coincidence. Real datasets with "Q4-2024", "Q1-2025" // would break. Also no cyclic semantics for wrap-around analysis. // // WITH "Quarter": // Guaranteed Q1→Q2→Q3→Q4 order. Cyclic flag enables wrap-around // for cross-year comparisons. // ============================================================================ function genQuarterCanonicalOrderTest(): TestCase { const rand = seeded(2601); // Deliberately shuffled quarters const shuffledQuarters = ['Q3', 'Q1', 'Q4', 'Q2']; const data: Record<string, any>[] = []; for (const region of ['North', 'South', 'East']) { for (const q of shuffledQuarters) { data.push({ quarter: q, region, sales: Math.round(50000 + rand() * 200000), }); } } return { title: 'Quarter Canonical Order (Q1→Q4)', description: 'Semantic type "Quarter" sorts Q1→Q2→Q3→Q4 in canonical order ' + 'regardless of data encounter order. Quarter is cyclic (Q4→Q1 wraps). ' + 'WITHOUT this: alphabetical order happens to work here, but no guarantee ' + 'for variant labels or cross-year data.', tags: ['semantic', 'ordering', 'quarter', 'cyclic', 'canonical'], chartType: 'Bar Chart', data, fields: [makeField('quarter'), makeField('region'), makeField('sales')], metadata: { quarter: { type: Type.String, semanticType: 'Quarter', levels: shuffledQuarters }, region: { type: Type.String, semanticType: 'Category', levels: ['North', 'South', 'East'] }, sales: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { x: makeEncodingItem('quarter'), y: makeEncodingItem('sales'), color: makeEncodingItem('region'), }, }; } // ============================================================================ // 27. Cost currency format — another currency type (additive) // Semantic type: "Cost" → currency prefix ($) + SI abbreviation // // Cost shares formatClass='currency' with Revenue/Amount but has // aggRole='additive' — costs sum to a meaningful total. // // WITHOUT this annotation: // Axis shows raw numbers: 450000, 1200000. No $ symbol. // // WITH "Cost": // Axis labels show "$450K", "$1.2M". Currency + abbreviation. // ============================================================================ function genCostCurrencyFormatTest(): TestCase { const rand = seeded(2701); const departments = ['Engineering', 'Marketing', 'Sales', 'Support', 'HR', 'Legal']; const data = departments.map(d => ({ department: d, annual_cost: Math.round(200000 + rand() * 1800000), })); return { title: 'Cost Currency Format ($)', description: 'Semantic type "Cost" uses currency format ($450K, $1.2M). ' + 'Cost is additive — department costs sum to total company cost. ' + 'WITHOUT this: plain numbers without currency context.', tags: ['semantic', 'format', 'currency', 'cost'], chartType: 'Bar Chart', data, fields: [makeField('department'), makeField('annual_cost')], metadata: { department: { type: Type.String, semanticType: 'Category', levels: departments }, annual_cost: { type: Type.Number, semanticType: 'Cost', levels: [] }, }, encodingMap: { x: makeEncodingItem('department'), y: makeEncodingItem('annual_cost'), }, }; } // ============================================================================ // 28. Direction compass order + cyclic // Semantic type: "Direction" → compass order N→NE→E→SE→S→SW→W→NW // // Direction is cyclic (NW wraps to N) and uses ordinal/nominal encoding. // The canonical order ensures compass directions appear in clockwise order. // // WITHOUT this annotation: // Directions sorted alphabetically: E, N, NE, NW, S, SE, SW, W. // Completely wrong — geographic/compass ordering is destroyed. // // WITH "Direction": // Canonical clockwise order: N, NE, E, SE, S, SW, W, NW. // ============================================================================ function genDirectionCompassOrderTest(): TestCase { const rand = seeded(2801); // Deliberately shuffled const shuffledDirs = ['SW', 'N', 'E', 'NW', 'S', 'NE', 'W', 'SE']; const data = shuffledDirs.map(d => ({ direction: d, wind_speed: Math.round(5 + rand() * 45), })); return { title: 'Direction Compass Order (N→NW)', description: 'Semantic type "Direction" sorts compass directions in clockwise order ' + '(N→NE→E→SE→S→SW→W→NW). Direction is cyclic (NW→N wraps). ' + 'WITHOUT this: alphabetical (E, N, NE, NW...) destroys geographic sense.', tags: ['semantic', 'ordering', 'direction', 'cyclic', 'compass', 'canonical'], chartType: 'Bar Chart', data, fields: [makeField('direction'), makeField('wind_speed')], metadata: { direction: { type: Type.String, semanticType: 'Direction', levels: shuffledDirs }, wind_speed: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('direction'), y: makeEncodingItem('wind_speed'), }, }; } // ============================================================================ // 29. AgeGroup — binned ordinal with inherent order // Semantic type: "AgeGroup" → ordinal encoding, sequential color // // AgeGroup is a binned type: continuous ages discretized into ranges. // Values have inherent order (18-24 < 25-34 < 35-44...) unlike // pure nominal categories. Uses ordinal encoding. // // WITHOUT this annotation: // Age groups treated as nominal — no guaranteed order, potentially // sorted alphabetically which breaks the natural sequence. // // WITH "AgeGroup": // Ordinal encoding preserves the range order. Color uses sequential // scheme reflecting the ordered nature. // ============================================================================ function genAgeGroupOrdinalTest(): TestCase { const rand = seeded(2901); const ageGroups = ['18-24', '25-34', '35-44', '45-54', '55-64', '65+']; const data = ageGroups.map(ag => ({ age_group: ag, population: Math.round(500000 + rand() * 2000000), avg_income: Math.round(25000 + rand() * 75000), })); return { title: 'AgeGroup Binned Ordinal', description: 'Semantic type "AgeGroup" is a binned ordinal — age ranges have inherent ' + 'order (18-24 < 25-34 < ...). Uses ordinal encoding, not nominal. ' + 'WITHOUT this: treated as unordered categories, alphabetical sorting.', tags: ['semantic', 'ordinal', 'binned', 'agegroup'], chartType: 'Bar Chart', data, fields: [makeField('age_group'), makeField('population'), makeField('avg_income')], metadata: { age_group: { type: Type.String, semanticType: 'AgeGroup', levels: ageGroups }, population: { type: Type.Number, semanticType: 'Count', levels: [] }, avg_income: { type: Type.Number, semanticType: 'Amount', levels: [] }, }, semanticAnnotations: { age_group: { semanticType: 'AgeGroup', sortOrder: ageGroups }, }, encodingMap: { x: makeEncodingItem('age_group'), y: makeEncodingItem('population'), }, }; } // ============================================================================ // 30. Boolean on color — fixed domain, categorical color, 2 values // Semantic type: "Boolean" → nominal encoding, fixed domain // // Boolean has a fixed domainShape — only 2 values exist (True/False). // Uses nominal encoding and categorical color scheme. // // WITHOUT this annotation: // Treated as generic string. No indication of the fixed 2-value nature. // // WITH "Boolean": // Fixed domain recognized. Categorical color with 2 distinct hues. // ============================================================================ function genBooleanColorTest(): TestCase { const rand = seeded(3001); const data: Record<string, any>[] = []; for (let i = 0; i < 30; i++) { data.push({ customer_id: `C${i + 1}`, purchase_amount: Math.round(20 + rand() * 480), is_member: rand() > 0.5 ? 'True' : 'False', }); } return { title: 'Boolean Color (True/False)', description: 'Semantic type "Boolean" on color uses categorical scheme with ' + 'fixed 2-value domain. Ideal for binary classification display. ' + 'WITHOUT this: treated as generic string, no fixed-domain awareness.', tags: ['semantic', 'color', 'boolean', 'categorical', 'fixed'], chartType: 'Scatter Plot', data, fields: [makeField('customer_id'), makeField('purchase_amount'), makeField('is_member')], metadata: { customer_id: { type: Type.String, semanticType: 'ID', levels: data.map(d => d.customer_id) }, purchase_amount: { type: Type.Number, semanticType: 'Amount', levels: [] }, is_member: { type: Type.String, semanticType: 'Boolean', levels: ['True', 'False'] }, }, encodingMap: { x: makeEncodingItem('customer_id'), y: makeEncodingItem('purchase_amount'), color: makeEncodingItem('is_member'), }, }; } // ============================================================================ // 31. Longitude — hard domain [-180, 180] with clamp // Semantic type: "Longitude" → scale.domain = [-180, 180], clamp = true // // Mirrors the Latitude test (test 12) but for the horizontal coordinate. // Longitude is a fixed geographic domain — values physically cannot // exceed ±180°. // // WITHOUT this annotation: // Axis auto-scales to data range. No geographic context. // // WITH "Longitude": // Full -180 to 180 range. Clamp prevents out-of-range rendering. // ============================================================================ function genLongitudeDomainClampTest(): TestCase { const cities = [ { city: 'Tokyo', longitude: 139.69, population_M: 13.96 }, { city: 'Delhi', longitude: 77.21, population_M: 32.94 }, { city: 'London', longitude: -0.12, population_M: 9.00 }, { city: 'New York', longitude: -74.01, population_M: 8.34 }, { city: 'São Paulo', longitude: -46.63, population_M: 12.33 }, { city: 'Sydney', longitude: 151.21, population_M: 5.31 }, { city: 'Cairo', longitude: 31.24, population_M: 21.32 }, { city: 'Los Angeles',longitude:-118.24, population_M: 3.90 }, ]; return { title: 'Longitude Hard Domain [-180°, 180°]', description: 'Semantic type "Longitude" pins axis to [-180, 180] with clamp=true ' + '(hard physical domain). Shows where cities fall in global E-W context. ' + 'WITHOUT this: axis scales to data range (-118 to 151), no global frame.', tags: ['semantic', 'domain', 'clamp', 'longitude', 'geographic'], chartType: 'Scatter Plot', data: cities, fields: [makeField('city'), makeField('longitude'), makeField('population_M')], metadata: { city: { type: Type.String, semanticType: 'City', levels: cities.map(c => c.city) }, longitude: { type: Type.Number, semanticType: 'Longitude', levels: [] }, population_M: { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('longitude'), y: makeEncodingItem('population_M'), }, }; } // ============================================================================ // 32. Index — ordinal, NOT reversed (contrast with Rank) // Semantic type: "Index" → ordinal encoding, ascending sort // // Unlike Rank (which reverses so #1 appears at top), Index is a // sequence number (position, row number). Index 1 should appear first // in data order — ascending, not reversed. // // WITHOUT this annotation: // Treated as generic number → quantitative encoding. Index values // like 1, 2, 3 would have fractional ticks and continuous axis. // // WITH "Index": // Ordinal encoding (no fractional values). Ascending sort direction. // Integer ticks only. Contrast with Rank which reverses. // ============================================================================ function genIndexOrdinalTest(): TestCase { const rand = seeded(3201); const data = Array.from({ length: 10 }, (_, i) => ({ trial_number: i + 1, response_time_ms: Math.round(200 + rand() * 800), })); return { title: 'Index Ordinal (Not Reversed)', description: 'Semantic type "Index" uses ordinal encoding with ascending sort. ' + 'Contrasts with Rank which reverses (best-first). Index is a plain ' + 'sequence: trial 1 → trial 10, no reversal. ' + 'WITHOUT this: treated as quantitative with fractional ticks.', tags: ['semantic', 'ordinal', 'index', 'ascending'], chartType: 'Line Chart', data, fields: [makeField('trial_number'), makeField('response_time_ms')], metadata: { trial_number: { type: Type.Number, semanticType: 'Index', levels: [] }, response_time_ms:{ type: Type.Number, semanticType: 'Duration', levels: [] }, }, semanticAnnotations: { response_time_ms: { semanticType: 'Duration', unit: 'ms' }, }, encodingMap: { x: makeEncodingItem('trial_number'), y: makeEncodingItem('response_time_ms'), }, }; } // ============================================================================ // 33. Percentage 0-100 representation (whole numbers) // Semantic type: "Percentage" with whole-number data (40, 85, 72) // // The format resolver detects 0-100 vs 0-1 representation. // For 0-100: uses data precision format + "%" suffix (not d3's .% which // multiplies by 100). // // WITHOUT this: // Values 72, 85, 40 shown as plain numbers. No % context. // // WITH "Percentage" (0-100): // Axis shows "72%", "85%", "40%". Data precision preserved. // ============================================================================ function genPercentageWholeNumberTest(): TestCase { const rand = seeded(3301); const subjects = ['Math', 'Science', 'English', 'History', 'Art', 'PE']; const data = subjects.map(s => ({ subject: s, pass_rate: Math.round(45 + rand() * 50), })); return { title: 'Percentage Whole-Number (0-100)', description: 'Percentage field with whole-number data (45, 72, 85). Format resolver ' + 'detects 0-100 representation and uses "d" + "%" suffix instead of ' + 'd3\'s .% format (which would multiply by 100, showing "7200%"). ' + 'WITHOUT this: bare numbers without %. Different from 0-1 test (#2).', tags: ['semantic', 'format', 'percentage', 'whole-number'], chartType: 'Bar Chart', data, fields: [makeField('subject'), makeField('pass_rate')], metadata: { subject: { type: Type.String, semanticType: 'Category', levels: subjects }, pass_rate: { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, semanticAnnotations: { pass_rate: { semanticType: 'Percentage', intrinsicDomain: [0, 100] }, }, encodingMap: { x: makeEncodingItem('subject'), y: makeEncodingItem('pass_rate'), }, }; } // ============================================================================ // 34. Score on color channel — conditional diverging // Semantic type: "Score" on color → diverging color when data spans // both sides of the domain midpoint // // Score with domain [0, 100] has a midpoint at 50. When data spans // both below and above 50, the color scheme should be diverging // (e.g., below-50 = red, above-50 = blue). // // WITHOUT this: // Generic sequential palette. No midpoint awareness. // // WITH "Score" on color: // Diverging color scheme centered on midpoint 50. // ============================================================================ function genScoreColorDivergingTest(): TestCase { const rand = seeded(3401); const students = ['Alice', 'Bob', 'Carol', 'Dave', 'Eve', 'Frank', 'Grace', 'Heidi', 'Ivan', 'Judy', 'Karl', 'Laura']; const data = students.map(s => ({ student: s, exam_score: Math.round(20 + rand() * 75), study_hours: Math.round(1 + rand() * 30), })); return { title: 'Score on Color (Conditional Diverging)', description: 'Score with intrinsicDomain [0, 100] on color. Domain midpoint = 50. ' + 'Data spans both sides → diverging color scheme centered at 50. ' + 'Below-50 = one hue, above-50 = another. ' + 'WITHOUT this: sequential palette, no midpoint.', tags: ['semantic', 'colorScheme', 'diverging', 'score', 'conditional'], chartType: 'Scatter Plot', data, fields: [makeField('student'), makeField('exam_score'), makeField('study_hours')], metadata: { student: { type: Type.String, semanticType: 'Category', levels: students }, exam_score: { type: Type.Number, semanticType: 'Score', levels: [] }, study_hours:{ type: Type.Number, semanticType: 'Quantity', levels: [] }, }, semanticAnnotations: { exam_score: { semanticType: 'Score', intrinsicDomain: [0, 100] }, }, encodingMap: { x: makeEncodingItem('study_hours'), y: makeEncodingItem('student'), color: makeEncodingItem('exam_score'), }, }; } // ============================================================================ // 35. Price with EUR currency override // Semantic type: "Price" + unit: "EUR" → "€15.50" on axis // // Annotation.unit overrides the default $ prefix for currency types. // Price also uses intensive aggRole (unit price averages, doesn't sum) // and always shows 2 decimal places for cents. // // WITHOUT this: // Default $ prefix. Or bare numbers with no currency. // // WITH "Price" + unit: "EUR": // Axis labels show "€15.50". Euro prefix from CURRENCY_MAP. // ============================================================================ function genPriceEurCurrencyTest(): TestCase { const rand = seeded(3501); const products = ['Espresso', 'Latte', 'Cappuccino', 'Mocha', 'Americano', 'Macchiato', 'Flat White', 'Cortado']; const data = products.map(p => ({ drink: p, price_eur: Math.round((2.50 + rand() * 5.50) * 100) / 100, daily_sales: Math.round(20 + rand() * 180), })); return { title: 'Price with EUR Currency Override', description: 'Annotation unit "EUR" overrides default $ to show "€3.50" on axis. ' + 'Price is intensive (average makes sense, sum doesn\'t) with 2 decimal ' + 'places for cents. ' + 'WITHOUT this: default $ prefix or bare numbers.', tags: ['semantic', 'format', 'currency', 'price', 'unit-override'], chartType: 'Bar Chart', data, fields: [makeField('drink'), makeField('price_eur'), makeField('daily_sales')], metadata: { drink: { type: Type.String, semanticType: 'Category', levels: products }, price_eur: { type: Type.Number, semanticType: 'Price', levels: [] }, daily_sales: { type: Type.Number, semanticType: 'Count', levels: [] }, }, semanticAnnotations: { price_eur: { semanticType: 'Price', unit: 'EUR' }, }, encodingMap: { x: makeEncodingItem('drink'), y: makeEncodingItem('price_eur'), }, }; } // ============================================================================ // 36. Year temporal vs ordinal disambiguation // Semantic type: "Year" with few values (≤6) → ordinal // Semantic type: "Year" with many values (>6) → temporal // // Year's registry entry has visEncodings: ['temporal', 'ordinal']. // resolveDefaultVisType disambiguates using distinct count: // ≤6 distinct → ordinal (e.g., 3 years: 2022, 2023, 2024) // >6 distinct → temporal (e.g., 20 years trend) // // This test uses 4 years → should pick ordinal. // Contrast with test #20 (Year format) which uses 7 years → temporal. // ============================================================================ function genYearOrdinalDisambiguationTest(): TestCase { const rand = seeded(3601); const years = [2021, 2022, 2023, 2024]; const data: Record<string, any>[] = []; for (const y of years) { for (const region of ['North', 'South']) { data.push({ year: y, region, revenue: Math.round(500000 + rand() * 1500000), }); } } return { title: 'Year Ordinal (≤6 Distinct Values)', description: 'Year with only 4 distinct values → ordinal encoding (not temporal). ' + 'resolveDefaultVisType picks ordinal when distinct ≤ 6. ' + 'Contrast with test #20 which uses 7 years → temporal. ' + 'WITHOUT disambiguation: always temporal, wasting axis resolution on 4 points.', tags: ['semantic', 'defaultVisType', 'year', 'ordinal', 'disambiguation'], chartType: 'Bar Chart', data, fields: [makeField('year'), makeField('region'), makeField('revenue')], metadata: { year: { type: Type.Number, semanticType: 'Year', levels: years }, region: { type: Type.String, semanticType: 'Category', levels: ['North', 'South'] }, revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { x: makeEncodingItem('year'), y: makeEncodingItem('revenue'), color: makeEncodingItem('region'), }, }; } // ============================================================================ // 37. Profit on color — signed currency diverging color // Semantic type: "Profit" on color → diverging color scheme // centered at 0, signed-currency format // // When Profit is on the color channel instead of axis, the diverging // analysis applies to color: positive = one hue, negative = another, // midpoint at 0. // // WITHOUT this: // Sequential palette for color. No sign awareness. Losses and gains // blend into the same gradient. // // WITH "Profit" on color: // Diverging scheme with domainMid = 0. (+) green, (-) red conceptually. // ============================================================================ function genProfitColorDivergingTest(): TestCase { const rand = seeded(3701); const divisions = ['Product A', 'Product B', 'Product C', 'Product D', 'Product E', 'Product F']; const data = divisions.map(d => ({ product: d, revenue: Math.round(100000 + rand() * 500000), profit: Math.round(-80000 + rand() * 220000), })); return { title: 'Profit on Color (Diverging)', description: 'Profit on color channel triggers diverging color scheme (midpoint 0). ' + 'Positive profit → one hue, negative → another, making P&L obvious. ' + 'WITHOUT this: sequential gradient, losses and gains look alike.', tags: ['semantic', 'colorScheme', 'diverging', 'profit', 'signed-currency'], chartType: 'Bar Chart', data, fields: [makeField('product'), makeField('revenue'), makeField('profit')], metadata: { product: { type: Type.String, semanticType: 'Category', levels: divisions }, revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, profit: { type: Type.Number, semanticType: 'Profit', levels: [] }, }, encodingMap: { x: makeEncodingItem('product'), y: makeEncodingItem('revenue'), color: makeEncodingItem('profit'), }, }; } // ============================================================================ // 38. Count integer format — comma grouping // Semantic type: "Count" → integer format with comma grouping (,d) // // Count uses format class 'integer' → ",d" format for axis labels. // This ensures tick marks are at whole numbers (no fractional counts) // and large counts show thousands separators (1,234 not 1234). // // Also tests aggregationDefault = 'sum' (additive measure — counts // of subgroups sum to the total). // // WITHOUT this: // Generic number format. Fractional ticks possible (1.5 items?). // No comma grouping on large values. // // WITH "Count": // Integer-only ticks with comma grouping. Meaningful zero. // ============================================================================ function genCountIntegerFormatTest(): TestCase { const rand = seeded(3801); const categories = ['Electronics', 'Clothing', 'Food', 'Books', 'Toys', 'Sports']; const data = categories.map(c => ({ category: c, item_count: Math.round(500 + rand() * 9500), })); return { title: 'Count Integer Format (,d)', description: 'Semantic type "Count" uses integer format ",d" — comma grouping ' + 'with integer-only ticks (no fractional counts). Count is additive: ' + 'sub-counts sum to total. ' + 'WITHOUT this: fractional ticks (1,500.5?) and no commas.', tags: ['semantic', 'format', 'integer', 'count', 'aggregation'], chartType: 'Bar Chart', data, fields: [makeField('category'), makeField('item_count')], metadata: { category: { type: Type.String, semanticType: 'Category', levels: categories }, item_count: { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('category'), y: makeEncodingItem('item_count'), }, }; } // ============================================================================ // 39. Unregistered semantic type — fallback to data-driven behavior // Semantic type: "CustomMetric" (not in registry) // // When a semantic type is not registered, the system falls back to // data-driven inference: if values are numeric → quantitative encoding, // aggregationDefault = 'sum', zeroClass = 'meaningful'. // // This tests that unregistered types don't crash and behave sensibly. // // WITHOUT this (before fallback was added): // Unknown types → nominal encoding even for numeric data. // // WITH fallback: // Numeric data → quantitative, sensible defaults applied. // ============================================================================ function genUnregisteredTypeFallbackTest(): TestCase { const rand = seeded(3901); const items = ['Item A', 'Item B', 'Item C', 'Item D', 'Item E', 'Item F']; const data = items.map(item => ({ item, custom_metric: Math.round(100 + rand() * 900), })); return { title: 'Unregistered Type Fallback', description: 'Semantic type "CustomMetric" is NOT in the registry. Falls back to ' + 'data-driven inference: numeric data → quantitative encoding, ' + 'aggregationDefault = sum, zeroClass = meaningful. ' + 'Tests that unknown types don\'t crash and behave sensibly.', tags: ['semantic', 'fallback', 'unregistered', 'data-driven'], chartType: 'Bar Chart', data, fields: [makeField('item'), makeField('custom_metric')], metadata: { item: { type: Type.String, semanticType: 'Category', levels: items }, custom_metric: { type: Type.Number, semanticType: 'CustomMetric', levels: [] }, }, encodingMap: { x: makeEncodingItem('item'), y: makeEncodingItem('custom_metric'), }, }; } // ############################################################################ // SNAP-TO-BOUND TESTS // // Demonstrate the snap-to-bound heuristic for Percentage and PercentageChange. // Each bound snaps independently when data reaches within 25% of the // intrinsic range from that edge. Data exceeding a bound never snaps. // ############################################################################ // ============================================================================ // S1. Percentage — data far from both bounds → no snap // Data 35–65%, threshold=25. Neither end close → axis data-fits. // ============================================================================ function genSnapPctNoSnapTest(): TestCase { const rand = seeded(7001); const weeks = Array.from({ length: 8 }, (_, i) => `Week ${i + 1}`); const data = weeks.map(w => ({ week: w, usage: Math.round(35 + rand() * 30), })); return { title: 'Snap: Pct 35–65% → no snap', description: 'Usage data ranges 35–65%. Both ends are far from 0 and 100 ' + '(threshold = 25% of range = 25), so no snap occurs. ' + 'Axis data-fits to show detail in the 35–65% band.', tags: ['semantic', 'snap', 'percentage', 'no-snap'], chartType: 'Line Chart', data, fields: [makeField('week'), makeField('usage')], metadata: { week: { type: Type.String, semanticType: 'Category', levels: weeks }, usage: { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, semanticAnnotations: { usage: { semanticType: 'Percentage', intrinsicDomain: [0, 100] }, }, encodingMap: { x: makeEncodingItem('week'), y: makeEncodingItem('usage'), }, }; } // ============================================================================ // S2. Percentage — data near upper bound only → snap max to 100 // Data 60–97%, 97 within threshold of 100 → domainMax=100. // Lower end (60) far from 0 → no snap min. // ============================================================================ function genSnapPctMaxOnlyTest(): TestCase { const rand = seeded(7002); const depts = ['Engineering', 'Marketing', 'Sales', 'Support', 'Design', 'Legal']; const data = depts.map(d => ({ dept: d, completion: Math.round(60 + rand() * 37), })); return { title: 'Snap: Pct 84–96% → snap max 100', description: 'Completion data reaches 96%. Since 96 is within 25 (threshold = 25% of 100) ' + 'of 100, the upper bound snaps to 100%. Lower end (84%) is far from 0 → ' + 'no snap. Shows domainMax=100 while the lower end auto-fits.', tags: ['semantic', 'snap', 'percentage', 'snap-max'], chartType: 'Bar Chart', data, fields: [makeField('dept'), makeField('completion')], metadata: { dept: { type: Type.String, semanticType: 'Category', levels: depts }, completion: { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, semanticAnnotations: { completion: { semanticType: 'Percentage', intrinsicDomain: [0, 100] }, }, encodingMap: { x: makeEncodingItem('dept'), y: makeEncodingItem('completion'), }, }; } // ============================================================================ // S3. Percentage — data near lower bound only → snap min to 0 // Data 2–25%, 2 within threshold of 0 → domainMin=0. // Upper end (25) far from 100 → no snap max. // ============================================================================ function genSnapPctMinOnlyTest(): TestCase { const rand = seeded(7003); const items = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']; const data = items.map(item => ({ item, error_rate: Math.round(2 + rand() * 23), })); return { title: 'Snap: Pct 4–20% → snap min 0', description: 'Error rates range 4–20%. Since 4 is within 25 (threshold = 25% of 100) ' + 'of 0, the lower bound snaps to 0%. Upper end (20%) is far from 100 → ' + 'no snap. Shows domainMin=0 while the upper end auto-fits.', tags: ['semantic', 'snap', 'percentage', 'snap-min'], chartType: 'Bar Chart', data, fields: [makeField('item'), makeField('error_rate')], metadata: { item: { type: Type.String, semanticType: 'Category', levels: items }, error_rate: { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, semanticAnnotations: { error_rate: { semanticType: 'Percentage', intrinsicDomain: [0, 100] }, }, encodingMap: { x: makeEncodingItem('item'), y: makeEncodingItem('error_rate'), }, }; } // ============================================================================ // S4. Percentage — data near both bounds → snap both [0, 100] // Data 3–95%, both ends close → full [0, 100] domain. // ============================================================================ function genSnapPctBothTest(): TestCase { const rand = seeded(7004); const months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']; const data = months.map((m, i) => ({ month: m, capacity: Math.round(3 + (i / 11) * 87 + rand() * 5), })); return { title: 'Snap: Pct 3–95% → snap both [0,100]', description: 'Capacity ranges 3–95%. Both 3 (within 25 of 0) and 95 (within 25 of 100) ' + 'trigger snap, producing the full [0, 100] domain. Natural for data that ' + 'spans nearly the entire percentage range.', tags: ['semantic', 'snap', 'percentage', 'snap-both'], chartType: 'Line Chart', data, fields: [makeField('month'), makeField('capacity')], metadata: { month: { type: Type.String, semanticType: 'Month', levels: months }, capacity: { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, semanticAnnotations: { capacity: { semanticType: 'Percentage', intrinsicDomain: [0, 100] }, }, encodingMap: { x: makeEncodingItem('month'), y: makeEncodingItem('capacity'), }, }; } // ============================================================================ // S5. Percentage — data exceeds upper bound → no snap // Data 30–130%, 130 exceeds 100 → no snap on either end. // (30 is also far from 0.) // ============================================================================ function genSnapPctExceedTest(): TestCase { const rand = seeded(7005); const quarters = ['Q1', 'Q2', 'Q3', 'Q4']; const data = quarters.map(q => ({ quarter: q, growth: Math.round(30 + rand() * 100), })); return { title: 'Snap: Pct 30–130% → no snap (exceeds)', description: 'Growth data ranges 30–130%. The max (130) exceeds the intrinsic upper ' + 'bound (100), so no snap — VL auto-extends. The min (30) is also far from 0. ' + 'Axis data-fits to the actual data range.', tags: ['semantic', 'snap', 'percentage', 'exceed'], chartType: 'Bar Chart', data, fields: [makeField('quarter'), makeField('growth')], metadata: { quarter: { type: Type.String, semanticType: 'Category', levels: quarters }, growth: { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, semanticAnnotations: { growth: { semanticType: 'Percentage', intrinsicDomain: [0, 100] }, }, encodingMap: { x: makeEncodingItem('quarter'), y: makeEncodingItem('growth'), }, }; } // ============================================================================ // S6. PercentageChange — small range near zero → no snap // Data -3% to +5%, both far from ±100% → axis data-fits. // ============================================================================ function genSnapPctChangeNoSnapTest(): TestCase { const rand = seeded(7006); const stocks = ['AAPL', 'GOOG', 'MSFT', 'AMZN', 'META', 'NVDA', 'TSLA']; const data = stocks.map(s => ({ stock: s, daily_change: Math.round((-0.03 + rand() * 0.08) * 1000) / 1000, })); return { title: 'Snap: PctChange ±3–5% → no snap', description: 'Daily stock changes range -3% to +5%. Both ends are far from ±1 ' + '(threshold = 0.25 per side), so no snap. Axis data-fits around the small range. ' + 'Contextual zero-baseline still includes 0 for bar marks.', tags: ['semantic', 'snap', 'percentage-change', 'no-snap'], chartType: 'Bar Chart', data, fields: [makeField('stock'), makeField('daily_change')], metadata: { stock: { type: Type.String, semanticType: 'Category', levels: stocks }, daily_change: { type: Type.Number, semanticType: 'PercentageChange', levels: [] }, }, semanticAnnotations: { daily_change: { semanticType: 'PercentageChange', intrinsicDomain: [-1, 1] }, }, encodingMap: { x: makeEncodingItem('stock'), y: makeEncodingItem('daily_change'), }, }; } // ============================================================================ // S7. PercentageChange — near negative bound → snap min to -1 // Data -0.96 to +0.25, -0.96 within 0.25 (25% of 1) of -1 → snap min=-1. // ============================================================================ function genSnapPctChangeMinTest(): TestCase { const data = [ { sector: 'Tech', ytd_return: -0.96 }, { sector: 'Energy', ytd_return: -0.42 }, { sector: 'Finance', ytd_return: -0.71 }, { sector: 'Healthcare', ytd_return: 0.25 }, { sector: 'Retail', ytd_return: -0.55 }, { sector: 'Telecom', ytd_return: -0.83 }, ]; const sectors = data.map(d => d.sector); return { title: 'Snap: PctChange -96% to +25% → snap min -100%', description: 'Sector returns range -96% to +25%. The min (-0.96) is within 0.25 (threshold = ' + '25% of distance 1 from zero to -1) of -1, so the lower bound snaps to -100%. ' + 'Upper end (+25%) is far from +100% → no snap. Shows domainMin=-1 with ' + 'auto-fit upper end.', tags: ['semantic', 'snap', 'percentage-change', 'snap-min'], chartType: 'Bar Chart', data, fields: [makeField('sector'), makeField('ytd_return')], metadata: { sector: { type: Type.String, semanticType: 'Category', levels: sectors }, ytd_return: { type: Type.Number, semanticType: 'PercentageChange', levels: [] }, }, semanticAnnotations: { ytd_return: { semanticType: 'PercentageChange', intrinsicDomain: [-1, 1] }, }, encodingMap: { x: makeEncodingItem('sector'), y: makeEncodingItem('ytd_return'), }, }; } // ============================================================================ // S8. PercentageChange — near both bounds → snap both [-1, 1] // Data -0.95 to +0.93, both within 0.25 (25% of per-side distance 1) → full [-1, 1]. // ============================================================================ function genSnapPctChangeBothTest(): TestCase { const data = [ { strategy: 'Strategy A', performance: -0.95 }, { strategy: 'Strategy B', performance: -0.30 }, { strategy: 'Strategy C', performance: 0.15 }, { strategy: 'Strategy D', performance: -0.60 }, { strategy: 'Strategy E', performance: 0.48 }, { strategy: 'Strategy F', performance: 0.93 }, ]; const strategies = data.map(d => d.strategy); return { title: 'Snap: PctChange ±95–93% → snap both [-1,1]', description: 'Strategy performance ranges -95% to +93%. Both -0.95 (within 0.25 of -1) ' + 'and +0.93 (within 0.25 of +1) trigger snap (threshold = 25% of each side\'s ' + 'distance from zero = 0.25), producing the full [-1, 1] domain.', tags: ['semantic', 'snap', 'percentage-change', 'snap-both'], chartType: 'Bar Chart', data, fields: [makeField('strategy'), makeField('performance')], metadata: { strategy: { type: Type.String, semanticType: 'Category', levels: strategies }, performance: { type: Type.Number, semanticType: 'PercentageChange', levels: [] }, }, semanticAnnotations: { performance: { semanticType: 'PercentageChange', intrinsicDomain: [-1, 1] }, }, encodingMap: { x: makeEncodingItem('strategy'), y: makeEncodingItem('performance'), }, }; } // ============================================================================ // S9. Stacked bar — percentages sum to ~100% per group → keep snap // Individual values 30–40%, each group has 3 series summing to ~98%. // Individual values are far from 100% (no individual snap), but the stacked // totals (~98%) are within 25% of 100 → re-snap fires domainMax=100. // ============================================================================ function genSnapStackedSumNear100Test(): TestCase { const categories = ['Alpha', 'Beta', 'Gamma', 'Delta']; const series = ['Part A', 'Part B', 'Part C']; // Each group's parts sum to ~97–99% const groupTotals: Record<string, number[]> = { 'Alpha': [35, 30, 33], // sum = 98 'Beta': [40, 28, 30], // sum = 98 'Gamma': [32, 35, 30], // sum = 97 'Delta': [38, 27, 34], // sum = 99 }; const data: Record<string, any>[] = []; for (const cat of categories) { const parts = groupTotals[cat]; series.forEach((s, i) => { data.push({ category: cat, series: s, share: parts[i] }); }); } return { title: 'Snap: Stacked Pct sum≈100% → snap max 100', description: 'Stacked bar with 3 series per group. Individual values range 27–40% (far from 100, ' + 'no individual snap), but stacked totals per group are 97–99%. Since 99 is within ' + '25 (threshold) of 100, the stacked-total re-snap fires → domainMax=100. ' + 'This keeps the axis at a natural percentage ceiling.', tags: ['semantic', 'snap', 'percentage', 'stacked', 'sum-near-bound'], chartType: 'Stacked Bar Chart', data, fields: [makeField('category'), makeField('share'), makeField('series')], metadata: { category: { type: Type.String, semanticType: 'Category', levels: categories }, series: { type: Type.String, semanticType: 'Category', levels: series }, share: { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, semanticAnnotations: { share: { semanticType: 'Percentage', intrinsicDomain: [0, 100] }, }, encodingMap: { x: makeEncodingItem('category'), y: makeEncodingItem('share'), color: makeEncodingItem('series'), }, }; } // ============================================================================ // S10. Stacked bar — percentages sum to ~150% per group → skip snap // Individual values 30–60%, but 3 series stack to ~150%. // Totals exceed intrinsic upper bound (100) → skip domain constraint. // ============================================================================ function genSnapStackedSumExceedsTest(): TestCase { const categories = ['East', 'West', 'North', 'South']; const series = ['Source X', 'Source Y', 'Source Z']; // Each group's parts sum to ~140–160% const groupTotals: Record<string, number[]> = { 'East': [55, 48, 42], // sum = 145 'West': [60, 50, 45], // sum = 155 'North': [45, 52, 43], // sum = 140 'South': [58, 55, 47], // sum = 160 }; const data: Record<string, any>[] = []; for (const cat of categories) { const parts = groupTotals[cat]; series.forEach((s, i) => { data.push({ region: cat, source: s, overlap_pct: parts[i] }); }); } return { title: 'Snap: Stacked Pct sum≈150% → no snap (exceeds)', description: 'Stacked bar with 3 overlapping percentage sources per region. Individual values ' + 'range 42–60%, but stacked totals are 140–160% — exceeding the intrinsic upper ' + 'bound of 100%. Domain constraint is skipped so VL auto-extends the axis.', tags: ['semantic', 'snap', 'percentage', 'stacked', 'sum-exceeds'], chartType: 'Stacked Bar Chart', data, fields: [makeField('region'), makeField('overlap_pct'), makeField('source')], metadata: { region: { type: Type.String, semanticType: 'Category', levels: categories }, source: { type: Type.String, semanticType: 'Category', levels: series }, overlap_pct: { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, semanticAnnotations: { overlap_pct: { semanticType: 'Percentage', intrinsicDomain: [0, 100] }, }, encodingMap: { x: makeEncodingItem('region'), y: makeEncodingItem('overlap_pct'), color: makeEncodingItem('source'), }, }; } // ============================================================================ // S11. Stacked bar — individual values snap, totals fit → keep snap // Individual values 80–95% (near 100, snap fires individually). // Stacked totals ~270% exceed 100, but the individual snap already // fired — this test verifies the stacked-total check correctly // overrides it: totals 270% > 100 → skip domain constraint. // ============================================================================ function genSnapStackedIndividualSnapButTotalsExceedTest(): TestCase { const categories = ['Jan', 'Feb', 'Mar', 'Apr']; const series = ['Metric 1', 'Metric 2', 'Metric 3']; // Individual values 80–95% (all snap individually), but sum ~270% const groupTotals: Record<string, number[]> = { 'Jan': [92, 88, 85], // sum = 265 'Feb': [95, 90, 87], // sum = 272 'Mar': [91, 93, 84], // sum = 268 'Apr': [94, 89, 91], // sum = 274 }; const data: Record<string, any>[] = []; for (const cat of categories) { const parts = groupTotals[cat]; series.forEach((s, i) => { data.push({ month: cat, metric: s, score: parts[i] }); }); } return { title: 'Snap: Stacked Pct indiv snap but sum>100% → skip', description: 'Individual scores range 84–95% — close to 100%, so snap fires on individual ' + 'values. But when stacked (3 metrics per month), totals reach 265–274%. Since ' + 'totals exceed intrinsic bound (100), the stacked-total check overrides → ' + 'domain constraint is skipped, VL auto-extends.', tags: ['semantic', 'snap', 'percentage', 'stacked', 'individual-snap-override'], chartType: 'Stacked Bar Chart', data, fields: [makeField('month'), makeField('score'), makeField('metric')], metadata: { month: { type: Type.String, semanticType: 'Category', levels: categories }, metric: { type: Type.String, semanticType: 'Category', levels: series }, score: { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, semanticAnnotations: { score: { semanticType: 'Percentage', intrinsicDomain: [0, 100] }, }, encodingMap: { x: makeEncodingItem('month'), y: makeEncodingItem('score'), color: makeEncodingItem('metric'), }, }; } // ============================================================================ // S12. Stacked bar — individual values 30–50%, totals far from 100% → no snap // Individual values are mid-range (no individual snap). // Stacked totals ~58–70%, also far from 100% → no re-snap either. // Axis should data-fit. // ============================================================================ function genSnapStackedTotalsFarFromBoundTest(): TestCase { const categories = ['Q1', 'Q2', 'Q3', 'Q4']; const series = ['Channel A', 'Channel B']; // Each group sums to ~58–70% const groupTotals: Record<string, number[]> = { 'Q1': [30, 28], // sum = 58 'Q2': [35, 30], // sum = 65 'Q3': [28, 32], // sum = 60 'Q4': [38, 32], // sum = 70 }; const data: Record<string, any>[] = []; for (const cat of categories) { const parts = groupTotals[cat]; series.forEach((s, i) => { data.push({ quarter: cat, channel: s, coverage: parts[i] }); }); } return { title: 'Snap: Stacked Pct sum≈58–70% → no snap', description: 'Stacked bar with 2 series per quarter. Individual values range 28–38% ' + '(far from 100%, no individual snap). Stacked totals are 58–70% — also ' + 'far from 100% (threshold = 25). No snap fires on either individual values ' + 'or stacked totals. Axis data-fits.', tags: ['semantic', 'snap', 'percentage', 'stacked', 'no-snap'], chartType: 'Stacked Bar Chart', data, fields: [makeField('quarter'), makeField('coverage'), makeField('channel')], metadata: { quarter: { type: Type.String, semanticType: 'Category', levels: categories }, channel: { type: Type.String, semanticType: 'Category', levels: series }, coverage: { type: Type.Number, semanticType: 'Percentage', levels: [] }, }, semanticAnnotations: { coverage: { semanticType: 'Percentage', intrinsicDomain: [0, 100] }, }, encodingMap: { x: makeEncodingItem('quarter'), y: makeEncodingItem('coverage'), color: makeEncodingItem('channel'), }, }; } /** * Generate all snap-to-bound test cases. * * Demonstrates the snap-to-bound heuristic for Percentage [0,100] * and PercentageChange [-1,1]. Each bound snaps independently when * data reaches within 25% of the effective side range from that edge. * For zero-straddling domains (lo < 0 < hi), each side's threshold is * computed relative to its distance from zero, not the full range. */ export function genSnapToBoundTests(): TestCase[] { return [ genSnapPctNoSnapTest(), genSnapPctMaxOnlyTest(), genSnapPctMinOnlyTest(), genSnapPctBothTest(), genSnapPctExceedTest(), genSnapPctChangeNoSnapTest(), genSnapPctChangeMinTest(), genSnapPctChangeBothTest(), genSnapStackedSumNear100Test(), genSnapStackedSumExceedsTest(), genSnapStackedIndividualSnapButTotalsExceedTest(), genSnapStackedTotalsFarFromBoundTest(), ]; } ================================================ FILE: src/lib/agents-chart/test-data/specialized-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { Type } from '../../../data/types'; import { Channel, EncodingItem } from '../../../components/ComponentType'; import { TestCase, makeField, makeEncodingItem, buildMetadata } from './types'; import { seededRandom, genDates, genMonths, genCategories } from './generators'; // ------ Heatmap ------ export function genHeatmapTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(500); // 1. Small nominal × nominal { const xs = genCategories('Category', 5); const ys = genMonths(6); const data: any[] = []; for (const x of xs) for (const y of ys) { data.push({ Category: x, Month: y, Value: Math.round(rand() * 100) }); } tests.push({ title: 'Nominal × Nominal (small, 5×6)', description: '5 categories × 6 months — basic heatmap', tags: ['nominal', 'ordinal', 'color', 'small'], chartType: 'Heatmap', data, fields: [makeField('Category'), makeField('Month'), makeField('Value')], metadata: { Category: { type: Type.String, semanticType: 'Category', levels: xs }, Month: { type: Type.String, semanticType: 'Month', levels: ys }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Category'), y: makeEncodingItem('Month'), color: makeEncodingItem('Value') }, }); } // 2. Quantitative × quantitative (tests applyDynamicMarkResizing with nominalThreshold) { const xs = Array.from({ length: 10 }, (_, i) => i * 10); const ys = Array.from({ length: 8 }, (_, i) => i * 5); const data: any[] = []; for (const x of xs) for (const y of ys) { data.push({ X: x, Y: y, Density: Math.round(rand() * 100) }); } tests.push({ title: 'Quant × Quant (small cardinality → nominal)', description: '10×8 grid — should convert to nominal (≤20 threshold)', tags: ['quantitative', 'small', 'dtype-conversion'], chartType: 'Heatmap', data, fields: [makeField('X'), makeField('Y'), makeField('Density')], metadata: { X: { type: Type.Number, semanticType: 'Quantity', levels: xs }, Y: { type: Type.Number, semanticType: 'Quantity', levels: ys }, Density: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('X'), y: makeEncodingItem('Y'), color: makeEncodingItem('Density') }, }); } // 3. Large quantitative (should resize rect, not convert) { const data: any[] = []; for (let x = 0; x < 50; x++) for (let y = 0; y < 30; y++) { data.push({ Hour: x, Day: y, Activity: Math.round(rand() * 100) }); } tests.push({ title: 'Quant × Quant (large cardinality → resize)', description: '50×30 grid — should resize rect width/height, not convert to nominal', tags: ['quantitative', 'large', 'dtype-conversion'], chartType: 'Heatmap', data, fields: [makeField('Hour'), makeField('Day'), makeField('Activity')], metadata: { Hour: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Day: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Activity: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Hour'), y: makeEncodingItem('Day'), color: makeEncodingItem('Activity') }, }); } // 4. Temporal × nominal { const months = genMonths(12); const products = genCategories('Product', 6); const data: any[] = []; for (const m of months) for (const p of products) { data.push({ Month: m, Product: p, Sales: Math.round(rand() * 1000) }); } tests.push({ title: 'Ordinal × Nominal (12×6)', description: '12 months × 6 products', tags: ['ordinal', 'nominal', 'color', 'medium'], chartType: 'Heatmap', data, fields: [makeField('Month'), makeField('Product'), makeField('Sales')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: months }, Product: { type: Type.String, semanticType: 'Product', levels: products }, Sales: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Product'), color: makeEncodingItem('Sales') }, }); } // 5. Large temporal × nominal (80 dates × 5 categories) { const dates = genDates(80, 2016); const cats = genCategories('Category', 5); const data: any[] = []; for (const d of dates) for (const c of cats) { data.push({ Date: d, Category: c, Intensity: Math.round(rand() * 100) }); } tests.push({ title: 'Temporal × Nominal (large, 80×5)', description: '80 dates × 5 categories — tests large temporal heatmap with rect sizing', tags: ['temporal', 'nominal', 'color', 'very-large'], chartType: 'Heatmap', data, fields: [makeField('Date'), makeField('Category'), makeField('Intensity')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Category: { type: Type.String, semanticType: 'Category', levels: cats }, Intensity: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Date'), y: makeEncodingItem('Category'), color: makeEncodingItem('Intensity') }, }); } // 6. Nominal × large temporal (swapped, 5 × 80 dates on y) { const dates = genDates(80, 2016); const cats = genCategories('Product', 5); const data: any[] = []; for (const c of cats) for (const d of dates) { data.push({ Product: c, Date: d, Score: Math.round(rand() * 100) }); } tests.push({ title: 'Nominal × Temporal (large, 5×80)', description: '5 products × 80 dates on y-axis — large temporal on y', tags: ['nominal', 'temporal', 'color', 'very-large', 'swap-axis'], chartType: 'Heatmap', data, fields: [makeField('Product'), makeField('Date'), makeField('Score')], metadata: { Product: { type: Type.String, semanticType: 'Product', levels: cats }, Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Score: { type: Type.Number, semanticType: 'Score', levels: [] }, }, encodingMap: { x: makeEncodingItem('Product'), y: makeEncodingItem('Date'), color: makeEncodingItem('Score') }, }); } // 7. Large temporal × large temporal (60×40 date grid) { const xDates = genDates(60, 2018); const yDates = genDates(40, 2020); const data: any[] = []; for (const xd of xDates) for (const yd of yDates) { data.push({ StartDate: xd, EndDate: yd, Correlation: Math.round(-100 + rand() * 200) / 100 }); } tests.push({ title: 'Temporal × Temporal (large, 60×40)', description: '60×40 date grid — both axes temporal, tests rect sizing on both', tags: ['temporal', 'color', 'very-large'], chartType: 'Heatmap', data, fields: [makeField('StartDate'), makeField('EndDate'), makeField('Correlation')], metadata: { StartDate: { type: Type.Date, semanticType: 'Date', levels: [] }, EndDate: { type: Type.Date, semanticType: 'Date', levels: [] }, Correlation: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('StartDate'), y: makeEncodingItem('EndDate'), color: makeEncodingItem('Correlation') }, }); } // 8. Asymmetric discrete: 5 categories on Y × 80 categories on X (400 cells) { const xCats = Array.from({ length: 80 }, (_, i) => `C${String(i + 1).padStart(2, '0')}`); const yCats = genCategories('Category', 5); const data: any[] = []; for (const x of xCats) for (const y of yCats) data.push({ Category: x, Group: y, Value: Math.round(rand() * 100) }); tests.push({ title: 'Nominal × Nominal (asymmetric wide, 80×5)', description: '80 categories on X × 5 on Y (400 cells) — tests wide asymmetric discrete axes', tags: ['nominal', 'color', 'asymmetric', 'very-large'], chartType: 'Heatmap', data, fields: [makeField('Category'), makeField('Group'), makeField('Value')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Category'), y: makeEncodingItem('Group'), color: makeEncodingItem('Value') }, }); } // 9. Asymmetric discrete: 80 categories on Y × 5 categories on X (400 cells) { const xCats = genCategories('Category', 5); const yCats = Array.from({ length: 80 }, (_, i) => `C${String(i + 1).padStart(2, '0')}`); const data: any[] = []; for (const x of xCats) for (const y of yCats) data.push({ Group: x, Category: y, Value: Math.round(rand() * 100) }); tests.push({ title: 'Nominal × Nominal (asymmetric tall, 5×80)', description: '5 categories on X × 80 on Y (400 cells) — tests tall asymmetric discrete axes', tags: ['nominal', 'color', 'asymmetric', 'very-large'], chartType: 'Heatmap', data, fields: [makeField('Group'), makeField('Category'), makeField('Value')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Group'), y: makeEncodingItem('Category'), color: makeEncodingItem('Value') }, }); } return tests; } // ------ Pie Chart ------ export function genPieTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(800); // 1. Small { const cats = genCategories('Category', 4); const data = cats.map(c => ({ Category: c, Value: Math.round(100 + rand() * 500) })); tests.push({ title: 'Pie (small, 4 slices)', description: '4 categories', tags: ['nominal', 'quantitative', 'small'], chartType: 'Pie Chart', data, fields: [makeField('Category'), makeField('Value')], metadata: { Category: { type: Type.String, semanticType: 'Category', levels: cats }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { size: makeEncodingItem('Value'), color: makeEncodingItem('Category') }, }); } // 2. Medium { const cats = genCategories('Product', 10); const data = cats.map(c => ({ Product: c, Revenue: Math.round(1000 + rand() * 9000) })); tests.push({ title: 'Pie (medium, 10 slices)', description: '10 products — tests color scheme at boundary', tags: ['nominal', 'quantitative', 'medium'], chartType: 'Pie Chart', data, fields: [makeField('Product'), makeField('Revenue')], metadata: { Product: { type: Type.String, semanticType: 'Product', levels: cats }, Revenue: { type: Type.Number, semanticType: 'Revenue', levels: [] }, }, encodingMap: { size: makeEncodingItem('Revenue'), color: makeEncodingItem('Product') }, }); } // 3. Large — 25 slices (text overlay should be disabled) { const cats = genCategories('Region', 25); const data = cats.map(c => ({ Region: c, Sales: Math.round(500 + rand() * 5000) })); tests.push({ title: 'Pie (large, 25 slices)', description: '25 regions — too many slices for text overlay, legend + tooltip only', tags: ['nominal', 'quantitative', 'large', 'stress'], chartType: 'Pie Chart', data, fields: [makeField('Region'), makeField('Sales')], metadata: { Region: { type: Type.String, semanticType: 'Category', levels: cats }, Sales: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { size: makeEncodingItem('Sales'), color: makeEncodingItem('Region') }, }); } // 4. Skewed — one dominant slice + several tiny ones { const cats = ['Dominant', 'Small-A', 'Small-B', 'Small-C', 'Tiny-1', 'Tiny-2']; const vals = [5000, 200, 180, 150, 30, 20]; const data = cats.map((c, i) => ({ Category: c, Value: vals[i] })); tests.push({ title: 'Pie (skewed, 6 slices)', description: 'One dominant slice ~90%, tests circumference pressure with effective bar count', tags: ['nominal', 'quantitative', 'skewed'], chartType: 'Pie Chart', data, fields: [makeField('Category'), makeField('Value')], metadata: { Category: { type: Type.String, semanticType: 'Category', levels: cats }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { size: makeEncodingItem('Value'), color: makeEncodingItem('Category') }, }); } return tests; } // ------ Ranged Dot Plot ------ export function genRangedDotPlotTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(850); { const cats = genCategories('Country', 8); const data: any[] = []; for (const c of cats) { data.push({ Country: c, Value: Math.round(30 + rand() * 40), Metric: 'Min' }); data.push({ Country: c, Value: Math.round(60 + rand() * 40), Metric: 'Max' }); } tests.push({ title: 'Ranged Dot Plot (8 items)', description: '8 countries with min/max range', tags: ['nominal', 'quantitative', 'color', 'small'], chartType: 'Ranged Dot Plot', data, fields: [makeField('Value'), makeField('Country'), makeField('Metric')], metadata: { Country: { type: Type.String, semanticType: 'Country', levels: cats }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Metric: { type: Type.String, semanticType: 'Category', levels: ['Min', 'Max'] }, }, encodingMap: { x: makeEncodingItem('Value'), y: makeEncodingItem('Country'), color: makeEncodingItem('Metric') }, }); } return tests; } // ------ Lollipop Chart ------ export function genLollipopTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(930); // 1. Nominal × Quant (vertical lollipop) { const countries = ['USA', 'China', 'Japan', 'Germany', 'UK', 'France', 'India', 'Brazil']; const data = countries.map(c => ({ Country: c, GDP: Math.round(500 + rand() * 20000) })); tests.push({ title: 'Nominal × Quant (vertical lollipop)', description: '8 countries — rule from 0 to value + dot', tags: ['nominal', 'quantitative', 'small'], chartType: 'Lollipop Chart', data, fields: [makeField('Country'), makeField('GDP')], metadata: { Country: { type: Type.String, semanticType: 'Country', levels: countries }, GDP: { type: Type.Number, semanticType: 'GDP', levels: [] }, }, encodingMap: { x: makeEncodingItem('Country'), y: makeEncodingItem('GDP') }, }); } // 2. Nominal × Quant + Color { const items = genCategories('Product', 10); const data = items.map(p => ({ Product: p, Sales: Math.round(100 + rand() * 900), Region: rand() > 0.5 ? 'East' : 'West', })); tests.push({ title: 'Nominal × Quant + Color (10 items)', description: 'Products with color-coded region', tags: ['nominal', 'quantitative', 'color', 'medium'], chartType: 'Lollipop Chart', data, fields: [makeField('Product'), makeField('Sales'), makeField('Region')], metadata: { Product: { type: Type.String, semanticType: 'Category', levels: items }, Sales: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Region: { type: Type.String, semanticType: 'Category', levels: ['East', 'West'] }, }, encodingMap: { x: makeEncodingItem('Product'), y: makeEncodingItem('Sales'), color: makeEncodingItem('Region') }, }); } // 3. Horizontal lollipop (quant on x, nominal on y) { const departments = ['Engineering', 'Marketing', 'Sales', 'Support', 'HR', 'Finance']; const data = departments.map(d => ({ Department: d, Score: Math.round(40 + rand() * 60) })); tests.push({ title: 'Quant × Nominal (horizontal lollipop)', description: '6 departments — horizontal layout', tags: ['nominal', 'quantitative', 'small'], chartType: 'Lollipop Chart', data, fields: [makeField('Score'), makeField('Department')], metadata: { Score: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Department: { type: Type.String, semanticType: 'Category', levels: departments }, }, encodingMap: { x: makeEncodingItem('Score'), y: makeEncodingItem('Department') }, }); } // 4. Color + Column facet { const regions = ['North', 'South']; const categories = genCategories('Item', 6); const data: any[] = []; for (const r of regions) { for (const c of categories) { data.push({ Item: c, Revenue: Math.round(200 + rand() * 800), Region: r, Tier: rand() > 0.5 ? 'Premium' : 'Standard', }); } } tests.push({ title: 'Color + Column Facet', description: '6 items × 2 regions faceted, color by tier', tags: ['nominal', 'quantitative', 'color', 'facet', 'medium'], chartType: 'Lollipop Chart', data, fields: [makeField('Item'), makeField('Revenue'), makeField('Region'), makeField('Tier')], metadata: { Item: { type: Type.String, semanticType: 'Category', levels: categories }, Revenue: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Region: { type: Type.String, semanticType: 'Category', levels: regions }, Tier: { type: Type.String, semanticType: 'Category', levels: ['Premium', 'Standard'] }, }, encodingMap: { x: makeEncodingItem('Item'), y: makeEncodingItem('Revenue'), color: makeEncodingItem('Tier'), column: makeEncodingItem('Region'), }, }); } return tests; } // ------ Custom Charts (Point, Line, Bar, Rect, Area) ------ export function genCustomTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(900); // Custom Point { const data = Array.from({ length: 50 }, () => ({ X: Math.round(rand() * 100), Y: Math.round(rand() * 100), Size: Math.round(rand() * 50), })); tests.push({ title: 'Custom Point (50 pts)', description: 'Basic custom point with size encoding', tags: ['quantitative', 'size', 'medium'], chartType: 'Custom Point', data, fields: [makeField('X'), makeField('Y'), makeField('Size')], metadata: { X: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Y: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Size: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('X'), y: makeEncodingItem('Y'), size: makeEncodingItem('Size') }, }); } // Custom Bar { const cats = genCategories('Status', 5); const data = cats.map(c => ({ Status: c, Count: Math.round(10 + rand() * 100) })); tests.push({ title: 'Custom Bar (5 bars)', description: 'Status × Count', tags: ['nominal', 'quantitative', 'small'], chartType: 'Custom Bar', data, fields: [makeField('Status'), makeField('Count')], metadata: { Status: { type: Type.String, semanticType: 'Status', levels: cats }, Count: { type: Type.Number, semanticType: 'Count', levels: [] }, }, encodingMap: { x: makeEncodingItem('Status'), y: makeEncodingItem('Count') }, }); } // Custom Area { const dates = genDates(40, 2022); const data = dates.map((d, i) => ({ Date: d, Value: Math.round(50 + rand() * 200 + i * 2) })); tests.push({ title: 'Custom Area (40 pts)', description: 'Temporal area chart', tags: ['temporal', 'quantitative', 'medium'], chartType: 'Custom Area', data, fields: [makeField('Date'), makeField('Value')], metadata: { Date: { type: Type.Date, semanticType: 'Date', levels: [] }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Date'), y: makeEncodingItem('Value') }, }); } return tests; } // ------ Waterfall Chart ------ export function genWaterfallTests(): TestCase[] { const tests: TestCase[] = []; // 1. Simple P&L waterfall — no explicit type column (auto-inferred) { const data = [ { Category: 'Revenue', Amount: 1000 }, { Category: 'COGS', Amount: -400 }, { Category: 'Gross Profit', Amount: -150 }, { Category: 'Operating Exp', Amount: -200 }, { Category: 'Tax', Amount: -80 }, { Category: 'Net Income', Amount: 170 }, ]; tests.push({ title: 'Simple P&L (6 steps, auto type)', description: 'Auto-detects first=start, last=end, rest=delta', tags: ['nominal', 'small'], chartType: 'Waterfall Chart', data, fields: [makeField('Category'), makeField('Amount')], metadata: { Category: { type: Type.String, semanticType: 'Category', levels: data.map(d => d.Category) }, Amount: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Category'), y: makeEncodingItem('Amount') }, }); } // 2. With explicit Type column (start/delta/end) { const data = [ { Step: 'Starting Balance', Value: 5000, Type: 'start' }, { Step: 'Sales', Value: 2200, Type: 'delta' }, { Step: 'Returns', Value: -350, Type: 'delta' }, { Step: 'Payroll', Value: -1800, Type: 'delta' }, { Step: 'Rent', Value: -600, Type: 'delta' }, { Step: 'Marketing', Value: -400, Type: 'delta' }, { Step: 'Ending Balance', Value: 4050, Type: 'end' }, ]; tests.push({ title: 'Explicit Type Column (7 steps)', description: 'User-provided start/delta/end type field', tags: ['nominal', 'color', 'small'], chartType: 'Waterfall Chart', data, fields: [makeField('Step'), makeField('Value'), makeField('Type')], metadata: { Step: { type: Type.String, semanticType: 'Category', levels: data.map(d => d.Step) }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Type: { type: Type.String, semanticType: 'Category', levels: ['start', 'delta', 'end'] }, }, encodingMap: { x: makeEncodingItem('Step'), y: makeEncodingItem('Value'), color: makeEncodingItem('Type'), }, }); } // 3. Budget variance (all deltas — no start/end) { const data = [ { Department: 'Engineering', Variance: 120 }, { Department: 'Sales', Variance: -45 }, { Department: 'Marketing', Variance: -80 }, { Department: 'Operations', Variance: 35 }, { Department: 'HR', Variance: -20 }, { Department: 'Finance', Variance: 15 }, { Department: 'Support', Variance: -30 }, { Department: 'Total', Variance: -5 }, ]; tests.push({ title: 'Budget Variance (8 depts)', description: 'Mixed positive/negative deltas with auto start/end', tags: ['nominal', 'medium'], chartType: 'Waterfall Chart', data, fields: [makeField('Department'), makeField('Variance')], metadata: { Department: { type: Type.String, semanticType: 'Category', levels: data.map(d => d.Department) }, Variance: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Department'), y: makeEncodingItem('Variance') }, }); } // 4. Larger waterfall — monthly cash flow { const months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']; const amounts = [500, 200, -150, 300, -100, -250, 400, 150, -300, 200, -50, 100]; const data = [ { Month: 'Opening', Amount: 10000, Type: 'start' }, ...months.map((m, i) => ({ Month: m, Amount: amounts[i], Type: 'delta' })), { Month: 'Closing', Amount: 11000, Type: 'end' }, ]; tests.push({ title: 'Monthly Cash Flow (14 steps)', description: '12 months with opening & closing balances', tags: ['nominal', 'color', 'medium'], chartType: 'Waterfall Chart', data, fields: [makeField('Month'), makeField('Amount'), makeField('Type')], metadata: { Month: { type: Type.String, semanticType: 'Category', levels: data.map(d => d.Month) }, Amount: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Type: { type: Type.String, semanticType: 'Category', levels: ['start', 'delta', 'end'] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Amount'), color: makeEncodingItem('Type'), }, }); } return tests; } // ------ Candlestick Chart ------ export function genCandlestickTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(950); // Helper: generate OHLC data function genOHLC(days: number, startPrice: number) { const data: any[] = []; let price = startPrice; const baseDate = new Date('2024-01-02'); for (let i = 0; i < days; i++) { const date = new Date(baseDate); date.setDate(baseDate.getDate() + i); const change = (rand() - 0.48) * 4; // slight upward bias const open = Math.round(price * 100) / 100; const close = Math.round((price + change) * 100) / 100; const high = Math.round((Math.max(open, close) + rand() * 2) * 100) / 100; const low = Math.round((Math.min(open, close) - rand() * 2) * 100) / 100; data.push({ Date: date.toISOString().slice(0, 10), Open: open, High: high, Low: low, Close: close, }); price = close; } return data; } // 1. 30-day stock price { const data = genOHLC(30, 150); tests.push({ title: '30-day OHLC', description: 'One month of stock data — classic candlestick', tags: ['temporal', 'quantitative', 'small'], chartType: 'Candlestick Chart', data, fields: [makeField('Date'), makeField('Open'), makeField('High'), makeField('Low'), makeField('Close')], metadata: { Date: { type: Type.String, semanticType: 'Date', levels: [] }, Open: { type: Type.Number, semanticType: 'Quantity', levels: [] }, High: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Low: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Close: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Date'), open: makeEncodingItem('Open'), high: makeEncodingItem('High'), low: makeEncodingItem('Low'), close: makeEncodingItem('Close'), }, }); } // 2. 90-day (denser candles) { const data = genOHLC(90, 50); tests.push({ title: '90-day OHLC (dense)', description: 'Three months — tests bar width auto-sizing', tags: ['temporal', 'quantitative', 'medium'], chartType: 'Candlestick Chart', data, fields: [makeField('Date'), makeField('Open'), makeField('High'), makeField('Low'), makeField('Close')], metadata: { Date: { type: Type.String, semanticType: 'Date', levels: [] }, Open: { type: Type.Number, semanticType: 'Quantity', levels: [] }, High: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Low: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Close: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Date'), open: makeEncodingItem('Open'), high: makeEncodingItem('High'), low: makeEncodingItem('Low'), close: makeEncodingItem('Close'), }, }); } // 3. Penny stock (low prices, high volatility) { const data = genOHLC(20, 3); tests.push({ title: 'Penny stock (20 days)', description: 'Low prices near zero — tests scale: {zero: false}', tags: ['temporal', 'quantitative', 'small'], chartType: 'Candlestick Chart', data, fields: [makeField('Date'), makeField('Open'), makeField('High'), makeField('Low'), makeField('Close')], metadata: { Date: { type: Type.String, semanticType: 'Date', levels: [] }, Open: { type: Type.Number, semanticType: 'Quantity', levels: [] }, High: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Low: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Close: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Date'), open: makeEncodingItem('Open'), high: makeEncodingItem('High'), low: makeEncodingItem('Low'), close: makeEncodingItem('Close'), }, }); } // 4. Multi-stock column facet { const tickers = ['AAPL', 'GOOG', 'MSFT']; const data: any[] = []; for (const ticker of tickers) { const startPrice = ticker === 'AAPL' ? 180 : ticker === 'GOOG' ? 140 : 350; let price = startPrice; const baseDate = new Date('2024-03-01'); for (let i = 0; i < 20; i++) { const date = new Date(baseDate); date.setDate(baseDate.getDate() + i); const change = (rand() - 0.48) * 4; const open = Math.round(price * 100) / 100; const close = Math.round((price + change) * 100) / 100; const high = Math.round((Math.max(open, close) + rand() * 2) * 100) / 100; const low = Math.round((Math.min(open, close) - rand() * 2) * 100) / 100; data.push({ Date: date.toISOString().slice(0, 10), Ticker: ticker, Open: open, High: high, Low: low, Close: close }); price = close; } } tests.push({ title: 'Multi-stock facet (3 tickers)', description: '3 stocks side-by-side — faceted by ticker', tags: ['temporal', 'quantitative', 'facet', 'medium'], chartType: 'Candlestick Chart', data, fields: [makeField('Date'), makeField('Ticker'), makeField('Open'), makeField('High'), makeField('Low'), makeField('Close')], metadata: { Date: { type: Type.String, semanticType: 'Date', levels: [] }, Ticker: { type: Type.String, semanticType: 'Category', levels: tickers }, Open: { type: Type.Number, semanticType: 'Quantity', levels: [] }, High: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Low: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Close: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Date'), open: makeEncodingItem('Open'), high: makeEncodingItem('High'), low: makeEncodingItem('Low'), close: makeEncodingItem('Close'), column: makeEncodingItem('Ticker'), }, }); } return tests; } // ------ Radar Chart ------ export function genRadarTests(): TestCase[] { const tests: TestCase[] = []; // 1. Single entity, 5 axes (long format) { const data = [ { Player: 'Player A', Metric: 'Speed', Value: 85 }, { Player: 'Player A', Metric: 'Shooting', Value: 70 }, { Player: 'Player A', Metric: 'Passing', Value: 90 }, { Player: 'Player A', Metric: 'Dribbling', Value: 80 }, { Player: 'Player A', Metric: 'Defense', Value: 60 }, ]; tests.push({ title: 'Single Player Stats (5 axes)', description: 'One polygon, 5 numeric dimensions, long format', tags: ['radar', 'single'], chartType: 'Radar Chart', data, fields: [makeField('Player'), makeField('Metric'), makeField('Value')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Metric'), y: makeEncodingItem('Value'), color: makeEncodingItem('Player') }, }); } // 2. Two entities comparison { const data = [ { Team: 'Team A', Metric: 'Attack', Value: 85 }, { Team: 'Team A', Metric: 'Defense', Value: 70 }, { Team: 'Team A', Metric: 'Midfield', Value: 78 }, { Team: 'Team A', Metric: 'Speed', Value: 90 }, { Team: 'Team A', Metric: 'Stamina', Value: 65 }, { Team: 'Team A', Metric: 'Tactics', Value: 80 }, { Team: 'Team B', Metric: 'Attack', Value: 72 }, { Team: 'Team B', Metric: 'Defense', Value: 88 }, { Team: 'Team B', Metric: 'Midfield', Value: 82 }, { Team: 'Team B', Metric: 'Speed', Value: 68 }, { Team: 'Team B', Metric: 'Stamina', Value: 85 }, { Team: 'Team B', Metric: 'Tactics', Value: 75 }, ]; tests.push({ title: 'Two Teams Comparison (6 axes)', description: 'Two overlapping polygons, long format', tags: ['radar', 'comparison'], chartType: 'Radar Chart', data, fields: [makeField('Team'), makeField('Metric'), makeField('Value')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Metric'), y: makeEncodingItem('Value'), color: makeEncodingItem('Team') }, }); } // 3. Three entities, no fill { const data = [ { Product: 'Widget', Metric: 'Quality', Value: 90 }, { Product: 'Widget', Metric: 'Price', Value: 60 }, { Product: 'Widget', Metric: 'Durability', Value: 80 }, { Product: 'Widget', Metric: 'Design', Value: 75 }, { Product: 'Widget', Metric: 'Support', Value: 85 }, { Product: 'Gadget', Metric: 'Quality', Value: 70 }, { Product: 'Gadget', Metric: 'Price', Value: 85 }, { Product: 'Gadget', Metric: 'Durability', Value: 65 }, { Product: 'Gadget', Metric: 'Design', Value: 90 }, { Product: 'Gadget', Metric: 'Support', Value: 50 }, { Product: 'Doohickey', Metric: 'Quality', Value: 80 }, { Product: 'Doohickey', Metric: 'Price', Value: 70 }, { Product: 'Doohickey', Metric: 'Durability', Value: 90 }, { Product: 'Doohickey', Metric: 'Design', Value: 60 }, { Product: 'Doohickey', Metric: 'Support', Value: 70 }, ]; tests.push({ title: 'Product Comparison (unfilled)', description: 'Three polygons, filled=false, long format', tags: ['radar', 'multi', 'config'], chartType: 'Radar Chart', data, fields: [makeField('Product'), makeField('Metric'), makeField('Value')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Metric'), y: makeEncodingItem('Value'), color: makeEncodingItem('Product') }, chartProperties: { filled: false }, }); } // 4. Faceted radar — one radar per region { const data = [ { Region: 'North', Metric: 'Sales', Value: 80 }, { Region: 'North', Metric: 'Profit', Value: 65 }, { Region: 'North', Metric: 'Growth', Value: 90 }, { Region: 'North', Metric: 'Retention', Value: 70 }, { Region: 'South', Metric: 'Sales', Value: 60 }, { Region: 'South', Metric: 'Profit', Value: 85 }, { Region: 'South', Metric: 'Growth', Value: 50 }, { Region: 'South', Metric: 'Retention', Value: 75 }, { Region: 'East', Metric: 'Sales', Value: 70 }, { Region: 'East', Metric: 'Profit', Value: 72 }, { Region: 'East', Metric: 'Growth', Value: 68 }, { Region: 'East', Metric: 'Retention', Value: 88 }, ]; tests.push({ title: 'Faceted Radar by Region', description: 'One radar per region via column facet', tags: ['radar', 'facet'], chartType: 'Radar Chart', data, fields: [makeField('Region'), makeField('Metric'), makeField('Value')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Metric'), y: makeEncodingItem('Value'), column: makeEncodingItem('Region') }, }); } // 5. Long labels — test that labels don't overlap the chart { const data = [ { Category: 'Customer Satisfaction Score', Assessment: 'Product A', Score: 82 }, { Category: 'Annual Revenue Growth Rate', Assessment: 'Product A', Score: 91 }, { Category: 'Employee Retention', Assessment: 'Product A', Score: 74 }, { Category: 'Market Share Percentage', Assessment: 'Product A', Score: 68 }, { Category: 'Net Promoter Score', Assessment: 'Product A', Score: 88 }, { Category: 'Customer Satisfaction Score', Assessment: 'Product B', Score: 70 }, { Category: 'Annual Revenue Growth Rate', Assessment: 'Product B', Score: 65 }, { Category: 'Employee Retention', Assessment: 'Product B', Score: 85 }, { Category: 'Market Share Percentage', Assessment: 'Product B', Score: 78 }, { Category: 'Net Promoter Score', Assessment: 'Product B', Score: 60 }, ]; tests.push({ title: 'Long Labels (5 axes)', description: 'Labels with long text should not overlap the radar', tags: ['radar', 'labels'], chartType: 'Radar Chart', data, fields: [makeField('Category'), makeField('Assessment'), makeField('Score')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Category'), y: makeEncodingItem('Score'), color: makeEncodingItem('Assessment') }, }); } // 6. Many axes with long labels (8) { const metrics = [ 'Overall User Experience', 'First Contentful Paint', 'Time to Interactive', 'Cumulative Layout Shift', 'Server Response Time', 'Error Rate per Minute', 'Database Query Latency', 'API Throughput', ]; const data = metrics.flatMap(m => [ { App: 'Frontend', KPI: m, Rating: Math.round(50 + Math.random() * 50) }, { App: 'Backend', KPI: m, Rating: Math.round(40 + Math.random() * 60) }, ]); tests.push({ title: 'Many Axes + Long Labels (8 axes)', description: '8 axes with verbose labels, two groups', tags: ['radar', 'labels', 'many-axes'], chartType: 'Radar Chart', data, fields: [makeField('App'), makeField('KPI'), makeField('Rating')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('KPI'), y: makeEncodingItem('Rating'), color: makeEncodingItem('App') }, }); } // 7. Many many metrics (12 axes) { const metrics = [ 'Revenue', 'Profit Margin', 'Customer Retention', 'Brand Awareness', 'Market Penetration', 'Product Quality', 'Employee Engagement', 'Innovation Index', 'Supply Chain Efficiency', 'Digital Transformation', 'Sustainability Rating', 'Compliance Score', ]; const data = metrics.flatMap(m => [ { Division: 'Americas', Factor: m, Score: Math.round(30 + Math.random() * 70) }, { Division: 'EMEA', Factor: m, Score: Math.round(30 + Math.random() * 70) }, { Division: 'APAC', Factor: m, Score: Math.round(30 + Math.random() * 70) }, ]); tests.push({ title: 'Many Metrics (12 axes)', description: '12 axes with 3 groups — tests label crowding', tags: ['radar', 'labels', 'crowded'], chartType: 'Radar Chart', data, fields: [makeField('Division'), makeField('Factor'), makeField('Score')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Factor'), y: makeEncodingItem('Score'), color: makeEncodingItem('Division') }, }); } return tests; } // ------ Pyramid Chart ------ export function genPyramidTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(777); // Helper to generate long-format pyramid data from two groups const makeLongData = (categories: string[], groupA: string, groupB: string, valField: string, minA: number, rangeA: number, minB: number, rangeB: number) => { const data: any[] = []; for (const cat of categories) { data.push({ [valField]: Math.round(minA + rand() * rangeA), Group: groupA, Category: cat }); data.push({ [valField]: Math.round(minB + rand() * rangeB), Group: groupB, Category: cat }); } return data; }; // 1. Classic population pyramid — Age Group × Gender × Population { const ageGroups = ['0-9', '10-19', '20-29', '30-39', '40-49', '50-59', '60-69', '70-79', '80+']; const data: any[] = []; for (const ag of ageGroups) { data.push({ 'Age Group': ag, Gender: 'Male', Population: Math.round(500 + rand() * 4500) }); data.push({ 'Age Group': ag, Gender: 'Female', Population: Math.round(500 + rand() * 4500) }); } tests.push({ title: 'Population pyramid (9 age groups)', description: 'Classic population pyramid — long format with Gender as color', tags: ['nominal', 'quantitative', 'color', 'small'], chartType: 'Pyramid Chart', data, fields: [makeField('Age Group'), makeField('Population'), makeField('Gender')], metadata: { 'Age Group': { type: Type.String, semanticType: 'Category', levels: ageGroups }, Population: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Gender: { type: Type.String, semanticType: 'Category', levels: ['Male', 'Female'] }, }, encodingMap: { y: makeEncodingItem('Age Group'), x: makeEncodingItem('Population'), color: makeEncodingItem('Gender') }, }); } // 2. Workforce pyramid { const grades = ['Junior', 'Mid-level', 'Senior', 'Lead', 'Manager', 'Director']; const data: any[] = []; for (const g of grades) { data.push({ Grade: g, Type: 'Full-Time', Count: Math.round(20 + rand() * 200) }); data.push({ Grade: g, Type: 'Part-Time', Count: Math.round(10 + rand() * 80) }); } tests.push({ title: 'Workforce pyramid (6 grades)', description: 'Grade levels on y, Count on x, Full-Time vs Part-Time as color', tags: ['nominal', 'quantitative', 'color', 'small'], chartType: 'Pyramid Chart', data, fields: [makeField('Grade'), makeField('Count'), makeField('Type')], metadata: { Grade: { type: Type.String, semanticType: 'Category', levels: grades }, Count: { type: Type.Number, semanticType: 'Count', levels: [] }, Type: { type: Type.String, semanticType: 'Category', levels: ['Full-Time', 'Part-Time'] }, }, encodingMap: { y: makeEncodingItem('Grade'), x: makeEncodingItem('Count'), color: makeEncodingItem('Type') }, }); } // 3. Survey responses { const levels = ['Very Low', 'Low', 'Medium', 'High', 'Very High']; const data: any[] = []; for (const lv of levels) { data.push({ 'Satisfaction Level': lv, Response: 'Agree', Count: Math.round(50 + rand() * 300) }); data.push({ 'Satisfaction Level': lv, Response: 'Disagree', Count: Math.round(30 + rand() * 250) }); } tests.push({ title: 'Survey pyramid (5 levels)', description: 'Satisfaction levels — Agree vs Disagree', tags: ['ordinal', 'quantitative', 'color', 'small'], chartType: 'Pyramid Chart', data, fields: [makeField('Satisfaction Level'), makeField('Count'), makeField('Response')], metadata: { 'Satisfaction Level': { type: Type.String, semanticType: 'Category', levels: levels }, Count: { type: Type.Number, semanticType: 'Count', levels: [] }, Response: { type: Type.String, semanticType: 'Category', levels: ['Agree', 'Disagree'] }, }, encodingMap: { y: makeEncodingItem('Satisfaction Level'), x: makeEncodingItem('Count'), color: makeEncodingItem('Response') }, }); } // 4. Income bracket pyramid (medium) { const brackets = ['<$20K', '$20-30K', '$30-40K', '$40-50K', '$50-60K', '$60-70K', '$70-80K', '$80-90K', '$90-100K', '$100-120K', '$120-150K', '$150K+']; const data: any[] = []; for (const b of brackets) { data.push({ 'Income Bracket': b, Area: 'Urban', Count: Math.round(100 + rand() * 3000) }); data.push({ 'Income Bracket': b, Area: 'Rural', Count: Math.round(80 + rand() * 2000) }); } tests.push({ title: 'Income pyramid (12 brackets)', description: '12 income bands — Urban vs Rural', tags: ['ordinal', 'quantitative', 'color', 'medium'], chartType: 'Pyramid Chart', data, fields: [makeField('Income Bracket'), makeField('Count'), makeField('Area')], metadata: { 'Income Bracket': { type: Type.String, semanticType: 'Category', levels: brackets }, Count: { type: Type.Number, semanticType: 'Count', levels: [] }, Area: { type: Type.String, semanticType: 'Category', levels: ['Urban', 'Rural'] }, }, encodingMap: { y: makeEncodingItem('Income Bracket'), x: makeEncodingItem('Count'), color: makeEncodingItem('Area') }, }); } // 5. Education pyramid { const degrees = ['High School', 'Associate', 'Bachelor', 'Master', 'Doctorate']; const data: any[] = []; for (const d of degrees) { data.push({ 'Degree Level': d, Outcome: 'Admitted', Count: Math.round(200 + rand() * 5000) }); data.push({ 'Degree Level': d, Outcome: 'Rejected', Count: Math.round(100 + rand() * 3000) }); } tests.push({ title: 'Education pyramid (5 degrees)', description: 'Degree levels — Admitted vs Rejected applicants', tags: ['ordinal', 'quantitative', 'color', 'small'], chartType: 'Pyramid Chart', data, fields: [makeField('Degree Level'), makeField('Count'), makeField('Outcome')], metadata: { 'Degree Level': { type: Type.String, semanticType: 'Category', levels: degrees }, Count: { type: Type.Number, semanticType: 'Count', levels: [] }, Outcome: { type: Type.String, semanticType: 'Category', levels: ['Admitted', 'Rejected'] }, }, encodingMap: { y: makeEncodingItem('Degree Level'), x: makeEncodingItem('Count'), color: makeEncodingItem('Outcome') }, }); } // 6. Country comparison { const countries = genCategories('Country', 10); const data: any[] = []; for (const c of countries) { data.push({ Country: c, Direction: 'Import', Value: Math.round(1000 + rand() * 50000) }); data.push({ Country: c, Direction: 'Export', Value: Math.round(1000 + rand() * 50000) }); } tests.push({ title: 'Trade pyramid (10 countries)', description: '10 countries — Import vs Export trade values', tags: ['nominal', 'quantitative', 'color', 'medium'], chartType: 'Pyramid Chart', data, fields: [makeField('Country'), makeField('Value'), makeField('Direction')], metadata: { Country: { type: Type.String, semanticType: 'Country', levels: countries }, Value: { type: Type.Number, semanticType: 'Amount', levels: [] }, Direction: { type: Type.String, semanticType: 'Category', levels: ['Import', 'Export'] }, }, encodingMap: { y: makeEncodingItem('Country'), x: makeEncodingItem('Value'), color: makeEncodingItem('Direction') }, }); } // 7. Large cardinality (20 age bands) { const ageBands = Array.from({ length: 20 }, (_, i) => { const lo = i * 5; const hi = lo + 4; return `${lo}-${hi}`; }); const data: any[] = []; for (const ag of ageBands) { data.push({ 'Age Band': ag, Gender: 'Male', Population: Math.round(200 + rand() * 8000) }); data.push({ 'Age Band': ag, Gender: 'Female', Population: Math.round(200 + rand() * 8000) }); } tests.push({ title: 'Overstretch pyramid (20 age bands)', description: '20 fine-grained age bands — tests y-axis elastic overstretch', tags: ['nominal', 'quantitative', 'color', 'large', 'overstretch'], chartType: 'Pyramid Chart', data, fields: [makeField('Age Band'), makeField('Population'), makeField('Gender')], metadata: { 'Age Band': { type: Type.String, semanticType: 'Category', levels: ageBands }, Population: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Gender: { type: Type.String, semanticType: 'Category', levels: ['Male', 'Female'] }, }, encodingMap: { y: makeEncodingItem('Age Band'), x: makeEncodingItem('Population'), color: makeEncodingItem('Gender') }, }); } // 8. Negative values — should trigger warning { const ageGroups = ['0-14', '15-29', '30-44', '45-59', '60-74', '75+']; const data: any[] = []; for (const ag of ageGroups) { data.push({ 'Age Group': ag, Gender: 'Male', Population: Math.round(-500 + rand() * 4000) }); data.push({ 'Age Group': ag, Gender: 'Female', Population: Math.round(-300 + rand() * 3500) }); } tests.push({ title: 'Negative values warning (6 groups)', description: 'Some values are negative — should trigger negative-value warnings', tags: ['nominal', 'quantitative', 'color', 'small', 'warning', 'negative'], chartType: 'Pyramid Chart', data, fields: [makeField('Age Group'), makeField('Population'), makeField('Gender')], metadata: { 'Age Group': { type: Type.String, semanticType: 'Category', levels: ageGroups }, Population: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Gender: { type: Type.String, semanticType: 'Category', levels: ['Male', 'Female'] }, }, encodingMap: { y: makeEncodingItem('Age Group'), x: makeEncodingItem('Population'), color: makeEncodingItem('Gender') }, }); } return tests; } // ------ Rose Chart (Nightingale / Coxcomb) ------ export function genRoseTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(1100); // 1. Basic rose — wind directions × speed { const directions = ['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW']; const data = directions.map(d => ({ Direction: d, Speed: Math.round(5 + rand() * 25) })); tests.push({ title: 'Rose (basic, 8 directions)', description: 'Wind speed by compass direction — classic coxcomb', tags: ['nominal', 'quantitative', 'small'], chartType: 'Rose Chart', data, fields: [makeField('Direction'), makeField('Speed')], metadata: { Direction: { type: Type.String, semanticType: 'Category', levels: directions }, Speed: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Direction'), y: makeEncodingItem('Speed') }, chartProperties: { alignment: 'center' }, }); } // 2. Stacked rose — wind directions × speed × season { const directions = ['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW']; const seasons = ['Spring', 'Summer', 'Autumn', 'Winter']; const data: any[] = []; for (const d of directions) { for (const s of seasons) { data.push({ Direction: d, Speed: Math.round(3 + rand() * 20), Season: s }); } } tests.push({ title: 'Stacked Rose (8 dirs × 4 seasons)', description: 'Wind speed stacked by season — polar stacked bar', tags: ['nominal', 'quantitative', 'color', 'stacked'], chartType: 'Rose Chart', data, fields: [makeField('Direction'), makeField('Speed'), makeField('Season')], metadata: { Direction: { type: Type.String, semanticType: 'Category', levels: directions }, Speed: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Season: { type: Type.String, semanticType: 'Category', levels: seasons }, }, encodingMap: { x: makeEncodingItem('Direction'), y: makeEncodingItem('Speed'), color: makeEncodingItem('Season') }, chartProperties: { alignment: 'center' }, }); } // 3. Rose with many categories { const cats = genCategories('Category', 12); const data = cats.map(c => ({ Category: c, Value: Math.round(10 + rand() * 90) })); tests.push({ title: 'Rose (medium, 12 categories)', description: '12 categories — tests angular spacing with more slices', tags: ['nominal', 'quantitative', 'medium'], chartType: 'Rose Chart', data, fields: [makeField('Category'), makeField('Value')], metadata: { Category: { type: Type.String, semanticType: 'Category', levels: cats }, Value: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Category'), y: makeEncodingItem('Value') }, }); } // 4. Rose with color stacking and many groups { const products = genCategories('Product', 6); const regions = ['North', 'South', 'East', 'West']; const data: any[] = []; for (const p of products) { for (const r of regions) { data.push({ Product: p, Sales: Math.round(100 + rand() * 900), Region: r }); } } tests.push({ title: 'Stacked Rose (6 products × 4 regions)', description: 'Product sales stacked by region', tags: ['nominal', 'quantitative', 'color', 'stacked', 'medium'], chartType: 'Rose Chart', data, fields: [makeField('Product'), makeField('Sales'), makeField('Region')], metadata: { Product: { type: Type.String, semanticType: 'Product', levels: products }, Sales: { type: Type.Number, semanticType: 'Revenue', levels: [] }, Region: { type: Type.String, semanticType: 'Category', levels: regions }, }, encodingMap: { x: makeEncodingItem('Product'), y: makeEncodingItem('Sales'), color: makeEncodingItem('Region') }, }); } // 5. Rose with inner radius (donut-rose) { const months = genMonths(12); const data = months.map(m => ({ Month: m, Rainfall: Math.round(20 + rand() * 150) })); tests.push({ title: 'Donut Rose (12 months, innerRadius)', description: 'Monthly rainfall with inner radius — donut style rose', tags: ['ordinal', 'quantitative', 'properties'], chartType: 'Rose Chart', data, fields: [makeField('Month'), makeField('Rainfall')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: months }, Rainfall: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Rainfall') }, chartProperties: { innerRadius: 40 }, }); } // 6. Rose with padAngle { const departments = genCategories('Department', 8); const data = departments.map(d => ({ Department: d, Score: Math.round(50 + rand() * 50) })); tests.push({ title: 'Rose with gap (padAngle)', description: 'Departments with angle padding between slices', tags: ['nominal', 'quantitative', 'properties'], chartType: 'Rose Chart', data, fields: [makeField('Department'), makeField('Score')], metadata: { Department: { type: Type.String, semanticType: 'Department', levels: departments }, Score: { type: Type.Number, semanticType: 'Quantity', levels: [] }, }, encodingMap: { x: makeEncodingItem('Department'), y: makeEncodingItem('Score') }, chartProperties: { padAngle: 0.03 }, }); } // 7. Faceted rose — directions by year (column facet) { const directions = ['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW']; const years = ['2022', '2023', '2024']; const data: any[] = []; for (const yr of years) { for (const d of directions) { data.push({ Direction: d, Speed: Math.round(4 + rand() * 20), Year: yr }); } } tests.push({ title: 'Faceted Rose (column)', description: 'Wind rose per year — faceted by column', tags: ['nominal', 'quantitative', 'facet'], chartType: 'Rose Chart', data, fields: [makeField('Direction'), makeField('Speed'), makeField('Year')], metadata: { Direction: { type: Type.String, semanticType: 'Direction', levels: directions }, Speed: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Year: { type: Type.String, semanticType: 'Year', levels: years }, }, encodingMap: { x: makeEncodingItem('Direction'), y: makeEncodingItem('Speed'), column: makeEncodingItem('Year') }, chartProperties: { alignment: 'center' }, }); } // 8. Faceted stacked rose — directions × season, faceted by location { const directions = ['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW']; const seasons = ['Spring', 'Summer', 'Autumn', 'Winter']; const locations = ['Coastal', 'Inland']; const data: any[] = []; for (const loc of locations) { for (const d of directions) { for (const s of seasons) { data.push({ Direction: d, Speed: Math.round(3 + rand() * 18), Season: s, Location: loc }); } } } tests.push({ title: 'Faceted Stacked Rose (column)', description: 'Stacked wind rose by season, faceted by location', tags: ['nominal', 'quantitative', 'color', 'stacked', 'facet'], chartType: 'Rose Chart', data, fields: [makeField('Direction'), makeField('Speed'), makeField('Season'), makeField('Location')], metadata: { Direction: { type: Type.String, semanticType: 'Direction', levels: directions }, Speed: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Season: { type: Type.String, semanticType: 'Category', levels: seasons }, Location: { type: Type.String, semanticType: 'Category', levels: locations }, }, encodingMap: { x: makeEncodingItem('Direction'), y: makeEncodingItem('Speed'), color: makeEncodingItem('Season'), column: makeEncodingItem('Location'), }, chartProperties: { alignment: 'center' }, }); } // 9. Faceted rose — monthly rainfall by region (3 regions) { const months = genMonths(12); const regions = ['North', 'Central', 'South']; const data: any[] = []; for (const r of regions) { for (const m of months) { data.push({ Month: m, Rainfall: Math.round(10 + rand() * 140), Region: r }); } } tests.push({ title: 'Faceted Rose (monthly × region)', description: 'Monthly rainfall rose faceted by region', tags: ['ordinal', 'quantitative', 'facet'], chartType: 'Rose Chart', data, fields: [makeField('Month'), makeField('Rainfall'), makeField('Region')], metadata: { Month: { type: Type.String, semanticType: 'Month', levels: months }, Rainfall: { type: Type.Number, semanticType: 'Quantity', levels: [] }, Region: { type: Type.String, semanticType: 'Category', levels: regions }, }, encodingMap: { x: makeEncodingItem('Month'), y: makeEncodingItem('Rainfall'), column: makeEncodingItem('Region') }, }); } return tests; } ================================================ FILE: src/lib/agents-chart/test-data/stress-tests.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { Type } from '../../../data/types'; import { Channel, EncodingItem } from '../../../components/ComponentType'; import { AssembleOptions } from '../core/types'; import { TestCase, makeField, makeEncodingItem, buildMetadata } from './types'; import { seededRandom, genCategories } from './generators'; // ------ Overflow / Discrete Value Capping ------ export function genOverflowTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(777); // Helper: generate N category names const cats = (prefix: string, n: number) => { const pad = String(n).length; return Array.from({ length: n }, (_, i) => `${prefix}${String(i + 1).padStart(pad, '0')}`); }; // ---- 1. Bar chart: increasing x cardinality ---- for (const n of [50, 100, 150, 200]) { const categories = cats('Cat', n); const data = categories.map(c => ({ Category: c, Sales: Math.round(10 + rand() * 500) })); tests.push({ title: `Bar — ${n} x-categories`, description: `Bar chart with ${n} discrete x values to test capping`, tags: ['overflow', 'bar', `n-${n}`], chartType: 'Bar Chart', data, fields: [makeField('Category'), makeField('Sales')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Category'), y: makeEncodingItem('Sales') }, }); } // ---- 2. Horizontal bar: increasing y cardinality ---- for (const n of [50, 100, 200]) { const categories = cats('Item', n); const data = categories.map(c => ({ Item: c, Revenue: Math.round(10 + rand() * 800) })); tests.push({ title: `Horiz Bar — ${n} y-categories`, description: `Horizontal bar chart with ${n} discrete y values`, tags: ['overflow', 'bar', 'horizontal', `n-${n}`], chartType: 'Bar Chart', data, fields: [makeField('Item'), makeField('Revenue')], metadata: buildMetadata(data), encodingMap: { y: makeEncodingItem('Item'), x: makeEncodingItem('Revenue') }, }); } // ---- 3. Grouped bar: x × color overflow ---- for (const [xN, colorN] of [[30, 5], [50, 5], [80, 3], [50, 10]] as const) { const xCats = cats('Product', xN); const colors = cats('Region', colorN); const data: any[] = []; for (const x of xCats) { for (const c of colors) { data.push({ Product: x, Region: c, Sales: Math.round(10 + rand() * 400) }); } } tests.push({ title: `Grouped — ${xN}x × ${colorN}color`, description: `Grouped bar: ${xN} x-values × ${colorN} color groups = ${xN * colorN} sub-bars`, tags: ['overflow', 'grouped', `x-${xN}`, `color-${colorN}`], chartType: 'Grouped Bar Chart', data, fields: [makeField('Product'), makeField('Region'), makeField('Sales')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Product'), y: makeEncodingItem('Sales'), group: makeEncodingItem('Region'), }, }); } // ---- 4. Stacked bar: many x values ---- for (const n of [50, 150]) { const xCats = cats('City', n); const segments = cats('Seg', 3); const data: any[] = []; for (const x of xCats) { for (const s of segments) { data.push({ City: x, Segment: s, Amount: Math.round(10 + rand() * 300) }); } } tests.push({ title: `Stacked — ${n} x-categories`, description: `Stacked bar with ${n} x values and 3 segments`, tags: ['overflow', 'stacked', `n-${n}`], chartType: 'Stacked Bar Chart', data, fields: [makeField('City'), makeField('Segment'), makeField('Amount')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('City'), y: makeEncodingItem('Amount'), color: makeEncodingItem('Segment'), }, }); } // ---- 5. Heatmap: large x × large y ---- for (const [xN, yN] of [[30, 30], [80, 20], [50, 50]] as const) { const xCats = cats('Col', xN); const yCats = cats('Row', yN); const data: any[] = []; for (const x of xCats) { for (const y of yCats) { data.push({ Col: x, Row: y, Value: Math.round(rand() * 100) }); } } tests.push({ title: `Heatmap — ${xN}x × ${yN}y`, description: `Heatmap with ${xN}×${yN} = ${xN * yN} cells`, tags: ['overflow', 'heatmap', `x-${xN}`, `y-${yN}`], chartType: 'Heatmap', data, fields: [makeField('Col'), makeField('Row'), makeField('Value')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Col'), y: makeEncodingItem('Row'), color: makeEncodingItem('Value'), }, }); } // ---- 6. Large color cardinality (numeric on color) ---- { const countries = cats('Country', 5); const data: any[] = []; for (const c of countries) { for (let age = 15; age <= 85; age++) { data.push({ Country: c, Age: age, Count: Math.round(50 + rand() * 200) }); } } tests.push({ title: `Color overflow — 71 numeric values`, description: `Age 15-85 on color channel (71 unique values)`, tags: ['overflow', 'color', 'numeric-color'], chartType: 'Bar Chart', data, fields: [makeField('Country'), makeField('Age'), makeField('Count')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Country'), y: makeEncodingItem('Count'), color: makeEncodingItem('Age'), }, }); } // ---- 7. Boxplot with many categories ---- for (const n of [50, 120]) { const categories = cats('Group', n); const data: any[] = []; for (const c of categories) { for (let i = 0; i < 10; i++) { data.push({ Group: c, Score: Math.round(rand() * 100) }); } } tests.push({ title: `Boxplot — ${n} groups`, description: `Boxplot with ${n} discrete groups on x-axis`, tags: ['overflow', 'boxplot', `n-${n}`], chartType: 'Boxplot', data, fields: [makeField('Group'), makeField('Score')], metadata: buildMetadata(data), encodingMap: { x: makeEncodingItem('Group'), y: makeEncodingItem('Score') }, }); } // ---- 8. Faceted + overflow within subplots ---- { const regions = cats('Region', 4); const products = cats('Prod', 80); const data: any[] = []; for (const r of regions) { for (const p of products) { data.push({ Region: r, Product: p, Sales: Math.round(10 + rand() * 500) }); } } tests.push({ title: `Facet + 80 x-categories`, description: `4 column facets each with 80 x-categories`, tags: ['overflow', 'facet', 'n-80'], chartType: 'Bar Chart', data, fields: [makeField('Region'), makeField('Product'), makeField('Sales')], metadata: buildMetadata(data), encodingMap: { column: makeEncodingItem('Region'), x: makeEncodingItem('Product'), y: makeEncodingItem('Sales'), }, }); } // ---- 9. Many facet columns with small discrete x ---- for (const [facetN, xN] of [[8, 3], [12, 4], [15, 2], [20, 3]] as const) { const facets = cats('Panel', facetN); const xCats = cats('Type', xN); const data: any[] = []; for (const f of facets) { for (const x of xCats) { data.push({ Panel: f, Type: x, Value: Math.round(10 + rand() * 300) }); } } tests.push({ title: `${facetN} facets × ${xN} x-bars`, description: `${facetN} column facets, each with only ${xN} x-categories — thin subplots`, tags: ['overflow', 'facet', 'thin', `facet-${facetN}`, `x-${xN}`], chartType: 'Bar Chart', data, fields: [makeField('Panel'), makeField('Type'), makeField('Value')], metadata: buildMetadata(data), encodingMap: { column: makeEncodingItem('Panel'), x: makeEncodingItem('Type'), y: makeEncodingItem('Value'), }, }); } return tests; } // ------ Elasticity & Stretch Comparison ------ export function genElasticityTests(): TestCase[] { const tests: TestCase[] = []; const rand = seededRandom(999); // ========== Helper to build a bar-chart test case with given layout ========== function makeBarTest( title: string, description: string, tags: string[], xCategories: string[], assembleOptions: AssembleOptions, facetCategories?: string[], ): TestCase { const data: Record<string, any>[] = []; if (facetCategories) { for (const f of facetCategories) { for (const x of xCategories) { data.push({ Panel: f, Category: x, Value: Math.round(10 + rand() * 500) }); } } } else { for (const x of xCategories) { data.push({ Category: x, Value: Math.round(10 + rand() * 500) }); } } const fields = facetCategories ? [makeField('Panel'), makeField('Category'), makeField('Value')] : [makeField('Category'), makeField('Value')]; const encodingMap: Partial<Record<Channel, EncodingItem>> = { x: makeEncodingItem('Category'), y: makeEncodingItem('Value'), }; if (facetCategories) { encodingMap.column = makeEncodingItem('Panel'); } return { title, description, tags, chartType: 'Bar Chart', data, fields, metadata: buildMetadata(data), encodingMap, assembleOptions, }; } // ========================================================================= // Group A: Axis-only (no facets) — compare elasticity / maxStretch // ========================================================================= const mediumCategories = genCategories('Country', 15); const largeCategories = genCategories('Name', 30); // A1: 15 x-categories — default tests.push(makeBarTest( '15 bars — default (e=0.5, s=2)', '15 x-categories, default axis elasticity & stretch', ['axis-only', 'medium', 'default'], mediumCategories, { elasticity: 0.5, maxStretch: 2 }, )); // A2: 15 x-categories — low elasticity tests.push(makeBarTest( '15 bars — low axis (e=0.2, s=1.2)', '15 x-categories, conservative axis stretch', ['axis-only', 'medium', 'low'], mediumCategories, { elasticity: 0.2, maxStretch: 1.2 }, )); // A3: 15 x-categories — high elasticity tests.push(makeBarTest( '15 bars — high axis (e=0.8, s=3)', '15 x-categories, aggressive axis stretch', ['axis-only', 'medium', 'high'], mediumCategories, { elasticity: 0.8, maxStretch: 3 }, )); // A4: 30 x-categories — default tests.push(makeBarTest( '30 bars — default (e=0.5, s=2)', '30 x-categories, default axis elasticity & stretch', ['axis-only', 'large', 'default'], largeCategories, { elasticity: 0.5, maxStretch: 2 }, )); // A5: 30 x-categories — low tests.push(makeBarTest( '30 bars — low axis (e=0.2, s=1.2)', '30 x-categories, conservative axis stretch', ['axis-only', 'large', 'low'], largeCategories, { elasticity: 0.2, maxStretch: 1.2 }, )); // A6: 30 x-categories — high tests.push(makeBarTest( '30 bars — high axis (e=0.8, s=3)', '30 x-categories, aggressive axis stretch', ['axis-only', 'large', 'high'], largeCategories, { elasticity: 0.8, maxStretch: 3 }, )); // ========================================================================= // Group B: Facet + axis — compare facet vs axis params independently // ========================================================================= const facets8 = genCategories('Department', 8); const bars5 = genCategories('Category', 5); const facets12 = genCategories('Company', 12); const bars8 = genCategories('Category', 8); // B1: 8 facets × 5 bars — all defaults tests.push(makeBarTest( '8F × 5B — all default', '8 column facets, 5 bars each, default axis & facet settings', ['facet+axis', 'default'], bars5, { elasticity: 0.5, maxStretch: 2, facetElasticity: 0.3 }, facets8, )); // B2: 8 facets × 5 bars — conservative facet, default axis tests.push(makeBarTest( '8F × 5B — conservative facet (fe=0.15, ms=1.5)', '8 column facets, 5 bars, conservative unified stretch', ['facet+axis', 'conservative-facet'], bars5, { elasticity: 0.5, maxStretch: 1.5, facetElasticity: 0.15 }, facets8, )); // B3: 8 facets × 5 bars — aggressive facet, default axis tests.push(makeBarTest( '8F × 5B — aggressive (fe=0.6, ms=2.5)', '8 column facets, 5 bars, aggressive unified stretch', ['facet+axis', 'aggressive-facet'], bars5, { elasticity: 0.5, maxStretch: 2.5, facetElasticity: 0.6 }, facets8, )); // B4: 12 facets × 8 bars — all defaults tests.push(makeBarTest( '12F × 8B — all default', '12 column facets, 8 bars each, default settings', ['facet+axis', 'large', 'default'], bars8, { elasticity: 0.5, maxStretch: 2, facetElasticity: 0.3 }, facets12, )); // B5: 12 facets × 8 bars — tight budget, aggressive axis elasticity tests.push(makeBarTest( '12F × 8B — tight (ms=1.5, fe=0.15, e=0.8)', '12 facets, 8 bars, tight unified budget + high axis elasticity', ['facet+axis', 'large', 'mixed-tight-facet'], bars8, { elasticity: 0.8, maxStretch: 1.5, facetElasticity: 0.15 }, facets12, )); // B6: 12 facets × 8 bars — wide budget, low axis elasticity tests.push(makeBarTest( '12F × 8B — wide (ms=3, fe=0.6, e=0.2)', '12 facets, 8 bars, wide unified budget + low axis elasticity', ['facet+axis', 'large', 'mixed-wide-facet'], bars8, { elasticity: 0.2, maxStretch: 3, facetElasticity: 0.6 }, facets12, )); // B7: 8 facets × 5 bars — no stretch at all tests.push(makeBarTest( '8F × 5B — no stretch (all 1.0)', '8 column facets, 5 bars, stretch disabled', ['facet+axis', 'no-stretch'], bars5, { elasticity: 0, maxStretch: 1, facetElasticity: 0 }, facets8, )); return tests; } ================================================ FILE: src/lib/agents-chart/test-data/types.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Types and helper utilities for chart gallery test cases. * No React/UI dependencies — pure TypeScript. */ import { Type } from '../../../data/types'; import { Channel, EncodingItem, FieldItem } from '../../../components/ComponentType'; import { AssembleOptions } from '../core/types'; import type { SemanticAnnotation } from '../core/field-semantics'; // ============================================================================ // Test Case Definition // ============================================================================ export interface TestCase { title: string; description: string; tags: string[]; // e.g., ['temporal', 'large-cardinality', 'color'] chartType: string; data: Record<string, any>[]; fields: FieldItem[]; metadata: Record<string, { type: Type; semanticType: string; levels: any[] }>; encodingMap: Partial<Record<Channel, EncodingItem>>; chartProperties?: Record<string, any>; assembleOptions?: AssembleOptions; /** * Enriched semantic annotations that override the plain semanticType strings * in metadata. Use this when a field needs extra info like intrinsicDomain or unit. * E.g., { rating: { semanticType: 'Rating', intrinsicDomain: [1, 5] } } */ semanticAnnotations?: Record<string, SemanticAnnotation>; } /** Date format definition for date stress tests */ export interface DateFormat { label: string; description: string; values: any[]; fieldName: string; expectedType: Type; semanticType: string; } /** Gallery section definition */ export interface GallerySection { label: string; description: string; entries: string[]; // keys into TEST_GENERATORS } // ============================================================================ // Helper Functions // ============================================================================ export function makeField(name: string, tableRef = 'test'): FieldItem { return { id: name, name, source: 'original', tableRef }; } export function makeEncodingItem(fieldID: string, opts?: Partial<EncodingItem>): EncodingItem { return { fieldID, ...opts }; } export function inferType(values: any[]): Type { if (values.length === 0) return Type.String; const sample = values.find(v => v != null); if (typeof sample === 'number') return Type.Number; if (typeof sample === 'boolean') return Type.String; if (sample instanceof Date) return Type.Date; // Check if string looks like a date if (typeof sample === 'string' && !isNaN(Date.parse(sample)) && sample.length > 4) return Type.Date; return Type.String; } export function buildMetadata(data: Record<string, any>[]): Record<string, { type: Type; semanticType: string; levels: any[] }> { if (data.length === 0) return {}; const meta: Record<string, { type: Type; semanticType: string; levels: any[] }> = {}; for (const key of Object.keys(data[0])) { const values = data.map(r => r[key]).filter(v => v != null); const type = inferType(values); const levels = [...new Set(values)]; // Assign semantic types heuristically let semanticType = ''; if (type === Type.Date) semanticType = 'Date'; else if (type === Type.Number) semanticType = 'Quantity'; else semanticType = 'Category'; meta[key] = { type, semanticType, levels }; } return meta; } ================================================ FILE: src/lib/agents-chart/vegalite/README.md ================================================ # Vega-Lite Backend The primary and most feature-complete backend for agents-chart. Compiles the core semantic layer into declarative [Vega-Lite](https://vega.github.io/vega-lite/) JSON specifications. ## Output Format ```jsonc { "mark": "bar", "encoding": { "x": { "field": "Name", "type": "nominal" }, ... }, "data": { "values": [...] }, "width": 400, "height": 300 } ``` A declarative JSON spec consumed by the Vega-Lite renderer. Bar sizing uses `width: { step: N }`, axis/legend/tooltip configuration is fully declarative via encodings and config blocks, and faceting uses VL's native `facet` + `columns` structure. ## Assembly Pipeline All backends share Phases 0 and 1 from `core/`. Phase 2 is VL-specific. | Phase | Step | Description | |-------|------|-------------| | **0** | `resolveSemantics` | Resolve field types, aggregates, ordinal sort orders | | 0a | `declareLayoutMode` | Template declares banded axes, sizing hints, auto-detect binned axes | | 0b | `convertTemporalData` | Parse temporal strings to JS Dates | | 0c | `filterOverflow` | Truncate categories that overflow the canvas | | **1** | `computeLayout` | Compute step sizes, subplot dimensions, tick params (target-agnostic) | | **2** | `buildVLEncodings` → `template.instantiate` → `restructureFacets` → `vlApplyLayoutToSpec` → post-layout (facet binning, independent scales, tooltips) | Final VL spec | **Unique to VL:** The `buildVLEncodings` step translates abstract `ChannelSemantics` into VL encoding objects before templates run — other backends skip this and let templates read `channelSemantics` directly. ## File Structure ``` vegalite/ assemble.ts – assembleVegaLite(): Phase 2 assembly instantiate-spec.ts – vlApplyLayoutToSpec(), vlApplyTooltips() index.ts – barrel exports templates/ index.ts – template registry (27 templates, 7 categories) scatter.ts – Scatter Plot, Linear Regression bar.ts – Bar, Grouped Bar, Stacked Bar, Histogram, Lollipop, Pyramid line.ts – Line, Dotted Line, Bump Chart area.ts – Area, Streamgraph pie.ts – Pie Chart rose.ts – Rose Chart radar.ts – Radar Chart density.ts – Density Plot candlestick.ts – Candlestick Chart waterfall.ts – Waterfall Chart lollipop.ts – Lollipop (also in bar.ts registry) jitter.ts – Strip Plot bump.ts – Bump Chart helpers custom.ts – Custom Point/Line/Bar/Rect/Area map.ts – US Map, World Map utils.ts – shared template utilities ``` ## Template Definitions (27 templates) | Category | Charts | |----------|--------| | Scatter & Point | Scatter Plot, Linear Regression, Boxplot, Strip Plot | | Bar | Bar Chart, Grouped Bar, Stacked Bar, Histogram, Lollipop, Pyramid | | Line & Area | Line Chart, Dotted Line, Bump Chart, Area Chart, Streamgraph | | Part-to-Whole | Pie Chart, Rose Chart, Heatmap, Waterfall Chart | | Statistical | Density Plot, Ranged Dot Plot, Radar Chart, Candlestick Chart | | Map | US Map, World Map | | Custom | Custom Point, Custom Line, Custom Bar, Custom Rect, Custom Area | ## Known Issues & Notes - **Richest template set** of all backends (27 templates). Maps and statistical charts (density, candlestick, waterfall) are only available in VL. - `instantiate-spec.ts` (421 lines) is the only file that contains Vega-Lite syntax knowledge — all other files work with abstract semantics. - Faceting is handled via VL's native `facet` mechanism; `restructureFacets` converts column-only facets to the `facet` + `columns` structure. - The `buildVLEncodings` step is an extra translation layer not present in other backends — it converts `ChannelSemantics` to `{ field, type, scale, axis, ... }` encoding objects before template instantiation. - VL natively handles zero-baseline via `scale: { zero: true }`, bar step sizing via `width: { step }`, and legend positioning — no manual pixel math needed. ================================================ FILE: src/lib/agents-chart/vegalite/assemble.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Core chart assembly logic — Two-Stage Pipeline Coordinator. * * Given data, encoding definitions, and semantic types, * produces a complete Vega-Lite specification in two stages: * * ── ANALYSIS (VL-free) ────────────────────────────────────── * Phase 0: resolveChannelSemantics → ChannelSemantics * Step 0a: declareLayoutMode → LayoutDeclaration * Step 0b: convertTemporalData → converted data * Step 0c: filterOverflow → filtered data, nominalCounts * Phase 1: computeLayout → LayoutResult * * ── INSTANTIATE (VL-specific) ─────────────────────────────── * buildVLEncodings → resolvedEncodings * template.instantiate * restructureFacets * vlApplyLayoutToSpec * post-layout adjustments (facet binning, independent scales, tooltips) * * ── Backend Translation Responsibilities ──────────────────── * The LayoutResult from Phase 1 is target-agnostic. This assembler * translates it into Vega-Lite-specific structures: * * subplotWidth / subplotHeight * → VL `width` / `height` on the spec (or `width: {step: N}` for * banded discrete axes). * * xStep / yStep / stepPadding * → VL `width: {step: N}`, `encoding.x.scale.paddingInner`, etc. * VL handles bar sizing natively from the step declaration. * * Facet wrapping * → `restructureFacets()` converts column-only to `facet` + * `columns: N`. The wrapping decision uses the same parameters * (maxStretch, minStep, minSubplotSize) as ECharts. * * Axis titles, labels, legends * → VL handles these declaratively via encoding / config. * * This module has NO React, Redux, or UI framework dependencies. */ import { ChartEncoding, ChartTemplateDef, ChartAssemblyInput, AssembleOptions, LayoutDeclaration, InstantiateContext, } from '../core/types'; import type { ChartWarning } from '../core/types'; import { vlGetTemplateDef } from './templates'; import { inferVisCategory, computeZeroDecision } from '../core/semantic-types'; import { resolveChannelSemantics, convertTemporalData } from '../core/resolve-semantics'; import { toTypeString, type SemanticAnnotation } from '../core/field-semantics'; import { filterOverflow } from '../core/filter-overflow'; import { computeLayout, computeChannelBudgets, computeMinSubplotDimensions } from '../core/compute-layout'; import { vlApplyLayoutToSpec, vlApplyTooltips } from './instantiate-spec'; // --------------------------------------------------------------------------- // Public API // --------------------------------------------------------------------------- /** * Assemble a Vega-Lite specification. * * ```ts * const spec = assembleVegaLite({ * data: { values: myRows }, * semantic_types: { weight: 'Quantity', mpg: 'Quantity' }, * chart_spec: { * chartType: 'Scatter Plot', * encodings: { x: { field: 'weight' }, y: { field: 'mpg' } }, * canvasSize: { width: 400, height: 300 }, * }, * options: { addTooltips: true }, * }); * ``` */ export function assembleVegaLite(input: ChartAssemblyInput): any { const chartType = input.chart_spec.chartType; const encodings = input.chart_spec.encodings; const data = input.data.values ?? []; const semanticTypes = input.semantic_types ?? {}; const canvasSize = input.chart_spec.canvasSize ?? { width: 400, height: 320 }; const chartProperties = input.chart_spec.chartProperties; const options = input.options ?? {}; const chartTemplate = vlGetTemplateDef(chartType) as ChartTemplateDef; if (!chartTemplate) { throw new Error(`Unknown chart type: ${chartType}`); } const warnings: ChartWarning[] = []; // ═══════════════════════════════════════════════════════════════════════ // PHASE 0: Resolve Semantics (VL-free) // ═══════════════════════════════════════════════════════════════════════ const tplMark = chartTemplate.template?.mark; const templateMarkType = typeof tplMark === 'string' ? tplMark : tplMark?.type; // Convert temporal data once — feeds semantic resolution and all downstream stages const convertedData = convertTemporalData(data, semanticTypes); const channelSemantics = resolveChannelSemantics( encodings, data, semanticTypes, convertedData, ); // Finalize zero-baseline (requires template mark knowledge) const effectiveMarkType = templateMarkType || 'point'; for (const [channel, cs] of Object.entries(channelSemantics)) { if ((channel === 'x' || channel === 'y') && cs.type === 'quantitative') { const numericValues = data .map(r => r[cs.field]) .filter((v: any) => v != null && typeof v === 'number' && !isNaN(v)); cs.zero = computeZeroDecision( cs.semanticAnnotation.semanticType, channel, effectiveMarkType, numericValues, ); } } // ═══════════════════════════════════════════════════════════════════════ // STEP 0a: declareLayoutMode (VL-free template hook) // ═══════════════════════════════════════════════════════════════════════ const declaration: LayoutDeclaration = chartTemplate.declareLayoutMode ? chartTemplate.declareLayoutMode(channelSemantics, data, chartProperties) : {}; // Auto-detect binnedAxes from template encoding if not declared if (!declaration.binnedAxes) { const templateEnc = chartTemplate.template?.encoding; if (templateEnc) { const binnedAxes: Record<string, boolean | { maxbins?: number }> = {}; for (const axis of ['x', 'y']) { if (templateEnc[axis]?.bin) { // Use chartProperties.binCount when available so layout // sizing matches the actual number of bins rendered. const propBins = chartProperties?.binCount; if (propBins != null) { binnedAxes[axis] = { maxbins: propBins }; } else if (typeof templateEnc[axis].bin === 'object' && templateEnc[axis].bin.maxbins) { binnedAxes[axis] = templateEnc[axis].bin; } else { // Find the template's default binCount from its property definitions const binPropDef = chartTemplate.properties?.find( (p: any) => p.key === 'binCount' ); const defaultBins = binPropDef?.defaultValue ?? 10; binnedAxes[axis] = { maxbins: defaultBins }; } } } if (Object.keys(binnedAxes).length > 0) { declaration.binnedAxes = binnedAxes; } } } // Merge paramOverrides into effective options const effectiveOptions: AssembleOptions = { ...options, ...(declaration.paramOverrides || {}), }; const { addTooltips: addTooltipsOpt = false, maxStretch: maxStretchVal = 2, minSubplotSize: minSubplotVal = 60, } = effectiveOptions; // VL facet overhead: // Fixed: y-axis labels (~35px width) + x-axis labels (~22px height) // + titles/legend margin. // Gap: config.facet.spacing between panels; shrunk post-layout for // small subplots (see facetGapVal below). if (effectiveOptions.facetFixedPadding == null) { effectiveOptions.facetFixedPadding = { width: 50, height: 40 }; } if (effectiveOptions.facetGap == null) { effectiveOptions.facetGap = 10; } const facetFixW = effectiveOptions.facetFixedPadding.width; const facetFixH = effectiveOptions.facetFixedPadding.height; // ═══════════════════════════════════════════════════════════════════════ // STEP 0b: filterOverflow (VL-free) // ═══════════════════════════════════════════════════════════════════════ // Collect mark types for sort strategy const allMarkTypes = new Set<string>(); if (templateMarkType) allMarkTypes.add(templateMarkType); if (Array.isArray(chartTemplate.template?.layer)) { for (const layer of chartTemplate.template.layer) { const lm = typeof layer.mark === 'string' ? layer.mark : layer.mark?.type; if (lm) allMarkTypes.add(lm); } } // ── Channel budgets (shared, in layout module) ───────────────────── // Computes per-channel max-to-keep using the most conservative // assumptions (minStep, maxStretch). Also decides facet grid. const budgets = computeChannelBudgets( channelSemantics, declaration, convertedData, canvasSize, effectiveOptions, ); const facetGridResult = budgets.facetGrid; const overflowResult = filterOverflow( channelSemantics, declaration, encodings, convertedData, budgets, allMarkTypes, ); let values = overflowResult.filteredData; const nominalCounts = overflowResult.nominalCounts; warnings.push(...overflowResult.warnings); // ═══════════════════════════════════════════════════════════════════════ // PHASE 1: Compute Layout (VL-free) // ═══════════════════════════════════════════════════════════════════════ const layoutResult = computeLayout( channelSemantics, declaration, values, // post-overflow filtered data canvasSize, effectiveOptions, facetGridResult, ); // Attach overflow truncations from filterOverflow layoutResult.truncations = overflowResult.truncations; // ═══════════════════════════════════════════════════════════════════════ // PHASE 2: Instantiate VL Spec // ═══════════════════════════════════════════════════════════════════════ // --- Build VL encodings (abstract semantics → VL encoding objects) --- const resolvedEncodings = buildVLEncodings( encodings, channelSemantics, declaration, data, canvasSize, semanticTypes, templateMarkType, chartTemplate, ); // --- Align sort/domain arrays to converted data types --- // buildVLEncodings uses the original data, but the VL spec embeds // post-conversion data (e.g. Year 1980 → "1980"). Re-map sort and // scale.domain entries so VL can match them against data.values. for (const enc of Object.values(resolvedEncodings)) { const field = enc?.field; if (!field) continue; // Build a lookup from the converted (spec-embedded) data const valMap = new Map<string, any>(); for (const r of values) { const v = r[field]; if (v != null && !valMap.has(String(v))) valMap.set(String(v), v); } if (valMap.size === 0) continue; const remap = (arr: any[]) => arr.map(v => { const key = String(v); return valMap.has(key) ? valMap.get(key) : v; }); if (Array.isArray(enc.sort)) enc.sort = remap(enc.sort); if (Array.isArray(enc.scale?.domain)) enc.scale.domain = remap(enc.scale.domain); } // Detect x/y discrete counts inside template layers for layered specs const isDiscreteType = (t: string | undefined) => t === 'nominal' || t === 'ordinal'; if (Array.isArray(chartTemplate.template?.layer)) { for (const axis of ['x', 'y'] as const) { if (nominalCounts[axis] === 0) { for (const layer of chartTemplate.template.layer) { const layerEnc = layer.encoding?.[axis]; if (layerEnc?.field && isDiscreteType(layerEnc.type)) { nominalCounts[axis] = new Set(values.map((r: any) => r[layerEnc.field])).size; break; } } if (nominalCounts[axis] === 0 && resolvedEncodings[axis]?.field) { const enc = resolvedEncodings[axis]; if (isDiscreteType(enc.type)) { nominalCounts[axis] = new Set(values.map((r: any) => r[enc.field])).size; } } } } } // --- template.instantiate (template-specific VL spec building) --- const vgObj = structuredClone(chartTemplate.template); const instantiateContext: InstantiateContext = { channelSemantics, layout: layoutResult, table: values, resolvedEncodings, encodings, chartProperties, canvasSize, semanticTypes, chartType, assembleOptions: effectiveOptions, }; chartTemplate.instantiate(vgObj, instantiateContext); // Merge any warnings emitted by instantiate if (vgObj._warnings && Array.isArray(vgObj._warnings)) { warnings.push(...vgObj._warnings); delete vgObj._warnings; } // --- restructureFacets (VL-specific) --- // The facet grid (including wrapping) was already decided by // computeChannelBudgets. restructureFacets only performs the VL structural // transform (column encoding → facet + spec for layered specs). restructureFacets(vgObj, nominalCounts, facetGridResult); // --- vlApplyLayoutToSpec (VL-specific: config, sizing, formatting) --- vlApplyLayoutToSpec(vgObj, instantiateContext, warnings); // --- Post-layout adjustments (VL-specific) --- const defaultChartWidth = canvasSize.width; const defaultChartHeight = canvasSize.height; // Compute banded-aware minimum subplot dimensions from core helper. const { minSubplotWidth, minSubplotHeight } = computeMinSubplotDimensions( channelSemantics, declaration, values, effectiveOptions, ); // Shrink gap for small subplots: scale linearly from the reference // (10px gap at 100px subplot), with a floor of 4px. const refGap = effectiveOptions.facetGap ?? 0; const subplotDim = Math.min(layoutResult.subplotWidth, layoutResult.subplotHeight); const REF_SUBPLOT = 100; const facetGapVal = Math.max(6, Math.round(refGap * subplotDim / REF_SUBPLOT)); // Apply the computed gap to the VL spec so Vega-Lite uses it for spacing. vgObj.config = vgObj.config || {}; vgObj.config.facet = { spacing: facetGapVal }; const maxFacetColumns = Math.max(2, Math.floor((defaultChartWidth * maxStretchVal - facetFixW) / (minSubplotWidth + facetGapVal))); const maxFacetRows = Math.max(2, Math.floor((defaultChartHeight * maxStretchVal - facetFixH) / (minSubplotHeight + facetGapVal))); const maxFacetNominalValues = maxFacetColumns * maxFacetRows; // Bin quantitative facets for (const channel of ['facet', 'column', 'row']) { const enc = vgObj.encoding?.[channel]; if (enc?.type === 'quantitative') { const fieldName = enc.field; const uniqueValues = [...new Set(values.map((r: any) => r[fieldName]))]; if (uniqueValues.length > maxFacetNominalValues) { enc.bin = true; } } } // Independent y-axis scaling for faceted charts with vastly different ranges const effectiveEncoding = vgObj.spec?.encoding || vgObj.encoding; const effectiveFacet = vgObj.facet || vgObj.encoding?.facet; if (effectiveFacet != undefined && effectiveEncoding?.y?.type === 'quantitative') { const yField = effectiveEncoding.y.field; const columnField = effectiveFacet.field; if (yField && columnField) { const columnGroups = new Map<any, number>(); for (const row of data) { const columnValue = row[columnField]; const yValue = row[yField]; if (yValue != null && !isNaN(yValue)) { const currentMax = columnGroups.get(columnValue) || 0; columnGroups.set(columnValue, Math.max(currentMax, Math.abs(yValue))); } } const maxValues = Array.from(columnGroups.values()).filter(v => v > 0); if (maxValues.length >= 2) { const maxValue = Math.max(...maxValues); const minValue = Math.min(...maxValues); const ratio = maxValue / minValue; const totalFacets = (layoutResult.facet?.columns ?? 1) * (layoutResult.facet?.rows ?? 1); if (ratio >= 100 && totalFacets < 6) { if (!vgObj.resolve) vgObj.resolve = {}; if (!vgObj.resolve.scale) vgObj.resolve.scale = {}; vgObj.resolve.scale.y = "independent"; } } } } if (addTooltipsOpt) { vlApplyTooltips(vgObj); } // ═══════════════════════════════════════════════════════════════════════ // RESULT // ═══════════════════════════════════════════════════════════════════════ const result: any = { ...vgObj, data: { values } }; if (warnings.length > 0) { result._warnings = warnings; } result._width = layoutResult.subplotWidth; result._height = layoutResult.subplotHeight; return result; } // =========================================================================== // buildVLEncodings — Translate abstract semantics → VL encoding objects // =========================================================================== /** * Translate Phase 0 channel semantics + declaration overrides into * concrete Vega-Lite encoding objects. This is the only place outside * template.instantiate() that constructs VL-specific syntax. */ function buildVLEncodings( encodings: Record<string, ChartEncoding>, channelSemantics: Record<string, import('../core/types').ChannelSemantics>, declaration: LayoutDeclaration, data: any[], canvasSize: { width: number; height: number }, semanticTypes: Record<string, string | SemanticAnnotation>, templateMarkType: string | undefined, chartTemplate: ChartTemplateDef, ): Record<string, any> { const resolvedEncodings: Record<string, any> = {}; // Only process channels the template declares (plus facets which are always valid) const templateChannels = new Set([ ...(chartTemplate.channels || []), 'column', 'row', // faceting is always allowed ]); for (const [channel, encoding] of Object.entries(encodings)) { // Skip channels not supported by this chart type if (!templateChannels.has(channel)) continue; const encodingObj: any = {}; const fieldName = encoding.field; const cs = channelSemantics[channel]; if (channel === "radius") { encodingObj.scale = { type: "sqrt", zero: true }; } // Handle count aggregate without a field if (!fieldName && encoding.aggregate === "count") { encodingObj.field = "_count"; encodingObj.title = "Count"; encodingObj.type = "quantitative"; } if (fieldName) { encodingObj.field = fieldName; // Use Phase 0's resolved type encodingObj.type = cs?.type || 'nominal'; // Explicit type override if (encoding.type) { encodingObj.type = encoding.type; } else if (channel === 'column' || channel === 'row') { if (encodingObj.type !== 'nominal' && encodingObj.type !== 'ordinal') { encodingObj.type = 'nominal'; } } // Aggregation handling — data is always pre-aggregated if (encoding.aggregate) { if (encoding.aggregate === "count") { encodingObj.field = "_count"; encodingObj.title = "Count"; encodingObj.type = "quantitative"; } else { encodingObj.field = `${fieldName}_${encoding.aggregate}`; encodingObj.type = "quantitative"; } } // Scale: quantitative X axes need tight domains for line-like marks if (encodingObj.type === "quantitative" && channel === "x") { if (templateMarkType === 'line' || templateMarkType === 'area' || templateMarkType === 'trail' || templateMarkType === 'point') { encodingObj.scale = { nice: false }; } } // Legend sizing for high-cardinality nominal color/group if (encodingObj.type === "nominal" && (channel === 'color' || channel === 'group')) { const actualDomain = [...new Set(data.map(r => r[fieldName]))]; if (actualDomain.length >= 16) { if (!encodingObj.legend) encodingObj.legend = {}; encodingObj.legend.symbolSize = 12; encodingObj.legend.labelFontSize = 8; } } } // Size channel: set scale based on resolved encoding type if (channel === "size") { const vlDefaultMax = 361; const plotArea = canvasSize.width * canvasSize.height; const n = Math.max(data.length, 1); const fairShare = plotArea / n; const targetPct = 0.6; const absoluteMin = 16; const isQuantitative = encodingObj.type === 'quantitative' || encodingObj.type === 'temporal'; if (isQuantitative) { const maxSize = Math.round(Math.max(absoluteMin, Math.min(vlDefaultMax, fairShare * targetPct))); const minSize = 9; encodingObj.scale = { type: "sqrt", zero: true, range: [minSize, maxSize] }; } else { const maxSize = Math.round(Math.max(absoluteMin, Math.min(vlDefaultMax, fairShare * targetPct))); const minSize = Math.round(maxSize / 4); encodingObj.scale = { range: [minSize, maxSize] }; } } // --- Sorting --- // Helper: when the field's actual data values are numeric but the // encoding is nominal, domain/sort arrays must keep numeric types // so Vega-Lite can match them against the data. const fieldIsNumeric = fieldName ? data.some(r => typeof r[fieldName] === 'number') : false; const preserveDomainTypes = (arr: any[]): any[] => { if (!fieldIsNumeric) return arr; return arr.map(v => { if (typeof v === 'string') { const n = Number(v); if (!isNaN(n) && String(n) === v.trim()) return n; } return v; }); }; if (encoding.sortBy || encoding.sortOrder) { if (!encoding.sortBy) { if (encoding.sortOrder) { encodingObj.sort = encoding.sortOrder; } } else if (encoding.sortBy === 'x' || encoding.sortBy === 'y') { if (encoding.sortBy === channel) { encodingObj.sort = `${encoding.sortOrder === "descending" ? "-" : ""}${encoding.sortBy}`; } else { encodingObj.sort = `${encoding.sortOrder === "ascending" ? "" : "-"}${encoding.sortBy}`; } } else if (encoding.sortBy === 'color') { if (encodings.color?.field) { encodingObj.sort = `${encoding.sortOrder === "ascending" ? "" : "-"}${encoding.sortBy}`; } } else { try { if (fieldName) { const fieldSemType = toTypeString(semanticTypes[fieldName]); const fieldVisCat = inferVisCategory(data.map(r => r[fieldName])); let sortedValues = JSON.parse(encoding.sortBy); if (fieldVisCat === 'temporal' || fieldSemType === "Year" || fieldSemType === "Decade") { sortedValues = sortedValues.map((v: any) => v.toString()); } // Preserve numeric types for nominal fields with numeric data sortedValues = preserveDomainTypes(sortedValues); encodingObj.sort = (encoding.sortOrder === "ascending" || !encoding.sortOrder) ? sortedValues : sortedValues.reverse(); } } catch { console.warn(`sort error > ${encoding.sortBy}`); } } } else { // Auto-sort: apply canonical ordinal sort (months, days, quarters, etc.) // when available. Otherwise, set `sort: null` so VL preserves the data // encounter order rather than its default alphabetical sort. // Alphabetical sort breaks labels like "Stage 1", "Stage 10", "Stage 2" // which should follow their natural data order. const isDiscreteType = encodingObj.type === 'nominal' || encodingObj.type === 'ordinal'; if (isDiscreteType) { if (cs?.ordinalSortOrder && cs.ordinalSortOrder.length > 0) { encodingObj.sort = preserveDomainTypes(cs.ordinalSortOrder); } else if (fieldIsNumeric && fieldName) { // Numeric data treated as nominal/ordinal: sort by numeric // value so labels appear as 0,1,2,3… instead of data-encounter // order. Use "ascending" instead of an explicit value array // to keep the spec compact (avoids enumerating every unique // value, which can be hundreds for fields like Rank). encodingObj.sort = "ascending"; } else { encodingObj.sort = null; } } } // Color scheme from Phase 0 decisions if (channel === "color" || channel === "group") { if (encoding.scheme && encoding.scheme !== "default") { if ('scale' in encodingObj) { encodingObj.scale.scheme = encoding.scheme; } else { encodingObj.scale = { scheme: encoding.scheme }; } } else if (fieldName && cs?.colorScheme) { if (!('scale' in encodingObj)) { encodingObj.scale = {}; } encodingObj.scale.scheme = cs.colorScheme.scheme; if (cs.colorScheme.type === 'diverging' && cs.colorScheme.domainMid !== undefined) { encodingObj.scale.domainMid = cs.colorScheme.domainMid; } } } // --- Collect resolved encoding --- if (Object.keys(encodingObj).length !== 0) { resolvedEncodings[channel] = encodingObj; } } // --- Apply declaration overrides --- // Apply resolved types from declareLayoutMode if (declaration.resolvedTypes) { for (const [ch, type] of Object.entries(declaration.resolvedTypes)) { if (resolvedEncodings[ch]) { resolvedEncodings[ch].type = type; } } } // Translate group channel → VL color + xOffset/yOffset encodings const groupCS = channelSemantics.group; if (groupCS?.field && resolvedEncodings.group) { // Determine which axis the group subdivides (the discrete one) const xType = resolvedEncodings.x?.type; const yType = resolvedEncodings.y?.type; const isDiscreteT = (t: string | undefined) => t === 'nominal' || t === 'ordinal'; const groupAxis = isDiscreteT(xType) ? 'x' : isDiscreteT(yType) ? 'y' : 'x'; const offsetChannel = groupAxis === 'x' ? 'xOffset' : 'yOffset'; // Map group → color encoding if (!resolvedEncodings.color) { resolvedEncodings.color = { ...resolvedEncodings.group }; } delete resolvedEncodings.group; // Create offset encoding for position subdivision if (!resolvedEncodings[offsetChannel]) { resolvedEncodings[offsetChannel] = { field: groupCS.field, type: 'nominal' }; } } // Merge template encoding defaults (bin, aggregate, etc.) const templateEncoding = chartTemplate.template?.encoding; if (templateEncoding) { for (const [ch, enc] of Object.entries(templateEncoding)) { if (enc && typeof enc === 'object' && Object.keys(enc as any).length > 0) { if (resolvedEncodings[ch]) { resolvedEncodings[ch] = { ...(enc as any), ...resolvedEncodings[ch] }; } } } } return resolvedEncodings; } // =========================================================================== // restructureFacets — VL-specific spec transforms for faceted charts // =========================================================================== /** * Purely structural VL transform for faceted charts. * * This function does NOT decide wrapping or column counts — that is done * earlier by computeFacetGrid, which returns a `FacetGridResult`. This function * only: * 1. Moves `encoding.column` → `encoding.facet` (with `columns: N`). * 2. For layered specs, hoists to top-level `facet` + `spec`. */ function restructureFacets( vgObj: any, nominalCounts: Record<string, number>, facetGrid?: { columns: number; rows: number }, ): void { if (vgObj.encoding?.column != undefined && vgObj.encoding?.row == undefined) { vgObj.encoding.facet = vgObj.encoding.column; // Use the grid decided by computeFacetGrid. const numCols = facetGrid?.columns ?? (nominalCounts.column || 1); const numRows = facetGrid?.rows ?? 1; vgObj.encoding.facet.columns = numCols; // Axis title suppression for faceted charts is handled by // vlApplyLayoutToSpec, which uses actual subplot dimensions // to decide whether titles should be hidden (size-based threshold). delete vgObj.encoding.column; // For layered specs, VL doesn't support encoding.facet inline — // restructure to top-level facet + spec. // IMPORTANT: In top-level facet mode, `columns` must be a sibling // of `facet`, not nested inside it. (VL ignores columns inside // the facet object when it's a top-level property.) if (vgObj.layer && Array.isArray(vgObj.layer)) { const facetDef = { ...vgObj.encoding.facet }; const wrapColumns = facetDef.columns; delete facetDef.columns; // remove from facet object delete vgObj.encoding.facet; vgObj.facet = facetDef; if (wrapColumns != null) { vgObj.columns = wrapColumns; // top-level sibling } vgObj.spec = { layer: vgObj.layer, encoding: vgObj.encoding, }; delete vgObj.layer; delete vgObj.encoding; } return; } // For layered specs with row-only or column+row facets if (vgObj.layer && Array.isArray(vgObj.layer) && (vgObj.encoding?.column || vgObj.encoding?.row)) { const facetDef: any = {}; if (vgObj.encoding.column) { facetDef.column = vgObj.encoding.column; delete vgObj.encoding.column; } if (vgObj.encoding.row) { facetDef.row = vgObj.encoding.row; delete vgObj.encoding.row; } vgObj.facet = facetDef; vgObj.spec = { layer: vgObj.layer, encoding: vgObj.encoding, }; delete vgObj.layer; delete vgObj.encoding; } } ================================================ FILE: src/lib/agents-chart/vegalite/index.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * @module agents-chart/vegalite * * Vega-Lite backend for agents-chart. * * Compiles the core semantic layer into Vega-Lite specifications. * Contains VL-specific assembly, spec instantiation, and chart templates. */ // VL assembly function export { assembleVegaLite } from './assemble'; // VL spec instantiation (Phase 2) export { vlApplyLayoutToSpec, vlApplyTooltips } from './instantiate-spec'; // VL template registry export { vlTemplateDefs, vlAllTemplateDefs, vlGetTemplateDef, vlGetTemplateChannels, } from './templates'; // VL recommendation & adaptation export { vlAdaptChart, vlRecommendEncodings } from './recommendation'; ================================================ FILE: src/lib/agents-chart/vegalite/instantiate-spec.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ============================================================================= * PHASE 2: INSTANTIATE SPEC * ============================================================================= * * Combine semantic decisions (Phase 0) and layout dimensions (Phase 1) to * produce the final Vega-Lite specification. * * This is the **only phase that knows about Vega-Lite** (or whichever * output format is targeted). * * VL dependency: **Yes — this is where VL lives** * ============================================================================= */ import type { ChannelSemantics, LayoutResult, InstantiateContext, ChartWarning, } from '../core/types'; import type { FormatSpec } from '../core/field-semantics'; import { looksLikeDateString, analyzeTemporalField, computeDataVotes, pickBestLevel, levelToFormat, SEMANTIC_LEVEL, } from '../core/resolve-semantics'; import { getVisCategory, inferVisCategory, } from '../core/semantic-types'; import { toTypeString, snapToBoundHeuristic } from '../core/field-semantics'; // --------------------------------------------------------------------------- // Public API: instantiateSpec // --------------------------------------------------------------------------- /** * Phase 2: Build the final VL specification from semantic decisions and * layout results. * * This is the shared assembler logic that translates abstract decisions * into VL syntax. Template-specific logic is handled by * template.instantiate(spec, context). * * This function handles the VL-specific plumbing that is common across * all templates: * - Canvas dimensions (config.view.continuousWidth/Height) * - Discrete step sizing (width: {step: N}) * - Zero-baseline application * - Color scheme application * - Temporal format application * - Label sizing application * - Overflow warning styling * * @param vgObj The mutated VL spec (after template encoding construction) * @param context Combined context from all phases * @param warnings Array to append warnings to */ export function vlApplyLayoutToSpec( vgObj: any, context: InstantiateContext, warnings: ChartWarning[], ): void { const { channelSemantics, layout, canvasSize } = context; const xIsDiscrete = layout.xNominalCount > 0; const yIsDiscrete = layout.yNominalCount > 0; // --- Helper: iterate encoding targets across top-level, spec, and layers --- // After facet restructuring, encodings may live under vgObj.spec instead // of vgObj directly, and layers may be under vgObj.spec.layer. const collectEncodingTargets = (ch: string): any[] => { const targets: any[] = []; if (vgObj.encoding?.[ch]) targets.push(vgObj.encoding[ch]); if (vgObj.spec?.encoding?.[ch]) targets.push(vgObj.spec.encoding[ch]); if (Array.isArray(vgObj.layer)) { for (const layer of vgObj.layer) { if (layer.encoding?.[ch]) targets.push(layer.encoding[ch]); } } if (Array.isArray(vgObj.spec?.layer)) { for (const layer of vgObj.spec.layer) { if (layer.encoding?.[ch]) targets.push(layer.encoding[ch]); } } return targets; }; // --- Apply zero-baseline decisions --- for (const ch of ['x', 'y'] as const) { const cs = channelSemantics[ch]; if (!cs?.zero) continue; const decision = cs.zero; const targets = collectEncodingTargets(ch) .filter(enc => enc.type === 'quantitative'); for (const enc of targets) { // Skip binned encodings — the bin axis represents data values, // not bar length, so zero-baseline is inappropriate (e.g. histograms). if (enc.bin) continue; if (!enc.scale) enc.scale = {}; if (enc.scale.zero !== undefined) continue; if (enc.scale.domain && Array.isArray(enc.scale.domain)) continue; enc.scale.zero = decision.zero; // No explicit domain padding — VL's native `nice` rounding // (on by default) already provides breathing room with clean // tick-aligned bounds, which is superior to computed fractional // bounds like [1.86, 4.94] that also conflict with semantic // domain constraints and log scales. } } // --- Apply field-context semantic decisions (format, domain, ticks, etc.) --- vlApplyFieldContext(vgObj, channelSemantics, collectEncodingTargets, context); // --- Apply temporal formatting --- // For positional temporal axes (x/y), do NOT set axis.format — VL's // built-in multi-level temporal labeling (e.g. "2016" at year boundaries, // "April" / "July" within a year) is far superior to a single uniform // format string which loses hierarchical context. // We still apply the format to color legends where multi-level is unavailable. const applyTemporalFormat = (enc: any, channel: string, cs: ChannelSemantics | undefined) => { if (!enc || !cs?.temporalFormat) return; if (enc.type === 'temporal') { if (channel === 'color') { if (!enc.legend) enc.legend = {}; enc.legend.format = cs.temporalFormat; } // x/y: intentionally omitted — let VL use native multi-level labels } }; const applyOrdinalTemporalFormat = (enc: any, channel: string, cs: ChannelSemantics | undefined) => { if (!enc || !enc.field) return; if (enc.type !== 'ordinal' && enc.type !== 'nominal') return; if (!cs) return; const semanticType = toTypeString(context.semanticTypes[enc.field]); const stCategory = semanticType ? getVisCategory(semanticType) : null; if (stCategory !== 'temporal') return; const fieldVals = context.table.map((r: any) => r[enc.field]).filter((v: any) => v != null); const datelikeCnt = fieldVals.filter((v: any) => typeof v !== 'string' || looksLikeDateString(String(v)) ).length; if (datelikeCnt < fieldVals.length * 0.5) return; const analysis = analyzeTemporalField(fieldVals); if (!analysis) return; const votes = computeDataVotes(analysis.same); const semLevel = SEMANTIC_LEVEL[semanticType]; if (semLevel !== undefined) votes[semLevel] += 3; const { level, score } = pickBestLevel(votes); if (score < 5) return; const fmt = levelToFormat(level, analysis); if (!fmt) return; const expr = `isValid(toDate(datum.label)) ? timeFormat(toDate(datum.label), '${fmt}') : datum.label`; if (channel === 'x' || channel === 'y') { if (!enc.axis) enc.axis = {}; enc.axis.labelExpr = expr; } else if (channel === 'color') { if (!enc.legend) enc.legend = {}; enc.legend.labelExpr = expr; } }; // Iterate all encoding locations (top-level, spec, layers) const applyTemporalToEncoding = (encoding: Record<string, any>) => { for (const [ch, enc] of Object.entries(encoding)) { applyTemporalFormat(enc, ch, channelSemantics[ch]); applyOrdinalTemporalFormat(enc, ch, channelSemantics[ch]); } }; if (vgObj.encoding) applyTemporalToEncoding(vgObj.encoding); if (vgObj.spec?.encoding) applyTemporalToEncoding(vgObj.spec.encoding); if (Array.isArray(vgObj.layer)) { for (const layer of vgObj.layer) { if (layer.encoding) applyTemporalToEncoding(layer.encoding); } } if (Array.isArray(vgObj.spec?.layer)) { for (const layer of vgObj.spec.layer) { if (layer.encoding) applyTemporalToEncoding(layer.encoding); } } // --- Banded continuous axis domain padding --- // For banded continuous axes (e.g. Heatmap with quantitative X/Y), // add a half-step buffer so edge cells aren't clipped at the boundary. // Without this, the domain starts/ends exactly at min/max data values // and rect marks at the edges are only half-visible. for (const axis of ['x', 'y'] as const) { const bandedCount = axis === 'x' ? layout.xContinuousAsDiscrete : layout.yContinuousAsDiscrete; if (bandedCount <= 1) continue; const enc = vgObj.encoding?.[axis] || vgObj.spec?.encoding?.[axis]; if (!enc) continue; // Skip binned encodings — VL handles bin domain automatically if (enc.bin) continue; const isTemporal = enc.type === 'temporal'; const isContinuous = enc.type === 'quantitative' || isTemporal; if (!isContinuous) continue; if (enc.scale?.domain) continue; const numericVals = context.table .map((r: any) => { const raw = r[enc.field]; if (raw == null) return NaN; if (isTemporal) return +new Date(raw); return +raw; }) .filter((v: number) => !isNaN(v)); if (numericVals.length <= 1) continue; const minVal = Math.min(...numericVals); const maxVal = Math.max(...numericVals); const dataRange = maxVal - minVal; if (dataRange === 0) continue; const pad = dataRange / (bandedCount - 1) / 2; if (!enc.scale) enc.scale = {}; enc.scale.nice = false; if (isTemporal) { enc.scale.domain = [ new Date(minVal - pad).toISOString(), new Date(maxVal + pad).toISOString(), ]; } else { enc.scale.domain = [minVal - pad, maxVal + pad]; } } // --- Canvas sizing --- const axisXConfig: Record<string, any> = { labelLimit: layout.xLabel.labelLimit, labelFontSize: layout.xLabel.fontSize, }; if (layout.xLabel.labelAngle !== undefined) { axisXConfig.labelAngle = layout.xLabel.labelAngle; axisXConfig.labelAlign = layout.xLabel.labelAlign; axisXConfig.labelBaseline = layout.xLabel.labelBaseline; } const axisYConfig: Record<string, any> = { labelFontSize: layout.yLabel.fontSize }; vgObj.config = { view: { continuousWidth: layout.subplotWidth, continuousHeight: layout.subplotHeight, ...(!vgObj.encoding && { stroke: null }), }, axisX: axisXConfig, axisY: axisYConfig, }; // --- Step-based sizing for discrete axes --- if (xIsDiscrete && typeof vgObj.width !== 'number') { vgObj.width = layout.xStepUnit === 'group' ? { step: layout.xStep, for: 'position' } : { step: layout.xStep }; } if (yIsDiscrete && typeof vgObj.height !== 'number') { vgObj.height = layout.yStepUnit === 'group' ? { step: layout.yStep, for: 'position' } : { step: layout.yStep }; } // Sync hardcoded template width/height to config.view if (typeof vgObj.width === 'number') { vgObj.config.view.continuousWidth = vgObj.width; } else if (vgObj.width && typeof vgObj.width === 'object' && 'step' in vgObj.width) { vgObj.width = layout.xStepUnit === 'group' ? { step: layout.xStep, for: 'position' } : { step: layout.xStep }; } if (typeof vgObj.height === 'number') { vgObj.config.view.continuousHeight = vgObj.height; } else if (vgObj.height && typeof vgObj.height === 'object' && 'step' in vgObj.height) { vgObj.height = layout.yStepUnit === 'group' ? { step: layout.yStep, for: 'position' } : { step: layout.yStep }; } // Facet header sizing const totalFacets = (layout.facet?.columns ?? 1) * (layout.facet?.rows ?? 1); if (totalFacets > 6) { vgObj.config.header = { labelLimit: 120, labelFontSize: 9 }; } // In faceted charts, use lighter axis title styling to reduce clutter const facetRows = layout.facet?.rows ?? 1; const facetCols = layout.facet?.columns ?? 1; const encTarget = vgObj.spec?.encoding || vgObj.encoding; if (facetRows > 1 || facetCols > 1) { if (!vgObj.config) vgObj.config = {}; const lightTitle = { titleFontWeight: 'normal' as const, titleFontSize: 11, titleColor: '#666' }; vgObj.config.axisX = { ...(vgObj.config.axisX || {}), ...lightTitle }; vgObj.config.axisY = { ...(vgObj.config.axisY || {}), ...lightTitle }; } // When row faceting is used, use lighter y axis title styling; // hide it entirely if y is nominal (the labels speak for themselves). if (encTarget?.row || (facetRows > 1 && encTarget?.y)) { if (encTarget?.y?.type === 'nominal') { if (!vgObj.config) vgObj.config = {}; vgObj.config.axisY = { ...(vgObj.config.axisY || {}), title: null }; if (!encTarget.y.axis) encTarget.y.axis = {}; encTarget.y.axis.title = null; } } // --- Dual-legend repositioning --- // When multiple channels produce legends (e.g. color + size, color + opacity), // Vega-Lite stacks them all on the right, which eats into the plot area on a // 400×300 canvas. Detect this and move the categorical (nominal/ordinal) // legend to the bottom with horizontal orientation, keeping the quantitative // legend compact on the right — BUT only when the total chart height is too // short to fit both stacked on the right comfortably. const legendChannels = (['color', 'size', 'shape', 'opacity', 'strokeDash', 'strokeWidth'] as const) .filter(ch => { const targets = collectEncodingTargets(ch); return targets.some(enc => enc.field && enc.legend !== null); }); if (legendChannels.length >= 2) { // Separate categorical vs quantitative legend channels const categoricalChs: string[] = []; const quantitativeChs: string[] = []; for (const ch of legendChannels) { const targets = collectEncodingTargets(ch); const isQuant = targets.some(enc => enc.type === 'quantitative' || enc.type === 'temporal'); if (isQuant) { quantitativeChs.push(ch); } else { categoricalChs.push(ch); } } // Move categorical legends to bottom, keep quantitative ones on right // — but only if the total chart height can't comfortably fit both. if (categoricalChs.length > 0 && quantitativeChs.length > 0) { // Estimate total right-side legend height: // Quantitative legend ≈ 100px (gradient + title + labels) // Categorical legend ≈ title(20px) + entries × 20px each const QUANT_LEGEND_HEIGHT = 100; const CAT_TITLE_HEIGHT = 20; const CAT_ENTRY_HEIGHT = 20; // Estimate domain sizes for categorical legends let totalCatEntries = 0; for (const ch of categoricalChs) { const targets = collectEncodingTargets(ch); for (const enc of targets) { if (!enc.field) continue; const domainSize = new Set(context.table.map((r: any) => r[enc.field])).size; totalCatEntries += domainSize; } } const estCatHeight = CAT_TITLE_HEIGHT * categoricalChs.length + totalCatEntries * CAT_ENTRY_HEIGHT; const estTotalLegendHeight = QUANT_LEGEND_HEIGHT + estCatHeight + 20; // 20px gap between legends // Total chart height = subplot height × facet rows + overhead const totalChartHeight = layout.subplotHeight * (layout.facet?.rows ?? 1) + (layout.facet?.rows ?? 1) * 10; // approx facet spacing const fitsOnRight = totalChartHeight >= estTotalLegendHeight; if (!fitsOnRight) { for (const ch of categoricalChs) { const targets = collectEncodingTargets(ch); for (const enc of targets) { if (!enc.field) continue; if (!enc.legend) enc.legend = {}; enc.legend.orient = 'bottom'; enc.legend.direction = 'horizontal'; // Responsive columns: estimate how many legend entries fit // per row based on the available canvas width. // Each entry ≈ symbol(16) + label + padding. Estimate label // width from the longest domain value, then derive columns. const domainValues = [...new Set(context.table.map((r: any) => r[enc.field]))]; const domainSize = domainValues.length; const maxLabelLen = Math.max( ...domainValues.map((v: any) => String(v ?? '').length), 3, ); // VL legend: symbol (~15px) + label (~5px/char at 11px proportional font) + gap (~8px) const entryWidth = 15 + maxLabelLen * 5 + 8; // Available width: VL's bottom legend spans the full SVG width, // which includes the plot area plus the right-side quantitative // legend (~130px). Use that total for column estimation. const rightLegendWidth = 130; const availableWidth = canvasSize.width + rightLegendWidth; const columnsByWidth = Math.max(1, Math.floor(availableWidth / entryWidth)); enc.legend.columns = Math.min(columnsByWidth, domainSize); // For very high cardinality, cap visible symbols to keep // the bottom legend from growing too tall. const maxRows = 4; const maxVisible = columnsByWidth * maxRows; if (domainSize > maxVisible) { enc.legend.symbolLimit = maxVisible; } } } } // else: chart is tall enough — leave both legends on the right (VL default) } } // --- Overflow styling (from TruncationWarning[]) --- // Applied AFTER template.instantiate and facet restructuring, // so we modify the spec's actual encoding objects. for (const trunc of layout.truncations) { const ch = trunc.channel; const targets = collectEncodingTargets(ch); for (const enc of targets) { if (!enc.field) continue; // Axis/legend label color: grey for placeholder if (ch === 'x' || ch === 'y') { if (!enc.axis) enc.axis = {}; enc.axis.labelColor = { condition: { test: `datum.label == '${trunc.placeholder}'`, value: "#999999", }, value: "#000000", }; // Set domain to kept values + placeholder if (!enc.scale) enc.scale = {}; enc.scale.domain = [...trunc.keptValues, trunc.placeholder]; } else if (ch === 'color') { if (!enc.legend) enc.legend = {}; enc.legend.values = [...trunc.keptValues, trunc.placeholder]; } } } } // --------------------------------------------------------------------------- // vlApplyFieldContext — Apply field-level semantic decisions to VL encodings // --------------------------------------------------------------------------- /** * Build a Vega expression that abbreviates large numbers using a small * set of universally understood suffixes: K (thousands), M (millions), * B (billions), T (trillions). * * The expression is a nested ternary that picks the right divisor: * abs(v) >= 1e12 → v/1e12 + "T" * abs(v) >= 1e9 → v/1e9 + "B" * abs(v) >= 1e6 → v/1e6 + "M" * abs(v) >= 1e3 → v/1e3 + "K" * else → plain number * * @param prefix Optional prefix (e.g., "$") * @param suffix Optional suffix (e.g., " kg") * @returns A Vega labelExpr string */ function buildAbbreviationExpr(prefix?: string, suffix?: string): string { const pfx = prefix ? `'${prefix}' + ` : ''; const sfx = suffix ? ` + '${suffix}'` : ''; // Use ~g to drop trailing zeros from the fractional digit return ( `${pfx}(abs(datum.value) >= 1e12 ? format(datum.value / 1e12, '~g') + 'T' : ` + `abs(datum.value) >= 1e9 ? format(datum.value / 1e9, '~g') + 'B' : ` + `abs(datum.value) >= 1e6 ? format(datum.value / 1e6, '~g') + 'M' : ` + `abs(datum.value) >= 1e3 ? format(datum.value / 1e3, '~g') + 'K' : ` + `format(datum.value, ','))${sfx}` ); } /** * Build a VL-compatible format expression from a FormatSpec. * * - d3's `format()` handles the numeric pattern. * - Prefix/suffix are prepended/appended via a Vega expression. * - When `abbreviate` is true, large values are compacted (1K, 1M, 1B, 1T). * * Returns an `axis.labelExpr` / `legend.labelExpr` string, or null if * no formatting is needed (plain data labels suffice). */ function formatSpecToLabelExpr(fmt: FormatSpec): string | null { // Abbreviation takes priority — produces its own complete expression if (fmt.abbreviate) { return buildAbbreviationExpr(fmt.prefix, fmt.suffix); } if (!fmt.pattern) return null; const hasPrefix = !!fmt.prefix; const hasSuffix = !!fmt.suffix; if (!hasPrefix && !hasSuffix) { // Pure d3-format — can use axis.format directly (no expr needed) return null; } // Build Vega expression: format(datum.value, pattern) with prefix/suffix const pfx = hasPrefix ? `'${fmt.prefix}' + ` : ''; const sfx = hasSuffix ? ` + '${fmt.suffix}'` : ''; return `${pfx}format(datum.value, '${fmt.pattern}')${sfx}`; } /** * Compute the maximum stacked (group) total for a quantitative field. * * For a stacked bar chart with: * x = category (grouping), y = value (stacked), color = series * * This computes sum(value) for each category group and returns the max. * Used to check whether stacked totals exceed an intrinsic domain bound * (e.g., percentages summing to >100%). * * Returns undefined if the grouping field can't be determined. */ function computeMaxStackedTotal( table: any[], measureField: string, measureChannel: string, channelSemantics: Record<string, ChannelSemantics>, ): number | undefined { if (!table || table.length === 0) return undefined; // The grouping axis is the *other* positional channel const groupChannel = measureChannel === 'y' ? 'x' : 'y'; const groupCS = channelSemantics[groupChannel]; if (!groupCS) return undefined; const groupField = groupCS.field; if (!groupField) return undefined; // Also consider facet fields (row/column) as additional grouping const facetFields: string[] = []; for (const ch of ['row', 'column']) { const fcs = channelSemantics[ch]; if (fcs?.field) facetFields.push(fcs.field); } // Group rows and sum the measure field per group const totals = new Map<string, number>(); for (const row of table) { const val = row[measureField]; if (typeof val !== 'number' || isNaN(val)) continue; // Build group key from grouping field + facet fields const keyParts = [String(row[groupField])]; for (const ff of facetFields) { keyParts.push(String(row[ff])); } const key = keyParts.join('|||'); totals.set(key, (totals.get(key) ?? 0) + val); } if (totals.size === 0) return undefined; return Math.max(...totals.values()); } /** * Get the effective intrinsic domain for a field, even when no explicit * `intrinsicDomain` is provided in the annotation. * * Mirrors steps 2–3 of `resolveDomainConstraint` (in field-semantics.ts) * which infer intrinsic bounds from the semantic type: * - Percentage → [0, 1] or [0, 100] depending on data scale * - Latitude → [-90, 90] * - Longitude → [-180, 180] * - Correlation → [-1, 1] * * Without this: stacked charts with Percentage fields that lack explicit * annotation never get domain constraints, because the stacking re-check * in vlApplyFieldContext couldn't find the intrinsic bounds. */ function getEffectiveIntrinsicDomain( cs: ChannelSemantics, table: any[], field: string, ): [number, number] | undefined { // 1. Explicit annotation — authoritative if (cs.semanticAnnotation?.intrinsicDomain) { return cs.semanticAnnotation.intrinsicDomain; } // 2. Infer from semantic type const semanticType = cs.semanticAnnotation?.semanticType; if (!semanticType) return undefined; if (semanticType === 'Latitude') return [-90, 90]; if (semanticType === 'Longitude') return [-180, 180]; if (semanticType === 'Correlation') return [-1, 1]; if (semanticType === 'Percentage') { const nums = table .map(r => r[field]) .filter((v: any) => typeof v === 'number' && !isNaN(v)); if (nums.length > 0) { // Inline scale detection: if ≥80% of |values| are ≤1, it's 0-1 scale const countBelow1 = nums.filter(v => Math.abs(v) <= 1).length; const isFractional = countBelow1 / nums.length >= 0.8; return isFractional ? [0, 1] : [0, 100]; } } return undefined; } /** * Apply field-context semantic properties to VL encoding objects. * * Consumes the following ChannelSemantics properties that were previously * dead writes (computed by resolveChannelSemantics but never read): * * 1. format → axis.format / axis.labelExpr (number formatting) * 2. tooltipFormat → tooltip encoding format * 3. domainConstraint → scale.domain + scale.clamp (bounded types) * 4. tickConstraint → axis.tickMinStep + axis.values (integer ticks) * 5. reversed → scale.reverse (rank axes) * 6. nice → scale.nice (bounded types disable nice) * 7. scaleType → scale.type (log, sqrt, symlog) */ function vlApplyFieldContext( vgObj: any, channelSemantics: Record<string, ChannelSemantics>, collectEncodingTargets: (ch: string) => any[], context: InstantiateContext, ): void { for (const [ch, cs] of Object.entries(channelSemantics)) { const targets = collectEncodingTargets(ch); if (targets.length === 0) continue; for (const enc of targets) { if (!enc.field) continue; // ── 0. Temporal + bin incompatibility guard ── // VL's `bin` operates on numeric values. Setting `type: "temporal"` // with `bin` causes VL to parse values (e.g., year 2004) as dates // and bin in milliseconds, producing nonsensical time-of-day labels. // For temporal binning, VL expects `timeUnit` instead. // Fix: demote to `quantitative` so bins work on raw numbers. if (enc.bin && enc.type === 'temporal') { enc.type = 'quantitative'; // Year/Decade values should show as plain integers, not "2,004" if (!enc.axis) enc.axis = {}; if (!enc.axis.format) enc.axis.format = 'd'; } // ── 1. Number format (axis.format / axis.labelExpr) ── // Only apply to quantitative positional channels. // Skip binned encodings — VL formats bin ranges natively and // our semantic format (e.g. percent) would misinterpret the // bin boundaries. // Without this: axes show raw numbers like "1000000" instead of "$1,000,000". if ((cs.format?.pattern || cs.format?.abbreviate) && (ch === 'x' || ch === 'y') && enc.type === 'quantitative' && !enc.bin) { // Skip if the encoding already has an explicit format if (!enc.axis?.format && !enc.axis?.labelExpr) { if (!enc.axis) enc.axis = {}; const expr = formatSpecToLabelExpr(cs.format); if (expr) { enc.axis.labelExpr = expr; } else { enc.axis.format = cs.format.pattern; } } } // ── 2. Tooltip format ── // Tooltip formatting is handled via VL's tooltip encoding with format. // Without this: tooltips show raw floats like "0.4812" instead of "48.12%". // NOTE: VL's `config.mark.tooltip: true` uses default formatting; // explicit tooltip channels would need encoding-level format. // For now, we set formatType on the main encoding when tooltipFormat // has a simple pattern (no prefix/suffix). // Full tooltip encoding is deferred to template-level implementation. // ── 3. Domain constraint (scale.domain + scale.clamp) ── // Semantic domain constraints represent *intrinsic* field bounds. // Full constraints (both min+max) set scale.domain directly. // Partial constraints (only min or max) use VL's domainMin/domainMax // for single-ended bounds, letting the other end auto-fit from data. // Skip binned encodings — VL handles bin domain automatically. // // Stacking interaction: // Sum-stacked charts (default / "zero" / "center") show stacked // totals on the axis, not individual values. The field-level snap // heuristic only saw individual values, which may not reflect the // actual axis range. We recompute: if the max group total still // fits within the intrinsic bound, the snap constraint is safe // (e.g., percentages summing to exactly 100%). If totals exceed // the bound, we skip the constraint to avoid clipping bars. // Normalize-stacked (stack: "normalize"): VL normalizes to [0,1], // so domain is always [0,1]. Constraint is harmless. → Keep. // Layered / no stack (stack: null/false): each bar is // independent. → Apply domain constraints as normal. // // VL auto-stacks bar/area marks when a color encoding is present // (unless stack: null/false). // // Without this: Rating gets auto-fitted to data range (e.g., 2-4.5) // instead of showing the full 1-5 scale. const isExplicitlyStacked = enc.stack !== undefined && enc.stack !== null && enc.stack !== false; const markType = typeof vgObj.mark === 'string' ? vgObj.mark : vgObj.mark?.type; const isBarLike = ['bar', 'area', 'rect'].includes(markType); // Check for color encoding at top level, in layers, or in faceted spec const hasColorEncoding = !!( vgObj.encoding?.color?.field || (Array.isArray(vgObj.layer) && vgObj.layer.some((l: any) => l.encoding?.color?.field)) || vgObj.spec?.encoding?.color?.field ); const isImplicitlyStacked = isBarLike && hasColorEncoding && enc.stack !== null; const isStacked = isExplicitlyStacked || isImplicitlyStacked; const isNormalizeStacked = enc.stack === 'normalize'; const isSumStacked = isStacked && !isNormalizeStacked; // For sum-stacked charts, check if stacked totals exceed the // intrinsic domain. If they do, skip the domain constraint. // // Also, if snap didn't fire on individual values but stacked // totals are near the intrinsic bound, re-run snap on the totals. // Example: individual percentages range 20–50% (no snap), but // they sum to ~100% per group → should snap to 100%. let skipDomain = false; let effectiveDomainConstraint = cs.domainConstraint; if (isSumStacked) { // Use explicit intrinsicDomain from annotation, or infer from // semantic type for known bounded types (Percentage, Lat/Lon, etc.) // Without this: Percentage fields without explicit annotation // never get a domain constraint on stacked charts because // individual values don't trigger snap, and the stacked re-check // can't find the intrinsic bounds to snap totals against. const intrinsic = getEffectiveIntrinsicDomain(cs, context.table, enc.field); if (intrinsic) { const maxTotal = computeMaxStackedTotal( context.table, enc.field, ch, channelSemantics, ); if (maxTotal !== undefined && maxTotal > intrinsic[1]) { // Stacked totals exceed the intrinsic bound → // skip domain constraint to avoid clipping. // Use a small epsilon tolerance for floating-point // imprecision (e.g., shares summing to 1.0000000001 // instead of exactly 1.0 should still be treated as // within bounds). Scale epsilon to the domain range. const range = intrinsic[1] - intrinsic[0]; const epsilon = range * 1e-6; if (maxTotal > intrinsic[1] + epsilon) { if (cs.domainConstraint) { skipDomain = true; } } else { // Within epsilon — treat as equal to the bound. // Re-run snap so the bound gets applied. const stackedSnap = snapToBoundHeuristic(intrinsic, [intrinsic[1]]); if (stackedSnap) { if (cs.domainConstraint) { effectiveDomainConstraint = { min: cs.domainConstraint.min ?? stackedSnap.min, max: cs.domainConstraint.max ?? stackedSnap.max, clamp: cs.domainConstraint.clamp || stackedSnap.clamp, }; } else { effectiveDomainConstraint = stackedSnap; } } } } else if (maxTotal !== undefined) { // Stacked totals are within intrinsic bounds. // Re-run snap on stacked totals to pick up bounds // that individual values missed (e.g., individual // shares of 20–40% don't snap to 100%, but stacked // totals of ~100% should). const stackedSnap = snapToBoundHeuristic(intrinsic, [maxTotal]); if (stackedSnap) { // Merge with existing constraint: keep any bound // already snapped from individual values, add any // new bound from stacked totals. if (cs.domainConstraint) { effectiveDomainConstraint = { min: cs.domainConstraint.min ?? stackedSnap.min, max: cs.domainConstraint.max ?? stackedSnap.max, clamp: cs.domainConstraint.clamp || stackedSnap.clamp, }; } else { effectiveDomainConstraint = stackedSnap; } } } } else if (cs.domainConstraint) { // No intrinsic domain to compare against → skip to be safe skipDomain = true; } } if (effectiveDomainConstraint && enc.type === 'quantitative' && (ch === 'x' || ch === 'y') && !enc.bin && !skipDomain) { if (!enc.scale) enc.scale = {}; const { min, max, clamp } = effectiveDomainConstraint; if (min !== undefined && max !== undefined) { enc.scale.domain = [min, max]; // For non-bar marks (scatter, line, etc.), the explicit // semantic domain is authoritative — clear zero so VL // doesn't extend beyond intrinsic bounds (e.g., Rating // scatter [1,5] shouldn't stretch to [0,5]). // For bar/area marks, keep zero:true so bars grow from // zero with correct proportional lengths — VL extends // the domain to include 0, and the upper bound is still // capped by the domain constraint (e.g., [0,5] not [0,6]). if (!isBarLike && enc.scale.zero !== undefined) { delete enc.scale.zero; } } else { // Partial constraint — snap one end while auto-fitting the other. // E.g., Percentage data at 97% → domainMax = 100, domainMin auto-fits. if (min !== undefined) enc.scale.domainMin = min; if (max !== undefined) enc.scale.domainMax = max; // VL may suppress nice rounding on the free end when // domainMin/domainMax is set, causing data to touch the // chart border. Force nice so the unconstrained end // gets proper headroom (e.g., data at +26% rounds to +40%). enc.scale.nice = true; } if (clamp) { enc.scale.clamp = true; } } // ── 4. Tick constraint (axis.tickMinStep + axis.values) ── // Skip binned encodings — VL handles bin ticks natively. // Without this: Rating 1-5 and Count axes show fractional ticks // like 1.5, 2.5, 3.5 that have no physical meaning. if (cs.tickConstraint && (ch === 'x' || ch === 'y') && enc.type === 'quantitative' && !enc.bin) { if (!enc.axis) enc.axis = {}; if (cs.tickConstraint.integersOnly && enc.axis.tickMinStep === undefined) { enc.axis.tickMinStep = cs.tickConstraint.minStep ?? 1; } if (cs.tickConstraint.exactTicks && !enc.axis.values) { enc.axis.values = cs.tickConstraint.exactTicks; } // Hide fractional tick labels for integer-only fields. // VL may still generate fractional ticks when the domain // span is small (e.g., all values = 1 → domain [0,1] → // ticks at 0, 0.2, 0.4…). The `,d` format rounds these // to duplicate labels. This labelExpr suppresses them. if (cs.tickConstraint.integersOnly && !enc.axis.labelExpr && !enc.axis.values) { enc.axis.labelExpr = "datum.value === ceil(datum.value) ? format(datum.value, ',d') : ''"; } // When ticks are integers, axis labels should show integers // even if the underlying data has decimals (e.g., Rating // data 3.7 with ticks at 1,2,3,4,5 → labels "1,2,3,4,5" // not "1.0,2.0,3.0"). if (cs.tickConstraint.integersOnly && enc.axis.format) { // Replace decimal format with integer format for axis only enc.axis.format = enc.axis.format.replace(/\.\d+f$/, 'd'); } // Same for labelExpr — swap the d3-format pattern inside if (cs.tickConstraint.integersOnly && enc.axis.labelExpr) { enc.axis.labelExpr = enc.axis.labelExpr.replace( /format\(datum\.value,\s*'([^']*)\.\d+f'\)/, "format(datum.value, '$1d')", ); } } // ── 5. Reversed axis (scale.reverse) ── // Only for quantitative axes. Ordinal y-axes already place the // first domain value (rank 1) at the top by default, so adding // scale.reverse there would double-reverse, putting 1 back at // the bottom. // Skip binned encodings — VL handles bin axis direction natively. if (cs.reversed && (ch === 'x' || ch === 'y') && enc.type === 'quantitative' && !enc.bin) { if (!enc.scale) enc.scale = {}; if (enc.scale.reverse === undefined) { enc.scale.reverse = true; } } // ── 6. Nice rounding (scale.nice) ── // Without this: Domain [1, 5] for ratings gets "nice-rounded" // to [0, 6] which wastes space and implies values that don't exist. // Skip binned encodings — VL computes bin extents automatically. if (cs.nice === false && enc.type === 'quantitative' && !enc.bin) { if (!enc.scale) enc.scale = {}; if (enc.scale.nice === undefined) { enc.scale.nice = false; } } // ── 7. Scale type (scale.type) ── // Only applies for specific semantic types (Population, GDP, etc.) // when data spans ≥ 4 orders of magnitude. Conservative policy // to avoid surprising users on normal datasets. // Skip binned encodings — log/sqrt scales conflict with VL's // linear bin computation and break when data contains zeros. if (cs.scaleType && cs.scaleType !== 'linear' && enc.type === 'quantitative' && !enc.bin) { if (!enc.scale) enc.scale = {}; if (!enc.scale.type) { enc.scale.type = cs.scaleType; // Log/symlog scales don't support zero baseline — clean it up if (cs.scaleType === 'log' || cs.scaleType === 'symlog') { if (enc.scale.zero !== undefined) { delete enc.scale.zero; } // Log axes produce many grid lines (1,2,3…9 per // decade). Make them very light so they convey the // log-scale structure without competing with data. if (ch === 'x' || ch === 'y') { if (!enc.axis) enc.axis = {}; enc.axis.gridColor = '#e8e8e8'; enc.axis.gridOpacity = 0.5; } } } } } } } /** * Apply tooltip configuration to a VL spec. */ export function vlApplyTooltips(vgObj: any): void { if (!vgObj.config) vgObj.config = {}; vgObj.config.mark = { ...vgObj.config.mark, tooltip: true }; } ================================================ FILE: src/lib/agents-chart/vegalite/recommendation.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Vega-Lite recommendation & adaptation wrappers. * * Extends core/recommendation.ts with VL-only chart types (Regression, * Ranged Dot Plot, Pyramid, Lollipop, Dotted Line, Bump, Density, Waterfall, * Strip, US/World Map) and filters results to VL-valid channels. */ import { adaptChannels, recommendChannels, getRecommendation, type InternalTableView, type RecommendFn, // Pick utilities for VL-specific chart types pick, pickQuantitative, pickTemporal, pickNominal, pickDiscrete, pickLowCardNominal, pickLowCardDiscrete, pickGeo, pickSeriesAxis, hasMultipleValuesPerField, pickBestGroupingField, pickLineChartColorField, isValidLineSeriesData, nameMatches, } from '../core/recommendation'; import { vlGetTemplateChannels } from './templates'; // ── VL-extended recommendation ────────────────────────────────────────── /** * VL-specific recommendation function. Handles VL-only chart types first, * then falls back to the core recommendation engine for shared types. */ function vlGetRecommendation(chartType: string, tv: InternalTableView): Record<string, string> { const used = new Set<string>(); const rec: Record<string, string> = {}; const assign = (channel: string, fieldName: string | undefined) => { if (fieldName) rec[channel] = fieldName; }; switch (chartType) { case 'Regression': // Regression uses the same logic as Scatter Plot in the core engine return getRecommendation('Scatter Plot', tv); case 'Ranged Dot Plot': { const yField = pickGeo(tv, used) ?? pickDiscrete(tv, used); const xField = pickQuantitative(tv, used); if (!xField || !yField) return {}; assign('y', yField); assign('x', xField); return rec; } case 'Pyramid Chart': { const yField = pickDiscrete(tv, used); const xField = pickQuantitative(tv, used); const colorField = pickDiscrete(tv, used); if (!xField || !yField || !colorField) return {}; assign('y', yField); assign('x', xField); assign('color', colorField); return rec; } case 'Dotted Line Chart': case 'Bump Chart': { // Same logic as Line Chart const xField = pickSeriesAxis(tv, used); const yField = pickQuantitative(tv, used); if (!xField || !yField) return {}; assign('x', xField); assign('y', yField); if (!isValidLineSeriesData(tv, xField, undefined)) { const colorField = pickLineChartColorField(tv, used, xField, 20) ?? pickLineChartColorField(tv, used, xField, 200); if (!colorField) return {}; assign('color', colorField); } return rec; } case 'Lollipop Chart': { const xField = pickDiscrete(tv, used); const yField = pickQuantitative(tv, used); if (!xField || !yField) return {}; assign('x', xField); assign('y', yField); if (hasMultipleValuesPerField(tv, xField)) { assign('color', pickBestGroupingField(tv, used, xField)); } return rec; } case 'Density Plot': { const xField = pickQuantitative(tv, used); if (!xField) return {}; assign('x', xField); assign('color', pickLowCardNominal(tv, used, 15)); return rec; } case 'Waterfall Chart': { const xField = pickDiscrete(tv, used); const yField = pickQuantitative(tv, used); if (!xField || !yField) return {}; assign('x', xField); assign('y', yField); return rec; } case 'Strip Plot': { const xField = pickDiscrete(tv, used); const yField = pickQuantitative(tv, used); if (!xField || !yField) return {}; assign('x', xField); assign('y', yField); assign('color', pickLowCardDiscrete(tv, used, 20)); return rec; } case 'US Map': case 'World Map': { const latField = pick(tv, used, (_n, _ty, st) => st === 'Latitude') ?? pick(tv, used, (n) => nameMatches(n, ['latitude', 'lat'])); const lonField = pick(tv, used, (_n, _ty, st) => st === 'Longitude') ?? pick(tv, used, (n) => nameMatches(n, ['longitude', 'lon', 'lng', 'long'])); if (!latField || !lonField) return {}; assign('latitude', latField); assign('longitude', lonField); assign('color', pickQuantitative(tv, used) ?? pickLowCardNominal(tv, used)); return rec; } default: // Fall through to core recommendation engine return getRecommendation(chartType, tv); } } // ── Public API ────────────────────────────────────────────────────────── /** * Adapt encodings when switching between Vega-Lite chart types. * * @param sourceType Current chart type name * @param targetType Target chart type name * @param encodings Current channel->fieldName map (filled channels only) * @param data (optional) Data rows for recommendation-based adaptation * @param semanticTypes (optional) Field->semantic-type map * @returns Remapped channel->fieldName for the target */ export function vlAdaptChart( sourceType: string, targetType: string, encodings: Record<string, string>, data?: any[], semanticTypes?: Record<string, string>, ): Record<string, string> { const targetChannels = vlGetTemplateChannels(targetType); return adaptChannels(sourceType, targetType, targetChannels, encodings, data, semanticTypes, vlGetRecommendation); } /** * Recommend field->channel assignments for a Vega-Lite chart type. * * @param chartType Chart template name (e.g. "Bar Chart") * @param data Array of row objects * @param semanticTypes Field->semantic-type map (e.g. { weight: "Quantity" }) * @returns channel->fieldName map (only VL-valid channels) */ export function vlRecommendEncodings( chartType: string, data: any[], semanticTypes: Record<string, string>, ): Record<string, string> { const rec = recommendChannels(chartType, data, semanticTypes, vlGetRecommendation); const validChannels = vlGetTemplateChannels(chartType); const result: Record<string, string> = {}; for (const [ch, field] of Object.entries(rec)) { if (validChannels.includes(ch)) { result[ch] = field; } } return result; } ================================================ FILE: src/lib/agents-chart/vegalite/templates/area.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { defaultBuildEncodings, setMarkProp } from './utils'; const interpolateConfigProperty: ChartPropertyDef = { key: "interpolate", label: "Curve", type: "discrete", options: [ { value: undefined, label: "Default (linear)" }, { value: "linear", label: "Linear" }, { value: "monotone", label: "Monotone (smooth)" }, { value: "step", label: "Step" }, { value: "step-before", label: "Step Before" }, { value: "step-after", label: "Step After" }, { value: "basis", label: "Basis (smooth)" }, { value: "cardinal", label: "Cardinal" }, { value: "catmull-rom", label: "Catmull-Rom" }, ], }; function applyInterpolate(vgSpec: any, config?: Record<string, any>): void { if (!config?.interpolate) return; vgSpec.mark = setMarkProp(vgSpec.mark, 'interpolate', config.interpolate); } /** * Check for continuous (quantitative/temporal) color on a connected mark. */ function hasContinuousColorOnConnectedMark(resolvedEncodings: Record<string, any>): boolean { const colorEnc = resolvedEncodings.color; return colorEnc?.field && (colorEnc.type === 'quantitative' || colorEnc.type === 'temporal'); } /** * Build a layered area + point spec for continuous color. * Area layer draws the filled shape; point layer overlays colored dots. */ function buildContinuousColorAreaLayers( spec: any, resolvedEncodings: Record<string, any>, config?: Record<string, any>, dataLength: number = 30, ): void { const colorEnc = { ...resolvedEncodings.color }; const sharedEncodings: Record<string, any> = {}; for (const [ch, enc] of Object.entries(resolvedEncodings)) { if (ch !== 'color') { sharedEncodings[ch] = enc; } } const areaMark: any = { type: 'area', opacity: config?.opacity ?? 0.3 }; if (config?.interpolate) { areaMark.interpolate = config.interpolate; } const pointSize = Math.round(Math.max(15, Math.min(60, 1200 / dataLength))); const pointMark: any = { type: 'point', filled: true, size: pointSize }; spec.layer = [ { mark: areaMark, encoding: { ...sharedEncodings } }, { mark: pointMark, encoding: { ...sharedEncodings, color: colorEnc } }, ]; delete spec.mark; spec.encoding = {}; } export const areaChartDef: ChartTemplateDef = { chart: "Area Chart", template: { mark: "area", encoding: {} }, channels: ["x", "y", "color", "opacity", "column", "row"], markCognitiveChannel: 'area', declareLayoutMode: () => ({ paramOverrides: { continuousMarkCrossSection: { x: 100, y: 20, seriesCountAxis: 'auto' } }, }), instantiate: (spec, ctx) => { // Continuous color on area marks has the same grouping problem as lines. // Use area + point layer approach. if (hasContinuousColorOnConnectedMark(ctx.resolvedEncodings)) { buildContinuousColorAreaLayers(spec, ctx.resolvedEncodings, ctx.chartProperties, ctx.table.length); } else { defaultBuildEncodings(spec, ctx.resolvedEncodings); const config = ctx.chartProperties; applyInterpolate(spec, config); if (config) { if (config.opacity !== undefined && config.opacity < 1) { spec.mark = setMarkProp(spec.mark, 'opacity', config.opacity); } if (config.stackMode) { for (const axis of ['x', 'y'] as const) { if (spec.encoding?.[axis]?.type === 'quantitative' || spec.encoding?.[axis]?.aggregate) { spec.encoding[axis].stack = config.stackMode === 'layered' ? null : config.stackMode; break; } } } } } }, properties: [ interpolateConfigProperty, { key: "opacity", label: "Opacity", type: "continuous", min: 0.1, max: 1, step: 0.05, defaultValue: 0.7 }, { key: "stackMode", label: "Stack", type: "discrete", options: [ { value: undefined, label: "Stacked (default)" }, { value: "normalize", label: "Normalize (100%)" }, { value: "center", label: "Center" }, { value: "layered", label: "Layered (overlap)" }, ] }, ] as ChartPropertyDef[], }; export const streamgraphDef: ChartTemplateDef = { chart: "Streamgraph", template: { mark: "area", encoding: {} }, channels: ["x", "y", "color", "column", "row"], markCognitiveChannel: 'area', declareLayoutMode: () => ({ paramOverrides: { continuousMarkCrossSection: { x: 100, y: 20, seriesCountAxis: 'auto' } }, }), instantiate: (spec, ctx) => { defaultBuildEncodings(spec, ctx.resolvedEncodings); // Force center stacking on the measure axis if (spec.encoding?.y && !spec.encoding.y.stack) { spec.encoding.y.stack = "center"; spec.encoding.y.axis = null; } else if (spec.encoding?.x && !spec.encoding.x.stack) { spec.encoding.x.stack = "center"; spec.encoding.x.axis = null; } applyInterpolate(spec, ctx.chartProperties); }, properties: [interpolateConfigProperty] as ChartPropertyDef[], }; ================================================ FILE: src/lib/agents-chart/vegalite/templates/bar.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { defaultBuildEncodings, setMarkProp, adjustBarMarks, adjustRectTiling, detectBandedAxisFromSemantics, detectBandedAxisForceDiscrete, resolveAsDiscrete, ensureDiscreteTypes, } from './utils'; // ─── Bar Chart ────────────────────────────────────────────────────────────── export const barChartDef: ChartTemplateDef = { chart: "Bar Chart", template: { mark: "bar", encoding: {} }, channels: ["x", "y", "color", "opacity", "column", "row"], markCognitiveChannel: 'length', declareLayoutMode: (cs, table) => { const result = detectBandedAxisFromSemantics(cs, table, { preferAxis: 'x' }); return { axisFlags: result ? { [result.axis]: { banded: true } } : { x: { banded: true } }, resolvedTypes: result?.resolvedTypes, }; }, instantiate: (spec, ctx) => { defaultBuildEncodings(spec, ctx.resolvedEncodings); const config = ctx.chartProperties; if (config && config.cornerRadius > 0) { spec.mark = setMarkProp(spec.mark, 'cornerRadius', config.cornerRadius); } adjustBarMarks(spec, ctx); }, properties: [ { key: "cornerRadius", label: "Corners", type: "continuous", min: 0, max: 15, step: 1, defaultValue: 0 }, ] as ChartPropertyDef[], }; // ─── Pyramid Chart ────────────────────────────────────────────────────────── export const pyramidChartDef: ChartTemplateDef = { chart: "Pyramid Chart", template: { spacing: 0, resolve: { scale: { y: "shared" } }, hconcat: [ { mark: "bar", encoding: { y: {}, x: { scale: { reverse: true }, stack: null }, opacity: { value: 0.9 }, color: { value: "#4e79a7" }, }, }, { mark: "bar", encoding: { y: { axis: null }, x: { stack: null }, opacity: { value: 0.9 }, color: { value: "#e15759" }, }, }, ], config: { view: { stroke: null }, axis: { grid: false } }, }, channels: ["x", "y", "color"], markCognitiveChannel: 'length', declareLayoutMode: () => ({ axisFlags: { y: { banded: true } }, }), instantiate: (spec, ctx) => { let { y, x, color } = ctx.resolvedEncodings; // Auto-detect flipped axes const isDiscreteType = (enc: any) => enc && (enc.type === 'nominal' || enc.type === 'ordinal'); const isQuant = (enc: any) => enc && (enc.type === 'quantitative' || enc.type === 'temporal'); if (isDiscreteType(x) && isQuant(y)) { [x, y] = [y, x]; } // y → both panels (shared category axis, always discrete) if (y) { const yEnc = { ...y }; resolveAsDiscrete(yEnc, ctx.table); spec.hconcat[0].encoding.y = { ...spec.hconcat[0].encoding.y, ...yEnc }; spec.hconcat[1].encoding.y = { ...spec.hconcat[1].encoding.y, ...yEnc }; } // x → both panels if (x) { spec.hconcat[0].encoding.x = { ...spec.hconcat[0].encoding.x, ...x }; spec.hconcat[1].encoding.x = { ...spec.hconcat[1].encoding.x, ...x }; } // --- Pyramid-specific configuration --- const colorField = color?.field; const table = ctx.table; const canvasSize = ctx.canvasSize; try { if (table && colorField) { const groups = [...new Set(table.map(r => r[colorField]))] as string[]; const leftGroup = groups[0]; const rightGroup = groups.length > 1 ? groups[1] : groups[0]; spec.hconcat[0].transform = [{ filter: { field: colorField, equal: leftGroup } }]; spec.hconcat[1].transform = [{ filter: { field: colorField, equal: rightGroup } }]; spec.hconcat[0].title = String(leftGroup); spec.hconcat[1].title = String(rightGroup); if (groups.length > 2) { if (!spec._warnings) spec._warnings = []; spec._warnings.push({ severity: 'warning', code: 'too-many-groups-pyramid', message: `Pyramid chart works best with exactly 2 groups, but found ${groups.length} (${groups.map((g: string) => `'${g}'`).join(', ')}). Only the first two are shown.`, channel: 'color', field: colorField, }); } } if (table) { const xField = spec.hconcat[0].encoding.x?.field; if (xField) { const allVals = table.map(r => r[xField]).filter((v: any) => typeof v === 'number'); if (allVals.length > 0) { const domain = [Math.min(0, ...allVals), Math.max(...allVals)]; spec.hconcat[0].encoding.x.scale = { ...spec.hconcat[0].encoding.x.scale, domain }; spec.hconcat[1].encoding.x.scale = { ...spec.hconcat[1].encoding.x.scale, domain }; } if (allVals.some((v: number) => v < 0)) { if (!spec._warnings) spec._warnings = []; spec._warnings.push({ severity: 'warning', code: 'negative-values-pyramid', message: `Negative values detected in '${xField}'. Pyramid charts work best with non-negative values.`, channel: 'x', field: xField, }); } } const baseWidth = canvasSize?.width ?? 400; const baseHeight = canvasSize?.height ?? 320; const facetCols = 2; const facetStretch = Math.min(1.5, Math.pow(facetCols, 0.3)); const panelWidth = Math.round(Math.max(40, baseWidth * facetStretch / facetCols)); const yField = spec.hconcat[0].encoding.y?.field; let panelHeight = baseHeight; if (yField) { const yCardinality = new Set(table.map(r => r[yField])).size; const baseRefSize = 300; const sizeRatio = Math.max(baseWidth, baseHeight) / baseRefSize; const defaultStep = Math.round(20 * Math.max(1, sizeRatio)); if (yCardinality > 0) { const pressure = (yCardinality * defaultStep) / baseHeight; if (pressure > 1) { const stretch = Math.min(2, Math.pow(pressure, 0.5)); panelHeight = Math.round(baseHeight * stretch); } } } for (const panel of spec.hconcat) { panel.width = panelWidth; panel.height = panelHeight; } } } catch { // ignore errors } }, }; // ─── Grouped Bar Chart ────────────────────────────────────────────────────── export const groupedBarChartDef: ChartTemplateDef = { chart: "Grouped Bar Chart", template: { mark: "bar", encoding: {} }, channels: ["x", "y", "group", "column", "row"], markCognitiveChannel: 'length', declareLayoutMode: (cs, table) => { const result = detectBandedAxisForceDiscrete(cs, table, { preferAxis: 'x' }); const axis = result?.axis || 'x'; return { axisFlags: { [axis]: { banded: true } }, resolvedTypes: result?.resolvedTypes, }; }, instantiate: (spec, ctx) => { // resolvedEncodings already includes color + xOffset/yOffset from group channel defaultBuildEncodings(spec, ctx.resolvedEncodings); adjustBarMarks(spec, ctx); }, }; // ─── Stacked Bar Chart ────────────────────────────────────────────────────── export const stackedBarChartDef: ChartTemplateDef = { chart: "Stacked Bar Chart", template: { mark: "bar", encoding: {} }, channels: ["x", "y", "color", "column", "row"], markCognitiveChannel: 'length', declareLayoutMode: (cs, table) => { const result = detectBandedAxisFromSemantics(cs, table, { preferAxis: 'x' }); return { axisFlags: result ? { [result.axis]: { banded: true } } : { x: { banded: true } }, resolvedTypes: result?.resolvedTypes, paramOverrides: { continuousMarkCrossSection: { x: 20, y: 20, seriesCountAxis: 'auto' } }, }; }, instantiate: (spec, ctx) => { defaultBuildEncodings(spec, ctx.resolvedEncodings); // Apply stack mode const config = ctx.chartProperties; if (config?.stackMode) { for (const axis of ['x', 'y'] as const) { if (spec.encoding?.[axis]?.type === 'quantitative' || spec.encoding?.[axis]?.aggregate) { spec.encoding[axis].stack = config.stackMode === 'layered' ? null : config.stackMode; break; } } } adjustBarMarks(spec, ctx); }, properties: [ { key: "stackMode", label: "Stack", type: "discrete", options: [ { value: undefined, label: "Stacked (default)" }, { value: "normalize", label: "Normalize (100%)" }, { value: "center", label: "Center" }, { value: "layered", label: "Layered (overlap)" }, ] }, ] as ChartPropertyDef[], }; // ─── Histogram ────────────────────────────────────────────────────────────── export const histogramDef: ChartTemplateDef = { chart: "Histogram", template: { mark: "bar", encoding: { x: { bin: true }, y: { aggregate: "count" }, }, }, channels: ["x", "color", "column", "row"], markCognitiveChannel: 'length', instantiate: (spec, ctx) => { defaultBuildEncodings(spec, ctx.resolvedEncodings); // Apply bin count from chart properties const config = ctx.chartProperties; if (config?.binCount !== undefined && spec.encoding?.x) { spec.encoding.x.bin = { maxbins: config.binCount }; } adjustBarMarks(spec, ctx); }, properties: [ { key: "binCount", label: "Bins", type: "continuous", min: 5, max: 50, step: 1, defaultValue: 10 }, ] as ChartPropertyDef[], }; // ─── Heatmap ──────────────────────────────────────────────────────────────── export const heatmapDef: ChartTemplateDef = { chart: "Heatmap", template: { mark: "rect", encoding: {} }, channels: ["x", "y", "color", "column", "row"], markCognitiveChannel: 'color', declareLayoutMode: () => ({ axisFlags: { x: { banded: true }, y: { banded: true } }, }), instantiate: (spec, ctx) => { defaultBuildEncodings(spec, ctx.resolvedEncodings); // Apply color scheme from chart properties const config = ctx.chartProperties; if (config?.colorScheme && spec.encoding?.color) { if (!spec.encoding.color.scale) spec.encoding.color.scale = {}; spec.encoding.color.scale.scheme = config.colorScheme; } adjustBarMarks(spec, ctx); adjustRectTiling(spec, ctx); }, properties: [ { key: "colorScheme", label: "Scheme", type: "discrete", options: [ { value: undefined, label: "Default" }, { value: "viridis", label: "Viridis" }, { value: "inferno", label: "Inferno" }, { value: "magma", label: "Magma" }, { value: "plasma", label: "Plasma" }, { value: "turbo", label: "Turbo" }, { value: "blues", label: "Blues" }, { value: "reds", label: "Reds" }, { value: "greens", label: "Greens" }, { value: "oranges", label: "Oranges" }, { value: "purples", label: "Purples" }, { value: "greys", label: "Greys" }, { value: "blueorange", label: "Blue-Orange (diverging)" }, { value: "redblue", label: "Red-Blue (diverging)" }, ], }, ] as ChartPropertyDef[], }; ================================================ FILE: src/lib/agents-chart/vegalite/templates/bump.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { ChartTemplateDef } from '../../core/types'; import { defaultBuildEncodings } from './utils'; /** Semantic types that indicate a rank-like field */ const RANK_SEMANTIC_TYPES = new Set(['Rank', 'Index', 'Score', 'Rating', 'Level']); const isDiscrete = (type: string | undefined) => type === 'nominal' || type === 'ordinal'; export const bumpChartDef: ChartTemplateDef = { chart: "Bump Chart", template: { mark: { type: "line", point: true, interpolate: "monotone", strokeWidth: 2 }, encoding: {}, }, channels: ["x", "y", "color", "detail", "column", "row"], markCognitiveChannel: 'position', declareLayoutMode: () => ({ paramOverrides: { continuousMarkCrossSection: { x: 80, y: 20, seriesCountAxis: 'auto' } }, }), instantiate: (spec, ctx) => { defaultBuildEncodings(spec, ctx.resolvedEncodings); const xEnc = spec.encoding?.x; const yEnc = spec.encoding?.y; if (!xEnc || !yEnc) return; const semanticTypes = ctx.semanticTypes; // --- Decide which axis is rank --- let rankAxis: 'x' | 'y'; const xSemType = (xEnc.field && semanticTypes?.[xEnc.field]) || ''; const ySemType = (yEnc.field && semanticTypes?.[yEnc.field]) || ''; const xIsRank = RANK_SEMANTIC_TYPES.has(xSemType); const yIsRank = RANK_SEMANTIC_TYPES.has(ySemType); if (yIsRank && !xIsRank) { rankAxis = 'y'; } else if (xIsRank && !yIsRank) { rankAxis = 'x'; } else if (isDiscrete(xEnc.type) && !isDiscrete(yEnc.type)) { rankAxis = 'y'; } else if (isDiscrete(yEnc.type) && !isDiscrete(xEnc.type)) { rankAxis = 'x'; } else { rankAxis = 'y'; } // Y is rank → reverse Y so rank 1 is at top if (rankAxis === 'y') { yEnc.scale = { ...yEnc.scale, reverse: true }; } // X is rank → fix line connection order if (rankAxis === 'x' && yEnc.field) { spec.encoding.order = { field: yEnc.field, type: yEnc.type || "quantitative", }; } }, }; ================================================ FILE: src/lib/agents-chart/vegalite/templates/candlestick.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; export const candlestickChartDef: ChartTemplateDef = { chart: "Candlestick Chart", template: { encoding: {}, layer: [ { mark: "rule", encoding: {} }, { mark: { type: "bar", size: 14 }, encoding: {} }, ], }, channels: ["x", "open", "high", "low", "close", "column", "row"], markCognitiveChannel: 'position', declareLayoutMode: () => ({ axisFlags: { x: { banded: true } }, }), instantiate: (spec, ctx) => { const { x, open, high, low, close, column, row } = ctx.resolvedEncodings; if (!spec.encoding) spec.encoding = {}; if (x) { spec.encoding.x = x; if (x.type === 'nominal' || x.type === 'ordinal') { spec.encoding.x.sort = null; } } if (column) spec.encoding.column = column; if (row) spec.encoding.row = row; spec.encoding.y = { type: "quantitative", scale: { zero: false }, axis: { title: null }, }; spec.title = { text: "Price", anchor: "start", fontSize: 11, fontWeight: "normal", color: "#666" }; if (low) spec.layer[0].encoding.y = { field: low.field }; if (high) spec.layer[0].encoding.y2 = { field: high.field }; if (open) spec.layer[1].encoding.y = { field: open.field }; if (close) spec.layer[1].encoding.y2 = { field: close.field }; if (open?.field && close?.field) { spec.encoding.color = { condition: { test: `datum['${open.field}'] < datum['${close.field}']`, value: "#06982d", }, value: "#ae1325", }; } // Compute bar width from x-axis cardinality const table = ctx.table; const plotWidth = ctx.canvasSize?.width || 400; const xField = spec.encoding?.x?.field; let barSize: number; if (xField && table?.length > 0) { const cardinality = new Set(table.map((r: any) => r[xField])).size; barSize = Math.max(2, Math.min(20, Math.round(plotWidth * 0.6 / cardinality))); } else { barSize = 14; } spec.layer[1].mark = { ...spec.layer[1].mark, size: barSize }; // Independent Y-axis for faceted candlestick charts const config = ctx.chartProperties; if (config?.independentYAxis) { if (!spec.resolve) spec.resolve = {}; if (!spec.resolve.scale) spec.resolve.scale = {}; spec.resolve.scale.y = "independent"; } }, properties: [ { key: "independentYAxis", label: "Independent Y-Axis", type: "binary", defaultValue: false }, ], }; ================================================ FILE: src/lib/agents-chart/vegalite/templates/custom.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { ChartTemplateDef } from '../../core/types'; import { defaultBuildEncodings } from './utils'; export const customPointDef: ChartTemplateDef = { chart: "Custom Point", template: { mark: "point", encoding: {} }, channels: ["x", "y", "color", "opacity", "size", "shape", "column", "row"], markCognitiveChannel: 'position', instantiate: (spec, ctx) => defaultBuildEncodings(spec, ctx.resolvedEncodings), }; export const customLineDef: ChartTemplateDef = { chart: "Custom Line", template: { mark: "line", encoding: {} }, channels: ["x", "y", "color", "opacity", "detail", "column", "row"], markCognitiveChannel: 'position', instantiate: (spec, ctx) => defaultBuildEncodings(spec, ctx.resolvedEncodings), }; export const customBarDef: ChartTemplateDef = { chart: "Custom Bar", template: { mark: "bar", encoding: {} }, channels: ["x", "y", "color", "opacity", "size", "shape", "column", "row"], markCognitiveChannel: 'length', instantiate: (spec, ctx) => defaultBuildEncodings(spec, ctx.resolvedEncodings), }; export const customRectDef: ChartTemplateDef = { chart: "Custom Rect", template: { mark: "rect", encoding: {} }, channels: ["x", "y", "x2", "y2", "color", "opacity", "column", "row"], markCognitiveChannel: 'area', instantiate: (spec, ctx) => defaultBuildEncodings(spec, ctx.resolvedEncodings), }; export const customAreaDef: ChartTemplateDef = { chart: "Custom Area", template: { mark: "area", encoding: {} }, channels: ["x", "y", "x2", "y2", "color", "column", "row"], markCognitiveChannel: 'area', instantiate: (spec, ctx) => defaultBuildEncodings(spec, ctx.resolvedEncodings), }; ================================================ FILE: src/lib/agents-chart/vegalite/templates/density.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; export const densityPlotDef: ChartTemplateDef = { chart: "Density Plot", template: { mark: "area", transform: [{ density: "__field__" }], encoding: { x: { field: "value", type: "quantitative" }, y: { field: "density", type: "quantitative" }, }, }, channels: ["x", "color", "column", "row"], markCognitiveChannel: 'area', instantiate: (spec, ctx) => { const { x, color, column, row } = ctx.resolvedEncodings; if (x?.field) { spec.transform[0].density = x.field; spec.encoding.x.title = x.field; } if (color?.field) { spec.transform[0].groupby = [color.field]; spec.encoding.color = { ...(spec.encoding.color || {}), ...color }; } if (column) spec.encoding.column = column; if (row) spec.encoding.row = row; const config = ctx.chartProperties; if (config?.bandwidth && config.bandwidth > 0) { spec.transform[0].bandwidth = config.bandwidth; } }, properties: [ { key: "bandwidth", label: "Bandwidth", type: "continuous", min: 0.05, max: 2, step: 0.05, defaultValue: 0 }, ] as ChartPropertyDef[], }; ================================================ FILE: src/lib/agents-chart/vegalite/templates/index.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Template registry — collects all chart template definitions. * No UI/icon dependencies. This is the pure-data template catalog. * * Each template file exports individual ChartTemplateDef objects. * Categories are defined here to group related charts in the UI. */ import { ChartTemplateDef } from '../../core/types'; // --- Individual chart imports --- import { scatterPlotDef, regressionDef, rangedDotPlotDef, boxplotDef } from './scatter'; import { barChartDef, pyramidChartDef, groupedBarChartDef, stackedBarChartDef, histogramDef, heatmapDef } from './bar'; import { lineChartDef, dottedLineChartDef } from './line'; import { bumpChartDef } from './bump'; import { areaChartDef, streamgraphDef } from './area'; import { pieChartDef } from './pie'; import { lollipopChartDef } from './lollipop'; import { densityPlotDef } from './density'; import { stripPlotDef } from './jitter'; import { candlestickChartDef } from './candlestick'; import { waterfallChartDef } from './waterfall'; import { radarChartDef } from './radar'; import { roseChartDef } from './rose'; import { usMapDef, worldMapDef } from './map'; import { customPointDef, customLineDef, customBarDef, customRectDef, customAreaDef } from './custom'; /** * All chart template definitions, grouped by category. * Keys are category names shown in the UI, values are arrays of template definitions. */ export const vlTemplateDefs: { [key: string]: ChartTemplateDef[] } = { "Scatter & Point": [scatterPlotDef, regressionDef, boxplotDef, stripPlotDef], "Bar": [barChartDef, groupedBarChartDef, stackedBarChartDef, histogramDef, lollipopChartDef, pyramidChartDef], "Line & Area": [lineChartDef, dottedLineChartDef, bumpChartDef, areaChartDef, streamgraphDef], "Part-to-Whole": [pieChartDef, roseChartDef, heatmapDef, waterfallChartDef], "Statistical": [densityPlotDef, rangedDotPlotDef, radarChartDef, candlestickChartDef], "Map": [usMapDef, worldMapDef], "Custom": [customPointDef, customLineDef, customBarDef, customRectDef, customAreaDef], }; /** * Flat list of all Vega-Lite chart template definitions. */ export const vlAllTemplateDefs: ChartTemplateDef[] = Object.values(vlTemplateDefs).flat(); /** * Look up a Vega-Lite chart template definition by chart type name. */ export function vlGetTemplateDef(chartType: string): ChartTemplateDef | undefined { return vlAllTemplateDefs.find(t => t.chart === chartType); } /** * Get the available channels for a Vega-Lite chart type. */ export function vlGetTemplateChannels(chartType: string): string[] { return vlGetTemplateDef(chartType)?.channels || []; } ================================================ FILE: src/lib/agents-chart/vegalite/templates/jitter.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { defaultBuildEncodings } from './utils'; export const stripPlotDef: ChartTemplateDef = { chart: "Strip Plot", template: { mark: { type: "circle", opacity: 0.7 }, encoding: {}, }, channels: ["x", "y", "color", "size", "column", "row"], markCognitiveChannel: 'position', declareLayoutMode: () => ({ paramOverrides: { defaultStepMultiplier: 2, minStep: 16 }, }), instantiate: (spec, ctx) => { defaultBuildEncodings(spec, ctx.resolvedEncodings); const table = ctx.table; const canvasSize = ctx.canvasSize; const config = ctx.chartProperties; const stepWidth = config?.stepWidth ?? 20; let pointSize = config?.pointSize ?? 0; let opacity = config?.opacity ?? 0; // Determine which axis is categorical const xType = spec.encoding?.x?.type; const yType = spec.encoding?.y?.type; const catAxis = (xType === 'nominal' || xType === 'ordinal') ? 'x' : (yType === 'nominal' || yType === 'ordinal') ? 'y' : null; // Count points in the largest categorical group let maxGroupCount = table?.length ?? 0; if (catAxis && spec.encoding?.[catAxis]?.field && table) { const catField = spec.encoding[catAxis].field; const groupCounts: Record<string, number> = {}; for (const row of table) { const key = String(row[catField] ?? ''); groupCounts[key] = (groupCounts[key] || 0) + 1; } maxGroupCount = Math.max(1, ...Object.values(groupCounts)); } // Continuous axis length const contLen = catAxis === 'x' ? (canvasSize?.height || 400) : (canvasSize?.width || 400); const areaBudget = stepWidth * contLen; const targetCoverage = 0.35; // Auto-compute size if (pointSize === 0) { const idealSize = (targetCoverage * areaBudget) / maxGroupCount; pointSize = Math.max(5, Math.min(100, Math.round(idealSize))); } // Auto-compute opacity if (opacity === 0) { const density = (maxGroupCount * pointSize) / areaBudget; if (density < 0.2) { opacity = 0.8; } else if (density < 0.5) { opacity = 0.6; } else if (density < 1) { opacity = 0.4; } else { opacity = Math.max(0.1, 0.3 / density); } opacity = Math.round(opacity * 20) / 20; } // Apply mark properties if (typeof spec.mark === 'string') { spec.mark = { type: spec.mark }; } spec.mark.size = pointSize; spec.mark.opacity = opacity; // Set step width and derive jitter const jitterWidth = stepWidth * 0.8; if (catAxis === 'x') { spec.width = { step: stepWidth }; } else if (catAxis === 'y') { spec.height = { step: stepWidth }; } if (jitterWidth > 0) { if (!spec.transform) spec.transform = []; spec.transform.push({ calculate: `${-jitterWidth / 2} + random() * ${jitterWidth}`, as: "__jitter", }); const offsetEnc = { field: "__jitter", type: "quantitative", axis: null, scale: { domain: [-stepWidth / 2, stepWidth / 2] }, }; if (catAxis === 'x') { spec.encoding.xOffset = offsetEnc; } else if (catAxis === 'y') { spec.encoding.yOffset = offsetEnc; } else { spec.encoding.xOffset = offsetEnc; } } }, properties: [ { key: "stepWidth", label: "Jitter", type: "continuous", min: 10, max: 100, step: 5, defaultValue: 20 }, { key: "pointSize", label: "Size", type: "continuous", min: 0, max: 150, step: 5, defaultValue: 0 }, { key: "opacity", label: "Opacity", type: "continuous", min: 0, max: 1, step: 0.05, defaultValue: 0 }, ] as ChartPropertyDef[], }; ================================================ FILE: src/lib/agents-chart/vegalite/templates/line.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { defaultBuildEncodings, setMarkProp } from './utils'; const interpolateConfigProperty: ChartPropertyDef = { key: "interpolate", label: "Curve", type: "discrete", options: [ { value: undefined, label: "Default (linear)" }, { value: "linear", label: "Linear" }, { value: "monotone", label: "Monotone (smooth)" }, { value: "step", label: "Step" }, { value: "step-before", label: "Step Before" }, { value: "step-after", label: "Step After" }, { value: "basis", label: "Basis (smooth)" }, { value: "cardinal", label: "Cardinal" }, { value: "catmull-rom", label: "Catmull-Rom" }, ], }; function applyInterpolate(vgSpec: any, config?: Record<string, any>): void { if (!config?.interpolate) return; vgSpec.mark = setMarkProp(vgSpec.mark, 'interpolate', config.interpolate); } /** * Detect whether color is continuous (quantitative/temporal) on a connected mark. * * In Vega-Lite, encoding `color` on a `line` mark groups data by the * color field. With a continuous field each unique value becomes its own * group, producing isolated single-point segments. * * The idiomatic VL pattern: convert to a layered spec where the `line` * layer uses `detail` for series grouping (without color mapping) and an * overlaid `point` layer carries the quantitative color encoding. */ function hasContinuousColor(resolvedEncodings: Record<string, any>): boolean { const colorEnc = resolvedEncodings.color; return !!(colorEnc?.field && (colorEnc.type === 'quantitative' || colorEnc.type === 'temporal')); } /** * Build a layered line + point spec for continuous color on line marks. * * Handles both single-series and multi-series cases: * * Single-series (no `detail` / `opacity` nominal grouping): * line layer → plain connected path, neutral grey * point layer → colored dots with quantitative color encoding * * Multi-series (`detail` or nominal `opacity` encoding present): * line layer → connected paths grouped by `detail`/`opacity`, neutral grey * point layer → colored dots with the quantitative color encoding * * The `detail` encoding is preserved on the line layer so that series * remain individually connected. The quantitative `color` is only * applied to the point layer where grouping doesn't matter. */ function buildContinuousColorLineLayers( spec: any, resolvedEncodings: Record<string, any>, config?: Record<string, any>, dataLength: number = 30, ): void { const colorEnc = { ...resolvedEncodings.color }; // Separate encodings: everything except color goes to both layers. // The line layer may additionally get `detail` for series grouping. const sharedEncodings: Record<string, any> = {}; for (const [ch, enc] of Object.entries(resolvedEncodings)) { if (ch !== 'color') { sharedEncodings[ch] = enc; } } // Line layer: connected path, neutral color, no color encoding. // If `detail` is present it groups lines without mapping to color. const lineMark: any = { type: 'line', color: '#ccc' }; if (config?.interpolate) { lineMark.interpolate = config.interpolate; } // Point layer: individual colored dots — size scales inversely with density const pointSize = Math.round(Math.max(15, Math.min(60, 1200 / dataLength))); const pointMark: any = { type: 'point', filled: true, size: pointSize }; spec.layer = [ { mark: lineMark, encoding: { ...sharedEncodings } }, { mark: pointMark, encoding: { ...sharedEncodings, color: colorEnc } }, ]; // Remove top-level mark — layered spec uses per-layer marks delete spec.mark; // Encodings live inside each layer, not at the top spec.encoding = {}; } export const lineChartDef: ChartTemplateDef = { chart: "Line Chart", template: { mark: "line", encoding: {} }, channels: ["x", "y", "color", "strokeDash", "detail", "opacity", "column", "row"], markCognitiveChannel: 'position', declareLayoutMode: () => ({ paramOverrides: { continuousMarkCrossSection: { x: 100, y: 20, seriesCountAxis: 'auto' } }, }), instantiate: (spec, ctx) => { if (hasContinuousColor(ctx.resolvedEncodings)) { buildContinuousColorLineLayers(spec, ctx.resolvedEncodings, ctx.chartProperties, ctx.table.length); } else { defaultBuildEncodings(spec, ctx.resolvedEncodings); applyInterpolate(spec, ctx.chartProperties); } }, properties: [interpolateConfigProperty], }; export const dottedLineChartDef: ChartTemplateDef = { chart: "Dotted Line Chart", template: { mark: { type: "line", point: true }, encoding: {} }, channels: ["x", "y", "color", "detail", "column", "row"], markCognitiveChannel: 'position', declareLayoutMode: () => ({ paramOverrides: { continuousMarkCrossSection: { x: 100, y: 20, seriesCountAxis: 'auto' } }, }), instantiate: (spec, ctx) => { if (hasContinuousColor(ctx.resolvedEncodings)) { buildContinuousColorLineLayers(spec, ctx.resolvedEncodings, ctx.chartProperties, ctx.table.length); } else { defaultBuildEncodings(spec, ctx.resolvedEncodings); applyInterpolate(spec, ctx.chartProperties); } }, properties: [interpolateConfigProperty], }; ================================================ FILE: src/lib/agents-chart/vegalite/templates/lollipop.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; export const lollipopChartDef: ChartTemplateDef = { chart: "Lollipop Chart", template: { encoding: {}, layer: [ { mark: { type: "rule", strokeWidth: 1.5 }, encoding: {} }, { mark: { type: "circle", size: 80 }, encoding: {} }, ], }, channels: ["x", "y", "color", "column", "row"], markCognitiveChannel: 'length', instantiate: (spec, ctx) => { const { color, column, row, ...positional } = ctx.resolvedEncodings; for (const [ch, enc] of Object.entries(positional)) { for (const layer of spec.layer) { layer.encoding[ch] = { ...(layer.encoding[ch] || {}), ...enc }; } } if (color) { spec.layer[1].encoding.color = { ...(spec.layer[1].encoding.color || {}), ...color }; } if (!spec.encoding) spec.encoding = {}; if (column) spec.encoding.column = column; if (row) spec.encoding.row = row; // --- Lollipop-specific configuration --- const table = ctx.table; const config = ctx.chartProperties; // Anchor rule from 0 on the measure axis const xEnc = spec.layer[0]?.encoding?.x; const yEnc = spec.layer[0]?.encoding?.y; const xType = xEnc?.type; const yType = yEnc?.type; const isMeasure = (t: string | undefined) => t != null && t !== 'nominal' && t !== 'ordinal'; if (isMeasure(yType)) { spec.layer[0].encoding.y2 = { datum: 0 }; } else if (isMeasure(xType)) { spec.layer[0].encoding.x2 = { datum: 0 }; } // Adaptive rule strokeWidth based on overlap const discreteAxis = !isMeasure(xType) ? 'x' : !isMeasure(yType) ? 'y' : null; const discreteField = discreteAxis === 'x' ? xEnc?.field : discreteAxis === 'y' ? yEnc?.field : null; if (discreteField && table && table.length > 0) { const counts: Record<string, number> = {}; for (const row of table) { const key = String(row[discreteField] ?? ''); counts[key] = (counts[key] || 0) + 1; } const maxOverlap = Math.max(...Object.values(counts)); if (maxOverlap > 1) { const baseStroke = 1.5; const stroke = Math.max(0.3, baseStroke / Math.sqrt(maxOverlap)); spec.layer[0].mark = { ...spec.layer[0].mark, strokeWidth: stroke }; } } // Apply dot size from config if (config?.dotSize) { spec.layer[1].mark = { ...spec.layer[1].mark, size: config.dotSize }; } }, properties: [ { key: "dotSize", label: "Dot Size", type: "continuous", min: 20, max: 300, step: 10, defaultValue: 80 }, ] as ChartPropertyDef[], }; ================================================ FILE: src/lib/agents-chart/vegalite/templates/map.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; const mapProjections = [ { value: "mercator", label: "Mercator" }, { value: "equalEarth", label: "Equal Earth" }, { value: "orthographic", label: "Orthographic (Globe)" }, { value: "stereographic", label: "Stereographic" }, { value: "conicEqualArea", label: "Conic Equal Area" }, { value: "conicEquidistant", label: "Conic Equidistant" }, { value: "azimuthalEquidistant", label: "Azimuthal Equidistant" }, { value: "mollweide", label: "Mollweide" }, ] as const; const projectionCenterPresets: { label: string; center: [number, number] }[] = [ { label: "World (Atlantic)", center: [0, 0] }, { label: "World (Pacific)", center: [150, 0] }, { label: "China", center: [105, 35] }, { label: "USA", center: [-98, 39] }, { label: "Europe", center: [10, 50] }, { label: "Japan", center: [138, 36] }, { label: "India", center: [78, 22] }, { label: "Brazil", center: [-52, -14] }, { label: "Australia", center: [134, -25] }, { label: "Russia", center: [100, 60] }, { label: "Africa", center: [20, 0] }, { label: "Middle East", center: [45, 28] }, { label: "Southeast Asia", center: [115, 5] }, { label: "South America", center: [-60, -15] }, { label: "North America", center: [-100, 45] }, { label: "UK", center: [-2, 54] }, { label: "Germany", center: [10, 51] }, { label: "France", center: [2, 47] }, { label: "Korea", center: [128, 36] }, ]; export const usMapDef: ChartTemplateDef = { chart: "US Map", template: { width: 500, height: 300, layer: [ { data: { url: "https://vega.github.io/vega-lite/data/us-10m.json", format: { type: "topojson", feature: "states" }, }, projection: { type: "albersUsa" }, mark: { type: "geoshape", fill: "lightgray", stroke: "white" }, }, { projection: { type: "albersUsa" }, mark: "circle", encoding: { longitude: {}, latitude: {}, size: {}, color: {} }, }, ], }, channels: ["longitude", "latitude", "color", "size"], markCognitiveChannel: 'position', instantiate: (spec, ctx) => { if (!spec.layer[1].encoding) spec.layer[1].encoding = {}; for (const [ch, enc] of Object.entries(ctx.resolvedEncodings)) { spec.layer[1].encoding[ch] = { ...(spec.layer[1].encoding[ch] || {}), ...enc }; } }, properties: [] as ChartPropertyDef[], }; export const worldMapDef: ChartTemplateDef = { chart: "World Map", template: { width: 600, height: 350, layer: [ { data: { url: "https://vega.github.io/vega-lite/data/world-110m.json", format: { type: "topojson", feature: "countries" }, }, projection: { type: "equalEarth" }, mark: { type: "geoshape", fill: "lightgray", stroke: "white" }, }, { projection: { type: "equalEarth" }, mark: "circle", encoding: { longitude: {}, latitude: {}, size: {}, color: {}, opacity: {} }, }, ], }, channels: ["longitude", "latitude", "color", "size", "opacity"], markCognitiveChannel: 'position', instantiate: (spec, ctx) => { if (!spec.layer[1].encoding) spec.layer[1].encoding = {}; for (const [ch, enc] of Object.entries(ctx.resolvedEncodings)) { spec.layer[1].encoding[ch] = { ...(spec.layer[1].encoding[ch] || {}), ...enc }; } const config = ctx.chartProperties; if (config) { const projection = config.projection; const projectionCenter = config.projectionCenter; const applyProjection = (obj: any) => { if (obj?.projection) { if (projection && projection !== 'default') { obj.projection.type = projection; } if (projectionCenter && obj.projection.type !== 'albersUsa') { obj.projection.rotate = [-projectionCenter[0], -projectionCenter[1], 0]; } } }; if (spec.layer && Array.isArray(spec.layer)) { for (const layer of spec.layer) applyProjection(layer); } applyProjection(spec); } }, properties: [ { key: "projection", label: "Projection", type: "discrete", options: [ { value: "default", label: "Default" }, ...mapProjections.map(p => ({ value: p.value, label: p.label })), ], defaultValue: "default", }, { key: "projectionCenter", label: "Center", type: "discrete", options: [ { value: undefined, label: "Default" }, ...projectionCenterPresets.map(p => ({ value: p.center, label: `${p.label} [${p.center[0]}, ${p.center[1]}]`, })), ], defaultValue: undefined, }, ] as ChartPropertyDef[], }; ================================================ FILE: src/lib/agents-chart/vegalite/templates/pie.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { computeCircumferencePressure, computeEffectiveBarCount } from '../../core/decisions'; import { setMarkProp } from './utils'; export const pieChartDef: ChartTemplateDef = { chart: "Pie Chart", template: { mark: "arc", encoding: {} }, channels: ["size", "color", "column", "row"], markCognitiveChannel: 'area', instantiate: (spec, ctx) => { // Remap abstract channels to VL channels: // "size" → VL "theta" (angular extent of each slice) if (!spec.encoding) spec.encoding = {}; for (const [ch, enc] of Object.entries(ctx.resolvedEncodings)) { if (ch === 'size') { // Strip the sqrt/range scale that the assembler adds for generic // "size" channels — theta handles its own proportional scaling. const { scale: _scale, ...thetaEnc } = enc; spec.encoding.theta = thetaEnc; } else { spec.encoding[ch] = enc; } } // Fallback: when the user only maps color (no size/theta), use count // so every colour group gets a proportional slice. if (!spec.encoding.theta) { spec.encoding.theta = { aggregate: 'count', type: 'quantitative' }; } const config = ctx.chartProperties; if (config && config.innerRadius > 0) { spec.mark = setMarkProp(spec.mark, 'innerRadius', config.innerRadius); } // ── Circumference-pressure sizing (spring model) ────────────── // Compute effective bar count from slice values to determine // whether the pie needs to grow beyond the base canvas. const thetaField = spec.encoding.theta?.field; const colorField = spec.encoding.color?.field; let effectiveCount: number; if (thetaField && colorField) { // Aggregate values per color category const agg = new Map<string, number>(); for (const row of ctx.table) { const cat = String(row[colorField] ?? ''); const val = Number(row[thetaField]) || 0; agg.set(cat, (agg.get(cat) ?? 0) + val); } effectiveCount = computeEffectiveBarCount([...agg.values()]); } else if (colorField) { // Count-based: each category gets equal slice const cats = new Set(ctx.table.map((r: any) => String(r[colorField] ?? ''))); effectiveCount = cats.size; } else { effectiveCount = ctx.table.length; } const { radius, canvasW, canvasH } = computeCircumferencePressure( effectiveCount, ctx.canvasSize, { minArcPx: 45, minRadius: 60, maxStretch: ctx.assembleOptions?.maxStretch, margin: 50, // room for labels around pie }); // Set explicit width/height — overrides config.view defaults spec.width = canvasW; spec.height = canvasH; }, properties: [ { key: "innerRadius", label: "Donut", type: "continuous", min: 0, max: 100, step: 5, defaultValue: 0 }, ] as ChartPropertyDef[], }; ================================================ FILE: src/lib/agents-chart/vegalite/templates/radar.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; /** * Radar / Spider Chart * * Data model (long format): * - x (nominal): the metric / axis name * - y (quantitative): the value for that metric * - color (nominal): the entity / group * * Supports column/row faceting. */ // --------------------------------------------------------------------------- // Helper: round up to a "nice" ceiling for axis max // --------------------------------------------------------------------------- function niceMax(v: number): number { if (v <= 0) return 1; const pow = Math.pow(10, Math.floor(Math.log10(v))); const mantissa = v / pow; const nice = mantissa <= 1 ? 1 : mantissa <= 2 ? 2 : mantissa <= 2.5 ? 2.5 : mantissa <= 5 ? 5 : 10; return nice * pow; } // --------------------------------------------------------------------------- // Helper: build VL layers for a single radar from long-format rows // --------------------------------------------------------------------------- function buildRadarLayers( rows: any[], axisField: string, valueField: string, groupField: string | undefined, opts: { filled: boolean; fillOpacity: number; strokeWidth: number; domainPad: number }, ): any[] { const axes: string[] = []; const axisSet = new Set<string>(); for (const row of rows) { const a = String(row[axisField]); if (!axisSet.has(a)) { axisSet.add(a); axes.push(a); } } if (axes.length < 2) return []; const groups: string[] = []; if (groupField) { const groupSet = new Set<string>(); for (const row of rows) { const g = String(row[groupField]); if (!groupSet.has(g)) { groupSet.add(g); groups.push(g); } } } else { groups.push("_all"); } // Normalise each axis to 0-1 const axisMax: Record<string, number> = {}; for (const axis of axes) { const vals = rows .filter(r => String(r[axisField]) === axis) .map(r => Number(r[valueField])) .filter(v => isFinite(v)); const mx = vals.length > 0 ? Math.max(...vals) : 1; axisMax[axis] = niceMax(mx); } // Aggregate: average per (group, axis) const keyMap = new Map<string, { sum: number; rawSum: number; count: number }>(); for (const row of rows) { const grp = groupField ? String(row[groupField]) : "_all"; const axis = String(row[axisField]); const raw = Number(row[valueField]) || 0; const mx = axisMax[axis]; const norm = mx > 0 ? raw / mx : 0; const k = `${grp}|||${axis}`; if (!keyMap.has(k)) keyMap.set(k, { sum: 0, rawSum: 0, count: 0 }); const entry = keyMap.get(k)!; entry.sum += norm; entry.rawSum += raw; entry.count += 1; } // Compute polar coordinates const angleStep = 360 / axes.length; const finalData: any[] = []; for (const [k, v] of keyMap.entries()) { const [grp, axis] = k.split('|||'); const axisIndex = axes.indexOf(axis); const angle = axisIndex * angleStep; const normVal = v.sum / v.count; const rawVal = Math.round((v.rawSum / v.count) * 100) / 100; const rad = (angle * Math.PI) / 180; finalData.push({ __group: grp, __axis: axis, __value: normVal, __raw: rawVal, __angle: angle, __x: normVal * Math.sin(rad), __y: -normVal * Math.cos(rad), }); } // Grid data (spokes + concentric rings) const gridData: any[] = []; for (let idx = 0; idx < axes.length; idx++) { const ang = (idx * angleStep * Math.PI) / 180; gridData.push({ __type: "spoke", __x: 0, __y: 0, __x2: Math.sin(ang), __y2: -Math.cos(ang), }); } for (const level of [0.25, 0.5, 0.75, 1.0]) { const points: any[] = []; for (let i = 0; i <= axes.length; i++) { const ang = ((i % axes.length) * angleStep * Math.PI) / 180; points.push({ __x: level * Math.sin(ang), __y: -level * Math.cos(ang) }); } for (let i = 0; i < points.length - 1; i++) { gridData.push({ __type: "ring", __level: level, __x: points[i].__x, __y: points[i].__y, __x2: points[i + 1].__x, __y2: points[i + 1].__y, }); } } // Axis labels const labelData = axes.map((axis, i) => { const angDeg = i * angleStep; const ang = (angDeg * Math.PI) / 180; const r = 1.15; const mx = axisMax[axis]; const maxStr = mx % 1 === 0 ? String(mx) : mx.toFixed(1); const sinA = Math.sin(ang); const cosA = -Math.cos(ang); let align: string; let baseline: string; let dx = 0; let dy = 0; if (Math.abs(sinA) < 0.15) { align = 'center'; baseline = cosA < 0 ? 'bottom' : 'top'; dy = cosA < 0 ? -4 : 4; } else if (sinA > 0) { align = 'left'; baseline = Math.abs(cosA) < 0.3 ? 'middle' : (cosA < 0 ? 'bottom' : 'top'); dx = 4; } else { align = 'right'; baseline = Math.abs(cosA) < 0.3 ? 'middle' : (cosA < 0 ? 'bottom' : 'top'); dx = -4; } return { __label: [axis, `(${maxStr})`], __x: r * Math.sin(ang), __y: -r * Math.cos(ang), __align: align, __baseline: baseline, __dx: dx, __dy: dy, }; }); const { filled, fillOpacity, strokeWidth, domainPad } = opts; const layers: any[] = []; // Spokes layers.push({ data: { values: gridData.filter(d => d.__type === "spoke") }, mark: { type: "rule", stroke: "#ddd", strokeWidth: 0.8 }, encoding: { x: { field: "__x", type: "quantitative", scale: { domain: [-domainPad, domainPad] }, axis: null }, y: { field: "__y", type: "quantitative", scale: { domain: [-domainPad, domainPad] }, axis: null }, x2: { field: "__x2" }, y2: { field: "__y2" }, }, }); // Rings layers.push({ data: { values: gridData.filter(d => d.__type === "ring") }, mark: { type: "rule", stroke: "#e0e0e0", strokeWidth: 0.6 }, encoding: { x: { field: "__x", type: "quantitative", axis: null }, y: { field: "__y", type: "quantitative", axis: null }, x2: { field: "__x2" }, y2: { field: "__y2" }, }, }); // Labels for (const lbl of labelData) { const lines: string[] = lbl.__label; layers.push({ data: { values: [lbl] }, mark: { type: "text", fontSize: 10, fill: "#555", align: lbl.__align, baseline: lbl.__baseline, dx: lbl.__dx, dy: lbl.__dy, limit: 120, lineHeight: 13, }, encoding: { x: { field: "__x", type: "quantitative", axis: null }, y: { field: "__y", type: "quantitative", axis: null }, text: { value: lines }, }, }); } // Data polygon const lineLayer: any = { data: { values: finalData }, mark: { type: "line", interpolate: "linear-closed", strokeWidth, point: false, ...(filled ? { fillOpacity } : {}), }, encoding: { x: { field: "__x", type: "quantitative", axis: null }, y: { field: "__y", type: "quantitative", axis: null }, order: { field: "__angle", type: "quantitative" }, tooltip: [ { field: "__axis", type: "nominal", title: axisField }, { field: "__raw", type: "quantitative", title: valueField }, ], }, }; if (groups.length > 1 && groupField) { lineLayer.encoding.stroke = { field: "__group", type: "nominal", title: groupField }; if (filled) { lineLayer.encoding.fill = { field: "__group", type: "nominal", title: groupField, legend: null }; } } else if (filled) { lineLayer.mark.fill = "#4c78a8"; } layers.push(lineLayer); // Data points const pointLayer: any = { data: { values: finalData }, mark: { type: "point", filled: true, size: 25 }, encoding: { x: { field: "__x", type: "quantitative", axis: null }, y: { field: "__y", type: "quantitative", axis: null }, tooltip: [ ...(groupField ? [{ field: "__group", type: "nominal", title: groupField }] : []), { field: "__axis", type: "nominal", title: axisField }, { field: "__raw", type: "quantitative", title: valueField }, ], }, }; if (groups.length > 1 && groupField) { pointLayer.encoding.color = { field: "__group", type: "nominal", title: groupField, legend: null }; } layers.push(pointLayer); return layers; } // --------------------------------------------------------------------------- // Template definition // --------------------------------------------------------------------------- export const radarChartDef: ChartTemplateDef = { chart: "Radar Chart", template: { description: "Radar / Spider chart", mark: "point", encoding: {}, }, channels: ["x", "y", "color", "column", "row"], markCognitiveChannel: 'position', instantiate: (spec, ctx) => { const axisField: string | undefined = ctx.resolvedEncodings.x?.field; const valueField: string | undefined = ctx.resolvedEncodings.y?.field; const groupField: string | undefined = ctx.resolvedEncodings.color?.field; const columnField: string | undefined = ctx.resolvedEncodings.column?.field; const rowField: string | undefined = ctx.resolvedEncodings.row?.field; const table = ctx.table; const canvasSize = ctx.canvasSize; const config = ctx.chartProperties; const filled = config?.filled ?? true; const fillOpacity = config?.fillOpacity ?? 0.15; const strokeWidth = config?.strokeWidth ?? 1.5; if (!table || table.length === 0 || !axisField || !valueField) { spec.mark = "point"; return; } const size = Math.min(canvasSize?.width || 400, canvasSize?.height || 400); const layerOpts = { filled, fillOpacity, strokeWidth, domainPad: 1.18 }; // ---- No faceting ---- if (!columnField && !rowField) { const layers = buildRadarLayers(table, axisField, valueField, groupField, layerOpts); if (layers.length === 0) { spec.mark = "point"; return; } const finalSpec: any = { width: size, height: size, layer: layers, config: { view: { stroke: null } }, }; for (const key of Object.keys(spec)) delete spec[key]; Object.assign(spec, finalSpec); return; } // ---- Faceting ---- const colGroups: string[] = columnField ? [...new Set(table.map(r => String(r[columnField])))] as string[] : ["_all"]; const rowGroups: string[] = rowField ? [...new Set(table.map(r => String(r[rowField])))] as string[] : ["_all"]; const minSubplot = 200; const subplotSize = Math.max(minSubplot, size); const buildSubplot = (rows: any[], title?: string) => { const layers = buildRadarLayers(rows, axisField, valueField, groupField, layerOpts); if (layers.length === 0) return null; return { width: subplotSize, height: subplotSize, layer: layers, title: title || undefined, }; }; let finalSpec: any; const concatSpacing = 5; if (rowField && columnField) { const vconcat: any[] = []; for (const rg of rowGroups) { const hconcat: any[] = []; for (const cg of colGroups) { const subset = table.filter(r => String(r[rowField]) === rg && String(r[columnField]) === cg); const s = buildSubplot(subset, `${cg}`); if (s) hconcat.push(s); } if (hconcat.length > 0) { vconcat.push({ hconcat, spacing: concatSpacing, title: rg }); } } finalSpec = { vconcat, spacing: concatSpacing, config: { view: { stroke: null } } }; } else if (columnField) { const hconcat: any[] = []; for (const cg of colGroups) { const subset = table.filter(r => String(r[columnField]) === cg); const s = buildSubplot(subset, cg); if (s) hconcat.push(s); } finalSpec = { hconcat, spacing: concatSpacing, config: { view: { stroke: null } } }; } else { const vconcat: any[] = []; for (const rg of rowGroups) { const subset = table.filter(r => String(r[rowField!]) === rg); const s = buildSubplot(subset, rg); if (s) vconcat.push(s); } finalSpec = { vconcat, spacing: concatSpacing, config: { view: { stroke: null } } }; } for (const key of Object.keys(spec)) delete spec[key]; Object.assign(spec, finalSpec); }, properties: [ { key: "filled", label: "Filled", type: "binary", defaultValue: true }, { key: "fillOpacity", label: "Fill Opacity", type: "continuous", min: 0, max: 0.5, step: 0.05, defaultValue: 0.15 }, { key: "strokeWidth", label: "Line Width", type: "continuous", min: 0.5, max: 4, step: 0.5, defaultValue: 1.5 }, ] as ChartPropertyDef[], }; ================================================ FILE: src/lib/agents-chart/vegalite/templates/rose.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Rose Chart (Nightingale / Coxcomb chart) template. * * A rose chart is essentially a bar chart on a polar axis: * - x (categorical) → theta (angular position of each category) * - y (quantitative) → radius (bar height / sector radius) * - color → stacking within each angular slice (like stacked bar) * * Implementation note: the template uses a flat (non-layered) spec as its * base. For non-faceted charts the instantiate function dynamically adds * a text-label layer. Faceted charts stay flat and use encoding.facet, * because the restructureFacets pipeline path (facet + spec.layer) does * not render arc marks correctly. */ import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { setMarkProp } from './utils'; export const roseChartDef: ChartTemplateDef = { chart: "Rose Chart", template: { mark: { type: "arc", stroke: "white", padAngle: 0.02, }, encoding: {}, }, channels: ["x", "y", "color", "column", "row"], markCognitiveChannel: 'area', // Polar charts have no positional axes — declare no banded axes // so the layout pipeline won't produce step-based sizing. declareLayoutMode: () => ({}), instantiate: (spec, ctx) => { if (!spec.encoding) spec.encoding = {}; const { x, y, color, column, row, ...rest } = ctx.resolvedEncodings; const isFaceted = !!(column || row); // ── theta encoding (shared) ── if (x) { const thetaEnc: any = { ...x }; if (thetaEnc.type === 'quantitative' || thetaEnc.type === 'temporal') { thetaEnc.type = 'nominal'; } // Remove sort references to positional channels (x/y) — they are // remapped to theta/radius and Vega-Lite can't resolve "-y" etc. if (typeof thetaEnc.sort === 'string' && /^-?[xy]$/.test(thetaEnc.sort)) { delete thetaEnc.sort; } delete thetaEnc.scale; thetaEnc.stack = true; // Rotate so the first slice aligns to 12 o'clock. // 'center' (default): wedge center at top; 'left': wedge left edge at top. const n = new Set(ctx.table.map((r: any) => r[x.field])).size; if (n > 0) { const alignment = ctx.chartProperties?.alignment ?? 'left'; if (alignment === 'center') { const halfSlice = Math.PI / n; thetaEnc.scale = { range: [-halfSlice, 2 * Math.PI - halfSlice] }; } // 'left': no range offset needed — default [0, 2π] puts left edge at top } spec.encoding.theta = thetaEnc; } // ── Build radius encoding ── let radiusEnc: any | undefined; let radiusField: string | undefined; if (y) { radiusEnc = { ...y }; if (typeof radiusEnc.sort === 'string' && /^-?[xy]$/.test(radiusEnc.sort)) { delete radiusEnc.sort; } radiusEnc.scale = { type: 'sqrt' }; if (color) { radiusEnc.stack = true; } radiusField = radiusEnc.field; } // ── Build color encoding ── let colorEnc: any | undefined; if (color) { colorEnc = color; } else if (x) { // No color channel: color by category (like a pie chart) colorEnc = { field: x.field, type: x.type || 'nominal' }; if (Array.isArray(x.sort)) { colorEnc.sort = x.sort; } } if (isFaceted) { // ── FACETED: keep flat spec with encoding.facet ── // The VL facet + spec.layer restructuring doesn't render arc // marks correctly. Using encoding.facet inline on a unit // spec (single mark) works reliably. if (radiusEnc) spec.encoding.radius = radiusEnc; if (colorEnc) spec.encoding.color = colorEnc; if (column && !row) { const facetEnc: any = { ...column }; const facetCount = new Set(ctx.table.map((r: any) => r[column.field])).size; facetEnc.columns = facetCount <= 6 ? facetCount : Math.ceil(Math.sqrt(facetCount)); spec.encoding.facet = facetEnc; } else if (row && !column) { spec.encoding.row = row; } else { spec.encoding.column = column; spec.encoding.row = row; } } else { // ── NON-FACETED: convert to layered spec for text labels ── const arcMark = spec.mark; spec.layer = [ { mark: arcMark, encoding: {} as any }, { mark: { type: "text", radiusOffset: 15, fontSize: 11 }, encoding: {} as any, }, ]; delete spec.mark; // radius → arc layer only if (radiusEnc) { spec.layer[0].encoding.radius = radiusEnc; } // color → arc layer only (so text layer isn't split by color) if (colorEnc) { spec.layer[0].encoding.color = colorEnc; } // text label layer: one label per category if (x && spec.layer[1]) { const textLayer = spec.layer[1]; textLayer.encoding.text = { field: x.field, type: x.type || 'nominal' }; if (radiusField) { // Aggregate: collapse to one row per category so labels // aren't duplicated per color segment. textLayer.transform = [ { aggregate: [{ op: 'sum', field: radiusField, as: radiusField }], groupby: [x.field], }, ]; textLayer.encoding.radius = { field: radiusField, type: 'quantitative', scale: { type: 'sqrt' }, }; } } } // ── Fallbacks ── const hasRadius = spec.encoding.radius || spec.layer?.[0]?.encoding?.radius; if (!hasRadius) { const fallback = { aggregate: 'count', type: 'quantitative', scale: { type: 'sqrt' } }; if (spec.layer) { spec.layer[0].encoding.radius = fallback; } else { spec.encoding.radius = fallback; } } if (!spec.encoding.theta) { spec.encoding.theta = { aggregate: 'count', type: 'quantitative' }; } // ── Pass-through channels ── const mappedChannels = new Set(['x', 'y', 'color', 'column', 'row', 'radius', 'size', 'theta', 'facet']); for (const [ch, enc] of Object.entries(rest)) { if (mappedChannels.has(ch)) continue; if (!enc.field && !enc.aggregate) continue; spec.encoding[ch] = enc; } // ── Sizing: square aspect ratio ── // Use subplot dimensions (which account for faceting). const subW = ctx.layout.subplotWidth ?? ctx.canvasSize.width; const subH = ctx.layout.subplotHeight ?? ctx.canvasSize.height; const size = Math.min(subW, subH); spec.width = size; spec.height = size; // ── Chart properties ── const config = ctx.chartProperties; if (config) { const markTarget = spec.layer ? spec.layer[0] : spec; if (config.innerRadius > 0) { markTarget.mark = setMarkProp(markTarget.mark, 'innerRadius', config.innerRadius); } if (config.padAngle > 0) { markTarget.mark = setMarkProp(markTarget.mark, 'padAngle', config.padAngle); } } }, properties: [ { key: "innerRadius", label: "Inner Radius", type: "continuous", min: 0, max: 100, step: 5, defaultValue: 0 }, { key: "padAngle", label: "Gap", type: "continuous", min: 0, max: 0.1, step: 0.005, defaultValue: 0 }, { key: 'alignment', label: 'Alignment', type: 'discrete', options: [ { value: 'left', label: 'Left (default)' }, { value: 'center', label: 'Center' }, ], }, ] as ChartPropertyDef[], }; ================================================ FILE: src/lib/agents-chart/vegalite/templates/scatter.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; import { defaultBuildEncodings, applyPointSizeScaling, setMarkProp, detectBandedAxisForceDiscrete, } from './utils'; export const scatterPlotDef: ChartTemplateDef = { chart: "Scatter Plot", template: { mark: "circle", encoding: {} }, channels: ["x", "y", "color", "size", "opacity", "column", "row"], markCognitiveChannel: 'position', instantiate: (spec, ctx) => { defaultBuildEncodings(spec, ctx.resolvedEncodings); applyPointSizeScaling(spec, ctx.table, ctx.canvasSize?.width, ctx.canvasSize?.height); const config = ctx.chartProperties; if (config?.opacity !== undefined && config.opacity < 1) { spec.mark = setMarkProp(spec.mark, 'opacity', config.opacity); } }, properties: [ { key: "opacity", label: "Opacity", type: "continuous", min: 0.1, max: 1, step: 0.05, defaultValue: 1 }, ] as ChartPropertyDef[], }; export const regressionDef: ChartTemplateDef = { chart: "Regression", template: { layer: [ { mark: "circle", encoding: { x: {}, y: {}, color: {}, size: {} }, }, { mark: { type: "line", color: "red" }, transform: [{ regression: "field1", on: "field2", group: "field3" }], encoding: { x: {}, y: {} }, }, ], }, channels: ["x", "y", "size", "color", "column", "row"], markCognitiveChannel: 'position', instantiate: (spec, ctx) => { const { x, y, color, size, column, row } = ctx.resolvedEncodings; const config = ctx.chartProperties; // x & y → both layers + transform field names if (x) { spec.layer[0].encoding.x = { ...spec.layer[0].encoding.x, ...x }; spec.layer[1].encoding.x = { ...spec.layer[1].encoding.x, ...x }; if (x.field) spec.layer[1].transform[0].on = x.field; } if (y) { spec.layer[0].encoding.y = { ...spec.layer[0].encoding.y, ...y }; spec.layer[1].encoding.y = { ...spec.layer[1].encoding.y, ...y }; if (y.field) spec.layer[1].transform[0].regression = y.field; } // Regression method (default: linear) const method = config?.regressionMethod; if (method && method !== 'linear') { spec.layer[1].transform[0].method = method; // For polynomial, allow configurable order if (method === 'poly') { const order = config?.polyOrder ?? 3; spec.layer[1].transform[0].order = order; } } // color → scatter layer always; if present, also group regression by color field if (color) { spec.layer[0].encoding.color = { ...spec.layer[0].encoding.color, ...color }; if (color.field) { // Group regression by color field so each class gets its own trend line spec.layer[1].transform[0].groupby = [color.field]; // Pass color encoding to regression layer so lines match scatter colors spec.layer[1].encoding.color = { ...color }; // Remove the hardcoded red so Vega-Lite uses the shared color scale spec.layer[1].mark = { type: "line" }; } } if (size) spec.layer[0].encoding.size = { ...spec.layer[0].encoding.size, ...size }; // facets → top-level encoding if (!spec.encoding) spec.encoding = {}; if (column) spec.encoding.column = column; if (row) spec.encoding.row = row; }, properties: [ { key: "regressionMethod", label: "Method", type: "discrete", options: [ { value: "linear", label: "Linear" }, { value: "log", label: "Logarithmic" }, { value: "exp", label: "Exponential" }, { value: "pow", label: "Power" }, { value: "quad", label: "Quadratic" }, { value: "poly", label: "Polynomial" }, ], defaultValue: "linear", }, { key: "polyOrder", label: "Poly Order", type: "continuous", min: 2, max: 10, step: 1, defaultValue: 3, }, ] as ChartPropertyDef[], }; export const rangedDotPlotDef: ChartTemplateDef = { chart: "Ranged Dot Plot", template: { encoding: {}, layer: [ { mark: "line", encoding: { detail: {} } }, { mark: { type: "point", filled: true }, encoding: { color: {} } }, ], }, channels: ["x", "y", "color"], markCognitiveChannel: 'position', instantiate: (spec, ctx) => { const { color, ...rest } = ctx.resolvedEncodings; if (!spec.encoding) spec.encoding = {}; for (const [ch, enc] of Object.entries(rest)) { spec.encoding[ch] = { ...(spec.encoding[ch] || {}), ...enc }; } if (color) { spec.layer[1].encoding.color = { ...(spec.layer[1].encoding.color || {}), ...color }; } // Copy nominal axis into detail encoding for line layer if (spec.encoding.y?.type === "nominal") { spec.layer[0].encoding.detail = JSON.parse(JSON.stringify(spec.encoding.y)); } else if (spec.encoding.x?.type === "nominal") { spec.layer[0].encoding.detail = JSON.parse(JSON.stringify(spec.encoding.x)); } }, }; export const boxplotDef: ChartTemplateDef = { chart: "Boxplot", template: { mark: "boxplot", encoding: {} }, channels: ["x", "y", "color", "opacity", "column", "row"], markCognitiveChannel: 'position', declareLayoutMode: (cs, table) => { if (!cs.x?.field || !cs.y?.field) return {}; const result = detectBandedAxisForceDiscrete(cs, table, { preferAxis: 'x' }); if (!result) return {}; return { axisFlags: { [result.axis]: { banded: true } }, resolvedTypes: result.resolvedTypes, }; }, instantiate: (spec, ctx) => { defaultBuildEncodings(spec, ctx.resolvedEncodings); // Scale box width to the step size of the discrete axis const layout = ctx.layout; if (layout.xNominalCount > 0 || layout.yNominalCount > 0) { const boxStep = layout.xNominalCount > 0 ? layout.xStep : layout.yStep; const boxSize = Math.max(4, Math.round(boxStep * 0.7)); spec.mark = setMarkProp(spec.mark, 'size', boxSize); } }, }; ================================================ FILE: src/lib/agents-chart/vegalite/templates/utils.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * Shared helper functions for chart template hooks (v2 pipeline). * Pure logic — no UI dependencies. */ import type { ChannelSemantics, InstantiateContext } from '../../core/types'; // --------------------------------------------------------------------------- // Discrete-dimension helpers // --------------------------------------------------------------------------- const isDiscrete = (type: string | undefined) => type === "nominal" || type === "ordinal"; /** * Check whether a numeric field's values are equally strided (uniform spacing). */ export function isEquallyStrided(field: string, table: any[]): boolean { const vals = [...new Set(table.map(r => r[field]).filter((v: any) => v != null && typeof v === 'number'))]; if (vals.length <= 1) return true; vals.sort((a, b) => a - b); const diffs = []; for (let i = 1; i < vals.length; i++) { diffs.push(vals[i] - vals[i - 1]); } const medianDiff = diffs.slice().sort((a, b) => a - b)[Math.floor(diffs.length / 2)]; if (medianDiff === 0) return false; const tolerance = 0.01 * Math.abs(medianDiff); return diffs.every(d => Math.abs(d - medianDiff) <= tolerance); } /** * Get the number of unique non-null values for a field in the data table. */ export function getFieldCardinality(field: string, table: any[]): number { return new Set(table.map((r: any) => r[field]).filter((v: any) => v != null)).size; } /** * Determine the discrete type for a given encoding type. * Returns the appropriate discrete type without mutating anything. */ export function resolveDiscreteType( currentType: string, field: string | undefined, table: any[], ): 'nominal' | 'ordinal' { if (currentType === 'nominal') return 'nominal'; if (currentType === 'ordinal') return 'ordinal'; if (currentType === 'temporal') return 'ordinal'; if (currentType === 'quantitative' && field && table.length > 0) { const cardinality = getFieldCardinality(field, table); return cardinality <= 20 ? 'ordinal' : 'nominal'; } return 'nominal'; } /** * Convert a single encoding to a discrete VL type in-place. */ export function resolveAsDiscrete( encodingObj: any, table: any[], ): 'nominal' | 'ordinal' { if (!encodingObj) return 'nominal'; const result = resolveDiscreteType(encodingObj.type, encodingObj.field, table); encodingObj.type = result; return result; } /** * Detect which positional axis should be the banded/category axis, * working from ChannelSemantics (v2 pipeline). * * Used by declareLayoutMode to set axisFlags and resolvedTypes. * * @returns axis: which axis is banded * resolvedTypes: type overrides if conversion was needed */ export function detectBandedAxisFromSemantics( channelSemantics: Record<string, ChannelSemantics>, table: any[], options: { preferAxis?: 'x' | 'y' } = {}, ): { axis: 'x' | 'y'; resolvedTypes?: Record<string, 'nominal' | 'ordinal' | 'quantitative' | 'temporal'> } | null { const xType = channelSemantics.x?.type; const yType = channelSemantics.y?.type; // Already discrete? if (xType && isDiscrete(xType)) return { axis: 'x' }; if (yType && isDiscrete(yType)) return { axis: 'y' }; // Both continuous — don't convert, the banded flag handles sizing if (xType && yType) { if (xType === 'quantitative' && yType !== 'quantitative') { return { axis: 'y' }; } if (yType === 'quantitative' && xType !== 'quantitative') { return { axis: 'x' }; } return { axis: options.preferAxis || 'x' }; } // Only one axis — convert to discrete if (xType) { const newType = resolveDiscreteType(xType, channelSemantics.x?.field, table); return { axis: 'x', resolvedTypes: { x: newType } }; } if (yType) { const newType = resolveDiscreteType(yType, channelSemantics.y?.field, table); return { axis: 'y', resolvedTypes: { y: newType } }; } return null; } /** * Detect which axis is banded, and also force discrete conversion * when needed (grouped bar, boxplot must have a truly discrete axis). * * Returns resolvedTypes with the forced conversion. */ export function detectBandedAxisForceDiscrete( channelSemantics: Record<string, ChannelSemantics>, table: any[], options: { preferAxis?: 'x' | 'y' } = {}, ): { axis: 'x' | 'y'; resolvedTypes?: Record<string, 'nominal' | 'ordinal' | 'quantitative' | 'temporal'> } | null { const result = detectBandedAxisFromSemantics(channelSemantics, table, options); if (!result) return null; const axis = result.axis; const cs = channelSemantics[axis]; if (!cs) return result; // If the axis is NOT already discrete, force conversion if (!isDiscrete(cs.type)) { const newType = resolveDiscreteType(cs.type, cs.field, table); return { axis, resolvedTypes: { ...result.resolvedTypes, [axis]: newType }, }; } return result; } /** * Default instantiate implementation for simple templates. * Maps each resolved encoding channel directly to spec.encoding[channel]. */ export const defaultBuildEncodings = (spec: any, encodings: Record<string, any>): void => { if (!spec.encoding) spec.encoding = {}; for (const [channel, encodingObj] of Object.entries(encodings)) { if (Object.keys(encodingObj).length > 0) { const existing = spec.encoding[channel]; if (existing && typeof existing === 'object') { spec.encoding[channel] = { ...existing, ...encodingObj }; } else { spec.encoding[channel] = encodingObj; } } } }; // --------------------------------------------------------------------------- // Mark sizing helpers (used by v2 instantiate hooks) // --------------------------------------------------------------------------- /** * Set a property on a mark object (handles both string and object forms). */ export function setMarkProp(mark: any, key: string, value: any): any { if (typeof mark === 'string') { return { type: mark, [key]: value }; } return { ...mark, [key]: value }; } /** * Coverage-based point sizing. */ export const applyPointSizeScaling = ( vgSpec: any, table: any[], plotWidth: number = 400, plotHeight: number = 300, targetCoverage: number = 0.15, defaultSize: number = 30, minSize: number = 4, ): any => { if (!table || table.length === 0) return vgSpec; const markType = typeof vgSpec.mark === 'string' ? vgSpec.mark : vgSpec.mark?.type; if (!['circle', 'point', 'square'].includes(markType)) return vgSpec; if (vgSpec.encoding?.size?.field) return vgSpec; if (typeof vgSpec.mark === 'object' && vgSpec.mark.size != null) return vgSpec; const n = table.length; const plotArea = plotWidth * plotHeight; const currentCoverage = (n * defaultSize) / plotArea; if (currentCoverage <= targetCoverage) return vgSpec; const size = Math.round(Math.max(minSize, (targetCoverage * plotArea) / n)); vgSpec.mark = setMarkProp(vgSpec.mark, 'size', size); return vgSpec; }; /** * Compute the maximum non-overlapping mark size (in pixels) for a continuous * banded axis. */ function maxNonOverlapSize( field: string, table: any[], isTemporal: boolean, subplotDim: number, count: number, minSize: number = 2, ): number { const nums = [...new Set( table.map((r: any) => { const v = r[field]; if (v == null) return NaN; return isTemporal ? +new Date(v) : +v; }).filter((v: number) => !isNaN(v)), )]; if (nums.length < 2) return Infinity; nums.sort((a, b) => a - b); let minGap = Infinity; for (let i = 1; i < nums.length; i++) { const gap = nums[i] - nums[i - 1]; if (gap > 0 && gap < minGap) minGap = gap; } if (!isFinite(minGap)) return Infinity; const dataRange = nums[nums.length - 1] - nums[0]; if (dataRange <= 0) return Infinity; const pixelsPerUnit = subplotDim * (count - 1) / (dataRange * count); const maxWidth = Math.floor(minGap * pixelsPerUnit); return Math.max(minSize, maxWidth); } /** * Adjust bar/rect marks for continuous-as-discrete axes. * v2 version: reads layout info from InstantiateContext. */ export function adjustBarMarks(spec: any, ctx: InstantiateContext): void { const layout = ctx.layout; for (const axis of ['x', 'y'] as const) { const count = axis === 'x' ? layout.xContinuousAsDiscrete : layout.yContinuousAsDiscrete; if (count <= 0) continue; const enc = spec.encoding?.[axis]; if (enc?.bin) continue; const effStep = axis === 'x' ? layout.xStep : layout.yStep; const allMarkTypes = new Set<string>(); const mt = typeof spec.mark === 'string' ? spec.mark : spec.mark?.type; if (mt) allMarkTypes.add(mt); if (Array.isArray(spec.layer)) { for (const layer of spec.layer) { const lm = typeof layer.mark === 'string' ? layer.mark : layer.mark?.type; if (lm) allMarkTypes.add(lm); } } const sizeKey = allMarkTypes.has('rect') ? (axis === 'x' ? 'width' : 'height') : 'size'; const subplotDim = axis === 'x' ? layout.subplotWidth : layout.subplotHeight; const isTemporal = enc?.type === 'temporal'; const maxSize = enc?.field ? maxNonOverlapSize(enc.field, ctx.table, isTemporal, subplotDim, count) : Infinity; const cellSize = Math.max(2, Math.min(Math.round(effStep * 0.9), maxSize)); if (Array.isArray(spec.layer)) { for (const layer of spec.layer) { const lm = typeof layer.mark === 'string' ? layer.mark : layer.mark?.type; if (lm === 'bar' || lm === 'rect') { layer.mark = setMarkProp(layer.mark, sizeKey, cellSize); } } } else if (spec.mark) { const markType = typeof spec.mark === 'string' ? spec.mark : spec.mark?.type; if (markType === 'bar' || markType === 'rect') { spec.mark = setMarkProp(spec.mark, sizeKey, cellSize); } } } } /** * Adjust rect marks for edge-to-edge tiling on continuous axes. * v2 version: reads layout info from InstantiateContext. */ export function adjustRectTiling(spec: any, ctx: InstantiateContext): void { const layout = ctx.layout; for (const axis of ['x', 'y'] as const) { const enc = spec.encoding?.[axis]; if (!enc?.field) continue; const t = enc.type; if (t === 'nominal' || t === 'ordinal') continue; if (enc.aggregate) continue; const uniqueVals = [...new Set(ctx.table.map((r: any) => r[enc.field]))]; const cardinality = uniqueVals.length; if (cardinality <= 1) continue; const count = axis === 'x' ? layout.xContinuousAsDiscrete : layout.yContinuousAsDiscrete; const effStep = axis === 'x' ? layout.xStep : layout.yStep; const pixelSpacing = count > 0 ? effStep * (count + 1) / count : effStep; const subplotDim = axis === 'x' ? layout.subplotWidth : layout.subplotHeight; const isTemporal = t === 'temporal'; const maxSize = maxNonOverlapSize(enc.field, ctx.table, isTemporal, subplotDim, count); const cellSize = Math.max(1, Math.min(Math.floor(pixelSpacing * 0.98), maxSize)); const sizeKey = axis === 'x' ? 'width' : 'height'; spec.mark = setMarkProp(spec.mark, sizeKey, cellSize); } } /** * Convert both positional axes to discrete types if they aren't already. * Returns resolvedTypes for layout declaration. */ export function ensureDiscreteTypes( channelSemantics: Record<string, ChannelSemantics>, table: any[], ): Record<string, 'nominal' | 'ordinal' | 'quantitative' | 'temporal'> { const resolvedTypes: Record<string, 'nominal' | 'ordinal' | 'quantitative' | 'temporal'> = {}; for (const axis of ['x', 'y'] as const) { const cs = channelSemantics[axis]; if (!cs?.field || isDiscrete(cs.type)) continue; resolvedTypes[axis] = resolveDiscreteType(cs.type, cs.field, table); } return resolvedTypes; } ================================================ FILE: src/lib/agents-chart/vegalite/templates/waterfall.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { ChartTemplateDef, ChartPropertyDef } from '../../core/types'; /** * Waterfall Chart template. * * Expects data with: * - x (nominal): step labels in display order * - y (quantitative): the delta/amount for each step * - color (nominal, optional): a "Type" column with values like * "start", "delta", "end" * * Uses a layered spec: bars + connector rules. */ export const waterfallChartDef: ChartTemplateDef = { chart: "Waterfall Chart", template: { mark: "bar", encoding: {} }, channels: ["x", "y", "color", "column", "row"], markCognitiveChannel: 'length', declareLayoutMode: () => ({ axisFlags: { x: { banded: true } }, }), instantiate: (spec, ctx) => { const { x, y, color, column, row } = ctx.resolvedEncodings; const config = ctx.chartProperties; const xField: string = x?.field || 'Category'; const yField: string = y?.field || 'Amount'; const colorField: string | undefined = color?.field; if (!spec.encoding) spec.encoding = {}; if (column) spec.encoding.column = column; if (row) spec.encoding.row = row; const hasTypeCol = !!colorField; const typeField = colorField || '__wf_type'; // ── Transforms ─────────────────────────────────────────────── const transforms: any[] = []; if (!hasTypeCol) { transforms.push( { window: [{ op: "row_number", as: "__wf_row" }] }, { joinaggregate: [{ op: "count", as: "__wf_total" }] }, { calculate: `datum.__wf_row === 1 ? 'start' : datum.__wf_row === datum.__wf_total ? 'end' : 'delta'`, as: typeField, }, ); } transforms.push({ window: [{ op: "sum", field: yField, as: "__wf_sum_raw" }], }); transforms.push({ calculate: `datum['${typeField}'] === 'end' ? datum.__wf_sum_raw - datum['${yField}'] : datum.__wf_sum_raw`, as: "__wf_sum", }); transforms.push({ calculate: `datum['${typeField}'] === 'end' ? 0 : datum.__wf_sum - datum['${yField}']`, as: "__wf_prev_sum", }); transforms.push({ calculate: `datum['${typeField}'] !== 'delta' ? 'total' : datum['${yField}'] >= 0 ? 'increase' : 'decrease'`, as: "__wf_color", }); spec.transform = transforms; // ── Shared x encoding ──────────────────────────────────────── const xEnc = { field: xField, type: "ordinal" as const, sort: null, axis: { labelAngle: -45 }, }; // ── Preserve facet encodings ───────────────────────────────── const facetEncodings: any = {}; if (spec.encoding?.column) facetEncodings.column = spec.encoding.column; if (spec.encoding?.row) facetEncodings.row = spec.encoding.row; const cornerRadius = (config?.cornerRadius && config.cornerRadius > 0) ? config.cornerRadius : 0; spec.encoding = { x: xEnc, ...facetEncodings, }; spec.layer = [ { mark: { type: "bar", ...(cornerRadius > 0 ? { cornerRadius: cornerRadius } : {}), }, encoding: { y: { field: "__wf_prev_sum", type: "quantitative", title: yField, }, y2: { field: "__wf_sum" }, color: { field: "__wf_color", type: "nominal", scale: { domain: ["total", "increase", "decrease"], range: ["#f7e0b6", "#93c4aa", "#f78a64"], }, legend: { title: "Type" }, }, }, }, ]; delete spec.mark; }, properties: [ { key: "cornerRadius", label: "Corners", type: "continuous", min: 0, max: 8, step: 1, defaultValue: 0 }, ] as ChartPropertyDef[], }; ================================================ FILE: src/scss/App.scss ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. h2.view-title { margin: 0; line-height: 1.75; display: inline-block; color: #717171; //font-weight: 600; //text-transform: uppercase; font-size: 14px; display: inline-block; } .Resizer { background: #000; opacity: 0.2; z-index: 1; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; box-sizing: border-box; -moz-background-clip: padding; -webkit-background-clip: padding; background-clip: padding-box; } .Resizer:hover { -webkit-transition: all 2s ease; transition: all 2s ease; } .Resizer.horizontal { height: 11px; margin: -5px 0; border-top: 5px solid rgba(255, 255, 255, 0); border-bottom: 5px solid rgba(255, 255, 255, 0); cursor: row-resize; width: 100%; } .Resizer.horizontal:hover { border-top: 5px solid rgba(0, 0, 0, 0.5); border-bottom: 5px solid rgba(0, 0, 0, 0.5); } .Resizer.vertical { width: 11px; margin: 0 -5px; border-left: 5px solid rgba(255, 255, 255, 0); border-right: 5px solid rgba(255, 255, 255, 0); cursor: col-resize; } .Resizer.vertical:hover { border-left: 5px solid rgba(0, 0, 0, 0.5); border-right: 5px solid rgba(0, 0, 0, 0.5); } .Resizer.disabled { cursor: not-allowed; } .Resizer.disabled:hover { border-color: transparent; } .GroupHeader { position: sticky; padding: 4px 8px; color: rgba(0, 0, 0, 0.6); font-size: 12px; } .GroupItems { padding: 0; } // Custom scrollbar styling with hover effects * { // Webkit browsers (Chrome, Safari, Edge) &::-webkit-scrollbar { width: 8px; height: 8px; } &::-webkit-scrollbar-track { background: #f1f1f1; border-radius: 4px; } &::-webkit-scrollbar-thumb { background: #c1c1c1; border-radius: 4px; transition: background-color 0.3s ease; } &::-webkit-scrollbar-thumb:hover { background: #a8a8a8; } &::-webkit-scrollbar-corner { background: #f1f1f1; } // Firefox scrollbar-width: auto; scrollbar-color: lightgray transparent; // Firefox hover effect (limited support) &:hover { scrollbar-color: #c6c6c6 transparent; } } // Specific styling for main scrollable containers .encoding-shelf, .vega-container, .data-fields-group { &::-webkit-scrollbar-thumb { background: #d1d1d1; border-radius: 4px; transition: background-color 0.3s ease; } &::-webkit-scrollbar-thumb:hover { background: #b8b8b8; } } // Pencil writing animation @keyframes writing-pencil { 0%, 100% { transform: rotate(-12deg) translateY(0px); } 25% { transform: rotate(-8deg) translateY(-1px); } 50% { transform: rotate(-12deg) translateY(0px); } 75% { transform: rotate(-15deg) translateY(1px); } } .pencil { display: inline-block; transform-origin: bottom right; animation: writing-pencil 0.8s ease-in-out infinite; } // Fix MUI Dialog bottom scrolling issues .MuiDialog-paper { display: flex; flex-direction: column; max-height: calc(100% - 64px); .MuiDialogTitle-root { flex-shrink: 0; } .MuiDialogContent-root { flex: 1 1 auto; overflow-y: auto; overflow-x: hidden; box-sizing: border-box; min-height: 0; // Ensures content can shrink in flex container } .MuiDialogActions-root { flex-shrink: 0; } } // Allotment sash (divider) styling // Outer allotment: trim sash to avoid overlapping with rounded corners of the vis panel .outer-allotment { // Hide the gray separator border (it would overlap with the rounded vis panel corners) --separator-border: transparent !important; --focus-border: #007fd4 !important; --sash-hover-transition-duration: 0s !important; [class*="sash_"][class*="vertical"] { top: 16px !important; height: calc(100% - 32px) !important; } } // Inner allotment: keep default/normal sash style with hover effect .inner-allotment { --separator-border: rgba(128, 128, 128, 0.35) !important; --focus-border: #007fd4 !important; --sash-hover-transition-duration: 0s !important; } ================================================ FILE: src/scss/DataView.scss ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /* or write CSS selectors to make customisations beyond what the parameters support */ .table-container { height: 100%; position: relative; //background-color: #fff; display: -webkit-box; display: -ms-flexbox; display: flex; -webkit-box-orient: vertical; -webkit-box-direction: normal; -ms-flex-direction: column; flex-direction: column; overflow: hidden; will-change: transform; } .table-header-container { display: flex; height: 100%; flex-wrap: nowrap; justify-content: flex-start; align-items: center; background-color: #fafafa; } .table-header-icon { padding: 0 6px 0 0; margin-top: 4px; display: inline-block; font-size: 18px; color: #717171; } .table-container-small { .derived-column-header { background-color: rgba(255, 215, 0, 0.1); } .highlighted-column-header { background-color:rgba(88, 24, 69, 0.05); } .data-view-header-label { font-size: 10px; } } .table-box { height: 100%; margin: 0px; background: white; } .table-footer-container { .table-search-input { font-size: small; input.MuiOutlinedInput-input.MuiInputBase-input { height: 1.2em; font-size: small; } } .table-footer-number { font-size: small; line-height: 1.5; color: rgba(0, 0, 0, 0.54); } } span.bold { font-weight: 700; } .data-view-header-cell.MuiTableCell-root { flex: 1 1 auto; overflow: clip; //border-bottom: none; border-bottom-width: 2px; &:last-child { .data-view-header-container { margin-right: 0px; } } &.original { .data-view-header-container { border-bottom: 2px solid #0288d1; } } &.derived { .data-view-header-container { background-color: #fffef1; border-bottom: 2px solid #ffd700; } } &.custom { .data-view-header-container { background-color: #fff9f8; border-bottom: 2px solid #ffb395; } } .data-view-header-container { display: flex; flex-direction: row; margin: 0 2px 0 0; padding: 4px 4px; } } ================================================ FILE: src/scss/DraggableCard.scss ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. .draggable-card { .draggable-card-header { display: flex; flex-direction: row; justify-content: space-between; height: auto; padding: 1px 8px 1px 0px; margin-left: 8px; position: relative; .draggable-card-title { margin: auto; margin-left: 3px; display: flex; align-items: center; } } .draggable-card-header.uninstantiated { font-style: italic; color: rgab(80, 80, 80); } .draggable-card-header.uninstantiated.custom { border: 0.5px dashed lightsalmon; border-left: none; } .draggable-card-header.uninstantiated.derived { border: 0.5px dashed gold; border-left: none; } & .draggable-card-action-button { visibility: hidden; opacity: 0; transition: visibility 0s, opacity 0.3s linear; } &:hover .draggable-card-action-button { visibility: visible; opacity: 1; } .draggable-card-body { padding: 0px 8px 8px 4px !important; margin-left: 8px; background-color: rgba(255, 255, 255, 0.93); } .draggable-card-body-edit-mode { background-color: rgba(255, 255, 255, 0.98); padding: 8px 8px 8px 4px !important; margin-left: 8px; } .draggable-card-example-values { display: block; text-overflow: ellipsis; margin-left: 8px; max-width: calc(100% - 96px); font-size: smaller; font-style: italic; color: rebeccapurple; .draggable-card-example-values-chip { height: auto; max-width: 120px; margin: 3px; border-radius: 0px; } } } ================================================ FILE: src/scss/EncodingShelf.scss ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. .encoding-shelf { padding: 10px; //width: 100%; width: 220px; overflow-y: auto; transition: height 300ms cubic-bezier(0.4, 0, 0.2, 1) 0ms; //overflow: hidden; align-items: flex-start; border-left: 1px dashed rgba(0,0,0,0.1); .example-panel { display: flex; //border: 1px grey dashed; flex-direction: column; justify-content: center } } .encoding-shelf-trigger-card { margin: 6px 2px; min-width: 80px; } .encoding-shelf-compact { .example-panel { display: flex; //border: 1px grey dashed; flex-direction: column; justify-content: center } .table-list-item { display: flex; } } .auto-sort-option-label { width: calc(100% - 24px); overflow: hidden; display: -webkit-box; line-clamp: 2; -webkit-line-clamp: 2; -webkit-box-orient: vertical; background: #fff; text-align: left; white-space: normal; font-size: xx-small !important; } .channel-shelf-box { width: auto !important; justify-content: space-between; white-space: nowrap; } .channel-encoded-field { width: 100%; //max-width: 144px; //min-width: 200px; min-height: 24px; display: flex; justify-content: space-between; & .encoding-shelf-action-button { visibility: hidden; opacity: 0; transition: visibility 0s, opacity 0.3s linear; } &:hover .encoding-shelf-action-button { visibility: visible; opacity: 1; } } .channel-encoded-field-compact { width: 100%; //max-width: 144px; min-width: 0px; min-height: 24px; display: flex; justify-content: space-between; & .encoding-shelf-action-button { visibility: hidden; opacity: 0; transition: visibility 0s, opacity 0.3s linear; } &:hover .encoding-shelf-action-button { visibility: visible; opacity: 1; } } .collapse-channel-label { //width: 60px !important; overflow: initial !important; text-align: left; padding-right: 2px !important; vertical-align: 'middle'; display: 'inline-flex'; font-size: "0.875em"; svg { vertical-align: bottom; } } .encoding-list { padding: 0; margin: 6px 3px; display: block; //flex-direction: column; //justify-content: space-evenly; } .encoding-item { display: block; margin-bottom: 4px; .encoding-button-container { position: relative; outline: 0; margin: auto; display: -webkit-box; display: -webkit-flex; display: -ms-flexbox; display: flex; -webkit-box-pack: center; -ms-flex-pack: center; -webkit-justify-content: center; justify-content: center; padding: 6px; .MuiButton-root { flex-grow: 1; } } .encoding-active-item { //width: calc(100% - 24px) !important; //width: auto; //min-width: 120px; flex-grow: 1; flex-shrink: 1; border-radius: 0; display: flex; justify-content: space-between; //background-color: rgb(2 136 209 / 0.07); .MuiChip-root { justify-content: flex-start; .MuiChip-deleteIcon { margin: 0 2px 0 auto !important; } } } // .encoding-active-item-original { // background-color: rgb(2 136 209 / 0.07); // } // .encoding-active-item-custom { // background-color: rgb(249 105 14 / 0.07); // } // .encoding-active-item-derived { // background-color: rgb(255 215 0 / 0.07); // gold // } // .encoding-active-item-custom { // //width: calc(100% - 24px) !important; // width: auto; // flex-grow: 1; // min-width: 120px; // border-radius: 0; // display: flex; // justify-content: space-between; // background-color: rgb(249 105 14 / 0.07); // .MuiChip-root { // justify-content: flex-start; // .MuiChip-deleteIcon { // margin: 0 2px 0 auto !important; // } // } // } .encoding-prop-chip { border-radius: 0px; font-size: 12px; font-style: italic; border-right: lightgray 1px solid; min-width: 42px; flex: 0; //height: 14px; .MuiChip-label { padding-left: 2px; padding-right: 2px; } .MuiChip-deleteIcon { margin-left: 0; margin: 0 2px 0 auto !important; font-size: inherit; } } .encoding-prop-chip:last-child { flex: 1; border-right: 0; } } .chart-wrapper canvas { max-width: 100%; height: auto !important; } ================================================ FILE: src/scss/VisualizationView.scss ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. .visualization-gallery { padding: 0; //0px 10px 10px 10px; overflow: hidden; flex-grow: 1; display: flex; .vega-container { display: flex; flex-wrap: wrap; padding-top: 30px; padding-left: 20px; //margin: 5px; overflow-y: auto; align-content: flex-start; width: 100%; } .vega-thumbnail-box { display: flex; //color: #fff; border-radius: 3px; //margin: auto; padding: 6px; cursor: pointer !important; } .vega-thumbnail-box-animation { transform-origin: bottom left; animation: appear 0.5s ease-out; } .vega-thumbnail { padding: 0px 10px; margin: auto; canvas { margin: 6px auto; } } .vega-thumbnail canvas{ cursor: pointer !important; } .vega-thumbnail-box:hover { transform: translateY(-1px); box-shadow: '0 2px 6px rgba(0,0,0,0.1)', } } // vega action menu .vega-embed .vega-actions a { padding: 8px 16px !important; font-family: Arial,Roboto,Helvetica Neue,sans-serif !important; font-size: 14px !important; font-weight: 100 !important; white-space: nowrap !important; color: #434a56 !important; text-decoration: none !important; } ; $standard-ease: cubic-bezier(0.4, 0.0, 0.2, 1); $decelerate-ease: cubic-bezier(0.0, 0.0, 0.2, 1); $accelerate-ease: cubic-bezier(0.4, 0.0, 1, 1); @keyframes bump { 0% { background-color: #fff; } 50% { background-color: rgba( 2, 136, 209, 0.2); } 100% { background-color: #fff; } } @keyframes appear { 0% { opacity: 0; } 100% { //background-color: #fff; } } @keyframes spin { 0%, to { transform: rotate(0) } 10% { transform: scale(1.1) } 20% { transform: rotate(4deg) translateY(-12px) } 40% { transform: rotate(2deg) translateY(0) } 60% { transform: rotate(-2deg) translateY(-8px) } 80% { transform: rotate(-1deg) translateY(0) } } .vega-container-carousel { width: 200px; min-width: 200px; display: flex; flex-direction: column; overflow-y: scroll; border-right: 1px lightgray solid; align-content: flex-start; padding-top: 0px; direction: rtl; padding-bottom: 6px; height: calc(100vh - 50px); .vega-thumbnail { //border-bottom: 0.5px dashed lightgray; //width: calc(100% - 16px); //margin: 0 8px 0 12px; display: flex; cursor: pointer !important; border: lightgray solid 0.5px; canvas { margin: 10px auto; } } .vega-thumbnail-box { //width: calc(100% - 16px); display: block; //color: #fff; border-radius: 3px; } .vega-thumbnail-box-animation { transform-origin: bottom left; animation: appear 0.5s ease-out; } .vega-thumbnail:hover { transform: translateY(1px); box-shadow: 0 0 3px rgba(33,33,33,.2); z-index: 1; } } .visualization-carousel { display: flex; flex-grow: 1; height: calc(100%); .vega-focused { flex: 1; height: 100%; display: flex; overflow: auto; .chart-box { width: calc(100% - 48px); overflow: auto; display: flex; color: #fff; border-radius: 3px; } .chart-box-animation { transform-origin: bottom left; animation: appear 0.5s ease-out } } .vega-container { width: 200px; min-width: 200px; display: flex; flex-wrap: wrap; overflow-y: scroll; border-right: 1px lightgray solid; align-content: flex-start; padding-top: 0px; direction: rtl; padding-bottom: 6px; } } .data-thread { .thread-view-animation { transform-origin: bottom left; animation: appear 0.5s ease-out } .vega-thumbnail { display: flex; cursor: pointer !important; .vega-embed { display: flex; cursor: pointer !important; } canvas { margin: 4px auto; } } .vega-thumbnail-box { border-radius: 3px; cursor: pointer !important; & .data-thread-chart-card-action-button { visibility: hidden; opacity: 0; transition: visibility 0s, opacity 0.3s linear; } &:hover .data-thread-chart-card-action-button { visibility: visible; opacity: 1; } } .vega-thumbnail-box-animation { transform-origin: bottom left; animation: appear 0.5s ease-out; } } .data-thread-card { cursor: pointer; } .data-thread-card:hover { transform: translateY(-1px); box-shadow: 0 1px 4px rgba(0,0,0,0.12); z-index: 1; } .hover-card { cursor: pointer; canvas { cursor: pointer; } } .hover-card:hover { transform: translateY(-1px); box-shadow: 0 0 3px rgba(33,33,33,.2); } .visualization-carousel { display: flex; flex-grow: 1; height: calc(100%); .vega-focused { flex: 1; height: 100%; display: flex; overflow: auto; .chart-box { display: flex; color: #fff; padding: 5px; border-radius: 3px; margin: auto; } .chart-box-animation { transform-origin: bottom left; animation: appear 0.5s ease-out } } .vega-container { width: 200px; min-width: 200px; display: flex; flex-wrap: wrap; overflow-y: scroll; border-right: 1px lightgray solid; align-content: flex-start; padding-top: 0px; direction: rtl; padding-bottom: 6px; } } ================================================ FILE: src/types.d.ts ================================================ declare module "*.png" { const content: any; export default content; } declare module "*.svg" { const content: string; export default content; } declare module "prettier"; declare module "prettier/parser-babel"; declare module "vm-browserify"; declare module "vega-lite" { export function compile(spec: any, options?: any): { spec: any }; } ================================================ FILE: src/views/About.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { Box, Typography, Button, useTheme, alpha, Divider } from "@mui/material"; import { borderColor, radius } from '../app/tokens'; import React, { FC } from "react"; import GridViewIcon from '@mui/icons-material/GridView'; import GitHubIcon from '@mui/icons-material/GitHub'; import YouTubeIcon from '@mui/icons-material/YouTube'; import { AgentIcon as PrecisionManufacturingIcon } from '../icons'; import { toolName } from "../app/App"; import { useSelector } from "react-redux"; import { DataFormulatorState } from "../app/dfSlice"; interface Feature { title: string; description: string; media: string; mediaType: 'image' | 'video'; } const features: Feature[] = [ { title: "Load (Almost) Any Data", description: "Load structured data, connect to databases. Ask AI agents to extract and clean (small) ad-hoc data from screenshots, text blocks.", media: "/feature-extract-data.mp4", mediaType: "video" }, { title: "Agent Mode", description: "Vibe with your data. Hands-off and let agents automatically explore and visualize data from high-level goals.", media: "/feature-agent-mode.mp4", mediaType: "video" }, { title: "Interactive Control", description: "Use UI interactions and natural language to precisely describe chart designs. Ask AI agents for recommendations. Use Data Threads to backtrack, explore new branches, or follow up.", media: "/feature-interactive-control.mp4", mediaType: "video" }, { title: "Verify & Share Insights", description: "Interact with charts, inspect data, formulas, and code. Create reports to share insights grounded in your exploration.", media: "/feature-generate-report.mp4", mediaType: "video" } ]; export const About: FC<{}> = function About({ }) { const theme = useTheme(); let actionButtons = ( <Box component="nav" aria-label="Primary actions" sx={{ display: 'flex', justifyContent: 'center', gap: 1.5, mb: 4, flexWrap: 'wrap' }}> <Button size="large" variant="outlined" color="primary" sx={{ textTransform: 'none' }} startIcon={<Box component="img" sx={{ width: 20, height: 20 }} alt="" aria-hidden="true" src="/pip-logo.svg" />} target="_blank" rel="noopener noreferrer" aria-label="Install Locally via pip (opens in new tab)" href="https://pypi.org/project/data-formulator/" >Install Locally</Button> <Button size="large" variant="contained" color="primary" sx={{ textTransform: 'none' }} startIcon={<GridViewIcon aria-hidden="true" />} href="/app" >Try Online Demo</Button> <Button size="large" variant="outlined" color="primary" sx={{ textTransform: 'none' }} startIcon={<YouTubeIcon sx={{ color: '#FF0000' }} aria-hidden="true" />} target="_blank" rel="noopener noreferrer" aria-label="Watch Video on YouTube (opens in new tab)" href="https://www.youtube.com/watch?v=GfTE2FLyMrs" >Video</Button> <Button size="large" variant="outlined" color="primary" sx={{ textTransform: 'none' }} startIcon={<GitHubIcon aria-hidden="true" />} target="_blank" rel="noopener noreferrer" aria-label="View on GitHub (opens in new tab)" href="https://github.com/microsoft/data-formulator" >GitHub</Button> </Box> ); return ( <Box component="main" role="main" sx={{ display: "flex", flexDirection: "column", overflowY: "auto", width: '100%', height: '100%', background: ` linear-gradient(90deg, ${alpha(theme.palette.text.secondary, 0.01)} 1px, transparent 1px), linear-gradient(0deg, ${alpha(theme.palette.text.secondary, 0.01)} 1px, transparent 1px) `, backgroundSize: '16px 16px', }} > <Box sx={{margin:'auto', py: 4, display: "flex", flexDirection: "column", textAlign: "center", maxWidth: 1000, width: '100%', px: 3}}> {/* Header with title */} <Box component="header" sx={{display: 'flex', flexDirection: 'column', mx: 'auto', mt: 2}}> <Typography component="h1" fontSize={84} sx={{letterSpacing: '0.05em'}}>{toolName}</Typography> <Typography component="p" sx={{ fontSize: 24, color: theme.palette.text.secondary, textAlign: 'center', mb: 4}}> Explore data with visualizations, powered by AI agents. </Typography> </Box> {actionButtons} {/* Features Grid - 2 columns, 4 rows */} <Box component="section" aria-label="Features" sx={{ display: 'flex', flexDirection: 'column', gap: 5, mt: 2 }} > {features.map((feature, index) => ( <Box component="article" key={index} aria-labelledby={`feature-title-${index}`} sx={{ display: 'flex', flexDirection: index % 2 === 0 ? 'row' : 'row-reverse', gap: 4, alignItems: 'center', }} > {/* Text Content */} <Box sx={{ flex: 1, textAlign: index % 2 === 0 ? 'left' : 'right', display: 'flex', flexDirection: 'column', justifyContent: 'center', }}> <Typography component="h2" id={`feature-title-${index}`} sx={{ fontSize: 28, fontWeight: 500, mb: 1.5, color: theme.palette.text.primary, }} > {feature.title} </Typography> <Typography component="p" sx={{ fontSize: 17, color: 'text.secondary', lineHeight: 1.7, }} > {feature.description} </Typography> </Box> {/* Media Content */} <Box sx={{ flex: 1, borderRadius: radius.md, overflow: 'hidden', border: `1px solid ${borderColor.divider}`, }}> {feature.mediaType === 'video' ? ( <Box component="video" src={feature.media} autoPlay loop muted playsInline preload="metadata" aria-label={`Video demonstration: ${feature.title}`} sx={{ width: '100%', height: 'auto', display: 'block', }} /> ) : ( <Box component="img" src={feature.media} alt={feature.title} loading="lazy" sx={{ width: '100%', height: 'auto', display: 'block', }} /> )} </Box> </Box> ))} </Box> <Box sx={{ mt: 6, mx: 2 }}> <Typography variant="body2" sx={{ color: 'text.secondary' }}> <strong>Data handling:</strong> Data stored in browser only • Local install runs Python locally; online demo processes server-side (not stored) • LLM receives small samples with prompts </Typography> <Typography variant="body2" sx={{ display: 'block', mt: 1, color: 'text.secondary' }}> Research Prototype from Microsoft Research </Typography> </Box> </Box> {/* Footer */} <Box component="footer" role="contentinfo" sx={{ color: 'text.secondary', display: 'flex', backgroundColor: 'rgba(255, 255, 255, 0.89)', alignItems: 'center', justifyContent: 'center' }} > <Button size="small" color="inherit" sx={{ textTransform: 'none' }} target="_blank" rel="noopener noreferrer" aria-label="Privacy & Cookies (opens in new tab)" href="https://www.microsoft.com/en-us/privacy/privacystatement">Privacy & Cookies</Button> <Divider orientation="vertical" variant="middle" flexItem sx={{ mx: 1 }} aria-hidden="true" /> <Button size="small" color="inherit" sx={{ textTransform: 'none' }} target="_blank" rel="noopener noreferrer" aria-label="Terms of Use (opens in new tab)" href="https://www.microsoft.com/en-us/legal/intellectualproperty/copyright">Terms of Use</Button> <Divider orientation="vertical" variant="middle" flexItem sx={{ mx: 1 }} aria-hidden="true" /> <Button size="small" color="inherit" sx={{ textTransform: 'none' }} target="_blank" rel="noopener noreferrer" aria-label="Contact Us (opens in new tab)" href="https://github.com/microsoft/data-formulator/issues">Contact Us</Button> <Typography component="span" sx={{ fontSize: '12px', ml: 1 }}>© {new Date().getFullYear()}</Typography> </Box> </Box>) } ================================================ FILE: src/views/AgentRulesDialog.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React, { FC, useState, useEffect } from 'react'; import { radius } from '../app/tokens'; import { Button, Typography, Box, DialogTitle, Dialog, DialogContent, DialogActions, Divider, IconButton, useTheme, Badge, alpha, } from '@mui/material'; import RuleIcon from '@mui/icons-material/Rule'; import CloseIcon from '@mui/icons-material/Close'; import { useDispatch, useSelector } from 'react-redux'; import { DataFormulatorState, dfActions } from '../app/dfSlice'; import Editor from 'react-simple-code-editor'; export const AgentRulesDialog: React.FC<{ externalOpen?: boolean; onExternalClose?: () => void; }> = ({ externalOpen, onExternalClose }) => { const theme = useTheme(); const [internalOpen, setInternalOpen] = useState(false); const open = externalOpen !== undefined ? externalOpen : internalOpen; const dispatch = useDispatch(); const agentRules = useSelector((state: DataFormulatorState) => state.agentRules); // Local state for editing const [codingRules, setCodingRules] = useState(agentRules.coding); const [explorationRules, setExplorationRules] = useState(agentRules.exploration); // Placeholder content const codingPlaceholder = `Example Rules: ## Computation - ROI (return on investment) should be computed as (revenue - cost) / cost. - When compute moving average for date field, the window size should be 7. - When performing forecasting, by default use linear models. ## Coding - When a string column contains placeholder '-' for missing values, convert them to ''. - Date should all be formated as 'YYYY-MM-DD'. `; const explorationPlaceholder = `Example Rules: ## Simpicity - Keep the questions simple and concise, do not overcomplicate the exploration. ## Question Generation - When you see outliers in the data, generate a question to investigate the outliers. ## Domain Knowledge - When exploring large product dataset, include questions about top 20 based on different criteria. `; // Update local state when dialog opens useEffect(() => { if (open) { setCodingRules(agentRules.coding); setExplorationRules(agentRules.exploration); } }, [open, agentRules]); const handleSaveCoding = () => { dispatch(dfActions.setAgentRules({ coding: codingRules, exploration: agentRules.exploration })); }; const handleSaveExploration = () => { dispatch(dfActions.setAgentRules({ coding: agentRules.coding, exploration: explorationRules })); }; const handleClose = () => { // Reset to original values setCodingRules(agentRules.coding); setExplorationRules(agentRules.exploration); if (onExternalClose) { onExternalClose(); } else { setInternalOpen(false); } }; // Check if there are changes for each tab const hasCodingChanges = codingRules !== agentRules.coding; const hasExplorationChanges = explorationRules !== agentRules.exploration; // Check if any rules are set const ruleCount = Number(agentRules.coding && agentRules.coding.trim().length > 0) + Number(agentRules.exploration && agentRules.exploration.trim().length > 0); return ( <> {externalOpen === undefined && ( <Badge color="primary" variant="standard" invisible={ruleCount === 0} badgeContent={ruleCount} sx={{ '& .MuiBadge-badge': { minWidth: 0, height: 12, fontSize: 8, top: 12, right: 8, px: 0.5, color: theme.palette.primary.textColor || theme.palette.primary.main, background: alpha(theme.palette.primary.light, 0.2), }, }} > <Button variant="text" sx={{ textTransform: 'none' }} onClick={() => setInternalOpen(true)} startIcon={<RuleIcon />} > Agent Rules </Button> </Badge> )} <Dialog onClose={handleClose} open={open} sx={{ '& .MuiDialog-paper': { maxWidth: 800, maxHeight: '90vh', width: '90%' } }} maxWidth={false} > <DialogTitle sx={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center' }}> <Typography variant="h6">Agent Rules</Typography> <IconButton aria-label="close" onClick={handleClose} sx={{ color: (theme) => theme.palette.grey[500] }} > <CloseIcon /> </IconButton> </DialogTitle> <DialogContent> {/* Coding Agent Rules Section */} <Box sx={{ mb: 3 }}> <Typography variant="body2" sx={{ mb: 1, fontWeight: 600, color: 'primary.main' }}> Coding Rules <Typography variant="body2" component="span" color="text.secondary" sx={{ ml: 1, fontSize: 12 }}> (Rules that guide AI agents when generating code to transform data and recommend visualizations.) </Typography> </Typography> <Box sx={{ border: `1px solid ${theme.palette.primary.main}`, borderRadius: radius.sm, overflow: 'auto', height: 180, boxShadow: `0 2px 8px ${theme.palette.primary.main}40`, transition: 'box-shadow 0.3s ease-in-out', '&:hover': { boxShadow: `0 4px 12px ${theme.palette.primary.main}60`, } }} > <Editor value={codingRules} onValueChange={(code) => setCodingRules(code)} highlight={(code) => code} padding={16} placeholder={codingPlaceholder} onKeyDown={(e) => { if (e.key === 'Tab' && !codingRules) { e.preventDefault(); setCodingRules(codingPlaceholder); } }} style={{ fontFamily: 'Monaco, Menlo, "Ubuntu Mono", monospace', fontSize: 10, lineHeight: 1.2, minHeight: 180, whiteSpace: 'pre-wrap', outline: 'none', resize: 'none', }} /> </Box> </Box> <Divider sx={{ my: 3 }} /> {/* Exploration Agent Rules Section */} <Box> <Typography variant="body2" sx={{ mb: 1, fontWeight: 600, color: 'secondary.main' }}> Exploration Rules <Typography variant="body2" component="span" color="text.secondary" sx={{ ml: 1, fontSize: 12 }}> (Rules that guide AI agents when exploring datasets, generating questions, and discovering insights) </Typography> </Typography> <Box sx={{ border: `1px solid ${theme.palette.secondary.main}`, borderRadius: radius.sm, overflow: 'auto', height: 180, boxShadow: `0 2px 8px ${theme.palette.secondary.main}40`, transition: 'box-shadow 0.3s ease-in-out', '&:hover': { boxShadow: `0 4px 12px ${theme.palette.secondary.main}60`, } }} > <Editor value={explorationRules} onValueChange={(code) => setExplorationRules(code)} highlight={(code) => code} padding={16} placeholder={explorationPlaceholder} onKeyDown={(e) => { if (e.key === 'Tab' && !explorationRules) { e.preventDefault(); setExplorationRules(explorationPlaceholder); } }} style={{ fontFamily: 'Monaco, Menlo, "Ubuntu Mono", monospace', fontSize: 10, lineHeight: 1.2, minHeight: 180, whiteSpace: 'pre-wrap', outline: 'none', resize: 'none', }} /> </Box> <Box sx={{ mt: 2, display: 'flex', justifyContent: 'flex-end' }}> <Button variant="text" disabled={!hasCodingChanges} onClick={handleSaveCoding} sx={{ textTransform: 'none' }} > Save Coding Rules </Button> <Button variant="text" disabled={!hasExplorationChanges} onClick={handleSaveExploration} color="secondary" sx={{ textTransform: 'none' }} > Save Exploration Rules </Button> </Box> </Box> </DialogContent> </Dialog> </> ); }; ================================================ FILE: src/views/ChartGallery.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ChartGallery — Visual gallery for Vega-Lite chart assembly. * * All synthetic test-data generators and types live in * `src/lib/agents-chart/test-data/`. This file contains only the * React components that render the gallery UI. */ import React, { useCallback, useEffect, useRef, useState, useMemo } from 'react'; import { Box, Tabs, Tab, Typography, Paper, Chip, Button, } from '@mui/material'; import ContentCopyIcon from '@mui/icons-material/ContentCopy'; import embed from 'vega-embed'; import * as echarts from 'echarts'; import { Chart, registerables } from 'chart.js'; import { assembleVegaChart } from '../app/utils'; import { Channel, EncodingItem } from '../components/ComponentType'; import { channels } from '../components/ChartTemplates'; import { ChartWarning, ChartEncoding, ChartAssemblyInput, assembleVegaLite, assembleECharts, assembleChartjs, assembleGoFish, GoFishSpec } from '../lib/agents-chart'; import { TestCase, TEST_GENERATORS, GALLERY_SECTIONS } from '../lib/agents-chart/test-data'; // Register all Chart.js components Chart.register(...registerables); // ============================================================================ // Chart Rendering Component // ============================================================================ const VegaChart: React.FC<{ testCase: TestCase }> = React.memo(({ testCase }) => { const containerRef = useRef<HTMLDivElement>(null); const [error, setError] = useState<string | null>(null); const [specJson, setSpecJson] = useState<string>(''); const [warnings, setWarnings] = useState<ChartWarning[]>([]); const [specOptions, setSpecOptions] = useState<string>(''); const [inferredSize, setInferredSize] = useState<string>(''); useEffect(() => { if (!containerRef.current) return; try { // Build encoding map with all channels defaulting to empty const fullEncodingMap: Record<string, EncodingItem> = {}; for (const ch of channels) { fullEncodingMap[ch as string] = testCase.encodingMap[ch as Channel] || {}; } const vlSpec = assembleVegaChart( testCase.chartType, fullEncodingMap as any, testCase.fields, testCase.data, testCase.metadata, 400, // baseChartWidth 300, // baseChartHeight true, // addTooltips testCase.chartProperties, 1, // scaleFactor testCase.assembleOptions?.maxStretch, testCase.assembleOptions, testCase.semanticAnnotations, ); if (!vlSpec) { setError('assembleVegaChart returned no spec'); return; } // Extract warnings const specAny = vlSpec as any; setWarnings(specAny._warnings || []); setInferredSize(`${specAny._width ?? '?'} × ${specAny._height ?? '?'}`); // Build the exact input sent to assembleVegaLite() const encodings: Record<string, any> = {}; for (const [ch, ei] of Object.entries(testCase.encodingMap)) { if (ei && ei.fieldID) { const entry: Record<string, any> = { field: ei.fieldID }; if (ei.dtype) entry.type = ei.dtype; if (ei.aggregate) entry.aggregate = ei.aggregate; if (ei.sortOrder) entry.sortOrder = ei.sortOrder; if (ei.sortBy) entry.sortBy = ei.sortBy; if (ei.scheme) entry.scheme = ei.scheme; encodings[ch] = entry; } } const semanticTypes: Record<string, string | any> = {}; for (const [fieldName, meta] of Object.entries(testCase.metadata)) { if (meta.semanticType) { semanticTypes[fieldName] = meta.semanticType; } } // Override with enriched annotations when present (e.g., intrinsicDomain, unit) if (testCase.semanticAnnotations) { for (const [fieldName, annotation] of Object.entries(testCase.semanticAnnotations)) { semanticTypes[fieldName] = annotation; } } const assembleInput: Record<string, any> = { data: `[${testCase.data.length} rows]`, semantic_types: semanticTypes, chart_spec: { chartType: testCase.chartType, encodings, ...(testCase.chartProperties && Object.keys(testCase.chartProperties).length > 0 ? { chartProperties: testCase.chartProperties } : {}), }, ...(testCase.assembleOptions && Object.keys(testCase.assembleOptions).length > 0 ? { options: testCase.assembleOptions } : {}), }; // Custom serializer: compact each encoding entry to one line const compactJson = (obj: any): string => { const raw = JSON.stringify(obj, null, 2); // Replace multi-line encoding entries with single-line versions // Match patterns like: "x": {\n "field": ...\n } return raw.replace( /"(\w+)":\s*\{([^{}]+)\}/g, (match, key, body) => { // Only compact if inside encodings (body has "field") if (!body.includes('"field"')) return match; const compact = body.replace(/\s*\n\s*/g, ' ').trim(); return `"${key}": { ${compact} }`; } ); }; setSpecOptions(compactJson(assembleInput)); setSpecJson(JSON.stringify(vlSpec, null, 2)); const spec = { ...vlSpec as any, } as any; delete spec._warnings; // Don't set explicit width/height — let config.view.step handle // discrete axes (step × count) and config.view.continuousWidth/Height // handle continuous axes. Forcing width/height here overrides step sizing. embed(containerRef.current, spec, { actions: { export: true, source: true, compiled: true, editor: true }, renderer: 'svg', }).catch(err => { setError(`Vega embed error: ${err.message}`); }); } catch (err: any) { setError(`Assembly error: ${err.message}`); } }, [testCase]); return ( <Paper elevation={1} sx={{ p: 2, mb: 2, width: 'fit-content', border: error ? '2px solid #f44336' : '1px solid #e0e0e0', }} > {/* Text block: width:0 + minWidth:100% prevents text from expanding the card beyond the chart width */} <Box sx={{ width: 0, minWidth: '100%', overflow: 'hidden' }}> <Typography variant="subtitle2" fontWeight={600} gutterBottom> {testCase.title} </Typography> <Typography variant="caption" color="text.secondary" display="block" mb={1} sx={{ wordBreak: 'break-word' }}> {testCase.description} </Typography> <Box sx={{ display: 'flex', gap: 0.5, flexWrap: 'wrap', mb: 1 }}> {testCase.tags.map(tag => ( <Chip key={tag} label={tag} size="small" variant="outlined" sx={{ fontSize: 10, height: 20 }} /> ))} </Box> {inferredSize && ( <Typography variant="caption" color="text.secondary" sx={{ display: 'block', mb: 0.5, fontSize: 10 }}> Inferred size: {inferredSize} </Typography> )} </Box> {error ? ( <Typography color="error" variant="body2" sx={{ whiteSpace: 'pre-wrap', fontSize: 11 }}> {error} </Typography> ) : ( <Box ref={containerRef} sx={{ minHeight: 200 }} /> )} {warnings.length > 0 && ( <Box sx={{ mt: 1, p: 1, bgcolor: '#fff3e0', borderLeft: '3px solid #ff9800', width: 0, minWidth: '100%' }}> <Typography variant="body2" color="warning.dark" sx={{ fontSize: 11, fontWeight: 600, mb: 0.5 }}> Warning: </Typography> {warnings.map((w, i) => ( <Typography key={i} variant="body2" color="warning.dark" sx={{ fontSize: 11 }}> {w.message} </Typography> ))} </Box> )} {/* Debug copy button — only for debug-tagged tests */} {testCase.tags.includes('debug') && ( <Box sx={{ mt: 1 }}> <Button size="small" variant="outlined" startIcon={<ContentCopyIcon sx={{ fontSize: 12 }} />} sx={{ fontSize: 10, textTransform: 'none', py: 0.25, px: 1 }} onClick={() => { const parts: string[] = []; if (specOptions) { parts.push('## agents-chart input spec\n' + specOptions); } if (specJson) { const vlLines = specJson.split('\n').slice(0, 50).join('\n'); parts.push('## vega-lite output spec (first 50 lines)\n' + vlLines); } navigator.clipboard.writeText(parts.join('\n\n')); }} > Copy Spec + VL </Button> </Box> )} {specOptions && ( <details style={{ marginTop: 8, width: 0, minWidth: '100%', overflow: 'hidden' }}> <summary style={{ cursor: 'pointer', fontSize: 11, color: '#888' }}> Spec </summary> <pre style={{ fontSize: 10, maxHeight: 200, overflow: 'auto', background: '#f0f4ff', padding: 8, borderRadius: 4 }}> {specOptions} </pre> </details> )} {/* {specJson && ( <details style={{ marginTop: 4 }}> <summary style={{ cursor: 'pointer', fontSize: 11, color: '#888' }}> Vega-Lite Spec </summary> <pre style={{ fontSize: 10, maxHeight: 300, overflow: 'auto', background: '#f5f5f5', padding: 8, borderRadius: 4 }}> {specJson} </pre> </details> )} */} </Paper> ); }); // ============================================================================ // ECharts Rendering Component // ============================================================================ /** * Build the shared input spec (same for VL and EC) for display. * Returns a JSON-serializable object and a compact string. */ function buildSharedInputSpec(testCase: TestCase): { obj: Record<string, unknown>; compact: string } { const encodings: Record<string, unknown> = {}; for (const [ch, ei] of Object.entries(testCase.encodingMap)) { if (ei && (ei as any).fieldID) { const entry: Record<string, unknown> = { field: (ei as any).fieldID }; if ((ei as any).dtype) entry.type = (ei as any).dtype; if ((ei as any).aggregate) entry.aggregate = (ei as any).aggregate; if ((ei as any).sortOrder) entry.sortOrder = (ei as any).sortOrder; if ((ei as any).sortBy) entry.sortBy = (ei as any).sortBy; if ((ei as any).scheme) entry.scheme = (ei as any).scheme; encodings[ch] = entry; } } const semanticTypes: Record<string, string> = {}; for (const [fieldName, meta] of Object.entries(testCase.metadata)) { if (meta.semanticType) semanticTypes[fieldName] = meta.semanticType; } const obj: Record<string, unknown> = { data: `[${testCase.data.length} rows]`, semantic_types: semanticTypes, chart_spec: { chartType: testCase.chartType, encodings, ...(testCase.chartProperties && Object.keys(testCase.chartProperties).length > 0 ? { chartProperties: testCase.chartProperties } : {}), }, ...(testCase.assembleOptions && Object.keys(testCase.assembleOptions || {}).length > 0 ? { options: testCase.assembleOptions } : {}), }; const raw = JSON.stringify(obj, null, 2); const compact = raw.replace( /"(\w+)":\s*\{([^{}]+)\}/g, (match, key, body) => { if (!body.includes('"field"')) return match; const single = body.replace(/\s*\n\s*/g, ' ').trim(); return `"${key}": { ${single} }`; }, ); return { obj, compact }; } /** * Convert a TestCase into a ChartAssemblyInput for assembleECharts. */ function testCaseToEChartsInput(testCase: TestCase, canvasSize: { width: number; height: number }): ChartAssemblyInput { const encodings: Record<string, ChartEncoding> = {}; for (const [channel, encoding] of Object.entries(testCase.encodingMap)) { if (encoding && encoding.fieldID) { encodings[channel] = { field: encoding.fieldID, type: encoding.dtype, aggregate: encoding.aggregate, sortOrder: encoding.sortOrder, sortBy: encoding.sortBy, scheme: encoding.scheme, }; } } const semanticTypes: Record<string, string> = {}; for (const [fieldName, meta] of Object.entries(testCase.metadata)) { if (meta.semanticType) { semanticTypes[fieldName] = meta.semanticType; } } return { data: { values: testCase.data }, semantic_types: semanticTypes, chart_spec: { chartType: testCase.chartType, encodings, canvasSize, chartProperties: testCase.chartProperties, }, options: testCase.assembleOptions, }; } /** Stable default so useEffect dependencies don't change on every render. */ const DEFAULT_CANVAS_SIZE = { width: 400, height: 300 } as const; const EChartsChart: React.FC<{ testCase: TestCase; canvasSize?: { width: number; height: number } }> = React.memo(({ testCase, canvasSize = DEFAULT_CANVAS_SIZE }) => { const containerRef = useRef<HTMLDivElement>(null); const chartRef = useRef<echarts.ECharts | null>(null); const [error, setError] = useState<string | null>(null); const [warnings, setWarnings] = useState<ChartWarning[]>([]); const [specJson, setSpecJson] = useState<string>(''); const [inferredSize, setInferredSize] = useState<string>(''); useEffect(() => { if (!containerRef.current) return; try { const ecOption = assembleECharts(testCaseToEChartsInput(testCase, canvasSize)); if (!ecOption) { setError('assembleECharts returned no option'); return; } // Extract warnings setWarnings(ecOption._warnings || []); setInferredSize(`${ecOption._width ?? '?'} × ${ecOption._height ?? '?'}`); // Build displayable JSON (strip internal props and data for readability) const displayOption = { ...ecOption }; delete displayOption._warnings; delete displayOption._dataLength; delete displayOption._width; delete displayOption._height; delete displayOption._legendWidth; setSpecJson(JSON.stringify(displayOption, null, 2)); // Initialize or reuse ECharts instance if (chartRef.current) { chartRef.current.dispose(); } const chart = echarts.init(containerRef.current, undefined, { width: ecOption._width || 400, height: ecOption._height || 300, }); chartRef.current = chart; // Clean the option for ECharts (remove internal props) const cleanOption = { ...ecOption }; delete cleanOption._warnings; delete cleanOption._dataLength; delete cleanOption._width; delete cleanOption._height; delete cleanOption._legendWidth; chart.setOption(cleanOption); setError(null); } catch (err: any) { setError(`ECharts error: ${err.message}`); } return () => { if (chartRef.current) { chartRef.current.dispose(); chartRef.current = null; } }; }, [testCase, canvasSize]); return ( <Box sx={{ width: 'fit-content' }}> <Typography variant="caption" fontWeight={600} color="#e65100" sx={{ display: 'block', mb: 0.5, fontSize: 11, letterSpacing: 0.5 }}> ECharts </Typography> {inferredSize && ( <Typography variant="caption" color="text.secondary" sx={{ display: 'block', mb: 0.5, fontSize: 10 }}> Inferred size: {inferredSize} </Typography> )} {error ? ( <Typography color="error" variant="body2" sx={{ whiteSpace: 'pre-wrap', fontSize: 11 }}> {error} </Typography> ) : ( <Box ref={containerRef} sx={{ minHeight: 200 }} /> )} {warnings.length > 0 && ( <Box sx={{ mt: 1, p: 1, bgcolor: '#fff3e0', borderLeft: '3px solid #ff9800' }}> {warnings.map((w, i) => ( <Typography key={i} variant="body2" color="warning.dark" sx={{ fontSize: 11 }}> {w.message} </Typography> ))} </Box> )} {specJson && ( <details style={{ marginTop: 8 }}> <summary style={{ cursor: 'pointer', fontSize: 11, color: '#888' }}> ECharts Option </summary> <pre style={{ fontSize: 10, maxHeight: 200, overflow: 'auto', background: '#fff3e0', padding: 8, borderRadius: 4 }}> {specJson} </pre> </details> )} </Box> ); }); // ============================================================================ // Dual Render: VL + ECharts side-by-side // ============================================================================ const DualChart: React.FC<{ testCase: TestCase }> = React.memo(({ testCase }) => { const sharedSpec = useMemo(() => buildSharedInputSpec(testCase), [testCase]); return ( <Paper elevation={1} sx={{ p: 2, mb: 2, maxWidth: 800, border: '1px solid #e0e0e0', }} > <Typography variant="subtitle2" fontWeight={600} gutterBottom> {testCase.title} </Typography> <Typography variant="caption" color="text.secondary" display="block" mb={1} sx={{ wordBreak: 'break-word' }}> {testCase.description} </Typography> <Box sx={{ display: 'flex', gap: 0.5, flexWrap: 'wrap', mb: 1 }}> {testCase.tags.map(tag => ( <Chip key={tag} label={tag} size="small" variant="outlined" sx={{ fontSize: 10, height: 20 }} /> ))} </Box> <details style={{ marginBottom: 12 }}> <summary style={{ cursor: 'pointer', fontSize: 11, color: '#666', fontWeight: 600 }}> Spec </summary> <pre style={{ fontSize: 10, maxHeight: 220, overflow: 'auto', background: '#f5f5f5', padding: 8, borderRadius: 4, marginTop: 4 }}> {sharedSpec.compact} </pre> </details> <Box sx={{ display: 'flex', gap: 3, flexWrap: 'wrap' }}> {/* Vega-Lite side */} <VegaChartInline testCase={testCase} /> {/* ECharts side */} <EChartsChart testCase={testCase} /> </Box> </Paper> ); }); /** * Inline VL chart (no Paper wrapper — used inside DualChart). */ const VegaChartInline: React.FC<{ testCase: TestCase; canvasSize?: { width: number; height: number } }> = React.memo(({ testCase, canvasSize = DEFAULT_CANVAS_SIZE }) => { const containerRef = useRef<HTMLDivElement>(null); const [error, setError] = useState<string | null>(null); const [specJson, setSpecJson] = useState<string>(''); const [inferredSize, setInferredSize] = useState<string>(''); useEffect(() => { if (!containerRef.current) return; try { const fullEncodingMap: Record<string, EncodingItem> = {}; for (const ch of channels) { fullEncodingMap[ch as string] = testCase.encodingMap[ch as Channel] || {}; } const vlSpec = assembleVegaChart( testCase.chartType, fullEncodingMap as any, testCase.fields, testCase.data, testCase.metadata, canvasSize.width, canvasSize.height, true, testCase.chartProperties, 1, testCase.assembleOptions?.maxStretch, testCase.assembleOptions, ); if (!vlSpec) { setError('No VL spec'); return; } const specAny = vlSpec as any; setInferredSize(`${specAny._width ?? '?'} × ${specAny._height ?? '?'}`); const displaySpec = { ...specAny }; delete displaySpec._warnings; delete displaySpec._width; delete displaySpec._height; setSpecJson(JSON.stringify(displaySpec, null, 2)); const spec = { ...specAny }; delete spec._warnings; delete spec._width; delete spec._height; embed(containerRef.current, spec, { actions: { export: true, source: true, compiled: true, editor: true }, renderer: 'svg', }).catch(err => setError(`VL embed error: ${err.message}`)); } catch (err: any) { setError(`VL error: ${err.message}`); } }, [testCase, canvasSize]); return ( <Box sx={{ width: 'fit-content' }}> <Typography variant="caption" fontWeight={600} color="#1565c0" sx={{ display: 'block', mb: 0.5, fontSize: 11, letterSpacing: 0.5 }}> Vega-Lite </Typography> {inferredSize && ( <Typography variant="caption" color="text.secondary" sx={{ display: 'block', mb: 0.5, fontSize: 10 }}> Inferred size: {inferredSize} </Typography> )} {error ? ( <Typography color="error" variant="body2" sx={{ whiteSpace: 'pre-wrap', fontSize: 11 }}> {error} </Typography> ) : ( <Box ref={containerRef} sx={{ minHeight: 200 }} /> )} {specJson && ( <details style={{ marginTop: 8 }}> <summary style={{ cursor: 'pointer', fontSize: 11, color: '#888' }}> Vega-Lite Spec </summary> <pre style={{ fontSize: 10, maxHeight: 200, overflow: 'auto', background: '#f0f4ff', padding: 8, borderRadius: 4 }}> {specJson} </pre> </details> )} </Box> ); }); // ============================================================================ // Chart.js Rendering Component // ============================================================================ /** * Convert a TestCase into a ChartAssemblyInput for assembleChartjs. * (Same conversion as testCaseToEChartsInput — shared data model.) */ function testCaseToChartJsInput(testCase: TestCase, canvasSize: { width: number; height: number }): ChartAssemblyInput { const encodings: Record<string, ChartEncoding> = {}; for (const [channel, encoding] of Object.entries(testCase.encodingMap)) { if (encoding && encoding.fieldID) { encodings[channel] = { field: encoding.fieldID, type: encoding.dtype, aggregate: encoding.aggregate, sortOrder: encoding.sortOrder, sortBy: encoding.sortBy, scheme: encoding.scheme, }; } } const semanticTypes: Record<string, string> = {}; for (const [fieldName, meta] of Object.entries(testCase.metadata)) { if (meta.semanticType) { semanticTypes[fieldName] = meta.semanticType; } } return { data: { values: testCase.data }, semantic_types: semanticTypes, chart_spec: { chartType: testCase.chartType, encodings, canvasSize, chartProperties: testCase.chartProperties, }, options: testCase.assembleOptions, }; } const ChartJsChart: React.FC<{ testCase: TestCase; canvasSize?: { width: number; height: number } }> = React.memo(({ testCase, canvasSize = DEFAULT_CANVAS_SIZE }) => { const canvasRef = useRef<HTMLCanvasElement>(null); const chartRef = useRef<Chart | null>(null); const [error, setError] = useState<string | null>(null); const [warnings, setWarnings] = useState<ChartWarning[]>([]); const [specJson, setSpecJson] = useState<string>(''); const [inferredSize, setInferredSize] = useState<string>(''); useEffect(() => { if (!canvasRef.current) return; try { const cjsConfig = assembleChartjs(testCaseToChartJsInput(testCase, canvasSize)); if (!cjsConfig) { setError('assembleChartjs returned no config'); return; } // Extract warnings setWarnings(cjsConfig._warnings || []); setInferredSize(`${cjsConfig._width ?? '?'} × ${cjsConfig._height ?? '?'}`); // Build displayable JSON const displayConfig = { ...cjsConfig }; delete displayConfig._warnings; delete displayConfig._dataLength; delete displayConfig._width; delete displayConfig._height; setSpecJson(JSON.stringify(displayConfig, null, 2)); // Set container size — Chart.js responsive mode will fill it const w = cjsConfig._width || 400; const h = cjsConfig._height || 300; const container = canvasRef.current.parentElement; if (container) { container.style.width = `${w}px`; container.style.height = `${h}px`; container.style.position = 'relative'; } // Destroy previous chart if (chartRef.current) { chartRef.current.destroy(); chartRef.current = null; } // Clean config for Chart.js const cleanConfig = { ...cjsConfig }; delete cleanConfig._warnings; delete cleanConfig._dataLength; delete cleanConfig._width; delete cleanConfig._height; // Ensure animation is disabled for gallery rendering if (!cleanConfig.options) cleanConfig.options = {}; cleanConfig.options.animation = false; cleanConfig.options.responsive = true; cleanConfig.options.maintainAspectRatio = false; chartRef.current = new Chart(canvasRef.current, cleanConfig); setError(null); } catch (err: any) { setError(`Chart.js error: ${err.message}`); } return () => { if (chartRef.current) { chartRef.current.destroy(); chartRef.current = null; } }; }, [testCase, canvasSize]); return ( <Box sx={{ width: 'fit-content' }}> <Typography variant="caption" fontWeight={600} color="#2e7d32" sx={{ display: 'block', mb: 0.5, fontSize: 11, letterSpacing: 0.5 }}> Chart.js </Typography> {inferredSize && ( <Typography variant="caption" color="text.secondary" sx={{ display: 'block', mb: 0.5, fontSize: 10 }}> Inferred size: {inferredSize} </Typography> )} {error ? ( <Typography color="error" variant="body2" sx={{ whiteSpace: 'pre-wrap', fontSize: 11 }}> {error} </Typography> ) : ( <div style={{ position: 'relative' }}> <canvas ref={canvasRef} /> </div> )} {warnings.length > 0 && ( <Box sx={{ mt: 1, p: 1, bgcolor: '#fff3e0', borderLeft: '3px solid #ff9800' }}> {warnings.map((w, i) => ( <Typography key={i} variant="body2" color="warning.dark" sx={{ fontSize: 11 }}> {w.message} </Typography> ))} </Box> )} {specJson && ( <details style={{ marginTop: 8 }}> <summary style={{ cursor: 'pointer', fontSize: 11, color: '#888' }}> Chart.js Config </summary> <pre style={{ fontSize: 10, maxHeight: 200, overflow: 'auto', background: '#e8f5e9', padding: 8, borderRadius: 4 }}> {specJson} </pre> </details> )} </Box> ); }); // ============================================================================ // Triple Render: VL + ECharts + Chart.js side-by-side // ============================================================================ const TripleChart: React.FC<{ testCase: TestCase }> = React.memo(({ testCase }) => { return ( <Paper elevation={1} sx={{ p: 2, mb: 2, maxWidth: 900, border: '1px solid #e0e0e0', }} > <Typography variant="subtitle2" fontWeight={600} gutterBottom> {testCase.title} </Typography> <Typography variant="caption" color="text.secondary" display="block" mb={1} sx={{ wordBreak: 'break-word' }}> {testCase.description} </Typography> <Box sx={{ display: 'flex', gap: 0.5, flexWrap: 'wrap', mb: 1 }}> {testCase.tags.map(tag => ( <Chip key={tag} label={tag} size="small" variant="outlined" sx={{ fontSize: 10, height: 20 }} /> ))} </Box> <Box sx={{ display: 'flex', gap: 3, flexWrap: 'wrap' }}> <VegaChartInline testCase={testCase} canvasSize={{ width: 240, height: 200 }} /> <EChartsChart testCase={testCase} canvasSize={{ width: 240, height: 200 }} /> <ChartJsChart testCase={testCase} canvasSize={{ width: 240, height: 200 }} /> </Box> </Paper> ); }); // ============================================================================ // GoFish Rendering Component // ============================================================================ /** * Convert a TestCase into a ChartAssemblyInput for assembleGoFish. * (Same conversion as other backends — shared data model.) */ function testCaseToGoFishInput(testCase: TestCase, canvasSize: { width: number; height: number }): ChartAssemblyInput { const encodings: Record<string, ChartEncoding> = {}; for (const [channel, encoding] of Object.entries(testCase.encodingMap)) { if (encoding && encoding.fieldID) { encodings[channel] = { field: encoding.fieldID, type: encoding.dtype, aggregate: encoding.aggregate, sortOrder: encoding.sortOrder, sortBy: encoding.sortBy, scheme: encoding.scheme, }; } } const semanticTypes: Record<string, string> = {}; for (const [fieldName, meta] of Object.entries(testCase.metadata)) { if (meta.semanticType) { semanticTypes[fieldName] = meta.semanticType; } } return { data: { values: testCase.data }, semantic_types: semanticTypes, chart_spec: { chartType: testCase.chartType, encodings, canvasSize, chartProperties: testCase.chartProperties, }, options: testCase.assembleOptions, }; } const GoFishChart: React.FC<{ testCase: TestCase; canvasSize?: { width: number; height: number } }> = React.memo(({ testCase, canvasSize = DEFAULT_CANVAS_SIZE }) => { const containerRef = useRef<HTMLDivElement>(null); const [error, setError] = useState<string | null>(null); const [warnings, setWarnings] = useState<ChartWarning[]>([]); const [specDescription, setSpecDescription] = useState<string>(''); const [inferredSize, setInferredSize] = useState<string>(''); useEffect(() => { if (!containerRef.current) return; try { const gfSpec = assembleGoFish(testCaseToGoFishInput(testCase, canvasSize)); if (!gfSpec) { setError('assembleGoFish returned no spec'); return; } // Extract warnings setWarnings(gfSpec._warnings || []); setInferredSize(`${gfSpec._width ?? '?'} × ${gfSpec._height ?? '?'}`); setSpecDescription(gfSpec._specDescription || ''); // Set container size if (containerRef.current) { containerRef.current.style.width = `${gfSpec._width || 400}px`; containerRef.current.style.height = `${gfSpec._height || 300}px`; containerRef.current.innerHTML = ''; } // Render GoFish chart into the container gfSpec.render(containerRef.current); setError(null); } catch (err: any) { setError(`GoFish error: ${err.message}`); } }, [testCase, canvasSize]); return ( <Box sx={{ width: 'fit-content' }}> <Typography variant="caption" fontWeight={600} color="#6a1b9a" sx={{ display: 'block', mb: 0.5, fontSize: 11, letterSpacing: 0.5 }}> GoFish </Typography> {inferredSize && ( <Typography variant="caption" color="text.secondary" sx={{ display: 'block', mb: 0.5, fontSize: 10 }}> Inferred size: {inferredSize} </Typography> )} {error ? ( <Typography color="error" variant="body2" sx={{ whiteSpace: 'pre-wrap', fontSize: 11 }}> {error} </Typography> ) : ( <Box ref={containerRef} sx={{ minHeight: 200 }} /> )} {warnings.length > 0 && ( <Box sx={{ mt: 1, p: 1, bgcolor: '#f3e5f5', borderLeft: '3px solid #9c27b0' }}> {warnings.map((w, i) => ( <Typography key={i} variant="body2" color="secondary.dark" sx={{ fontSize: 11 }}> {w.message} </Typography> ))} </Box> )} {specDescription && ( <details style={{ marginTop: 8 }}> <summary style={{ cursor: 'pointer', fontSize: 11, color: '#888' }}> GoFish Spec </summary> <pre style={{ fontSize: 10, maxHeight: 200, overflow: 'auto', background: '#f3e5f5', padding: 8, borderRadius: 4 }}> {specDescription} </pre> </details> )} </Box> ); }); // ============================================================================ // Quad Render: VL + GoFish side-by-side (for GoFish backend tests) // ============================================================================ const QuadChart: React.FC<{ testCase: TestCase }> = React.memo(({ testCase }) => { return ( <Paper elevation={1} sx={{ p: 2, mb: 2, maxWidth: 800, border: '1px solid #e0e0e0', }} > <Typography variant="subtitle2" fontWeight={600} gutterBottom> {testCase.title} </Typography> <Typography variant="caption" color="text.secondary" display="block" mb={1} sx={{ wordBreak: 'break-word' }}> {testCase.description} </Typography> <Box sx={{ display: 'flex', gap: 0.5, flexWrap: 'wrap', mb: 1 }}> {testCase.tags.map(tag => ( <Chip key={tag} label={tag} size="small" variant="outlined" sx={{ fontSize: 10, height: 20 }} /> ))} </Box> <Box sx={{ display: 'flex', gap: 3, flexWrap: 'wrap' }}> <VegaChartInline testCase={testCase} canvasSize={{ width: 300, height: 250 }} /> <GoFishChart testCase={testCase} canvasSize={{ width: 300, height: 250 }} /> </Box> </Paper> ); }); // ============================================================================ // Sub-page for a single chart type // ============================================================================ const ChartTypeTestPanel: React.FC<{ chartGroup: string; sectionLabel?: string }> = ({ chartGroup, sectionLabel }) => { const tests = useMemo(() => { const gen = TEST_GENERATORS[chartGroup]; return gen ? gen() : []; }, [chartGroup]); const isChartJsGroup = chartGroup.startsWith('Chart.js:'); const isGoFishGroup = chartGroup.startsWith('GoFish'); const isEChartsSection = sectionLabel === 'ECharts Backend'; if (tests.length === 0) { return ( <Box sx={{ p: 4, textAlign: 'center' }}> <Typography color="text.secondary">No test cases defined for "{chartGroup}"</Typography> </Box> ); } return ( <Box sx={{ p: 2, display: 'flex', flexWrap: 'wrap', gap: 2, justifyContent: 'flex-start' }}> {tests.map((tc, i) => isGoFishGroup ? <QuadChart key={`${chartGroup}-${i}`} testCase={tc} /> : isChartJsGroup ? <TripleChart key={`${chartGroup}-${i}`} testCase={tc} /> : isEChartsSection ? <DualChart key={`${chartGroup}-${i}`} testCase={tc} /> : <VegaChart key={`${chartGroup}-${i}`} testCase={tc} /> )} </Box> ); }; // ============================================================================ // Main Page // ============================================================================ const ChartGallery: React.FC = () => { const [activeSection, setActiveSection] = useState(0); const [activeCategory, setActiveCategory] = useState(0); const section = GALLERY_SECTIONS[activeSection]; const activeCategoryName = section?.entries[activeCategory] ?? ''; return ( <Box sx={{ display: 'flex', flexDirection: 'column', height: '100vh', overflow: 'hidden' }}> {/* Section tabs (top level) */} <Box sx={{ borderBottom: 1, borderColor: 'divider', px: 2 }}> <Tabs value={activeSection} onChange={(_, v) => { setActiveSection(v); setActiveCategory(0); }} sx={{ minHeight: 36, '& .MuiTab-root': { minHeight: 36, py: 0.5, textTransform: 'none', fontWeight: 600, fontSize: 14 }, }} > {GALLERY_SECTIONS.map((s, i) => ( <Tab key={s.label} label={s.label} value={i} /> ))} </Tabs> </Box> {/* Category chips within the active section */} <Box sx={{ px: 2, py: 1, display: 'flex', gap: 0.5, flexWrap: 'wrap', bgcolor: '#f5f5f5', borderBottom: 1, borderColor: 'divider' }}> <Typography variant="caption" color="text.secondary" sx={{ mr: 1, lineHeight: '28px' }}> {section.description}: </Typography> {section.entries.map((entry, ei) => ( <Chip key={entry} label={entry} size="small" onClick={() => setActiveCategory(ei)} variant={ei === activeCategory ? 'filled' : 'outlined'} color={ei === activeCategory ? 'primary' : 'default'} sx={{ fontSize: 12, height: 26, cursor: 'pointer' }} /> ))} </Box> {/* Chart content */} <Box sx={{ flex: 1, overflow: 'auto', bgcolor: '#fafafa' }}> <ChartTypeTestPanel chartGroup={activeCategoryName} sectionLabel={section?.label} /> </Box> </Box> ); }; export default ChartGallery; ================================================ FILE: src/views/ChartRecBox.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { FC, useEffect, useState, useRef } from 'react' import { transition } from '../app/tokens'; import { useSelector, useDispatch } from 'react-redux' import { DataFormulatorState, dfActions, generateFreshChart } from '../app/dfSlice'; import { AppDispatch } from '../app/store'; import { Box, Typography, IconButton, Tooltip, TextField, Card, SxProps, LinearProgress, CircularProgress, alpha, useTheme, Theme, } from '@mui/material'; import React from 'react'; import { Chart } from "../components/ComponentType"; import '../scss/EncodingShelf.scss'; import { resolveRecommendedChart } from '../app/utils'; import { useFormulateData } from '../app/useFormulateData'; import { AgentIcon as PrecisionManufacturing } from '../icons'; import TipsAndUpdatesIcon from '@mui/icons-material/TipsAndUpdates'; import { renderTextWithEmphasis } from './EncodingShelfCard'; import { ThinkingBufferEffect } from '../components/FunComponents'; // when this is set to true, the new chart will be focused automatically const AUTO_FOCUS_NEW_CHART = false; export interface ChartRecBoxProps { tableId: string; placeHolderChartId?: string; sx?: SxProps; } export const IdeaChip: FC<{ mini?: boolean, idea: {text?: string, questions?: string[], goal: string, difficulty: 'easy' | 'medium' | 'hard', type?: 'branch' | 'deep_dive'} theme: Theme, onClick: () => void, sx?: SxProps, disabled?: boolean, }> = function ({mini, idea, theme, onClick, sx, disabled}) { const getDifficultyColor = (difficulty: 'easy' | 'medium' | 'hard') => { switch (difficulty) { case 'easy': return theme.palette.success.main; case 'medium': return theme.palette.primary.main; case 'hard': return theme.palette.warning.main; default: return theme.palette.text.secondary; } }; let styleColor = getDifficultyColor(idea.difficulty || 'medium'); let ideaText = idea.goal; let ideaTextComponent = renderTextWithEmphasis(ideaText, { borderRadius: '0px', borderBottom: `1px solid`, borderColor: alpha(styleColor, 0.4), fontSize: '11px', lineHeight: 1.4, backgroundColor: alpha(styleColor, 0.05), }); return ( <Box sx={{ display: 'inline-flex', alignItems: 'center', padding: '4px 6px', fontSize: '11px', minHeight: '24px', height: 'auto', borderRadius: 2, border: `1px solid ${alpha(styleColor, 0.2)}`, transition: transition.fast, backgroundColor: alpha(theme.palette.background.paper, 0.9), cursor: disabled ? 'default' : 'pointer', opacity: disabled ? 0.6 : 1, '&:hover': disabled ? 'none' : { borderColor: alpha(styleColor, 0.7), transform: 'translateY(-1px)', }, ...sx }} onClick={disabled ? undefined : onClick} > <Typography component="div" sx={{ fontSize: '11px', color: getDifficultyColor(idea.difficulty || 'medium') }}> {ideaTextComponent} </Typography> </Box> ); }; export const ChartRecBox: FC<ChartRecBoxProps> = function ({ tableId, placeHolderChartId, sx }) { const dispatch = useDispatch<AppDispatch>(); const theme = useTheme(); // reference to states const tables = useSelector((state: DataFormulatorState) => state.tables); const { streamIdeas, formulateData } = useFormulateData(); const focusNextChartRef = useRef<boolean>(true); const modeColor = theme.palette.secondary.main; const [prompt, setPrompt] = useState<string>(""); const [isFormulating, setIsFormulating] = useState<boolean>(false); const [ideas, setIdeas] = useState<{text: string, goal: string, difficulty: 'easy' | 'medium' | 'hard'}[]>([]); const [thinkingBuffer, setThinkingBuffer] = useState<string>(""); let thinkingBufferEffect = <ThinkingBufferEffect text={thinkingBuffer.slice(-60)} sx={{ width: '46%' }} />; // Add state for loading ideas const [isLoadingIdeas, setIsLoadingIdeas] = useState<boolean>(false); // Use the provided tableId and find additional available tables for multi-table operations const currentTable = tables.find(t => t.id === tableId); // All root/anchored tables, with current source tables ordered first for context priority const rootTables = tables.filter(t => t.derive === undefined || t.anchored); const priorityIds = (currentTable?.derive && !currentTable.anchored) ? currentTable.derive.source : [tableId]; let selectedTableIds = [ ...priorityIds.filter(id => rootTables.some(t => t.id === id)), ...rootTables.map(t => t.id).filter(id => !priorityIds.includes(id)) ]; // Function to get a question from the list with cycling const getQuestion = (): string => { return "show something interesting about the data"; }; // Function to get ideas from the interactive explore agent const getIdeasFromAgent = async (startQuestion?: string) => { if (!currentTable || isLoadingIdeas) return; await streamIdeas({ actionTableIds: selectedTableIds, currentTable, onIdeas: setIdeas, onThinkingBuffer: setThinkingBuffer, onLoadingChange: setIsLoadingIdeas, startQuestion, }); setThinkingBuffer(""); }; useEffect(() => { setIdeas([]); }, [tableId]); // Handle tab key press for auto-completion const handleKeyDown = (event: React.KeyboardEvent) => { if (event.key === 'Tab' && !event.shiftKey) { event.preventDefault(); if (prompt.trim() === "") { setPrompt(getQuestion()); } } else if (event.key === 'Enter' && prompt.trim() !== "") { event.preventDefault(); focusNextChartRef.current = true; deriveDataFromNL(prompt.trim()); } }; const deriveDataFromNL = (instruction: string) => { if (selectedTableIds.length === 0 || instruction.trim() === "") { return; } if (placeHolderChartId) { dispatch(dfActions.changeChartRunningStatus({chartId: placeHolderChartId, status: true})); } const actionId = `deriveDataFromNL_${String(Date.now())}`; dispatch(dfActions.updateAgentWorkInProgress({actionId: actionId, originTableId: tableId, description: instruction, status: 'running', hidden: false, message: { content: instruction, role: 'user', observeTableId: tableId }})); // Validate table selection const firstTableId = selectedTableIds[0]; if (!firstTableId) { dispatch(dfActions.addMessages({ "timestamp": Date.now(), "type": "error", "component": "chart builder", "value": "No table selected for data formulation.", })); return; } let refChart = generateFreshChart(tableId, 'Auto') as Chart; refChart.source = 'trigger'; formulateData({ instruction, mode: 'formulate', actionTableIds: selectedTableIds, currentTable: currentTable!, triggerChart: refChart, createChart: ({ candidateTable, refinedGoal, currentConcepts }) => { let newChart = resolveRecommendedChart(refinedGoal, currentConcepts, candidateTable); dispatch(dfActions.addChart(newChart)); if (focusNextChartRef.current || AUTO_FOCUS_NEW_CHART) { focusNextChartRef.current = false; dispatch(dfActions.setFocused({ type: 'chart', chartId: newChart.id })); } return newChart.id; }, onStarted: () => { setIsFormulating(true); }, onSuccess: ({ displayInstruction, candidateTable }) => { dispatch(dfActions.addMessages({ "timestamp": Date.now(), "component": "chart builder", "type": "success", "value": `Data formulation: "${displayInstruction}"` })); dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: displayInstruction || instruction, status: 'completed', hidden: false, message: { content: displayInstruction || instruction, role: 'action', resultTableId: candidateTable.id } })); setPrompt(""); }, onError: () => { dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: instruction, status: 'failed', hidden: false, message: { content: 'Data formulation failed.', role: 'error' } })); }, onFinally: () => { setIsFormulating(false); if (placeHolderChartId) { dispatch(dfActions.changeChartRunningStatus({chartId: placeHolderChartId, status: false})); } }, }); }; return ( <Box sx={{ maxWidth: "600px", display: 'flex', flexDirection: 'column', ...sx }}> <Box sx={{ display: 'flex', flexDirection: 'row', alignItems: 'center', gap: 1 }}> <Card variant='outlined' sx={{ flex: 1, px: 1, pt: 0.5, pb: 0.25, display: 'flex', flexDirection: 'column', gap: 0.5, position: 'relative', borderWidth: 1.5, borderColor: alpha(theme.palette.text.primary, 0.15), borderRadius: '8px', overflow: 'visible', transition: transition.fast, '&:hover': { borderColor: alpha(theme.palette.primary.main, 0.7), }, '&:focus-within': { borderColor: alpha(theme.palette.primary.main, 0.8), }, }}> {isFormulating && ( <LinearProgress sx={{ position: 'absolute', bottom: 0, left: 0, right: 0, zIndex: 1000, height: '2px', borderRadius: '0 0 8px 8px', backgroundColor: alpha(modeColor, 0.15), '& .MuiLinearProgress-bar': { backgroundColor: modeColor } }} /> )} <Box sx={{ display: 'flex', flexDirection: 'row', gap: 0.5, alignItems: 'flex-end' }}> <TextField variant="standard" sx={{ flex: 1, "& .MuiInput-input": { fontSize: '14px', lineHeight: 1.5 }, "& .MuiInput-underline:before": { borderBottom: 'none' }, "& .MuiInput-underline:hover:not(.Mui-disabled):before": { borderBottom: 'none' }, "& .MuiInput-underline:not(.Mui-disabled):before": { borderBottom: 'none' }, "& .MuiInput-underline.Mui-disabled:before": { borderBottom: 'none' }, "& .MuiInput-underline.Mui-disabled:after": { borderBottom: 'none' }, "& .MuiInput-underline:after": { borderBottom: 'none' }, }} disabled={isFormulating || isLoadingIdeas} onChange={(event) => setPrompt(event.target.value)} onKeyDown={handleKeyDown} slotProps={{ inputLabel: { shrink: true }, input: { endAdornment: <Box sx={{ display: 'flex', alignItems: 'center', gap: 0.25, flexShrink: 0 }}> <Tooltip title="Generate chart from description"> <span> <IconButton size="medium" disabled={isFormulating || isLoadingIdeas || !currentTable || prompt.trim() === ""} sx={{ color: modeColor, '&:hover': { backgroundColor: alpha(modeColor, 0.08) } }} onClick={() => { focusNextChartRef.current = true; deriveDataFromNL(prompt.trim()); }} > {isFormulating ? <Box sx={{ display: 'flex', alignItems: 'center', justifyContent: 'center'}}> <CircularProgress size={24} sx={{ color: modeColor }} /> </Box> : <PrecisionManufacturing sx={{fontSize: 24}} />} </IconButton> </span> </Tooltip> </Box> } }} value={prompt} placeholder={`${getQuestion()}`} fullWidth multiline maxRows={4} minRows={1} /> </Box> </Card> <Tooltip title={ideas.length > 0 ? "Refresh ideas" : "Get ideas"}> <span> <IconButton size="medium" disabled={isFormulating || isLoadingIdeas || !currentTable} sx={{ color: modeColor, '&:hover': { backgroundColor: alpha(modeColor, 0.08) } }} onClick={() => getIdeasFromAgent()} > {isLoadingIdeas ? <CircularProgress size={24} sx={{ color: modeColor }} /> : <TipsAndUpdatesIcon sx={{ fontSize: 24, animation: ideas.length == 0 ? 'colorWipe 5s ease-in-out infinite' : 'none', '@keyframes colorWipe': { '0%, 90%': { scale: 1 }, '95%': { scale: 1.2 }, '100%': { scale: 1 }, }, }} />} </IconButton> </span> </Tooltip> </Box> {(ideas.length > 0 || thinkingBuffer) && ( <Box sx={{ display: 'flex', flexWrap: 'wrap', gap: 0.5, py: 1, }}> {ideas.map((idea, index) => ( <IdeaChip mini key={index} idea={idea} theme={theme} onClick={() => { focusNextChartRef.current = true; setPrompt(idea.text); deriveDataFromNL(idea.text); }} disabled={isFormulating} sx={{ width: 'calc(50% - 16px)', }} /> ))} {isLoadingIdeas && thinkingBuffer && thinkingBufferEffect} </Box> )} </Box> ); }; ================================================ FILE: src/views/ChartRenderService.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. /** * ChartRenderService — headless chart rendering background service. * * This invisible component watches all charts in the Redux store and * renders them off-screen using Vega's headless rendering pipeline: * vegaLite.compile() → vega.parse() → new vega.View() → toSVG() / toImageURL('png') * * Results are stored in: * 1. Module-level chartCache (SVG + PNG) — for VisualizationView to read * 2. chart.thumbnail in Redux (PNG data URL) — for DataThread <img> tags * * This eliminates redundant DOM-based Vega rendering in DataThread * and EncodingShelfThread, replacing heavy <VegaLite> / embed() calls * with lightweight <img src={thumbnail}> elements. */ import { FC, useEffect, useRef, useCallback } from 'react'; import { useSelector, useDispatch } from 'react-redux'; import { DataFormulatorState, dfActions, dfSelectors } from '../app/dfSlice'; import { Chart, DictTable, FieldItem } from '../components/ComponentType'; import { assembleVegaChart, prepVisTable } from '../app/utils'; import { getDataTable, checkChartAvailability } from './VisualizationView'; import { getCachedChart, setCachedChart, computeCacheKey, invalidateChart, ChartCacheEntry } from '../app/chartCache'; import { compile } from 'vega-lite'; import { parse, View } from 'vega'; import _ from 'lodash'; /** Thumbnail rendering dimensions (matches DataThread's 100×80 spec assembly) */ const THUMB_WIDTH = 120; const THUMB_HEIGHT = 90; /** Full-size base rendering dimensions */ const FULL_WIDTH = 300; const FULL_HEIGHT = 300; interface RenderJob { chart: Chart; table: DictTable; conceptShelfItems: FieldItem[]; cacheKey: string; } /** * Render a Vega-Lite spec headlessly to SVG and PNG. * Uses vega.View with no DOM attachment — pure string/canvas output. */ async function renderHeadless( vlSpec: any, ): Promise<{ svg: string; pngDataUrl: string }> { // Compile Vega-Lite → Vega spec const vgSpec = compile(vlSpec as any).spec; // Parse into Vega runtime dataflow const runtime = parse(vgSpec); // Create a headless View (no DOM container needed) const view = new View(runtime, { renderer: 'none' }); // Initialize the view (runs dataflow) await view.runAsync(); // Generate SVG string and PNG data URL in parallel const [svg, pngDataUrl] = await Promise.all([ view.toSVG(), view.toImageURL('png', 2), // scale factor 2 for retina ]); // Finalize the view to free resources view.finalize(); return { svg, pngDataUrl }; } /** * Scale a PNG data URL down to the given dimensions. * Renders the full-size image onto a smaller canvas to produce a * properly downscaled thumbnail that preserves the chart's visual * appearance (avoiding Vega-Lite layout optimizations for small sizes). */ async function scalePngDown( pngDataUrl: string, targetWidth: number, targetHeight: number, ): Promise<string> { const img = new Image(); await new Promise<void>((resolve, reject) => { img.onload = () => resolve(); img.onerror = reject; img.src = pngDataUrl; }); // Compute scaled size that fits within target bounds while preserving aspect ratio const scale = Math.min(targetWidth / img.width, targetHeight / img.height, 1); const w = Math.round(img.width * scale); const h = Math.round(img.height * scale); const canvas = document.createElement('canvas'); canvas.width = w * 2; // 2x for retina canvas.height = h * 2; const ctx = canvas.getContext('2d')!; ctx.fillStyle = 'white'; ctx.fillRect(0, 0, canvas.width, canvas.height); ctx.drawImage(img, 0, 0, canvas.width, canvas.height); return canvas.toDataURL('image/png'); } export const ChartRenderService: FC = () => { const dispatch = useDispatch(); const charts = useSelector(dfSelectors.getAllCharts); const tables = useSelector((state: DataFormulatorState) => state.tables); const conceptShelfItems = useSelector((state: DataFormulatorState) => state.conceptShelfItems); const chartSynthesisInProgress = useSelector((state: DataFormulatorState) => state.chartSynthesisInProgress); const maxStretchFactor = useSelector((state: DataFormulatorState) => state.config.maxStretchFactor); // Track which charts are currently being rendered to avoid duplicates const renderingRef = useRef<Set<string>>(new Set()); // Track previous chart count for cleanup const prevChartIdsRef = useRef<Set<string>>(new Set()); const processChart = useCallback(async (job: RenderJob) => { const { chart, table, conceptShelfItems: items, cacheKey } = job; // Skip if already rendering this chart if (renderingRef.current.has(chart.id)) return; renderingRef.current.add(chart.id); try { // --- Prepare data (mirror MemoizedChartObject's pipeline) --- // Use all rows so the thumbnail faithfully matches the main chart's // appearance (sort order, aggregation, overflow filtering, etc.). let visTableRows: any[] = structuredClone(table.rows); // Pre-aggregate for the encoding map visTableRows = prepVisTable(visTableRows, items, chart.encodingMap); // --- Assemble full-size spec (with tooltips) --- // We render at the default full size and scale down for the thumbnail, // so the chart looks like a miniature of the real thing instead of a // completely different chart due to Vega-Lite layout optimizations. const fullSpec = assembleVegaChart( chart.chartType, chart.encodingMap, items, visTableRows, table.metadata, FULL_WIDTH, FULL_HEIGHT, true, // add tooltips chart.config, 1, maxStretchFactor, ); if (!fullSpec || fullSpec === "Table") return; fullSpec['background'] = 'white'; // --- Render headlessly (single full-size render) --- const fullResult = await renderHeadless(fullSpec); // Scale the full-size PNG down to thumbnail dimensions const thumbnailPng = await scalePngDown( fullResult.pngDataUrl, THUMB_WIDTH, THUMB_HEIGHT, ); // --- Store in cache --- const entry: ChartCacheEntry = { svg: fullResult.svg, thumbnailDataUrl: thumbnailPng, specKey: cacheKey, }; setCachedChart(chart.id, entry); // --- Dispatch thumbnail to Redux for DataThread --- dispatch(dfActions.updateChartThumbnail({ chartId: chart.id, thumbnail: thumbnailPng, })); } catch (err) { // Rendering failures are non-fatal — chart will just show skeleton console.warn(`ChartRenderService: failed to render chart ${chart.id}`, err); } finally { renderingRef.current.delete(chart.id); } }, [dispatch]); useEffect(() => { // Clean up cache entries for deleted charts const currentIds = new Set(charts.map(c => c.id)); for (const prevId of prevChartIdsRef.current) { if (!currentIds.has(prevId)) { invalidateChart(prevId); } } prevChartIdsRef.current = currentIds; // Build render jobs for charts that need (re-)rendering const jobs: RenderJob[] = []; for (const chart of charts) { // Skip non-renderable chart types if (['Auto', '?', 'Table'].includes(chart.chartType)) continue; // Skip charts whose synthesis is in progress if (chartSynthesisInProgress.includes(chart.id)) continue; // Get the chart's data table const table = getDataTable(chart, tables, charts, conceptShelfItems); if (!table || table.rows.length === 0) continue; // Check if chart fields are available in the table if (!checkChartAvailability(chart, conceptShelfItems, table.rows)) continue; // Compute cache key and check if rendering is needed const cacheKey = computeCacheKey( chart.chartType, chart.encodingMap, chart.config, table.rows.length, table.contentHash, table.id, table.metadata, ); const cached = getCachedChart(chart.id); if (cached && cached.specKey === cacheKey) { // Already up-to-date — but ensure Redux thumbnail is set // (e.g., after page reload where module cache is cleared but Redux persisted) if (!chart.thumbnail || chart.thumbnail !== cached.thumbnailDataUrl) { dispatch(dfActions.updateChartThumbnail({ chartId: chart.id, thumbnail: cached.thumbnailDataUrl, })); } continue; } jobs.push({ chart, table, conceptShelfItems, cacheKey }); } // Process jobs sequentially to avoid overwhelming the browser // Use a small delay between renders to keep the UI responsive if (jobs.length > 0) { let cancelled = false; const processJobs = async () => { for (const job of jobs) { if (cancelled) break; await processChart(job); // Small yield to let React process updates await new Promise(resolve => setTimeout(resolve, 16)); } }; processJobs(); return () => { cancelled = true; }; } }, [charts, tables, conceptShelfItems, chartSynthesisInProgress, processChart, dispatch]); // This component renders nothing return null; }; ================================================ FILE: src/views/ChartifactDialog.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { Chart, DictTable, FieldItem } from '../components/ComponentType'; import { assembleVegaChart, prepVisTable } from '../app/utils'; import { exportTableToDsv } from '../data/utils'; import { ClientConfig } from '../app/dfSlice'; // Function to generate CSS styling based on report type const generateStyleCSS = (style: string): string => { // Font families const FONT_FAMILY_SYSTEM = '-apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, "Apple Color Emoji", Arial, sans-serif'; const FONT_FAMILY_SERIF = 'Georgia, Cambria, "Times New Roman", Times, serif'; const FONT_FAMILY_MONO = '"SFMono-Regular", Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace'; if (style === 'social post' || style === 'short note') { // Twitter/X style - compact, modern return `\`\`\`css body { margin: 20px; padding: 20px; background-color: white; border: 1px solid rgb(207, 217, 222); border-radius: 12px; font-family: ${FONT_FAMILY_SYSTEM}; font-size: 0.875rem; font-weight: 400; line-height: 1.4; color: rgb(15, 20, 25); } h1, h2, h3, h4, h5, h6 { color: rgb(15, 20, 25); font-weight: 700; } code { background-color: rgba(29, 155, 240, 0.1); color: rgb(29, 155, 240); padding: 0.1em 0.25em; border-radius: 3px; font-size: 0.8125rem; font-weight: 500; font-family: ${FONT_FAMILY_MONO}; } strong { font-weight: 600; color: rgb(15, 20, 25); } \`\`\` `; } else if (style === 'executive summary') { // Professional/business look return `\`\`\`css body { max-width: 700px; margin: 20px auto; padding: 20px; background-color: white; font-family: ${FONT_FAMILY_SERIF}; font-size: 0.875rem; line-height: 1.5; color: rgb(33, 37, 41); } h1, h2, h3, h4, h5, h6 { color: rgb(20, 24, 28); font-weight: 600; } code { background-color: rgb(248, 249, 250); color: rgb(0, 123, 255); padding: 0.1em 0.25em; border-radius: 2px; font-size: 0.75rem; font-family: ${FONT_FAMILY_MONO}; } strong { font-weight: 600; color: rgb(20, 24, 28); } \`\`\` `; } else { // Default "blog post" style - Notion-like return `\`\`\`css body { max-width: 800px; margin: 20px auto; padding: 0 48px; background-color: #ffffff; font-family: ${FONT_FAMILY_SYSTEM}; font-size: 0.9375rem; line-height: 1.75; font-weight: 400; letter-spacing: 0.003em; color: rgb(55, 53, 47); } h1, h2, h3, h4, h5, h6 { color: rgb(37, 37, 37); font-weight: 700; letter-spacing: -0.01em; } code { background-color: rgba(135, 131, 120, 0.15); color: #eb5757; padding: 0.2em 0.4em; border-radius: 3px; font-size: 0.875rem; font-weight: 500; font-family: ${FONT_FAMILY_MONO}; } strong { font-weight: 600; color: rgb(37, 37, 37); } \`\`\` `; } }; // Function to convert report markdown to Chartifact format export const convertToChartifact = (reportMarkdown: string, reportStyle: string, charts: Chart[], tables: DictTable[], conceptShelfItems: FieldItem[], config: ClientConfig) => { try { // Extract chart IDs from the report markdown images // Images are in format: [IMAGE(chart-id)] const imageRegex = /\[IMAGE\(([^)]+)\)\]/g; let result = reportMarkdown; let match; const chartReplacements: Array<{ original: string; specReplacement: string; dataName: string; csvContent: string }> = []; while ((match = imageRegex.exec(reportMarkdown)) !== null) { const [fullMatch, chartId] = match; // Find the chart in the store using the chart ID const chart = charts.find(c => c.id === chartId); if (!chart) { console.warn(`Chart with id ${chartId} not found in store`); continue; } // Get the chart's data table from the store using chart.tableRef const chartTable = tables.find(t => t.id === chart.tableRef); if (!chartTable) { console.warn(`Table for chart ${chartId} not found`); continue; } // Skip non-visual chart types if (chart.chartType === 'Table' || chart.chartType === '?') { continue; } try { // Preprocess the data for aggregations const processedRows = prepVisTable(chartTable.rows, conceptShelfItems, chart.encodingMap); // Assemble the Vega-Lite spec const vegaSpec = assembleVegaChart( chart.chartType, chart.encodingMap, conceptShelfItems, processedRows, chartTable.metadata, config.defaultChartWidth, config.defaultChartHeight, true, chart.config, 1, config.maxStretchFactor, ); // Convert the spec to use named data source const dataName = `chartData_${chartId.replace(/[^a-zA-Z0-9]/g, '_')}`; const modifiedSpec = { ...vegaSpec, data: { name: dataName } }; // Convert table rows to CSV format using the utility function const csvContent = exportTableToDsv(chartTable, ','); // Create the Chartifact spec replacement (without CSV) const specReplacement = ` \`\`\`json vega-lite ${JSON.stringify(modifiedSpec, null, 2)} \`\`\` `; chartReplacements.push({ original: fullMatch, specReplacement, dataName, csvContent }); } catch (error) { console.error(`Error processing chart ${chartId}:`, error); } } // Apply spec replacements to the markdown for (const { original, specReplacement } of chartReplacements) { result = result.replace(original, specReplacement); } result += '\n\n---\ncreated with AI using [Data Formulator](https://github.com/microsoft/data-formulator)\n\n'; // Prepend CSS styling based on report type const cssStyles = generateStyleCSS(reportStyle); result += cssStyles; // Append all CSV data blocks at the bottom if (chartReplacements.length > 0) { result += '\n\n'; for (const { dataName, csvContent } of chartReplacements) { result += `\n\`\`\`csv ${dataName}\n${csvContent}\n\`\`\`\n`; } } return result; } catch (error) { console.error('Error converting to Chartifact:', error); throw error; } }; // Function to open Chartifact in a new tab and send markdown via postMessage export const openChartifactViewer = async (chartifactMarkdown: string) => { try { // Open the Chartifact viewer in a new tab const chartifactWindow = window.open( 'https://microsoft.github.io/chartifact/view/?post', '_blank' ); if (!chartifactWindow) { //showMessage('Failed to open Chartifact viewer. Please allow popups.', 'error'); return; } // Listen for hostStatus messages from the Chartifact viewer const handleMessage = (event: MessageEvent) => { // Verify the message is from the Chartifact viewer if (event.origin !== 'https://microsoft.github.io') { return; } const message = event.data; // Check if this is a hostStatus message if (message.type === 'hostStatus' && message.hostStatus === 'ready') { // Send the render request when the host is ready const renderRequest: { type: 'hostRenderRequest'; title: string; markdown?: string; interactiveDocument?: any; } = { type: 'hostRenderRequest', title: 'Data Formulator Report', markdown: chartifactMarkdown }; chartifactWindow.postMessage(renderRequest, 'https://microsoft.github.io'); //Call here to show source const toolbarControl: { type: 'hostToolbarControl'; showSource?: boolean; } = { type: 'hostToolbarControl', showSource: true }; chartifactWindow.postMessage(toolbarControl, 'https://microsoft.github.io'); // Remove the event listener after sending window.removeEventListener('message', handleMessage); } }; // Add event listener for messages from the Chartifact viewer window.addEventListener('message', handleMessage); //showMessage('Opened Chartifact viewer in a new tab', 'success'); } catch (error) { console.error('Error opening Chartifact viewer:', error); //showMessage('Failed to prepare Chartifact report', 'error'); } }; ================================================ FILE: src/views/ChatDialog.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { FC, useRef, useEffect } from 'react' import { radius } from '../app/tokens'; import { Divider } from "@mui/material"; import { Card, Box, Typography, Dialog, DialogTitle, DialogContent, DialogActions, Button, styled, CardContent, } from '@mui/material'; import React from 'react'; import { alpha, useTheme } from '@mui/material/styles'; import { CodeBox } from './VisualizationView'; export const GroupHeader = styled('div')(({ theme }) => ({ position: 'sticky', top: '-8px', padding: '4px 4px', color: "darkgray", fontSize: "12px", })); export const GroupItems = styled('ul')({ padding: 0, }); // Function to parse message content and render code blocks const renderMessageContent = (role: string, message: string) => { // Split message by code blocks (```language ... ```) const codeBlockRegex = /```(\w+)?\n([\s\S]*?)```/g; const parts = []; let lastIndex = 0; let match; while ((match = codeBlockRegex.exec(message)) !== null) { // Add text before code block if (match.index > lastIndex) { const textContent = message.slice(lastIndex, match.index); if (textContent.trim()) { parts.push( <Typography key={`text-${lastIndex}`} sx={{ fontSize: 13, whiteSpace: 'pre-wrap', lineHeight: 1.4, marginBottom: 1 }}> {textContent} </Typography> ); } } // Add code block const language = match[1] || 'text'; const code = match[2].trim(); parts.push( <Box key={`code-${match.index}`} sx={{ margin: '0', borderRadius: 1, overflow: 'auto' }}> <CodeBox code={code} language={language} fontSize={10} /> </Box> ); lastIndex = match.index + match[0].length; } // Add remaining text after last code block if (lastIndex < message.length) { const textContent = message.slice(lastIndex); if (textContent.trim()) { parts.push( <Typography key={`text-${lastIndex}`} sx={{ fontSize: 13, whiteSpace: 'pre-wrap', lineHeight: 1.4 }}> {textContent} </Typography> ); } } // If no code blocks found, return original message if (parts.length === 0) { return ( <Typography sx={{ fontSize: 13, whiteSpace: 'pre-wrap', lineHeight: 1.4 }}> {message} </Typography> ); } return <Box>{parts}</Box>; }; export interface ChatDialogProps { code: string, // final code generated dialog: any[], open: boolean, handleCloseDialog: () => void, } export const ChatDialog: FC<ChatDialogProps> = function ChatDialog({code, dialog, open, handleCloseDialog}) { let theme = useTheme(); const dialogContentRef = useRef<HTMLDivElement>(null); // Auto-scroll to bottom when dialog opens useEffect(() => { if (open) { setTimeout(() => { if (dialogContentRef.current) { dialogContentRef.current.scrollTop = dialogContentRef.current.scrollHeight; } }, 100); } }, [open]); let body = undefined if (dialog == undefined) { body = <Box sx={{display: "flex", overflowX: "auto", flexDirection: "column", justifyContent: "space-between", position: "relative", marginTop: "10px", minHeight: "50px"}}> <Typography sx={{ fontSize: 14 }} color="text.secondary" gutterBottom> {"There is no conversation history yet"} </Typography> </Box> } else { body = <Box sx={{display: "flex", overflowX: "auto", flexDirection: "column", justifyContent: "space-between", position: "relative", marginTop: "10px", minHeight: "50px"}}> {/* filter out system messages */} {dialog.filter(entry => entry["role"] != 'system').map((chatEntry, idx) => { let role = chatEntry['role']; let content : any = chatEntry['content']; const isUser = role === 'user'; // Handle multimodal content (array with text + image_url objects) let message: string; if (Array.isArray(content)) { message = content .filter((part: any) => part.type === 'text') .map((part: any) => part.text) .join('\n'); } else { message = content as string; } message = message.trimEnd(); return <Card variant="outlined" key={`chat-dialog-${idx}`} sx={{ minWidth: "280px", maxWidth: "1920px", display: "flex", flexGrow: 1, margin: "6px", backgroundColor: isUser ? alpha(theme.palette.primary.main, 0.05) : alpha(theme.palette.custom.main, 0.05), border: isUser ? "1px solid" : "1px solid", borderColor: isUser ? alpha(theme.palette.primary.main, 0.2) : alpha(theme.palette.custom.main, 0.2), borderRadius: radius.md, }}> <CardContent sx={{display: "flex", flexDirection: "column", flexGrow: 1, padding: '8px 12px', paddingBottom: '8px !important'}}> <Typography sx={{ fontSize: 12, fontWeight: 600, color: isUser ? 'primary.main' : 'custom.main', textTransform: 'uppercase', letterSpacing: '0.5px' }} gutterBottom> {isUser ? 'You' : 'Assistant'} </Typography> <Box sx={{display: 'flex', flexDirection: "column", alignItems: "flex-start", flex: 'auto'}}> <Box sx={{maxWidth: 800, width: 'fit-content', display: 'flex', flexDirection: 'column'}}> <Box sx={{ color: isUser ? 'primary.dark' : 'custom.dark', }}> {renderMessageContent(role, message)} </Box> </Box> </Box> </CardContent> </Card> })} </Box> } return ( <Dialog sx={{ '& .MuiDialog-paper': { maxWidth: '95%', maxHeight: '90%', minWidth: 300 } }} maxWidth={false} open={open} key="chat-dialog-dialog" > <DialogTitle><Typography>Agent Log</Typography></DialogTitle> <DialogContent ref={dialogContentRef} sx={{overflowY: "auto", overflowX: "hidden"}} dividers> {body} </DialogContent> <DialogActions> <Button onClick={()=>{ handleCloseDialog() }}>Close</Button> </DialogActions> </Dialog> ); } ================================================ FILE: src/views/ChatThreadView.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React, { FC, useRef, useEffect, useMemo, useState, useCallback } from 'react'; import { Box, Typography, Card, CircularProgress, Divider, useTheme, } from '@mui/material'; import { useDispatch, useSelector } from 'react-redux'; import { DataFormulatorState, dfActions, dfSelectors } from '../app/dfSlice'; import { alpha } from '@mui/material/styles'; import PersonOutlineIcon from '@mui/icons-material/PersonOutline'; import SmartToyOutlinedIcon from '@mui/icons-material/SmartToyOutlined'; import StorageOutlinedIcon from '@mui/icons-material/StorageOutlined'; import ExpandMoreIcon from '@mui/icons-material/ExpandMore'; import ChevronRightIcon from '@mui/icons-material/ChevronRight'; import { renderTextWithEmphasis } from './EncodingShelfCard'; import { ThinkingBufferEffect } from '../components/FunComponents'; type ThreadItem = { content: string; role: 'user' | 'thinking' | 'action' | 'completion' | 'error' | 'clarify'; tableId?: string; chartId?: string; observeTableId?: string; resultTableId?: string; timestamp: number; actionId: string; isRunning: boolean; }; /** A conversation thread groups an origin table with its agentAction messages. */ type ConversationThread = { kind: 'action'; actionId: string; originTableId: string; messages: ThreadItem[]; status: 'running' | 'completed' | 'warning' | 'failed'; } | { kind: 'orphan-table'; tableId: string; }; /** A thread group chains threads whose end tables feed into the next thread's origin. */ type ThreadGroup = ConversationThread[]; /** * ChatThreadView renders the full conversation timeline from agentActions, * grouped into separate conversation threads separated by dividers. */ export const ChatThreadView: FC = function () { const tables = useSelector((state: DataFormulatorState) => state.tables); const focusedId = useSelector((state: DataFormulatorState) => state.focusedId); const charts = useSelector(dfSelectors.getAllCharts); const agentActions = useSelector((state: DataFormulatorState) => state.agentActions); const theme = useTheme(); const dispatch = useDispatch(); const scrollRef = useRef<HTMLDivElement>(null); const focusedTableId = useMemo(() => { if (!focusedId) return undefined; if (focusedId.type === 'table') return focusedId.tableId; const chart = charts.find(c => c.id === focusedId.chartId); return chart?.tableRef; }, [focusedId, charts]); const focusedChartId = focusedId?.type === 'chart' ? focusedId.chartId : undefined; // Track which threads are collapsed (keyed by actionId) const [collapsedThreads, setCollapsedThreads] = useState<Set<string>>(new Set()); const toggleThread = useCallback((threadId: string) => { setCollapsedThreads(prev => { const next = new Set(prev); if (next.has(threadId)) next.delete(threadId); else next.add(threadId); return next; }); }, []); // Build conversation threads and chain them into groups const threadGroups = useMemo((): ThreadGroup[] => { const threads: ConversationThread[] = []; // Collect all table IDs referenced by agentActions (origin + result) const tablesInActions = new Set<string>(); for (const action of agentActions) { if (action.hidden) continue; if (action.originTableId) tablesInActions.add(action.originTableId); for (const m of (action.messages || [])) { if (m.resultTableId) tablesInActions.add(m.resultTableId); if (m.observeTableId) tablesInActions.add(m.observeTableId); } } // Find root tables (no derive, or anchored) not referenced by any action const orphanRootTables = tables.filter(t => (t.derive === undefined || t.anchored) && !tablesInActions.has(t.id) ); // Each orphan root table gets its own thread entry for (const t of orphanRootTables) { threads.push({ kind: 'orphan-table', tableId: t.id }); } // Each non-hidden agentAction becomes a conversation thread const sortedActions = [...agentActions] .filter(a => !a.hidden) .sort((a, b) => (a.messages?.[0]?.timestamp || a.lastUpdate) - (b.messages?.[0]?.timestamp || b.lastUpdate)); for (const action of sortedActions) { const messages: ThreadItem[] = []; for (const m of (action.messages || [])) { const effectiveObserveId = m.observeTableId || (m.role === 'user' ? action.originTableId : undefined); messages.push({ content: m.content, role: m.role, observeTableId: effectiveObserveId, resultTableId: m.resultTableId, timestamp: m.timestamp, actionId: action.actionId, isRunning: false, }); } // Live running status if (action.status === 'running') { const lastMsg = action.messages?.[action.messages.length - 1]; if (!lastMsg || lastMsg.content !== action.description) { messages.push({ content: action.description || 'thinking...', role: 'thinking', timestamp: action.lastUpdate, actionId: action.actionId, isRunning: true, }); } } threads.push({ kind: 'action', actionId: action.actionId, originTableId: action.originTableId, messages, status: action.status, }); } // Chain threads into groups: if thread B's originTableId matches any // resultTableId from thread A, display them together without a divider. // Build a map from resultTableId → thread index for action threads const resultTableToThread = new Map<string, number>(); for (let i = 0; i < threads.length; i++) { const t = threads[i]; if (t.kind === 'action') { for (const m of t.messages) { if (m.resultTableId) { resultTableToThread.set(m.resultTableId, i); } } } } // For each action thread, find its parent (the thread whose result is this thread's origin) const parentOf = new Map<number, number>(); // child index → parent index for (let i = 0; i < threads.length; i++) { const t = threads[i]; if (t.kind === 'action' && t.originTableId) { const parentIdx = resultTableToThread.get(t.originTableId); if (parentIdx !== undefined && parentIdx !== i) { parentOf.set(i, parentIdx); } } } // Find chain roots (threads that aren't children of another thread) const childIndices = new Set(parentOf.keys()); const visited = new Set<number>(); const groups: ThreadGroup[] = []; // Build chain starting from each root for (let i = 0; i < threads.length; i++) { if (childIndices.has(i) || visited.has(i)) continue; // This is a root — walk forward to build the chain const group: ConversationThread[] = [threads[i]]; visited.add(i); // Find children: threads whose originTableId is a result of the current tail let current = i; while (true) { // Find the next thread in the chain let nextIdx: number | undefined; for (const [child, parent] of parentOf.entries()) { if (parent === current && !visited.has(child)) { nextIdx = child; break; } } if (nextIdx === undefined) break; group.push(threads[nextIdx]); visited.add(nextIdx); current = nextIdx; } groups.push(group); } // Add any remaining unvisited threads (shouldn't happen, but safety) for (let i = 0; i < threads.length; i++) { if (!visited.has(i)) { groups.push([threads[i]]); } } return groups; }, [agentActions, tables]); // Auto-scroll to bottom when threads change useEffect(() => { if (scrollRef.current) { scrollRef.current.scrollTop = scrollRef.current.scrollHeight; } }, [threadGroups]); const primaryColor = theme.palette.primary.main; const customColor = theme.palette.secondary.main; const TIMELINE_W = 14; const TIMELINE_GAP = '6px'; const getTimelineIcon = (msg: ThreadItem) => { if (msg.isRunning) { return <CircularProgress size={14} thickness={5} sx={{ color: customColor }} />; } switch (msg.role) { case 'user': return <PersonOutlineIcon sx={{ fontSize: 16, color: primaryColor }} />; case 'thinking': return <SmartToyOutlinedIcon sx={{ fontSize: 15, color: customColor }} />; case 'action': return <SmartToyOutlinedIcon sx={{ fontSize: 15, color: customColor }} />; case 'completion': return <SmartToyOutlinedIcon sx={{ fontSize: 15, color: theme.palette.success.main }} />; case 'error': return <SmartToyOutlinedIcon sx={{ fontSize: 15, color: theme.palette.error.main }} />; case 'clarify': return <SmartToyOutlinedIcon sx={{ fontSize: 15, color: customColor }} />; default: return <Box sx={{ width: 6, height: 6, borderRadius: '50%', backgroundColor: 'rgba(0,0,0,0.2)' }} />; } }; const tableNode = (tableId: string) => { const table = tables.find(t => t.id === tableId); if (!table) return null; const rowCount = table.virtual?.rowCount ?? table.rows?.length ?? 0; const isFocused = tableId === focusedTableId; return ( <Card elevation={0} sx={{ display: 'inline-flex', alignItems: 'center', gap: '4px', px: '6px', py: '3px', borderRadius: '6px', width: 'fit-content', border: isFocused ? `1.5px solid ${alpha(theme.palette.primary.main, 0.9)}` : '1px solid rgba(0,0,0,0.12)', backgroundColor: isFocused ? alpha(theme.palette.primary.main, 0.06) : alpha(theme.palette.grey[500], 0.06), cursor: 'pointer', '&:hover': { backgroundColor: alpha(theme.palette.primary.main, 0.08) }, }} onClick={(e) => { e.stopPropagation(); dispatch(dfActions.setFocused({ type: 'table', tableId })); }} > <Typography sx={{ fontSize: 11, fontWeight: 500, color: theme.palette.text.primary, overflow: 'hidden', textOverflow: 'ellipsis', whiteSpace: 'nowrap' }}> {table.displayId || tableId} <Typography component="span" sx={{ fontSize: 10, color: theme.palette.text.disabled, ml: 0.5 }}> {rowCount}r × {table.names.length}c </Typography> </Typography> </Card> ); }; const chartThumbnails = (tableId: string) => { const tableCharts = charts.filter(c => c.tableRef === tableId && c.thumbnail); if (tableCharts.length === 0) return null; return ( <Box sx={{ display: 'flex', flexWrap: 'wrap', gap: '4px', alignItems: 'flex-start' }}> {tableCharts.map(chart => { const isChartFocused = chart.id === focusedChartId; return ( <Box key={chart.id} onClick={(e) => { e.stopPropagation(); dispatch(dfActions.setFocused({ type: 'chart', chartId: chart.id })); }} sx={{ display: 'inline-flex', borderRadius: '6px', overflow: 'hidden', border: isChartFocused ? `1.5px solid ${alpha(theme.palette.primary.main, 0.9)}` : '1.5px solid transparent', backgroundColor: 'white', cursor: 'pointer', }} > <img src={chart.thumbnail} alt={`${chart.chartType} chart`} style={{ minWidth: 80, maxWidth: 120, maxHeight: 80, objectFit: 'contain' }} /> </Box> ); })} </Box> ); }; /** Generate a one-line plain-text summary for a message. */ const summarize = (msg: ThreadItem): string => { const raw = typeof msg.content === 'string' ? msg.content.replace(/\*\*/g, '') : String(msg.content || msg.role); const oneLine = raw.replace(/\n+/g, ' ').trim(); return oneLine.length > 80 ? oneLine.slice(0, 80) + '…' : oneLine; }; const renderContent = (msg: ThreadItem, collapsed = false) => { const fs = 12; const fsStr = '12px'; if (msg.isRunning) { return <ThinkingBufferEffect text={msg.content || 'thinking...'} sx={{ width: '100%' }} />; } // Collapsed: single-line truncated summary with role-appropriate color if (collapsed) { const color = msg.role === 'completion' ? theme.palette.success.main : msg.role === 'error' ? theme.palette.error.main : msg.role === 'thinking' ? 'rgba(0,0,0,0.5)' : 'rgba(0,0,0,0.6)'; return ( <Box> <Typography sx={{ fontSize: 11, color, overflow: 'hidden', textOverflow: 'ellipsis', whiteSpace: 'nowrap', ...(msg.role === 'user' ? { fontStyle: 'italic' } : {}) }}> {summarize(msg)} </Typography> {msg.resultTableId && ( <Box sx={{ display: 'flex', flexDirection: 'column', alignItems: 'flex-start', gap: '4px', mt: '4px' }}> {tableNode(msg.resultTableId)} {chartThumbnails(msg.resultTableId)} </Box> )} </Box> ); } const textColor = 'rgba(0,0,0,0.87)'; const textSecondary = 'rgba(0,0,0,0.7)'; switch (msg.role) { case 'user': return ( <Typography component="div" sx={{ fontSize: fs, color: textColor, fontStyle: 'italic', whiteSpace: 'pre-wrap', wordBreak: 'break-word' }}> {msg.content} </Typography> ); case 'thinking': return ( <Typography component="div" sx={{ fontSize: fs, color: textSecondary, whiteSpace: 'pre-wrap', wordBreak: 'break-word' }}> {renderTextWithEmphasis(msg.content, { borderRadius: '2px', fontSize: fsStr, backgroundColor: alpha(customColor, 0.08), })} </Typography> ); case 'action': return ( <Box> <Typography component="div" sx={{ fontSize: fs, color: textColor, whiteSpace: 'pre-wrap', wordBreak: 'break-word' }}> {renderTextWithEmphasis(msg.content, { borderRadius: '2px', fontSize: fsStr, backgroundColor: alpha(customColor, 0.08), })} </Typography> {msg.resultTableId && ( <Box sx={{ display: 'flex', flexDirection: 'column', alignItems: 'flex-start', gap: '4px', mt: '4px' }}> {tableNode(msg.resultTableId)} {chartThumbnails(msg.resultTableId)} </Box> )} </Box> ); case 'completion': return ( <Box> <Typography sx={{ fontSize: fs, color: theme.palette.success.main, whiteSpace: 'pre-wrap', wordBreak: 'break-word' }}> {renderTextWithEmphasis(msg.content, { borderRadius: '2px', fontSize: fsStr, })} </Typography> {msg.resultTableId && ( <Box sx={{ display: 'flex', flexDirection: 'column', alignItems: 'flex-start', gap: '4px', mt: '4px' }}> {tableNode(msg.resultTableId)} {chartThumbnails(msg.resultTableId)} </Box> )} </Box> ); case 'error': return ( <Typography sx={{ fontSize: fs, color: textColor, whiteSpace: 'pre-wrap', wordBreak: 'break-word' }}> {msg.content} </Typography> ); case 'clarify': return ( <Typography component="div" sx={{ fontSize: fs, color: textColor, whiteSpace: 'pre-wrap', wordBreak: 'break-word' }}> {renderTextWithEmphasis(msg.content, { borderRadius: '2px', fontSize: fsStr, backgroundColor: alpha(customColor, 0.08), })} </Typography> ); default: return null; } }; /** Render a single timeline row (icon + connecting lines + content). */ const renderTimelineRow = ( content: React.ReactNode, icon: React.ReactNode, opts: { isFirst: boolean; isLast: boolean; bgColor?: string; lineColor?: string; bottomLineColor?: string; onClick?: (e: React.MouseEvent) => void; // optional click handler for the row } ) => { const lc = opts.lineColor || 'rgba(0,0,0,0.12)'; const blc = opts.bottomLineColor || lc; const clickable = !!opts.onClick; return ( <Box onClick={opts.onClick} sx={{ display: 'flex', flexDirection: 'row', position: 'relative', ...(clickable ? { cursor: 'pointer' } : {}), ...(opts.bgColor && opts.bgColor !== 'transparent' ? { backgroundColor: opts.bgColor, borderRadius: '6px', mx: '-4px', px: '4px', } : {}), }}> <Box sx={{ width: TIMELINE_W, flexShrink: 0, display: 'flex', flexDirection: 'column', alignItems: 'center', pt: '5px', }}> {!opts.isFirst && ( <Box sx={{ width: 0, flexShrink: 0, height: 4, borderLeft: `1px solid ${lc}`, mt: '-5px' }} /> )} <Box sx={{ flexShrink: 0, zIndex: 1, display: 'flex', alignItems: 'center', justifyContent: 'center' }}> {icon} </Box> {!opts.isLast ? <Box sx={{ width: 0, flex: '1 1 0', minHeight: 2, borderLeft: `1px solid ${blc}` }} /> : <Box sx={{ flex: '1 1 0', minHeight: 2 }} /> } </Box> <Box sx={{ flex: 1, minWidth: 0, py: '6px', pl: TIMELINE_GAP }}> {content} </Box> </Box> ); }; /** Render a full action-based conversation thread. skipOriginTable omits the * origin table row when it was already shown as a result of the previous thread in the chain. */ const renderActionThread = (thread: ConversationThread & { kind: 'action' }, skipOriginTable = false) => { const originTable = skipOriginTable ? undefined : tables.find(t => t.id === thread.originTableId); const isRunningThread = thread.status === 'running'; const lastMsgIsRunning = thread.messages.length > 0 && thread.messages[thread.messages.length - 1].isRunning; // If the thread is running but the last message doesn't already have isRunning, // we'll append an extra "working..." row, so account for it in totalRows. const needsRunningRow = isRunningThread && !lastMsgIsRunning; const totalRows = (originTable ? 1 : 0) + thread.messages.length + (needsRunningRow ? 1 : 0); // When origin is skipped (chained), the first message continues the timeline from the previous thread const chainContinuation = skipOriginTable; const isCollapsed = collapsedThreads.has(thread.actionId); return ( <Box sx={{ display: 'flex', flexDirection: 'column', gap: '6px' }}> {/* Origin table — click to collapse/expand the entire thread */} {originTable && renderTimelineRow( <Box sx={{ display: 'flex', flexDirection: 'column', gap: '4px' }}> {tableNode(originTable.id)} {chartThumbnails(originTable.id)} </Box>, isCollapsed ? <Box sx={{ display: 'flex', alignItems: 'center', justifyContent: 'center', width: 18, height: 18, borderRadius: '50%', border: `1px solid ${alpha(theme.palette.text.secondary, 0.4)}`, cursor: 'pointer', '&:hover': { backgroundColor: alpha(theme.palette.action.hover, 0.12) } }}> <ChevronRightIcon sx={{ fontSize: 13, color: theme.palette.text.secondary }} /> </Box> : <Box sx={{ display: 'flex', alignItems: 'center', justifyContent: 'center', width: 18, height: 18, borderRadius: '50%', border: `1px solid ${alpha(theme.palette.text.secondary, 0.4)}`, cursor: 'pointer', '&:hover': { backgroundColor: alpha(theme.palette.action.hover, 0.12) } }}> <ExpandMoreIcon sx={{ fontSize: 13, color: theme.palette.text.secondary }} /> </Box>, { isFirst: true, isLast: totalRows <= 1, onClick: (e) => { e.stopPropagation(); toggleThread(thread.actionId); } }, )} {/* Messages — condensed to single-line summaries when collapsed */} {thread.messages.map((msg, idx) => { const rowIdx = (originTable ? 1 : 0) + idx; const isFirst = rowIdx === 0 && !chainContinuation; const isLast = rowIdx === totalRows - 1; const bgColor = isCollapsed ? 'transparent' : msg.role === 'user' ? alpha(theme.palette.primary.main, 0.10) : msg.role === 'error' ? alpha(theme.palette.error.main, 0.10) : 'transparent'; return ( <React.Fragment key={`msg-${idx}`}> {renderTimelineRow( renderContent(msg, isCollapsed), getTimelineIcon(msg), { isFirst, isLast, bgColor }, )} </React.Fragment> ); })} {/* Extra running indicator when thread is running but last message isn't already a running row */} {needsRunningRow && renderTimelineRow( <Typography variant="body2" sx={{ fontSize: 11, color: customColor, fontStyle: 'italic' }}> working... </Typography>, <CircularProgress size={14} thickness={5} sx={{ color: customColor }} />, { isFirst: totalRows <= 1, isLast: true }, )} </Box> ); }; /** Render an orphan table thread (table + its charts, no agent messages). */ const renderOrphanThread = (tableId: string) => { return ( <Box sx={{ display: 'flex', flexDirection: 'column', gap: '6px' }}> {renderTimelineRow( <Box sx={{ display: 'flex', flexDirection: 'column', gap: '4px' }}> {tableNode(tableId)} {chartThumbnails(tableId)} </Box>, <StorageOutlinedIcon sx={{ fontSize: 12, color: theme.palette.text.secondary }} />, { isFirst: true, isLast: true }, )} </Box> ); }; if (threadGroups.length === 0) { return ( <Box sx={{ flex: 1, display: 'flex', alignItems: 'center', justifyContent: 'center', minHeight: 0 }}> <Typography variant="caption" sx={{ color: theme.palette.text.disabled, fontSize: 11 }}> Start a conversation to explore your data. </Typography> </Box> ); } /** Collect all result table IDs from an action thread's messages. */ const getResultTableIds = (thread: ConversationThread): Set<string> => { const ids = new Set<string>(); if (thread.kind === 'action') { for (const m of thread.messages) { if (m.resultTableId) ids.add(m.resultTableId); } } return ids; }; return ( <Box ref={scrollRef} sx={{ flex: 1, overflowY: 'auto', overflowX: 'hidden', px: 1, py: 1, display: 'flex', flexDirection: 'column', gap: '6px', minHeight: 0, }}> {threadGroups.map((group, gIdx) => ( <React.Fragment key={`group-${gIdx}`}> {gIdx > 0 && ( <Box sx={{ display: 'flex', alignItems: 'center', my: '12px', px: 1 }}> <Box sx={{ flex: 1, borderBottom: `1.5px dashed ${alpha(theme.palette.text.disabled, 0.3)}` }} /> </Box> )} {group.map((thread, tIdx) => { // Skip origin table if it was a result of the previous thread in the chain const prevThread = tIdx > 0 ? group[tIdx - 1] : undefined; const prevResultIds = prevThread ? getResultTableIds(prevThread) : new Set<string>(); const skipOrigin = thread.kind === 'action' && prevResultIds.has(thread.originTableId); return ( <React.Fragment key={`thread-${tIdx}`}> {thread.kind === 'action' ? renderActionThread(thread, skipOrigin) : renderOrphanThread(thread.tableId) } </React.Fragment> ); })} </React.Fragment> ))} </Box> ); }; ================================================ FILE: src/views/DBTableManager.tsx ================================================ // TableManager.tsx import React, { useState, useEffect, useCallback, FC, useRef, useMemo } from 'react'; import { Card, CardContent, Typography, Button, Grid, Box, IconButton, Paper, TextField, Divider, SxProps, CircularProgress, ButtonGroup, ToggleButton, ToggleButtonGroup, MenuItem, Menu, Chip, Checkbox, FormControlLabel, styled, useTheme, Link, alpha, } from '@mui/material'; import SearchIcon from '@mui/icons-material/Search'; import Autocomplete from '@mui/material/Autocomplete'; import { getUrls, fetchWithIdentity } from '../app/utils'; import { borderColor } from '../app/tokens'; import { CustomReactTable } from './ReactTable'; import { DataSourceConfig, DictTable } from '../components/ComponentType'; import { Type } from '../data/types'; import { useDispatch, useSelector } from 'react-redux'; import { dfActions, dfSelectors } from '../app/dfSlice'; import { DataFormulatorState } from '../app/dfSlice'; import { fetchFieldSemanticType } from '../app/dfSlice'; import { loadTable } from '../app/tableThunks'; import { AppDispatch } from '../app/store'; import Markdown from 'markdown-to-jsx'; import CheckIcon from '@mui/icons-material/Check'; import MuiMarkdown from 'mui-markdown'; import CleaningServicesIcon from '@mui/icons-material/CleaningServices'; import MoreVertIcon from '@mui/icons-material/MoreVert'; import UploadFileIcon from '@mui/icons-material/UploadFile'; import DownloadIcon from '@mui/icons-material/Download'; import RestartAltIcon from '@mui/icons-material/RestartAlt'; import CloudUploadIcon from '@mui/icons-material/CloudUpload'; export const handleDBDownload = async (identityId: string) => { try { const response = await fetchWithIdentity( getUrls().DOWNLOAD_DB_FILE, { method: 'GET' } ); // Check if the response is ok if (!response.ok) { const errorData = await response.json(); throw new Error(errorData.error || errorData.message || 'Failed to download database file'); } // Get the blob directly from response const blob = await response.blob(); const url = URL.createObjectURL(blob); // Create a temporary link element const link = document.createElement('a'); link.href = url; link.download = `df_${identityId?.slice(0, 4) || 'db'}.db`; document.body.appendChild(link); // Trigger download link.click(); // Clean up document.body.removeChild(link); URL.revokeObjectURL(url); } catch (error) { throw error; } }; interface DBTable { name: string; columns: { name: string; type: string; }[]; row_count: number; sample_rows: any[]; view_source: string | null; // Source metadata for refreshable tables (from data loaders) // Backend stores connection info; frontend manages refresh timing source_metadata?: { table_name: string; data_loader_type: string; data_loader_params: Record<string, any>; source_table_name?: string; source_query?: string; last_refreshed?: string; } | null; } interface ColumnStatistics { column: string; type: string; statistics: { count: number; unique_count: number; null_count: number; min?: number; max?: number; avg?: number; }; } export const DBManagerPane: React.FC<{ onClose?: () => void; storeOnServer?: boolean; }> = function DBManagerPane({ onClose, storeOnServer = true }) { const theme = useTheme(); const dispatch = useDispatch<AppDispatch>(); const identity = useSelector((state: DataFormulatorState) => state.identity); const tables = useSelector((state: DataFormulatorState) => state.tables); const serverConfig = useSelector((state: DataFormulatorState) => state.serverConfig); const dataLoaderConnectParams = useSelector((state: DataFormulatorState) => state.dataLoaderConnectParams); // maps data loader type to list of param defs const [dataLoaderMetadata, setDataLoaderMetadata] = useState<Record<string, { params: {name: string, default: string, type: string, required: boolean, description: string}[], auth_instructions: string}>>({}); const [dbTables, setDbTables] = useState<DBTable[]>([]); const [selectedTabKey, setSelectedTabKey] = useState(""); const [selectedDataLoader, setSelectedDataLoader] = useState<string>(""); const [isUploading, setIsUploading] = useState<boolean>(false); let setSystemMessage = (content: string, severity: "error" | "warning" | "info" | "success") => { dispatch(dfActions.addMessages({ "timestamp": Date.now(), "component": "DB manager", "type": severity, "value": content })); } useEffect(() => { fetchDataLoaders(); }, []); useEffect(() => { if (selectedDataLoader === "") { if (dbTables.length == 0) { setSelectedTabKey(""); } else if (dbTables.find(t => t.name === selectedTabKey) == undefined) { setSelectedTabKey(dbTables[0]?.name || ""); } } }, [dbTables, selectedDataLoader]); // Fetch list of tables const fetchTables = async (): Promise<DBTable[] | undefined> => { try { const response = await fetchWithIdentity(getUrls().LIST_TABLES, { method: 'GET' }); const data = await response.json(); if (data.status === 'success') { setDbTables(data.tables); return data.tables; } } catch (error) { setSystemMessage('Failed to fetch tables, please check if the server is running', "error"); } return undefined; }; const fetchDataLoaders = async () => { fetchWithIdentity(getUrls().DATA_LOADER_LIST_DATA_LOADERS, { method: 'GET', headers: { 'Content-Type': 'application/json', }, }) .then(response => response.json()) .then(data => { if (data.status === "success") { setDataLoaderMetadata(data.data_loaders); } else { console.error('Failed to fetch data loader params:', data.error); } }) .catch(error => { console.error('Failed to fetch data loader params:', error); }); } useEffect(() => { fetchTables(); }, []); let tableSelectionPanel = <Box sx={{ pt: 1, display: 'flex', flexDirection: 'column', width: '100%', }}> {/* Data loaders */} {Object.keys(dataLoaderMetadata ?? {}).map((dataLoaderType) => ( <Button key={dataLoaderType} variant="text" size="small" color="primary" onClick={() => setSelectedDataLoader(dataLoaderType)} sx={{ fontSize: 12, textTransform: "none", width: '100%', justifyContent: 'flex-start', textAlign: 'left', borderRadius: 0, py: 1, px: 2, color: selectedDataLoader === dataLoaderType ? 'primary.main' : 'text.secondary', borderRight: selectedDataLoader === dataLoaderType ? 2 : 0, borderColor: 'primary.main', }} > {dataLoaderType} </Button> ))} </Box> let dataConnectorView = <Box sx={{ position: 'absolute', top: 0, left: 0, right: 0, bottom: 0, overflowY: 'auto', overflowX: 'hidden', p: 2, pb: 4, display: 'flex', flexDirection: 'column', minWidth: 0, overscrollBehavior: 'contain' }}> {/* Empty state when no loader selected */} {selectedDataLoader === '' && ( <Box sx={{ display: 'flex', alignItems: 'center', justifyContent: 'center', flex: 1, color: 'text.disabled' }}> <Typography variant="body2" sx={{ fontStyle: 'italic' }}> Select a data loader from the left panel </Typography> </Box> )} {/* Data loader forms */} {dataLoaderMetadata && Object.entries(dataLoaderMetadata).map(([dataLoaderType, metadata]) => ( selectedDataLoader === dataLoaderType && ( <Box key={`dataLoader:${dataLoaderType}`} sx={{ position: "relative", maxWidth: '100%', flexShrink: 0 }}> <DataLoaderForm key={`data-loader-form-${dataLoaderType}`} dataLoaderType={dataLoaderType} paramDefs={metadata.params} authInstructions={metadata.auth_instructions} storeOnServer={storeOnServer} onImport={() => { setIsUploading(true); }} onFinish={(status, message, importedTables) => { setIsUploading(false); if (status === "success") { setSystemMessage(message, "success"); } else { setSystemMessage(message, "error"); } }} /> </Box> ) ))} </Box>; let mainContent = <Box sx={{ display: 'flex', flexDirection: 'column', bgcolor: 'white', flex: 1, overflow: 'hidden', height: '100%' }}> <Box sx={{ display: 'flex', flexDirection: 'row', flex: 1, overflow: 'hidden', minHeight: 0, height: '100%' }}> {/* Button navigation - similar to TableSelectionView */} <Box sx={{ display: 'flex', flexDirection: 'column', px: 0, width: 150, minWidth: 150, maxWidth: 150, overflow: 'hidden', height: '100%' }}> <Box sx={{ display: 'flex', flexDirection: 'column', overflowY: 'auto', overflowX: 'hidden', flex: 1, minHeight: 0, height: '100%', position: 'relative', borderRight: `1px solid ${borderColor.view}`, overscrollBehavior: 'contain' }}> {/* Available Tables Section - always visible */} {tableSelectionPanel} </Box> </Box> {/* Content area - show selected data loader form */} <Box sx={{ flex: 1, overflow: 'hidden', minWidth: 0, minHeight: 0, height: '100%', position: 'relative' }}> {dataConnectorView} </Box> </Box> </Box> return ( <Box sx={{ position: 'relative', display: 'flex', flexDirection: 'column', height: '100%', width: '100%' }}> {mainContent} {isUploading && ( <Box sx={{ position: 'absolute', top: 0, left: 0, width: '100%', height: '100%', display: 'flex', flexDirection: 'column', alignItems: 'center', justifyContent: 'center', backgroundColor: 'rgba(255, 255, 255, 0.85)', backdropFilter: 'blur(4px)', zIndex: 1000, gap: 2, }}> <CircularProgress size={60} thickness={5} /> <Typography variant="body2" color="text.secondary"> Uploading data... </Typography> <Button variant="text" size="small" onClick={() => setIsUploading(false)} sx={{ mt: 1, textTransform: 'none', color: 'text.secondary' }} > Cancel </Button> </Box> )} </Box> ); } export const DataLoaderForm: React.FC<{ dataLoaderType: string, paramDefs: {name: string, default: string, type: string, required: boolean, description: string}[], authInstructions: string, storeOnServer?: boolean, onImport: () => void, onFinish: (status: "success" | "error", message: string, importedTables?: string[]) => void }> = ({dataLoaderType, paramDefs, authInstructions, storeOnServer = true, onImport, onFinish}) => { const dispatch = useDispatch<AppDispatch>(); const theme = useTheme(); const params = useSelector((state: DataFormulatorState) => state.dataLoaderConnectParams[dataLoaderType] ?? {}); const frontendRowLimit = useSelector((state: DataFormulatorState) => state.config?.frontendRowLimit ?? 50000); const workspaceTables = useSelector((state: DataFormulatorState) => state.tables); const [tableMetadata, setTableMetadata] = useState<Record<string, any>>({}); const [selectedPreviewTable, setSelectedPreviewTable] = useState<string | null>(null); let [tableFilter, setTableFilter] = useState<string>(""); // Import mode for the currently selected table const [importMode, setImportMode] = useState<'full' | 'subset'>('full'); const [subsetConfig, setSubsetConfig] = useState<{ rowLimit: number; sortColumns: string[]; sortOrder: 'asc' | 'desc' }>({ rowLimit: 1000, sortColumns: [], sortOrder: 'asc' }); // Track which tables have been loaded and how (persists across table selections) const [loadedTables, setLoadedTables] = useState<Record<string, 'full' | 'subset'>>({}); // Cross-reference workspace tables with database tables to detect already-loaded ones const workspaceLoadedTables = useMemo(() => { const result: Record<string, 'full' | 'subset'> = {}; for (const wt of workspaceTables) { const dbTableName = wt.source?.databaseTable; if (dbTableName && wt.source?.type === 'database') { // Determine if subset: if virtual exists and rowCount > loaded rows, it's a subset const isSubset = wt.virtual ? wt.rows.length < wt.virtual.rowCount : false; result[dbTableName] = isSubset ? 'subset' : 'full'; } } return result; }, [workspaceTables]); // Merge: local session loads take priority over workspace-detected loads const effectiveLoadedTables = useMemo(() => { return { ...workspaceLoadedTables, ...loadedTables }; }, [workspaceLoadedTables, loadedTables]); let [isConnecting, setIsConnecting] = useState(false); // Auto-select first table for preview when metadata loads useEffect(() => { const tableNames = Object.keys(tableMetadata); if (tableNames.length > 0 && (!selectedPreviewTable || !tableMetadata[selectedPreviewTable])) { setSelectedPreviewTable(tableNames[0]); } }, [tableMetadata]); // Reset import mode when switching tables useEffect(() => { if (selectedPreviewTable && tableMetadata[selectedPreviewTable]) { setImportMode('full'); const metadata = tableMetadata[selectedPreviewTable]; setSubsetConfig({ rowLimit: Math.min(1000, metadata.row_count || 1000), sortColumns: [], sortOrder: 'asc' }); } }, [selectedPreviewTable]); // Build preview DictTable for the selected table const previewTable: DictTable | null = useMemo(() => { if (!selectedPreviewTable || !tableMetadata[selectedPreviewTable]) return null; const metadata = tableMetadata[selectedPreviewTable]; const sampleRows = metadata.sample_rows || []; const columns = metadata.columns || []; const names = columns.map((c: any) => c.name); return { id: selectedPreviewTable, displayId: selectedPreviewTable, names, rows: sampleRows, metadata: names.reduce((acc: Record<string, any>, name: string) => ({ ...acc, [name]: { type: 'string' as any, semanticType: '', levels: [] } }), {}), anchored: true, createdBy: 'user' as const, attachedMetadata: '', }; }, [selectedPreviewTable, tableMetadata]); const tableNames = Object.keys(tableMetadata); let tableMetadataBox = [ // Tables as chips + preview below tableNames.length > 0 && ( <Box key="table-chips-preview" sx={{ mt: 1 }}> {/* Table chips */} <Box sx={{ display: 'flex', flexWrap: 'wrap', gap: 0.5, mb: 1.5 }}> {tableNames.map((tableName) => { const metadata = tableMetadata[tableName]; const isSelected = tableName === selectedPreviewTable; const loaded = effectiveLoadedTables[tableName]; return ( <Chip key={tableName} label={tableName} size="small" onClick={() => setSelectedPreviewTable(tableName)} icon={loaded ? <CheckIcon sx={{ fontSize: 14 }} /> : undefined} sx={{ cursor: 'pointer', fontSize: 11, height: 26, borderRadius: 1, ...(loaded === 'full' ? { backgroundColor: alpha(theme.palette.success.main, 0.12), borderColor: alpha(theme.palette.success.main, 0.5), color: theme.palette.success.dark, '& .MuiChip-icon': { color: theme.palette.success.main }, } : loaded === 'subset' ? { backgroundColor: alpha('#f9a825', 0.15), borderColor: alpha('#f9a825', 0.5), color: '#e65100', '& .MuiChip-icon': { color: '#f9a825' }, } : isSelected ? { backgroundColor: alpha(theme.palette.primary.main, 0.12), borderColor: alpha(theme.palette.primary.main, 0.5), color: theme.palette.primary.main, } : {}), border: '1px solid', borderColor: loaded === 'full' ? alpha(theme.palette.success.main, 0.5) : loaded === 'subset' ? alpha('#f9a825', 0.5) : isSelected ? alpha(theme.palette.primary.main, 0.5) : 'rgba(0,0,0,0.15)', '&:hover': { backgroundColor: loaded === 'full' ? alpha(theme.palette.success.main, 0.18) : loaded === 'subset' ? alpha('#f9a825', 0.22) : alpha(theme.palette.primary.main, 0.08), }, }} /> ); })} </Box> {/* Preview + load controls */} {previewTable && selectedPreviewTable && ( <Box> <Card variant="outlined" sx={{ pb: 0.5 }}> <CustomReactTable rows={previewTable.rows.slice(0, 12)} columnDefs={previewTable.names.map(name => ({ id: name, label: name, minWidth: 60, }))} rowsPerPageNum={-1} compact={false} isIncompleteTable={previewTable.rows.length > 12} maxHeight={240} /> </Card> <Typography variant="caption" color="text.secondary" sx={{ mt: 0.5, display: 'block' }}> {tableMetadata[selectedPreviewTable]?.row_count > 0 ? `${tableMetadata[selectedPreviewTable].row_count.toLocaleString()} rows` : `${previewTable.rows.length} sample rows` } × {previewTable.names.length} columns </Typography> {/* Load controls */} <Box sx={{ mt: 1.5, pt: 1, display: 'flex', alignItems: 'center', justifyContent: 'center', gap: 2, flexWrap: 'nowrap' }}> {/* Subset option - hidden when already loaded */} {!effectiveLoadedTables[selectedPreviewTable] && <Box sx={{ display: 'flex', alignItems: 'center', gap: 0.5, flexWrap: 'nowrap' }}> <Checkbox checked={importMode === 'subset'} onChange={(e) => setImportMode(e.target.checked ? 'subset' : 'full')} size="small" sx={{ p: 0.25 }} /> <Typography variant="body2" sx={{ fontSize: 12, cursor: 'pointer', userSelect: 'none', whiteSpace: 'nowrap' }} onClick={() => setImportMode(importMode === 'subset' ? 'full' : 'subset')} > Load a subset </Typography> {importMode === 'subset' && selectedPreviewTable && tableMetadata[selectedPreviewTable] && (() => { const metadata = tableMetadata[selectedPreviewTable]; return ( <> <Box sx={{ display: 'flex', alignItems: 'center', gap: 0.5, flexShrink: 0 }}> <Typography variant="caption" sx={{ fontSize: 11, color: 'text.secondary', whiteSpace: 'nowrap' }}>Rows:</Typography> <TextField size="small" type="number" value={subsetConfig.rowLimit} onChange={(e) => { const value = parseInt(e.target.value) || 1; const maxRows = metadata.row_count || 100000; setSubsetConfig(prev => ({ ...prev, rowLimit: Math.min(Math.max(1, value), maxRows) })); }} slotProps={{ input: { inputProps: { min: 1, max: metadata.row_count || 100000, step: 100 } } }} sx={{ width: 90, '& .MuiInputBase-root': { fontSize: 11, height: 26 }, '& .MuiInputBase-input': { py: 0.25, px: 0.75 } }} /> <Typography variant="caption" sx={{ fontSize: 10, color: 'text.disabled', whiteSpace: 'nowrap' }}>/ {(metadata.row_count || '?').toLocaleString()}</Typography> </Box> <Box sx={{ display: 'flex', alignItems: 'center', gap: 0.5, minWidth: 0, maxWidth: 400 }}> <Typography variant="caption" sx={{ fontSize: 11, color: 'text.secondary', whiteSpace: 'nowrap' }}>Sort:</Typography> <Autocomplete multiple size="small" options={metadata.columns.map((col: any) => col.name)} value={subsetConfig.sortColumns} onChange={(_, newValue) => setSubsetConfig(prev => ({ ...prev, sortColumns: newValue }))} renderInput={(params) => ( <TextField {...params} placeholder="columns..." size="small" sx={{ minWidth: 120, '& .MuiInputBase-root': { fontSize: 11, minHeight: 26, py: 0 } }} /> )} renderTags={(value, getTagProps) => value.map((option, index) => ( <Chip {...getTagProps({ index })} key={option} label={option} size="small" sx={{ height: 18, fontSize: 10 }} /> )) } slotProps={{ paper: { sx: { fontSize: 12, '& .MuiAutocomplete-option': { fontSize: 12, py: 0.5, minHeight: 28 } } } }} sx={{ flex: 1, minWidth: 0 }} /> {subsetConfig.sortColumns.length > 0 && ( <ToggleButtonGroup value={subsetConfig.sortOrder} exclusive onChange={(_, v) => { if (v) setSubsetConfig(prev => ({ ...prev, sortOrder: v })); }} size="small" sx={{ height: 24 }} > <ToggleButton value="asc" sx={{ px: 1, py: 0, fontSize: 10, textTransform: 'none' }}>↑</ToggleButton> <ToggleButton value="desc" sx={{ px: 1, py: 0, fontSize: 10, textTransform: 'none' }}>↓</ToggleButton> </ToggleButtonGroup> )} </Box> </> ); })()} </Box>} {/* Load Table button */} {effectiveLoadedTables[selectedPreviewTable] ? ( <Box sx={{ display: 'flex', alignItems: 'center', gap: 0.5, flexShrink: 0 }}> <Button variant="outlined" size="medium" disabled startIcon={<CheckIcon sx={{ fontSize: 16 }} />} sx={{ textTransform: 'none', fontSize: 13, px: 3, height: 34, color: 'success.main', borderColor: 'success.main', '&.Mui-disabled': { color: 'success.main', borderColor: 'success.main', opacity: 0.8 }, }} > {effectiveLoadedTables[selectedPreviewTable] === 'subset' ? 'Subset loaded' : 'Loaded'} </Button> <Button variant="text" size="small" onClick={() => { const tableName = selectedPreviewTable; // Find and remove the workspace table that matches this database table const wt = workspaceTables.find(t => t.source?.databaseTable === tableName && t.source?.type === 'database'); if (wt) { dispatch(dfActions.deleteTable(wt.id)); } setLoadedTables(prev => { const next = { ...prev }; delete next[tableName]; return next; }); }} sx={{ textTransform: 'none', fontSize: 11, px: 1, minWidth: 0, height: 28, color: 'text.secondary', '&:hover': { color: 'error.main', backgroundColor: 'rgba(211,47,47,0.04)' }, }} > Unload </Button> </Box> ) : ( <Button variant="contained" size="medium" sx={{ textTransform: 'none', fontSize: 13, px: 4, height: 34, flexShrink: 0 }} onClick={() => { const tableName = selectedPreviewTable; const metadata = tableMetadata[tableName]; if (!metadata) return; const importOptions: any = {}; if (importMode === 'subset') { importOptions.rowLimit = subsetConfig.rowLimit; if (subsetConfig.sortColumns.length > 0) { importOptions.sortColumns = subsetConfig.sortColumns; importOptions.sortOrder = subsetConfig.sortOrder; } } const sampleRows = metadata.sample_rows || []; const columns = metadata.columns || []; const tableObj: DictTable = { id: tableName.split('.').pop() || tableName, displayId: tableName, names: columns.map((c: any) => c.name), metadata: columns.reduce((acc: Record<string, any>, col: any) => ({ ...acc, [col.name]: { type: 'string' as any, semanticType: '', levels: [] } }), {}), rows: sampleRows, anchored: true, createdBy: 'user' as const, attachedMetadata: '', source: { type: 'database' as const, databaseTable: tableName, canRefresh: true, lastRefreshed: Date.now(), }, }; onImport(); dispatch(loadTable({ table: tableObj, storeOnServer, dataLoaderType, dataLoaderParams: params, sourceTableName: tableName, importOptions: Object.keys(importOptions).length > 0 ? importOptions : undefined, })).unwrap() .then((result) => { setLoadedTables(prev => ({ ...prev, [tableName]: importMode })); onFinish("success", `Loaded table "${tableName}"`, [result.table.id]); }) .catch((error) => { console.error('Failed to load data:', error); onFinish("error", `Failed to load "${tableName}": ${error}`); }); }} > {importMode === 'subset' ? 'Load Table Subset' : 'Load Table'} </Button> )} </Box> </Box> )} </Box> ), ] const isConnected = Object.keys(tableMetadata).length > 0; return ( <Box sx={{p: 0, pb: 2}}> {isConnecting && <Box sx={{ position: "absolute", top: 0, left: 0, width: "100%", height: "100%", display: "flex", alignItems: "center", justifyContent: "center", zIndex: 1000, backgroundColor: "rgba(255, 255, 255, 0.7)" }}> <CircularProgress size={20} /> </Box>} {isConnected ? ( // Connected state: show connection parameters and disconnect button <Box sx={{}}> <Box sx={{mb: 1.5}}> <Box sx={{display: "flex", flexDirection: "row", alignItems: "center", gap: 0.5, mb: 1.5, flexWrap: "wrap"}}> <Typography variant="body2" component="span" sx={{fontSize: 11, color: 'secondary.main', fontWeight: 600, mr: 0.5}}> {dataLoaderType} </Typography> {paramDefs.filter((paramDef) => params[paramDef.name]).length > 0 && ( <Typography variant="body2" component="span" sx={{fontSize: 11, color: 'text.disabled', mr: 0.5}}>·</Typography> )} {paramDefs.filter((paramDef) => params[paramDef.name]).map((paramDef, index) => ( <React.Fragment key={paramDef.name}> <Typography variant="body2" component="span" sx={{fontSize: 11, color: 'text.secondary'}}> {paramDef.name}: </Typography> <Typography variant="body2" component="span" sx={{fontSize: 11, color: 'text.primary', fontWeight: 500, mr: 0.5}}> {params[paramDef.name] || '(empty)'} </Typography> {index < paramDefs.filter((p) => params[p.name]).length - 1 && ( <Typography variant="body2" component="span" sx={{fontSize: 11, color: 'text.disabled', mr: 0.5}}>·</Typography> )} </React.Fragment> ))} </Box> <Box sx={{display: "flex", flexDirection: "row", alignItems: "flex-end", gap: 1.5}}> <Box sx={{ display: "flex", flexDirection: "column", gap: 0.25, flex: 1, maxWidth: 300 }}> <Typography sx={{ fontSize: 11, fontWeight: 500, color: 'text.secondary', lineHeight: 1.3, display: 'flex', alignItems: 'center', gap: 0.5 }}> <SearchIcon sx={{ fontSize: 11 }} /> table filter </Typography> <TextField sx={{ '& .MuiInputBase-root': {fontSize: 12, height: '30px'}, '& .MuiInputBase-input': {fontSize: 12, py: 0.5, px: 1}, '& .MuiInputBase-input::placeholder': {fontSize: 11, fontStyle: "italic"}, '& .MuiOutlinedInput-root': { '& fieldset': { borderColor: 'rgba(0,0,0,0.15)' }, } }} variant="outlined" size="small" fullWidth autoComplete="off" placeholder="filter tables by keyword" value={tableFilter} onChange={(event) => setTableFilter(event.target.value)} /> </Box> <Button variant="outlined" size="small" sx={{textTransform: "none", height: 30, fontSize: 12}} onClick={() => { setIsConnecting(true); fetchWithIdentity(getUrls().DATA_LOADER_LIST_TABLES, { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ data_loader_type: dataLoaderType, data_loader_params: params, table_filter: tableFilter.trim() || null }) }).then((response: Response) => response.json()) .then((data: any) => { if (data.status === "success") { console.log(data.tables); setTableMetadata(Object.fromEntries(data.tables.map((table: any) => { return [table.name, table.metadata]; }))); } else { console.error('Failed to fetch data loader tables: {}', data.message); onFinish("error", `Failed to fetch data loader tables: ${data.message}`); } setIsConnecting(false); }) .catch((error: any) => { onFinish("error", `Failed to fetch data loader tables, please check the server is running`); setIsConnecting(false); }); }} > Refresh </Button> <Button variant="outlined" size="small" sx={{textTransform: "none", height: 30, fontSize: 12}} onClick={() => { setTableMetadata({}); setTableFilter(""); }} > Disconnect </Button> </Box> </Box> {tableMetadataBox} </Box> ) : ( // Not connected: show connection forms <> <Typography variant="body2" sx={{fontSize: 12, color: 'secondary.main', fontWeight: 600, mt: 1}}> {dataLoaderType} </Typography> <Box sx={{ display: "grid", gridTemplateColumns: "repeat(3, 1fr)", gap: 1.5, mt: 1, }}> {paramDefs.map((paramDef) => ( <Box key={paramDef.name} sx={{ display: "flex", flexDirection: "column", gap: 0.25 }}> <Typography sx={{ fontSize: 11, fontWeight: 500, color: paramDef.required ? 'text.primary' : 'text.secondary', lineHeight: 1.3, }} > {paramDef.name} {paramDef.required && <span style={{color: '#d32f2f'}}> *</span>} </Typography> <TextField sx={{ '& .MuiInputBase-root': {fontSize: 12, height: '30px'}, '& .MuiInputBase-input': {fontSize: 12, py: 0.5, px: 1}, '& .MuiInputBase-input::placeholder': {fontSize: 11, fontStyle: "italic"}, '& .MuiOutlinedInput-root': { '& fieldset': { borderColor: 'rgba(0,0,0,0.15)' }, } }} variant="outlined" size="small" fullWidth required={paramDef.required} value={params[paramDef.name] ?? ''} placeholder={paramDef.default ? `${paramDef.default}` : paramDef.description || ''} onChange={(event: any) => { dispatch(dfActions.updateDataLoaderConnectParam({ dataLoaderType, paramName: paramDef.name, paramValue: event.target.value})); }} /> </Box> ))} </Box> <Box sx={{ display: "flex", flexDirection: "row", alignItems: "flex-end", gap: 1.5, mt: 2 }}> <Box sx={{ display: "flex", flexDirection: "column", gap: 0.25, flex: 1, maxWidth: 300 }}> <Typography sx={{ fontSize: 11, fontWeight: 500, color: 'text.secondary', lineHeight: 1.3, display: 'flex', alignItems: 'center', gap: 0.5 }}> <SearchIcon sx={{ fontSize: 11 }} /> table filter </Typography> <TextField sx={{ '& .MuiInputBase-root': {fontSize: 12, height: '30px'}, '& .MuiInputBase-input': {fontSize: 12, py: 0.5, px: 1}, '& .MuiInputBase-input::placeholder': {fontSize: 11, fontStyle: "italic"}, '& .MuiOutlinedInput-root': { '& fieldset': { borderColor: 'rgba(0,0,0,0.15)' }, } }} variant="outlined" size="small" fullWidth autoComplete="off" placeholder="filter tables by keyword" value={tableFilter} onChange={(event) => setTableFilter(event.target.value)} /> </Box> {paramDefs.length > 0 && <Button variant="contained" color="primary" size="small" sx={{textTransform: "none", minWidth: 100, height: 30}} onClick={() => { setIsConnecting(true); fetchWithIdentity(getUrls().DATA_LOADER_LIST_TABLES, { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ data_loader_type: dataLoaderType, data_loader_params: params, table_filter: tableFilter.trim() || null }) }).then((response: Response) => response.json()) .then((data: any) => { if (data.status === "success") { console.log(data.tables); setTableMetadata(Object.fromEntries(data.tables.map((table: any) => { return [table.name, table.metadata]; }))); } else { console.error('Failed to fetch data loader tables: {}', data.message); onFinish("error", `Failed to fetch data loader tables: ${data.message}`); } setIsConnecting(false); }) .catch((error: any) => { onFinish("error", `Failed to fetch data loader tables, please check the server is running`); setIsConnecting(false); }); }}> Connect{tableFilter.trim() ? " with filter" : ""} </Button>} </Box> {authInstructions.trim() && ( <Box sx={(theme) => ({ mt: 2, px: 1.5, py: 1, backgroundColor: 'rgba(0,0,0,0.02)', borderRadius: 1, border: '1px solid rgba(0,0,0,0.06)', fontFamily: theme.typography.fontFamily, fontSize: '11px', color: 'text.secondary', lineHeight: 1.6, '& *': { fontFamily: theme.typography.fontFamily, fontSize: 'inherit', lineHeight: 'inherit', color: 'inherit' }, '& p': { margin: '0 0 4px 0', '&:last-child': { marginBottom: 0 } }, '& code': { fontSize: '10px', fontFamily: 'monospace !important', backgroundColor: 'rgba(0,0,0,0.06)', padding: '1px 4px', borderRadius: '3px' }, '& pre': { fontSize: '10px', fontFamily: 'monospace !important', backgroundColor: 'rgba(0,0,0,0.04)', padding: '8px', borderRadius: '4px', overflow: 'auto', margin: '4px 0', '& code': { backgroundColor: 'transparent', padding: 0 } }, '& a': { color: 'primary.main' }, '& ul, & ol': { paddingLeft: '20px', margin: '4px 0' }, '& li': { marginBottom: '2px' }, '& strong': { fontWeight: 600, color: 'text.primary' }, '& h1, & h2, & h3, & h4': { fontSize: '12px', fontWeight: 600, color: 'text.primary', margin: '4px 0' }, })}> <Markdown>{authInstructions.trim()}</Markdown> </Box> )}</> )} </Box> ); } ================================================ FILE: src/views/DataFormulator.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React, { useEffect, useRef, useState, useCallback, useMemo } from 'react'; import '../scss/App.scss'; import { useDispatch, useSelector } from "react-redux"; /* code change */ import { DataFormulatorState, dfActions, dfSelectors, ModelConfig, } from '../app/dfSlice' import _ from 'lodash'; import { Allotment, AllotmentHandle } from "allotment"; import "allotment/dist/style.css"; import { Typography, Box, Tooltip, Button, Divider, useTheme, alpha, CircularProgress, Backdrop, } from '@mui/material'; import { borderColor, shadow, radius } from '../app/tokens'; import { FreeDataViewFC } from './DataView'; import { VisualizationViewFC } from './VisualizationView'; import { DndProvider } from 'react-dnd' import { HTML5Backend } from 'react-dnd-html5-backend' import { toolName } from '../app/App'; import { DataThread } from './DataThread'; import dfLogo from '../assets/df-logo.png'; import exampleImageTable from "../assets/example-image-table.png"; import { ModelSelectionButton } from './ModelSelectionDialog'; import { getUrls, fetchWithIdentity } from '../app/utils'; import { UnifiedDataUploadDialog, UploadTabType, DataLoadMenu } from './UnifiedDataUploadDialog'; import { ReportView } from './ReportView'; import GitHubIcon from '@mui/icons-material/GitHub'; import YouTubeIcon from '@mui/icons-material/YouTube'; import { ExampleSession, exampleSessions, ExampleSessionCard } from './ExampleSessions'; import { useDataRefresh, useDerivedTableRefresh } from '../app/useDataRefresh'; import type { DictTable } from '../components/ComponentType'; export const DataFormulatorFC = ({ }) => { const tables = useSelector((state: DataFormulatorState) => state.tables); const focusedId = useSelector((state: DataFormulatorState) => state.focusedId); const models = useSelector((state: DataFormulatorState) => state.models); const selectedModelId = useSelector((state: DataFormulatorState) => state.selectedModelId); const viewMode = useSelector((state: DataFormulatorState) => state.viewMode); const serverConfig = useSelector((state: DataFormulatorState) => state.serverConfig); const theme = useTheme(); const dispatch = useDispatch(); // Set up automatic refresh of derived tables when source data changes useDerivedTableRefresh(); // State for unified data upload dialog const [uploadDialogOpen, setUploadDialogOpen] = useState(false); const [uploadDialogInitialTab, setUploadDialogInitialTab] = useState<UploadTabType>('menu'); // Loading state for sessions (from Redux, shared with App.tsx) const sessionLoading = useSelector((state: DataFormulatorState) => state.sessionLoading); const sessionLoadingLabel = useSelector((state: DataFormulatorState) => state.sessionLoadingLabel); const openUploadDialog = (tab: UploadTabType) => { setUploadDialogInitialTab(tab); setUploadDialogOpen(true); }; const handleLoadExampleSession = (session: ExampleSession) => { dispatch(dfActions.setSessionLoading({ loading: true, label: `Loading ${session.title}...` })); dispatch(dfActions.addMessages({ timestamp: Date.now(), type: 'info', component: 'data formulator', value: `Loading example session: ${session.title}`, })); // Load the complete state from the JSON file fetch(session.dataFile) .then(res => res.json()) .then(savedState => { // Use loadState to restore the complete session state dispatch(dfActions.loadState(savedState)); dispatch(dfActions.addMessages({ timestamp: Date.now(), type: 'success', component: 'data formulator', value: `Successfully loaded ${session.title}`, })); }) .catch(error => { console.error('Error loading session:', error); dispatch(dfActions.addMessages({ timestamp: Date.now(), type: 'error', component: 'data formulator', value: `Failed to load ${session.title}: ${error.message}`, })); }) .finally(() => { dispatch(dfActions.setSessionLoading({ loading: false })); }); }; useEffect(() => { document.title = toolName; // Preload imported images (public images are preloaded in index.html) const imagesToPreload = [ { src: dfLogo, type: 'image/png' }, { src: exampleImageTable, type: 'image/png' }, ]; const preloadLinks: HTMLLinkElement[] = []; imagesToPreload.forEach(({ src, type }) => { // Use link preload for better priority const link = document.createElement('link'); link.rel = 'preload'; link.as = 'image'; link.href = src; link.type = type; document.head.appendChild(link); preloadLinks.push(link); }); // Cleanup function to remove preload links when component unmounts return () => { preloadLinks.forEach(link => { if (link.parentNode) { link.parentNode.removeChild(link); } }); }; }, []); useEffect(() => { const findWorkingModel = async () => { let selectedModel = models.find(m => m.id == selectedModelId); let otherModels = models.filter(m => m.id != selectedModelId); let modelsToTest = [selectedModel, ...otherModels].filter(m => m != undefined); let testModel = async (model: ModelConfig) => { const message = { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ model }), }; try { const response = await fetchWithIdentity(getUrls().TEST_MODEL, {...message }); const data = await response.json(); const status = data["status"] || 'error'; return {model, status, message: data["message"] || ""}; } catch (error) { return {model, status: 'error', message: (error as Error).message || 'Failed to test model'}; } } // Then test unassigned models sequentially until one works for (let model of modelsToTest) { if (!model) continue; let testResult = await testModel(model); dispatch(dfActions.updateModelStatus({ id: model.id, status: testResult.status, message: testResult.message })); if (testResult.status == 'ok') { dispatch(dfActions.selectModel(model.id)); return; }; } }; if (models.length > 0) { findWorkingModel(); } }, []); const visPaneMain = ( <Box sx={{ width: "100%", overflow: "hidden", display: "flex", flexDirection: "row" }}> <VisualizationViewFC /> </Box>); const visPane = ( <Box className="inner-allotment" sx={{width: '100%', height: '100%', "& .split-view-view:first-of-type": { display: 'flex', overflow: 'hidden', }}}> <Allotment vertical> <Allotment.Pane minSize={200} > {visPaneMain} </Allotment.Pane> <Allotment.Pane minSize={0} preferredSize={180}> <Box className="table-box"> <FreeDataViewFC /> </Box> </Allotment.Pane> </Allotment> </Box>); let borderBoxStyle = { border: `1px solid ${borderColor.view}`, borderRadius: radius.pill, //boxShadow: '0 0 5px rgba(0,0,0,0.1)', } // Discrete column snapping for DataThread const CARD_WIDTH = 220; const CARD_GAP = 12; const COLUMN_WIDTH = CARD_WIDTH + CARD_GAP; const PANEL_PADDING = 32 ; const columnSize = (n: number) => n * COLUMN_WIDTH + 2 * PANEL_PADDING; const allotmentRef = useRef<AllotmentHandle>(null); const containerRef = useRef<HTMLDivElement>(null); const snapToColumns = useCallback((sizes: number[]) => { if (!allotmentRef.current || sizes.length < 2) return; const raw = sizes[0]; // Find nearest discrete column count (1-3) let bestCols = 1; let bestDist = Infinity; for (let n = 1; n <= 3; n++) { const dist = Math.abs(raw - columnSize(n)); if (dist < bestDist) { bestDist = dist; bestCols = n; } } const snapped = columnSize(bestCols); if (Math.abs(raw - snapped) > 2) { const totalWidth = sizes.reduce((a, b) => a + b, 0); allotmentRef.current.resize([snapped, totalWidth - snapped]); } }, []); // Compute thread count to decide preferred pane width: // A "thread" is a leaf table's derivation chain displayed as a column. // Must match the chain-splitting logic in DataThread (MAX_CHAIN_TABLES). const threadCount = useMemo(() => { // A table is a "leaf" if no other non-anchored table derives from it const hasNonAnchoredChild = new Set<string>(); tables.forEach(t => { if (t.derive && !t.anchored) { hasNonAnchoredChild.add(t.derive.trigger.tableId); } }); const leafTables = tables.filter(t => !hasNonAnchoredChild.has(t.id)); // Threads = leaf tables with derivation chains + 1 group for hanging (source) tables const threaded = leafTables.filter(t => t.derive); const hanging = leafTables.filter(t => !t.derive); let count = threaded.length + (hanging.length > 0 ? 1 : 0); // Account for chain-splitting: long chains are broken into sub-threads // (mirrors MAX_CHAIN_TABLES logic in DataThread) const MAX_CHAIN_TABLES = 5; const tableById = new Map(tables.map(t => [t.id, t])); const getChainLength = (t: DictTable): number => { let len = 1; let cur = t; while (cur.derive && !cur.anchored) { len++; const parent = tableById.get(cur.derive.trigger.tableId); if (!parent) break; cur = parent; } return len; }; const claimedForCount = new Set<string>(); for (const lt of threaded) { // Walk chain const chainIds: string[] = [lt.id]; let cur = lt; while (cur.derive && !cur.anchored) { const pid = cur.derive.trigger.tableId; chainIds.push(pid); const parent = tableById.get(pid); if (!parent) break; cur = parent; } const ownedIds = chainIds.filter(id => !claimedForCount.has(id)); if (ownedIds.length > MAX_CHAIN_TABLES) { // Each extra split adds one more thread entry const extraSplits = Math.floor((ownedIds.length - 1) / MAX_CHAIN_TABLES); count += extraSplits; } chainIds.forEach(id => claimedForCount.add(id)); } return count; }, [tables]); const preferredColumns = threadCount <= 1 ? 1 : 2; // Track previous thread count to auto-resize intelligently const prevThreadCountRef = useRef(threadCount); useEffect(() => { const prev = prevThreadCountRef.current; prevThreadCountRef.current = threadCount; if (!allotmentRef.current || !containerRef.current) return; // When there are no tables the first Allotment.Pane is unmounted, // so the Allotment only has one child – calling resize with two // sizes would crash (accessing .minimumSize on an undefined pane). if (tables.length === 0) return; const totalWidth = containerRef.current.clientWidth; if (totalWidth <= 0) return; let newSize: number | null = null; if (prev <= 1 && threadCount > 1) { // Case 1: was 1 thread, now 2+ → expand to 2 columns newSize = columnSize(2); } else if (prev > 1 && threadCount <= 1) { // Case 2: was 2+ threads, now 1 → shrink to 1 column newSize = columnSize(1); } // Case 3: was 2+ threads and still 2+ → don't change (respect user's manual setting) if (newSize !== null) { // Defer resize to the next animation frame so the Allotment has // re-rendered its pane children before we call resize. const finalSize = newSize; const rafId = requestAnimationFrame(() => { try { const w = containerRef.current?.clientWidth ?? totalWidth; allotmentRef.current?.resize([finalSize, w - finalSize]); } catch { // Allotment pane structure may not yet match; ignore. } }); return () => cancelAnimationFrame(rafId); } }, [threadCount, tables.length]); const fixedSplitPane = ( <Box sx={{display: 'flex', flexDirection: 'row', height: '100%'}}> <Box ref={containerRef} className="outer-allotment" sx={{ margin: '4px 8px 8px 8px', backgroundColor: 'white', display: 'flex', height: 'calc(100% - 12px)', width: '100%', flexDirection: 'column', overflow: 'hidden', position: 'relative'}}> <Allotment ref={allotmentRef} onDragEnd={snapToColumns} proportionalLayout={false}> {tables.length > 0 ? ( <Allotment.Pane minSize={columnSize(1)} preferredSize={columnSize(preferredColumns)} maxSize={columnSize(3)} snap={false}> <DataThread sx={{ display: 'flex', flexDirection: 'column', overflow: 'hidden', alignContent: 'flex-start', height: '100%', mr: 0.5, }}/> </Allotment.Pane> ) : null} <Allotment.Pane minSize={300}> <Box sx={{ ...borderBoxStyle, height: '100%', overflow: 'hidden', display: 'flex', flexDirection: 'column', boxSizing: 'border-box' }}> {viewMode === 'editor' ? ( visPane ) : ( <ReportView /> )} </Box> </Allotment.Pane> </Allotment> </Box> </Box> ); let footer = <Box sx={{ color: 'text.secondary', display: 'flex', backgroundColor: 'rgba(255, 255, 255, 0.89)', alignItems: 'center', justifyContent: 'center' }}> <Button size="small" color="inherit" sx={{ textTransform: 'none'}} target="_blank" rel="noopener noreferrer" href="https://www.microsoft.com/en-us/privacy/privacystatement">Privacy & Cookies</Button> <Divider orientation="vertical" variant="middle" flexItem sx={{ mx: 1 }} /> <Button size="small" color="inherit" sx={{ textTransform: 'none'}} target="_blank" rel="noopener noreferrer" href="https://www.microsoft.com/en-us/legal/intellectualproperty/copyright">Terms of Use</Button> <Divider orientation="vertical" variant="middle" flexItem sx={{ mx: 1 }} /> <Button size="small" color="inherit" sx={{ textTransform: 'none'}} target="_blank" rel="noopener noreferrer" href="https://github.com/microsoft/data-formulator/issues">Contact Us</Button> <Typography sx={{ display: 'inline', fontSize: '12px', ml: 1 }}> @ {new Date().getFullYear()}</Typography> </Box> let dataUploadRequestBox = <Box sx={{ margin: '4px 4px 4px 8px', background: ` linear-gradient(90deg, ${alpha(theme.palette.text.secondary, 0.01)} 1px, transparent 1px), linear-gradient(0deg, ${alpha(theme.palette.text.secondary, 0.01)} 1px, transparent 1px) `, backgroundSize: '16px 16px', width: 'calc(100vw - 16px)', overflow: 'auto', display: 'flex', flexDirection: 'column', height: '100%', }}> <Box sx={{margin:'auto', pb: '5%', display: "flex", flexDirection: "column", textAlign: "center" }}> <Box sx={{display: 'flex', mx: 'auto'}}> <Typography fontSize={84} sx={{ml: 2, letterSpacing: '0.05em'}}>{toolName}</Typography> </Box> <Typography sx={{ fontSize: 24, color: theme.palette.text.secondary, textAlign: 'center', mb: 2}}> Explore data with visualizations, powered by AI agents. </Typography> {serverConfig.PROJECT_FRONT_PAGE && ( <Box component="nav" aria-label="Resources" sx={{ display: 'flex', justifyContent: 'center', gap: 1, mb: 3, flexWrap: 'wrap' }}> <Button size="small" variant="text" color="primary" sx={{ textTransform: 'none', fontSize: 13 }} startIcon={<Box component="img" sx={{ width: 15, height: 15 }} alt="" aria-hidden="true" src="/pip-logo.svg" />} target="_blank" rel="noopener noreferrer" href="https://pypi.org/project/data-formulator/" >Install Locally</Button> <Button size="small" variant="text" color="primary" sx={{ textTransform: 'none', fontSize: 13 }} startIcon={<YouTubeIcon sx={{ color: '#FF0000', fontSize: 17 }} aria-hidden="true" />} target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/watch?v=GfTE2FLyMrs" >Video</Button> <Button size="small" variant="text" color="primary" sx={{ textTransform: 'none', fontSize: 13 }} startIcon={<GitHubIcon aria-hidden="true" sx={{ fontSize: 17 }} />} target="_blank" rel="noopener noreferrer" href="https://github.com/microsoft/data-formulator" >GitHub</Button> </Box> )} <Box sx={{my: 4}}> <DataLoadMenu onSelectTab={(tab) => openUploadDialog(tab)} serverConfig={serverConfig} variant="page" /> </Box> <Box sx={{mt: 4}}> <Divider sx={{width: '200px', mx: 'auto', mb: 3, fontSize: '1.2rem'}}> <Typography sx={{ color: 'text.secondary' }}> demos </Typography> </Divider> <Box sx={{ display: 'grid', gridTemplateColumns: 'repeat(auto-fit, minmax(200px, 1fr))', gap: 2, }}> {exampleSessions.map((session) => ( <ExampleSessionCard key={session.id} session={session} theme={theme} onClick={() => handleLoadExampleSession(session)} /> ))} </Box> </Box> </Box> {footer} </Box>; return ( <Box sx={{ display: 'block', width: "100%", height: '100%', position: 'relative' }}> <DndProvider backend={HTML5Backend}> {tables.length > 0 ? fixedSplitPane : dataUploadRequestBox} <UnifiedDataUploadDialog open={uploadDialogOpen} onClose={() => setUploadDialogOpen(false)} initialTab={uploadDialogInitialTab} /> {/* Loading overlay for session loading */} <Backdrop open={sessionLoading} sx={{ position: 'absolute', zIndex: 999, backgroundColor: alpha(theme.palette.background.default, 0.85), backdropFilter: 'blur(4px)', display: 'flex', flexDirection: 'column', gap: 2, }} > <CircularProgress size={40} /> <Typography variant="body1" color="text.secondary"> {sessionLoadingLabel || 'Loading session...'} </Typography> <Button variant="text" size="small" onClick={() => dispatch(dfActions.setSessionLoading({ loading: false }))} sx={{ mt: 1, textTransform: 'none', color: 'text.secondary' }} > Cancel </Button> </Backdrop> {selectedModelId == undefined && ( <Box sx={{ position: 'absolute', top: 0, left: 0, right: 0, bottom: 0, backgroundColor: alpha(theme.palette.background.default, 0.85), backdropFilter: 'blur(4px)', display: 'flex', flexDirection: 'column', zIndex: 1000, }}> <Box sx={{margin:'auto', pb: '5%', display: "flex", flexDirection: "column", textAlign: "center"}}> <Box component="img" sx={{ width: 196, margin: "auto" }} alt="Data Formulator logo" src={dfLogo} fetchPriority="high" /> <Typography variant="h3" sx={{marginTop: "20px", fontWeight: 200, letterSpacing: '0.05em'}}> {toolName} </Typography> <Typography variant="h4" sx={{mt: 3, fontSize: 28, letterSpacing: '0.02em'}}> First, let's <ModelSelectionButton /> </Typography> <Typography color="text.secondary" variant="body1" sx={{mt: 2, width: 600}}>💡 Models with strong code generation capabilities (e.g., gpt-5, claude-sonnet-4-5) provide best experience with Data Formulator.</Typography> </Box> {footer} </Box> )} </DndProvider> </Box>); } ================================================ FILE: src/views/DataLoadingChat.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import * as React from 'react'; import { useEffect, useRef, useState } from 'react'; import { Box, Button, Divider, IconButton, Typography, Tooltip, CircularProgress, alpha, useTheme } from '@mui/material'; import { borderColor, transition, radius } from '../app/tokens'; import { useDispatch, useSelector } from 'react-redux'; import { AppDispatch } from '../app/store'; import { DataFormulatorState, dfActions, fetchFieldSemanticType } from '../app/dfSlice'; import { createTableFromText } from '../data/utils'; import { loadTable } from '../app/tableThunks'; import { createOrderedThreadBlocks, DataLoadingInputBox, DataPreviewBox, SingleDataCleanThreadView } from './DataLoadingThread'; const generateDefaultName = (seed: string) => { const hash = seed.split('').reduce((acc, c) => ((acc << 5) - acc) + c.charCodeAt(0) | 0, 0); return `data-${Math.abs(hash).toString(36).slice(0, 5)}`; }; const getUniqueTableName = (baseName: string, existingNames: Set<string>): string => { let uniqueName = baseName; let counter = 1; while (existingNames.has(uniqueName)) { uniqueName = `${baseName}_${counter}`; counter += 1; } return uniqueName; }; export const DataLoadingChat: React.FC<{storeOnServer?: boolean}> = ({storeOnServer = true}) => { const theme = useTheme(); const dispatch = useDispatch<AppDispatch>(); const inputBoxRef = useRef<(() => void) | null>(null); const abortControllerRef = useRef<AbortController | null>(null); const cleanInProgress = useSelector((state: DataFormulatorState) => state.cleanInProgress); const existingTables = useSelector((state: DataFormulatorState) => state.tables); const dataCleanBlocks = useSelector((state: DataFormulatorState) => state.dataCleanBlocks); const focusedDataCleanBlockId = useSelector((state: DataFormulatorState) => state.focusedDataCleanBlockId); const [streamingContent, setStreamingContent] = useState(''); const existingNames = new Set(existingTables.map(t => t.id)); let existOutputBlocks = dataCleanBlocks.length > 0; let dataCleanBlocksThread = createOrderedThreadBlocks(dataCleanBlocks); let threadsComponent = dataCleanBlocksThread.map((thread, i) => { return <SingleDataCleanThreadView key={`data-clean-thread-${i}`} thread={thread} sx={{ backgroundColor: 'white', borderRadius: radius.md, padding: 1, flex: 'none', display: 'flex', flexDirection: 'column', height: 'fit-content', transition: transition.fast, }} /> }) // Get the selected CSV data from Redux state const selectedTable = (() => { if (focusedDataCleanBlockId) { let block = dataCleanBlocks.find(block => block.id === focusedDataCleanBlockId.blockId); if (block) { return block.items?.[focusedDataCleanBlockId.itemId]; } } return undefined; })(); const handleUpload = () => { if (!selectedTable) return; const suggestedName = selectedTable.name || generateDefaultName(selectedTable.content.value.slice(0, 96)); console.log(selectedTable); const base = suggestedName.trim(); const unique = getUniqueTableName(base, existingNames); const table = createTableFromText(unique, selectedTable.content.value, selectedTable.context); if (table) { const tableWithSource = { ...table, source: { type: 'extract' as const } }; dispatch(loadTable({ table: tableWithSource, storeOnServer })); } }; const thinkingBanner = ( <Box sx={{ py: 0.5, display: 'flex', position: 'relative', overflow: 'hidden', '&::before': { content: '""', position: 'absolute', top: 0, left: 0, width: '100%', height: '100%', background: 'linear-gradient(90deg, transparent 0%, rgba(255, 255, 255, 0.8) 50%, transparent 100%)', animation: 'shimmer 2s ease-in-out infinite', zIndex: 1, pointerEvents: 'none', }, '@keyframes shimmer': { '0%': { transform: 'translateX(-100%)', }, '100%': { transform: 'translateX(100%)', }, } }}> <Box sx={{ py: 1, display: 'flex', alignItems: 'center', justifyContent: 'left', }}> <CircularProgress size={10} sx={{ color: 'text.secondary' }} /> <Typography variant="body2" sx={{ ml: 1, fontSize: 10, color: 'rgba(0, 0, 0, 0.7) !important' }}> extracting data... </Typography> </Box> </Box> ); // Empty state - centered layout similar to upload dialog if (!existOutputBlocks && !streamingContent) { return ( <Box sx={{ width: '100%', height: '100%', display: 'flex', justifyContent: 'center', alignItems: 'center', p: 2, boxSizing: 'border-box' }}> <Box sx={{ width: '100%', maxWidth: 720, display: 'flex', flexDirection: 'column', gap: 2, }}> <DataLoadingInputBox maxLines={24} onStreamingContentUpdate={setStreamingContent} abortControllerRef={abortControllerRef} /> </Box> </Box> ); } // Main layout with sidebar (similar to DBTablePane) return ( <Box sx={{ display: 'flex', flexDirection: 'column', bgcolor: 'white', flex: 1, overflow: 'hidden', height: '100%', width: '100%' }}> <Box sx={{ display: 'flex', flexDirection: 'row', flex: 1, overflow: 'hidden', minHeight: 0, height: '100%' }}> {/* Left sidebar - Thread list (similar to DBTablePane) */} <Box sx={{ display: 'flex', flexDirection: 'column', width: 240, minWidth: 240, maxWidth: 240, overflow: 'hidden', height: '100%', borderRight: `1px solid ${borderColor.view}` }}> <Box sx={{ display: 'flex', flexDirection: 'column', overflowY: 'auto', overflowX: 'hidden', flex: 1, minHeight: 0, height: '100%', position: 'relative', overscrollBehavior: 'contain', px: 0.5, pt: 1 }}> {threadsComponent.length > 0 ? ( threadsComponent ) : ( <Typography variant="caption" sx={{ color: "text.disabled", px: 2, py: 0.5, fontStyle: "italic", textAlign: 'center' }}> No extraction threads yet </Typography> )} </Box> <Box sx={{ borderTop: `1px solid ${borderColor.divider}`, p: 1 }}> <DataLoadingInputBox ref={inputBoxRef} maxLines={4} onStreamingContentUpdate={setStreamingContent} abortControllerRef={abortControllerRef} /> </Box> </Box> {/* Right content area */} <Box sx={{ flex: 1, overflow: 'hidden', minWidth: 0, minHeight: 0, height: '100%', position: 'relative', display: 'flex', flexDirection: 'column' }}> {streamingContent ? ( <Box sx={{ display: 'flex', flexDirection: 'column', padding: 2, height: '100%', overflow: 'auto' }} > {thinkingBanner} <Typography variant="body2" color="text.secondary" sx={{ mt: 2, fontSize: '11px', whiteSpace: 'pre-wrap', overflow: 'clip', maxHeight: '100%', overflowY: 'auto' }} > {streamingContent.trim()} </Typography> </Box> ) : existOutputBlocks ? ( <Box sx={{ display: 'flex', flexDirection: 'column', padding: 2, height: '100%', overflow: 'hidden' }} > {selectedTable && ( <Typography sx={{ fontSize: 14, marginBottom: 1, fontWeight: 500, color: 'text.primary', display: 'flex', alignItems: 'center', gap: 0.5 }} gutterBottom > {selectedTable?.name} </Typography> )} <Box sx={{ display: 'flex', flexDirection: 'column', flex: 1, gap: 1, overflow: 'hidden', minHeight: 0 }} > {selectedTable ? ( <Box sx={{ flex: 1, overflow: 'auto', minHeight: 0 }}> <DataPreviewBox /> </Box> ) : ( <Box sx={{ display: 'flex', alignItems: 'center', justifyContent: 'center', flex: 1, minHeight: 0 }}> <Typography variant="body2" color="text.secondary" sx={{ textAlign: 'center', fontSize: '11px' }}> Select a table from the left to preview </Typography> </Box> )} {/* Bottom submit bar */} {selectedTable && ( <Box sx={{ mt: 'auto', pt: 1, display: 'flex', flexDirection: 'row', alignItems: 'center', gap: 1, borderTop: `1px solid ${borderColor.divider}`, '& .MuiButton-root': { textTransform: 'none' } }}> <Button variant="contained" onClick={() => { if (inputBoxRef.current) { inputBoxRef.current(); } }} disabled={!selectedTable || selectedTable.content.type !== 'image_url' || cleanInProgress} size="small" > Extract data from image </Button> <Button variant="contained" sx={{ ml: 'auto' }} onClick={handleUpload} disabled={!selectedTable || selectedTable.content.type !== 'csv'} size="small" > Load table </Button> </Box> )} </Box> </Box> ) : null} </Box> </Box> </Box> ); }; ================================================ FILE: src/views/DataLoadingThread.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import * as React from 'react'; import { useEffect, useRef, useState } from 'react'; import { Box, Button, Card, CardContent, Divider, IconButton, Paper, Stack, TextField, Typography, alpha, useTheme, Dialog, DialogTitle, DialogContent, Tooltip, LinearProgress, CircularProgress, Chip, SxProps, Theme } from '@mui/material'; import UploadIcon from '@mui/icons-material/Upload'; import SendIcon from '@mui/icons-material/Send'; import RestartAltIcon from '@mui/icons-material/RestartAlt'; import CancelIcon from '@mui/icons-material/Cancel'; import PanoramaFishEyeIcon from '@mui/icons-material/PanoramaFishEye'; import CloseIcon from '@mui/icons-material/Close'; import CircleIcon from '@mui/icons-material/Circle'; import TableIcon from '@mui/icons-material/TableChart'; import LinkIcon from '@mui/icons-material/Link'; import DeleteIcon from '@mui/icons-material/Delete'; import { AgentIcon as PrecisionManufacturingIcon } from '../icons'; import SouthIcon from '@mui/icons-material/South'; import LanguageIcon from '@mui/icons-material/Language'; import ImageIcon from '@mui/icons-material/Image'; import TextFieldsIcon from '@mui/icons-material/TextFields'; import DatasetIcon from '@mui/icons-material/Dataset'; import StopIcon from '@mui/icons-material/Stop'; import exampleImageTable from "../assets/example-image-table.png"; import { useDispatch, useSelector } from 'react-redux'; import { AppDispatch } from '../app/store'; import { DataFormulatorState, dfActions, dfSelectors, fetchFieldSemanticType } from '../app/dfSlice'; import { borderColor, shadow, transition, radius } from '../app/tokens'; import { DataCleanBlock, DataCleanTableOutput } from '../components/ComponentType'; import { getUrls, fetchWithIdentity } from '../app/utils'; import { CustomReactTable } from './ReactTable'; import { createTableFromText } from '../data/utils'; type DialogContentItem = { type: 'text'; text: string; } | { type: 'image_url'; image_url: { url: string; detail?: string; }; }; type DialogMessage = { role: 'user' | 'assistant' | 'system'; content: string | DialogContentItem[]; }; const generateDefaultName = (seed: string) => { const hash = seed.split('').reduce((acc, c) => ((acc << 5) - acc) + c.charCodeAt(0) | 0, 0); return `data-${Math.abs(hash).toString(36).slice(0, 5)}`; }; // Sample task card component const SampleTaskChip: React.FC<{ task: { text: string; icon?: React.ReactElement; image?: string }; theme: Theme; onClick: () => void; disabled?: boolean; }> = ({ task, theme, onClick, disabled }) => { return ( <Box sx={{ display: 'inline-flex', alignItems: 'center', padding: '6px 8px', fontSize: '12px', minHeight: '32px', height: 'auto', borderRadius: radius.md, border: `1px solid ${alpha(theme.palette.primary.main, 0.2)}`, boxShadow: shadow.sm, transition: transition.normal, backgroundColor: alpha(theme.palette.background.paper, 0.9), cursor: disabled ? 'default' : 'pointer', opacity: disabled ? 0.6 : 1, '&:hover': disabled ? {} : { boxShadow: shadow.md, borderColor: alpha(theme.palette.primary.main, 0.5), transform: 'translateY(-1px)', }, }} onClick={disabled ? undefined : onClick} > {/* {task.icon && ( <Box sx={{ mr: 1, display: 'flex', alignItems: 'center', color: theme.palette.primary.main }}> {task.icon} </Box> )} */} {task.image && ( <Box component="img" src={task.image} sx={{ width: 24, height: 24, objectFit: 'cover', borderRadius: 0.5, mr: 1, border: `1px solid ${borderColor.divider}` }} /> )} <Typography component="div" sx={{ fontSize: '12px', color: theme.palette.text.primary, lineHeight: 1.4, }} > {task.text} </Typography> </Box> ); }; export const DataPreviewBox: React.FC<{sx?: SxProps}> = ({sx}) => { const dispatch = useDispatch<AppDispatch>(); const dataCleanBlocks = useSelector((state: DataFormulatorState) => state.dataCleanBlocks); const focusedDataCleanBlockId = useSelector((state: DataFormulatorState) => state.focusedDataCleanBlockId); const existingTables = useSelector((state: DataFormulatorState) => state.tables); let selectedBlock = dataCleanBlocks.find(block => block.id === focusedDataCleanBlockId?.blockId) || dataCleanBlocks[dataCleanBlocks.length - 1]; let selectedTable = focusedDataCleanBlockId ? selectedBlock?.items?.[focusedDataCleanBlockId.itemId] : undefined; if (!selectedTable) { return <Paper variant="outlined" sx={{ p: 1, display: 'flex', flexDirection: 'column', gap: 1, ...sx }}> <Typography variant="body2" sx={{ fontSize: '10px', color: 'text.secondary' }}> No data selected </Typography> </Paper> } if (selectedTable.content.type === 'csv' && selectedTable.content.value) { const suggestedName = selectedTable.name || generateDefaultName(selectedTable.content.value.slice(0, 96)); const tableComponent = createTableFromText(suggestedName, selectedTable.content.value); if (tableComponent) { return <CustomReactTable rows={tableComponent.rows} rowsPerPageNum={-1} compact={false} columnDefs={tableComponent.names.map((name) => ({ id: name, label: name, minWidth: 60, align: undefined, format: (v: any) => v, }))} maxHeight={600} /> } return <Paper variant="outlined" sx={{ p: 1, display: 'flex', flexDirection: 'column', gap: 1, ...sx }}> <Typography variant="body2" sx={{ fontSize: '10px', color: 'text.secondary' }}> {selectedTable.content.value} </Typography> </Paper> } // Handle image_url content type if (selectedTable.content.type === 'image_url') { return <Paper variant="outlined" sx={{ p: 1, display: 'flex', flexDirection: 'column', gap: 1, ...sx }}> <Typography variant="body2" sx={{ fontSize: '10px', color: 'text.secondary' }}> Image URL: {selectedTable.content.value} </Typography> <Box component="img" src={selectedTable.content.value} alt={`Image from ${selectedTable.name || 'data source'}`} sx={{ maxWidth: '100%', maxHeight: '400px', objectFit: 'contain', borderRadius: 1 }} /> </Paper> } // Handle data_url content type if (selectedTable.content.type === 'web_url') { return <Paper variant="outlined" sx={{ p: 1, display: 'flex', flexDirection: 'column', gap: 1, ...sx }}> <Typography variant="body2" sx={{ fontSize: '12px', color: 'text.primary', fontWeight: 500 }}> Data URL </Typography> <Typography variant="body2" sx={{ fontSize: '10px', color: 'text.secondary', wordBreak: 'break-all' }}> {selectedTable.content.value} </Typography> </Paper> } // Fallback for other content types return <Paper variant="outlined" sx={{ p: 1, display: 'flex', flexDirection: 'column', gap: 1, ...sx }}> <Typography variant="body2" sx={{ fontSize: '10px', color: 'text.secondary' }}> {selectedTable.content.value} </Typography> </Paper> } export const DataLoadingInputBox = React.forwardRef<(() => void) | null, {maxLines?: number, onStreamingContentUpdate?: (content: string) => void, abortControllerRef?: React.MutableRefObject<AbortController | null>}>(({maxLines = 4, onStreamingContentUpdate, abortControllerRef}, ref) => { const dispatch = useDispatch<AppDispatch>(); const theme = useTheme(); const activeModel = useSelector(dfSelectors.getActiveModel); const dataCleanBlocks = useSelector((state: DataFormulatorState) => state.dataCleanBlocks); const cleanInProgress = useSelector((state: DataFormulatorState) => state.cleanInProgress); const focusedDataCleanBlockId = useSelector((state: DataFormulatorState) => state.focusedDataCleanBlockId); let selectedBlock = focusedDataCleanBlockId ? dataCleanBlocks.find(block => block.id === focusedDataCleanBlockId.blockId) : undefined; let selectedTable = focusedDataCleanBlockId ? selectedBlock?.items?.[focusedDataCleanBlockId.itemId] : undefined; const [userImages, setUserImages] = useState<string[]>([]); const [prompt, setPrompt] = useState(''); const existOutputBlocks = dataCleanBlocks.length > 0; // Reconstruct dialog from Redux state for API compatibility const dialog: DialogMessage[] = (() => { const reconstructedDialog: DialogMessage[] = []; // Build dialog backwards from selected block until there's no parent let currentBlockId = focusedDataCleanBlockId?.blockId; const processedBlocks = new Set<string>(); while (currentBlockId && !processedBlocks.has(currentBlockId)) { const block = dataCleanBlocks.find(b => b.id === currentBlockId); if (!block) break; processedBlocks.add(currentBlockId); // Add user message from block's derive field const content: any[] = []; if (block.derive.prompt) { content.push({ type: 'text', text: block.derive.prompt }); } if (block.derive.artifacts) { block.derive.artifacts.forEach(artifact => { if (artifact.type === 'image_url') { content.push({ type: 'image_url', image_url: { url: artifact.value } }); } }); } reconstructedDialog.unshift({ role: 'user', content: content.length === 1 && content[0].type === 'text' ? content[0].text : content }); // Add assistant message if dialogItem exists if (block.dialogItem) { reconstructedDialog.unshift(block.dialogItem); } // Move to parent block currentBlockId = block.derive.sourceId; } return reconstructedDialog; })(); // Define sample tasks const sampleTasks = [ { text: "Extract top repos from https://github.com/microsoft", fullText: "extract the top repos information from https://github.com/microsoft?q=&type=all&language=&sort=stargazers", icon: <LanguageIcon sx={{ fontSize: 18 }} /> }, { text: "Extract data from this image", fullText: "help me extract data from this image", icon: <ImageIcon sx={{ fontSize: 18 }} />, image: exampleImageTable }, { text: "Extract growth data from text", fullText: `help me extract sub-segment growth data from this text\n\n\"Revenue in Productivity and Business Processes was $33.1 billion and increased 16% (up 14% in constant currency), with the following business highlights: · Microsoft 365 Commercial products and cloud services revenue increased 16% (up 15% in constant currency) driven by Microsoft 365 Commercial cloud revenue growth of 18% (up 16% in constant currency) · Microsoft 365 Consumer products and cloud services revenue increased 21% driven by Microsoft 365 Consumer cloud revenue growth of 20% · LinkedIn revenue increased 9% (up 8% in constant currency) · Dynamics products and cloud services revenue increased 18% (up 17% in constant currency) driven by Dynamics 365 revenue growth of 23% (up 21% in constant currency) Revenue in Intelligent Cloud was $29.9 billion and increased 26% (up 25% in constant currency), with the following business highlights: · Server products and cloud services revenue increased 27% driven by Azure and other cloud services revenue growth of 39% Revenue in More Personal Computing was $13.5 billion and increased 9%, with the following business highlights: · Windows OEM and Devices revenue increased 3% · Xbox content and services revenue increased 13% (up 12% in constant currency) · Search and news advertising revenue excluding traffic acquisition costs increased 21% (up 20% in constant currency)\"`, icon: <TextFieldsIcon sx={{ fontSize: 18 }} /> }, { text: "Generate UK dynasty dataset", fullText: "help me generate a dataset about uk dynasty with their years of reign and their monarchs", icon: <DatasetIcon sx={{ fontSize: 18 }} /> } ]; const placeholder = (existOutputBlocks) ? (selectedTable && selectedTable.content.type === 'image_url' ? "extract data from this image" : "follow-up instruction (e.g., fix headers, remove totals, generate 15 rows, etc.)") : "paste the content (website, image, text block, etc.) and ask AI to extract / clean data from it"; let additionalImages = (() => { if (selectedTable && selectedTable.content.type === 'image_url') { return [selectedTable.content.value]; } return []; })(); const canSend = (() => { // Allow sending if there's prompt text or image data const hasPrompt = prompt.trim().length > 0; const hasImageData = userImages.length > 0 || additionalImages.length > 0; return (hasPrompt || hasImageData) && !cleanInProgress; })(); // Function to extract URLs from the current prompt const extractedUrls = (() => { const urlRegex = /(https?:\/\/[^\s]+)/gi; const matches = prompt.match(urlRegex); if (!matches) return []; // Remove trailing commas and periods from URLs const cleanedUrls = matches.map(url => { return url.replace(/[,.]$/, ''); }); return [...new Set(cleanedUrls)]; // Remove duplicates })(); const handlePasteImage = (e: React.ClipboardEvent<HTMLDivElement>) => { if (e.clipboardData && e.clipboardData.files && e.clipboardData.files.length > 0) { const files = Array.from(e.clipboardData.files); const imageFiles = files.filter(file => file.type.startsWith('image/')); if (imageFiles.length > 0) { const newImages: string[] = []; let processedCount = 0; imageFiles.forEach(file => { const reader = new FileReader(); reader.onload = () => { const res = reader.result as string; if (res) { newImages.push(res); } processedCount++; if (processedCount === imageFiles.length) { setUserImages(prev => [...prev, ...newImages]); } }; reader.readAsDataURL(file); }); } } }; const removeImage = (index: number) => { setUserImages(prev => prev.filter((_, i) => i !== index)); }; const stopGeneration = () => { if (abortControllerRef?.current) { abortControllerRef.current.abort(); } }; const sendRequest = (promptToUse: string, imagesToUse: string[]) => { // Check if we can send with the provided or state values const hasPrompt = promptToUse.trim().length > 0; const hasImageData = imagesToUse.length > 0 || additionalImages.length > 0; if (!hasPrompt && !hasImageData) return; if (cleanInProgress) return; dispatch(dfActions.setCleanInProgress(true)); const token = String(Date.now()); let prompt_to_send = promptToUse.trim() || (hasImageData ? 'extract data from the image' : 'let\'s generate some interesting data'); let images_to_send = [...additionalImages, ...imagesToUse]; // Extract URLs from the prompt const urlRegex = /(https?:\/\/[^\s]+)/gi; const matches = prompt_to_send.match(urlRegex); const extractedUrlsFromPrompt = matches ? [...new Set(matches)] : []; // Construct payload - simplified to match backend API const payload: any = { token, model: activeModel, prompt: prompt_to_send, artifacts: [ ...images_to_send.map(image => ({ type: 'image_url', content: image })), ...extractedUrlsFromPrompt.map(url => ({ type: 'web_url', content: url })), ], dialog: dialog }; // Create abort controller const controller = new AbortController(); if (abortControllerRef) { abortControllerRef.current = controller; } fetchWithIdentity(getUrls().CLEAN_DATA_URL, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(payload), signal: controller.signal, }) .then(async (response) => { if (!response.ok) { throw new Error(`HTTP error! status: ${response.status}`); } const reader = response.body?.getReader(); if (!reader) { throw new Error('No response body reader available'); } const decoder = new TextDecoder(); let buffer = ''; let finalResult: any = null; try { while (true) { const { done, value } = await reader.read(); if (done) { break; } buffer += decoder.decode(value, { stream: true }); onStreamingContentUpdate?.(buffer); // Split by newlines to get individual JSON objects const lastLine = buffer.split('\n').filter(line => line.trim() !== "").pop(); // Process each line if (lastLine) { try { const data = JSON.parse(lastLine); if (data.status === "ok" && data.content) { finalResult = data; break; } } catch (parseError) { continue } } } if (finalResult && finalResult.status === 'ok' && finalResult.content) { const tables = finalResult.content; const updatedDialog = finalResult.dialog || []; // Create new DataCleanBlock const newBlock: DataCleanBlock = { id: `block-${Date.now()}`, items: tables, derive: { sourceId: focusedDataCleanBlockId?.blockId, prompt: prompt_to_send, artifacts: [ ...images_to_send.map(image => ({ type: 'image_url' as const, value: image })), ...extractedUrls.map(url => ({ type: 'web_url' as const, value: url })), ] }, dialogItem: updatedDialog.length > 0 ? updatedDialog[updatedDialog.length - 1] : undefined }; onStreamingContentUpdate?.(''); dispatch(dfActions.addDataCleanBlock(newBlock)); dispatch(dfActions.setFocusedDataCleanBlockId({blockId: newBlock.id, itemId: 0})); // Clear input fields only after successful completion setPrompt(''); setUserImages([]); } else { // Generation failed dispatch(dfActions.addMessages({ timestamp: Date.now(), type: 'error', component: 'data loader', value: finalResult?.content || 'Unable to extract tables from response', })); // Clear input fields only after failed completion setPrompt(''); onStreamingContentUpdate?.(''); setUserImages([]); } } finally { reader.releaseLock(); dispatch(dfActions.setCleanInProgress(false)); if (abortControllerRef) { abortControllerRef.current = null; } } }) .catch((error) => { dispatch(dfActions.setCleanInProgress(false)); if (abortControllerRef) { abortControllerRef.current = null; } // Check if this was an abort (user stopped the generation) if (error.name === 'AbortError') { dispatch(dfActions.addMessages({ timestamp: Date.now(), type: 'info', component: 'data loader', value: 'Generation stopped by user', })); } else { // Generation failed const errorMessage = `Server error while processing data: ${error.message}`; dispatch(dfActions.addMessages({ timestamp: Date.now(), type: 'error', component: 'data loader', value: errorMessage, })); } // Clear input fields only after failed completion setPrompt(''); setUserImages([]); onStreamingContentUpdate?.(''); }); }; // Expose sendRequest function to parent via ref React.useEffect(() => { if (ref && typeof ref === 'object' && 'current' in ref) { ref.current = () => sendRequest(prompt, userImages); } }, [canSend, prompt, additionalImages, userImages, extractedUrls, dialog, activeModel, focusedDataCleanBlockId, dispatch]); let inputImages = [...userImages, ...additionalImages]; return ( <Stack sx={{ py: 1, position: 'relative' }} direction="row" spacing={1} alignItems="flex-end"> {cleanInProgress && <LinearProgress sx={{ width: '100%', height: '100%', position: 'absolute', opacity: 0.1, top: 0, left: 0, right: 0, zIndex: 1, pointerEvents: 'none' }} />} <Box sx={{ flex: 1, position: 'relative' }}> {/* HTML Address Chips */} {extractedUrls.length > 0 && ( <Box sx={{ display: 'flex', flexDirection: 'row', flexWrap: 'wrap', gap: 0.5, mb: 1, position: 'relative' }}> {extractedUrls.map((url, index) => ( <Chip key={index} icon={<LinkIcon sx={{ fontSize: 16 }} />} label={url.length > 50 ? `${url.substring(0, 47)}...` : url} variant="outlined" color="primary" size="small" sx={{ maxWidth: existOutputBlocks ? 280 : 400, backgroundColor: 'primary.50', borderColor: 'primary.200', color: 'primary.700', borderRadius: 2, '& .MuiChip-label': { fontSize: existOutputBlocks ? '11px' : '12px', maxWidth: '100%', overflow: 'hidden', textOverflow: 'ellipsis' } }} /> ))} </Box> )} {inputImages.length > 0 && ( <Box sx={{ display: 'flex', flexDirection: 'row', flexWrap: 'wrap', gap: 0.5, mt: 0.5, position: 'relative' }}> {inputImages.map((imageUrl, index) => ( <Box key={index} sx={{ display: 'block', position: 'relative' }}> <Box component="img" src={imageUrl} alt={`Pasted image ${index + 1}`} sx={{ maxHeight: existOutputBlocks ? 60 : 600, maxWidth: inputImages.length > 1 ? '30%' : 600, objectFit: 'cover', borderRadius: 1, border: `1px solid ${borderColor.divider}` }} /> {userImages.includes(imageUrl) ? <IconButton sx={{position: 'absolute', top: 0, right: 0}} size="small" onClick={() => removeImage(index)} > <CancelIcon fontSize="small" /> </IconButton> : <Typography sx={{fontSize: '10px', color: 'text.secondary', position: 'absolute', top: 0, right: 0, width: 16, height: 16, display: 'flex', alignItems: 'center', justifyContent: 'center'}}> ➤ </Typography>} </Box> ))} </Box> )} <TextField sx={{ '& .MuiInputBase-root': { p: 1, fontSize: existOutputBlocks ? '12px' : '14px', position: 'relative', zIndex: 2 } }} placeholder={cleanInProgress ? 'extracting data...' : placeholder} variant="standard" multiline value={prompt} onChange={(e) => setPrompt(e.target.value)} fullWidth disabled={cleanInProgress} autoComplete="off" maxRows={maxLines} onKeyDown={(e) => { if (e.key === 'Enter' && !e.shiftKey) { e.preventDefault(); sendRequest(prompt, userImages); } }} slotProps={{ input: { endAdornment: cleanInProgress ? ( <Tooltip title="Stop generation"> <IconButton color='error' size="small" onClick={stopGeneration}> <StopIcon /> </IconButton> </Tooltip> ) : ( <IconButton color='primary' size="small" disabled={!canSend} onClick={() => sendRequest(prompt, userImages)}> <PrecisionManufacturingIcon /> </IconButton> ) } }} onPaste={handlePasteImage} /> {/* Sample Task Cards - Show only when no output blocks exist and not processing */} {!existOutputBlocks && !cleanInProgress && ( <Box sx={{ mt: 2, mb: 1 }}> <Typography sx={{ fontSize: '11px', color: 'text.secondary', mb: 1 }}> examples </Typography> <Box sx={{ display: 'flex', flexWrap: 'wrap', gap: 1, }}> {sampleTasks.map((task, index) => ( <SampleTaskChip key={index} task={task} theme={theme} onClick={async () => { let imagesToSend: string[] = []; if (task.image) { // Convert example image to data URL try { const response = await fetch(task.image); const blob = await response.blob(); const reader = new FileReader(); await new Promise<void>((resolve) => { reader.onload = () => { const dataUrl = reader.result as string; imagesToSend = [dataUrl]; setUserImages([dataUrl]); resolve(); }; reader.readAsDataURL(blob); }); } catch (error) { console.error('Failed to load image:', error); } } // Set prompt for display setPrompt(task.fullText); // Call sendRequest with explicit parameters sendRequest(task.fullText, imagesToSend); }} disabled={cleanInProgress} /> ))} </Box> </Box> )} </Box> </Stack> ); }); // Utility function to convert dataCleanBlocks into ordered threadBlocks export interface ThreadBlock { threadIndex: number; blocks: DataCleanBlock[]; leafBlock: DataCleanBlock; } export const createOrderedThreadBlocks = (dataCleanBlocks: DataCleanBlock[]): ThreadBlock[] => { // Helper function to get the path from root to a block const getBlockPath = (blockId: string): DataCleanBlock[] => { const path: DataCleanBlock[] = []; let currentBlock = dataCleanBlocks.find(b => b.id === blockId); while (currentBlock) { path.unshift(currentBlock); currentBlock = dataCleanBlocks.find(b => b.id === currentBlock?.derive.sourceId); } return path; }; // Identify leaf blocks (blocks that have no children) const getLeafBlocks = (): DataCleanBlock[] => { return dataCleanBlocks.filter(block => { // A block is a leaf if no other block has it as a parent return !dataCleanBlocks.some(otherBlock => otherBlock.derive.sourceId === block.id); }); }; // Get blocks that should be displayed in a thread (avoiding repetition) const getThreadBlocks = (leafBlock: DataCleanBlock, usedBlockIds: Set<string>): DataCleanBlock[] => { const path = getBlockPath(leafBlock.id); // Find the first block in the path that hasn't been used in previous threads let startIndex = 0; for (let i = 0; i < path.length; i++) { if (!usedBlockIds.has(path[i].id)) { startIndex = i; break; } } return path.slice(startIndex); }; // Sort leaf blocks by their creation order (using block IDs which contain timestamps) const leafBlocks = getLeafBlocks().sort((a, b) => { const aTime = parseInt(a.id.split('-')[1] || '0'); const bTime = parseInt(b.id.split('-')[1] || '0'); return aTime - bTime; }); // Build threads const threads: ThreadBlock[] = leafBlocks.map((leafBlock, threadIndex) => { const usedBlockIds = new Set<string>(); // Collect all block IDs used in previous threads for (let i = 0; i < threadIndex; i++) { const previousThreadBlocks = getThreadBlocks(leafBlocks[i], new Set()); previousThreadBlocks.forEach(block => usedBlockIds.add(block.id)); } const threadBlocks = getThreadBlocks(leafBlock, usedBlockIds); return { threadIndex, blocks: threadBlocks, leafBlock }; }); return threads; }; export const SingleDataCleanThreadView: React.FC<{thread: ThreadBlock, sx?: SxProps}> = ({thread, sx}) => { const {threadIndex, blocks, leafBlock} = thread; const theme = useTheme(); const dispatch = useDispatch<AppDispatch>(); const focusedDataCleanBlockId = useSelector((state: DataFormulatorState) => state.focusedDataCleanBlockId); let isThreadFocused = blocks.some(block => block.id === focusedDataCleanBlockId?.blockId); return ( <Box sx={{ display: 'flex', flexDirection: 'column', gap: 0, mb: 2, borderRadius: radius.md, transition: transition.normal, ...sx }}> {/* Thread header */} <Box sx={{ display: 'flex', direction: 'ltr', margin: '2px 2px 8px 2px' }}> <Divider flexItem sx={{ margin: 'auto', "& .MuiDivider-wrapper": { display: 'flex', flexDirection: 'row' }, "&::before, &::after": { borderColor: alpha(theme.palette.divider, 0.2), borderWidth: '2px', width: 60 }, }}> <Typography sx={{ fontSize: "10px", textTransform: 'none' }}> {`loading - ${threadIndex + 1}`} </Typography> </Divider> </Box> {/* Thread content */} {blocks.map((block, blockIndex) => { const isLastBlock = blockIndex === blocks.length - 1; return ( <Box key={block.id} sx={{ display: 'flex', flexDirection: 'column', gap: 0}}> {/* Start circle for the first block */} {blockIndex === 0 && ( <Box sx={{ display: 'flex', alignItems: 'center', ml: '5px' }}> <PanoramaFishEyeIcon sx={{ fontSize: 7, color: 'darkgray' }} /> </Box> )} <Box sx={{borderLeft: isThreadFocused ? '3px solid' : '1px dashed', borderColor: isThreadFocused ? 'primary.light' : ' rgba(0, 0, 0, 0.3)', py: blockIndex === 0 ? 0.5 : 1.5, px: 1, display: 'flex', alignItems: 'center', ml: isThreadFocused ? '7px' : '8px'}}> </Box> {/* User Instruction Card (styled like TriggerCard) */} <Card variant="outlined" sx={{ backgroundColor: alpha(theme.palette.custom.main, 0.05), fontSize: '10px', display: 'flex', flexDirection: 'row', alignItems: 'center', gap: '2px', }} > <PrecisionManufacturingIcon sx={{ml: 1, color: 'darkgray', width: '14px', height: '14px'}} /> <Box sx={{margin: "4px 8px 4px 2px"}}> {/* Text prompt */} {block.derive.prompt && ( <Typography fontSize="inherit" sx={{ textAlign: 'center', textWrap: 'balance', minWidth: '40px', color: 'rgba(0,0,0,0.7)', display: '-webkit-box', overflow: 'auto', textOverflow: 'ellipsis', wordBreak: 'break-word', maxHeight: 100 }}> "{block.derive.prompt}" </Typography> )} {/* Images if present */} {block.derive.artifacts && block.derive.artifacts.filter(a => a.type === 'image_url').length > 0 && ( <Box sx={{ display: 'flex', flexWrap: 'wrap', gap: 0.5, py: 0.5, }}> {block.derive.artifacts .filter(artifact => artifact.type === 'image_url') .map((artifact, imgIndex) => ( <Box key={imgIndex} component="img" src={artifact.value} alt={`User uploaded image ${imgIndex + 1}`} sx={{ width: 'auto', height: 40, maxWidth: 100, objectFit: 'cover', borderRadius: 0.5, border: `1px solid ${borderColor.divider}` }} /> ))} </Box> )} </Box> </Card> {/* Down arrow connecting instruction to output */} <Box sx={{ display: 'flex', alignItems: 'center',}}> <SouthIcon color={isThreadFocused ? 'primary' : 'secondary'} sx={{ fontSize: 16, ml: 0.5 }} /> </Box> {/* Output Cards (styled like primary colored cards) */} {block.items?.map((table, itemId) => { const isItemSelected = block.id === focusedDataCleanBlockId?.blockId && itemId === focusedDataCleanBlockId.itemId; return ( <Card variant="outlined" sx={{ py: 0, mt: itemId === 0 ? 0 : 0.5, border: isItemSelected ? `2px solid ${theme.palette.primary.light}` : `1px solid ${borderColor.component}`, cursor: 'pointer', backgroundColor: alpha(theme.palette.primary.light, 0.1), '&:hover': { boxShadow: shadow.md, transform: "translate(0px, 1px)", } }} onClick={() => dispatch(dfActions.setFocusedDataCleanBlockId({blockId: block.id, itemId: itemId}))} > <Box sx={{ margin: '0px', display: 'flex', py: 0.5 }}> <Stack direction="row" sx={{ marginLeft: 0.5, marginRight: 'auto', fontSize: 12, width: 'calc(100% - 8px)', alignItems: 'center' }} gap={"2px"}> {table.content.type === 'csv' && <TableIcon sx={{color: 'darkgray', fontSize: 14}} />} {table.content.type === 'image_url' && <LinkIcon sx={{color: 'darkgray', fontSize: 14}} />} {table.content.type === 'web_url' && <LinkIcon sx={{color: 'darkgray', fontSize: 14}} />} <Typography fontSize="inherit" sx={{ ml: 0.5, color: 'rgba(0,0,0,0.7)', overflow: 'hidden', textOverflow: 'ellipsis', display: '-webkit-box', WebkitLineClamp: 2, WebkitBoxOrient: 'vertical', }}> {table.name} </Typography> {isLastBlock && <Tooltip title="delete table"> <IconButton aria-label="share" size="small" sx={{ ml: 'auto', padding: 0.25, '&:hover': { transform: 'scale(1.2)', transition: 'all 0.2s ease' } }} onClick={(event) => { event.stopPropagation(); dispatch(dfActions.removeDataCleanBlocks({ blockIds: [block.id] })) }}> <DeleteIcon fontSize="small" sx={{ fontSize: 18 }} color='warning'/> </IconButton> </Tooltip> } </Stack> </Box> </Card> ); })} </Box> ); })} </Box> ); }; export const DataLoadingThread: React.FC = () => { const dispatch = useDispatch<AppDispatch>(); const dataCleanBlocks = useSelector((state: DataFormulatorState) => state.dataCleanBlocks); // Use the utility function to create ordered thread blocks const threads = createOrderedThreadBlocks(dataCleanBlocks); let threadDisplay = <Box sx={{ flex: 1, width: 'calc(100% - 8px)', p: 0.5, display: 'flex', flexDirection: 'column', maxHeight: '400px', overflowY: 'auto', overflowX: 'hidden' }}> {/* Render each thread */} {threads.map((thread) => ( <SingleDataCleanThreadView key={`thread-${thread.threadIndex}`} thread={thread} /> ))} </Box> return threadDisplay; }; ================================================ FILE: src/views/DataThread.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React, { FC, useEffect, useMemo, useRef, useState, memo } from 'react'; import { Box, Typography, LinearProgress, ListItemIcon, IconButton, Tooltip, useTheme, SxProps, Button, TextField, CircularProgress, Popper, Paper, ClickAwayListener, Badge, Menu, MenuItem, Switch, FormControlLabel, Collapse, Card, } from '@mui/material'; import '../scss/VisualizationView.scss'; import { batch, useDispatch, useSelector } from 'react-redux'; import { DataFormulatorState, dfActions, dfSelectors, SSEMessage } from '../app/dfSlice'; import { getTriggers, getUrls, fetchWithIdentity } from '../app/utils'; import { Chart, DictTable, Trigger } from "../components/ComponentType"; import DeleteIcon from '@mui/icons-material/Delete'; import StarIcon from '@mui/icons-material/Star'; import { TableIcon, AnchorIcon, InsightIcon, StreamIcon, AgentIcon } from '../icons'; import _ from 'lodash'; import { getChartTemplate } from '../components/ChartTemplates'; import 'prismjs/components/prism-python' // Language import 'prismjs/components/prism-typescript' // Language import 'prismjs/themes/prism.css'; //Example style, you can use another import { checkChartAvailability, generateChartSkeleton, getDataTable } from './VisualizationView'; import AttachFileIcon from '@mui/icons-material/AttachFile'; import EditIcon from '@mui/icons-material/Edit'; import RefreshIcon from '@mui/icons-material/Refresh'; import ForumOutlinedIcon from '@mui/icons-material/ForumOutlined'; import AddIcon from '@mui/icons-material/Add'; import ExpandMoreIcon from '@mui/icons-material/ExpandMore'; import ChevronRightIcon from '@mui/icons-material/ChevronRight'; import { alpha } from '@mui/material/styles'; import { RefreshDataDialog } from './RefreshDataDialog'; import { AppDispatch } from '../app/store'; import StopIcon from '@mui/icons-material/Stop'; import UnfoldMoreIcon from '@mui/icons-material/UnfoldMore'; import UnfoldLessIcon from '@mui/icons-material/UnfoldLess'; import BarChartIcon from '@mui/icons-material/BarChart'; import ShowChartIcon from '@mui/icons-material/ShowChart'; import ScatterPlotIcon from '@mui/icons-material/ScatterPlot'; import PieChartOutlineIcon from '@mui/icons-material/PieChartOutline'; import GridOnIcon from '@mui/icons-material/GridOn'; import { useDataRefresh } from '../app/useDataRefresh'; import { buildChartCard, buildTriggerCard, buildTableCard, BuildTableCardProps } from './DataThreadCards'; import { UnifiedDataUploadDialog } from './UnifiedDataUploadDialog'; import { AgentRulesDialog } from './AgentRulesDialog'; import RuleIcon from '@mui/icons-material/Rule'; import AccountTreeIcon from '@mui/icons-material/AccountTree'; import HourglassEmptyIcon from '@mui/icons-material/HourglassEmpty'; import CheckCircleOutlineIcon from '@mui/icons-material/CheckCircleOutline'; import { ViewBorderStyle, transition, radius, borderColor } from '../app/tokens'; import { SimpleChartRecBox } from './SimpleChartRecBox'; import { ChatThreadView } from './ChatThreadView'; export const ThinkingBanner = (message: string, sx?: SxProps) => ( <Box sx={{ display: 'flex', position: 'relative', overflow: 'hidden', '&::before': { content: '""', position: 'absolute', top: 0, left: 0, width: '100%', height: '100%', background: 'linear-gradient(90deg, transparent 0%, rgba(255, 255, 255, 0.8) 50%, transparent 100%)', animation: 'windowWipe 2s ease-in-out infinite', zIndex: 1, pointerEvents: 'none', }, '@keyframes windowWipe': { '0%': { transform: 'translateX(-100%)', }, '100%': { transform: 'translateX(100%)', }, }, ...sx }}> <Typography variant="body2" sx={{ fontSize: 10, color: 'rgba(0, 0, 0, 0.7) !important' }}> {message} </Typography> </Box> ); // Streaming Settings Popup Component const StreamingSettingsPopup = memo<{ open: boolean; anchorEl: HTMLElement | null; onClose: () => void; table: DictTable; onUpdateSettings: (autoRefresh: boolean, refreshIntervalSeconds?: number) => void; onRefreshNow?: () => void; }>(({ open, anchorEl, onClose, table, onUpdateSettings, onRefreshNow }) => { const [refreshInterval, setRefreshInterval] = useState<number>( table.source?.refreshIntervalSeconds || 60 ); const [autoRefresh, setAutoRefresh] = useState<boolean>( table.source?.autoRefresh || false ); const [selectMenuOpen, setSelectMenuOpen] = useState<boolean>(false); const [isRefreshing, setIsRefreshing] = useState<boolean>(false); useEffect(() => { if (open) { setRefreshInterval(table.source?.refreshIntervalSeconds || 60); setAutoRefresh(table.source?.autoRefresh || false); } }, [open, table.source]); const handleAutoRefreshChange = (enabled: boolean) => { setAutoRefresh(enabled); onUpdateSettings(enabled, enabled ? refreshInterval : undefined); if (!enabled) { onClose(); } }; const handleIntervalChange = (interval: number) => { setRefreshInterval(interval); if (autoRefresh) { onUpdateSettings(true, interval); } }; const handleRefreshNow = async () => { if (onRefreshNow && !isRefreshing) { setIsRefreshing(true); try { await onRefreshNow(); } finally { setIsRefreshing(false); } } }; const handleClickAway = (event: MouseEvent | TouchEvent) => { // Don't close if the select menu is open if (selectMenuOpen) { return; } // Don't close if clicking on the select menu or menu items const target = event.target as HTMLElement; if ( target.closest('.MuiMenu-root') || target.closest('.MuiPaper-root')?.classList.contains('MuiMenu-paper') || target.closest('[role="menuitem"]') || target.closest('[role="listbox"]') ) { return; } onClose(); }; return ( <Popper open={open} anchorEl={anchorEl} placement="bottom-start" style={{ zIndex: 1300 }} > <ClickAwayListener onClickAway={handleClickAway} mouseEvent="onMouseDown"> <Paper elevation={8} sx={{ fontSize: 12, p: 1.5, mt: 1, ...ViewBorderStyle, }} > <Box sx={{ display: 'flex', flexDirection: 'column', gap: 1 }}> <Box sx={{ display: 'flex', alignItems: 'center', gap: 1, flexWrap: 'nowrap' }}> <FormControlLabel control={ <Switch checked={autoRefresh} onChange={(e) => handleAutoRefreshChange(e.target.checked)} size="small" /> } label={ <Typography variant="body2" sx={{ fontSize: 11 }}> Watch for updates </Typography> } sx={{ mr: 0 }} /> {autoRefresh && ( <Box sx={{ display: 'flex', alignItems: 'center', gap: 0.5, minWidth: 100 }}> <Typography variant="body2" sx={{ fontSize: 11, color: 'text.secondary' }}> every </Typography> <TextField select size="small" value={refreshInterval} onChange={(e) => handleIntervalChange(Number(e.target.value))} slotProps={{ select: { open: selectMenuOpen, onOpen: () => setSelectMenuOpen(true), onClose: () => setSelectMenuOpen(false) } }} sx={{ minWidth: 70, '& .MuiInputBase-root': { fontSize: 11, height: 28 }, '& .MuiSelect-select': { py: 0.5 } }} > <MenuItem value={1}>1s</MenuItem> <MenuItem value={10}>10s</MenuItem> <MenuItem value={30}>30s</MenuItem> <MenuItem value={60}>1m</MenuItem> <MenuItem value={300}>5m</MenuItem> <MenuItem value={600}>10m</MenuItem> <MenuItem value={1800}>30m</MenuItem> <MenuItem value={3600}>1h</MenuItem> <MenuItem value={86400}>24h</MenuItem> </TextField> </Box> )} {onRefreshNow && ( <Button variant="outlined" size="small" onClick={handleRefreshNow} disabled={isRefreshing} startIcon={isRefreshing ? <CircularProgress size={14} /> : <RefreshIcon sx={{ fontSize: 14 }} />} sx={{ fontSize: 11, textTransform: 'none', height: 28, alignSelf: 'flex-start' }} > Refresh now </Button> )} </Box> </Box> </Paper> </ClickAwayListener> </Popper> ); }); // Metadata Popup Component const MetadataPopup = memo<{ open: boolean; anchorEl: HTMLElement | null; onClose: () => void; onSave: (metadata: string) => void; initialValue: string; tableName: string; }>(({ open, anchorEl, onClose, onSave, initialValue, tableName }) => { const [metadata, setMetadata] = useState(initialValue); let hasChanges = metadata !== initialValue; useEffect(() => { setMetadata(initialValue); }, [initialValue, open]); const handleSave = () => { onSave(metadata); onClose(); }; const handleCancel = () => { setMetadata(initialValue); onClose(); }; const handleKeyDown = (e: React.KeyboardEvent) => { if (e.key === 'Escape') { handleCancel(); } else if (e.key === 'Enter' && e.ctrlKey) { handleSave(); } }; return ( <Popper open={open} anchorEl={anchorEl} placement="bottom-start" style={{ zIndex: 1300 }} > <ClickAwayListener onClickAway={handleCancel}> <Paper elevation={8} sx={{ width: 480, fontSize: 12, p: 2, mt: 1, ...ViewBorderStyle, }} > <Typography variant="subtitle2" sx={{ mb: 1 }}> Attach metadata to <Typography component="span" sx={{ fontSize: 'inherit', color: 'primary.main'}}>{tableName}</Typography> </Typography> <TextField autoFocus label="metadata" placeholder="Attach additional contexts or guidance so that AI agents can better understand and process the data." fullWidth multiline slotProps={{ inputLabel: {shrink: true}, }} minRows={3} maxRows={20} variant="outlined" size="small" value={metadata} onChange={(e) => setMetadata(e.target.value)} onKeyDown={handleKeyDown} sx={{ my: 1, '& .MuiInputBase-input': { fontSize: 12 } }} /> <Box sx={{ mt: 1, display: 'flex', gap: 1, alignItems: 'center' }}> <Button size="small" sx={{ml: 'auto'}} onClick={handleCancel} color="primary">Cancel</Button> <Button size="small" onClick={handleSave} color="primary" disabled={!hasChanges}>Save</Button> </Box> </Paper> </ClickAwayListener> </Popper> ); }); // Rename table popup - opens as a small popper with a text field const RenameTablePopup = memo<{ open: boolean; anchorEl: HTMLElement | null; onClose: () => void; onSave: (newName: string) => void; initialValue: string; tableName: string; }>(({ open, anchorEl, onClose, onSave, initialValue, tableName }) => { const [name, setName] = useState(initialValue); useEffect(() => { setName(initialValue); }, [initialValue, open]); const handleSave = () => { if (name.trim() !== '') { onSave(name.trim()); onClose(); } }; const handleKeyDown = (e: React.KeyboardEvent) => { e.stopPropagation(); if (e.key === 'Enter') { handleSave(); } else if (e.key === 'Escape') { onClose(); } }; return ( <Popper open={open} anchorEl={anchorEl} placement="bottom-start" style={{ zIndex: 1300 }} > <ClickAwayListener onClickAway={onClose}> <Paper elevation={8} sx={{ width: 240, fontSize: 12, p: 1.5, mt: 1, ...ViewBorderStyle }} > <Typography variant="subtitle2" sx={{ mb: 0.5, fontSize: 12 }}> Rename table </Typography> <TextField autoFocus fullWidth variant="outlined" size="small" value={name} onChange={(e) => setName(e.target.value)} onKeyDown={handleKeyDown} sx={{ my: 0.5, '& .MuiInputBase-input': { fontSize: 12 } }} /> <Box sx={{ mt: 0.5, display: 'flex', gap: 1, justifyContent: 'flex-end' }}> <Button size="small" onClick={onClose}>Cancel</Button> <Button size="small" onClick={handleSave} color="primary" disabled={name.trim() === '' || name.trim() === initialValue}>Save</Button> </Box> </Paper> </ClickAwayListener> </Popper> ); }); const WorkspacePanel: FC<{ tables: DictTable[], chartElements: { tableId: string, chartId: string, element: any }[], suppressScrollRef?: React.MutableRefObject<boolean>, sx?: SxProps, }> = function ({ tables, chartElements, suppressScrollRef, sx }) { const theme = useTheme(); const dispatch = useDispatch(); const charts = useSelector(dfSelectors.getAllCharts); const focusedId = useSelector((state: DataFormulatorState) => state.focusedId); const focusedTableId = React.useMemo(() => { if (!focusedId) return undefined; if (focusedId.type === 'table') return focusedId.tableId; const chartId = focusedId.chartId; const chart = charts.find(c => c.id === chartId); return chart?.tableRef; }, [focusedId, charts]); const focusedChartId = focusedId?.type === 'chart' ? focusedId.chartId : undefined; const conceptShelfItems = useSelector((state: DataFormulatorState) => state.conceptShelfItems); const [uploadDialogOpen, setUploadDialogOpen] = useState(false); const [agentRulesOpen, setAgentRulesOpen] = useState(false); const [workspaceExpanded, setWorkspaceExpanded] = useState(false); const fileItemSx = (isActive: boolean, isNested: boolean = false) => ({ display: 'flex', alignItems: 'center', gap: 0.5, px: 0.75, py: '3px', borderRadius: '3px', cursor: 'pointer', fontSize: 11, transition: transition.fast, backgroundColor: isActive ? alpha(theme.palette.primary.main, 0.08) : 'transparent', '&:hover': { backgroundColor: isActive ? alpha(theme.palette.primary.main, 0.12) : 'rgba(0,0,0,0.04)', }, }); const getTableIcon = (table: DictTable) => { const isStreaming = (table.source?.type === 'stream' || table.source?.type === 'database') && table.source?.autoRefresh; const iconSx = { width: 14, height: 14, color: 'text.secondary', flexShrink: 0 }; if (isStreaming) return <StreamIcon sx={{ ...iconSx, color: theme.palette.success.main, animation: 'pulse 2s infinite', '@keyframes pulse': { '0%': { opacity: 1 }, '50%': { opacity: 0.4 }, '100%': { opacity: 1 } } }} />; if (table.virtual) return <TableIcon sx={{ ...iconSx, width: 14, height: 14 }} />; return <TableIcon sx={iconSx} />; }; const getChartIcon = (chartType: string) => { const template = getChartTemplate(chartType); if (template && template.icon) { // Use chart template icon (it's an image path) if (typeof template.icon === 'string') { return <Box component="img" src={template.icon} sx={{ width: 14, height: 14, objectFit: 'contain' }} />; } // Or it could be a React component return <Box sx={{ width: 14, height: 14, display: 'flex', alignItems: 'center', justifyContent: 'center', color: 'text.secondary' }}>{template.icon}</Box>; } // Fallback to generic chart icon return <InsightIcon sx={{ fontSize: 14, color: 'text.secondary' }} />; }; const getChartFields = (chart: Chart) => { const encodings = Object.entries(chart.encodingMap) .filter(([_, encoding]) => encoding.fieldID) .map(([channel, encoding]) => { const field = conceptShelfItems.find(f => f.id === encoding.fieldID); return field?.name || encoding.fieldID; }) .filter(Boolean); return encodings.slice(0, 3).join(', ') + (encodings.length > 3 ? '...' : ''); }; const getTableSourceName = (table: DictTable) => { // Get the source file name from table source if (table.source?.type === 'file' && table.source?.fileName) { return table.source.fileName; } if (table.source?.type === 'database' && table.source?.databaseTable) { return table.source.databaseTable; } if (table.source?.type === 'stream' && table.source?.url) { // Extract a meaningful name from URL try { const url = new URL(table.source.url); return url.hostname; } catch { return 'stream'; } } return null; }; return ( <Box sx={{ ...sx, py: 0, mb: 0.5, backgroundColor: 'rgba(0,0,0,0.02)', borderBottom: `1px solid ${borderColor.divider}`, userSelect: 'none', }}> <Box sx={{ display: 'flex', alignItems: 'center', px: 0.75, py: '4px', borderRadius: '3px', }} > <Box sx={{ display: 'flex', alignItems: 'center', cursor: 'pointer', borderRadius: '3px', '&:hover': { backgroundColor: 'rgba(0,0,0,0.04)' }, pr: 0.5, py: 0.5, }} onClick={() => setWorkspaceExpanded(!workspaceExpanded)} > {workspaceExpanded ? <ExpandMoreIcon sx={{ fontSize: 14, color: 'rgba(0,0,0,0.5)' }} /> : <ChevronRightIcon sx={{ fontSize: 14, color: 'rgba(0,0,0,0.5)' }} /> } <Typography sx={{ fontSize: 11, fontWeight: 600, color: 'rgba(0,0,0,0.55)', textTransform: 'uppercase', letterSpacing: '0.5px', ml: 0.5 }}> Workspace </Typography> </Box> <Box onClick={(e) => { e.stopPropagation(); setUploadDialogOpen(true); }} sx={{ display: 'flex', alignItems: 'center', gap: '2px', ml: 'auto', px: '5px', py: '2px', borderRadius: '3px', cursor: 'pointer', color: theme.palette.primary.textColor || theme.palette.primary.main, '&:hover': { backgroundColor: alpha(theme.palette.primary.main, 0.08) }, }} > <AddIcon sx={{ fontSize: 14 }} /> <Typography sx={{ fontSize: 11, fontWeight: 600 }}>add data</Typography> </Box> </Box> <Collapse in={workspaceExpanded} timeout={150}> <Box sx={{ display: 'flex', flexDirection: 'column', gap: '2px', mt: '2px', ml: '14px', py: 0.5 }}> {tables.map((table, tableIndex) => { const isTableActive = focusedTableId === table.id; const tableCharts = chartElements.filter(ce => ce.tableId === table.id); const sourceName = getTableSourceName(table); const isLastTable = tableIndex === tables.length - 1; const handleTableClick = () => { if (suppressScrollRef) suppressScrollRef.current = true; dispatch(dfActions.setFocused({ type: 'table', tableId: table.id })); }; return ( <Box key={table.id} sx={{ position: 'relative', pl: 1.5, '&::before': { content: '""', position: 'absolute', left: 0, top: 0, bottom: isLastTable ? 'calc(100% - 10px)' : 0, width: '1px', backgroundColor: 'rgba(0,0,0,0.1)', }, '&::after': { content: '""', position: 'absolute', left: 0, top: '10px', width: '8px', height: '1px', backgroundColor: 'rgba(0,0,0,0.1)', } }} > <Box sx={fileItemSx(isTableActive)} onClick={handleTableClick} > {getTableIcon(table)} <Typography sx={{ fontSize: 11, fontWeight: isTableActive ? 600 : 400, color: isTableActive ? 'primary.main' : 'text.primary', overflow: 'hidden', textOverflow: 'ellipsis', whiteSpace: 'nowrap', flex: 1, minWidth: 0, }}> {table.displayId || table.id} {sourceName && ( <Typography component="span" sx={{ fontSize: 10, color: 'text.disabled', ml: 0.5, }}> ({sourceName}) </Typography> )} </Typography> {table.attachedMetadata && ( <AttachFileIcon sx={{ fontSize: 10, color: 'text.disabled', flexShrink: 0 }} /> )} </Box> {/* Show all charts for this table with vertical guide line */} {tableCharts.length > 0 && ( <Box sx={{ position: 'relative', ml: '14px', mt: '2px', }}> {tableCharts.map((chartElement, idx) => { const chart = charts.find(c => c.id === chartElement.chartId); if (!chart) return null; const isChartActive = focusedChartId === chart.id; const isLast = idx === tableCharts.length - 1; const handleChartClick = () => { if (suppressScrollRef) suppressScrollRef.current = true; dispatch(dfActions.setFocused({ type: 'chart', chartId: chart.id })); }; return ( <Box key={chart.id} sx={{ position: 'relative', pl: 1.5, // Connector area '&::before': { content: '""', position: 'absolute', left: 0, top: 0, bottom: isLast ? '50%' : 0, width: '1px', backgroundColor: 'rgba(0,0,0,0.1)', }, '&::after': { content: '""', position: 'absolute', left: 0, top: '50%', width: '8px', height: '1px', backgroundColor: 'rgba(0,0,0,0.1)', } }} > <Box sx={fileItemSx(isChartActive, true)} onClick={handleChartClick} > {getChartIcon(chart.chartType)} <Typography sx={{ fontSize: 11, fontWeight: isChartActive ? 600 : 400, color: isChartActive ? 'primary.main' : 'text.primary', overflow: 'hidden', textOverflow: 'ellipsis', whiteSpace: 'nowrap', flex: 1, minWidth: 0, }}> {chart.chartType} </Typography> </Box> </Box> ); })} </Box> )} </Box> ); })} </Box> </Collapse> <UnifiedDataUploadDialog open={uploadDialogOpen} onClose={() => setUploadDialogOpen(false)} initialTab="menu" /> <AgentRulesDialog externalOpen={agentRulesOpen} onExternalClose={() => setAgentRulesOpen(false)} /> </Box> ); }; /** A status message that collapses to a single line on click, and expands back on click. */ const CollapsibleStatusMessage: FC<{ text: string; color: string }> = ({ text, color }) => { const [collapsed, setCollapsed] = useState(true); return ( <Typography variant="body2" onClick={(e) => { e.stopPropagation(); setCollapsed(prev => !prev); }} sx={{ fontSize: 10, color, px: 1, py: 0.5, cursor: 'pointer', '&:hover': { textDecoration: 'underline', textDecorationStyle: 'dotted' }, ...(collapsed ? { overflow: 'hidden', textOverflow: 'ellipsis', whiteSpace: 'nowrap', maxWidth: '100%', } : { whiteSpace: 'pre-wrap', wordBreak: 'break-word', }), }} > {text} </Typography> ); }; let SingleThreadGroupView: FC<{ scrollRef: any, threadIdx: number, threadLabel?: string, // Custom label like "thread 1.1" for split sub-threads isSplitThread?: boolean, // When true, truncate used tables to immediate parent + "..." hideLabel?: boolean, // When true, hide the thread label divider leafTables: DictTable[]; chartElements: { tableId: string, chartId: string, element: any }[]; usedIntermediateTableIds: string[], globalHighlightedTableIds: string[], focusedThreadLeafId?: string, // The leaf table ID of the thread containing the focused table chatboxFocused?: boolean, // Whether the chatbox input is focused sx?: SxProps }> = function ({ scrollRef, threadIdx, threadLabel, isSplitThread = false, hideLabel = false, leafTables, chartElements, usedIntermediateTableIds, // tables that have been used globalHighlightedTableIds, focusedThreadLeafId, chatboxFocused = false, sx }) { let tables = useSelector((state: DataFormulatorState) => state.tables); const { manualRefresh } = useDataRefresh(); const tableById = useMemo(() => new Map(tables.map(t => [t.id, t])), [tables]); let leafTableIds = leafTables.map(lt => lt.id); // Thread is highlighted only if this thread's leaf tables include the focused thread's leaf const threadHighlighted = focusedThreadLeafId ? leafTableIds.includes(focusedThreadLeafId) : false; // Ancestor thread: not the focused thread, but *owns* some highlighted tables // (tables that only appear as used/shared references don't count) const isAncestorThread = !threadHighlighted && globalHighlightedTableIds.length > 0 && leafTables.some(lt => { const trigs = getTriggers(lt, tables); const chainIds = [...trigs.map(t => t.tableId), lt.id]; const ownedIds = chainIds.filter(id => !usedIntermediateTableIds.includes(id)); return ownedIds.some(id => globalHighlightedTableIds.includes(id)); }); const shouldHighlightThread = threadHighlighted || isAncestorThread; let parentTableId = leafTables[0].derive?.trigger.tableId || undefined; let parentTable = (parentTableId ? tableById.get(parentTableId) : undefined) as DictTable; let charts = useSelector(dfSelectors.getAllCharts); let focusedId = useSelector((state: DataFormulatorState) => state.focusedId); let focusedChartId = focusedId?.type === 'chart' ? focusedId.chartId : undefined; let focusedTableId = useMemo(() => { if (!focusedId) return undefined; if (focusedId.type === 'table') return focusedId.tableId; const chartId = focusedId.chartId; const chart = charts.find(c => c.id === chartId); return chart?.tableRef; }, [focusedId, charts]); let agentActions = useSelector((state: DataFormulatorState) => state.agentActions); // Pre-index running agent table IDs for O(1) lookup const runningAgentTableIds = useMemo(() => { const ids = new Map<string, typeof agentActions[0]>(); for (const a of agentActions) { if (!a.hidden && a.status === 'running') { // Find the last table created in the chain so far const lastResultTableId = a.messages ?.filter(m => m.resultTableId) .pop()?.resultTableId; ids.set(lastResultTableId || a.originTableId, a); } } return ids; }, [agentActions]); // Pre-index tables with pending clarification const clarifyAgentTableIds = useMemo(() => { const ids = new Map<string, typeof agentActions[0]>(); for (const a of agentActions) { if (!a.hidden && a.pendingClarification) { const lastResultTableId = a.pendingClarification.lastCreatedTableId || a.messages?.filter(m => m.resultTableId).pop()?.resultTableId; ids.set(lastResultTableId || a.originTableId, a); } } return ids; }, [agentActions]); // Pre-index tables with completed agent actions const completedAgentTableIds = useMemo(() => { const ids = new Map<string, typeof agentActions[0]>(); for (const a of agentActions) { if (!a.hidden && (a.status === 'completed' || a.status === 'warning' || a.status === 'failed') && !a.pendingClarification) { const lastResultTableId = a.messages ?.filter(m => m.resultTableId) .pop()?.resultTableId; ids.set(lastResultTableId || a.originTableId, a); } } return ids; }, [agentActions]); // Metadata popup state const [metadataPopupOpen, setMetadataPopupOpen] = useState(false); const [selectedTableForMetadata, setSelectedTableForMetadata] = useState<DictTable | null>(null); const [metadataAnchorEl, setMetadataAnchorEl] = useState<HTMLElement | null>(null); // Table menu state const [tableMenuAnchorEl, setTableMenuAnchorEl] = useState<HTMLElement | null>(null); const [selectedTableForMenu, setSelectedTableForMenu] = useState<DictTable | null>(null); // Refresh data dialog state const [refreshDialogOpen, setRefreshDialogOpen] = useState(false); const [selectedTableForRefresh, setSelectedTableForRefresh] = useState<DictTable | null>(null); const [isRefreshing, setIsRefreshing] = useState(false); // Streaming settings popup state const [streamingSettingsPopupOpen, setStreamingSettingsPopupOpen] = useState(false); const [selectedTableForStreamingSettings, setSelectedTableForStreamingSettings] = useState<DictTable | null>(null); const [streamingSettingsAnchorEl, setStreamingSettingsAnchorEl] = useState<HTMLElement | null>(null); let handleUpdateTableDisplayId = (tableId: string, displayId: string) => { dispatch(dfActions.updateTableDisplayId({ tableId: tableId, displayId: displayId })); } // Rename popup state const [renamePopupOpen, setRenamePopupOpen] = useState(false); const [selectedTableForRename, setSelectedTableForRename] = useState<DictTable | null>(null); const [renameAnchorEl, setRenameAnchorEl] = useState<HTMLElement | null>(null); const handleOpenRenamePopup = (table: DictTable, anchorEl: HTMLElement) => { setSelectedTableForRename(table); setRenameAnchorEl(anchorEl); setRenamePopupOpen(true); }; const handleCloseRenamePopup = () => { setRenamePopupOpen(false); setSelectedTableForRename(null); setRenameAnchorEl(null); }; const handleSaveRename = (newName: string) => { if (selectedTableForRename) { handleUpdateTableDisplayId(selectedTableForRename.id, newName); } }; const handleOpenMetadataPopup = (table: DictTable, anchorEl: HTMLElement) => { setSelectedTableForMetadata(table); setMetadataAnchorEl(anchorEl); setMetadataPopupOpen(true); }; const handleCloseMetadataPopup = () => { setMetadataPopupOpen(false); setSelectedTableForMetadata(null); setMetadataAnchorEl(null); }; const handleSaveMetadata = (metadata: string) => { if (selectedTableForMetadata) { dispatch(dfActions.updateTableAttachedMetadata({ tableId: selectedTableForMetadata.id, attachedMetadata: metadata })); } }; // Table menu handlers const handleOpenTableMenu = (table: DictTable, anchorEl: HTMLElement) => { setSelectedTableForMenu(table); setTableMenuAnchorEl(anchorEl); }; const handleCloseTableMenu = () => { setTableMenuAnchorEl(null); setSelectedTableForMenu(null); }; // Refresh data handlers const handleOpenRefreshDialog = (table: DictTable) => { setSelectedTableForRefresh(table); setRefreshDialogOpen(true); handleCloseTableMenu(); }; const handleCloseRefreshDialog = () => { setRefreshDialogOpen(false); setSelectedTableForRefresh(null); }; // Streaming settings handlers const handleOpenStreamingSettingsPopup = (table: DictTable, anchorEl: HTMLElement) => { setSelectedTableForStreamingSettings(table); setStreamingSettingsAnchorEl(anchorEl); setStreamingSettingsPopupOpen(true); }; const handleCloseStreamingSettingsPopup = () => { setStreamingSettingsPopupOpen(false); setSelectedTableForStreamingSettings(null); setStreamingSettingsAnchorEl(null); }; const handleUpdateStreamingSettings = (autoRefresh: boolean, refreshIntervalSeconds?: number) => { if (selectedTableForStreamingSettings) { dispatch(dfActions.updateTableSourceRefreshSettings({ tableId: selectedTableForStreamingSettings.id, autoRefresh, refreshIntervalSeconds })); } }; // Function to refresh derived tables in batch. // Collects all results first, then dispatches a single updateMultipleTableRows. const refreshDerivedTables = async (sourceTableId: string, newRows: any[]) => { const derivedTables = tables.filter(t => t.derive?.source?.includes(sourceTableId)); if (derivedTables.length === 0) return; const refreshPromises = derivedTables .filter(dt => dt.derive && dt.derive.code && dt.derive.codeSignature) .map(async (derivedTable) => { const parentTableData = derivedTable.derive!.source.map(sourceId => { const sourceTable = tables.find(t => t.id === sourceId); if (sourceTable) { const rows = sourceId === sourceTableId ? newRows : sourceTable.rows; const tableName = sourceTable.virtual?.tableId || sourceTable.id.replace(/\.[^/.]+$/, ""); return { name: tableName, rows }; } return null; }).filter(t => t !== null); if (parentTableData.length === 0) return null; try { const requestBody: any = { input_tables: parentTableData, code: derivedTable.derive!.code, code_signature: derivedTable.derive!.codeSignature, // HMAC proof output_variable: derivedTable.derive!.outputVariable || 'result_df', virtual: !!derivedTable.virtual?.tableId, output_table_name: derivedTable.virtual?.tableId }; const response = await fetchWithIdentity(getUrls().REFRESH_DERIVED_DATA, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(requestBody) }); const result = await response.json(); if (result.status === 'ok' && result.rows) { return { tableId: derivedTable.id, rows: result.rows } as { tableId: string, rows: any[] }; } else { console.error(`Failed to refresh derived table ${derivedTable.id}:`, result.message); dispatch(dfActions.addMessages({ timestamp: Date.now(), type: 'error', component: 'data refresh', value: `Failed to refresh derived table "${derivedTable.displayId || derivedTable.id}": ${result.message || 'Unknown error'}` })); return null; } } catch (error) { console.error(`Error refreshing derived table ${derivedTable.id}:`, error); dispatch(dfActions.addMessages({ timestamp: Date.now(), type: 'error', component: 'data refresh', value: `Error refreshing derived table "${derivedTable.displayId || derivedTable.id}"` })); return null; } }); const results = await Promise.all(refreshPromises); const successfulUpdates = results.filter((r): r is { tableId: string, rows: any[] } => r !== null); if (successfulUpdates.length > 0) { // Single batch dispatch instead of N individual dispatches dispatch(dfActions.updateMultipleTableRows(successfulUpdates)); } }; const handleRefreshComplete = async (newRows: any[]) => { if (!selectedTableForRefresh) return; setIsRefreshing(true); try { // Update the source table with new rows dispatch(dfActions.updateTableRows({ tableId: selectedTableForRefresh.id, rows: newRows })); // Refresh all derived tables await refreshDerivedTables(selectedTableForRefresh.id, newRows); dispatch(dfActions.addMessages({ timestamp: Date.now(), type: 'success', component: 'data refresh', value: `Successfully refreshed data for "${selectedTableForRefresh.displayId || selectedTableForRefresh.id}" and updated derived tables.` })); } catch (error) { console.error('Error during refresh:', error); dispatch(dfActions.addMessages({ timestamp: Date.now(), type: 'error', component: 'data refresh', value: `Error refreshing data: ${error}` })); } finally { setIsRefreshing(false); } }; const theme = useTheme(); let focusedChart = useSelector((state: DataFormulatorState) => charts.find(c => c.id == focusedChartId)); const dispatch = useDispatch(); let [collapsed, setCollapsed] = useState<boolean>(false); const w: any = (a: any[], b: any[], spaceElement?: any) => a.length ? [a[0], b.length == 0 ? "" : (spaceElement || ""), ...w(b, a.slice(1), spaceElement)] : b; let triggers = parentTable ? getTriggers(parentTable, tables) : []; let tableIdList = parentTable ? [...triggers.map((trigger) => trigger.tableId), parentTable.id] : []; let usedTableIdsInThread = tableIdList.filter(id => usedIntermediateTableIds.includes(id)); let newTableIds = tableIdList.filter(id => !usedTableIdsInThread.includes(id)); let newTriggers = triggers.filter(tg => newTableIds.includes(tg.resultTableId)); // Use the global highlighted table IDs (computed at DataThread level from the focused table's full ancestor chain) let highlightedTableIds = globalHighlightedTableIds; let _buildTriggerCard = (trigger: Trigger, highlighted: boolean = false) => { return buildTriggerCard(trigger, focusedChartId, highlighted); } // Shared props for buildTableCard calls let tableCardProps: Omit<BuildTableCardProps, 'tableId'> = { tables, charts, chartElements, usedIntermediateTableIds, highlightedTableIds, focusedTableId, focusedChartId, focusedChart, parentTable, tableIdList, collapsed, scrollRef, dispatch, handleOpenTableMenu, primaryBgColor: theme.palette.primary.bgcolor, }; let _buildTableCard = (tableId: string) => { return buildTableCard({ tableId, ...tableCardProps }); } let tableElementList = newTableIds.map((tableId, i) => _buildTableCard(tableId)); let triggerCards = newTriggers.map((trigger) => { const triggerTableId = trigger.resultTableId; const isHL = triggerTableId ? highlightedTableIds.includes(triggerTableId) : false; return _buildTriggerCard(trigger, isHL); }); // Build a flat sequence of timeline items: [trigger, table, charts, trigger, table, charts, ...] let timelineItems: { key: string; element: React.ReactNode; type: 'used-table' | 'trigger' | 'table' | 'chart' | 'leaf-trigger' | 'leaf-table'; highlighted: boolean; tableId?: string; chartType?: string; isRunning?: boolean; isClarifying?: boolean; isCompleted?: boolean }[] = []; // Add used (shared) tables at the top // Only show the immediate parent + "..." for further ancestors let displayedUsedTableIds = usedTableIdsInThread; if (usedTableIdsInThread.length > 1) { // Keep only the last (immediate parent), prepend "..." placeholder displayedUsedTableIds = usedTableIdsInThread.slice(-1); timelineItems.push({ key: 'used-table-ellipsis', type: 'used-table', highlighted: false, element: ( <Typography sx={{ fontSize: '10px', color: 'text.disabled' }}> … </Typography> ), }); } displayedUsedTableIds.forEach((tableId, i) => { let table = tableById.get(tableId) as DictTable; timelineItems.push({ key: `used-table-${tableId}-${i}`, type: 'used-table', tableId: tableId, highlighted: highlightedTableIds.includes(tableId), element: ( <Typography sx={{ fontSize: '10px', cursor: 'pointer', width: 'fit-content', '&:hover': { backgroundColor: alpha(theme.palette.primary.light, 0.1), }, }} onClick={() => { dispatch(dfActions.setFocused({ type: 'table', tableId })) }}> {table.displayId || tableId} </Typography> ), }); }); // Interleave triggers and tables for the main thread body newTableIds.forEach((tableId, i) => { const trigger = newTriggers.find(t => t.resultTableId === tableId); const isHighlighted = highlightedTableIds.includes(tableId); // Add trigger card if exists if (trigger) { const triggerCard = triggerCards[newTriggers.indexOf(trigger)]; if (triggerCard) { timelineItems.push({ key: triggerCard?.key || `woven-trigger-${tableId}`, type: 'trigger', highlighted: isHighlighted, element: triggerCard, }); } } // Add table card and its charts const tableCard = tableElementList[i]; if (Array.isArray(tableCard)) { tableCard.forEach((subItem: any, j: number) => { if (!subItem) return; const subKey = subItem?.key || `woven-${tableId}-${j}`; const isChart = subKey.includes('chart'); // Extract chartType from the key (pattern: 'relevant-chart-{chartId}') let itemChartType: string | undefined; if (isChart) { const cIdMatch = subKey.match(/(?:chart)-(.+)$/); if (cIdMatch) { const cObj = charts.find(c => c.id === cIdMatch[1]); itemChartType = cObj?.chartType; } } timelineItems.push({ key: subKey, type: isChart ? 'chart' : 'table', tableId: isChart ? undefined : tableId, chartType: itemChartType, highlighted: isHighlighted, element: subItem, }); }); } // If an agent is running on this table, add a "working..." indicator if (runningAgentTableIds.has(tableId)) { const runningAction = runningAgentTableIds.get(tableId); const message = runningAction?.description || 'working...'; timelineItems.push({ key: `agent-running-${tableId}`, type: 'chart', highlighted: isHighlighted, isRunning: true, element: ThinkingBanner(message, { px: 1, py: 0.5 }), }); } else if (clarifyAgentTableIds.has(tableId)) { timelineItems.push({ key: `agent-clarify-${tableId}`, type: 'chart', highlighted: isHighlighted, isClarifying: true, element: <Typography variant="body2" sx={{ fontSize: 10, color: theme.palette.warning.main, px: 1, py: 0.5 }}>waiting for clarification...</Typography>, }); } else if (completedAgentTableIds.has(tableId)) { const completedAction = completedAgentTableIds.get(tableId); const completionMsg = completedAction?.messages?.filter(m => m.role === 'completion').pop(); if (completionMsg) { const color = completedAction?.status === 'failed' ? theme.palette.error.main : theme.palette.success.main; timelineItems.push({ key: `agent-completed-${tableId}`, type: 'chart', highlighted: isHighlighted, isCompleted: true, element: <CollapsibleStatusMessage text={typeof completionMsg.content === 'string' ? completionMsg.content : String(completionMsg.content)} color={color} />, }); } } }); // Add leaf table components leafTables.forEach((lt, i) => { let leafTrigger = lt.derive?.trigger; if (leafTrigger) { timelineItems.push({ key: `leaf-trigger-${lt.id}`, type: 'leaf-trigger', highlighted: highlightedTableIds.includes(lt.id), element: _buildTriggerCard(leafTrigger, highlightedTableIds.includes(lt.id)), }); } let leafCards = _buildTableCard(lt.id); if (Array.isArray(leafCards)) { leafCards.forEach((subItem: any, j: number) => { if (!subItem) return; const subKey = subItem?.key || `leaf-card-${lt.id}-${j}`; const isChart = subKey.includes('chart'); let leafChartType: string | undefined; if (isChart) { const cIdMatch = subKey.match(/(?:chart)-(.+)$/); if (cIdMatch) { const cObj = charts.find(c => c.id === cIdMatch[1]); leafChartType = cObj?.chartType; } } timelineItems.push({ key: subKey, type: isChart ? 'chart' : 'leaf-table', tableId: isChart ? undefined : lt.id, chartType: leafChartType, highlighted: highlightedTableIds.includes(lt.id), element: subItem, }); }); } // If an agent is running on this leaf table, add a "working..." indicator if (runningAgentTableIds.has(lt.id)) { const runningAction = runningAgentTableIds.get(lt.id); const message = runningAction?.description || 'working...'; timelineItems.push({ key: `agent-running-${lt.id}`, type: 'chart', highlighted: highlightedTableIds.includes(lt.id), isRunning: true, element: ThinkingBanner(message, { px: 1, py: 0.5 }), }); } else if (clarifyAgentTableIds.has(lt.id)) { timelineItems.push({ key: `agent-clarify-${lt.id}`, type: 'chart', highlighted: highlightedTableIds.includes(lt.id), isClarifying: true, element: <Typography variant="body2" sx={{ fontSize: 10, color: theme.palette.warning.main, px: 1, py: 0.5 }}>waiting for clarification...</Typography>, }); } else if (completedAgentTableIds.has(lt.id)) { const completedAction = completedAgentTableIds.get(lt.id); const completionMsg = completedAction?.messages?.filter(m => m.role === 'completion').pop(); if (completionMsg) { const color = completedAction?.status === 'failed' ? theme.palette.error.main : theme.palette.success.main; timelineItems.push({ key: `agent-completed-${lt.id}`, type: 'chart', highlighted: highlightedTableIds.includes(lt.id), isCompleted: true, element: <CollapsibleStatusMessage text={typeof completionMsg.content === 'string' ? completionMsg.content : String(completionMsg.content)} color={color} />, }); } } }); // Timeline rendering helper const TIMELINE_WIDTH = 14; const TIMELINE_GAP = '4px'; // gap between timeline and card content const DOT_SIZE = 6; const CARD_PY = '6px'; // vertical padding for each timeline row const getTimelineDot = (item: typeof timelineItems[0]) => { const isTable = item.type === 'table' || item.type === 'leaf-table' || item.type === 'used-table'; const color = item.highlighted ? theme.palette.primary.main : 'rgba(0,0,0,0.15)'; // For running agent items, show a spinner instead of a dot if (item.isRunning) { return <CircularProgress size={12} thickness={5} sx={{ color: theme.palette.primary.main }} />; } // For clarification items, show an hourglass icon if (item.isClarifying) { return <HourglassEmptyIcon sx={{ width: 12, height: 12, color: theme.palette.warning.main }} />; } // For completed items, show a checkmark icon if (item.isCompleted) { return <CheckCircleOutlineIcon sx={{ width: 12, height: 12, color: theme.palette.success.main }} />; } // For table items, show a type-specific icon instead of a dot if (isTable && item.tableId) { const tableForDot = tableById.get(item.tableId); const iconSx = { width: 14, height: 14, color }; const isStreaming = tableForDot && (tableForDot.source?.type === 'stream' || tableForDot.source?.type === 'database') && tableForDot.source?.autoRefresh; if (isStreaming) { return <StreamIcon sx={{ ...iconSx, color: item.highlighted ? theme.palette.success.main : 'rgba(0,0,0,0.15)', animation: 'pulse 2s infinite', '@keyframes pulse': { '0%': { opacity: 1 }, '50%': { opacity: 0.4 }, '100%': { opacity: 1 }, }, }} />; } if (tableForDot?.virtual) { return <TableIcon sx={{ ...iconSx, width: 14, height: 14 }} />; } return <TableIcon sx={iconSx} />; } // For chart items, show a chart-type-specific icon if (item.type === 'chart' && item.chartType) { const iconSx = { width: 14, height: 14, color }; const ct = item.chartType.toLowerCase(); if (ct.includes('scatter') || ct.includes('point') || ct.includes('dot') || ct.includes('boxplot')) { return <ScatterPlotIcon sx={iconSx} />; } if (ct.includes('line') || ct.includes('regression')) { return <ShowChartIcon sx={iconSx} />; } if (ct.includes('pie')) { return <PieChartOutlineIcon sx={iconSx} />; } if (ct.includes('heatmap')) { return <GridOnIcon sx={iconSx} />; } if (ct.includes('table')) { return <TableIcon sx={iconSx} />; } // Bar, histogram, stacked, grouped, pyramid, and default return <BarChartIcon sx={iconSx} />; } return <Box sx={{ width: DOT_SIZE, height: DOT_SIZE, borderRadius: '50%', backgroundColor: color, }} />; }; const hasHighlighting = highlightedTableIds.length > 0; // Whether the thread header is highlighted (any non-used-table item in this thread is highlighted) const headerHL = timelineItems.some(item => item.highlighted && item.type !== 'used-table'); const renderTimelineItem = (item: typeof timelineItems[0], index: number, isLast: boolean, nextHighlighted: boolean) => { const isTrigger = item.type === 'trigger' || item.type === 'leaf-trigger'; const isTable = item.type === 'table' || item.type === 'leaf-table' || item.type === 'used-table'; const isChart = item.type === 'chart'; const dashedColor = item.highlighted ? alpha(theme.palette.primary.main, 0.6) : 'rgba(0,0,0,0.1)'; const dashedWidth = item.highlighted ? '2px' : '1px'; const dashedStyle = 'solid'; // Bottom connector uses unhighlighted style if next item isn't highlighted const bottomHighlighted = item.highlighted && nextHighlighted; const bottomDashedColor = bottomHighlighted ? alpha(theme.palette.primary.main, 0.6) : 'rgba(0,0,0,0.1)'; const bottomDashedWidth = bottomHighlighted ? '2px' : '1px'; const bottomDashedStyle = 'solid'; const triggerColor = item.highlighted ? theme.palette.custom.main : 'rgba(0,0,0,0.15)'; // Dim non-highlighted cards when chatbox is focused const rowHighlightSx = (chatboxFocused && hasHighlighting && !item.highlighted) ? { opacity: 0.35 } : {}; // Triggers: agent icon on the timeline if (isTrigger) { return ( <Box key={`timeline-row-${item.key}`} sx={{ display: 'flex', flexDirection: 'row', position: 'relative', ...rowHighlightSx }}> <Box sx={{ width: TIMELINE_WIDTH, flexShrink: 0, display: 'flex', flexDirection: 'column', alignItems: 'center', }}> <Box sx={{ width: 0, flex: '1 1 0', minHeight: 2, borderLeft: `${dashedWidth} ${dashedStyle} ${dashedColor}` }} /> <Box sx={{ flexShrink: 0, zIndex: 1, position: 'relative', display: 'flex', alignItems: 'center', justifyContent: 'center' }}> <AgentIcon sx={{ width: 14, height: 14, color: triggerColor }} /> </Box> {!isLast && <Box sx={{ width: 0, flex: '1 1 0', minHeight: 2, borderLeft: `${bottomDashedWidth} ${bottomDashedStyle} ${bottomDashedColor}` }} />} {isLast && <Box sx={{ flex: '1 1 0', minHeight: 2 }} />} </Box> <Box sx={{ flex: 1, minWidth: 0, py: CARD_PY, pl: TIMELINE_GAP }}> {item.element} </Box> </Box> ); } // Charts: chart-type icon on the timeline if (isChart) { return ( <Box key={`timeline-row-${item.key}`} sx={{ display: 'flex', flexDirection: 'row', position: 'relative', ...rowHighlightSx }}> <Box sx={{ width: TIMELINE_WIDTH, flexShrink: 0, display: 'flex', flexDirection: 'column', alignItems: 'center', }}> <Box sx={{ width: 0, flex: '1 1 0', borderLeft: `${dashedWidth} ${dashedStyle} ${dashedColor}` }} /> <Box sx={{ flexShrink: 0, display: 'flex', alignItems: 'center', justifyContent: 'center' }}> {getTimelineDot(item)} </Box> {!isLast && <Box sx={{ width: 0, flex: '1 1 0', minHeight: 2, borderLeft: `${bottomDashedWidth} ${bottomDashedStyle} ${bottomDashedColor}` }} />} {isLast && <Box sx={{ flex: '1 1 0', minHeight: 2 }} />} </Box> <Box sx={{ flex: 1, minWidth: 0, py: CARD_PY, pl: TIMELINE_GAP }}> {item.element} </Box> </Box> ); } // Tables (primary nodes): settings icon on the timeline, more vertical spacing const tableForItem = item.tableId ? tableById.get(item.tableId) : undefined; return ( <Box key={`timeline-row-${item.key}`} sx={{ display: 'flex', flexDirection: 'row', position: 'relative', ...rowHighlightSx }}> <Box sx={{ width: TIMELINE_WIDTH, flexShrink: 0, display: 'flex', flexDirection: 'column', alignItems: 'center', position: 'relative', }}> {(index > 0 || !hideLabel) && (() => { // When connecting to the header (index 0, label visible), match the header's highlight state const useHeader = index === 0 && !hideLabel; const topColor = useHeader ? (headerHL ? alpha(theme.palette.primary.main, 0.6) : 'rgba(0,0,0,0.1)') : dashedColor; const topWidth = useHeader ? (headerHL ? '2px' : '1px') : dashedWidth; const topStyle = 'solid'; return <Box sx={{ width: 0, flex: '1 1 0', minHeight: 6, borderLeft: `${topWidth} ${topStyle} ${topColor}` }} />; })()} {index === 0 && hideLabel && <Box sx={{ flex: '1 1 0', minHeight: 6 }} />} <Box sx={{ flexShrink: 0, zIndex: 1, backgroundColor: 'white', display: 'flex', alignItems: 'center', justifyContent: 'center' }}> {getTimelineDot(item)} </Box> {!isLast && ( <Box sx={{ width: 0, flex: '1 1 0', minHeight: 6, borderLeft: `${bottomDashedWidth} ${bottomDashedStyle} ${bottomDashedColor}` }} /> )} {isLast && <Box sx={{ flex: '1 1 0', minHeight: 6 }} />} </Box> <Box sx={{ flex: 1, minWidth: 0, py: item.type === 'used-table' ? '1px' : CARD_PY, pl: TIMELINE_GAP, ...(item.type === 'used-table' && { display: 'flex', alignItems: 'center' }), }}> {item.element} </Box> </Box> ); }; return <Box sx={{ ...sx, '& .selected-card': { boxShadow: `0 0 0 2px ${theme.palette.primary.light}`, borderColor: 'transparent', margin: '1px 0', }, padding: '6px', ...(chatboxFocused && focusedThreadLeafId && !shouldHighlightThread ? { opacity: 0.35 } : {}), }} > <div style={{ padding: '2px 4px 2px 4px', marginTop: 0, direction: 'ltr' }}> {!hideLabel && (() => { const hlColor = theme.palette.primary.main; const nhColor = 'rgba(0,0,0,0.35)'; const connColor = headerHL ? alpha(theme.palette.primary.main, 0.6) : 'rgba(0,0,0,0.1)'; const connWidth = headerHL ? '2px' : '1px'; const connStyle = 'solid'; return ( <Box sx={{ display: 'flex', flexDirection: 'row', ...(chatboxFocused && hasHighlighting && !headerHL ? { opacity: 0.35 } : {}) }}> <Box sx={{ width: TIMELINE_WIDTH, flexShrink: 0, display: 'flex', flexDirection: 'column', alignItems: 'center', }}> <Box sx={{ flex: '1 1 0', minHeight: 6 }} /> <Box sx={{ width: 8, height: 8, borderRadius: '50%', border: `1.5px solid ${headerHL ? alpha(hlColor, 0.6) : 'rgba(0,0,0,0.15)'}`, backgroundColor: 'transparent', flexShrink: 0, }} /> <Box sx={{ width: 0, flex: '1 1 0', minHeight: 10, borderLeft: `${connWidth} ${connStyle} ${connColor}` }} /> </Box> <Box sx={{ flex: 1, minWidth: 0, display: 'flex', alignItems: 'center', pl: 0.5 }}> <Typography sx={{ fontSize: '11px', fontWeight: 700, textTransform: 'uppercase', letterSpacing: '0.02em', color: headerHL ? hlColor : 'rgba(0,0,0,0.55)', }}> {threadLabel || (threadIdx === -1 ? 'Thread 0' : `Thread ${threadIdx + 1}`)} </Typography> </Box> </Box> ); })()} {timelineItems.map((item, index) => renderTimelineItem(item, index, index === timelineItems.length - 1, timelineItems[index + 1]?.highlighted ?? false))} </div> <MetadataPopup open={metadataPopupOpen} anchorEl={metadataAnchorEl} onClose={handleCloseMetadataPopup} onSave={handleSaveMetadata} initialValue={selectedTableForMetadata?.attachedMetadata || ''} tableName={selectedTableForMetadata?.displayId || selectedTableForMetadata?.id || ''} /> <RenameTablePopup open={renamePopupOpen} anchorEl={renameAnchorEl} onClose={handleCloseRenamePopup} onSave={handleSaveRename} initialValue={selectedTableForRename?.displayId || selectedTableForRename?.id || ''} tableName={selectedTableForRename?.displayId || selectedTableForRename?.id || ''} /> {/* Table actions menu */} <Menu anchorEl={tableMenuAnchorEl} open={Boolean(tableMenuAnchorEl)} onClose={handleCloseTableMenu} onClick={(e) => e.stopPropagation()} > <MenuItem onClick={(e) => { e.stopPropagation(); if (selectedTableForMenu) { handleOpenRenamePopup(selectedTableForMenu, tableMenuAnchorEl!); } handleCloseTableMenu(); }} sx={{ fontSize: '12px', display: 'flex', alignItems: 'center', gap: 1 }} > <EditIcon sx={{ fontSize: 16, color: 'text.secondary' }}/> Rename </MenuItem> {/* Pin option - only for derived tables */} {selectedTableForMenu?.derive != undefined && ( <MenuItem onClick={(e) => { e.stopPropagation(); if (selectedTableForMenu) { dispatch(dfActions.updateTableAnchored({tableId: selectedTableForMenu.id, anchored: !selectedTableForMenu.anchored})); } handleCloseTableMenu(); }} sx={{ fontSize: '12px', display: 'flex', alignItems: 'center', gap: 1 }} > <AnchorIcon sx={{ fontSize: 16, color: selectedTableForMenu?.anchored ? 'primary.main' : 'text.secondary' }}/> {selectedTableForMenu?.anchored ? "Unpin table" : "Pin table"} </MenuItem> )} {/* Non-derived table options */} {selectedTableForMenu?.derive == undefined && ( <MenuItem onClick={(e) => { e.stopPropagation(); if (selectedTableForMenu) { handleOpenMetadataPopup(selectedTableForMenu, tableMenuAnchorEl!); } handleCloseTableMenu(); }} sx={{ fontSize: '12px', display: 'flex', alignItems: 'center', gap: 1 }} > <AttachFileIcon sx={{ fontSize: 16, color: selectedTableForMenu?.attachedMetadata ? 'secondary.main' : 'text.secondary', }}/> {selectedTableForMenu?.attachedMetadata ? "Edit metadata" : "Attach metadata"} </MenuItem> )} {/* Refresh settings - shown for stream/database sources to configure auto-refresh interval */} {selectedTableForMenu && selectedTableForMenu.derive == undefined && (selectedTableForMenu.source?.type === 'stream' || selectedTableForMenu.source?.type === 'database') && ( <MenuItem onClick={(e) => { e.stopPropagation(); if (selectedTableForMenu) { handleOpenStreamingSettingsPopup(selectedTableForMenu, tableMenuAnchorEl!); } handleCloseTableMenu(); }} sx={{ fontSize: '12px', display: 'flex', alignItems: 'center', gap: 1 }} > <StreamIcon sx={{ fontSize: 16, color: selectedTableForMenu.source?.autoRefresh ? 'success.main' : 'text.secondary' }}/> {selectedTableForMenu.source?.autoRefresh ? 'Refresh settings' : 'Watch for updates'} </MenuItem> )} {/* Refresh data - hidden for database tables and derived tables */} {selectedTableForMenu?.derive == undefined && selectedTableForMenu?.source?.type !== 'database' && ( <MenuItem onClick={(e) => { e.stopPropagation(); if (selectedTableForMenu) { handleOpenRefreshDialog(selectedTableForMenu); } }} sx={{ fontSize: '12px', display: 'flex', alignItems: 'center', gap: 1 }} > <RefreshIcon sx={{ fontSize: 16, color: 'primary.main' }}/> Replace data </MenuItem> )} <MenuItem onClick={(e) => { e.stopPropagation(); if (selectedTableForMenu) { dispatch(dfActions.deleteTable(selectedTableForMenu.id)); } handleCloseTableMenu(); }} disabled={selectedTableForMenu ? (!selectedTableForMenu.derive && tables.some(t => t.derive?.trigger.tableId === selectedTableForMenu.id)) : true} sx={{ fontSize: '12px', display: 'flex', alignItems: 'center', gap: 1, color: 'warning.main' }} > <DeleteIcon sx={{ fontSize: 16 }} color='warning'/> Delete table </MenuItem> </Menu> {/* Refresh data dialog */} {selectedTableForRefresh && ( <RefreshDataDialog open={refreshDialogOpen} onClose={handleCloseRefreshDialog} table={selectedTableForRefresh} onRefreshComplete={handleRefreshComplete} /> )} {/* Streaming settings popup */} {selectedTableForStreamingSettings && ( <StreamingSettingsPopup open={streamingSettingsPopupOpen} anchorEl={streamingSettingsAnchorEl} onClose={handleCloseStreamingSettingsPopup} table={selectedTableForStreamingSettings} onUpdateSettings={handleUpdateStreamingSettings} onRefreshNow={() => manualRefresh(selectedTableForStreamingSettings.id)} /> )} </Box> } /** Lightweight chart thumbnail — shows cached PNG, skeleton, or status icon. */ const ChartThumbnail: FC<{ chart: Chart; table: DictTable; status: 'available' | 'pending' | 'unavailable'; onChartClick: (chartId: string, tableId: string) => void; onDelete: (chartId: string) => void; }> = ({ chart, table, status, onChartClick, onDelete }) => { let deleteButton = <Box className='data-thread-chart-card-action-button' sx={{ zIndex: 10, color: 'blue', position: "absolute", right: 1, background: 'rgba(255, 255, 255, 0.95)' }}> <Tooltip title="delete chart"> <IconButton size="small" color="warning" onClick={(event) => { event.stopPropagation(); onDelete(chart.id); }}><DeleteIcon fontSize="small" /></IconButton> </Tooltip> </Box> const pendingOverlay = status == 'pending' ? <Box sx={{ position: "absolute", top: 0, left: -8, right: -8, bottom: 0, zIndex: 20, backgroundColor: "rgba(243, 243, 243, 0.8)", cursor: "pointer", borderRadius: '6px', display: 'flex', flexDirection: 'column', }}> <LinearProgress sx={{ width: "100%", height: "100%", opacity: 0.05 }} /> </Box> : null; if (['Auto', '?'].includes(chart.chartType)) { return <Box className="vega-thumbnail-box" onClick={() => onChartClick(chart.id, table.id)} sx={{ width: "100%", color: 'text.secondary', height: 48, display: "flex", backgroundColor: "white", position: 'relative', flexDirection: "column" }}> {pendingOverlay} <InsightIcon sx={{ margin: 'auto', color: 'darkgray' }} fontSize="medium" /> {deleteButton} </Box>; } if (status == 'unavailable' || chart.chartType == "Table") { let chartTemplate = getChartTemplate(chart.chartType); return <Box key={`unavailable-${chart.id}`} width={"100%"} className={"vega-thumbnail vega-thumbnail-box"} onClick={() => onChartClick(chart.id, table.id)} sx={{ display: "flex", backgroundColor: "white", position: 'relative', flexDirection: "column" }}> {pendingOverlay} <Box sx={{ display: "flex", flexDirection: "column", margin: "auto", height: 48}}> <Box sx={{ margin: "auto", transform: chart.chartType == 'Table' ? "rotate(15deg)" : undefined }} > {generateChartSkeleton(chartTemplate?.icon, 32, 32, chart.chartType == 'Table' ? 1 : 0.5)} </Box> {deleteButton} </Box> </Box>; } // ---- Thumbnail path: use cached PNG from ChartRenderService ---- if (chart.thumbnail) { return ( <Box onClick={() => onChartClick(chart.id, table.id)} className="vega-thumbnail-box" style={{ width: "100%", position: "relative", cursor: "pointer" }} > <Box sx={{ margin: "auto" }}> {chart.saved && <Typography sx={{ position: "absolute", margin: "5px", zIndex: 2 }}> <StarIcon sx={{ color: "gold" }} fontSize="small" /> </Typography>} {pendingOverlay} {deleteButton} <Box className={"vega-thumbnail"} sx={{ display: "flex", backgroundColor: chart.saved ? "rgba(255,215,0,0.05)" : "white", justifyContent: 'center', alignItems: 'center', minHeight: 48, minWidth: 60, }} > <img src={chart.thumbnail} alt={`${chart.chartType} chart`} style={{ maxWidth: 120, maxHeight: 100, objectFit: 'contain' }} /> </Box> </Box> </Box> ); } // ---- Fallback: skeleton while ChartRenderService is still processing ---- let chartTemplate = getChartTemplate(chart.chartType); return ( <Box onClick={() => onChartClick(chart.id, table.id)} className="vega-thumbnail-box" style={{ width: "100%", position: "relative", cursor: "pointer" }} > <Box sx={{ margin: "auto" }}> {chart.saved && <Typography sx={{ position: "absolute", margin: "5px", zIndex: 2 }}> <StarIcon sx={{ color: "gold" }} fontSize="small" /> </Typography>} {pendingOverlay} {deleteButton} <Box className={"vega-thumbnail"} sx={{ display: "flex", backgroundColor: chart.saved ? "rgba(255,215,0,0.05)" : "white", justifyContent: 'center', alignItems: 'center', minHeight: 60, }} > {generateChartSkeleton(chartTemplate?.icon, 48, 48, 0.3)} </Box> </Box> </Box> ); }; // Height estimation constants (px) – per-type heights + py:4px (8px) gap per row const LAYOUT_TABLE_HEIGHT = 28 + 8; // table card + row padding const LAYOUT_TRIGGER_HEIGHT = 43 + 8; // trigger card (2 lines) + row padding const LAYOUT_CHART_HEIGHT = 90 + 8; // chart card (~70-110) + row padding const LAYOUT_THREAD_OVERHEAD = 52; // header divider + thread padding const LAYOUT_THREAD_GAP = 8; // my: 0.5 = 4px top + 4px bottom between threads function estimateThreadHeight( tableCount: number, triggerCount: number, chartCount: number ): number { return LAYOUT_THREAD_OVERHEAD + tableCount * LAYOUT_TABLE_HEIGHT + triggerCount * LAYOUT_TRIGGER_HEIGHT + chartCount * LAYOUT_CHART_HEIGHT; } /** * Compute a balanced column layout for threads. * * @param heights – Estimated pixel height for each thread (in display order). * @param numColumns – Maximum number of columns to distribute into. * @param flexOrder – When true, threads may be reordered across columns for * better balance (LPT heuristic). When false, the original * order is preserved (optimal contiguous partitioning via * binary-search on the maximum column height). * @returns An array of columns, where each column is an array of original * thread indices. Empty columns are omitted. */ function computeThreadColumnLayout( heights: number[], numColumns: number, flexOrder: boolean = false, ): number[][] { if (heights.length === 0) return []; if (heights.length === 1) return [[0]]; const cols = Math.min(numColumns, heights.length); if (cols <= 1) return [heights.map((_, i) => i)]; return flexOrder ? layoutFlexOrder(heights, cols) : layoutPreserveOrder(heights, cols); } /** * Balanced layout *with* reordering (LPT – Longest Processing Time first). * Assigns the tallest unplaced thread to whichever column is currently shortest. */ function layoutFlexOrder(heights: number[], numColumns: number): number[][] { const indexed = heights.map((h, i) => ({ idx: i, h })); indexed.sort((a, b) => b.h - a.h); // tallest first const columns: number[][] = Array.from({ length: numColumns }, () => []); const colH: number[] = new Array(numColumns).fill(0); for (const item of indexed) { let minCol = 0; for (let c = 1; c < numColumns; c++) { if (colH[c] < colH[minCol]) minCol = c; } columns[minCol].push(item.idx); colH[minCol] += item.h; } return columns.filter(c => c.length > 0); } /** * Balanced layout *preserving* thread order. * * Uses binary-search on the maximum column height to find the tightest * contiguous partitioning of threads into ≤ numColumns groups. */ function layoutPreserveOrder(heights: number[], numColumns: number): number[][] { const maxH = Math.max(...heights); const totalH = heights.reduce((s, h) => s + h, 0); // Can we fit all threads into `numColumns` columns with no column > target? const canPartition = (target: number): boolean => { let cols = 1, cur = 0; for (const h of heights) { if (cur + h > target && cur > 0) { cols++; cur = h; if (cols > numColumns) return false; } else { cur += h; } } return true; }; // Binary-search for the minimum feasible max-column height let lo = maxH, hi = totalH; while (lo < hi) { const mid = Math.floor((lo + hi) / 2); if (canPartition(mid)) hi = mid; else lo = mid + 1; } // Build the actual partition with the optimal target const target = lo; const columns: number[][] = [[]]; let cur = 0; for (let i = 0; i < heights.length; i++) { if (cur + heights[i] > target && columns[columns.length - 1].length > 0) { columns.push([]); cur = 0; } columns[columns.length - 1].push(i); cur += heights[i]; } return columns; } /** * Choose the best column layout that balances scroll burden vs whitespace. * * 1. If a single column fits within SCROLL_TOLERANCE × viewportHeight, * use one column — the small scroll is preferable to the whitespace * of an extra column (e.g. one long thread + one tiny thread). * 2. Otherwise, evaluate layouts for 1 … maxColumns and pick the smallest * column count whose tallest column fits within viewportHeight. * 3. If nothing eliminates scrolling, pick the layout that minimises the * tallest column (least scrolling). */ const SCROLL_TOLERANCE = 1.5; // allow up to 50% overflow before adding columns function chooseBestColumnLayout( heights: number[], maxColumns: number, viewportHeight: number, flexOrder: boolean = false, minColumns: number = 1, ): number[][] { if (heights.length === 0) return []; const cap = Math.min(maxColumns, heights.length); const start = Math.min(Math.max(minColumns, 1), cap); const tolerantHeight = viewportHeight * SCROLL_TOLERANCE; // Compute effective column height including gaps between threads const columnEffectiveHeight = (col: number[]) => { const contentH = col.reduce((sum, idx) => sum + heights[idx], 0); const gapH = Math.max(0, col.length - 1) * LAYOUT_THREAD_GAP; return contentH + gapH; }; // Evaluate every candidate column count (start … cap). // Pick the smallest n whose tallest column fits within tolerance. // If none fits, pick the one with the shortest tallest column. let bestLayout: number[][] = []; let bestMaxH = Infinity; for (let n = start; n <= cap; n++) { const layout = computeThreadColumnLayout(heights, n, flexOrder); const maxH = Math.max(...layout.map(columnEffectiveHeight)); // Smallest n that fits within tolerance → least whitespace if (maxH <= tolerantHeight) { return layout; } // Otherwise track the layout with the shortest tallest column if (maxH < bestMaxH) { bestMaxH = maxH; bestLayout = layout; } } return bestLayout; } export const DataThread: FC<{sx?: SxProps}> = function ({ sx }) { let tables = useSelector((state: DataFormulatorState) => state.tables); let focusedId = useSelector((state: DataFormulatorState) => state.focusedId); let charts = useSelector(dfSelectors.getAllCharts); // Derive focusedTableId from focusedId for scroll/highlight logic let focusedTableId = useMemo(() => { if (!focusedId) return undefined; if (focusedId.type === 'table') return focusedId.tableId; const chartId = focusedId.chartId; const chart = charts.find(c => c.id === chartId); return chart?.tableRef; }, [focusedId, charts]); let chartSynthesisInProgress = useSelector((state: DataFormulatorState) => state.chartSynthesisInProgress); const conceptShelfItems = useSelector((state: DataFormulatorState) => state.conceptShelfItems); const scrollRef = useRef<null | HTMLDivElement>(null) const containerRef = useRef<null | HTMLDivElement>(null) const suppressScrollRef = useRef(false); const [expandedColumns, setExpandedColumns] = useState(false); const [containerWidth, setContainerWidth] = useState(0); const [chatboxFocused, setChatboxFocused] = useState(false); const [chatMode, setChatMode] = useState(false); // Re-attach ResizeObserver whenever chatMode changes (containerRef moves to a different element) useEffect(() => { const el = containerRef.current; if (!el) return; const ro = new ResizeObserver((entries) => { for (const entry of entries) { setContainerWidth(entry.contentRect.width); } }); ro.observe(el); return () => ro.disconnect(); }, [chatMode]); const theme = useTheme(); const executeScroll = (smooth: boolean = true, block: ScrollLogicalPosition = 'center') => { if (scrollRef.current != null) { scrollRef.current.scrollIntoView({ behavior: smooth ? 'smooth' : 'auto', block }) } } const dispatch = useDispatch(); // Track previous table/chart counts to detect agent additions const prevCountsRef = useRef({ tables: tables.length, charts: charts.length }); useEffect(() => { // Only auto-scroll when new tables or charts are added (e.g., by the agent), // not when the user simply changes focus by clicking. const prevCounts = prevCountsRef.current; const isNewItem = tables.length > prevCounts.tables || charts.length > prevCounts.charts; prevCountsRef.current = { tables: tables.length, charts: charts.length }; if (focusedTableId && isNewItem) { executeScroll(true); } }, [focusedTableId]); // When the chatbox panel expands, scroll the focused table above the overlay if needed useEffect(() => { if (chatboxFocused && focusedTableId && scrollRef.current && containerRef.current) { setTimeout(() => { if (!scrollRef.current || !containerRef.current) return; const container = containerRef.current; const el = scrollRef.current; const containerRect = container.getBoundingClientRect(); const elRect = el.getBoundingClientRect(); // The overlay panel is ~220px from bottom; ensure the focused element's bottom // is at least 240px above the container's bottom const safeZone = 400; const elBottomInContainer = elRect.bottom - containerRect.top; const visibleHeight = containerRect.height - safeZone; if (elBottomInContainer > visibleHeight) { // Scroll so the element top is near the top of the container el.scrollIntoView({ behavior: 'smooth', block: 'start' }); } }, 100); } }, [chatboxFocused]); // O(1) table lookup by ID const tableById = useMemo(() => new Map(tables.map(t => [t.id, t])), [tables]); // Cached getTriggers — avoids repeated chain walks within a single render const _tCache = new Map<string, Trigger[]>(); const getCachedTriggers = (lt: DictTable): Trigger[] => { if (_tCache.has(lt.id)) return _tCache.get(lt.id)!; const triggers = getTriggers(lt, tables); _tCache.set(lt.id, triggers); return triggers; }; // Now use useMemo to memoize the chartElements array let chartElements = useMemo(() => { return charts.filter(c => c.source == "user").map((chart) => { const table = getDataTable(chart, tables, charts, conceptShelfItems); let status: 'available' | 'pending' | 'unavailable' = chartSynthesisInProgress.includes(chart.id) ? 'pending' : checkChartAvailability(chart, conceptShelfItems, table.rows) ? 'available' : 'unavailable'; let element = <ChartThumbnail chart={chart} table={table} status={status} onChartClick={() => { dispatch(dfActions.setFocused({ type: 'chart', chartId: chart.id })); }} onDelete={() => {dispatch(dfActions.deleteChartById(chart.id))}} />; return { chartId: chart.id, tableId: table.id, element }; }); }, [charts, tables, conceptShelfItems, chartSynthesisInProgress]); // anchors are considered leaf tables to simplify the view let isLeafTable = (table: DictTable) => { let children = tables.filter(t => t.derive?.trigger.tableId == table.id); if (children.length == 0 || children.every(t => t.anchored)) { return true; } return false; } let leafTables = [ ...tables.filter(t => isLeafTable(t)) ]; // Split long derivation chains by promoting intermediate tables as additional "leaves". // If a chain has more than MAX_CHAIN_TABLES tables, we add a split point every // MAX_CHAIN_TABLES steps. The sort (shorter chains first) ensures the intermediate // leaf is processed before the real leaf, so its tables get claimed — making the // real leaf's thread show only the remaining (new) tables. // When counting chain length, exclude "used" tables (already claimed by an earlier // chain) so that shared ancestors don't inflate the count. The first chain to // contain a table still counts it as owned. const MAX_CHAIN_TABLES = 5; // Process leaves in order, tracking claimed tables to simulate the later claim loop. // A table is "used" for a chain only if a *previous* chain already claimed it. const claimedForSplit = new Set<string>(); const extraLeaves: DictTable[] = []; for (const lt of leafTables) { const triggers = getCachedTriggers(lt); const allChainIds = [lt.id, ...triggers.map(t => t.tableId)]; // Tables not yet claimed by an earlier chain count as owned const ownedIds = allChainIds.filter(id => !claimedForSplit.has(id)); if (ownedIds.length > MAX_CHAIN_TABLES) { // Walk only owned (unclaimed) triggers for split positions const ownedTriggers = triggers.filter(t => !claimedForSplit.has(t.tableId)); for (let pos = MAX_CHAIN_TABLES - 1; pos < ownedTriggers.length; pos += MAX_CHAIN_TABLES) { const midId = ownedTriggers[pos].tableId; const midTable = tableById.get(midId); if (midTable && !leafTables.includes(midTable) && !extraLeaves.includes(midTable)) { extraLeaves.push(midTable); } } } // Claim all tables in this chain for subsequent chains allChainIds.forEach(id => claimedForSplit.add(id)); } if (extraLeaves.length > 0) { leafTables.push(...extraLeaves); } // we want to sort the leaf tables by the order of their ancestors // for example if ancestor of list a is [0, 3] and the ancestor of list b is [0, 2] then b should come before a // when tables are anchored, we want to give them a higher order (so that they are displayed after their peers) let tableOrder = Object.fromEntries(tables.map((table, index) => [table.id, index + (table.anchored ? 1 : 0) * tables.length])); let getAncestorOrders = (leafTable: DictTable) => { let triggers = getCachedTriggers(leafTable); return [...triggers.map(t => tableOrder[t.tableId]), tableOrder[leafTable.id]]; } leafTables.sort((a, b) => { let aOrders = getAncestorOrders(a); let bOrders = getAncestorOrders(b); // If lengths are equal, compare orders in order for (let i = 0; i < Math.min(aOrders.length, bOrders.length); i++) { if (aOrders[i] !== bOrders[i]) { return aOrders[i] - bOrders[i]; } } // If all orders are equal, compare the leaf tables themselves return aOrders.length - bOrders.length; }); // Compute global highlighted table IDs from the focused table's full ancestor chain let globalHighlightedTableIds: string[] = useMemo(() => { if (!focusedTableId) return []; let focusedTable = tableById.get(focusedTableId); if (!focusedTable) return []; // Walk up the trigger chain from the focused table to collect all ancestor IDs let ids: string[] = [focusedTableId]; let current = focusedTable; while (current.derive && !current.anchored) { let parentId = current.derive.trigger.tableId; ids.unshift(parentId); let parent = tableById.get(parentId); if (!parent) break; current = parent; } return ids; }, [focusedTableId, tableById]); // Determine which leaf table's thread the focused table belongs to let focusedThreadLeafId: string | undefined = useMemo(() => { if (!focusedTableId) return undefined; // Check if focused table IS a leaf table let directLeaf = leafTables.find(lt => lt.id === focusedTableId); if (directLeaf) return directLeaf.id; // Otherwise, find the leaf table whose ancestor chain includes the focused table for (const lt of leafTables) { const triggers = getCachedTriggers(lt); const chainIds = [...triggers.map(t => t.tableId), lt.id]; if (chainIds.includes(focusedTableId)) { return lt.id; } } return undefined; }, [focusedTableId, leafTables, tables]); let hasContent = leafTables.length > 0 || tables.length > 0; // Collect all tables (including derived ones) for the workspace panel. let baseTables = tables; // Threaded tables: leaf tables that have a derivation chain let threadedTables = leafTables.filter(lt => { const triggers = getTriggers(lt, tables); return triggers.length + 1 > 1; }); // Build thread entries and their estimated heights for layout type ThreadEntry = { key: string; groupId: string; leafTables: DictTable[]; threadIdx: number; threadLabel?: string; isSplitThread?: boolean; usedTableIds?: string[]; hideLabel?: boolean }; let allThreadEntries: ThreadEntry[] = []; let allThreadHeights: number[] = []; // Track which leaf tables are promoted (split) vs real leaves const extraLeafIds = new Set(extraLeaves.map(t => t.id)); // Track which table IDs have been claimed by earlier threads let claimedTableIds = new Set<string>(); // Hanging tables: source tables with no children — displayed as a group before thread 1 let hangingTables = leafTables.filter(lt => !lt.derive); if (hangingTables.length > 0) { hangingTables.forEach(lt => claimedTableIds.add(lt.id)); let hangingChartCount = hangingTables.reduce((sum, lt) => sum + chartElements.filter(ce => ce.tableId === lt.id).length, 0); allThreadEntries.push({ key: 'hanging-tables', groupId: 'hanging-tables', leafTables: hangingTables, threadIdx: -1, hideLabel: true, }); allThreadHeights.push(estimateThreadHeight(hangingTables.length, 0, hangingChartCount)); } // Regular threads: one per threaded leaf table // Assign sub-thread numbering: split (promoted) threads get the main index (1, 2, ...), // real leaf tables whose chain was split get a sub-index (1.1, 1.2, ...) let realThreadIdx = 0; // counter for main threads // Pre-scan: find which real leaf each extra leaf belongs to let extraLeafToRealLeaf = new Map<string, string>(); // Also build reverse: real leaf -> list of extra leaves in its chain let realLeafToExtraLeaves = new Map<string, string[]>(); for (const lt of threadedTables) { if (!extraLeafIds.has(lt.id)) { // This is a real leaf — find all extra leaves that are ancestors of it const triggers = getCachedTriggers(lt); const chainIds = triggers.map(t => t.tableId); const myExtras: string[] = []; for (const extraId of extraLeafIds) { if (chainIds.includes(extraId)) { if (!extraLeafToRealLeaf.has(extraId)) { extraLeafToRealLeaf.set(extraId, lt.id); } myExtras.push(extraId); } } if (myExtras.length > 0) { realLeafToExtraLeaves.set(lt.id, myExtras); } } } // Map from extra leaf id -> its assigned main thread index let extraLeafToThreadIdx = new Map<string, number>(); // Track sub-index counters per chain (keyed by first extra leaf's thread idx) let subThreadCounters = new Map<number, number>(); threadedTables.forEach((lt, i) => { const triggers = getCachedTriggers(lt); // Collect all table IDs in this thread's chain let threadTableIds = new Set<string>(); triggers.forEach(t => threadTableIds.add(t.tableId)); threadTableIds.add(lt.id); // Only new (unclaimed) tables contribute to this thread's height let newTableIds = [...threadTableIds].filter(id => !claimedTableIds.has(id)); let newTriggerCount = triggers.filter(t => newTableIds.includes(t.resultTableId)).length; let chartCount = newTableIds.reduce((sum, tid) => sum + chartElements.filter(ce => ce.tableId === tid).length, 0); // +1 table and +1 trigger for the leaf table itself let totalTables = newTableIds.length + 1; let totalTriggers = newTriggerCount + 1; // Claim this thread's tables for subsequent threads threadTableIds.forEach(id => claimedTableIds.add(id)); // Determine thread label and whether this is a split sub-thread const isSplit = extraLeafIds.has(lt.id); // A real leaf is a "continuation" if it has extra leaves in its chain const isContinuation = !isSplit && realLeafToExtraLeaves.has(lt.id); let threadLabel: string; let threadIdxForEntry: number; if (isSplit) { // Promoted intermediate — gets a main thread index realThreadIdx++; extraLeafToThreadIdx.set(lt.id, realThreadIdx); threadLabel = `thread - ${realThreadIdx}`; threadIdxForEntry = realThreadIdx - 1; } else if (isContinuation) { // Real leaf whose chain was split — gets sub-index under the last extra leaf's index const myExtras = realLeafToExtraLeaves.get(lt.id) || []; // Use the last extra leaf's thread index (the one closest to this leaf in the chain) const lastExtra = myExtras[myExtras.length - 1]; const parentIdx = extraLeafToThreadIdx.get(lastExtra) ?? realThreadIdx; const subIdx = (subThreadCounters.get(parentIdx) || 0) + 1; subThreadCounters.set(parentIdx, subIdx); threadLabel = `thread - ${parentIdx}.${subIdx}`; threadIdxForEntry = i; } else { // Normal thread (no splitting involved) realThreadIdx++; threadLabel = `thread - ${realThreadIdx}`; threadIdxForEntry = realThreadIdx - 1; } allThreadEntries.push({ key: `thread-${lt.id}-${i}`, groupId: lt.id, leafTables: [lt], threadIdx: threadIdxForEntry, threadLabel, isSplitThread: isContinuation, }); allThreadHeights.push(estimateThreadHeight(totalTables, totalTriggers, chartCount)); }); // Pre-compute usedTableIds for each entry (avoids quadratic recomputation in renderThreadEntry) { let accumulated: string[] = []; for (const entry of allThreadEntries) { entry.usedTableIds = [...accumulated]; for (const lt of entry.leafTables) { const triggers = getCachedTriggers(lt); accumulated.push(...triggers.map(t => t.tableId), lt.id); } } } // Pick the best column layout: dynamically based on container width. const availableHeight = containerRef.current?.clientHeight ?? 600; const hasMultipleThreads = allThreadEntries.length > 1; const CARD_GAP = 12; // padding + spacing between cards in a column const PANEL_PADDING = 16; const CARD_WIDTH = 220; const COLUMN_WIDTH = CARD_WIDTH + CARD_GAP; // Determine how many columns fit in the available container width (1-3) const fittableColumns = Math.max(1, Math.min(3, Math.floor((containerWidth - PANEL_PADDING) / COLUMN_WIDTH))); const MAX_COLUMNS = fittableColumns; const columnLayout: number[][] = chooseBestColumnLayout( allThreadHeights, MAX_COLUMNS, availableHeight, /* flexOrder */ false, /* minColumns */ Math.min(fittableColumns, hasMultipleThreads ? 2 : 1) ); const actualColumns = columnLayout.length || 1; let renderThreadEntry = (entry: ThreadEntry) => { let usedTableIds = entry.usedTableIds || []; return <SingleThreadGroupView key={entry.key} scrollRef={scrollRef} threadIdx={entry.threadIdx} threadLabel={entry.threadLabel} isSplitThread={entry.isSplitThread} hideLabel={entry.hideLabel} leafTables={entry.leafTables} chartElements={chartElements} usedIntermediateTableIds={usedTableIds} globalHighlightedTableIds={globalHighlightedTableIds} focusedThreadLeafId={focusedThreadLeafId} chatboxFocused={chatboxFocused} sx={{ backgroundColor: 'white', borderRadius: radius.md, padding: 1, my: 0.5, flex: 'none', display: 'flex', flexDirection: 'column', height: 'fit-content', width: CARD_WIDTH, transition: transition.fast, }} />; }; // Let content fill available width; column count driven by container size const panelWidth = '100%'; let view = hasContent ? ( <Box sx={{ overflowY: 'auto', overflowX: 'hidden', position: 'relative', direction: 'ltr', height: 'calc(100% - 16px)', width: panelWidth, }}> <Box sx={{ display: 'flex', flexDirection: 'row', flexWrap: 'nowrap', justifyContent: 'center', gap: `${CARD_GAP}px`, py: 1, pb: chatboxFocused ? '180px' : 1, pl: `${PANEL_PADDING / 2}px`, pr: 0, }}> {/* First column: workspace panel + first batch of threads */} <Box key="thread-column-0" sx={{ display: 'flex', flexDirection: 'column', alignItems: 'center', gap: 0, width: CARD_WIDTH, flexShrink: 0, }}> {(columnLayout[0] || []).map((idx: number) => { const entry = allThreadEntries[idx]; return entry ? renderThreadEntry(entry) : null; })} </Box> {/* Remaining columns */} {columnLayout.slice(1).map((columnIndices: number[], colIdx: number) => ( <Box key={`thread-column-${colIdx + 1}`} sx={{ display: 'flex', flexDirection: 'column', alignItems: 'center', gap: 0, width: CARD_WIDTH, flexShrink: 0, }}> {columnIndices.map((idx: number) => { const entry = allThreadEntries[idx]; return entry ? renderThreadEntry(entry) : null; })} </Box> ))} </Box> </Box> ) : null; return ( <Box className="data-thread" sx={{ ...sx, position: 'relative', display: 'flex', flexDirection: 'column' }}> {/* Mode toggle button — floats on top-right corner */} <Box sx={{ position: 'absolute', top: 4, right: 16, zIndex: 5 }}> <Tooltip title={chatMode ? 'Switch to thread view' : 'Switch to chat view'}> <IconButton size="small" onClick={() => setChatMode(m => !m)} sx={{ p: 0.5, color: theme.palette.text.secondary, backgroundColor: alpha(theme.palette.action.selected, 0.04), borderRadius: '6px', '&:hover': { color: theme.palette.primary.main, backgroundColor: alpha(theme.palette.primary.main, 0.08) } }} > {chatMode ? <AccountTreeIcon sx={{ fontSize: 18 }} /> : <ForumOutlinedIcon sx={{ fontSize: 18 }} />} </IconButton> </Tooltip> </Box> {chatMode ? ( <Box ref={containerRef} sx={{ flex: 1, minHeight: 0, overflow: 'hidden', display: 'flex', flexDirection: 'column' }}> <ChatThreadView /> </Box> ) : ( <Box ref={containerRef} sx={{ overflow: 'hidden', direction: 'rtl', display: 'block', flex: 1, minHeight: 0, }}> {view} </Box> )} <SimpleChartRecBox /> </Box> ); } ================================================ FILE: src/views/DataThreadCards.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React, { memo } from 'react'; import { Box, Typography, Stack, Card, IconButton, Tooltip, ButtonGroup, useTheme, } from '@mui/material'; import { dfActions } from '../app/dfSlice'; import { Chart, DictTable, Trigger } from "../components/ComponentType"; import DeleteIcon from '@mui/icons-material/Delete'; import { AnchorIcon } from '../icons'; import MoreVertIcon from '@mui/icons-material/MoreVert'; import AddchartIcon from '@mui/icons-material/Addchart'; import { TriggerCard } from './EncodingShelfCard'; import { ComponentBorderStyle, shadow, transition } from '../app/tokens'; // ─── Chart Card ────────────────────────────────────────────────────────────── export let buildChartCard = ( chartElement: { tableId: string, chartId: string, element: any }, focusedChartId?: string, ) => { let selectedClassName = focusedChartId == chartElement.chartId ? 'selected-card' : ''; return <Card className={`data-thread-card ${selectedClassName}`} elevation={0} sx={{ width: 'fit-content', display: 'flex', position: 'relative', border: 'none', borderRadius: '6px', backgroundColor: 'white', px: 1, }}> {chartElement.element} </Card> } // ─── Trigger Card Wrapper ──────────────────────────────────────────────────── export let buildTriggerCard = ( trigger: Trigger, focusedChartId: string | undefined, highlighted: boolean = false, ) => { let selectedClassName = trigger.chart?.id == focusedChartId ? 'selected-card' : ''; let triggerCard = <div key={'thread-card-trigger-box'}> <Box sx={{ flex: 1 }} > <TriggerCard className={selectedClassName} trigger={trigger} hideFields={trigger.instruction != ""} highlighted={highlighted} sx={{ '& .MuiBox-root': { mx: 0.5, my: 0.25 }, '& .MuiSvgIcon-root': { width: '12px', height: '12px' }, }} /> </Box> </div>; return <Box sx={{ display: 'flex', flexDirection: 'column' }} key={`trigger-card-${trigger.chart?.id}`}> {triggerCard} </Box>; } // ─── Table Card ────────────────────────────────────────────────────────────── export interface BuildTableCardProps { tableId: string; tables: DictTable[]; charts: Chart[]; chartElements: { tableId: string, chartId: string, element: any }[]; usedIntermediateTableIds: string[]; highlightedTableIds: string[]; focusedTableId: string | undefined; focusedChartId: string | undefined; focusedChart: Chart | undefined; parentTable: DictTable | undefined; tableIdList: string[]; collapsed: boolean; scrollRef: any; dispatch: any; handleOpenTableMenu: (table: DictTable, anchorEl: HTMLElement) => void; primaryBgColor: string | undefined; } export let buildTableCard = (props: BuildTableCardProps) => { const { tableId, tables, charts, chartElements, usedIntermediateTableIds, highlightedTableIds, focusedTableId, focusedChartId, focusedChart, parentTable, tableIdList, collapsed, scrollRef, dispatch, handleOpenTableMenu, primaryBgColor, } = props; if (parentTable && tableId == parentTable.id && parentTable.anchored && tableIdList.length > 1) { let table = tables.find(t => t.id == tableId); return <Typography sx={{ background: 'transparent', }} > <Box sx={{ margin: '0px', width: 'fit-content', display: 'flex', cursor: 'pointer', padding: '2px 4px', borderRadius: '4px', '&:hover': { backgroundColor: 'rgba(0, 0, 0, 0.04)', boxShadow: shadow.sm } }} onClick={(event) => { event.stopPropagation(); dispatch(dfActions.setFocused({ type: 'table', tableId })); }} > <Stack direction="row" sx={{ marginLeft: 0.25, marginRight: 'auto', fontSize: 12 }} alignItems="center" gap={"2px"}> <AnchorIcon sx={{ fontSize: 14, color: 'rgba(0,0,0,0.5)' }} /> <Typography fontSize="inherit" sx={{ textAlign: 'center', color: 'rgba(0,0,0,0.7)', maxWidth: '100px', wordWrap: 'break-word', whiteSpace: 'normal' }}> {table?.displayId || tableId} </Typography> </Stack> </Box> </Typography> } // filter charts relevant to this let relevantCharts = chartElements.filter(ce => ce.tableId == tableId && !usedIntermediateTableIds.includes(tableId)); let table = tables.find(t => t.id == tableId); let selectedClassName = tableId == focusedTableId ? 'selected-card' : ''; let collapsedProps = collapsed ? { width: '50%', "& canvas": { width: 60, maxHeight: 50 } } : { width: '100%' } let releventChartElements = relevantCharts.map((ce, j) => <Box key={`relevant-chart-${ce.chartId}`} sx={{ display: 'flex', padding: 0, ...collapsedProps }}> {buildChartCard(ce, focusedChartId)} </Box>) const isHighlighted = highlightedTableIds.includes(tableId); let regularTableBox = <Box key={`regular-table-box-${tableId}`} ref={relevantCharts.some(c => c.chartId == focusedChartId) ? scrollRef : null} sx={{ padding: '0px' }}> <Card className={`data-thread-card ${selectedClassName}`} elevation={0} sx={{ width: '100%', backgroundColor: primaryBgColor, ...ComponentBorderStyle, ...(isHighlighted ? { borderLeft: '2px solid', borderLeftColor: 'primary.main' } : {}), borderRadius: '6px', }} onClick={() => { dispatch(dfActions.setFocused({ type: 'table', tableId })); }}> <Box sx={{ margin: '0px', display: 'flex', minWidth: 0 }}> <Stack direction="row" sx={{ marginLeft: 0.5, marginRight: 'auto', fontSize: 12, flex: 1, minWidth: 0, overflow: 'hidden' }} alignItems="center" gap={"2px"}> <Box sx={{ margin: '4px 8px 4px 2px', display: 'flex', alignItems: 'center', minWidth: 0, flex: 1 }}> <Typography fontSize="inherit" sx={{ color: 'text.primary', fontWeight: 500, display: '-webkit-box', WebkitLineClamp: 2, WebkitBoxOrient: 'vertical', overflow: 'hidden', wordBreak: 'break-all', }}>{table?.displayId || tableId}</Typography> </Box> </Stack> <ButtonGroup aria-label="Basic button group" variant="text" sx={{ textAlign: 'end', margin: "auto 2px auto auto", flexShrink: 0 }}> <Tooltip key="create-chart-btn-tooltip" title="create chart"> <IconButton className="create-chart-btn" color="primary" aria-label="create chart" size="small" sx={{ padding: 0.25, '&:hover': { transform: 'scale(1.2)', transition: transition.fast } }} onClick={(event) => { event.stopPropagation(); dispatch(dfActions.setFocused({ type: 'table', tableId })); }} > <AddchartIcon fontSize="small" sx={{ fontSize: 16 }} /> </IconButton> </Tooltip> <Tooltip key="more-options-btn-tooltip" title="more options"> <IconButton className="more-options-btn" color="primary" aria-label="more options" size="small" sx={{ padding: 0.25, '&:hover': { transform: 'scale(1.2)', transition: transition.fast } }} onClick={(event) => { event.stopPropagation(); handleOpenTableMenu(table!, event.currentTarget); }} > <MoreVertIcon fontSize="small" sx={{ fontSize: 16 }} /> </IconButton> </Tooltip> </ButtonGroup> </Box> </Card> </Box> return [ regularTableBox, ...releventChartElements, ] } ================================================ FILE: src/views/DataView.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React, { FC, useEffect, useMemo } from 'react'; import _ from 'lodash'; import { Typography, Box, Link, Breadcrumbs, useTheme, Fade } from '@mui/material'; import { alpha } from '@mui/material/styles'; import '../scss/DataView.scss'; import { DictTable } from '../components/ComponentType'; import { DataFormulatorState, dfActions, dfSelectors, FocusedId } from '../app/dfSlice'; import { useDispatch, useSelector } from 'react-redux'; import { Type } from '../data/types'; import { SelectableDataGrid } from './SelectableDataGrid'; export interface FreeDataViewProps { } export const FreeDataViewFC: FC<FreeDataViewProps> = function DataView() { const dispatch = useDispatch(); const tables = useSelector((state: DataFormulatorState) => state.tables); const conceptShelfItems = useSelector((state: DataFormulatorState) => state.conceptShelfItems); const focusedId = useSelector((state: DataFormulatorState) => state.focusedId); const allCharts = useSelector(dfSelectors.getAllCharts); // Derive the table to display based on focusedId const focusedTableId = React.useMemo(() => { if (!focusedId) return undefined; if (focusedId.type === 'table') return focusedId.tableId; // Chart focused: show the chart's backing table const chartId = focusedId.chartId; const chart = allCharts.find(c => c.id === chartId); return chart?.tableRef; }, [focusedId, allCharts]); useEffect(() => { if(focusedId == undefined && tables.length > 0) { dispatch(dfActions.setFocused({ type: 'table', tableId: tables[0].id })) } }, [tables]) // given a table render the table let renderTableBody = (targetTable: DictTable | undefined) => { let rowData = []; if (targetTable) { if (targetTable.virtual) { rowData = targetTable.rows; } else { rowData = targetTable.rows; rowData = rowData.map((r: any, i: number) => ({ ...r, "#rowId": i })); } } // Randomly sample up to 29 rows for column width calculation const sampleSize = Math.min(29, rowData.length); const sampledRows = _.sampleSize(rowData, sampleSize); // Calculate appropriate column widths based on content const calculateColumnWidth = (name: string) => { if (name === "#rowId") return { minWidth: 10, width: 40 }; // Default for row ID column // Get all values for this column from sampled rows const values = sampledRows.map(row => String(row[name] || '')); // Estimate width based on content length (simple approach) const avgLength = values.length > 0 ? values.reduce((sum, val) => sum + val.length, 0) / values.length : 0; // Adjust width based on average content length and column name length const nameSegments = name.split(/[\s-]+/); // Split by whitespace or hyphen const maxNameSegmentLength = nameSegments.length > 0 ? nameSegments.reduce((max, segment) => Math.max(max, segment.length), 0) : name.length; const contentLength = Math.max(maxNameSegmentLength, avgLength); const minWidth = Math.max(60, contentLength * 8 > 240 ? 240 : contentLength * 8) + 50; // 8px per character with 50px padding const width = minWidth; return { minWidth, width }; }; let colDefs = targetTable ? targetTable.names.map((name, i) => { const { minWidth, width } = calculateColumnWidth(name); return { id: name, label: name, minWidth, width, align: undefined, format: (value: any) => <Typography fontSize="inherit">{`${value}`}</Typography>, dataType: targetTable?.metadata[name].type as Type, source: conceptShelfItems.find(f => f.name == name)?.source || "original", }; }) : []; if (colDefs && !targetTable?.virtual) { colDefs = [{ id: "#rowId", label: "#", minWidth: 10, align: undefined, width: 40, format: (value: any) => <Typography fontSize="inherit" color="rgba(0,0,0,0.65)">{value}</Typography>, dataType: Type.Number, source: "original", }, ...colDefs] } return <Fade in={true} timeout={600} key={targetTable?.id}> <Box sx={{height: '100%'}}> <SelectableDataGrid tableId={targetTable?.id || ""} tableName={targetTable?.displayId || targetTable?.id || "table"} rows={rowData} columnDefs={colDefs} rowCount={targetTable?.virtual?.rowCount || targetTable?.rows.length || 0} virtual={targetTable?.virtual ? true : false} /> </Box> </Fade> } return ( <Box sx={{height: "100%", display: "flex", flexDirection: "column", background: "rgba(0,0,0,0.02)"}}> {renderTableBody(tables.find(t => t.id == focusedTableId))} </Box> ); } ================================================ FILE: src/views/EncodingBox.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { FC, useEffect, useState } from 'react' import { useSelector, useDispatch } from 'react-redux' import { DataFormulatorState, dfActions, dfSelectors } from '../app/dfSlice'; import { LinearProgress, styled, TextField, Tooltip } from '@mui/material'; import { useTheme } from '@mui/material/styles'; import { alpha } from "@mui/material"; import { Chip, Box, Typography, Button, FormControl, Select, MenuItem, Card, IconButton, FormLabel, RadioGroup, Radio, FormControlLabel, CardContent, ClickAwayListener, Popper, } from '@mui/material'; import Autocomplete, { createFilterOptions } from '@mui/material/Autocomplete'; import { useDrag, useDrop } from 'react-dnd' import React from 'react'; import ArrowDropDownIcon from '@mui/icons-material/ArrowDropDown'; import RefreshIcon from '@mui/icons-material/Refresh'; import BarChartIcon from '@mui/icons-material/BarChart'; import CategoryIcon from '@mui/icons-material/Category'; import CalendarMonthIcon from '@mui/icons-material/CalendarMonth'; import QuestionMarkIcon from '@mui/icons-material/QuestionMark'; import { FieldItem, Channel, EncodingItem, AggrOp, Chart, EncodingDropResult } from "../components/ComponentType"; import _ from 'lodash'; import '../scss/EncodingShelf.scss'; import AnimateHeight from 'react-animate-height'; import { getIconFromDtype, getIconFromType, groupConceptItems } from './ViewUtils'; import { getUrls, fetchWithIdentity } from '../app/utils'; import { Type } from '../data/types'; let getChannelDisplay = (channel: Channel) => { if (channel == "x") { return "x-axis"; } else if (channel == "y") { return "y-axis"; } return channel; } export interface LittleConceptCardProps { channel: Channel, field: FieldItem, encoding: EncodingItem, handleUnbind: () => void, tableMetadata: {[key: string]: {type: Type, semanticType: string, levels?: any[]}} } export const LittleConceptCard: FC<LittleConceptCardProps> = function LittleConceptCard({ channel, field, encoding, handleUnbind, tableMetadata }) { // concept cards are draggable cards that can be dropped into encoding shelf let theme = useTheme(); const [{ isDragging }, drag] = useDrag(() => ({ type: "concept-card", item: { type: "concept-card", channel: channel, fieldID: field.id, source: "encodingShelf", encoding: encoding }, collect: (monitor) => ({ isDragging: monitor.isDragging(), handlerId: monitor.getHandlerId(), }) })); const opacity = isDragging ? 0.4 : 1; const cursorStyle = isDragging ? "grabbing" : "grab"; let fieldClass = "encoding-active-item "; let backgroundColor = alpha(theme.palette.primary.main, 0.05); if (field.source == "original") { backgroundColor = alpha(theme.palette.primary.main, 0.05); } else if (field.source == "custom") { backgroundColor = alpha(theme.palette.custom.main, 0.05); } return ( <Chip ref={drag} className={`${fieldClass}`} color={'default'} label={field.name} size="small" sx={{ backgroundColor, opacity: opacity, cursor: cursorStyle, ".MuiChip-label": { /*width: "calc(100% - 36px)", maxWidth: "94px"*/ flexGrow: 1, flexShrink: 1, width: 0 }, ".MuiSvgIcon-root": { fontSize: "inherit" } }} variant="filled" onClick={(event) => {}} onDelete={handleUnbind} icon={getIconFromType(tableMetadata[field.name]?.type || Type.Auto)} /> ) } // The property of an encoding box export interface EncodingBoxProps { channel: Channel; chartId: string; tableId: string; } // the encoding boxes, allows export const EncodingBox: FC<EncodingBoxProps> = function EncodingBox({ channel, chartId, tableId }) { let theme = useTheme(); // use tables for infer domains const tables = useSelector((state: DataFormulatorState) => state.tables); let allCharts = useSelector(dfSelectors.getAllCharts); let activeModel = useSelector(dfSelectors.getActiveModel); let chart = allCharts.find(c => c.id == chartId) as Chart; let activeTable = tables.find(t => t.id == tableId); let encoding = chart.encodingMap[channel]; let handleSwapEncodingField = (channel1: Channel, channel2: Channel) => { dispatch(dfActions.swapChartEncoding({chartId, channel1, channel2})) } let handleResetEncoding = () => { dispatch(dfActions.updateChartEncoding({chartId, channel, encoding: { }})); } // updating a property of the encoding let updateEncProp = (prop: keyof EncodingItem, value: any) => { dispatch(dfActions.updateChartEncodingProp({chartId, channel, prop: prop as string, value})); } const conceptShelfItems = useSelector((state: DataFormulatorState) => state.conceptShelfItems); let field = conceptShelfItems.find((x: FieldItem) => x.id == encoding.fieldID); let fieldMetadata = field?.name && activeTable?.metadata[field?.name] ? activeTable?.metadata[field?.name] : undefined; let [autoSortResult, setAutoSortResult] = useState<any[] | undefined>(fieldMetadata?.levels); let [autoSortInferRunning, setAutoSortInferRunning] = useState<boolean>(false); const dispatch = useDispatch(); useEffect(() => { if (field?.name && activeTable?.metadata[field?.name]) { let levels = activeTable?.metadata[field?.name].levels; setAutoSortResult(levels); if (!chart.chartType.includes("Area") && levels && levels.length > 0) { updateEncProp('sortBy', JSON.stringify(levels)); } } }, [encoding.fieldID, activeTable]) // make this a drop element for concepts const [{ canDrop, isOver }, drop] = useDrop(() => ({ accept: ["concept-card", "operator-card"], // accepts only concept card items drop: (item: any): EncodingDropResult => { if (item.type === "concept-card") { if (item.source === "conceptShelf") { handleResetEncoding(); updateEncProp('fieldID', item.fieldID); } else if (item.source === "encodingShelf") { handleSwapEncodingField(channel, item.channel); } else { console.log("field error") } } if (item.type === 'operator-card') { dispatch(dfActions.updateChartEncodingProp({chartId, channel, prop: 'aggregate', value: item.operator as AggrOp})); } return { channel: channel } }, collect: (monitor) => ({ isOver: monitor.isOver(), canDrop: monitor.canDrop(), }), }), [chartId, encoding]); // add dependency //useEffect(() => {resetConfigOptions()}, [encoding]); // items that control the editor panel popover const [editMode, setEditMode] = React.useState<boolean>(false); const isActive = canDrop && isOver; let backgroundColor = ''; if (isActive) { backgroundColor = 'rgba(204, 239, 255, 0.5)'; } else if (canDrop) { backgroundColor = 'rgba(255, 251, 204, 0.5)'; } let fieldComponent = field === undefined ? "" : ( <LittleConceptCard channel={channel} key={`${channel}-${field.name}`} tableMetadata={activeTable?.metadata || {}} field={field} encoding={encoding} handleUnbind={() => { handleResetEncoding(); }} /> ) // define anchor open let channelDisplay = getChannelDisplay(channel); let radioLabel = (label: string | React.ReactNode, value: any, key: string, width: number = 80, disabled: boolean = false, tooltip: string = "") => { let comp = <FormControlLabel sx={{ width: width, margin: 0 }} key={key} disabled={disabled} value={value} control={<Radio size="small" sx={{ padding: "4px" }} />} label={<Box sx={{display: 'flex', alignItems: 'center', gap: '4px'}}> {label} </Box>} /> if (tooltip != "") { comp = <Tooltip key={`${key}-tooltip`} title={tooltip} arrow slotProps={{ tooltip: { sx: { bgcolor: 'rgba(255, 255, 255, 0.95)', color: 'rgba(0,0,0,0.95)', border: '1px solid darkgray' }, }, }}>{comp}</Tooltip> } return comp; } let dataTypeOpt = [ <FormLabel key={`enc-box-${channel}-data-type-label`} sx={{ fontSize: "inherit" }} id="data-type-option-radio-buttons-group" >Data Type</FormLabel>, <FormControl key={`enc-box-${channel}-data-type-form-control`} sx={{ paddingBottom: "2px", '& .MuiTypography-root': { fontSize: "inherit" }, flexDirection: "row", '& .MuiFormLabel-root': { fontSize: "inherit" } }}> <RadioGroup row aria-labelledby="data-type-option-radio-buttons-group" name="data-type-option-radio-buttons-group" value={encoding.dtype || "auto"} sx={{ width: 160 }} onChange={(event) => { if (event.target.value == "auto") { updateEncProp("dtype", undefined); } else { updateEncProp("dtype", event.target.value as "quantitative" | "qualitative" | "temporal"); } }} > {radioLabel(getIconFromDtype("auto"), "auto", `dtype-auto`, 40, false, "auto")} {radioLabel(getIconFromDtype("quantitative"), "quantitative", `dtype-quantitative`, 40, false, "quantitative")} {radioLabel(getIconFromDtype("nominal"), "nominal", `dtype-nominal`, 40, false, "nominal")} {radioLabel(getIconFromDtype("temporal"), "temporal", `dtype-temporal`, 40, false, "temporal")} </RadioGroup> </FormControl> ]; let stackOpt: any[] = []; let domainItems = (field && activeTable) ? activeTable.rows.map(row => row[field!.name]) : []; domainItems = [...new Set(domainItems)]; let autoSortEnabled = field && fieldMetadata?.type == Type.String && domainItems.length < 200; let autoSortFunction = () => { let token = domainItems.map(x => String(x)).join("--"); setAutoSortInferRunning(true); let message = { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ token: token, items: domainItems, field: field?.name, model: activeModel }), }; fetchWithIdentity(getUrls().SORT_DATA_URL, message) .then((response) => response.json()) .then((data) => { setAutoSortInferRunning(false); if (data["status"] == "ok") { if (data["token"] == token) { let candidate = data["result"][0]; if (candidate['status'] == 'ok') { let sortRes = {values: candidate['content']['sorted_values'], reason: candidate['content']['reason']} setAutoSortResult(sortRes.values); } } } else { // TODO: add warnings to show the user dispatch(dfActions.addMessages({ "timestamp": Date.now(), "component": "EncodingBox", "type": "error", "value": "unable to perform auto-sort." })); setAutoSortResult(undefined); } }).catch((error) => { setAutoSortInferRunning(false); setAutoSortResult(undefined); dispatch(dfActions.addMessages({ "timestamp": Date.now(), "component": "EncodingBox", "type": "error", "value": "unable to perform auto-sort due to server issue." })); }); } let sortByOptions = [ radioLabel("auto", "auto", `sort-by-auto`) ] // TODO: check sort options if (channel == "x" && (fieldMetadata?.type == Type.String || fieldMetadata?.type == Type.Auto)) { sortByOptions.push(radioLabel("x", "x", `sort-x-by-x-ascending`, 80)); sortByOptions.push(radioLabel("y", "y", `sort-x-by-y-ascending`, 80)); sortByOptions.push(radioLabel("color", "color", `sort-x-by-color-ascending`, 80)); } if (channel == "y" && (fieldMetadata?.type == Type.String || fieldMetadata?.type == Type.Auto)) { sortByOptions.push(radioLabel("x", "x", `sort-y-by-x-ascending`, 80)); sortByOptions.push(radioLabel("y", "y", `sort-y-by-y-ascending`, 80)); sortByOptions.push(radioLabel("color", "color", `sort-y-by-color-ascending`, 80)); } if (autoSortEnabled) { if (autoSortInferRunning) { sortByOptions = [ ...sortByOptions, <FormControlLabel sx={{ width: 180, margin: 0 }} key={"auto-btn"} disabled={autoSortInferRunning || !autoSortResult} value={JSON.stringify(autoSortResult)} control={<Radio size="small" sx={{ padding: "4px" }} />} label={<LinearProgress color="primary" sx={{ width: "120px", opacity: 0.4 }} />} /> ] } else { if (autoSortResult != undefined && autoSortResult.length > 0) { let autoSortOptTitle = <Box> <Box> <Typography sx={{fontWeight: 'bold'}} component='span' fontSize='inherit'>Sort Order: </Typography> {autoSortResult.map(x => x ? x.toString() : 'null').join(", ")} </Box> </Box> let autoSortOpt = <Tooltip title={autoSortOptTitle} arrow componentsProps={{ tooltip: { sx: { bgcolor: 'rgba(255, 255, 255, 0.95)', color: 'rgba(0,0,0,0.95)', border: '1px solid darkgray' }, }, }}> <Typography className="auto-sort-option-label"> {autoSortResult.map(x => x ? x.toString() : 'null').join(", ")} </Typography> </Tooltip>; sortByOptions = [ ...sortByOptions, <FormControlLabel sx={{ width: 180, margin: 0 }} key={"auto"} disabled={autoSortInferRunning || !autoSortResult} value={JSON.stringify(autoSortResult)} control={<Radio size="small" sx={{ padding: "4px" }} />} label={<Box sx={{width: '100%', display:'flex'}}> {autoSortOpt} <Tooltip title='rerun smart sort'> <IconButton onClick={autoSortFunction} size='small' color='primary'> <RefreshIcon /> </IconButton> </Tooltip> </Box>} /> ] } else { sortByOptions = [ ...sortByOptions, <FormControlLabel sx={{ width: 180, margin: 0 }} key={"auto-btn"} disabled={autoSortInferRunning || !autoSortResult} value={JSON.stringify(autoSortResult)} control={<Radio size="small" sx={{ padding: "4px" }} />} label={<Button size="small" variant="text" sx={{ textTransform: "none", padding: "2px 4px", marginLeft: "0px", minWidth: 0 }} onClick={autoSortFunction}>infer smart sort order</Button>} /> ] } } } let sortByOpt = [ <FormLabel sx={{ fontSize: "inherit" }} key={`enc-box-${channel}-sort-label`} id="sort-option-radio-buttons-group" >Sort By</FormLabel>, <FormControl key={`enc-box-${channel}-sort-form-control`} sx={{ paddingBottom: "4px", '& .MuiTypography-root': { fontSize: "inherit" }, flexDirection: "row", '& .MuiFormLabel-root': { fontSize: "inherit" } }}> <RadioGroup row aria-labelledby="sort-option-radio-buttons-group" name="sort-option-radio-buttons-group" value={encoding.sortBy || 'auto'} sx={{ width: 180 }} onChange={(event) => { updateEncProp("sortBy", event.target.value) }} > {sortByOptions} </RadioGroup> </FormControl> ] let sortOrderOpt = [ <FormLabel sx={{ fontSize: "inherit" }} key={`enc-box-${channel}-sort-order-label`} id="sort-option-radio-buttons-group" >Sort Order</FormLabel>, <FormControl key={`enc-box-${channel}-sort-order-form-control`} sx={{ paddingBottom: "2px", '& .MuiTypography-root': { fontSize: "inherit" }, flexDirection: "row", '& .MuiFormLabel-root': { fontSize: "inherit" } }}> <RadioGroup row aria-labelledby="sort-option-radio-buttons-group" name="sort-option-radio-buttons-group" value={encoding.sortOrder || "auto"} sx={{ width: 180 }} onChange={(event) => { updateEncProp("sortOrder", event.target.value) }} > {radioLabel("auto", "auto", `sort-auto`, 60)} {radioLabel("↑ asc", "ascending", `sort-ascending`, 60)} {radioLabel("↓ desc", "descending", `sort-descending`, 60)} </RadioGroup> </FormControl> ] let colorSchemeList = [ "category10", "category20", "tableau10", "blues", "oranges", "reds", "greys", "goldgreen", "bluepurple", "blueorange", "redyellowblue", "spectral" ] let colorSchemeOpt = channel == "color" ? [ <FormLabel sx={{ fontSize: "inherit" }} key={`enc-box-${channel}-color-scheme-label`} id="scheme-option-radio-buttons-group">Color scheme</FormLabel>, <FormControl key="color-sel-form" fullWidth size="small" sx={{textAlign: "initial", fontSize: "12px"}}> <Select labelId="color-scheme-select-label" variant="standard" id="color-scheme-select" label="" sx={{'& .MuiSelect-select': {fontSize: "12px", paddingLeft: '6px'}}} value={encoding.scheme || "default"} onChange={(event)=>{ updateEncProp("scheme", event.target.value) }} > <MenuItem value={"default"} key={"color-scheme--1"}><em>default</em></MenuItem> {colorSchemeList.map((t, i) => ( <MenuItem value={t} key={`color-scheme-${i}`}>{t}</MenuItem> ))} </Select> </FormControl> ] : [] let encodingConfigCard = ( <CardContent sx={{ display: "flex", '& svg': { fontSize: "inherit" }, '&:last-child': { pb: "12px", pt: "12px" }, margin: '0px 12px', padding: "6px", fontSize: "12px" }} > <Box sx={{margin: 'auto', display: "flex", width: "fit-content", textAlign: "center", flexDirection: "column", alignItems: "flex-start" }}> {dataTypeOpt} {stackOpt} {sortByOpt} {sortOrderOpt} {colorSchemeOpt} </Box> </CardContent> ) let optBackgroundColor = alpha(theme.palette.secondary.main, 0.07); let aggregateDisplay = encoding.aggregate ? (<Chip key="aggr-display" className="encoding-prop-chip" sx={{ backgroundColor: optBackgroundColor, width: field == undefined ? "100%" : "auto" }} onDelete={() => updateEncProp("aggregate", undefined)} color="default" //deleteIcon={<RemoveIcon />} label={encoding.aggregate == "average" ? "avg" : encoding.aggregate} size="small" />) : ""; let normalizedDisplay = ""; let handleSelectOption = (option: string) => { if (conceptShelfItems.map(f => f.name).includes(option)) { //console.log(`yah-haha: ${option}`); updateEncProp("fieldID", (conceptShelfItems.find(f => f.name == option) as FieldItem).id); } else { if (option == "") { console.log("nothing happens") } else { let newConept = { id: `concept-${Date.now()}`, name: option, source: "custom", tableRef: "custom", } as FieldItem; dispatch(dfActions.updateConceptItems(newConept)); updateEncProp("fieldID", newConept.id); } } } let conceptGroups = groupConceptItems(conceptShelfItems, tables); let groupNames = [...new Set(conceptGroups.map(g => g.group))]; conceptGroups.sort((a, b) => { if (groupNames.indexOf(a.group) < groupNames.indexOf(b.group)) { return -1; } else if (groupNames.indexOf(a.group) > groupNames.indexOf(b.group)) { return 1; } else { return activeTable && activeTable.names.includes(a.field.name) && !activeTable.names.includes(b.field.name) ? -1 : 1; } }) // Smart Popper component that switches between bottom-end and top-end const CustomPopper = (props: any) => { return ( <Popper {...props} placement="bottom-end" modifiers={[ { name: 'flip', enabled: true, options: { fallbackPlacements: ['top-end'], // Only flip to top-end }, }, { name: 'preventOverflow', enabled: true, options: { boundary: 'viewport', padding: 8, }, }, { name: 'offset', options: { offset: [0, 8], // [horizontal, vertical] offset }, }, ]} style={{ zIndex: 1300, // Ensure it's above other elements }} /> ); }; let createConceptInputBox = <Autocomplete key="concept-create-input-box" slots={{ popper: CustomPopper // Try changing to: CustomPopperCSS }} onChange={(event, value) => { if (value != null) { handleSelectOption(value) } }} // value={tempValue} filterOptions={(options, params) => { const filtered = filter(options, params); const { inputValue } = params; // Suggest the creation of a new value const isExisting = options.some((option) => inputValue === option); if (!isExisting) { return [`${inputValue}`, ...filtered, ] } else { return [...filtered]; } }} sx={{ flexGrow: 1, flexShrink: 1, "& .MuiInput-input": { padding: "0px 8px !important"}, "& .MuiAutocomplete-listbox": { maxHeight: '600px !important' } }} fullWidth selectOnFocus clearOnBlur handleHomeEndKeys autoHighlight id={`autocomplete-${chartId}-${channel}`} options={conceptGroups.map(g => g.field.name).filter(name => name != "")} getOptionLabel={(option) => { // Value selected with enter, right from the input return option; }} groupBy={(option) => { let groupItem = conceptGroups.find(item => item.field.name == option); if (groupItem && groupItem.field.name != "") { return `${groupItem.group}`; } else { return "create a new field" } }} renderGroup={(params) => ( <Box key={params.key}> <Box className="GroupHeader">{params.group}</Box> <Box className="GroupItems" sx={{ display: 'grid', gridTemplateColumns: 'repeat(2, 1fr)', padding: '4px' }}> {params.children} </Box> </Box> )} renderOption={(props, option) => { let renderOption = (conceptShelfItems.map(f => f.name).includes(option)) ? option : `${option}`; let otherStyle = option == `` ? {color: "darkgray", fontStyle: "italic"} : {} // Find the field item for this option const fieldItem = conceptShelfItems.find(f => f.name === option); if (fieldItem) { // Create a mini concept card let backgroundColor = theme.palette.primary.main; if (fieldItem.source == "original") { backgroundColor = theme.palette.primary.light; } else if (fieldItem.source == "custom") { backgroundColor = theme.palette.custom.main; } // Add overlay logic similar to ConceptCard - make fields not in focused table more transparent let draggleCardHeaderBgOverlay = 'rgba(255, 255, 255, 0.9)'; // Add subtle tint for non-focused fields if (activeTable && !activeTable.names.includes(fieldItem.name)) { draggleCardHeaderBgOverlay = 'rgba(255, 255, 255, 1)'; } // Extract only the compatible props for Card const { key, ...cardProps } = props; return ( <Card key={key} onClick={() => handleSelectOption(option)} sx={{ minWidth: 80, backgroundColor, position: "relative", border: "none", cursor: "pointer", margin: '2px 4px', "&:hover": { boxShadow: "0 2px 4px 0 rgb(0 0 0 / 20%)" } }} variant="outlined" className={`data-field-list-item draggable-card`} > <Box sx={{ cursor: "pointer", background: draggleCardHeaderBgOverlay, display: 'flex', alignItems: 'center', minHeight: '20px', ml: 0.5 }} className={`draggable-card-inner ${fieldItem.source}`} > <Typography sx={{ margin: '0px 4px', fontSize: 10, width: "100%", display: 'flex', alignItems: 'center', gap: '4px', }} component={'span'}> {getIconFromType(activeTable?.metadata[fieldItem.name]?.type || Type.Auto)} <span style={{ whiteSpace: "nowrap", overflow: "hidden", textOverflow: "ellipsis", flexShrink: 1 }}> {fieldItem.name} </span> </Typography> </Box> </Card> ); } else { // For non-existing options (like new field creation) return ( <Typography {...props} onClick={() => handleSelectOption(option)} sx={{ fontSize: "10px", padding: '4px 6px', margin: '2px 4px', cursor: 'pointer', border: '1px dashed #ccc', borderRadius: '4px', backgroundColor: 'rgba(0,0,0,0.02)', height: '24px', display: 'flex', alignItems: 'center', "&:hover": { backgroundColor: 'rgba(0,0,0,0.05)' }, ...otherStyle }} > {renderOption || "type a new field name"} </Typography> ); } }} freeSolo renderInput={(params) => ( <TextField {...params} variant="standard" autoComplete='off' placeholder='field' sx={{height: "24px", "& .MuiInput-root": {height: "24px", fontSize: "small"}}} /> )} slotProps={{ paper: { // Use paper instead of popper for styling sx: { width: '300px', maxWidth: '300px', '& .MuiAutocomplete-listbox': { maxHeight: '600px !important' }, } } }} /> const filter = createFilterOptions<string>(); // when there is no field added, allow users to directly type concepts here, and it will be created on the fly. const encContent = field == undefined ? (encoding.aggregate == 'count' ? [ aggregateDisplay ] : [ normalizedDisplay, aggregateDisplay, createConceptInputBox ]) : [ normalizedDisplay, aggregateDisplay, fieldComponent ] let encodingComp = ( <ClickAwayListener mouseEvent="onMouseUp" touchEvent="onTouchStart" onClickAway={() => { setEditMode(false) }} > <Box sx={{ display: 'flex', flexDirection: "column", alignItems: 'flex-start', width: "100%", marginBottom: "4px" }} component="form" className="channel-shelf-box encoding-item"> <Card sx={{ width: "100%" }} variant="outlined"> <Box ref={drop} className="channel-encoded-field"> <IconButton //className="encoding-shelf-action-button" onClick={() => { setEditMode(!editMode) }} color="default" aria-label="axis settings" component="span" size="small" sx={{ padding: "0px", borderRadius: 0, textAlign: "left", fontSize: "inherit", height: "auto", position: "relative", borderRight: "1px solid lightgray", width: '64px', display: "flex", justifyContent: "space-between" }}> <Typography variant="caption" component="span" sx={{ padding: "0px 0px 0px 6px" }}>{channelDisplay}</Typography> <ArrowDropDownIcon sx={{ position: "absolute", right: "0", paddingLeft: "2px", transform: editMode ? "rotate(180deg)" : "" }} fontSize="inherit" /> </IconButton> <Box sx={{ backgroundColor: backgroundColor, width: "calc(100% - 64px)", display: "flex", borderBottom: (editMode ? "1px solid rgba(0, 0, 0, 0.12)" : undefined) }}> {encContent} </Box> </Box> <AnimateHeight duration={200} height={editMode ? "auto" : 0} // see props documentation below > {encodingConfigCard} </AnimateHeight> </Card> </Box> </ClickAwayListener> ) return encodingComp; } ================================================ FILE: src/views/EncodingShelfCard.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { FC, useEffect, useState } from 'react' import { useSelector, useDispatch } from 'react-redux' import { DataFormulatorState, dfActions, dfSelectors, generateFreshChart } from '../app/dfSlice'; import embed from 'vega-embed'; import { Box, Typography, FormControl, InputLabel, Select, MenuItem, ListSubheader, ListItemIcon, ListItemText, IconButton, Tooltip, TextField, Card, Chip, Autocomplete, Menu, Divider, alpha, useTheme, SxProps, Theme, Slider, CircularProgress, Button, Collapse, Dialog, DialogTitle, DialogContent, DialogActions, } from '@mui/material'; import ExpandMoreIcon from '@mui/icons-material/ExpandMore'; import React from 'react'; import { useDragLayer } from 'react-dnd'; import { ThinkingBufferEffect } from '../components/FunComponents'; import { Channel, Chart, FieldItem, Trigger, duplicateChart } from "../components/ComponentType"; import _ from 'lodash'; import '../scss/EncodingShelf.scss'; import { DictTable } from "../components/ComponentType"; import { resolveChartFields, assembleVegaChart, resolveRecommendedChart } from '../app/utils'; import { EncodingBox } from './EncodingBox'; import { channelGroups, CHART_TEMPLATES, getChartChannels, getChartTemplate } from '../components/ChartTemplates'; import { checkChartAvailability, getDataTable } from './VisualizationView'; import { TableIcon, AgentIcon as PrecisionManufacturing } from '../icons'; import ChangeCircleOutlinedIcon from '@mui/icons-material/ChangeCircleOutlined'; import AddIcon from '@mui/icons-material/Add'; import CheckIcon from '@mui/icons-material/Check'; import { ThinkingBanner } from './DataThread'; import { AppDispatch } from '../app/store'; import { borderColor, radius, transition } from '../app/tokens'; import DeleteIcon from '@mui/icons-material/Delete'; import CloseIcon from '@mui/icons-material/Close'; import TipsAndUpdatesIcon from '@mui/icons-material/TipsAndUpdates'; import ArrowBackIcon from '@mui/icons-material/ArrowBack'; import { IdeaChip } from './ChartRecBox'; import { useFormulateData } from '../app/useFormulateData'; // Property and state of an encoding shelf export interface EncodingShelfCardProps { chartId: string; trigger?: Trigger; noBorder?: boolean; } // Add this utility function before the TriggerCard component export const renderTextWithEmphasis = (text: string | any, highlightChipSx?: SxProps<Theme>) => { if (typeof text !== 'string') { text = text == null ? '' : String(text); } text = text.replace(/_/g, '_\u200B'); // Split the prompt by ** patterns and create an array of text and highlighted segments const parts = text.split(/(\*\*.*?\*\*)/g); return parts.map((part, index) => { if (part.startsWith('**') && part.endsWith('**')) { // This is a highlighted part - remove the ** and wrap with styled component const content = part.slice(2, -2).replaceAll('_', ' '); return ( <Typography key={index} component="span" sx={{ color: 'inherit', padding: '0px 2px', borderRadius: radius.sm, ...highlightChipSx }} > {content} </Typography> ); } return part; }); }; export const TriggerCard: FC<{ className?: string, trigger: Trigger, hideFields?: boolean, mini?: boolean, highlighted?: boolean, sx?: SxProps<Theme>}> = function ({ className, trigger, hideFields, mini = false, highlighted = false, sx }) { let theme = useTheme(); let fieldItems = useSelector((state: DataFormulatorState) => state.conceptShelfItems); const dispatch = useDispatch<AppDispatch>(); let handleClick = () => { if (trigger.chart) { dispatch(dfActions.setFocused({ type: 'chart', chartId: trigger.chart.id })); } } let encodingComp : any = ''; let encFields = []; if (trigger.chart) { let chart = trigger.chart; let encodingMap = chart?.encodingMap; encFields = Object.entries(encodingMap) .filter(([channel, encoding]) => { return encoding.fieldID != undefined; }) .map(([channel, encoding], index) => { let field = fieldItems.find(f => f.id == encoding.fieldID) as FieldItem; return field.name; }); encodingComp = <Typography component="span" key="enc-fields" sx={{ fontSize: 'inherit', color: 'inherit' }}> {Object.entries(encodingMap) .filter(([channel, encoding]) => encoding.fieldID != undefined) .map(([channel, encoding], index) => { let field = fieldItems.find(f => f.id == encoding.fieldID) as FieldItem; return <React.Fragment key={`trigger-${channel}-${field?.id}`}> {index > 0 ? <span style={{ margin: '0 2px', opacity: 0.5 }}> × </span> : ''} <span>{field?.name}</span> </React.Fragment>; })} </Typography> } let prompt: string = trigger.displayInstruction; if (trigger.instruction == '' && encFields.length > 0) { prompt = ''; } else if (!trigger.displayInstruction || (trigger.instruction != '' && trigger.instruction.length <= trigger.displayInstruction.replace(/\*\*/g, '').length)) { prompt = trigger.instruction; } // Process the prompt to highlight content in ** ** const processedPrompt = renderTextWithEmphasis(prompt, { fontSize: mini ? 10 : 11, padding: '1px 4px', borderRadius: radius.sm, background: alpha(theme.palette.custom.main, 0.08), }); if (mini) { return <Typography component="div" sx={{ fontSize: '10px', color: theme.palette.text.secondary, my: '2px', textWrap: 'balance', '&:hover': { cursor: 'pointer', color: theme.palette.text.primary, }, '& .MuiChip-label': { px: 0.5, fontSize: "10px"}, ...sx, }} onClick={handleClick}> {processedPrompt} {hideFields ? "" : encodingComp} </Typography> } return <Typography component="div" className={`${className}`} sx={{ cursor: 'pointer', fontSize: '11px', color: 'rgba(0,0,0,0.75)', textAlign: 'left', py: 0.5, px: 1, borderRadius: radius.sm, backgroundColor: theme.palette.custom.bgcolor, border: `1px solid ${borderColor.component}`, ...(highlighted ? { borderLeft: `2px solid ${theme.palette.custom.main}` } : {}), '& .MuiChip-label': { px: 0.5, fontSize: "10px"}, ...sx, }} onClick={handleClick}> {processedPrompt} {hideFields ? "" : <>{" "}{encodingComp}</>} </Typography> } export const EncodingShelfCard: FC<EncodingShelfCardProps> = function ({ chartId }) { const theme = useTheme(); // reference to states const tables = useSelector((state: DataFormulatorState) => state.tables); const config = useSelector((state: DataFormulatorState) => state.config); const agentRules = useSelector((state: DataFormulatorState) => state.agentRules); const focusedId = useSelector((state: DataFormulatorState) => state.focusedId); let activeModel = useSelector(dfSelectors.getActiveModel); let allCharts = useSelector(dfSelectors.getAllCharts); // The table the user is currently looking at (from focused state) const focusedTableId = (() => { if (!focusedId) return undefined; if (focusedId.type === 'table') return focusedId.tableId; const focusedChart = allCharts.find(c => c.id === focusedId.chartId); return focusedChart?.tableRef; })(); let chart = allCharts.find(c => c.id == chartId) as Chart; let trigger = chart.source == "trigger" ? tables.find(t => t.derive?.trigger?.chart?.id == chartId)?.derive?.trigger : undefined; let [prompt, setPrompt] = useState<string>(trigger?.instruction || ""); useEffect(() => { setPrompt(trigger?.instruction || ""); }, [chartId]); let encodingMap = chart?.encodingMap; const dispatch = useDispatch<AppDispatch>(); const { streamIdeas, formulateData } = useFormulateData(); const [chartTypeMenuOpen, setChartTypeMenuOpen] = useState<boolean>(false); const [encodingHovered, setEncodingHovered] = useState<boolean>(false); // Auto-expand encoding shelf when dragging a concept or operator card const { isDraggingField } = useDragLayer((monitor) => ({ isDraggingField: monitor.isDragging() && (monitor.getItemType() === 'concept-card' || monitor.getItemType() === 'operator-card'), })); const shouldExpand = encodingHovered || isDraggingField; // When no fields are assigned to any channel, show all channels expanded const hasAnyField = Object.values(encodingMap).some(enc => enc?.fieldID); const shouldExpandAll = !hasAnyField || shouldExpand; let handleUpdateChartType = (newChartType: string) => { dispatch(dfActions.updateChartType({chartId, chartType: newChartType})); // Close the menu after selection setChartTypeMenuOpen(false); } const conceptShelfItems = useSelector((state: DataFormulatorState) => state.conceptShelfItems); let currentTable = getDataTable(chart, tables, allCharts, conceptShelfItems); // Check if chart is available let isChartAvailable = checkChartAvailability(chart, conceptShelfItems, currentTable.rows); // Consolidated chart state - maps chartId to its ideas, thinkingBuffer, and loading state const [chartState, setChartState] = useState<Record<string, { ideas: {text: string, goal: string, difficulty: 'easy' | 'medium' | 'hard'}[], thinkingBuffer: string, isLoading: boolean }>>({}); // Get current chart's state const currentState = chartState[chartId] || { ideas: [], thinkingBuffer: "", isLoading: false }; const currentChartIdeas = currentState.ideas; const thinkingBuffer = currentState.thinkingBuffer; const isLoadingIdeas = currentState.isLoading; // Helper functions to update current chart's state const setIdeas = (ideas: {text: string, goal: string, difficulty: 'easy' | 'medium' | 'hard'}[]) => { setChartState(prev => ({ ...prev, [chartId]: { ...prev[chartId] || { thinkingBuffer: "", isLoading: false }, ideas } })); }; const setThinkingBuffer = (thinkingBuffer: string) => { setChartState(prev => ({ ...prev, [chartId]: { ...prev[chartId] || { ideas: [], isLoading: false }, thinkingBuffer } })); }; const setIsLoadingIdeas = (isLoading: boolean) => { setChartState(prev => ({ ...prev, [chartId]: { ...prev[chartId] || { ideas: [], thinkingBuffer: "" }, isLoading } })); }; let encodingBoxGroups = Object.entries(channelGroups) .filter(([group, channelList]) => channelList.some(ch => Object.keys(encodingMap).includes(ch))) .map(([group, channelList]) => { let channels = channelList.filter(channel => Object.keys(encodingMap).includes(channel)); let occupiedChannels = channels.filter(ch => encodingMap[ch as Channel]?.fieldID); let unoccupiedChannels = channels.filter(ch => !encodingMap[ch as Channel]?.fieldID); let hasVisibleContent = occupiedChannels.length > 0 || shouldExpandAll; let component = <Box key={`encoding-group-box-${group}`} sx={{ mt: (group && shouldExpandAll) ? '6px' : 0 }}> {channels.map(channel => { const isOccupied = encodingMap[channel as Channel]?.fieldID; const box = <EncodingBox key={`shelf-${channel}`} channel={channel as Channel} chartId={chartId} tableId={currentTable.id} />; return isOccupied ? box : ( <Collapse key={`collapse-${channel}`} in={shouldExpandAll} timeout={200}> {box} </Collapse> ); })} </Box> return component; }); // derive active fields from encoding map so that we can keep the order of which fields will be visualized let activeFields = Object.values(encodingMap).map(enc => enc.fieldID).filter(fieldId => fieldId && conceptShelfItems.map(f => f.id) .includes(fieldId)).map(fieldId => conceptShelfItems.find(f => f.id == fieldId) as FieldItem); let activeSimpleEncodings: { [key: string]: string } = {}; for (let channel of getChartChannels(chart.chartType)) { if (chart.encodingMap[channel as Channel]?.fieldID) { activeSimpleEncodings[channel] = activeFields.find(f => f.id == chart.encodingMap[channel as Channel].fieldID)?.name as string; } } let activeCustomFields = activeFields.filter(field => field.source == "custom"); // check if the current table contains all fields already exists a table that fullfills the user's specification let existsWorkingTable = activeFields.length == 0 || activeFields.every(f => currentTable.names.includes(f.name)); // All root/anchored tables, with current source tables ordered first for context priority let rootTables = tables.filter(t => t.derive === undefined || t.anchored); let priorityIds = (currentTable.derive && !currentTable.anchored) ? currentTable.derive.source : [currentTable.id]; let actionTableIds = [ ...priorityIds.filter(id => rootTables.some(t => t.id === id)), ...rootTables.map(t => t.id).filter(id => !priorityIds.includes(id)) ]; let getIdeasForVisualization = async () => { if (!currentTable || isLoadingIdeas) return; let chartAvailable = checkChartAvailability(chart, conceptShelfItems, currentTable.rows); let currentChartPng = chartAvailable ? await vegaLiteSpecToPng(assembleVegaChart( chart.chartType, chart.encodingMap, activeFields, currentTable.rows, currentTable.metadata, 100, 80, false, chart.config)) : undefined; await streamIdeas({ actionTableIds, currentTable, onIdeas: setIdeas, onThinkingBuffer: setThinkingBuffer, onLoadingChange: setIsLoadingIdeas, currentChartImage: currentChartPng, currentDataSample: currentTable.rows.slice(0, 10), filterByType: true, }); } // Function to handle idea chip click const handleIdeaClick = (ideaText: string) => { setPrompt(ideaText); // Automatically start the data formulation process deriveNewData(ideaText, 'ideate'); }; let deriveNewData = async ( instruction: string, mode: 'formulate' | 'ideate' = 'formulate', overrideTableId?: string, ) => { if (actionTableIds.length == 0) return; // Short-circuit: if all fields exist in source table, just reference it if (currentTable.derive == undefined && instruction == "" && (activeFields.length > 0 && activeCustomFields.length == 0) && tables.some(t => t.derive == undefined && activeFields.every(f => currentTable.names.includes(f.name)))) { let tempTable = getDataTable(chart, tables, allCharts, conceptShelfItems, true); dispatch(dfActions.updateTableRef({chartId: chartId, tableRef: tempTable.id})); dispatch(dfActions.changeChartRunningStatus({chartId, status: true})); setTimeout(function(){ dispatch(dfActions.changeChartRunningStatus({chartId, status: false})); dispatch(dfActions.clearUnReferencedTables()); }, 400); return; } dispatch(dfActions.clearUnReferencedTables()); let fieldNamesStr = activeFields.map(f => f.name).reduce( (a: string, b: string, i, array) => a + (i == 0 ? "" : (i < array.length - 1 ? ', ' : ' and ')) + b, ""); const actionId = `deriveNewData_${String(Date.now())}`; const originTableId = focusedTableId || currentTable.id; const actionDescription = instruction || `Derive ${fieldNamesStr}`; dispatch(dfActions.updateAgentWorkInProgress({ actionId, originTableId, description: actionDescription, status: 'running', hidden: false, message: { content: actionDescription, role: 'user', observeTableId: originTableId } })); // Build chart visualization context let chartComplete = checkChartAvailability(chart, conceptShelfItems, currentTable.rows); let chartSpec = (mode == 'formulate' && Object.keys(activeSimpleEncodings).length > 0) ? { chart_type: chart.chartType, encodings: activeSimpleEncodings, ...(chart.config ? { config: chart.config } : {}) } : undefined; let currentChartImage: string | null | undefined = undefined; if (chartComplete && chartSpec) { currentChartImage = await vegaLiteSpecToPng(assembleVegaChart( chart.chartType, chart.encodingMap, activeFields, currentTable.rows, currentTable.metadata, 100, 80, false, chart.config )); } let currentVisualization = (chartComplete && chartSpec) ? { chart_spec: chartSpec, ...(currentChartImage ? { chart_image: currentChartImage } : {}) } : undefined; let expectedVisualization = (!chartComplete && chartSpec) ? { chart_spec: chartSpec } : undefined; let triggerChartSpec = duplicateChart(chart); triggerChartSpec.source = "trigger"; formulateData({ instruction, mode, actionTableIds, currentTable, overrideTableId, currentVisualization, expectedVisualization, triggerChart: triggerChartSpec, createChart: ({ candidateTable, refinedGoal, currentConcepts }) => { let needToCreateNewChart = true; let focusedChartId: string | undefined; if (mode != "ideate" && chart.chartType != "Auto" && overrideTableId != undefined && allCharts.filter(c => c.source == "user").find(c => c.tableRef == overrideTableId)) { let chartsFromOverrideTable = allCharts.filter(c => c.source == "user" && c.tableRef == overrideTableId); let chartsWithSameEncoding = chartsFromOverrideTable.filter(c => { let getSimpliedChartEnc = (ch: Chart) => { return ch.chartType + ":" + Object.entries(ch.encodingMap) .filter(([channel, enc]) => enc.fieldID != undefined) .map(([channel, enc]) => `${channel}:${enc.fieldID}:${enc.aggregate}:${enc.sortOrder}:${enc.sortBy}:${enc.scheme}`) .join(";"); } return getSimpliedChartEnc(c) == getSimpliedChartEnc(triggerChartSpec); }); if (chartsWithSameEncoding.length > 0) { focusedChartId = chartsWithSameEncoding[0].id; dispatch(dfActions.setFocused({ type: 'chart', chartId: focusedChartId })); needToCreateNewChart = false; } } if (needToCreateNewChart) { let newChart: Chart; if (mode == "ideate" || chart.chartType == "Auto") { newChart = resolveRecommendedChart(refinedGoal, currentConcepts, candidateTable); } else if (chart.chartType == "Table") { newChart = generateFreshChart(candidateTable.id, 'Table'); } else { newChart = structuredClone(chart) as Chart; newChart.source = "user"; newChart.id = `chart-${Date.now() - Math.floor(Math.random() * 10000)}`; newChart.saved = false; newChart.tableRef = candidateTable.id; let chartEncodings = refinedGoal['chart']?.['encodings'] || refinedGoal['chart_encodings'] || {}; newChart = resolveChartFields(newChart, currentConcepts, chartEncodings, candidateTable); } focusedChartId = newChart.id; dispatch(dfActions.addAndFocusChart(newChart)); } return focusedChartId; }, onStarted: () => { dispatch(dfActions.changeChartRunningStatus({chartId, status: true})); }, onSuccess: ({ displayInstruction, candidateTable, focusedChartId }) => { if (chart.chartType == "Table" || chart.chartType == "Auto" || (existsWorkingTable == false)) { dispatch(dfActions.deleteChartById(chartId)); } dispatch(dfActions.clearUnReferencedTables()); dispatch(dfActions.clearUnReferencedCustomConcepts()); dispatch(dfActions.setFocused({ type: 'chart', chartId: focusedChartId as string })); dispatch(dfActions.addMessages({ "timestamp": Date.now(), "component": "chart builder", "type": "success", "value": `Data formulation for ${fieldNamesStr} succeeded.` })); dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: displayInstruction || actionDescription, status: 'completed', hidden: false, message: { content: displayInstruction || actionDescription, role: 'action', resultTableId: candidateTable.id } })); }, onError: () => { dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: actionDescription, status: 'failed', hidden: false, message: { content: 'Data formulation failed.', role: 'error' } })); }, onFinally: () => { dispatch(dfActions.changeChartRunningStatus({chartId, status: false})); }, }); } // zip multiple components together const w: any = (a: any[], b: any[]) => a.length ? [a[0], ...w(b, a.slice(1))] : b; let formulateInputBox = <Card key='text-input-boxes' variant='outlined' sx={{ display: 'flex', flexDirection: 'column', px: 1, pt: 0.5, pb: 0.25, borderWidth: 1, borderColor: alpha(theme.palette.text.primary, 0.2), borderRadius: '8px', overflow: 'visible', flexShrink: 0, transition: transition.fast, '&:hover': { borderWidth: 1, borderColor: alpha(theme.palette.primary.main, 0.6), }, '&:focus-within': { borderWidth: 1, borderColor: alpha(theme.palette.primary.main, 0.8), }, }}> <TextField variant="standard" sx={{ flex: 1, "& .MuiInput-input": { fontSize: '12px', lineHeight: 1.5 }, "& .MuiInput-underline:before": { borderBottom: 'none' }, "& .MuiInput-underline:hover:not(.Mui-disabled):before": { borderBottom: 'none' }, "& .MuiInput-underline:after": { borderBottom: 'none' }, }} onChange={(event: any) => { setPrompt(event.target.value); }} onKeyDown={(event: any) => { if (event.key === 'Enter' && !event.shiftKey) { event.preventDefault(); if (prompt.trim().length > 0) { deriveNewData(prompt, 'formulate'); } } }} slotProps={{ inputLabel: { shrink: true }, }} value={prompt} placeholder={"follow up on this chart"} fullWidth multiline minRows={2} maxRows={5} /> <Box sx={{ display: 'flex', flexDirection: 'row', alignItems: 'center', justifyContent: 'flex-end', }}> <Tooltip title={currentChartIdeas.length > 0 ? "Refresh ideas" : "Get ideas"}> <span> <IconButton size="small" disabled={isLoadingIdeas} sx={{ p: 0.5, color: theme.palette.custom.textColor || theme.palette.custom.main, '&:hover': { backgroundColor: alpha(theme.palette.custom.main, 0.08) } }} onClick={() => getIdeasForVisualization()}> {isLoadingIdeas ? <CircularProgress size={20} sx={{ color: theme.palette.custom.main }} /> : <TipsAndUpdatesIcon sx={{ fontSize: 20 }} />} </IconButton> </span> </Tooltip> {trigger ? <Tooltip title={<Typography sx={{fontSize: 11}}>formulate and override <TableIcon sx={{width: 10, height: 10, marginBottom: '-1px'}}/>{trigger.resultTableId}</Typography>}> <span> <IconButton size="small" color={"warning"} sx={{ p: 0.5 }} onClick={() => { deriveNewData(trigger!.instruction, 'formulate', trigger!.resultTableId); }}> <ChangeCircleOutlinedIcon sx={{ fontSize: 18 }} /> </IconButton> </span> </Tooltip> : <Tooltip title={`Formulate`}> <span> <IconButton size="small" color={"primary"} sx={{ p: 0.5 }} onClick={() => { deriveNewData(prompt, 'formulate'); }}> <PrecisionManufacturing sx={{ fontSize: 20, ...(isChartAvailable ? {} : { animation: 'pulseAttention 3s ease-in-out infinite', '@keyframes pulseAttention': { '0%, 90%': { scale: 1 }, '95%': { scale: 1.2 }, '100%': { scale: 1 }, }, }), }} /> </IconButton> </span> </Tooltip> } </Box> </Card> let channelComponent = ( <Box sx={{ width: "100%", minWidth: "210px", height: '100%', display: "flex", flexDirection: "column", gap: '4px' }}> <Box key='mark-selector-box' sx={{ flex: '0 0 auto', display: 'flex', alignItems: 'center' }}> <FormControl sx={{ m: 1, minWidth: 120, flex: 1, margin: "0px 0"}} size="small"> <Select variant="standard" labelId="chart-mark-select-label" id="chart-mark-select" value={chart.chartType} // Add these props to control the open state open={chartTypeMenuOpen} onOpen={() => setChartTypeMenuOpen(true)} onClose={() => setChartTypeMenuOpen(false)} MenuProps={{ anchorOrigin: { vertical: 'bottom', horizontal: 'left', }, transformOrigin: { vertical: 'top', horizontal: 'left', }, PaperProps: { sx: { '& .MuiList-root': { display: 'grid', gridTemplateColumns: '1fr 1fr', gap: 0, padding: '8px' } } } }} renderValue={(value: string) => { const t = getChartTemplate(value); return ( <div style={{display: 'flex', padding: "0px 0px 0px 4px"}}> <ListItemIcon sx={{minWidth: "24px"}}> {typeof t?.icon == 'string' ? <img height="24px" width="24px" src={t?.icon} alt="" role="presentation" /> : <Box sx={{width: "24px", height: "24px"}}>{t?.icon}</Box>} </ListItemIcon> <ListItemText sx={{marginLeft: "2px", whiteSpace: "initial"}} slotProps={{primary: {fontSize: 12}}}>{t?.chart}</ListItemText> </div> ) }} onChange={(event) => { }}> {Object.entries(CHART_TEMPLATES).map(([group, templates]) => { return [ <ListSubheader sx={{ color: "text.secondary", lineHeight: 2, fontSize: 12, gridColumn: '1 / -1' // Make subheader span both columns }} key={group}>{group}</ListSubheader>, ...templates.map((t, i) => ( <MenuItem sx={{ fontSize: 12, paddingLeft: 2, paddingRight: 2, minHeight: '32px', margin: '1px 0' }} value={t.chart} key={`${group}-${i}`} onClick={(e) => { console.log('MenuItem clicked:', t.chart); // Manually trigger the chart type update (this will also close the menu) handleUpdateChartType(t.chart); }} > <Box sx={{display: 'flex'}}> <ListItemIcon sx={{minWidth: "20px"}}> {typeof t?.icon == 'string' ? <img height="20px" width="20px" src={t?.icon} alt="" role="presentation" /> : <Box sx={{width: "20px", height: "20px"}}>{t?.icon}</Box> } </ListItemIcon> <ListItemText slotProps={{primary: {fontSize: 11}}} sx={{ margin: 0 }} > {t.chart} </ListItemText> </Box> </MenuItem> )) ] })} </Select> </FormControl> </Box> {/* Template-driven config property selectors */} <Box key='encoding-and-config' sx={{ flex: '1 1 auto', }} style={{ height: "calc(100% - 100px)" }} className="encoding-list" onMouseEnter={() => setEncodingHovered(true)} onMouseLeave={() => setEncodingHovered(false)}> {(() => { const template = getChartTemplate(chart.chartType); const configProps = template?.properties; if (!configProps || configProps.length === 0) return null; return ( <Box sx={{ display: 'flex', flexDirection: 'column', gap: '1px', mb: '6px' }}> {configProps.map((propDef) => { // App-level visibility: hide certain properties unless relevant channels are assigned if (propDef.key === 'independentYAxis') { const hasFacet = chart.encodingMap['column' as Channel]?.fieldID != null || chart.encodingMap['row' as Channel]?.fieldID != null; if (!hasFacet) return null; } if (propDef.type === 'continuous') { const currentValue = chart.config?.[propDef.key] ?? propDef.defaultValue ?? propDef.min ?? 0; return ( <Box key={`config-${propDef.key}`} sx={{ display: 'flex', alignItems: 'center', borderRadius: '12px', minHeight: '18px', overflow: 'hidden', padding: '0px 10px 0px 0px', }}> <Typography variant="caption" sx={{ padding: '0px 6px', color: 'text.secondary', fontSize: 10, whiteSpace: 'nowrap', fontWeight: 500, minWidth: '40px', userSelect: 'none', }}> {propDef.label} </Typography> <Slider size="small" value={currentValue} min={propDef.min} max={propDef.max} step={propDef.step} onChange={(_event, newValue) => { dispatch(dfActions.updateChartConfig({chartId, key: propDef.key, value: newValue as number})); }} valueLabelDisplay="auto" sx={{ flex: 1, height: 3, mx: 0.5, '& .MuiSlider-thumb': { width: 10, height: 10 }, '& .MuiSlider-valueLabel': { fontSize: 10, padding: '2px 4px', lineHeight: 1.2 }, }} /> <Typography variant="caption" sx={{ fontSize: 10, color: 'text.secondary', minWidth: '20px', textAlign: 'right' }}> {currentValue} </Typography> </Box> ); } if (propDef.type === 'binary') { const currentValue = chart.config?.[propDef.key] ?? propDef.defaultValue ?? false; return ( <Box key={`config-${propDef.key}`} sx={{ display: 'flex', alignItems: 'center', borderRadius: '12px', minHeight: '18px', overflow: 'hidden', padding: '0px 8px', cursor: 'pointer', '&:hover': { backgroundColor: 'rgba(0,0,0,0.04)' }, }} onClick={() => { dispatch(dfActions.updateChartConfig({chartId, key: propDef.key, value: !currentValue})); }}> <Typography variant="caption" sx={{ flex: 1, color: 'text.secondary', fontSize: 10, whiteSpace: 'nowrap', fontWeight: 500, userSelect: 'none', }}> {propDef.label} </Typography> <Box sx={{ width: 28, height: 14, borderRadius: '7px', backgroundColor: currentValue ? theme.palette.primary.main : 'rgba(0,0,0,0.2)', position: 'relative', transition: 'background-color 0.2s', flexShrink: 0, }}> <Box sx={{ width: 10, height: 10, borderRadius: '50%', backgroundColor: 'white', position: 'absolute', top: 2, left: currentValue ? 16 : 2, transition: 'left 0.2s', }} /> </Box> </Box> ); } if (propDef.type !== 'discrete' || !propDef.options) return null; const currentValue = chart.config?.[propDef.key] ?? propDef.defaultValue; const options = propDef.options; // Find the index of the current value in options (deep compare via JSON) const currentSerialized = JSON.stringify(currentValue); let selectedIndex = options.findIndex(o => JSON.stringify(o.value) === currentSerialized); if (selectedIndex < 0) selectedIndex = 0; return ( <Box key={`config-${propDef.key}`} sx={{ display: 'flex', alignItems: 'center', borderRadius: '12px', minHeight: '22px', overflow: 'hidden', }}> <Typography variant="caption" sx={{ padding: '0px 8px', color: 'text.secondary', fontSize: 10, whiteSpace: 'nowrap', fontWeight: 500, userSelect: 'none', }}> {propDef.label} </Typography> <Select variant="standard" id={`config-${propDef.key}-select`} value={selectedIndex} onChange={(event) => { const idx = event.target.value as number; dispatch(dfActions.updateChartConfig({chartId, key: propDef.key, value: options[idx].value})); }} disableUnderline sx={{ flex: 1, fontSize: 11, height: '22px', backgroundColor: 'rgba(0,0,0,0.04)', borderRadius: '6px', '&:hover': { backgroundColor: 'rgba(0,0,0,0.07)' }, '& .MuiSelect-select': { padding: '1px 20px 1px 6px !important', fontSize: 11 }, '& .MuiSvgIcon-root': { fontSize: 14, right: 2 }, }} renderValue={(idx: number) => { return <span style={{fontSize: 11}}>{options[idx]?.label || "Default"}</span>; }} > {options.map((opt, i) => ( <MenuItem value={i} key={`config-${propDef.key}-${i}`} sx={{ fontSize: 11, minHeight: '28px' }}> {opt.label} </MenuItem> ))} </Select> </Box> ); })} </Box> ); })()} {encodingBoxGroups} </Box> {formulateInputBox} </Box>); const encodingShelfCard = ( <Box sx={{ padding: '4px 6px', maxWidth: "400px", display: 'flex', flexDirection: 'column', }}> <Box sx={{ padding: '4px 0px' }}> {channelComponent} </Box> {/* Ideas chips shown inline below the formulate box */} {(currentChartIdeas.length > 0 || (isLoadingIdeas && thinkingBuffer)) && ( <Box sx={{ display: 'flex', flexWrap: 'wrap', gap: 0.5, pt: 0.5, }}> {currentChartIdeas.map((idea, index) => ( <IdeaChip mini={true} key={index} idea={idea} theme={theme} onClick={() => handleIdeaClick(idea.text)} /> ))} {isLoadingIdeas && thinkingBuffer && <ThinkingBufferEffect text={thinkingBuffer.slice(-40)} sx={{ width: '100%' }} />} </Box> )} {isLoadingIdeas && !thinkingBuffer && ( <Box sx={{ padding: '2px 0' }}> {ThinkingBanner('ideating...')} </Box> )} </Box> ); return encodingShelfCard; } // Function to convert Vega-Lite spec to PNG data URL with improved resolution const vegaLiteSpecToPng = async (spec: any, scale: number = 2.0, quality: number = 1.0): Promise<string | null> => { try { // Create a temporary container const tempId = `temp-chart-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`; const tempContainer = document.createElement('div'); tempContainer.id = tempId; tempContainer.style.position = 'absolute'; tempContainer.style.left = '-9999px'; tempContainer.style.top = '-9999px'; document.body.appendChild(tempContainer); // Embed the chart with higher resolution settings const result = await embed('#' + tempId, spec, { actions: false, renderer: "canvas", scaleFactor: scale // Apply scale factor for higher resolution }); // Get the canvas and apply high-resolution rendering const canvas = await result.view.toCanvas(scale); // Pass scale to toCanvas const pngDataUrl = canvas.toDataURL('image/png', quality); // Clean up document.body.removeChild(tempContainer); return pngDataUrl; } catch (error) { console.error('Error converting Vega-Lite spec to PNG:', error); return null; } }; // Alternative method using toImageURL for even better quality const vegaLiteSpecToPngWithImageURL = async (spec: any, scale: number = 2.0): Promise<string | null> => { try { // Create a temporary container const tempId = `temp-chart-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`; const tempContainer = document.createElement('div'); tempContainer.id = tempId; tempContainer.style.position = 'absolute'; tempContainer.style.left = '-9999px'; tempContainer.style.top = '-9999px'; document.body.appendChild(tempContainer); // Embed the chart const result = await embed('#' + tempId, spec, { actions: false, renderer: "canvas", scaleFactor: scale }); // Use toImageURL for better quality const pngDataUrl = await result.view.toImageURL('png', scale); // Clean up document.body.removeChild(tempContainer); return pngDataUrl; } catch (error) { console.error('Error converting Vega-Lite spec to PNG:', error); return null; } }; ================================================ FILE: src/views/EncodingShelfThread.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { FC } from 'react' import { useSelector } from 'react-redux' import { DataFormulatorState, dfSelectors } from '../app/dfSlice'; import { Box, } from '@mui/material'; import React from 'react'; import { Chart } from "../components/ComponentType"; import '../scss/EncodingShelf.scss'; import { Type } from '../data/types'; import { getChartTemplate } from '../components/ChartTemplates'; import { checkChartAvailability, generateChartSkeleton } from './VisualizationView'; import { InsightIcon } from '../icons'; import { EncodingShelfCard } from './EncodingShelfCard'; // Property and state of an encoding shelf export interface EncodingShelfThreadProps { chartId: string, } export let ChartElementFC: FC<{ chart: Chart, tableRows: any[], tableMetadata: {[key: string]: {type: Type, semanticType: string, levels: any[]}}, boxWidth?: number, boxHeight?: number}> = function({chart, tableRows, tableMetadata, boxWidth, boxHeight}) { const conceptShelfItems = useSelector((state: DataFormulatorState) => state.conceptShelfItems); let WIDTH = boxWidth || 120; let HEIGHT = boxHeight || 80; let chartTemplate = getChartTemplate(chart.chartType); let available = checkChartAvailability(chart, conceptShelfItems, tableRows); if (chart.chartType == "Auto") { return <Box sx={{ position: "relative", display: "flex", flexDirection: "column", margin: 'auto', color: 'darkgray' }}> <InsightIcon fontSize="large"/> </Box> } if (!available || chart.chartType == "Table") { return <Box sx={{ margin: "auto" }} > {generateChartSkeleton(chartTemplate?.icon, 64, 64)} </Box> } // Use cached thumbnail from ChartRenderService when available if (chart.thumbnail) { return ( <Box sx={{ margin: "auto", display: 'flex', justifyContent: 'center', alignItems: 'center', backgroundColor: chart.saved ? "rgba(255,215,0,0.05)" : "white" }}> <img src={chart.thumbnail} alt={`${chart.chartType} chart`} style={{ maxWidth: WIDTH, maxHeight: HEIGHT, objectFit: 'contain' }} /> </Box> ); } // Fallback: skeleton while ChartRenderService is processing return ( <Box sx={{ margin: "auto", display: 'flex', justifyContent: 'center', alignItems: 'center', width: WIDTH, height: HEIGHT }}> {generateChartSkeleton(chartTemplate?.icon, 48, 48, 0.3)} </Box> ); } export const EncodingShelfThread: FC<EncodingShelfThreadProps> = function ({ chartId }) { const encodingShelf = ( <Box className="encoding-shelf-compact" sx={{height: '100%', width: 236, overflowY: 'auto', transition: 'height 150ms cubic-bezier(0.4, 0, 0.2, 1) 0ms', display: 'flex', flexDirection: 'column', alignItems: 'center', px: '8px', pt: '8px', }}> <EncodingShelfCard chartId={chartId}/> </Box> ) return encodingShelf; } ================================================ FILE: src/views/ExampleSessions.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React from 'react'; import { Typography, Box, Card, CardContent, Chip, alpha, useTheme, } from '@mui/material'; import { StreamIcon } from '../icons'; // Example session data for pre-built sessions export interface ExampleSession { id: string; title: string; description: string; previewImage: string; dataFile: string; live: boolean; } export const exampleSessions: ExampleSession[] = [ { id: 'stock-prices-live', title: 'Stock Prices (Live)', description: 'Live stock prices for different companies', previewImage: '/screenshot-stock-price-live-thumbnail.webp', dataFile: '/df_stock_prices_live.json', live: true, }, { id: 'gas-prices', title: 'Gas Prices', description: 'Weekly gas prices across different grades and formulations', previewImage: '/gas_prices-thumbnail.webp', dataFile: '/df_gas_prices.json', live: false, }, { id: 'global-energy', title: 'Global Energy', description: 'Explore global energy consumption and CO2 emissions data', previewImage: '/global_energy-thumbnail.webp', dataFile: '/df_global_energy.json', live: false, }, { id: 'movies', title: 'Movies', description: 'Analyze movie performance, budgets, and ratings data', previewImage: '/movies-thumbnail.webp', dataFile: '/df_movies.json', live: false, }, { id: 'unemployment', title: 'Unemployment', description: 'Unemployment rates across different industries over time', previewImage: '/unemployment-thumbnail.webp', dataFile: '/df_unemployment.json', live: false, } ]; // Session card component for displaying example sessions export const ExampleSessionCard: React.FC<{ session: ExampleSession; theme: any; onClick: () => void; disabled?: boolean; }> = ({ session, theme, onClick, disabled }) => { return ( <Card variant="outlined" sx={{ cursor: disabled ? 'default' : 'pointer', '&:hover': disabled ? {} : { transform: 'translateY(-2px)', borderColor: session.live ? alpha(theme.palette.secondary.main, 0.4) : alpha(theme.palette.primary.main, 0.4), }, }} onClick={disabled ? undefined : onClick} > <Box sx={{ height: 100, display: 'flex', alignItems: 'center', justifyContent: 'center', position: 'relative', overflow: 'hidden' }} > <Box component="img" src={session.previewImage} alt={session.title} sx={{ width: '100%', height: '100%', objectFit: 'cover', opacity: 0.8 }} /> </Box> {/* Content */} <CardContent sx={{ display: 'flex', flexDirection: 'column', justifyContent: 'space-between', py: 1, '&:last-child': { pb: 1 } }}> {/* Header */} <Box> <Typography variant="subtitle2" sx={{ fontSize: '12px', color: theme.palette.text.secondary, display: '-webkit-box', WebkitLineClamp: 2, WebkitBoxOrient: 'vertical', overflow: 'hidden' }} > {session.live && <StreamIcon sx={{ fontSize: 10, color: 'success.main' }} />} <span style={{textDecoration: 'underline'}}>{session.title}:</span> {session.description} </Typography> </Box> </CardContent> </Card> ); }; ================================================ FILE: src/views/ExplComponents.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React, { FC, useState } from 'react'; import { Box, Card, CardContent, Typography, IconButton, Tooltip, Chip, } from '@mui/material'; import { styled } from '@mui/material/styles'; import { alpha } from '@mui/material/styles'; import { borderColor, shadow, transition, radius } from '../app/tokens'; import 'katex/dist/katex.min.css'; import { InlineMath, BlockMath } from 'react-katex'; import LightbulbIcon from '@mui/icons-material/Lightbulb'; import InfoIcon from '@mui/icons-material/Info'; // Helper function to render text with LaTeX math expressions const renderWithMath = (text: string) => { const parts: Array<{ type: 'text' | 'inline' | 'block', content: string }> = []; let currentIndex = 0; let currentText = ''; while (currentIndex < text.length) { // Check for block math \[ ... \] if (text.slice(currentIndex, currentIndex + 2) === '\\[') { // Save any accumulated text if (currentText) { parts.push({ type: 'text', content: currentText }); currentText = ''; } // Find the closing \] let blockEnd = currentIndex + 2; let braceCount = 0; while (blockEnd < text.length) { if (text.slice(blockEnd, blockEnd + 2) === '\\]') { break; } if (text[blockEnd] === '{') braceCount++; if (text[blockEnd] === '}') braceCount--; blockEnd++; } if (blockEnd < text.length) { // Found complete block math const mathContent = text.slice(currentIndex + 2, blockEnd); parts.push({ type: 'block', content: mathContent }); currentIndex = blockEnd + 2; } else { // No closing bracket found, treat as text currentText += text[currentIndex]; currentIndex++; } } // Check for inline math \( ... \) else if (text.slice(currentIndex, currentIndex + 2) === '\\(') { // Save any accumulated text if (currentText) { parts.push({ type: 'text', content: currentText }); currentText = ''; } // Find the closing \) let inlineEnd = currentIndex + 2; let braceCount = 0; while (inlineEnd < text.length) { if (text.slice(inlineEnd, inlineEnd + 2) === '\\)') { break; } if (text[inlineEnd] === '{') braceCount++; if (text[inlineEnd] === '}') braceCount--; inlineEnd++; } if (inlineEnd < text.length) { // Found complete inline math const mathContent = text.slice(currentIndex + 2, inlineEnd); parts.push({ type: 'inline', content: mathContent }); currentIndex = inlineEnd + 2; } else { // No closing bracket found, treat as text currentText += text[currentIndex]; currentIndex++; } } // Regular character else { currentText += text[currentIndex]; currentIndex++; } } // Add any remaining text if (currentText) { parts.push({ type: 'text', content: currentText }); } return parts.map((part, index) => { if (part.type === 'inline') { try { return <InlineMath key={index} math={part.content} />; } catch (error) { return <span key={index}>{`\\(${part.content}\\)`}</span>; } } else if (part.type === 'block') { try { return <BlockMath key={index} math={part.content} />; } catch (error) { return <span key={index}>{`\\[${part.content}\\]`}</span>; } } else { return <span key={index}>{part.content}</span>; } }); }; // Styled components for the concept explanation cards const ConceptExplanationCard = styled(Card, { shouldForwardProp: (prop) => prop !== 'secondary', })<{ secondary: boolean }>(({ theme, secondary}) => ({ minWidth: 360, // Increased from 300 maxWidth: 480, // Increased from 360 margin: '4px', borderRadius: '6px', border: `1px solid ${borderColor.divider}`, boxShadow: shadow.sm, transition: transition.normal, backgroundColor: alpha(theme.palette.background.paper, 0.9), '&:hover': { boxShadow: shadow.lg, borderColor: !secondary ? theme.palette.primary.light : theme.palette.secondary.light, transform: 'translateY(-1px)', }, })); const ConceptName = styled(Typography, { shouldForwardProp: (prop) => prop !== 'secondary', })<{ secondary: boolean }>(({ theme, secondary }) => ({ fontSize: '12px', fontWeight: 600, color: secondary ? theme.palette.secondary.main : theme.palette.primary.main, marginBottom: '3px', display: 'flex', alignItems: 'center', gap: '4px', })); const ConceptExplanation = styled(Typography)(({ theme }) => ({ fontSize: '11px', lineHeight: 1.4, overflow: 'auto', color: theme.palette.text.primary, fontStyle: 'italic', '& .katex': { fontSize: '12px', lineHeight: 1.2, }, '& .katex-display': { margin: '4px 0', }, })); export interface ConceptExplanationItem { field: string; explanation: string; } export interface ConceptExplCardsProps { concepts: ConceptExplanationItem[]; title?: string; maxCards?: number; } export const ConceptExplCards: FC<ConceptExplCardsProps> = ({ concepts, maxCards = 8 }) => { const [expanded, setExpanded] = useState(false); if (!concepts || concepts.length === 0) { return null; } const displayConcepts = expanded ? concepts : concepts.slice(0, maxCards); const hasMoreConcepts = concepts.length > maxCards; return ( <Box sx={{ position: 'relative', display: 'flex', justifyContent: 'center' }}> {/* Concepts Grid */} <Box sx={{ display: 'grid', gridTemplateColumns: 'repeat(auto-fill, minmax(360px, 1fr))', // Increased from 180px gap: 1, overflow: 'hidden', }}> {displayConcepts.map((concept, index) => { let secondary = concept.field == "Statistical Analysis"; return ( <ConceptExplanationCard key={`${concept.field}-${index}`} secondary={secondary}> <CardContent sx={{ padding: '6px !important' }}> <ConceptName secondary={secondary}> {concept.field} </ConceptName> <ConceptExplanation> {renderWithMath(concept.explanation)} </ConceptExplanation> </CardContent> </ConceptExplanationCard> )})} </Box> {/* Show More/Less Button */} {hasMoreConcepts && ( <Box sx={{ display: 'flex', justifyContent: 'center', marginTop: 1, paddingTop: 1, borderTop: `1px solid ${borderColor.divider}`, }}> <Tooltip title={expanded ? "Show fewer concepts" : "Show all concepts"}> <IconButton size="small" onClick={() => setExpanded(!expanded)} sx={{ fontSize: '10px', color: 'text.secondary', '&:hover': { backgroundColor: 'action.hover', }, }} > <Typography variant="caption"> {expanded ? `Show first ${maxCards} concepts` : `Show all ${concepts.length} concepts` } </Typography> </IconButton> </Tooltip> </Box> )} </Box> ); }; // Helper function to extract concept explanations from table derivation export const extractConceptExplanations = (table: any): ConceptExplanationItem[] => { if (!table?.derive?.explanation?.concepts) { return []; } return table.derive.explanation.concepts.map((concept: any) => ({ field: concept.field, explanation: concept.explanation, })); }; // Shared component for data transformation cards export const CodeExplanationCard: FC<{ title: string; icon: React.ReactNode; children: React.ReactNode; }> = ({ title, icon, children }) => ( <Card variant="outlined" sx={{ minWidth: "280px", maxWidth: "1200px", display: "flex", flexGrow: 1, margin: 0, borderRadius: radius.md, border: `1px solid ${borderColor.divider}`, boxShadow: shadow.sm, transition: transition.normal, '&:hover': { boxShadow: shadow.lg, borderColor: 'primary.main', } }} > <CardContent sx={{ display: "flex", flexDirection: "column", flexGrow: 1, padding: 0, overflow: 'auto', '&:last-child': { paddingBottom: 0 } }} > <Typography sx={{ fontSize: 14, margin: 1.5, fontWeight: 500, color: 'text.primary', display: 'flex', alignItems: 'center', gap: 0.5 }} gutterBottom > {icon} {title} </Typography> <Box sx={{ display: 'flex', flexDirection: "row", alignItems: "flex-start", flex: 'auto', padding: 1.5, background: 'background.default', borderTop: `1px solid ${borderColor.divider}`, borderRadius: '0 0 8px 8px' }} > {children} </Box> </CardContent> </Card> ); ================================================ FILE: src/views/MessageSnackbar.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import * as React from 'react'; import Snackbar from '@mui/material/Snackbar'; import IconButton from '@mui/material/IconButton'; import CloseIcon from '@mui/icons-material/Close'; import { DataFormulatorState, dfActions } from '../app/dfSlice'; import { useDispatch, useSelector } from 'react-redux'; import { Alert, Box, Paper, Tooltip, Typography } from '@mui/material'; import { shadow, transition } from '../app/tokens'; import InfoIcon from '@mui/icons-material/Info'; import DeleteIcon from '@mui/icons-material/Delete'; export interface Message { type: "success" | "info" | "error" | "warning", component: string, // the component that generated the message timestamp: number, value: string, detail?: string, // error details code?: string // if this message is related to a code error, include code as well } const TYPE_SYMBOLS: Record<string, string> = { error: '✗', warning: '⚠', info: 'ℹ', success: '✓', }; const TYPE_COLORS: Record<string, string> = { error: '#d32f2f', warning: '#ed6c02', info: '#0288d1', success: '#2e7d32', }; // Helper function to format timestamp const formatTimestamp = (timestamp: number) => { const timestampMs = timestamp < 1e12 ? timestamp * 1000 : timestamp; return new Date(timestampMs).toLocaleString('en-US', { hour: "2-digit", minute: "2-digit", hour12: false }); }; export const MessageSnackbar = React.memo(function MessageSnackbar() { const messages = useSelector((state: DataFormulatorState) => state.messages); const displayedMessageIdx = useSelector((state: DataFormulatorState) => state.displayedMessageIdx); const dispatch = useDispatch(); const [openLastMessage, setOpenLastMessage] = React.useState(false); const [latestMessage, setLatestMessage] = React.useState<Message | undefined>(); const [openMessages, setOpenMessages] = React.useState(false); const [expandedMessages, setExpandedMessages] = React.useState<Set<number>>(new Set()); const messagesScrollRef = React.useRef<HTMLDivElement>(null); React.useEffect(()=>{ if (displayedMessageIdx < messages.length) { setOpenLastMessage(true); setLatestMessage(messages[displayedMessageIdx]); dispatch(dfActions.setDisplayedMessageIndex(displayedMessageIdx + 1)); } }, [messages]) React.useEffect(() => { messagesScrollRef.current?.scrollTo({ top: messagesScrollRef.current.scrollHeight, behavior: 'smooth' }); }, [messages, openMessages]); const handleClose = (event: React.SyntheticEvent | Event, reason?: string) => { if (reason === 'clickaway') { return; } setOpenLastMessage(false); setLatestMessage(undefined); }; // Only compute grouped messages when panel is open — show latest 30 messages only const MAX_DISPLAY_MESSAGES = 30; const groupedMessages = React.useMemo(() => { if (!openMessages) return []; const groups: Array<Message & { count: number; originalIndex: number }> = []; // Only process the latest 30 messages const startIdx = Math.max(0, messages.length - MAX_DISPLAY_MESSAGES); for (let i = startIdx; i < messages.length; i++) { const msg = messages[i]; const key = `${msg.value}|${msg.detail || ''}|${msg.code || ''}|${msg.type}`; const lastGroup = groups[groups.length - 1]; const lastKey = lastGroup ? `${lastGroup.value}|${lastGroup.detail || ''}|${lastGroup.code || ''}|${lastGroup.type}` : null; if (lastKey === key) { lastGroup.count++; if (msg.timestamp > lastGroup.timestamp) { lastGroup.timestamp = msg.timestamp; } } else { groups.push({ ...msg, count: 1, originalIndex: i }); } } return groups; }, [messages, openMessages]); const toggleExpand = React.useCallback((index: number) => { setExpandedMessages(prev => { const next = new Set(prev); if (next.has(index)) next.delete(index); else next.add(index); return next; }); }, []); return ( <Box sx={{ '& .snackbar-button': { width: 36, height: 36, zIndex: 10, backgroundColor: 'white', '&:hover': { transform: 'scale(1.1)', backgroundColor: 'white', }, border: '1px solid', boxShadow: shadow.xl, transition: transition.slow }}}> <Tooltip placement="left" title="view system messages"> <IconButton className='snackbar-button' color="warning" sx={{position: "absolute", bottom: 16, right: 16 }} onClick={() => setOpenMessages(true)} > <InfoIcon sx={{fontSize: 32}}/> </IconButton> </Tooltip> <Snackbar open={openMessages} anchorOrigin={{vertical: 'bottom', horizontal: 'right'}} sx={{maxWidth: '500px', maxHeight: '70vh'}} > <Paper elevation={3} sx={{ width: '100%', color: 'text.primary', display: 'flex', flexDirection: 'column', minWidth: '300px', py: 1, }}> {/* Header */} <Box sx={{display: 'flex', alignItems: 'center', px: 1.5}}> <Typography variant="subtitle1" sx={{fontSize: 12, flexGrow: 1, color: 'text.secondary'}}> system messages ({messages.length}){messages.length > MAX_DISPLAY_MESSAGES ? ` — showing latest ${MAX_DISPLAY_MESSAGES}` : ''} </Typography> <Tooltip title="clear all messages"> <IconButton size="small" color="warning" aria-label="delete" onClick={() => { dispatch(dfActions.clearMessages()); dispatch(dfActions.setDisplayedMessageIndex(0)); setOpenMessages(false); }} > <DeleteIcon fontSize="small" /> </IconButton> </Tooltip> <IconButton size="small" aria-label="close" onClick={() => setOpenMessages(false)} > <CloseIcon fontSize="small" /> </IconButton> </Box> {/* Message list — plain text, no MUI Alert per row */} <div ref={messagesScrollRef} style={{ overflow: 'auto', flexGrow: 1, maxHeight: '50vh', minHeight: 100, padding: '4px 12px', }} > {messages.length === 0 && ( <Typography fontSize={10} sx={{ opacity: 0.5, fontStyle: 'italic' }}>No messages yet</Typography> )} {groupedMessages.map((msg, index) => { const color = TYPE_COLORS[msg.type] || '#333'; const symbol = TYPE_SYMBOLS[msg.type] || '•'; const hasDetails = !!(msg.detail || msg.code); const isExpanded = expandedMessages.has(index); return ( <div key={index} style={{ borderBottom: '1px solid #f0f0f0', padding: '2px 0' }}> <Typography fontSize={10} component="div" sx={{ display: 'flex', alignItems: 'baseline', gap: 0.5 }}> <span style={{ color, fontWeight: 600 }}>{symbol}</span> <span style={{ color: '#888' }}>[{formatTimestamp(msg.timestamp)}]</span> <span>(<span style={{ color: '#888' }}>{msg.component}</span>) {msg.value}</span> {msg.count > 1 && ( <span style={{ color, fontWeight: 600, border: `1px solid ${color}`, borderRadius: 3, padding: '0 3px', marginLeft: 2 }}>×{msg.count}</span> )} {hasDetails && ( <span style={{ color: '#0288d1', cursor: 'pointer', userSelect: 'none' }} onClick={() => toggleExpand(index)} > {isExpanded ? '▾ collapse' : '▸ details'} </span> )} </Typography> {hasDetails && isExpanded && ( <div style={{ marginLeft: 20, padding: '4px 0', color: '#555' }}> {msg.detail && ( <div style={{ marginBottom: 4 }}> <Typography fontSize={10} sx={{ color: '#888' }}>— details —</Typography> <Typography fontSize={10}>{msg.detail}</Typography> </div> )} {msg.code && ( <div> <Typography fontSize={10} sx={{ color: '#888' }}>— code —</Typography> <pre style={{ whiteSpace: 'pre-wrap', wordBreak: 'break-word', fontSize: 10, margin: '2px 0', padding: '4px 8px', backgroundColor: '#f8f8f8', borderRadius: 3, }}> {msg.code.split('\n').filter(line => line.trim() !== '').join('\n')} </pre> </div> )} </div> )} </div> ); })} </div> </Paper> </Snackbar> {/* Last message toast — keep the single Alert for latest message popup */} {latestMessage != undefined ? <Snackbar open={openLastMessage} autoHideDuration={latestMessage?.type == "error" ? 20000 : 10000} anchorOrigin={{vertical: 'bottom', horizontal: 'right'}} onClose={handleClose} > <Alert onClose={handleClose} severity={latestMessage?.type} sx={{ maxWidth: '400px', maxHeight: '600px', overflow: 'auto' }}> <Typography fontSize={12} component="span" sx={{margin: "auto"}}> <b>[{formatTimestamp(latestMessage.timestamp)}] ({latestMessage.component})</b> {latestMessage?.value} </Typography> {latestMessage?.detail && <> <div style={{ borderTop: '1px solid #ddd', margin: '4px 0', fontSize: 12 }}>{latestMessage.detail}</div> </>} {latestMessage?.code && <pre style={{ whiteSpace: 'pre-wrap', wordBreak: 'break-word', fontSize: 10, opacity: 0.7, margin: '4px 0' }}> {latestMessage.code.split('\n').filter(line => line.trim() !== '').join('\n')} </pre> } </Alert> </Snackbar> : ""} </Box> ); }); ================================================ FILE: src/views/ModelSelectionDialog.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React, { useEffect, useState } from 'react'; import '../scss/App.scss'; import { useDispatch, useSelector } from "react-redux"; import { DataFormulatorState, dfActions, ModelConfig, dfSelectors, } from '../app/dfSlice' import _ from 'lodash'; import { Button, Tooltip, Typography, IconButton, DialogTitle, Dialog, DialogContent, DialogActions, Radio, TextField, TableContainer, TableHead, Table, TableCell, TableRow, TableBody, Autocomplete, CircularProgress, FormControl, Select, SelectChangeEvent, MenuItem, OutlinedInput, Paper, Box, Divider, Checkbox, } from '@mui/material'; import { alpha, styled, useTheme } from '@mui/material/styles'; import AddCircleIcon from '@mui/icons-material/AddCircle'; import ClearIcon from '@mui/icons-material/Clear'; import VisibilityIcon from '@mui/icons-material/Visibility'; import VisibilityOffIcon from '@mui/icons-material/VisibilityOff'; import CheckCircleOutlineIcon from '@mui/icons-material/CheckCircleOutline'; import ErrorOutlineIcon from '@mui/icons-material/ErrorOutline'; import HelpOutlineIcon from '@mui/icons-material/HelpOutline'; import InfoOutlinedIcon from '@mui/icons-material/InfoOutlined'; import { getUrls, fetchWithIdentity } from '../app/utils'; const decodeHtmlEntities = (text: string): string => { const textarea = document.createElement('textarea'); textarea.innerHTML = text; return textarea.value; }; // Add this helper function at the top of the file, after the imports const simpleHash = (str: string): string => { let hash = 0; for (let i = 0; i < str.length; i++) { const char = str.charCodeAt(i); hash = ((hash << 5) - hash) + char; hash = hash & hash; // Convert to 32-bit integer } return Math.abs(hash).toString(36); }; export const ModelSelectionButton: React.FC<{}> = ({ }) => { const theme = useTheme(); const dispatch = useDispatch(); const models = useSelector((state: DataFormulatorState) => state.models); const selectedModelId = useSelector((state: DataFormulatorState) => state.selectedModelId); const testedModels = useSelector((state: DataFormulatorState) => state.testedModels); const [modelDialogOpen, setModelDialogOpen] = useState<boolean>(false); const [showKeys, setShowKeys] = useState<boolean>(false); const [providerModelOptions, setProviderModelOptions] = useState<{[key: string]: string[]}>({ 'openai': [], 'azure': [], 'anthropic': [], 'gemini': [], 'ollama': [] }); const serverConfig = useSelector((state: DataFormulatorState) => state.serverConfig); let updateModelStatus = (model: ModelConfig, status: 'ok' | 'error' | 'testing' | 'unknown', message: string) => { dispatch(dfActions.updateModelStatus({id: model.id, status, message})); } let getStatus = (id: string | undefined) => { return id != undefined ? (testedModels.find(t => (t.id == id))?.status || 'unknown') : 'unknown'; } // Helper functions for slot management const [tempSelectedModelId, setTempSelectedModelId] = useState<string | undefined>(selectedModelId); const [newEndpoint, setNewEndpoint] = useState<string>(""); // openai, azure, ollama etc const [newModel, setNewModel] = useState<string>(""); const [newApiKey, setNewApiKey] = useState<string>(""); const [newApiBase, setNewApiBase] = useState<string>(""); const [newApiVersion, setNewApiVersion] = useState<string>(""); // Fetch available models from the API useEffect(() => { const fetchModelOptions = async () => { try { const response = await fetchWithIdentity(getUrls().CHECK_AVAILABLE_MODELS); const data = await response.json(); // Group models by provider const modelsByProvider: {[key: string]: string[]} = { 'openai': [], 'azure': [], 'anthropic': [], 'gemini': [], 'ollama': [] }; data.forEach((modelConfig: any) => { const provider = modelConfig.endpoint; const model = modelConfig.model; if (provider && model && !modelsByProvider[provider]) { modelsByProvider[provider] = []; } if (provider && model && !modelsByProvider[provider].includes(model)) { modelsByProvider[provider].push(model); } }); setProviderModelOptions(modelsByProvider); } catch (error) { console.error("Failed to fetch model options:", error); } }; fetchModelOptions(); }, []); let modelExists = models.some(m => m.endpoint == newEndpoint && m.model == newModel && m.api_base == newApiBase && m.api_key == newApiKey && m.api_version == newApiVersion); let testModel = (model: ModelConfig) => { updateModelStatus(model, 'testing', ""); let message = { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ model: model, }), }; fetchWithIdentity(getUrls().TEST_MODEL, {...message }) .then((response) => response.json()) .then((data) => { let status = data["status"] || 'error'; updateModelStatus(model, status, data["message"] || ""); // Auto-select the first good model if none is currently selected if (status === 'ok' && !tempSelectedModelId) { setTempSelectedModelId(model.id); } }).catch((error) => { updateModelStatus(model, 'error', error.message) }); } let readyToTest = newModel && (newApiKey || newApiBase); let newModelEntry = <TableRow key={`new-model-entry`} sx={{ '&:last-child td, &:last-child th': { border: 0 }, padding: "6px 6px"}} > <TableCell align="left" sx={{ width: '120px' }}> <Autocomplete freeSolo value={newEndpoint} onChange={(event: any, newValue: string | null) => { setNewEndpoint(newValue || ""); if (newModel == "" && newValue == "openai" && providerModelOptions.openai.length > 0) { setNewModel(providerModelOptions.openai[0]); } if (!newApiVersion && newValue == "azure") { setNewApiVersion("2024-02-15"); } }} options={['openai', 'azure', 'ollama', 'anthropic', 'gemini']} renderOption={(props, option) => ( <Typography {...props} onClick={() => setNewEndpoint(option)} sx={{fontSize: "0.875rem"}}> {option} </Typography> )} renderInput={(params) => ( <TextField {...params} placeholder="provider" slotProps={{ input: { ...params.InputProps, style: { fontSize: "0.75rem" } } }} size="small" onChange={(event: any) => setNewEndpoint(event.target.value)} /> )} slotProps={{ listbox: { style: { padding: 0 } }, }} slots={{ paper: (props) => { return <Paper {...props}> <Typography sx={{ p: 1, color: 'gray', fontStyle: 'italic', fontSize: '0.75rem' }}> examples </Typography> {props.children} </Paper> } }} /> </TableCell> <TableCell align="left" sx={{ minWidth: '180px' }}> <TextField fullWidth size="small" type={showKeys ? "text" : "password"} slotProps={{ input: { style: { fontSize: "0.75rem" } } }} placeholder='optional for keyless endpoint' value={newApiKey} onChange={(event: any) => { setNewApiKey(event.target.value); }} autoComplete='off' /> </TableCell> <TableCell align="left"> <TextField size="small" fullWidth value={newModel} onChange={(event) => { setNewModel(event.target.value); }} placeholder="e.g., gpt-5.1" error={newEndpoint != "" && !newModel} slotProps={{ input: { style: { fontSize: "0.75rem" }, 'aria-label': 'Enter a model name', } }} /> </TableCell> <TableCell align="right"> <TextField size="small" type="text" fullWidth placeholder="optional" slotProps={{ input: { style: { fontSize: "0.75rem" } } }} value={newApiBase} onChange={(event: any) => { setNewApiBase(event.target.value); }} autoComplete='off' /> </TableCell> <TableCell align="right"> <TextField size="small" type="text" fullWidth slotProps={{ input: { style: { fontSize: "0.75rem" } } }} value={newApiVersion} onChange={(event: any) => { setNewApiVersion(event.target.value); }} autoComplete='off' placeholder="optional" /> </TableCell> <TableCell align="right"> <Tooltip title={modelExists ? "provider + model already exists" : "add and test model"}> <span> <IconButton color={modelExists ? 'error' : 'primary'} disabled={!readyToTest} sx={{cursor: modelExists ? 'help' : 'pointer'}} onClick={(event) => { event.stopPropagation() let endpoint = newEndpoint; // Hash the ID to prevent API key exposure const idString = `${endpoint}-${newModel}-${newApiKey}-${newApiBase}-${newApiVersion}`; let id = simpleHash(idString); let model = {endpoint, model: newModel, api_key: newApiKey, api_base: newApiBase, api_version: newApiVersion, id: id}; dispatch(dfActions.addModel(model)); // Create a custom test function that assigns to slot on success const testAndAssignModel = (model: ModelConfig) => { updateModelStatus(model, 'testing', ""); let message = { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ model: model, }), }; fetch(getUrls().TEST_MODEL, {...message }) .then((response) => response.json()) .then((data) => { let status = data["status"] || 'error'; updateModelStatus(model, status, data["message"] || ""); // Only assign to slot if test is successful if (status === 'ok') { setTempSelectedModelId(id); } }).catch((error) => { updateModelStatus(model, 'error', error.message) }); }; testAndAssignModel(model); setNewEndpoint(""); setNewModel(""); setNewApiKey(""); setNewApiBase(""); setNewApiVersion(""); }}> <AddCircleIcon /> </IconButton> </span> </Tooltip> </TableCell> <TableCell align="right"> <Tooltip title={"clear"}> <IconButton onClick={(event) => { event.stopPropagation() setNewEndpoint(""); setNewModel(""); setNewApiKey(""); setNewApiBase(""); setNewApiVersion(""); }}> <ClearIcon /> </IconButton> </Tooltip> </TableCell> </TableRow> let modelTable = <TableContainer> <Table sx={{ minWidth: 600, "& .MuiTableCell-root": { padding: "4px 8px", borderBottom: "none", fontSize: '0.75rem' } }} size="small" > <TableHead> <TableRow> <TableCell sx={{fontWeight: 'bold', width: '120px'}}>Provider</TableCell> <TableCell sx={{fontWeight: 'bold', width: '160px'}}>API Key</TableCell> <TableCell sx={{fontWeight: 'bold', width: '160px'}} align="left">Model</TableCell> <TableCell sx={{fontWeight: 'bold', width: '200px'}} align="left">API Base</TableCell> <TableCell sx={{fontWeight: 'bold', width: '120px'}} align="left">API Version</TableCell> <TableCell sx={{fontWeight: 'bold'}} align="left">Status</TableCell> <TableCell sx={{fontWeight: 'bold'}} align="right"></TableCell> </TableRow> </TableHead> <TableBody> {models.map((model) => { let status = getStatus(model.id); let statusIcon = status == "unknown" ? <HelpOutlineIcon color="warning" fontSize="small" /> : ( status == 'testing' ? <CircularProgress size={20} />: (status == "ok" ? <CheckCircleOutlineIcon color="success" fontSize="small"/> : <ErrorOutlineIcon color="error" fontSize="small"/> )) let message = "Model is ready to use"; if (status == "unknown") { message = "Click to test if this model is working"; } else if (status == "error") { const rawMessage = testedModels.find(t => t.id == model.id)?.message || "Unknown error"; message = `Error: ${decodeHtmlEntities(rawMessage)}. Click to retest.`; } const borderStyle = ['error'].includes(status) ? '1px dashed lightgray' : undefined; const noBorderStyle = ['error'].includes(status) ? 'none' : undefined; const disabledStyle = status != 'ok' ? { cursor: 'default', opacity: 0.5 } : undefined; return ( <React.Fragment key={`${model.id}`}> <TableRow key={`${model.id}`} sx={{ '& .MuiTableCell-root': { fontSize: '0.75rem' }, '&:hover': { backgroundColor: '#f8f9fa' }, border: tempSelectedModelId == model.id ? `2px solid ${theme.palette.primary.main}` : 'none', cursor: status == 'ok' ? 'pointer' : 'default', }} onClick={() => status == 'ok' && setTempSelectedModelId(tempSelectedModelId == model.id ? undefined : model.id)} > <TableCell align="left" sx={{ borderBottom: noBorderStyle, ...disabledStyle }}> <Typography variant="body2" sx={{ fontWeight: 500, fontSize: 'inherit' }}> {model.endpoint} </Typography> </TableCell> <TableCell component="th" scope="row" sx={{ borderBottom: borderStyle, ...disabledStyle }}> {model.api_key ? (showKeys ? <Typography variant="body2" sx={{ maxWidth: '220px', wordBreak: 'break-all', whiteSpace: 'normal', fontSize: '0.5rem', fontFamily: 'monospace', lineHeight: 1.3 }} > {model.api_key} </Typography> : <Typography variant="body2" sx={{ fontSize: 'inherit', fontFamily: 'monospace', color: 'text.secondary' }}>••••••••••••</Typography>) : <Typography sx={{color: "text.secondary", fontSize: 'inherit', fontStyle: 'italic'}}>None</Typography> } </TableCell> <TableCell align="left" sx={{ borderBottom: borderStyle, ...disabledStyle }}> <Typography variant="body2" sx={{ fontSize: 'inherit', fontWeight: 500 }}> {model.model} </Typography> </TableCell> <TableCell align="left" sx={{ borderBottom: borderStyle, ...disabledStyle }}> {model.api_base ? ( <Typography variant="body2" sx={{ fontSize: 'inherit', maxWidth: '220px', wordBreak: 'break-all', lineHeight: 1.3 }}> {model.api_base} </Typography> ) : ( <Typography sx={{ color: "text.secondary", fontSize: 'inherit', fontStyle: 'italic' }}> Default </Typography> )} </TableCell> <TableCell align="left" sx={{ borderBottom: borderStyle, ...disabledStyle }}> {model.api_version ? ( <Typography variant="body2" sx={{ fontSize: 'inherit' }}> {model.api_version} </Typography> ) : ( <Typography sx={{ color: "text.secondary", fontSize: 'inherit', fontStyle: 'italic' }}> Default </Typography> )} </TableCell> <TableCell sx={{borderBottom: borderStyle}} align="left"> <Tooltip title={message}> <Button size="small" color={status == 'ok' ? 'success' : status == 'error' ? 'error' : 'warning'} onClick ={() => { testModel(model) }} sx={{ p: 0.75, fontSize: "0.75rem", textTransform: "none" }} startIcon={statusIcon} > {status == 'ok' ? 'Ready' : status == 'error' ? 'Retest' : 'Test'} </Button> </Tooltip> </TableCell> <TableCell sx={{ borderBottom: borderStyle }} align="right"> <Tooltip title="remove model"> <IconButton size="small" onClick={()=>{ dispatch(dfActions.removeModel(model.id)); // Remove from all slots if assigned if (tempSelectedModelId == model.id) { setTempSelectedModelId(undefined); } }} sx={{ p: 0.75 }} > <ClearIcon fontSize="small"/> </IconButton> </Tooltip> </TableCell> </TableRow> {['error'].includes(status) && ( <TableRow> <TableCell colSpan={1} align="right" ></TableCell> <TableCell colSpan={7}> <Typography variant="caption" color="#c82c2c" sx={{fontSize: "0.625rem"}}> {message} </Typography> </TableCell> </TableRow> )} </React.Fragment> ) })} {newModelEntry} </TableBody> </Table> </TableContainer> let modelNotReady = tempSelectedModelId == undefined || getStatus(tempSelectedModelId) !== 'ok'; let tempModel = models.find(m => m.id == tempSelectedModelId); let tempModelName = tempModel ? `${tempModel.endpoint}/${tempModel.model}` : 'Please select a model'; let selectedModelName = models.find(m => m.id == selectedModelId)?.model || 'Unselected'; return <> <Tooltip title="Select a model"> <Button sx={{fontSize: "inherit", textTransform: "none"}} variant="text" color={modelNotReady ? 'warning' : "primary"} onClick={()=>{setModelDialogOpen(true)}}> {modelNotReady ? 'Select Models' : selectedModelName} </Button> </Tooltip> <Dialog maxWidth="lg" open={modelDialogOpen} onClose={(event, reason) => { if (reason !== 'backdropClick') { setModelDialogOpen(false); } }} > <DialogTitle sx={{display: "flex", alignItems: "center"}}>Select a model</DialogTitle> <DialogContent > <Box sx={{ display: 'flex', color: 'text.secondary', alignItems: 'flex-start', mb: 2, p: 1.5, backgroundColor: alpha(theme.palette.info.main, 0.08), }}> <Box> <Typography variant="caption" component="div" sx={{ lineHeight: 1.6 }}> • Models with strong code generation capabilities (e.g., <code style={{ backgroundColor: alpha(theme.palette.text.primary, 0.05), padding: '1px 4px', borderRadius: 3, fontSize: '0.7rem' }}>gpt-5.1</code>, <code style={{ backgroundColor: alpha(theme.palette.text.primary, 0.05), padding: '1px 4px', borderRadius: 3, fontSize: '0.7rem' }}>claude-sonnet-4-5</code>) provide best experience. </Typography> <Typography variant="caption" component="div" sx={{ lineHeight: 1.6, mt: 0.5 }}> • Model configuration based on LiteLLM. <a href="https://docs.litellm.ai/docs/" target="_blank" rel="noopener noreferrer">See supported providers</a>. Use <code style={{ backgroundColor: alpha(theme.palette.text.primary, 0.05), padding: '1px 4px', borderRadius: 3, fontSize: '0.7rem' }}>openai</code> provider for OpenAI-compatible APIs. </Typography> </Box> </Box> {modelTable} </DialogContent> <DialogActions> {!serverConfig.DISABLE_DISPLAY_KEYS && ( <Button sx={{marginRight: 'auto'}} endIcon={showKeys ? <VisibilityOffIcon /> : <VisibilityIcon />} onClick={()=>{ setShowKeys(!showKeys);}}> {showKeys ? 'hide' : 'show'} keys </Button> )} <Button disabled={modelNotReady} sx={{textTransform: 'none'}} variant={modelNotReady ? 'text' : 'contained'} onClick={()=>{ dispatch(dfActions.selectModel(tempSelectedModelId)); setModelDialogOpen(false);}}>Use {tempModelName}</Button> <Button onClick={()=>{ setTempSelectedModelId(selectedModelId); setModelDialogOpen(false); }}>cancel</Button> </DialogActions> </Dialog> </>; } ================================================ FILE: src/views/MultiTablePreview.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import * as React from 'react'; import { useState, useEffect } from 'react'; import { Box, Chip, IconButton, Typography, Tooltip, LinearProgress, alpha, Card, } from '@mui/material'; import DeleteIcon from '@mui/icons-material/Delete'; import { DictTable } from '../components/ComponentType'; import { CustomReactTable } from './ReactTable'; export interface MultiTablePreviewProps { /** Loading state indicator */ loading?: boolean; /** Error message to display */ error?: string | null; /** Single table for backwards compatibility */ table?: DictTable | null; /** Array of tables to preview */ tables?: DictTable[] | null; /** Label to show when no tables are available */ emptyLabel?: string; /** Optional metadata string to display */ meta?: string; /** Callback when a table is removed (enables delete button) */ onRemoveTable?: (index: number) => void; /** Controlled active index */ activeIndex?: number; /** Callback when active index changes */ onActiveIndexChange?: (index: number) => void; /** Maximum height for the table container */ maxHeight?: number; /** Maximum number of rows to display */ maxRows?: number; /** Whether to use compact mode for the table */ compact?: boolean; /** Whether to show the "Preview" label */ showPreviewLabel?: boolean; /** Whether to hide the row count display */ hideRowCount?: boolean; } export const MultiTablePreview: React.FC<MultiTablePreviewProps> = ({ loading = false, error = null, table, tables, emptyLabel = 'No tables to preview.', meta, onRemoveTable, activeIndex: controlledActiveIndex, onActiveIndexChange, maxHeight = 200, maxRows = 12, compact = true, hideRowCount = false, }) => { const previewTables = tables ?? (table ? [table] : null); const [internalActiveIndex, setInternalActiveIndex] = useState(0); const activeIndex = controlledActiveIndex !== undefined ? controlledActiveIndex : internalActiveIndex; const setActiveIndex = onActiveIndexChange || setInternalActiveIndex; useEffect(() => { if (!previewTables || previewTables.length === 0) { if (onActiveIndexChange) { onActiveIndexChange(0); } else { setInternalActiveIndex(0); } return; } if (activeIndex > previewTables.length - 1) { const newIndex = previewTables.length - 1; if (onActiveIndexChange) { onActiveIndexChange(newIndex); } else { setInternalActiveIndex(newIndex); } } }, [previewTables, activeIndex, onActiveIndexChange]); const activeTable = previewTables && previewTables.length > 0 ? previewTables[activeIndex] : null; return ( <Box sx={{ p: 1, display: 'flex', flexDirection: 'column', gap: 1, minHeight: 120, }} > {loading && <LinearProgress />} {error && ( <Typography variant="caption" color="error"> {error} </Typography> )} {!loading && !error && (!previewTables || previewTables.length === 0) && ( <Typography variant="caption" color="text.secondary"> {emptyLabel} </Typography> )} {previewTables && previewTables.length > 0 && ( <Box> {/* Table selection chips */} <Box sx={{ display: 'flex', alignItems: 'center', flexWrap: 'wrap', gap: 0.25, mb: 0.5, pb: 0.25, }} > <Typography variant="caption" sx={{ mx: 0.5 }}> Preview </Typography> {previewTables.map((t, idx) => { const label = t.displayId || t.id; const isSelected = idx === activeIndex; return ( <Tooltip key={`${t.id}-${idx}`} title={label} placement="top" arrow> <Chip label={label} size="small" onClick={() => { if (onActiveIndexChange) { onActiveIndexChange(idx); } else { setInternalActiveIndex(idx); } }} sx={{ borderRadius: 1, cursor: 'pointer', maxWidth: 160, backgroundColor: (theme) => isSelected ? alpha(theme.palette.primary.main, 0.12) : 'transparent', borderColor: (theme) => isSelected ? alpha(theme.palette.primary.main, 0.5) : undefined, color: (theme) => isSelected ? theme.palette.primary.main : theme.palette.text.secondary, '& .MuiChip-label': { fontSize: '0.625rem', overflow: 'hidden', textOverflow: 'ellipsis', whiteSpace: 'nowrap', maxWidth: 140, }, '&:hover': { backgroundColor: (theme) => isSelected ? alpha(theme.palette.primary.main, 0.16) : alpha(theme.palette.primary.main, 0.08), }, }} /> </Tooltip> ); })} {onRemoveTable && ( <Tooltip title="Remove table" placement="top" arrow> <IconButton size="small" color="error" onClick={() => onRemoveTable(activeIndex)} sx={{ ml: 'auto', flexShrink: 0 }} aria-label="Remove table" > <DeleteIcon fontSize="small" /> </IconButton> </Tooltip> )} </Box> {activeTable && ( <Box> <Card variant="outlined" sx={{ pb: 0.5 }}> <CustomReactTable rows={activeTable.rows.slice(0, maxRows)} columnDefs={activeTable.names.map(name => ({ id: name, label: name, minWidth: 60, }))} rowsPerPageNum={-1} compact={compact} isIncompleteTable={activeTable.rows.length > maxRows} maxHeight={maxHeight} /> </Card> {!hideRowCount && ( <Typography variant="caption" color="text.secondary"> {activeTable.rows.length} rows × {activeTable.names.length} columns </Typography> )} </Box> )} {meta && ( <Typography variant="caption" color="text.secondary" sx={{ mt: 0.5, display: 'block' }}> {meta} </Typography> )} </Box> )} </Box> ); }; export default MultiTablePreview; ================================================ FILE: src/views/OperatorCard.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { FC } from 'react' import { useDrag } from 'react-dnd' import '../scss/DraggableCard.scss'; import { useTheme } from '@mui/material/styles'; import { Card, Box, Typography, } from '@mui/material'; import React from 'react'; export interface OperatorCardProp { operator: string } export const OperatorCard: FC<OperatorCardProp> = function OperatorCard({ operator }) { // concept cards are draggable cards that can be dropped into encoding shelf let theme = useTheme(); const [{ isDragging }, drag] = useDrag(() => ({ type: "operator-card", item: { type: 'operator-card', operator, source: "conceptShelf" }, collect: (monitor) => ({ isDragging: monitor.isDragging(), handlerId: monitor.getHandlerId(), }), })); let opacity = isDragging ? 0.4 : 1; let fontStyle = "inherit"; let border = "hidden"; const cursorStyle = isDragging ? "grabbing" : "grab"; let backgroundColor = theme.palette.secondary.light; let cardComponent = ( <Card sx={{ minWidth: 80, backgroundColor, width: 'calc(50% - 6px)' }} variant="outlined" style={{ opacity, border, fontStyle, marginLeft: '3px', }} color="secondary" className={`data-field-list-item draggable-card `}> <Box ref={drag} sx={{ cursor: cursorStyle, background: 'rgba(255, 255, 255, 0.93)'}} className={`draggable-card-header draggable-card-inner`}> <Typography className="draggable-card-title" sx={{ marginLeft: '6px !important', fontSize: 12, height: 24, width: "100%", fontStyle: 'italic' }} component={'span'} gutterBottom> {operator} </Typography> </Box> </Card> ) return cardComponent; } ================================================ FILE: src/views/ReactTable.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import * as React from 'react'; import Table from '@mui/material/Table'; import TableBody from '@mui/material/TableBody'; import TableCell from '@mui/material/TableCell'; import TableContainer from '@mui/material/TableContainer'; import TableHead from '@mui/material/TableHead'; import TablePagination from '@mui/material/TablePagination'; import TableRow from '@mui/material/TableRow'; import { alpha, Box, useTheme } from '@mui/system'; import Typography from '@mui/material/Typography'; export interface ColumnDef { id: string; label: string; minWidth?: number; align?: 'right'; source?: 'original' | 'custom' ; format?: (value: number) => string; } interface CustomReactTableProps { rows: any[]; columnDefs: ColumnDef[]; rowsPerPageNum: number; compact: boolean; maxCellWidth? : number; isIncompleteTable?: boolean; maxHeight?: number; } export const CustomReactTable: React.FC<CustomReactTableProps> = ({ rows, columnDefs, rowsPerPageNum, compact, maxCellWidth, isIncompleteTable, maxHeight = 340 }) => { let theme = useTheme(); const [page, setPage] = React.useState(0); const [rowsPerPage, setRowsPerPage] = React.useState(rowsPerPageNum == -1 ? (rows.length > 500 ? 100 : rows.length) : rowsPerPageNum); const handleChangePage = (event: unknown, newPage: number) => { setPage(newPage); }; const handleChangeRowsPerPage = (event: React.ChangeEvent<HTMLInputElement>) => { setRowsPerPage(+event.target.value); setPage(0); }; return ( <Box className="table-container table-container-small" sx={{ width: '100%', "& .MuiTableCell-root": { fontSize: 10, maxWidth: maxCellWidth || "60px", padding: compact ? "2px 4px" : "6px", overflow: "clip", textOverflow: "ellipsis", whiteSpace: "nowrap" } }}> <TableContainer sx={{ maxHeight: maxHeight }}> <Table stickyHeader aria-label="sticky table"> <TableHead> <TableRow> {columnDefs.map((column, i) => { let backgroundColor = "none"; let borderBottomColor = theme.palette.primary.main; if (column.source == "custom") { backgroundColor = alpha(theme.palette.custom.main, 0.05); borderBottomColor = theme.palette.custom.main; } return <TableCell key={column.id} align={column.align} sx={{ minWidth: column.minWidth, fontSize: 12, color: "#333", backgroundColor: backgroundColor, borderBottomColor, borderBottomWidth: '1px', borderBottomStyle: 'solid' }} > {column.label} </TableCell> }) } </TableRow> </TableHead> <TableBody> {rows.slice(page * rowsPerPage, page * rowsPerPage + rowsPerPage) .map((row, i) => { return ( <TableRow hover tabIndex={-1} key={i} sx={{ background: i % 2 == 0 ? '#F0F0F0' : "none" }}> {columnDefs.map((column, j) => { const value = row[column.id]; let backgroundColor = "none"; if (column.source == "custom") { backgroundColor = alpha(theme.palette.custom.main, 0.05); } return ( <TableCell key={column.id} align={column.align} sx={{ backgroundColor }}> {column.format ? column.format(value) : (typeof value === "boolean" ? `${value}` : value)} </TableCell> ); })} </TableRow> ); })} {isIncompleteTable && ( <TableRow> {columnDefs.map((column, i) => ( <TableCell key={i} sx={{padding: 0}} align="left"> ...... </TableCell> ))} </TableRow> )} </TableBody> </Table> </TableContainer> {rowsPerPage < rows.length ? <TablePagination sx={{ "color": "gray", "& .MuiInputBase-root": { fontSize: 10 }, "& .MuiTablePagination-selectLabel": { fontSize: 10 }, "& .MuiTablePagination-displayedRows": { fontSize: 10 }, "& .MuiButtonBase-root": { padding: 0 }, "& .MuiToolbar-root": { minHeight: 12, height: 18}, "& .MuiTablePagination-toolbar": { paddingRight: 0 }, "& .MuiSvgIcon-root": { fontSize: '1rem' } }} SelectProps={{ MenuProps: { sx: { '.MuiPaper-root': {}, '.MuiTablePagination-menuItem': { fontSize: 12 }, }, } }} rowsPerPageOptions={[10]} component="div" count={rows.length} rowsPerPage={rowsPerPage} page={page} onPageChange={handleChangePage} showFirstButton showLastButton //onRowsPerPageChange={handleChangeRowsPerPage} /> : ""} </Box> ); } ================================================ FILE: src/views/RefreshDataDialog.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React, { useState, useCallback, useRef } from 'react'; import { borderColor, transition, radius } from '../app/tokens'; import { Dialog, DialogTitle, DialogContent, DialogActions, Button, IconButton, Typography, Box, TextField, Tabs, Tab, LinearProgress, Input, Link, alpha, useTheme, } from '@mui/material'; import CloseIcon from '@mui/icons-material/Close'; import UploadFileIcon from '@mui/icons-material/UploadFile'; import { useSelector } from 'react-redux'; import { DataFormulatorState } from '../app/dfSlice'; import { DictTable } from '../components/ComponentType'; import { createTableFromText, loadTextDataWrapper, loadBinaryDataWrapper } from '../data/utils'; interface TabPanelProps { children?: React.ReactNode; index: number; value: number; } function TabPanel(props: TabPanelProps) { const { children, value, index, ...other } = props; return ( <div role="tabpanel" hidden={value !== index} id={`refresh-tabpanel-${index}`} aria-labelledby={`refresh-tab-${index}`} {...other} > {value === index && <Box sx={{ p: 2.5, pt: 3 }}>{children}</Box>} </div> ); } export interface RefreshDataDialogProps { open: boolean; onClose: () => void; table: DictTable; onRefreshComplete: (newRows: any[]) => void; } export const RefreshDataDialog: React.FC<RefreshDataDialogProps> = ({ open, onClose, table, onRefreshComplete, }) => { const theme = useTheme(); const [tabValue, setTabValue] = useState(0); const [pasteContent, setPasteContent] = useState(''); const [urlContent, setUrlContent] = useState(''); const [isLoading, setIsLoading] = useState(false); const [error, setError] = useState<string | null>(null); const fileInputRef = useRef<HTMLInputElement>(null); const serverConfig = useSelector((state: DataFormulatorState) => state.serverConfig); // Constants for content size limits const MAX_DISPLAY_LINES = 20; const LARGE_CONTENT_THRESHOLD = 50000; const MAX_CONTENT_SIZE = 2 * 1024 * 1024; // 2MB const [displayContent, setDisplayContent] = useState(''); const [isLargeContent, setIsLargeContent] = useState(false); const [showFullContent, setShowFullContent] = useState(false); const [isOverSizeLimit, setIsOverSizeLimit] = useState(false); const validateColumns = (newRows: any[]): { valid: boolean; message: string } => { if (!newRows || newRows.length === 0) { return { valid: false, message: 'No data found in the uploaded content.' }; } const newColumns = Object.keys(newRows[0]).sort(); const existingColumns = [...table.names].sort(); if (newColumns.length !== existingColumns.length) { return { valid: false, message: `Column count mismatch. Expected ${existingColumns.length} columns (${existingColumns.join(', ')}), but got ${newColumns.length} columns (${newColumns.join(', ')}).`, }; } const missingColumns = existingColumns.filter(col => !newColumns.includes(col)); const extraColumns = newColumns.filter(col => !existingColumns.includes(col)); if (missingColumns.length > 0 || extraColumns.length > 0) { let message = 'Column names do not match.'; if (missingColumns.length > 0) { message += ` Missing: ${missingColumns.join(', ')}.`; } if (extraColumns.length > 0) { message += ` Unexpected: ${extraColumns.join(', ')}.`; } return { valid: false, message }; } return { valid: true, message: '' }; }; const processAndValidateData = (newRows: any[]): boolean => { const validation = validateColumns(newRows); if (!validation.valid) { setError(validation.message); return false; } setError(null); onRefreshComplete(newRows); handleClose(); return true; }; const handleClose = () => { setPasteContent(''); setUrlContent(''); setDisplayContent(''); setError(null); setIsLoading(false); setIsLargeContent(false); setShowFullContent(false); setIsOverSizeLimit(false); onClose(); }; const handleTabChange = (_event: React.SyntheticEvent, newValue: number) => { setTabValue(newValue); setError(null); }; // Handle paste content change with optimization for large content const handlePasteContentChange = useCallback((event: React.ChangeEvent<HTMLInputElement>) => { const newContent = event.target.value; setPasteContent(newContent); const contentSizeBytes = new Blob([newContent]).size; const isOverLimit = contentSizeBytes > MAX_CONTENT_SIZE; setIsOverSizeLimit(isOverLimit); const isLarge = newContent.length > LARGE_CONTENT_THRESHOLD; setIsLargeContent(isLarge); if (isLarge && !showFullContent) { const lines = newContent.split('\n'); const previewLines = lines.slice(0, MAX_DISPLAY_LINES); const preview = previewLines.join('\n') + (lines.length > MAX_DISPLAY_LINES ? '\n... (truncated for performance)' : ''); setDisplayContent(preview); } else { setDisplayContent(newContent); } }, [showFullContent]); const toggleFullContent = useCallback(() => { setShowFullContent(!showFullContent); if (!showFullContent) { setDisplayContent(pasteContent); } else { const lines = pasteContent.split('\n'); const previewLines = lines.slice(0, MAX_DISPLAY_LINES); const preview = previewLines.join('\n') + (lines.length > MAX_DISPLAY_LINES ? '\n... (truncated for performance)' : ''); setDisplayContent(preview); } }, [showFullContent, pasteContent]); // Handle paste submit const handlePasteSubmit = () => { if (!pasteContent.trim()) { setError('Please paste some data.'); return; } setIsLoading(true); try { let newRows: any[] = []; try { const jsonContent = JSON.parse(pasteContent); if (Array.isArray(jsonContent)) { newRows = jsonContent; } else { setError('JSON content must be an array of objects.'); setIsLoading(false); return; } } catch { // Try parsing as CSV/TSV const tempTable = createTableFromText('temp', pasteContent); if (tempTable) { newRows = tempTable.rows; } else { setError('Could not parse the pasted content as JSON or CSV/TSV.'); setIsLoading(false); return; } } processAndValidateData(newRows); } catch (err) { setError('Failed to parse the pasted content.'); } finally { setIsLoading(false); } }; // Handle URL submit const handleUrlSubmit = () => { if (!urlContent.trim()) { setError('Please enter a URL.'); return; } const hasValidSuffix = urlContent.endsWith('.csv') || urlContent.endsWith('.tsv') || urlContent.endsWith('.json'); if (!hasValidSuffix) { setError('URL must point to a .csv, .tsv, or .json file.'); return; } setIsLoading(true); fetch(urlContent) .then(res => res.text()) .then(content => { let newRows: any[] = []; try { const jsonContent = JSON.parse(content); if (Array.isArray(jsonContent)) { newRows = jsonContent; } else { setError('JSON content must be an array of objects.'); setIsLoading(false); return; } } catch { const tempTable = createTableFromText('temp', content); if (tempTable) { newRows = tempTable.rows; } else { setError('Could not parse the URL content as JSON or CSV/TSV.'); setIsLoading(false); return; } } processAndValidateData(newRows); }) .catch(err => { setError(`Failed to fetch data from URL: ${err.message}`); }) .finally(() => { setIsLoading(false); }); }; // Handle file upload const handleFileUpload = (event: React.ChangeEvent<HTMLInputElement>) => { const files = event.target.files; if (!files || files.length === 0) return; const file = files[0]; setIsLoading(true); if (file.type === 'text/csv' || file.type === 'text/tab-separated-values' || file.type === 'application/json' || file.name.endsWith('.csv') || file.name.endsWith('.tsv') || file.name.endsWith('.json')) { const MAX_FILE_SIZE = 5 * 1024 * 1024; if (file.size > MAX_FILE_SIZE) { setError(`File is too large (${(file.size / (1024 * 1024)).toFixed(2)}MB). Maximum size is 5MB.`); setIsLoading(false); return; } file.text().then((text) => { let newRows: any[] = []; try { const jsonContent = JSON.parse(text); if (Array.isArray(jsonContent)) { newRows = jsonContent; } else { setError('JSON content must be an array of objects.'); setIsLoading(false); return; } } catch { const tempTable = loadTextDataWrapper('temp', text, file.type); if (tempTable) { newRows = tempTable.rows; } else { setError('Could not parse the file content.'); setIsLoading(false); return; } } processAndValidateData(newRows); }).catch(err => { setError(`Failed to read file: ${err.message}`); }).finally(() => { setIsLoading(false); }); } else if (file.type === 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet' || file.type === 'application/vnd.ms-excel' || file.name.endsWith('.xlsx') || file.name.endsWith('.xls')) { const reader = new FileReader(); reader.onload = async (e) => { const arrayBuffer = e.target?.result as ArrayBuffer; if (arrayBuffer) { try { const tables = await loadBinaryDataWrapper('temp', arrayBuffer); if (tables.length > 0) { processAndValidateData(tables[0].rows); } else { setError('Failed to parse Excel file.'); } } catch (err) { setError('Failed to parse Excel file.'); } } setIsLoading(false); }; reader.readAsArrayBuffer(file); } else { setError('Unsupported file format. Please use CSV, TSV, JSON, or Excel files.'); setIsLoading(false); } // Reset file input if (fileInputRef.current) { fileInputRef.current.value = ''; } }; const hasValidUrlSuffix = urlContent.endsWith('.csv') || urlContent.endsWith('.tsv') || urlContent.endsWith('.json'); return ( <Dialog open={open} onClose={handleClose} maxWidth="md" fullWidth sx={{ '& .MuiDialog-paper': { minHeight: 500 } }} > <DialogTitle sx={{ display: 'flex', alignItems: 'center', pb: 1 }}> <Typography variant="h6" component="span"> Refresh Data for "{table.displayId || table.id}" </Typography> <IconButton sx={{ marginLeft: 'auto' }} size="small" onClick={handleClose} > <CloseIcon fontSize="small" /> </IconButton> </DialogTitle> <DialogContent sx={{ p: 0 }}> <Box sx={{ px: 3, pt: 2.5, pb: 1.5 }}> <Typography variant="caption" sx={{ display: 'block', color: 'text.secondary', fontSize: '0.75rem', mb: 2, lineHeight: 1.5 }} > Upload new data to replace the current table content. Required columns: <strong style={{ color: 'inherit' }}>{table.names.join(', ')}</strong> </Typography> {error && ( <Box sx={{ mb: 1.5, p: 1, backgroundColor: alpha(theme.palette.error.main, 0.08), borderLeft: `3px solid ${theme.palette.error.main}`, borderRadius: '4px', }} > <Typography variant="caption" sx={{ color: 'error.main', fontSize: '0.75rem', lineHeight: 1.5 }}> {error} </Typography> </Box> )} {isLoading && <LinearProgress sx={{ mb: 2, height: 2 }} />} </Box> <Tabs value={tabValue} onChange={handleTabChange} sx={{ borderBottom: `1px solid ${borderColor.divider}`, px: 3 }}> <Tab label="Paste Data" sx={{ textTransform: 'none', fontSize: '0.875rem', minHeight: 48 }} /> <Tab label="Upload File" sx={{ textTransform: 'none', fontSize: '0.875rem', minHeight: 48 }} /> <Tab label="From URL" sx={{ textTransform: 'none', fontSize: '0.875rem', minHeight: 48 }} /> </Tabs> <TabPanel value={tabValue} index={0}> <Box sx={{ display: 'flex', flexDirection: 'column', gap: 1.5 }}> {isOverSizeLimit && ( <Box sx={{ p: 1, backgroundColor: alpha(theme.palette.error.main, 0.06), borderLeft: `3px solid ${theme.palette.error.main}`, borderRadius: '4px', }} > <Typography variant="caption" sx={{ color: 'error.main', fontSize: '0.75rem', lineHeight: 1.5 }}> Content exceeds {(MAX_CONTENT_SIZE / (1024 * 1024)).toFixed(0)}MB limit ({(new Blob([pasteContent]).size / (1024 * 1024)).toFixed(2)}MB) </Typography> </Box> )} {isLargeContent && !isOverSizeLimit && ( <Box sx={{ display: 'flex', alignItems: 'center', gap: 1.5, p: 1, backgroundColor: alpha(theme.palette.text.secondary, 0.04), borderRadius: 1, border: `1px solid ${borderColor.divider}` }}> <Typography variant="caption" sx={{ flex: 1, fontSize: '0.75rem', color: 'text.secondary' }}> Large content ({Math.round(pasteContent.length / 1000)}KB) • {showFullContent ? 'Full view' : 'Preview'} </Typography> <Button size="small" variant="text" onClick={toggleFullContent} sx={{ textTransform: 'none', minWidth: 'auto', fontSize: '0.75rem', color: 'text.secondary', '&:hover': { backgroundColor: alpha(theme.palette.text.secondary, 0.08), } }} > {showFullContent ? 'Preview' : 'Full'} </Button> </Box> )} <TextField autoFocus multiline fullWidth value={displayContent} onChange={handlePasteContentChange} placeholder="Paste your data here (CSV, TSV, or JSON format)" disabled={isLoading} sx={{ '& .MuiInputBase-root': { minHeight: 240, alignItems: 'flex-start', }, '& .MuiInputBase-input': { fontSize: 12, fontFamily: 'monospace', lineHeight: 1.5, } }} /> </Box> </TabPanel> <TabPanel value={tabValue} index={1}> {serverConfig.DISABLE_FILE_UPLOAD ? ( <Box sx={{ textAlign: 'center', py: 6 }}> <Typography color="text.secondary" sx={{ mb: 1.5, fontSize: '0.875rem' }}> File upload is disabled in this environment. </Typography> <Typography variant="body2" color="text.secondary" sx={{ fontSize: '0.75rem' }}> Install Data Formulator locally to enable file upload. <br /> <Link href="https://github.com/microsoft/data-formulator" target="_blank" rel="noopener noreferrer" sx={{ fontSize: '0.75rem' }} > https://github.com/microsoft/data-formulator </Link> </Typography> </Box> ) : ( <Box sx={{ display: 'flex', flexDirection: 'column', gap: 2 }}> <Input inputProps={{ accept: '.csv,.tsv,.json,.xlsx,.xls' }} type="file" sx={{ display: 'none' }} inputRef={fileInputRef} onChange={handleFileUpload} /> <Box sx={{ border: '2px dashed', borderColor: borderColor.divider, borderRadius: radius.md, p: 4, textAlign: 'center', cursor: 'pointer', transition: transition.normal, '&:hover': { borderColor: 'primary.main', backgroundColor: alpha(theme.palette.primary.main, 0.04), } }} onClick={() => fileInputRef.current?.click()} > <UploadFileIcon sx={{ fontSize: 40, color: 'text.secondary', mb: 1.5 }} /> <Typography variant="subtitle1" gutterBottom sx={{ fontSize: '0.9375rem', fontWeight: 500 }}> Drag & drop file here </Typography> <Typography variant="body2" color="text.secondary" sx={{ fontSize: '0.8125rem', mb: 0.5 }}> or <Link component="button" sx={{ textDecoration: 'underline', cursor: 'pointer' }}>Browse</Link> </Typography> <Typography variant="caption" color="text.secondary" sx={{ fontSize: '0.75rem' }}> Supported: CSV, TSV, JSON, Excel (xlsx, xls) </Typography> </Box> </Box> )} </TabPanel> <TabPanel value={tabValue} index={2}> <TextField fullWidth placeholder="Load a CSV, TSV, or JSON file from a URL, e.g. https://example.com/data.json" value={urlContent} onChange={(e) => setUrlContent(e.target.value.trim())} disabled={isLoading} error={urlContent !== '' && !hasValidUrlSuffix} helperText={urlContent !== '' && !hasValidUrlSuffix ? 'URL should link to a .csv, .tsv, or .json file' : ''} size="small" sx={{ '& .MuiInputBase-input': { fontSize: '0.875rem', }, '& .MuiInputBase-input::placeholder': { fontSize: '0.875rem', }, '& .MuiFormHelperText-root': { fontSize: '0.75rem', }, }} /> </TabPanel> </DialogContent> <DialogActions sx={{ px: 3, py: 2, gap: 1 }}> <Button onClick={handleClose} disabled={isLoading} sx={{ textTransform: 'none' }} > Cancel </Button> {tabValue === 0 && ( <Button variant="contained" onClick={handlePasteSubmit} disabled={isLoading || !pasteContent.trim() || isOverSizeLimit} sx={{ textTransform: 'none' }} > Refresh Data </Button> )} {tabValue === 2 && ( <Button variant="contained" onClick={handleUrlSubmit} disabled={isLoading || !urlContent.trim() || !hasValidUrlSuffix} sx={{ textTransform: 'none' }} > Refresh Data </Button> )} </DialogActions> </Dialog> ); }; ================================================ FILE: src/views/ReportView.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React, { FC, useState, useRef, useEffect, memo, useMemo } from 'react'; import { borderColor, shadow, transition, radius } from '../app/tokens'; import { Box, Button, Typography, Checkbox, IconButton, Card, CardContent, CircularProgress, Alert, Link, Divider, Paper, TableBody, TableCell, TableContainer, TableHead, TableRow, ToggleButton, ToggleButtonGroup, Tooltip, useTheme, alpha, } from '@mui/material'; import Masonry from '@mui/lab/Masonry'; import ArrowForwardIcon from '@mui/icons-material/ArrowForward'; import ArrowBackIcon from '@mui/icons-material/ArrowBack'; import CreateChartifact from '@mui/icons-material/Description'; import EditIcon from '@mui/icons-material/Edit'; import HelpOutlineIcon from '@mui/icons-material/HelpOutline'; import HistoryIcon from '@mui/icons-material/History'; import DeleteIcon from '@mui/icons-material/Delete'; import ShareIcon from '@mui/icons-material/Share'; import CheckCircleIcon from '@mui/icons-material/CheckCircle'; import html2canvas from 'html2canvas'; import { useDispatch, useSelector } from 'react-redux'; import { DataFormulatorState, dfActions, dfSelectors, GeneratedReport } from '../app/dfSlice'; import { Message } from './MessageSnackbar'; import { getUrls, assembleVegaChart, getTriggers, prepVisTable, fetchWithIdentity } from '../app/utils'; import { MuiMarkdown, getOverrides } from 'mui-markdown'; import embed from 'vega-embed'; import { getDataTable } from './VisualizationView'; import { DictTable } from '../components/ComponentType'; import { AppDispatch } from '../app/store'; import { Collapse } from '@mui/material'; import ExpandMoreIcon from '@mui/icons-material/ExpandMore'; import ExpandLessIcon from '@mui/icons-material/ExpandLess'; import { convertToChartifact, openChartifactViewer } from './ChartifactDialog'; import { StreamIcon } from '../icons'; // Typography constants const FONT_FAMILY_SYSTEM = '-apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, "Apple Color Emoji", Arial, sans-serif, "Segoe UI Emoji", "Segoe UI Symbol"'; const FONT_FAMILY_SERIF = 'Georgia, Cambria, "Times New Roman", Times, serif'; const FONT_FAMILY_MONO = '"SFMono-Regular", Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace'; // Color constants const COLOR_HEADING = 'rgb(37, 37, 37)'; const COLOR_BODY = 'rgb(55, 53, 47)'; const COLOR_MUTED = 'rgb(73, 73, 73)'; const COLOR_BG_LIGHT = 'rgba(247, 246, 243, 1)'; // Social post style constants (Twitter/X style) const COLOR_SOCIAL_TEXT = 'rgb(15, 20, 25)'; const COLOR_SOCIAL_BORDER = 'rgb(207, 217, 222)'; const COLOR_SOCIAL_ACCENT = 'rgb(29, 155, 240)'; // Executive summary style constants (professional/business look) const COLOR_EXEC_TEXT = 'rgb(33, 37, 41)'; const COLOR_EXEC_HEADING = 'rgb(20, 24, 28)'; const COLOR_EXEC_BORDER = 'rgb(108, 117, 125)'; const COLOR_EXEC_ACCENT = 'rgb(0, 123, 255)'; const COLOR_EXEC_BG = 'rgb(248, 249, 250)'; const HEADING_BASE = { fontFamily: FONT_FAMILY_SYSTEM, color: COLOR_HEADING, fontWeight: 700, letterSpacing: '-0.01em', }; const BODY_TEXT_BASE = { fontFamily: FONT_FAMILY_SYSTEM, fontSize: '0.9375rem', lineHeight: 1.75, fontWeight: 400, letterSpacing: '0.003em', color: COLOR_BODY, }; const TABLE_CELL_BASE = { fontFamily: FONT_FAMILY_SYSTEM, fontSize: '0.875rem', py: 1.5, px: 2, }; // Notion-style markdown overrides with MUI components const notionStyleMarkdownOverrides = { ...getOverrides(), h1: { component: Typography, props: { variant: 'h4', gutterBottom: true, sx: { ...HEADING_BASE, fontSize: '1.75rem', lineHeight: 1.25, letterSpacing: '-0.02em', pb: 0.5, mb: 3, mt: 4 } } }, h2: { component: Typography, props: { variant: 'h5', gutterBottom: true, sx: { ...HEADING_BASE, fontSize: '1.5rem', lineHeight: 1.3, pb: 0.5, mb: 2.5, mt: 3.5 } } }, h3: { component: Typography, props: { variant: 'h6', gutterBottom: true, sx: { ...HEADING_BASE, fontWeight: 600, fontSize: '1.25rem', lineHeight: 1.4, letterSpacing: '-0.005em', mb: 2, mt: 3 } } }, h4: { component: Typography, props: { variant: 'h6', gutterBottom: true, sx: { ...HEADING_BASE, fontWeight: 600, fontSize: '1.125rem', lineHeight: 1.4, mb: 1.5, mt: 2.5 } } }, h5: { component: Typography, props: { variant: 'subtitle1', gutterBottom: true, sx: { ...HEADING_BASE, fontWeight: 600, fontSize: '1rem', lineHeight: 1.5, mb: 1.5, mt: 2 } } }, h6: { component: Typography, props: { variant: 'subtitle2', gutterBottom: true, sx: { ...HEADING_BASE, fontWeight: 600, fontSize: '0.9375rem', lineHeight: 1.5, mb: 1.5, mt: 2 } } }, p: { component: Typography, props: { variant: 'body2', paragraph: true, sx: { ...BODY_TEXT_BASE, mb: 1.75 } } }, a: { component: Link, props: { underline: 'hover' as const, color: 'primary' as const, sx: { fontSize: 'inherit', fontWeight: 500 } } }, ul: { component: 'ul', props: { style: { paddingLeft: '1.8em', marginTop: '0.75em', marginBottom: '1.5em', fontFamily: FONT_FAMILY_SYSTEM } } }, ol: { component: 'ol', props: { style: { paddingLeft: '1.8em', marginTop: '0.75em', marginBottom: '1.5em', fontFamily: FONT_FAMILY_SYSTEM } } }, li: { component: Typography, props: { component: 'li', variant: 'body1', sx: { ...BODY_TEXT_BASE, mb: 0.5 } } }, blockquote: { component: Box, props: { sx: { borderLeft: '3px solid', borderColor: borderColor.component, pl: 2.5, py: 1, my: 2.5, fontFamily: FONT_FAMILY_SERIF, fontStyle: 'italic', color: COLOR_MUTED, fontSize: '1rem', lineHeight: 1.7 } } }, pre: { component: Paper, props: { elevation: 0, sx: { backgroundColor: COLOR_BG_LIGHT, p: 2, borderRadius: '4px', overflow: 'auto', my: 2, border: `1px solid ${borderColor.divider}`, '& code': { backgroundColor: 'transparent !important', padding: '0 !important', fontSize: '0.8125rem', fontFamily: FONT_FAMILY_MONO, lineHeight: 1.7, color: COLOR_BODY } } } }, table: { component: TableContainer, props: { component: Paper, elevation: 0, sx: { my: 2, border: `1px solid ${borderColor.divider}` } } }, thead: { component: TableHead, props: { sx: { backgroundColor: COLOR_BG_LIGHT } } }, tbody: { component: TableBody }, tr: { component: TableRow }, th: { component: TableCell, props: { sx: { ...TABLE_CELL_BASE, fontWeight: 600, borderBottom: `2px solid ${borderColor.divider}` } } }, td: { component: TableCell, props: { sx: { ...TABLE_CELL_BASE, borderBottom: `1px solid ${borderColor.divider}`, lineHeight: 1.6 } } }, hr: { component: Divider, props: { sx: { my: 3 } } } } as any; // Social post style markdown overrides (X/Twitter style) const socialStyleMarkdownOverrides = { ...notionStyleMarkdownOverrides, h1: { component: Typography, props: { variant: 'h6', gutterBottom: true, sx: { fontFamily: FONT_FAMILY_SYSTEM, fontWeight: 700, fontSize: '1.125rem', lineHeight: 1.25, color: COLOR_SOCIAL_TEXT, mb: 1.5, mt: 1.5 } } }, h2: { component: Typography, props: { variant: 'h6', gutterBottom: true, sx: { fontFamily: FONT_FAMILY_SYSTEM, fontWeight: 700, fontSize: '1rem', lineHeight: 1.25, color: COLOR_SOCIAL_TEXT, mb: 1.25, mt: 1.5 } } }, h3: { component: Typography, props: { variant: 'subtitle1', gutterBottom: true, sx: { fontFamily: FONT_FAMILY_SYSTEM, fontWeight: 600, fontSize: '0.9375rem', lineHeight: 1.3, color: COLOR_SOCIAL_TEXT, mb: 1, mt: 1.25 } } }, p: { component: Typography, props: { variant: 'body2', paragraph: true, sx: { fontFamily: FONT_FAMILY_SYSTEM, fontSize: '0.875rem', lineHeight: 1.4, fontWeight: 400, mb: 0.75, color: COLOR_SOCIAL_TEXT } } }, li: { component: Typography, props: { component: 'li', variant: 'body2', sx: { fontFamily: FONT_FAMILY_SYSTEM, fontSize: '0.875rem', lineHeight: 1.4, fontWeight: 400, mb: 0.25, color: COLOR_SOCIAL_TEXT } } } } as any; // Executive summary style markdown overrides (compact serif styling) const executiveSummaryMarkdownOverrides = { ...getOverrides(), h1: { component: Typography, props: { variant: 'h5', gutterBottom: true, sx: { fontFamily: FONT_FAMILY_SERIF, fontWeight: 700, fontSize: '1.25rem', lineHeight: 1.3, color: COLOR_EXEC_HEADING, mb: 2, mt: 2.5 } } }, h2: { component: Typography, props: { variant: 'h6', gutterBottom: true, sx: { fontFamily: FONT_FAMILY_SERIF, fontWeight: 600, fontSize: '1.125rem', lineHeight: 1.3, color: COLOR_EXEC_HEADING, mb: 1.5, mt: 2 } } }, h3: { component: Typography, props: { variant: 'h6', gutterBottom: true, sx: { fontFamily: FONT_FAMILY_SERIF, fontWeight: 600, fontSize: '1rem', lineHeight: 1.4, color: COLOR_EXEC_HEADING, mb: 1.25, mt: 1.5 } } }, h4: { component: Typography, props: { variant: 'subtitle1', gutterBottom: true, sx: { fontFamily: FONT_FAMILY_SERIF, fontWeight: 600, fontSize: '0.9375rem', lineHeight: 1.4, color: COLOR_EXEC_HEADING, mb: 1, mt: 1.5 } } }, p: { component: Typography, props: { variant: 'body2', paragraph: true, sx: { fontFamily: FONT_FAMILY_SERIF, fontSize: '0.875rem', lineHeight: 1.5, fontWeight: 400, color: COLOR_EXEC_TEXT, mb: 1.25, textAlign: 'justify' } } }, a: { component: Link, props: { underline: 'hover' as const, color: 'primary' as const, sx: { fontSize: 'inherit', fontWeight: 500, color: COLOR_EXEC_ACCENT, '&:hover': { color: 'rgb(0, 86, 179)' } } } }, ul: { component: 'ul', props: { style: { paddingLeft: '1.5em', marginTop: '0.5em', marginBottom: '1em', fontFamily: FONT_FAMILY_SERIF } } }, ol: { component: 'ol', props: { style: { paddingLeft: '1.5em', marginTop: '0.5em', marginBottom: '1em', fontFamily: FONT_FAMILY_SERIF } } }, li: { component: Typography, props: { component: 'li', variant: 'body2', sx: { fontFamily: FONT_FAMILY_SERIF, fontSize: '0.875rem', lineHeight: 1.5, fontWeight: 400, color: COLOR_EXEC_TEXT, mb: 0.25 } } }, blockquote: { component: Box, props: { sx: { borderLeft: '2px solid', borderLeftColor: COLOR_EXEC_ACCENT, pl: 2, py: 1, my: 1.5, backgroundColor: COLOR_EXEC_BG, fontFamily: FONT_FAMILY_SERIF, fontStyle: 'italic', color: COLOR_EXEC_TEXT, fontSize: '0.875rem', lineHeight: 1.6 } } }, pre: { component: Paper, props: { elevation: 0, sx: { backgroundColor: COLOR_EXEC_BG, p: 1.5, borderRadius: '4px', overflow: 'auto', my: 1.5, '& code': { backgroundColor: 'transparent !important', padding: '0 !important', fontSize: '0.75rem', fontFamily: FONT_FAMILY_MONO, lineHeight: 1.5, color: COLOR_EXEC_TEXT } } } }, table: { component: TableContainer, props: { component: Paper, elevation: 0, sx: { my: 1.5, borderRadius: '4px' } } }, thead: { component: TableHead, props: { sx: { backgroundColor: COLOR_EXEC_BG } } }, tbody: { component: TableBody }, tr: { component: TableRow }, th: { component: TableCell, props: { sx: { fontFamily: FONT_FAMILY_SERIF, fontSize: '0.8125rem', py: 1, px: 1.5, fontWeight: 600, borderBottom: '1px solid', borderColor: COLOR_EXEC_BORDER, color: COLOR_EXEC_HEADING } } }, td: { component: TableCell, props: { sx: { fontFamily: FONT_FAMILY_SERIF, fontSize: '0.8125rem', py: 1, px: 1.5, borderBottom: '1px solid', borderColor: COLOR_EXEC_BORDER, lineHeight: 1.5, color: COLOR_EXEC_TEXT } } }, hr: { component: Divider, props: { sx: { my: 2, borderColor: COLOR_EXEC_BORDER } } } } as any; export const ReportView: FC = () => { // Get all generated reports from Redux state const dispatch = useDispatch<AppDispatch>(); const charts = useSelector((state: DataFormulatorState) => state.charts); const tables = useSelector((state: DataFormulatorState) => state.tables); const selectedModelId = useSelector((state: DataFormulatorState) => state.selectedModelId); const models = useSelector((state: DataFormulatorState) => state.models); const conceptShelfItems = useSelector((state: DataFormulatorState) => state.conceptShelfItems); const config = useSelector((state: DataFormulatorState) => state.config); const allGeneratedReports = useSelector(dfSelectors.getAllGeneratedReports); const serverConfig = useSelector((state: DataFormulatorState) => state.serverConfig); const focusedId = useSelector((state: DataFormulatorState) => state.focusedId); const focusedChartId = focusedId?.type === 'chart' ? focusedId.chartId : undefined; const theme = useTheme(); const [selectedChartIds, setSelectedChartIds] = useState<Set<string>>(new Set(focusedChartId ? [focusedChartId] : [])); const [previewImages, setPreviewImages] = useState<Map<string, { url: string; width: number; height: number }>>(new Map()); const [isLoadingPreviews, setIsLoadingPreviews] = useState(false); const [isGenerating, setIsGenerating] = useState(false); const [error, setError] = useState<string>(''); const [style, setStyle] = useState<string>('social post'); const [mode, setMode] = useState<'compose' | 'post'>(allGeneratedReports.length > 0 ? 'post' : 'compose'); // Local state for current report const [currentReportId, setCurrentReportId] = useState<string | undefined>(undefined); const [generatedReport, setGeneratedReport] = useState<string>(''); const [generatedStyle, setGeneratedStyle] = useState<string>('social post'); const [cachedReportImages, setCachedReportImages] = useState<Record<string, { url: string; width: number; height: number }>>({}); const [shareButtonSuccess, setShareButtonSuccess] = useState(false); const [hideTableOfContents, setHideTableOfContents] = useState(false); const updateCachedReportImages = (chartId: string, blobUrl: string, width: number, height: number) => { setCachedReportImages(prev => ({ ...prev, [chartId]: { url: blobUrl, width, height } })); }; // Helper function to show messages using dfSlice const showMessage = (message: string, type: 'success' | 'error' | 'info' | 'warning' = 'success') => { const msg: Message = { type, component: 'ReportView', timestamp: Date.now(), value: message }; dispatch(dfActions.addMessages(msg)); }; // Function to capture and share report as image const shareReportAsImage = async () => { if (!currentReportId) return; try { // Find the report content element const reportElement = document.querySelector('[data-report-content]') as HTMLElement; if (!reportElement) { showMessage('Could not find report content to capture', 'error'); return; } // Capture the report as canvas with extra padding for borders const canvas = await html2canvas(reportElement, { backgroundColor: '#ffffff', scale: 2, // Higher quality useCORS: true, allowTaint: true, scrollX: 0, scrollY: 0, // Add extra padding to ensure borders are captured width: reportElement.scrollWidth + 4, height: reportElement.scrollHeight + 4, logging: false // Disable console logs }); // Convert canvas to blob canvas.toBlob((blob: Blob | null) => { if (!blob) { showMessage('Failed to generate image', 'error'); return; } // Copy to clipboard if (navigator.clipboard && navigator.clipboard.write) { navigator.clipboard.write([ new ClipboardItem({ 'image/png': blob }) ]).then(() => { showMessage('Report image copied to clipboard! You can now paste it anywhere to share.'); setShareButtonSuccess(true); setTimeout(() => setShareButtonSuccess(false), 2000); }).catch(() => { showMessage('Failed to copy to clipboard. Your browser may not support this feature.', 'error'); }); } else { showMessage('Clipboard API not supported in your browser. Please use a modern browser.', 'error'); } }, 'image/png', 0.95); } catch (error) { console.error('Error generating report image:', error); showMessage('Failed to generate report image. Please try again.', 'error'); } }; // Update like this: const processReport = (rawReport: string): string => { const markdownMatch = rawReport.match(/```markdown\n([\s\S]*?)(?:\n```)?$/); let processed = markdownMatch ? markdownMatch[1] : rawReport; Object.entries(cachedReportImages).forEach(([chartId, { url, width, height }]) => { processed = processed.replace( new RegExp(`\\[IMAGE\\(${chartId}\\)\\]`, 'g'), `<img src="${url}" alt="Chart" width="${width}" height="${height}" />` ); }); return processed; }; const loadReport = (reportId: string) => { const report = allGeneratedReports.find(r => r.id === reportId); if (report) { setCurrentReportId(reportId); setGeneratedReport(report.content); setGeneratedStyle(report.style); // load / assemble chart images for the report report.selectedChartIds.forEach((chartId) => { const chart = charts.find(c => c.id === chartId); if (!chart) return null; const chartTable = tables.find(t => t.id === chart.tableRef); if (!chartTable) return null; if (chart.chartType === 'Table' || chart.chartType === '?') { return null; } getChartImageFromVega(chart, chartTable).then(({ blobUrl, width, height }) => { if (blobUrl) { // Use blob URL for local display and caching updateCachedReportImages(chart.id, blobUrl, width, height); } }); }); } }; useEffect(() => { if (currentReportId === undefined && allGeneratedReports.length > 0) { loadReport(allGeneratedReports[0].id); } }, [currentReportId]); // Auto-refresh chart images when underlying table data changes // This enables real-time chart updates in reports when data is streaming const tableRowSignaturesRef = useRef<Map<string, string>>(new Map()); useEffect(() => { if (!currentReportId || mode !== 'post') return; const currentReport = allGeneratedReports.find(r => r.id === currentReportId); if (!currentReport) return; // Get all tables referenced by the report's charts const reportChartIds = currentReport.selectedChartIds; const affectedTableIds = new Set<string>(); reportChartIds.forEach(chartId => { const chart = charts.find(c => c.id === chartId); if (chart) { affectedTableIds.add(chart.tableRef); } }); // Check if any affected tables have changed let hasChanges = false; affectedTableIds.forEach(tableId => { const table = tables.find(t => t.id === tableId); if (table) { // Use contentHash if available (set by state management), otherwise fallback to lightweight rowCount // This avoids expensive JSON.stringify operations on every table change during streaming updates const signature = table.contentHash || `${table.rows.length}`; const prevSignature = tableRowSignaturesRef.current.get(tableId); if (prevSignature && prevSignature !== signature) { hasChanges = true; } tableRowSignaturesRef.current.set(tableId, signature); } }); // If data changed, regenerate chart images for the report if (hasChanges) { reportChartIds.forEach(chartId => { const chart = charts.find(c => c.id === chartId); if (!chart) return; const chartTable = tables.find(t => t.id === chart.tableRef); if (!chartTable) return; if (chart.chartType === 'Table' || chart.chartType === '?') { return; } getChartImageFromVega(chart, chartTable).then(({ blobUrl, width, height }) => { if (blobUrl) { updateCachedReportImages(chart.id, blobUrl, width, height); } }); }); } }, [tables, currentReportId, mode, allGeneratedReports, charts]); // Sort charts based on data thread ordering const sortedCharts = useMemo(() => { // Create table order mapping (anchored tables get higher order) const tableOrder = Object.fromEntries( tables.map((table, index) => [ table.id, index + (table.anchored ? 1 : 0) * tables.length ]) ); // Get ancestor orders for a table const getAncestorOrders = (table: DictTable): number[] => { const triggers = getTriggers(table, tables); return [...triggers.map(t => tableOrder[t.tableId]), tableOrder[table.id]]; }; // Sort charts by their associated table's ancestor orders return [...charts].sort((chartA, chartB) => { const tableA = getDataTable(chartA, tables, charts, conceptShelfItems); const tableB = getDataTable(chartB, tables, charts, conceptShelfItems); const ordersA = getAncestorOrders(tableA); const ordersB = getAncestorOrders(tableB); // Compare orders element by element for (let i = 0; i < Math.min(ordersA.length, ordersB.length); i++) { if (ordersA[i] !== ordersB[i]) { return ordersA[i] - ordersB[i]; } } // If all orders are equal, compare by length return ordersA.length - ordersB.length; }); }, [charts, tables, conceptShelfItems]); // Clean up Blob URLs on unmount useEffect(() => { return () => { // Clean up preview images (these are always blob URLs) previewImages.forEach(({ url }) => { if (url.startsWith('blob:')) { URL.revokeObjectURL(url); } }); }; // eslint-disable-next-line react-hooks/exhaustive-deps }, []); // Only cleanup on unmount, not when images change // Use existing thumbnails from ChartRenderService instead of re-rendering useEffect(() => { const newPreviewImages = new Map<string, { url: string; width: number; height: number }>(); // Clean up old blob URLs previewImages.forEach(({ url }) => { if (url.startsWith('blob:')) { URL.revokeObjectURL(url); } }); for (const chart of sortedCharts) { if (chart.chartType === 'Table' || chart.chartType === '?' || chart.chartType === 'Auto') continue; if (chart.thumbnail) { // Use the pre-rendered thumbnail from ChartRenderService newPreviewImages.set(chart.id, { url: chart.thumbnail, width: config.defaultChartWidth, height: config.defaultChartHeight, }); } } setPreviewImages(newPreviewImages); setIsLoadingPreviews(false); }, [sortedCharts, config]); const toggleChartSelection = (chartId: string) => { const newSelection = new Set(selectedChartIds); if (newSelection.has(chartId)) { newSelection.delete(chartId); } else { newSelection.add(chartId); } setSelectedChartIds(newSelection); }; const selectAll = () => { // Only select available charts (excluding Table, ?, Auto, and charts without preview images) const availableChartIds = sortedCharts .filter(chart => { const isUnavailable = chart.chartType === 'Table' || chart.chartType === '?' || chart.chartType === 'Auto'; const hasPreview = previewImages.has(chart.id); return !isUnavailable && hasPreview; }) .map(c => c.id); setSelectedChartIds(new Set(availableChartIds)); }; const deselectAll = () => { setSelectedChartIds(new Set()); }; const getChartImageFromVega = async (chart: any, chartTable: any): Promise<{ dataUrl: string; blobUrl: string; width: number; height: number }> => { try { // Preprocess the data for aggregations const processedRows = prepVisTable(chartTable.rows, conceptShelfItems, chart.encodingMap); // Assemble the Vega spec const assembledChart = assembleVegaChart( chart.chartType, chart.encodingMap, conceptShelfItems, processedRows, chartTable.metadata, config.defaultChartWidth, config.defaultChartHeight, true, chart.config, 1, config.maxStretchFactor, ); // Create a temporary container for embedding const tempId = `temp-chart-${chart.id}-${Date.now()}`; const tempDiv = document.createElement('div'); tempDiv.id = tempId; tempDiv.style.position = 'absolute'; tempDiv.style.left = '-9999px'; document.body.appendChild(tempDiv); try { // Embed the chart const result = await embed(`#${tempId}`, assembledChart, { actions: false, renderer: 'svg' }); // Export to SVG with high resolution const svgString = await result.view.toSVG(4); // Parse SVG to get original dimensions const parser = new DOMParser(); const svgDoc = parser.parseFromString(svgString, 'image/svg+xml'); const svgElement = svgDoc.querySelector('svg'); if (!svgElement) { throw new Error('Could not parse SVG'); } // Get original dimensions const originalWidth = parseFloat(svgElement.getAttribute('width') || '0'); const originalHeight = parseFloat(svgElement.getAttribute('height') || '0'); // Convert SVG to PNG using canvas const { dataUrl, blobUrl } = await new Promise<{ dataUrl: string; blobUrl: string }>((resolve, reject) => { const canvas = document.createElement('canvas'); const ctx = canvas.getContext('2d'); if (!ctx) { reject(new Error('Could not get canvas context')); return; } const img = new Image(); const svgBlob = new Blob([svgString], { type: 'image/svg+xml;charset=utf-8' }); const svgUrl = URL.createObjectURL(svgBlob); img.onload = () => { canvas.width = img.width; canvas.height = img.height; ctx.drawImage(img, 0, 0); URL.revokeObjectURL(svgUrl); const dataUrl = canvas.toDataURL('image/png'); canvas.toBlob((blob) => { if (blob) { const blobUrl = URL.createObjectURL(blob); resolve({ dataUrl, blobUrl }); } else { resolve({ dataUrl, blobUrl: '' }); } }, 'image/png'); }; img.onerror = (err) => { URL.revokeObjectURL(svgUrl); reject(err); }; img.src = svgUrl; }); document.body.removeChild(tempDiv); return { dataUrl, blobUrl, width: originalWidth, height: originalHeight }; } catch (error) { if (document.body.contains(tempDiv)) { document.body.removeChild(tempDiv); } throw error; } } catch (e) { console.warn('Could not capture chart image:', e); return { dataUrl: '', blobUrl: '', width: 0, height: 0 }; } }; const generateReport = async () => { if (selectedChartIds.size === 0) { setError('Please select at least one chart'); return; } setIsGenerating(true); setError(''); setGeneratedReport(''); setGeneratedStyle(style); // Create a new report ID const reportId = `report-${Date.now()}-${Math.floor(Math.random() * 10000)}`; try { let model = models.find(m => m.id == selectedModelId); if (!model) { throw new Error('No model selected'); } const maxRows = serverConfig.MAX_DISPLAY_ROWS; const inputTables = tables.filter(t => t.anchored).map(table => { const rows = table.rows.length > maxRows ? table.rows.slice(0, maxRows) : table.rows; return { name: table.id, rows, attached_metadata: table.attachedMetadata }; }); // Check if any table data was truncated const truncatedTables = tables.filter(t => t.anchored).filter(t => { const totalRows = t.virtual?.rowCount || t.rows.length; return totalRows > maxRows; }); const truncationNote = truncatedTables.length > 0 ? `\n\nNote: Some tables were truncated to ${maxRows.toLocaleString()} rows for this report. ` + `Tables affected: ${truncatedTables.map(t => `"${t.displayId || t.id}" (${(t.virtual?.rowCount || t.rows.length).toLocaleString()} total rows)`).join(', ')}.` : ''; const selectedCharts = await Promise.all( sortedCharts .filter(chart => selectedChartIds.has(chart.id)) .map(async (chart) => { const chartTable = tables.find(t => t.id === chart.tableRef); if (!chartTable) return null; if (chart.chartType === 'Table' || chart.chartType === '?') { return null; } const { dataUrl, blobUrl, width, height } = await getChartImageFromVega(chart, chartTable); if (blobUrl) { // Use blob URL for local display and caching updateCachedReportImages(chart.id, blobUrl, width, height); } return { chart_id: chart.id, code: chartTable.derive?.code || '', chart_data: { name: chartTable.id, rows: chartTable.rows.length > maxRows ? chartTable.rows.slice(0, maxRows) : chartTable.rows }, chart_url: dataUrl // use data_url to send to the agent }; }) ); const validCharts = selectedCharts.filter(c => c !== null); const requestBody = { model: model, input_tables: inputTables, charts: validCharts, style: style + truncationNote }; const response = await fetchWithIdentity(getUrls().GENERATE_REPORT_STREAM, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(requestBody) }); if (!response.ok) { throw new Error('Failed to generate report'); } const reader = response.body?.getReader(); if (!reader) { throw new Error('No response body'); } const decoder = new TextDecoder(); let accumulatedReport = ''; while (true) { const { done, value } = await reader.read(); if (done) { // Create the report object for saving to Redux const report: GeneratedReport = { id: reportId, content: accumulatedReport, style: style, selectedChartIds: Array.from(selectedChartIds), createdAt: Date.now(), }; // Save to Redux state dispatch(dfActions.saveGeneratedReport(report)); break; }; const chunk = decoder.decode(value, { stream: true }); if (chunk.startsWith('error:')) { const errorData = JSON.parse(chunk.substring(6)); throw new Error(errorData.content || 'Error generating report'); } accumulatedReport += chunk; // Update local state setGeneratedReport(accumulatedReport); setCurrentReportId(reportId); if (mode === 'compose') { setMode('post'); } } } catch (err) { setError((err as Error).message || 'Failed to generate report'); } finally { setIsGenerating(false); } }; const deleteReport = (reportId: string, event: React.MouseEvent) => { event.stopPropagation(); // Prevent triggering the card click dispatch(dfActions.deleteGeneratedReport(reportId)); // If we're deleting the currently viewed report, switch to another report or clear the view if (currentReportId === reportId) { const remainingReports = allGeneratedReports.filter(r => r.id !== reportId); if (remainingReports.length > 0) { // Switch to the first remaining report loadReport(remainingReports[0].id); } else { // No reports left, clear the view and go back to compose mode setCurrentReportId(undefined); setGeneratedReport(''); setGeneratedStyle('social post'); setMode('compose'); } } }; let displayedReport = isGenerating ? `${generatedReport} <span class="pencil" style="opacity: 0.4; margin-left: 2px;">✏️</span>` : generatedReport; displayedReport = processReport(displayedReport); return ( <Box sx={{ height: '100%', width: '100%', display: 'flex', flexDirection: 'column', overflow: 'hidden' }}> {mode === 'compose' ? ( <Box sx={{ overflowY: 'auto', position: 'relative', height: '100%' }}> <Box sx={{ p: 2, pb: 0, display: 'flex' }}> <Button variant="text" size="small" color='secondary' onClick={() => dispatch(dfActions.setViewMode('editor'))} sx={{ textTransform: 'none' }} startIcon={<ArrowBackIcon />} > back to explore </Button> <Divider orientation="vertical" sx={{ mx: 1 }} flexItem /> <Button variant="text" disabled={allGeneratedReports.length === 0} size="small" onClick={() => setMode('post')} sx={{ textTransform: 'none' }} endIcon={<ArrowForwardIcon />} > view reports </Button> </Box> {/* Centered Top Bar */} <Box sx={{ display: 'flex', justifyContent: 'center', p: 2, }}> <Paper elevation={0} sx={{ display: 'flex', alignItems: 'center', gap: 1, p: 1, borderRadius: 2, backgroundColor: 'rgba(255, 255, 255, 0.9)', backdropFilter: 'blur(12px)', border: '1px solid', borderColor: borderColor.view, boxShadow: shadow.lg, '&:hover': { backgroundColor: 'rgba(255, 255, 255, 0.95)', borderColor: borderColor.view, boxShadow: shadow.xl, transition: transition.normal }, '.MuiTypography-root': { fontSize: '1rem', } }} > {/* Natural Flow */} <Typography variant="body2" color="text.primary" sx={{ fontWeight: 500 }}> Create a </Typography> <ToggleButtonGroup value={style} exclusive onChange={(e, newStyle) => newStyle && setStyle(newStyle)} size="small" sx={{ '& .MuiToggleButtonGroup-grouped': { border: 'none', backgroundColor: 'action.hover', margin: '0 2px', borderRadius: '4px', '&:hover': { backgroundColor: 'action.selected', }, '&.Mui-selected': { backgroundColor: 'primary.main', color: 'white', '&:hover': { backgroundColor: 'primary.dark', } }, } }} > {[ { value: 'live report', label: 'live report' }, { value: 'blog post', label: 'blog post' }, { value: 'social post', label: 'social post' }, { value: 'executive summary', label: 'executive summary' }, ].map((option) => ( <ToggleButton key={option.value} value={option.value} sx={{ px: 1, py: 0.25, textTransform: 'none', fontSize: '1rem', minWidth: 'auto' }} > {option.value === 'live report' ? <StreamIcon sx={{ fontSize: 16, mr: 1 }} /> : <></>} {option.label} </ToggleButton> ))} </ToggleButtonGroup> <Typography variant="body2" color="text.primary" sx={{ fontWeight: 500 }}> from </Typography> <Typography variant="body2" color={selectedChartIds.size === 0 ? "warning.main" : 'primary.main'} sx={{ fontWeight: 'bold' }}> {selectedChartIds.size} </Typography> <Typography variant="body2" color="text.primary" sx={{ fontWeight: 500 }}> {selectedChartIds.size <= 1 ? 'chart' : 'charts'} </Typography> {/* Generate Button */} <Button variant="contained" disabled={isGenerating || selectedChartIds.size === 0} onClick={generateReport} size="small" sx={{ textTransform: 'none', ml: 2, px: 2, py: 0.75, borderRadius: 1.5, fontWeight: 500, fontSize: '1rem', minWidth: 'auto' }} startIcon={isGenerating ? <CircularProgress size={14} /> : <EditIcon sx={{ fontSize: 16 }} />} > {isGenerating ? 'composing...' : 'compose'} </Button> </Paper> </Box> <Box sx={{ py: 2, px: 6 }}> {error && ( <Alert severity="error" sx={{ mb: 2 }} onClose={() => setError('')}> {error} </Alert> )} {sortedCharts.length === 0 ? ( <Typography color="text.secondary"> No charts available. Create some visualizations first. </Typography> ) : isLoadingPreviews ? ( <Box sx={{ display: 'flex', alignItems: 'center', justifyContent: 'center', py: 4 }}> <CircularProgress size={18} sx={{ color: 'text.secondary' }} /> <Typography sx={{ ml: 2 }} color="text.secondary"> loading chart previews... </Typography> </Box> ) : (() => { // Filter out unavailable charts (Table, ?, Auto, and charts without preview images) const availableCharts = sortedCharts.filter(chart => { const isUnavailable = chart.chartType === 'Table' || chart.chartType === '?' || chart.chartType === 'Auto'; const hasPreview = previewImages.has(chart.id); return !isUnavailable && hasPreview; }); if (availableCharts.length === 0) { return ( <Typography color="text.secondary"> No available charts to display. Charts may still be loading or unavailable. </Typography> ); } return ( <Masonry columns={{ xs: 2, sm: 3, md: 4, lg: 5 }} spacing={2}> {availableCharts.map((chart) => { const table = tables.find(t => t.id === chart.tableRef); const previewImage = previewImages.get(chart.id); return ( <Card key={chart.id} variant="outlined" sx={{ cursor: 'pointer', position: 'relative', overflow: 'hidden', backgroundColor: selectedChartIds.has(chart.id) ? alpha(theme.palette.primary.main, 0.08) : 'background.paper', border: selectedChartIds.has(chart.id) ? '2px solid' : '1px solid', borderColor: selectedChartIds.has(chart.id) ? 'primary.main' : borderColor.divider, '&:hover': { backgroundColor: 'action.hover', boxShadow: 3, transform: 'translateY(-2px)', transition: transition.normal }, }} onClick={() => toggleChartSelection(chart.id)} > <Box sx={{ position: 'relative' }}> <Checkbox checked={selectedChartIds.has(chart.id)} onChange={() => toggleChartSelection(chart.id)} onClick={(e) => e.stopPropagation()} sx={{ position: 'absolute', top: 4, right: 4, p: 0.5, zIndex: 3, backgroundColor: 'rgba(255, 255, 255, 0.9)', borderRadius: 1, '&:hover': { backgroundColor: 'rgba(255, 255, 255, 1)' } }} /> <Box component="img" src={previewImage!.url} alt={chart.chartType} sx={{ p: 1, width: `calc(100% - 16px)`, height: 'auto', maxHeight: config.defaultChartHeight, display: 'block', objectFit: 'contain', backgroundColor: 'white' }} /> </Box> <CardContent sx={{ p: 1, '&:last-child': { pb: 1.5 } }}> <Typography variant="caption" sx={{ display: 'block', fontWeight: 500, overflow: 'hidden', textOverflow: 'ellipsis', whiteSpace: 'nowrap' }} > {chart.chartType} </Typography> {table?.displayId && ( <Typography variant="caption" color="text.secondary" sx={{ display: 'block', overflow: 'hidden', textOverflow: 'ellipsis', whiteSpace: 'nowrap' }} > {table.displayId} </Typography> )} </CardContent> </Card> ); })} </Masonry> ); })()} </Box> </Box> ) : mode === 'post' ? ( <Box sx={{ height: '100%', display: 'flex', flexDirection: 'column', overflow: 'hidden' }}> <Box sx={{ p: 2, display: 'flex', alignItems: 'center', justifyContent: 'space-between', }}> <Button size="small" disabled={isGenerating} startIcon={<ArrowBackIcon />} sx={{ textTransform: 'none' }} onClick={() => setMode('compose')} > create a new report </Button> <Typography variant="body2" color="text.secondary"> AI generated the post from the selected charts, and it could be inaccurate! </Typography> </Box> <Box sx={{ flex: 1, display: 'flex', overflow: 'hidden', position: 'relative' }}> {/* Table of Contents Sidebar */} {allGeneratedReports.length > 0 && ( <Box sx={{ position: 'absolute', top: 0, left: 8, zIndex: 1, width: 200, display: 'flex', overflowY: 'auto', flexDirection: 'column', borderRight: `1px solid ${borderColor.view}`, height: 'fit-content', background: alpha(theme.palette.background.paper, 0.9), }}> <Button size='small' color='primary' onClick={() => setHideTableOfContents(!hideTableOfContents)} sx={{ width: '100%', justifyContent: 'flex-start', textAlign: 'left', borderRadius: 0, textTransform: 'none', fontSize: 12, py: 1, px: 2, }}> {hideTableOfContents ? <ExpandMoreIcon sx={{ fontSize: 16, mr: 1 }} /> : <ExpandLessIcon sx={{ fontSize: 16, mr: 1 }} /> } {hideTableOfContents ? 'show all reports' : 'reports'} </Button> <Collapse in={!hideTableOfContents}>{allGeneratedReports.map((report) => ( <Box key={report.id} sx={{ position: 'relative' }}> <Button variant="text" size="small" color='primary' onClick={() => loadReport(report.id)} sx={{ fontSize: 12, textTransform: "none", width: '100%', justifyContent: 'flex-start', textAlign: 'left', borderRadius: 0, py: 1, px: 2, color: currentReportId === report.id ? 'primary.main' : 'text.secondary', borderRight: currentReportId === report.id ? 2 : 0, borderColor: 'primary.main', }} > <Box sx={{ flex: 1, minWidth: 0 }}> <Typography variant="body2" sx={{ fontSize: 'inherit', fontWeight: 500, mb: 0.25 }} > {report.content.split('\n')[0]} </Typography> <Typography variant="caption" color="text.secondary" sx={{ fontSize: 10, display: 'block', overflow: 'hidden', textOverflow: 'ellipsis', whiteSpace: 'nowrap' }} > {new Date(report.createdAt).toLocaleDateString()} • {report.style} </Typography> </Box> </Button> <Tooltip title="Delete report"> <IconButton size="small" disabled={isGenerating} color='warning' onClick={(e) => deleteReport(report.id, e)} sx={{ position: 'absolute', right: 4, top: '50%', transform: 'translateY(-50%)', width: 20, height: 20, '&:hover': { transform: 'translateY(-50%) scale(1.2)', transition: 'all 0.2s ease-in-out' } }} > <DeleteIcon sx={{ fontSize: 14 }} /> </IconButton> </Tooltip> </Box> ))} </Collapse> </Box> )} {/* Main Content Area */} <Box sx={{ flex: 1, overflowY: 'auto', position: 'relative' }}> {/* Action Buttons */} {currentReportId && ( <Box sx={{ position: 'absolute', top: 16, right: 16, zIndex: 10, display: 'flex', gap: 1 }}> <Tooltip title="Create Chartifact report"> <Button variant="contained" size="small" onClick={() => { // Convert report to Chartifact markdown format const chartifactMarkdown = convertToChartifact( generatedReport, generatedStyle, charts, tables, conceptShelfItems, config ); openChartifactViewer(chartifactMarkdown); }} sx={{ textTransform: 'none', backgroundColor: 'primary.main', color: 'white', '&:hover': { backgroundColor: 'primary.dark', }, }} startIcon={<CreateChartifact />} > Create Chartifact </Button> </Tooltip> <Tooltip title="Share report as image"> <Button variant="contained" size="small" startIcon={ shareButtonSuccess ? <CheckCircleIcon /> : <ShareIcon /> } onClick={shareReportAsImage} sx={{ textTransform: 'none', backgroundColor: shareButtonSuccess ? 'success.main' : 'primary.main', color: 'white', transition: 'all 0.4s cubic-bezier(0.4, 0, 0.2, 1)', opacity: shareButtonSuccess ? 0.9 : 1, transform: shareButtonSuccess ? 'scale(0.98)' : 'scale(1)', animation: shareButtonSuccess ? 'pulse 0.6s ease-in-out' : 'none', '@keyframes pulse': { '0%': { transform: 'scale(0.98)' }, '50%': { transform: 'scale(1.05)' }, '100%': { transform: 'scale(0.98)' } }, '&:hover': { backgroundColor: shareButtonSuccess ? 'success.dark' : 'primary.dark', }, }} > {shareButtonSuccess ? 'Copied!' : 'Share Image'} </Button> </Tooltip> </Box> )} <Box sx={{ display: 'flex', justifyContent: 'center', width: '100%', py: 3 }}> <Box data-report-content sx={{ // Common styles width: '100%', WebkitFontSmoothing: 'antialiased', MozOsxFontSmoothing: 'grayscale', '& em': { fontStyle: 'italic' }, // Conditional styles ...(generatedStyle === 'social post' || generatedStyle === 'short note' ? { maxWidth: '520px', borderRadius: '12px', border: '1px solid', borderColor: COLOR_SOCIAL_BORDER, p: 2.5, backgroundColor: 'white', fontFamily: FONT_FAMILY_SYSTEM, fontSize: '0.875rem', fontWeight: 400, lineHeight: 1.4, color: COLOR_SOCIAL_TEXT, '& code': { backgroundColor: `${COLOR_SOCIAL_ACCENT}1A`, color: COLOR_SOCIAL_ACCENT, padding: '0.1em 0.25em', borderRadius: '3px', fontSize: '0.8125rem', fontWeight: 500, fontFamily: FONT_FAMILY_MONO }, '& strong': { fontWeight: 600, color: COLOR_SOCIAL_TEXT }, '& img': { width: '100%', maxWidth: '100%', height: 'auto', maxHeight: '280px', objectFit: 'contain', borderRadius: '8px', marginTop: '8px', marginBottom: '8px' } } : generatedStyle === 'executive summary' ? { maxWidth: '700px', p: 2.5, backgroundColor: 'white', fontFamily: FONT_FAMILY_SERIF, fontSize: '0.875rem', lineHeight: 1.5, color: COLOR_EXEC_TEXT, '& code': { backgroundColor: COLOR_EXEC_BG, color: COLOR_EXEC_ACCENT, padding: '0.1em 0.25em', borderRadius: '2px', fontSize: '0.75rem', fontFamily: FONT_FAMILY_MONO }, '& strong': { fontWeight: 600, color: COLOR_EXEC_HEADING }, '& img': { maxWidth: '70%', maxHeight: config.defaultChartHeight * 1.5, objectFit: 'contain', width: 'auto', height: 'auto', borderRadius: '3px', marginTop: '1em', marginBottom: '1em' } } : { maxWidth: '800px', px: 6, py: 0, backgroundColor: 'background.paper', ...BODY_TEXT_BASE, '& code': { backgroundColor: 'rgba(135, 131, 120, 0.15)', color: '#eb5757', padding: '0.2em 0.4em', borderRadius: '3px', fontSize: '0.875rem', fontWeight: 500, fontFamily: FONT_FAMILY_MONO }, '& strong': { fontWeight: 600, color: COLOR_HEADING }, '& img': { maxWidth: '75%', maxHeight: config.defaultChartHeight * 1.5, width: 'auto', height: 'auto', objectFit: 'contain', borderRadius: '4px', marginTop: '1.75em', marginBottom: '1.75em', } }) }} > <MuiMarkdown overrides={ generatedStyle === 'social post' || generatedStyle === 'short note' ? socialStyleMarkdownOverrides : generatedStyle === 'executive summary' ? executiveSummaryMarkdownOverrides : notionStyleMarkdownOverrides }>{displayedReport}</MuiMarkdown> {/* Attribution */} <Box sx={{ mt: 3, pt: 2, borderTop: '1px solid #e0e0e0', textAlign: 'center', fontSize: '0.75rem', color: '#666' }}> created with AI using{' '} <Link href="https://github.com/microsoft/data-formulator" target="_blank" rel="noopener noreferrer" sx={{ color: '#1976d2', textDecoration: 'none', '&:hover': { textDecoration: 'underline' } }} > https://github.com/microsoft/data-formulator </Link> </Box> </Box> </Box> </Box> </Box> </Box> ) : null} </Box> ); }; ================================================ FILE: src/views/SelectableDataGrid.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import * as React from 'react'; import { shadow, transition } from '../app/tokens'; import { TableVirtuoso } from 'react-virtuoso'; import Table from '@mui/material/Table'; import TableBody from '@mui/material/TableBody'; import TableCell from '@mui/material/TableCell'; import TableContainer from '@mui/material/TableContainer'; import TableHead from '@mui/material/TableHead'; import TableRow from '@mui/material/TableRow'; import { Box } from '@mui/system'; import { useTheme } from '@mui/material/styles'; import { alpha, Paper, Tooltip, CircularProgress, Fade } from "@mui/material"; import { Type } from '../data/types'; import { getIconFromType } from './ViewUtils'; import { IconButton, TableSortLabel, Typography } from '@mui/material'; import _ from 'lodash'; import * as d3 from 'd3-dsv'; import { FieldSource, FieldItem } from '../components/ComponentType'; import FileDownloadIcon from '@mui/icons-material/FileDownload'; import { TableIcon } from '../icons'; import CasinoIcon from '@mui/icons-material/Casino'; import ArrowUpwardIcon from '@mui/icons-material/ArrowUpward'; import ArrowDownwardIcon from '@mui/icons-material/ArrowDownward'; import UnfoldMoreIcon from '@mui/icons-material/UnfoldMore'; import { getUrls, fetchWithIdentity } from '../app/utils'; import { useDrag } from 'react-dnd'; import { useSelector } from 'react-redux'; import { DataFormulatorState } from '../app/dfSlice'; export interface ColumnDef { id: string; label: string; dataType: Type; minWidth?: number; width?: number; align?: 'right'; format?: (value: number) => string | JSX.Element; source: FieldSource; } interface SelectableDataGridProps { tableId: string; tableName: string; rows: any[]; rowCount: number; virtual: boolean; columnDefs: ColumnDef[]; } function descendingComparator<T>(a: T, b: T, orderBy: keyof T) { if (b[orderBy] < a[orderBy]) { return -1; } if (b[orderBy] > a[orderBy]) { return 1; } return 0; } function getComparator<Key extends keyof any>( order: "asc" | "desc", orderBy: Key, ): ( a: { [key in Key]: number | string }, b: { [key in Key]: number | string }, ) => number { return order === 'desc' ? (a, b) => descendingComparator(a, b, orderBy) : (a, b) => -descendingComparator(a, b, orderBy); } // Get color for field source hover highlight function getColorForFieldSource(source: string | undefined, theme: any): string { if (!source) { return theme.palette.primary.main; // Default to primary for original fields } switch (source) { case "custom": return theme.palette.custom.main; // Orange for custom fields case "original": default: return theme.palette.primary.main; // Blue for original fields } } // Draggable header component interface DraggableHeaderProps { columnDef: ColumnDef; orderBy: string | undefined; order: 'asc' | 'desc'; onSortClick: () => void; tableId: string; } const DraggableHeader: React.FC<DraggableHeaderProps> = ({ columnDef, orderBy, order, onSortClick, tableId }) => { const theme = useTheme(); const conceptShelfItems = useSelector((state: DataFormulatorState) => state.conceptShelfItems); const tables = useSelector((state: DataFormulatorState) => state.tables); // Get semantic type from table metadata const table = tables.find(t => t.id === tableId); const semanticType = table?.metadata?.[columnDef.id]?.semanticType; // Find the corresponding FieldItem for this column // Try to find by name first, then by constructing the ID for original fields const field = conceptShelfItems.find(f => f.name === columnDef.id) || conceptShelfItems.find(f => f.id === `original--${tableId}--${columnDef.id}`); // Only make draggable if we have a field // react-dnd has a drag threshold, so clicks will still work for sorting const [{ isDragging }, dragSource, dragPreview] = useDrag(() => ({ type: "concept-card", item: field ? { type: 'concept-card', fieldID: field.id, source: "conceptShelf" } : undefined, canDrag: !!field, collect: (monitor) => ({ isDragging: monitor.isDragging(), handlerId: monitor.getHandlerId(), }), }), [field]); let backgroundColor: string; let borderBottomColor = theme.palette.primary.main; if (columnDef.source == "custom") { backgroundColor = theme.palette.custom.bgcolor || alpha(theme.palette.custom.main, 0.1); borderBottomColor = theme.palette.custom.main; } else { backgroundColor = theme.palette.primary.bgcolor || alpha(theme.palette.primary.main, 0.1); borderBottomColor = theme.palette.primary.main; } const opacity = isDragging ? 0.3 : 1; const cursorStyle = field ? (isDragging ? "grabbing" : "grab") : "default"; // Enhanced background color on hover for draggable headers - based on field source (derived/original) const hoverBackgroundColor = field ? alpha(getColorForFieldSource(field.source, theme), 0.1) : backgroundColor; // Determine sort icon const getSortIcon = () => { if (orderBy !== columnDef.id) { return <UnfoldMoreIcon sx={{ fontSize: 16 }} />; } return order === 'asc' ? <ArrowUpwardIcon sx={{ fontSize: 16 }} /> : <ArrowDownwardIcon sx={{ fontSize: 16 }} />; }; return ( <Box className="data-view-header-container" ref={dragPreview} sx={{ backgroundColor: backgroundColor, borderBottomColor, borderBottomWidth: '2px', borderBottomStyle: 'solid', opacity, display: 'flex', alignItems: 'center', position: 'relative', transition: 'background-color 0.2s ease, box-shadow 0.2s ease', // Ensure cursor applies to TableSortLabel and its children, but not IconButton '& .data-view-header-title, & .data-view-header-title *': { cursor: cursorStyle, }, ...(field && { '&:hover': { backgroundColor: hoverBackgroundColor, boxShadow: shadow.md, }, }), }} > {/* Main content area - draggable for concepts, using original TableSortLabel structure */} <TableSortLabel ref={field ? dragSource : undefined} className="data-view-header-title" sx={{ display: "flex", flexDirection: "row", flex: 1, width: 'calc(100% - 24px)', cursor: cursorStyle, // Inherit cursor from parent '& .MuiTableSortLabel-icon': { display: 'none', }, }} active={orderBy === columnDef.id} direction={orderBy === columnDef.id ? order : 'asc'} onClick={(e) => { // Prevent sort when dragging if (!isDragging) { e.stopPropagation(); onSortClick(); } }} > <span role="img" style={{ fontSize: "inherit", padding: "2px", display: "inline-flex", alignItems: "center" }}> {getIconFromType(columnDef.dataType)} </span> <Tooltip title={semanticType ? ( <Typography sx={{ fontSize: 11 }}> <b>{columnDef.label}</b>: <i>{semanticType}</i> </Typography> ) : ''} arrow placement="top" > <Typography sx={{fontSize: 12, overflow: 'hidden', textOverflow: 'ellipsis', whiteSpace: 'nowrap'}}> {columnDef.label} </Typography> </Tooltip> </TableSortLabel> {/* Separate sort handler button */} <Tooltip title={<Typography sx={{fontSize: 10}}>Sort by <b>{columnDef.label}</b></Typography>}> <IconButton size="small" onClick={(e) => { e.stopPropagation(); onSortClick(); }} sx={{ padding: '2px', marginLeft: '4px', marginRight: '2px', opacity: orderBy === columnDef.id ? 1 : 0.5, '&:hover': { opacity: 1, backgroundColor: alpha(theme.palette.action.hover, 0.2), }, }} > {getSortIcon()} </IconButton> </Tooltip> </Box> ); }; export const SelectableDataGrid: React.FC<SelectableDataGridProps> = ({ tableId, rows, tableName, columnDefs, rowCount, virtual }) => { const [orderBy, setOrderBy] = React.useState<string | undefined>(undefined); const [order, setOrder] = React.useState<'asc' | 'desc'>('asc'); let theme = useTheme(); const [rowsToDisplay, setRowsToDisplay] = React.useState<any[]>(rows); // Initialize as true to cover the initial mount delay const [isLoading, setIsLoading] = React.useState<boolean>(true); // Clear loading state after first render React.useEffect(() => { setIsLoading(false); }, []); React.useEffect(() => { if (orderBy && !isLoading) { setRowsToDisplay(rows.slice().sort(getComparator(order, orderBy))); } else { setRowsToDisplay(rows); } }, [rows, order, orderBy]) const TableComponents = { Scroller: React.forwardRef<HTMLDivElement>((props, ref) => ( <TableContainer {...props} ref={ref} /> )), Table: (props: any) => <Table {...props} />, TableHead: React.forwardRef<HTMLTableSectionElement>((props, ref) => ( <TableHead {...props} ref={ref} className='table-header-container' /> )), TableRow: (props: any) => { const index = props['data-index']; return <TableRow {...props} style={{backgroundColor: index % 2 == 0 ? "rgba(255, 255, 255, 0.05)" : "rgba(0, 0, 0, 0.02)"}}/> }, TableBody: React.forwardRef<HTMLTableSectionElement>((props, ref) => ( <TableBody {...props} ref={ref} /> )), } const handleDownload = async (format: 'csv' | 'tsv') => { const delimiter = format === 'tsv' ? '\t' : ','; const ext = format === 'tsv' ? 'tsv' : 'csv'; const mime = format === 'tsv' ? 'text/tab-separated-values' : 'text/csv'; if (virtual) { // Virtual table: fetch full data from server try { const response = await fetchWithIdentity(getUrls().EXPORT_TABLE_CSV, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ table_name: tableId, delimiter }), }); if (!response.ok) throw new Error('Export failed'); const blob = await response.blob(); const a = document.createElement('a'); a.href = URL.createObjectURL(blob); a.download = `${tableName}.${ext}`; a.click(); URL.revokeObjectURL(a.href); } catch (error) { console.error('Error downloading table:', error); } } else { // Local table: export from in-memory rows const csvContent = d3.dsvFormat(delimiter).format(rows); const blob = new Blob([csvContent], { type: mime }); const a = document.createElement('a'); a.href = URL.createObjectURL(blob); a.download = `${tableName}.${ext}`; a.click(); URL.revokeObjectURL(a.href); } }; const fetchVirtualData = (sortByColumnIds: string[], sortOrder: 'asc' | 'desc') => { // Set loading to true when starting the fetch setIsLoading(true); let message = sortByColumnIds.length > 0 ? { table: tableId, size: 1000, method: sortOrder === 'asc' ? 'head' : 'bottom', order_by_fields: sortByColumnIds } : { table: tableId, size: 1000, method: 'random' } // Use the SAMPLE_TABLE endpoint with appropriate ordering fetchWithIdentity(getUrls().SAMPLE_TABLE, { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify(message), }) .then(response => response.json()) .then(data => { if (data.status === 'success') { setRowsToDisplay(data.rows); } // Set loading to false when done setIsLoading(false); }) .catch(error => { console.error('Error fetching sorted table data:', error); // Ensure loading is set to false even on error setIsLoading(false); }); }; return ( <Box className="table-container table-container-small" sx={{ width: '100%', height: '100%', position: 'relative', "& .MuiTableCell-root": { fontSize: 12, maxWidth: "120px", py: '2px', cursor: "default", overflow: "clip", textOverflow: "ellipsis", whiteSpace: "nowrap" } }}> {/* Loading Overlay */} {isLoading && ( <Box sx={{ position: 'absolute', top: 0, left: 0, right: 0, zIndex: 10, display: 'flex', alignItems: 'center', justifyContent: 'center', backgroundColor: 'rgba(255, 255, 255, 0.7)', padding: '8px', height: '100%', borderTopLeftRadius: '4px', borderTopRightRadius: '4px' }}> <CircularProgress size={24} sx={{ mr: 1, color: 'lightgray' }} /> <Typography variant="body2" color="text.secondary">Loading ...</Typography> </Box> )} <Fade in={!isLoading} timeout={{appear: 300, enter: 300, exit: 2000}}> <Box sx={{ flex: '1 1', display: 'flex', flexDirection: 'column' }}> <TableVirtuoso style={{ flex: '1 1' }} data={rowsToDisplay} components={TableComponents} fixedHeaderContent={() => { return ( <TableRow key='header-fixed' style={{ paddingRight: 0, marginRight: '17px', height: '24px'}}> {columnDefs.map((columnDef, index) => { return ( <TableCell className='data-view-header-cell' key={columnDef.id} align={columnDef.align} sx={{p: 0, minWidth: columnDef.minWidth, width: columnDef.width,}} > <DraggableHeader columnDef={columnDef} orderBy={orderBy} order={order} tableId={tableId} onSortClick={() => { let newOrder: 'asc' | 'desc' = 'asc'; let newOrderBy : string | undefined = columnDef.id; if (orderBy === columnDef.id && order === 'asc') { newOrder = 'desc'; } else if (orderBy === columnDef.id && order === 'desc') { newOrder = 'asc'; newOrderBy = undefined; } else { newOrder = 'asc'; } setOrder(newOrder); setOrderBy(newOrderBy); if (virtual) { fetchVirtualData(newOrderBy ? [newOrderBy] : [], newOrder); } }} /> </TableCell> ); })} </TableRow> ) }} itemContent={(rowIndex, data) => { return ( <> {columnDefs.map((column, colIndex) => { let backgroundColor = "rgba(255,255,255,0.05)"; // if (column.source == "custom") { // backgroundColor = alpha(theme.palette.custom.main, 0.03); // } else { // backgroundColor = "rgba(255,255,255,0.05)"; // } return ( <TableCell key={`col-${colIndex}-row-${rowIndex}`} sx={{backgroundColor}} align={column.align || 'left'} > {column.format ? column.format(data[column.id]) : data[column.id]} </TableCell> ) })} </> ) }} /> </Box> </Fade> <Paper variant="outlined" sx={{ display: 'flex', flexDirection: 'row', position: 'absolute', bottom: 6, right: 12 }}> <Box sx={{display: 'flex', alignItems: 'center', mx: 1}}> <Typography sx={{display: 'flex', alignItems: 'center', fontSize: '12px'}}> {virtual && <TableIcon sx={{width: 14, height: 14, mr: 1}}/> } {`${rowCount} rows`} </Typography> {virtual && rowCount > 10000 && ( <Tooltip title="view 10000 random rows from this table"> <IconButton size="small" color="primary" sx={{marginRight: 1}} onClick={() => { fetchVirtualData([], 'asc'); }} > <CasinoIcon sx={{ fontSize: 18, '&:hover': { transform: 'rotate(180deg)' } }} /> </IconButton> </Tooltip> )} <Tooltip title="Download as CSV"> <IconButton size="small" color="primary" onClick={() => handleDownload('csv')} > <FileDownloadIcon sx={{ fontSize: 18 }} /> </IconButton> </Tooltip> </Box> </Paper> </Box > ); } ================================================ FILE: src/views/SimpleChartRecBox.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React, { FC, useState, useCallback, useRef, useEffect } from 'react'; import { Box, IconButton, Tooltip, Typography, useTheme, TextField, CircularProgress, Card, LinearProgress, Button, } from '@mui/material'; import { useDispatch, useSelector } from 'react-redux'; import { DataFormulatorState, dfActions, dfSelectors, fetchCodeExpl, fetchFieldSemanticType, fetchChartInsight, generateFreshChart } from '../app/dfSlice'; import { AppDispatch } from '../app/store'; import { resolveRecommendedChart, getUrls, fetchWithIdentity, getTriggers } from '../app/utils'; import { Chart, DictTable, FieldItem, createDictTable } from "../components/ComponentType"; import { alpha } from '@mui/material/styles'; import SendIcon from '@mui/icons-material/Send'; import CloseIcon from '@mui/icons-material/Close'; import AddIcon from '@mui/icons-material/Add'; import TipsAndUpdatesIcon from '@mui/icons-material/TipsAndUpdates'; import CancelOutlinedIcon from '@mui/icons-material/CancelOutlined'; import StopCircleOutlinedIcon from '@mui/icons-material/StopCircleOutlined'; import SmartToyOutlinedIcon from '@mui/icons-material/SmartToyOutlined'; import RefreshIcon from '@mui/icons-material/Refresh'; import ClearIcon from '@mui/icons-material/Clear'; import { renderTextWithEmphasis } from './EncodingShelfCard'; import { UnifiedDataUploadDialog } from './UnifiedDataUploadDialog'; import { Theme } from '@mui/material/styles'; const AgentWorkingOverlay: FC<{ relevantAgentActions: any[]; theme: Theme; onCancel?: () => void }> = ({ relevantAgentActions, theme, onCancel }) => { const runningAction = relevantAgentActions.find(a => a.status === 'running'); const latestMessage = runningAction?.description || 'thinking...'; return ( <Box sx={{ position: 'absolute', top: 0, left: 0, right: 0, bottom: 0, backgroundColor: alpha(theme.palette.background.paper, 0.88), backdropFilter: 'blur(3px)', display: 'flex', flexDirection: 'column', alignItems: 'center', justifyContent: 'center', gap: 0.5, zIndex: 2, borderRadius: 'inherit', px: 2, overflow: 'hidden', }}> <Box sx={{ display: 'flex', flexDirection: 'row', alignItems: 'center', gap: 1 }}> <Typography sx={{ fontSize: 10, animation: 'agentWriting 1.2s ease-in-out infinite', '@keyframes agentWriting': { '0%, 100%': { transform: 'rotate(-15deg) translate(0, 0)' }, '25%': { transform: 'rotate(-8deg) translate(2px, 1px)' }, '50%': { transform: 'rotate(-15deg) translate(0, 0)' }, '75%': { transform: 'rotate(-20deg) translate(-2px, 1px)' }, }, transformOrigin: 'bottom right', }}> ✏️ </Typography> <Typography variant="body2" color="text.secondary" sx={{ fontWeight: 500, fontSize: 10 }}> Agent is working... </Typography> </Box> {onCancel && ( <IconButton size="small" onClick={onCancel} sx={{ position: 'absolute', bottom: 6, right: 6, p: 1.5, width: 16, height: 16, color: theme.palette.warning.main }} > <StopCircleOutlinedIcon sx={{ fontSize: 14 }} /> </IconButton> )} <Typography variant="caption" sx={{ color: 'text.disabled', fontSize: 10, textAlign: 'center', display: '-webkit-box', WebkitLineClamp: 3, WebkitBoxOrient: 'vertical', overflow: 'hidden', lineHeight: 1.3, wordBreak: 'break-word', }}> {latestMessage} </Typography> <LinearProgress sx={{ position: 'absolute', bottom: 0, left: 0, right: 0, height: 2, borderRadius: '0 0 8px 8px' }} /> </Box> ); }; export const SimpleChartRecBox: FC = function () { const tables = useSelector((state: DataFormulatorState) => state.tables); const focusedId = useSelector((state: DataFormulatorState) => state.focusedId); const charts = useSelector(dfSelectors.getAllCharts); const conceptShelfItems = useSelector((state: DataFormulatorState) => state.conceptShelfItems); const config = useSelector((state: DataFormulatorState) => state.config); const agentRules = useSelector((state: DataFormulatorState) => state.agentRules); const activeModel = useSelector(dfSelectors.getActiveModel); const agentActions = useSelector((state: DataFormulatorState) => state.agentActions); const theme = useTheme(); const dispatch = useDispatch<AppDispatch>(); const [chatPrompt, setChatPrompt] = useState(""); const [isChatFormulating, setIsChatFormulating] = useState(false); const [ideas, setIdeas] = useState<{text: string, goal: string, difficulty: 'easy' | 'medium' | 'hard'}[]>([]); const [isLoadingIdeas, setIsLoadingIdeas] = useState(false); const [thinkingBuffer, setThinkingBuffer] = useState(''); const [uploadDialogOpen, setUploadDialogOpen] = useState(false); const agentAbortRef = useRef<AbortController | null>(null); const ideasAbortRef = useRef<AbortController | null>(null); // pendingClarification is now derived from Redux (stored on the agentAction itself) // so it persists when user clicks away and comes back. // On mount, clean up any stale "running" agent actions left over from a page refresh. // The streaming connection is lost on refresh, so these will never complete. // Only mark actions whose lastUpdate predates the current page load as stale. useEffect(() => { const pageLoadTime = performance.timeOrigin; // ms timestamp of when the page was loaded const staleRunning = agentActions.filter(a => a.status === 'running' && a.lastUpdate < pageLoadTime); if (staleRunning.length === 0) return; // The last stale action gets the visible message; others are silently marked warning const lastStale = staleRunning[staleRunning.length - 1]; for (const action of staleRunning) { dispatch(dfActions.updateAgentWorkInProgress({ actionId: action.actionId, description: action === lastStale ? 'Interrupted by page refresh' : action.description, status: 'warning', hidden: false, ...(action === lastStale ? { message: { content: 'Interrupted by page refresh', role: 'clarify' } } : {}), })); } }, []); // eslint-disable-line react-hooks/exhaustive-deps const inputCardRef = useRef<HTMLDivElement>(null); const focusedTableId = useCallback(() => { if (!focusedId) return undefined; if (focusedId.type === 'table') return focusedId.tableId; const chartId = focusedId.chartId; const chart = charts.find(c => c.id === chartId); return chart?.tableRef; }, [focusedId, charts])(); // Clear ideas when focused table changes — ideas are scoped per table useEffect(() => { setIdeas([]); setIsLoadingIdeas(false); }, [focusedTableId]); // Root tables and priority ordering for API calls const rootTables = tables.filter(t => t.derive === undefined || t.anchored); const currentTable = tables.find(t => t.id === focusedTableId); const priorityIds = (currentTable?.derive && !currentTable.anchored) ? currentTable.derive.source : focusedTableId ? [focusedTableId] : []; const selectedTableIds = [ ...priorityIds.filter(id => rootTables.some(t => t.id === id)), ...rootTables.map(t => t.id).filter(id => !priorityIds.includes(id)) ]; // Collect table IDs from root up to (and including) the focused table for agent action matching const threadTableIds = React.useMemo(() => { if (!focusedTableId) return new Set<string>(); const ids = new Set<string>(); // Walk up from focused table to root — only ancestors, not descendants let current = tables.find(t => t.id === focusedTableId); while (current) { ids.add(current.id); if (current.derive && !current.anchored && current.derive.trigger) { const parentId = current.derive.trigger.tableId; if (ids.has(parentId)) break; current = tables.find(t => t.id === parentId); } else { break; } } return ids; }, [focusedTableId, tables]); // Agent actions relevant to all tables in this thread, sorted by creation time // Agent actions relevant to the focused table's thread. // An action is relevant if: // - its originTableId is in the ancestor chain, AND // - it produced a table in the ancestor chain (resultTableId ∈ threadTableIds), // OR is still running, OR the focused table is the originTableId itself. const relevantAgentActions = React.useMemo(() => { if (threadTableIds.size === 0) return []; return agentActions .filter(a => { if (a.hidden) return false; if (!threadTableIds.has(a.originTableId)) return false; // Include if still running or waiting for clarification (live progress) if (a.status === 'running' || a.status === 'warning') return true; // Include if any message produced a table in the ancestor chain if (a.messages?.some(m => m.resultTableId && threadTableIds.has(m.resultTableId))) return true; // Include if the focused table IS the origin (user is at the starting table) if (focusedTableId === a.originTableId) return true; return false; }) .sort((a, b) => (a.messages?.[0]?.timestamp || a.lastUpdate) - (b.messages?.[0]?.timestamp || b.lastUpdate)); }, [agentActions, threadTableIds, focusedTableId]); const hasRunningAgent = relevantAgentActions.some(a => a.status === 'running'); // Derive pending clarification from the current thread's relevant actions (stored in Redux) const pendingClarification = React.useMemo(() => { const action = relevantAgentActions.find(a => a.pendingClarification); if (!action || !action.pendingClarification) return null; return { ...action.pendingClarification, actionId: action.actionId }; }, [relevantAgentActions]); // Extract the clarification question text from the last 'clarify' message const clarificationQuestion = React.useMemo(() => { if (!pendingClarification) return null; const action = agentActions.find(a => a.actionId === pendingClarification.actionId); if (!action?.messages) return null; const clarifyMsgs = action.messages.filter(m => m.role === 'clarify'); return clarifyMsgs.length > 0 ? clarifyMsgs[clarifyMsgs.length - 1].content : null; }, [pendingClarification, agentActions]); const getIdeasFromAgent = useCallback(async () => { if (!currentTable || isLoadingIdeas) return; setIsLoadingIdeas(true); setIdeas([]); setThinkingBuffer(''); try { let explorationThread: any[] = []; const sourceTables = selectedTableIds.map(id => tables.find(t => t.id === id) as DictTable); if (currentTable.derive && !currentTable.anchored) { const triggers = getTriggers(currentTable, tables); explorationThread = triggers.map(trigger => ({ name: trigger.resultTableId, rows: tables.find(t2 => t2.id === trigger.resultTableId)?.rows, description: `Derive from ${tables.find(t2 => t2.id === trigger.resultTableId)?.derive?.source} with instruction: ${trigger.instruction}`, })); } const messageBody = JSON.stringify({ token: String(Date.now()), model: activeModel, mode: 'interactive', input_tables: sourceTables.map(t => ({ name: t.virtual?.tableId || t.id.replace(/\.[^/.]+$/, ""), rows: t.rows, attached_metadata: t.attachedMetadata })), exploration_thread: explorationThread, agent_exploration_rules: agentRules.exploration }); const controller = new AbortController(); ideasAbortRef.current = controller; const timeoutId = setTimeout(() => controller.abort(), config.formulateTimeoutSeconds * 1000); const response = await fetchWithIdentity(getUrls().GET_RECOMMENDATION_QUESTIONS, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: messageBody, signal: controller.signal }); clearTimeout(timeoutId); if (!response.ok) throw new Error(`HTTP error! status: ${response.status}`); const reader = response.body?.getReader(); if (!reader) throw new Error('No response body reader available'); const decoder = new TextDecoder(); let buffer = ''; let lines: string[] = []; try { while (true) { const { done, value } = await reader.read(); if (done) break; buffer += decoder.decode(value, { stream: true }); const newLines = buffer.split('data: ').filter(l => l.trim() !== ''); buffer = newLines.pop() || ''; if (newLines.length > 0) { lines.push(...newLines); const parsed = lines .map(l => { try { return JSON.parse(l.trim()); } catch { return null; } }) .filter(Boolean) .map(b => ({ text: b.text, goal: b.goal, difficulty: b.difficulty })); setIdeas(parsed); } setThinkingBuffer(buffer.replace(/^data: /, '')); } } finally { reader.releaseLock(); } lines.push(buffer); const finalIdeas = lines .map(l => { try { return JSON.parse(l.trim()); } catch { return null; } }) .filter(Boolean) .map(b => ({ text: b.text, goal: b.goal, difficulty: b.difficulty })); setIdeas(finalIdeas); } catch (error) { console.error('Error getting ideas:', error); } finally { setIsLoadingIdeas(false); setThinkingBuffer(''); ideasAbortRef.current = null; } }, [currentTable, isLoadingIdeas, selectedTableIds, tables, activeModel, agentRules, config, dispatch]); const exploreFromChat = useCallback((prompt: string, clarificationContext?: { trajectory: any[]; completedStepCount: number; actionId: string; lastCreatedTableId: string | null; }) => { if (!focusedTableId || prompt.trim() === "") return; const rootTables = tables.filter(t => t.derive === undefined || t.anchored); const currentTable = tables.find(t => t.id === focusedTableId); const priorityIds = (currentTable?.derive && !currentTable.anchored) ? currentTable.derive.source : [focusedTableId]; const selectedTableIds = [ ...priorityIds.filter(id => rootTables.some(t => t.id === id)), ...rootTables.map(t => t.id).filter(id => !priorityIds.includes(id)) ]; if (selectedTableIds.length === 0) return; const isResume = !!clarificationContext; const actionId = isResume ? clarificationContext!.actionId : `exploreDataFromNL_${String(Date.now())}`; const actionTables = selectedTableIds.map(id => tables.find(t => t.id === id) as DictTable); setIsChatFormulating(true); if (isResume) { // Show user's clarification reply in the thread; clear pendingClarification in Redux dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: prompt, status: 'running', hidden: false, message: { content: prompt, role: 'user' }, pendingClarification: null })); } else { // User instruction with source table context dispatch(dfActions.updateAgentWorkInProgress({ actionId, originTableId: focusedTableId, description: prompt, status: 'running', hidden: false, message: { content: prompt, role: 'user' } })); } // Collect previous conversation messages for context (only for fresh starts) let conversationHistory: { role: string; content: string }[] | undefined = undefined; if (!isResume) { const history: { role: string; content: string }[] = []; for (const action of relevantAgentActions) { if (action.messages) { for (const m of action.messages) { if (m.role === 'user') { history.push({ role: 'user', content: m.content }); } else if (m.role === 'thinking' || m.role === 'completion') { history.push({ role: 'assistant', content: m.content }); } } } } if (history.length > 0) conversationHistory = history; } const token = String(Date.now()); const requestBody: any = { token, input_tables: actionTables.map(t => ({ name: t.virtual?.tableId || t.id.replace(/\.[^/.]+$/, ""), rows: t.rows, attached_metadata: t.attachedMetadata })), model: activeModel, max_iterations: 5, agent_exploration_rules: agentRules.exploration, agent_coding_rules: agentRules.coding }; if (isResume) { // Stateless resume: send back trajectory + user answer requestBody.trajectory = clarificationContext!.trajectory; requestBody.clarification_response = prompt; requestBody.completed_step_count = clarificationContext!.completedStepCount; } else { requestBody.user_question = prompt; if (conversationHistory) requestBody.conversation_history = conversationHistory; } const messageBody = JSON.stringify(requestBody); const controller = new AbortController(); agentAbortRef.current = controller; const timeoutId = setTimeout(() => controller.abort(), config.formulateTimeoutSeconds * 6 * 1000); let allResults: any[] = []; let createdTables: DictTable[] = []; let createdCharts: Chart[] = []; let allNewConcepts: FieldItem[] = []; let isCompleted = false; let lastCreatedTableId: string | null = isResume ? clarificationContext!.lastCreatedTableId : null; const genTableId = () => { let tableSuffix = Number.parseInt((Date.now() - Math.floor(Math.random() * 10000)).toString().slice(-6)); let tId = `table-${tableSuffix}`; while (tables.find(t => t.id === tId) !== undefined) { tableSuffix += 1; tId = `table-${tableSuffix}`; } return tId; }; const processStreamingResult = (result: any) => { // Agent thinking / choosing next action if (result.type === "action" && result.action === "visualize") { const thinkingMsg = result.thought || "Thinking..."; const currentObserveId = lastCreatedTableId || focusedTableId; dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: thinkingMsg, status: 'running', hidden: false, message: { content: thinkingMsg, role: 'thinking', observeTableId: currentObserveId } })); } // Visualization result (same shape as old data_transformation) if (result.type === "result" && result.status === "success") { const transformResult = result.content.result; if (!transformResult || transformResult.status !== 'ok') return; const transformedData = transformResult.content; const code = transformResult.code; const dialog = transformResult.dialog; const refinedGoal = transformResult.refined_goal; const question = result.content.question; if (!transformedData || !transformedData.rows || transformedData.rows.length === 0) return; const rows = transformedData.rows; const candidateTableId = transformedData.virtual?.table_name || genTableId(); const displayInstruction = refinedGoal?.display_instruction || `Exploration step ${createdTables.length + 1}: ${question}`; // Chain from last created table, or focused table if first const triggerTableId = lastCreatedTableId || focusedTableId!; const candidateTable = createDictTable(candidateTableId, rows, undefined); candidateTable.derive = { code: code || `# Exploration step ${createdTables.length + 1}`, codeSignature: result.content?.result?.code_signature, outputVariable: refinedGoal?.output_variable || 'result_df', source: selectedTableIds, dialog: dialog || [], trigger: { tableId: triggerTableId, instruction: question, displayInstruction, chart: undefined, resultTableId: candidateTableId } }; if (transformedData.virtual) { candidateTable.virtual = { tableId: transformedData.virtual.table_name, rowCount: transformedData.virtual.row_count }; } // Bootstrap metadata from agent field_metadata (temporary until fetchFieldSemanticType completes) const fieldMetadata = refinedGoal?.['field_metadata']; if (fieldMetadata && typeof fieldMetadata === 'object') { for (const [fieldName, meta] of Object.entries(fieldMetadata)) { if (!candidateTable.metadata[fieldName]) continue; if (typeof meta === 'string') { // Plain string format: { "field": "SemanticType" } candidateTable.metadata[fieldName].semanticType = meta; } else if (typeof meta === 'object' && meta !== null) { // Dict format: { "field": { "semantic_type": "...", "unit": "...", ... } } const m = meta as Record<string, any>; if (m['semantic_type']) { candidateTable.metadata[fieldName].semanticType = m['semantic_type']; } if (m['unit']) { candidateTable.metadata[fieldName].unit = m['unit']; } if (m['intrinsic_domain']) { candidateTable.metadata[fieldName].intrinsicDomain = m['intrinsic_domain']; } } } } createdTables.push(candidateTable); const observedTableId = lastCreatedTableId || focusedTableId; // table the agent was looking at before this step lastCreatedTableId = candidateTableId; dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: displayInstruction, status: 'running', hidden: false, message: { content: displayInstruction, role: 'action', observeTableId: observedTableId, resultTableId: candidateTableId } })); const names = candidateTable.names; const missingNames = names.filter(name => !conceptShelfItems.some(field => field.name === name) && !allNewConcepts.some(concept => concept.name === name) ); const conceptsToAdd = missingNames.map(name => ({ id: `concept-${name}-${Date.now()}-${Math.random()}`, name, source: "custom", tableRef: "custom", } as FieldItem)); allNewConcepts.push(...conceptsToAdd); let triggerChart = generateFreshChart(actionTables[0].id, 'Auto') as Chart; triggerChart.source = 'trigger'; if (candidateTable.derive) { candidateTable.derive.trigger.chart = triggerChart; } // DataRecAgent always returns a refined_goal (with chart info), // so we can rely on it to create the chart for every step. const currentConcepts = [...conceptShelfItems.filter(c => names.includes(c.name)), ...allNewConcepts, ...conceptsToAdd]; let newChart = resolveRecommendedChart(refinedGoal, currentConcepts, candidateTable); createdCharts.push(newChart); dispatch(dfActions.addChart(newChart)); dispatch(dfActions.setFocused({ type: 'chart', chartId: newChart.id })); if (conceptsToAdd.length > 0) { dispatch(dfActions.addConceptItems(conceptsToAdd)); } dispatch(dfActions.insertDerivedTables(candidateTable)); dispatch(fetchFieldSemanticType(candidateTable)); dispatch(fetchCodeExpl(candidateTable)); if (createdCharts.length > 0) { const lastChart = createdCharts[createdCharts.length - 1]; setTimeout(() => { dispatch(fetchChartInsight({ chartId: lastChart.id, tableId: candidateTable.id }) as any); }, 1500); } } // Agent asks for clarification — pause and let user respond if (result.type === "clarify") { const clarifyMsg = result.message || "Could you clarify?"; dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: clarifyMsg, status: 'warning', hidden: false, message: { content: clarifyMsg, role: 'clarify' }, pendingClarification: { trajectory: result.trajectory || [], completedStepCount: result.completed_step_count || 0, lastCreatedTableId, } })); setIsChatFormulating(false); agentAbortRef.current = null; clearTimeout(timeoutId); setChatPrompt(""); isCompleted = true; // prevent handleCompletion from firing } }; const handleCompletion = () => { if (isCompleted) return; isCompleted = true; setIsChatFormulating(false); agentAbortRef.current = null; clearTimeout(timeoutId); const completionResult = allResults.find((r: any) => r.type === "completion"); if (completionResult) { const summary = completionResult.content.summary || completionResult.content.message || ""; const status: "completed" | "warning" = completionResult.status === "success" ? "completed" : "warning"; dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: summary, status, hidden: false, message: { content: summary, role: 'completion' } })); setChatPrompt(""); } else { dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: "The agent got lost in the data.", status: 'warning', hidden: false, message: { content: "The agent got lost in the data.", role: 'clarify' } })); } }; fetchWithIdentity(getUrls().DATA_AGENT_STREAMING, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: messageBody, signal: controller.signal }) .then(async (response) => { if (!response.ok) throw new Error(`HTTP error! status: ${response.status}`); const reader = response.body?.getReader(); if (!reader) throw new Error('No response body reader available'); const decoder = new TextDecoder(); let buffer = ''; try { while (true) { const { done, value } = await reader.read(); if (done) { handleCompletion(); break; } buffer += decoder.decode(value, { stream: true }); const lines = buffer.split('\n'); buffer = lines.pop() || ''; for (let line of lines) { if (line.trim() !== "") { try { const data = JSON.parse(line); if (data.token === token) { if (data.status === "ok" && data.result) { allResults.push(data.result); processStreamingResult(data.result); if (data.result.type === "completion" || data.result.type === "clarify") { handleCompletion(); return; } } else if (data.status === "error") { setIsChatFormulating(false); clearTimeout(timeoutId); dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: data.error_message || "Error during exploration", status: 'failed', hidden: false, message: { content: data.error_message || "Error during exploration", role: 'error' } })); return; } } } catch (parseError) { console.warn('Failed to parse streaming response:', parseError); } } } } } finally { reader.releaseLock(); } }) .catch((error) => { setIsChatFormulating(false); agentAbortRef.current = null; clearTimeout(timeoutId); const isCancelled = error.name === 'AbortError' && !isCompleted; const errorMessage = isCancelled ? "Exploration cancelled" : error.name === 'AbortError' ? "Exploration timed out" : `Exploration failed: ${error.message}`; dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: errorMessage, status: isCancelled ? 'warning' : 'failed', hidden: false, message: { content: errorMessage, role: isCancelled ? 'clarify' : 'error' } })); }); }, [focusedTableId, tables, activeModel, agentRules, config, conceptShelfItems, dispatch, relevantAgentActions]); const cancelAgent = useCallback(() => { if (agentAbortRef.current) { agentAbortRef.current.abort(); agentAbortRef.current = null; } // Also dismiss any pending clarification if (pendingClarification) { dispatch(dfActions.updateAgentWorkInProgress({ actionId: pendingClarification.actionId, description: "Conversation ended by user.", status: 'completed', hidden: false, message: { content: "Conversation ended by user.", role: 'completion' }, pendingClarification: null, })); } }, [pendingClarification, dispatch]); const gradientBorder = `linear-gradient(135deg, ${alpha(theme.palette.primary.main, 0.6)}, ${alpha(theme.palette.secondary.main, 0.55)})`; const workingBorder = `linear-gradient(135deg, ${alpha(theme.palette.primary.main, 0.3)}, ${alpha(theme.palette.secondary.main, 0.25)})`; const inputBox = ( <Card ref={inputCardRef} variant="outlined" sx={{ display: 'flex', flexDirection: 'column', mx: 1, mb: 1, mt: 0.5, px: 1, pt: 0.5, pb: 0.25, borderRadius: '8px', border: 'none', outline: 'none', position: 'relative', overflow: isChatFormulating ? 'hidden' : 'visible', flexShrink: 0, transition: 'box-shadow 0.2s ease, background-color 0.2s ease', ...(isChatFormulating ? { backgroundColor: alpha(theme.palette.action.disabledBackground, 0.06) } : {}), // Gradient border via pseudo-element (works with border-radius) '&::before': { content: '""', position: 'absolute', inset: 0, borderRadius: 'inherit', padding: '1.5px', background: isChatFormulating ? workingBorder : gradientBorder, WebkitMask: 'linear-gradient(#fff 0 0) content-box, linear-gradient(#fff 0 0)', WebkitMaskComposite: 'xor', maskComposite: 'exclude', pointerEvents: 'none', zIndex: 3, }, }} > {/* Show clarification question above input when agent is asking */} {clarificationQuestion && pendingClarification && !isChatFormulating && ( <Box sx={{ display: 'flex', alignItems: 'flex-start', gap: '6px', px: 0.5, py: '6px', borderBottom: `1px solid ${alpha(theme.palette.warning.main, 0.2)}`, backgroundColor: alpha(theme.palette.warning.main, 0.05), borderRadius: '8px 8px 0 0', mx: '-8px', mt: '-4px', mb: '4px', pt: '8px', pb: '6px', }}> <SmartToyOutlinedIcon sx={{ fontSize: 14, color: theme.palette.warning.main, mt: '1px', flexShrink: 0 }} /> <Typography sx={{ fontSize: 12, color: theme.palette.text.primary, lineHeight: 1.5, whiteSpace: 'pre-wrap', wordBreak: 'break-word' }}> {clarificationQuestion} </Typography> </Box> )} {/* Idea chips inline */} {ideas.length > 0 && !isChatFormulating && ( <Box sx={{ display: 'flex', flexDirection: 'column', gap: '4px', borderBottom: `1px solid ${alpha(theme.palette.secondary.main, 0.15)}`, backgroundColor: alpha(theme.palette.secondary.main, 0.03), borderRadius: clarificationQuestion ? 0 : '8px 8px 0 0', mx: '-8px', mt: clarificationQuestion ? 0 : '-4px', mb: '4px', px: '10px', pt: '6px', pb: '6px', }}> <Box sx={{ display: 'flex', alignItems: 'center', gap: '4px', mb: '2px' }}> <TipsAndUpdatesIcon sx={{ fontSize: 12, color: theme.palette.secondary.main }} /> <Typography sx={{ fontSize: 11, fontWeight: 600, color: theme.palette.secondary.main, flex: 1 }}>Ideas</Typography> <Tooltip title="Regenerate ideas"> <IconButton size="small" onClick={() => getIdeasFromAgent()} sx={{ p: '2px', color: theme.palette.text.secondary, '&:hover': { color: theme.palette.primary.main } }}> <RefreshIcon sx={{ fontSize: 12 }} /> </IconButton> </Tooltip> <Tooltip title="Clear ideas"> <IconButton size="small" onClick={() => setIdeas([])} sx={{ p: '2px', color: theme.palette.text.secondary, '&:hover': { color: theme.palette.error.main } }}> <ClearIcon sx={{ fontSize: 12 }} /> </IconButton> </Tooltip> </Box> <Box sx={{ display: 'flex', flexWrap: 'wrap', gap: '4px' }}> {ideas.map((idea, idx) => { const color = idea.difficulty === 'easy' ? theme.palette.success.main : idea.difficulty === 'hard' ? theme.palette.warning.main : theme.palette.primary.main; return ( <Box key={idx} sx={{ px: '6px', py: '3px', borderRadius: '4px', border: `1px solid ${alpha(color, 0.2)}`, backgroundColor: alpha(color, 0.04), cursor: 'pointer', transition: 'all 0.15s ease', '&:hover': { borderColor: alpha(color, 0.6), backgroundColor: alpha(color, 0.08) }, }} onClick={() => { setChatPrompt(idea.text); exploreFromChat(idea.text); }}> <Typography component="div" sx={{ fontSize: 10, lineHeight: 1.3, color }}> {renderTextWithEmphasis(idea.goal, { borderRadius: '0px', borderBottom: '1px solid', borderColor: alpha(color, 0.4), fontSize: '10px', lineHeight: 1.3, backgroundColor: alpha(color, 0.05), })} </Typography> </Box> ); })} </Box> </Box> )} <TextField variant="standard" sx={{ flex: 1, "& .MuiInput-input": { fontSize: '12px', lineHeight: 1.5 }, "& .MuiInput-underline:before": { borderBottom: 'none !important' }, "& .MuiInput-underline:hover:not(.Mui-disabled):before": { borderBottom: 'none !important' }, "& .MuiInput-underline:after": { borderBottom: 'none !important' }, "& .MuiInputBase-root": { borderBottom: 'none !important' }, ...(isChatFormulating ? { "& .MuiInput-input": { fontSize: '12px', lineHeight: 1.5, color: 'text.disabled' }, } : {}), }} onChange={(event: any) => { if (!isChatFormulating) setChatPrompt(event.target.value); }} onKeyDown={(event: any) => { if (event.key === 'Tab' && !event.shiftKey && chatPrompt.trim() === '' && !isChatFormulating) { event.preventDefault(); setChatPrompt('help me suggest some exploration directions from this thread'); } if (event.key === 'Enter' && !event.shiftKey) { event.preventDefault(); if (chatPrompt.trim().length > 0 && !isChatFormulating) { if (pendingClarification) { exploreFromChat(chatPrompt, pendingClarification); } else { exploreFromChat(chatPrompt); } } } }} onFocus={() => { // Scroll to the focused table card, positioning it near the bottom of the visible area const el = document.querySelector('.data-thread-card.selected-card') as HTMLElement | null; if (el) { // Find nearest scrollable ancestor let scrollContainer: HTMLElement | null = el.parentElement; while (scrollContainer) { const ov = getComputedStyle(scrollContainer).overflowY; if (ov === 'auto' || ov === 'scroll') break; scrollContainer = scrollContainer.parentElement; } if (scrollContainer) { const containerRect = scrollContainer.getBoundingClientRect(); const elRect = el.getBoundingClientRect(); // Place the element so its bottom sits ~80px above the container's bottom edge const targetBottom = containerRect.bottom - 80; const offset = elRect.bottom - targetBottom; scrollContainer.scrollBy({ top: offset, behavior: 'smooth' }); } else { el.scrollIntoView({ behavior: 'smooth', block: 'end' }); } } }} slotProps={{ inputLabel: { shrink: true }, input: { readOnly: isChatFormulating }, }} value={chatPrompt} placeholder={pendingClarification ? "Reply to agent's question..." : "Ask agent to explore a new direction"} fullWidth multiline minRows={2} maxRows={4} /> <Box sx={{ display: 'flex', flexDirection: 'row', alignItems: 'center', justifyContent: 'space-between', gap: 0.5 }}> {/* Action buttons */} <Box sx={{ display: 'flex', flexDirection: 'row', alignItems: 'center', gap: 0.5, overflow: 'hidden', flex: 1 }}> <Tooltip title="Add more data to the workspace"> <IconButton size="small" onClick={(e) => { e.stopPropagation(); setUploadDialogOpen(true); }} sx={{ p: 0, width: 18, height: 18, color: theme.palette.text.secondary, borderRadius: '4px', '&:hover': { color: theme.palette.primary.main, borderColor: alpha(theme.palette.primary.main, 0.5) } }} > <AddIcon sx={{ fontSize: 12 }} /> </IconButton> </Tooltip> </Box> <Box sx={{ display: 'flex', flexDirection: 'row', alignItems: 'center', gap: 0.25, flexShrink: 0 }}> {isChatFormulating ? ( <CircularProgress size={18} sx={{ m: 0.5 }} /> ) : ( <> <Tooltip title="Get idea suggestions"> <span> <IconButton size="small" sx={{ p: 0.5, color: theme.palette.secondary.main }} disabled={!focusedTableId || isLoadingIdeas} onClick={() => { if (ideas.length > 0) { setIdeas([]); } else { getIdeasFromAgent(); } }} > {isLoadingIdeas ? <CircularProgress size={18} sx={{ color: theme.palette.warning.main }} /> : <TipsAndUpdatesIcon sx={{ fontSize: 18 }} />} </IconButton> </span> </Tooltip> {pendingClarification && !isChatFormulating && ( <Tooltip title="End conversation"> <IconButton size="small" sx={{ p: 0.5, color: theme.palette.warning.main }} onClick={() => cancelAgent()} > <StopCircleOutlinedIcon sx={{ fontSize: 18 }} /> </IconButton> </Tooltip> )} <Tooltip title={pendingClarification ? "Send reply" : "Explore"}> <span> <IconButton size="small" color="primary" sx={{ p: 0.5 }} disabled={chatPrompt.trim().length === 0 || !focusedTableId} onClick={() => { if (pendingClarification) { exploreFromChat(chatPrompt, pendingClarification); } else { exploreFromChat(chatPrompt); } }} > <SendIcon sx={{ fontSize: 18 }} /> </IconButton> </span> </Tooltip> </> )} </Box> </Box> {/* Agent working overlay */} {(isChatFormulating || isLoadingIdeas) && ( <AgentWorkingOverlay relevantAgentActions={isLoadingIdeas && !isChatFormulating ? [{ status: 'running', description: 'Generating exploration ideas...' }] : relevantAgentActions} theme={theme} onCancel={isLoadingIdeas && !isChatFormulating ? () => { ideasAbortRef.current?.abort(); ideasAbortRef.current = null; setIsLoadingIdeas(false); setIdeas([]); } : cancelAgent} /> )} </Card> ); return ( <Box> {/* The input box */} {inputBox} <UnifiedDataUploadDialog open={uploadDialogOpen} onClose={() => setUploadDialogOpen(false)} initialTab="menu" /> </Box> ); }; ================================================ FILE: src/views/TableSelectionView.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import * as React from 'react'; import { useEffect, useState, useMemo } from 'react'; import Typography from '@mui/material/Typography'; import Box from '@mui/material/Box'; import { Button, Chip } from '@mui/material'; import { borderColor } from '../app/tokens'; import { StreamIcon } from '../icons'; import { createTableFromFromObjectArray } from '../data/utils'; import { MultiTablePreview } from './MultiTablePreview'; import { DictTable } from '../components/ComponentType'; // Update the interface to support multiple tables per dataset export interface DatasetMetadata { name: string; description: string; source: string; tables: { table_name: string; url: string; format: string; sample: any[]; }[]; // Live/streaming dataset properties live?: boolean; refreshIntervalSeconds?: number; } export interface DatasetSelectionViewProps { datasets: DatasetMetadata[]; handleSelectDataset: (datasetMetadata: DatasetMetadata) => void; hideRowNum?: boolean; } export const DatasetSelectionView: React.FC<DatasetSelectionViewProps> = function DatasetSelectionView({ datasets, handleSelectDataset, hideRowNum }) { const [selectedDatasetName, setSelectedDatasetName] = useState<string | undefined>(undefined); const [tableActiveIndex, setTableActiveIndex] = useState<number>(0); useEffect(() => { if (datasets.length > 0) { setSelectedDatasetName(datasets[0].name); } }, [datasets]); // Reset table active index when dataset changes useEffect(() => { setTableActiveIndex(0); }, [selectedDatasetName]); const handleDatasetSelect = (index: number) => { setSelectedDatasetName(datasets[index].name); }; let datasetTitles : string[] = []; for (let i = 0; i < datasets.length; i ++) { let k = 0; let title = datasets[i].name; while (datasetTitles.includes(title)) { k = k + 1; title = `${title}_${k}`; } datasetTitles.push(title); } // Convert dataset tables to DictTable objects for the preview const selectedDataset = datasets.find(d => d.name === selectedDatasetName); const previewTables: DictTable[] = useMemo(() => { if (!selectedDataset) return []; return selectedDataset.tables.map((table) => { const dictTable = createTableFromFromObjectArray(table.table_name, table.sample, true); // Use the table name from URL as displayId for better labeling const displayName = table.url.split("/").pop()?.split(".")[0]?.split("?")[0] || table.table_name; return { ...dictTable, displayId: displayName, }; }); }, [selectedDataset]); return ( <Box sx={{ bgcolor: 'background.paper', display: 'flex', height: '100%', borderRadius: 2, overflow: 'hidden' }} > {/* Button navigation */} <Box sx={{ minWidth: 180, maxWidth: 180, width: 180, display: 'flex', flexDirection: 'column', borderRight: `1px solid ${borderColor.view}`, overflow: 'hidden', height: '100%' }}> <Box sx={{ display: 'flex', flexDirection: 'column', overflowY: 'auto', overflowX: 'hidden', flex: 1, minHeight: 0, height: '100%', position: 'relative', overscrollBehavior: 'contain' }}> {datasetTitles.map((title, i) => ( <Button key={i} variant="text" size="small" color='primary' onClick={() => handleDatasetSelect(i)} sx={{ fontSize: 12, textTransform: "none", width: 180, justifyContent: 'flex-start', textAlign: 'left', borderRadius: 0, py: 1, px: 2, color: selectedDatasetName === title ? 'primary.main' : 'text.secondary', borderRight: selectedDatasetName === title ? 2 : 0, borderColor: 'primary.main', }} > <Box sx={{ display: 'flex', alignItems: 'center', gap: 0.5 }}> {datasets[i]?.live && ( <StreamIcon sx={{ fontSize: 14, color: 'success.main' }} /> )} <span>{title}</span> </Box> </Button> ))} </Box> </Box> {/* Content area */} <Box sx={{ flex: 1, overflow: 'hidden', minWidth: 0, minHeight: 0, height: '100%', position: 'relative' }}> <Box sx={{ height: '100%', overflowY: 'auto', overflowX: 'hidden', p: 2, minWidth: 0, overscrollBehavior: 'contain' }}> {selectedDataset && ( <Box> <Box sx={{mb: 1, gap: 1, maxWidth: 800, display: "flex", alignItems: "center", flexWrap: "wrap"}}> <Typography sx={{fontSize: 12, flex: 1, minWidth: 200}}> {selectedDataset.description} <Typography variant="caption" sx={{color: "primary.light", fontSize: 10, mx: 0.5}}>[from {selectedDataset.source}]</Typography> </Typography> </Box> <Box sx={{ maxWidth: 800 }}> <MultiTablePreview tables={previewTables} emptyLabel="No tables available." activeIndex={tableActiveIndex} onActiveIndexChange={setTableActiveIndex} maxHeight={280} maxRows={12} compact={false} showPreviewLabel={false} hideRowCount={hideRowNum} /> </Box> <Box sx={{display: 'flex', justifyContent: 'center', mt: 2}} > <Button variant="contained" sx={{ width: 240 }} onClick={(event: React.MouseEvent<HTMLElement>) => { handleSelectDataset(selectedDataset); }}> load dataset </Button> </Box> </Box> )} </Box> </Box> </Box> ); } ================================================ FILE: src/views/TestPanel.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React from 'react'; import { Box, Button, Typography, } from '@mui/material'; import embed from 'vega-embed'; import '../scss/VisualizationView.scss'; export interface TestPanelProps { } export interface TestPanelState { vgSpecs: any[] } export default class TestPanel extends React.Component<TestPanelProps, TestPanelState> { constructor(props: TestPanelProps) { super(props); this.state = { vgSpecs: [] }; } private processText(reader: any, state: any) : any { // Result objects contain two properties: // done - true if the stream has already given you all its data. // value - some data. Always undefined when done is true. let { done, value } = state; if (done) { console.log("Stream complete"); return; } // value for fetch streams is a Uint8Array const chunk = value; console.log(`Current chunk = ${chunk}`); if (value) { let vgObj = JSON.parse(value); let vgSpecs = this.state.vgSpecs; vgSpecs.push(vgObj); this.setState({ vgSpecs }); } // Read some more, and call this function again return reader.read().then((state: any) => this.processText(reader, state)); } private testStreamingChart = () => { fetch('http://127.0.0.1:5000/stream') // Retrieve its body as ReadableStream .then((response) => response.body) // Create a gray-scaled PNG stream out of the original .then((body) => (body as ReadableStream).pipeThrough(new TextDecoderStream()).getReader()) .then((reader) => { reader.read().then((state: any) => this.processText(reader, state)) }) } public render = () => { return ( <Box className="visualization-container"> <Typography variant="subtitle1" component="h2"> TestPanel </Typography> <Button variant="contained" onClick={()=>{this.testStreamingChart()}}>Contained</Button> <Box className="vega-container"> {this.state.vgSpecs.map((spec: any, index: any) => { const id = `chart-element-${index}`; const element = <div id={id} key={`chart-${index}`}></div>; embed('#' + id, spec); return element; })} </Box> </Box>); }; } ================================================ FILE: src/views/UnifiedDataUploadDialog.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import * as React from 'react'; import { useState, useCallback, useEffect, useRef } from 'react'; import { borderColor, transition, radius } from '../app/tokens'; import { Box, Button, Chip, Dialog, DialogContent, DialogTitle, IconButton, TextField, Typography, Tooltip, Link, Input, alpha, useTheme, } from '@mui/material'; import CloseIcon from '@mui/icons-material/Close'; import UploadFileIcon from '@mui/icons-material/UploadFile'; import ContentPasteIcon from '@mui/icons-material/ContentPaste'; import LinkIcon from '@mui/icons-material/Link'; import { StreamIcon } from '../icons'; import StorageIcon from '@mui/icons-material/Storage'; import ImageSearchIcon from '@mui/icons-material/ImageSearch'; import ExploreIcon from '@mui/icons-material/Explore'; import RestartAltIcon from '@mui/icons-material/RestartAlt'; import ArrowBackIcon from '@mui/icons-material/ArrowBack'; import Paper from '@mui/material/Paper'; import CircularProgress from '@mui/material/CircularProgress'; import Backdrop from '@mui/material/Backdrop'; import { useDispatch, useSelector } from 'react-redux'; import { DataFormulatorState, dfActions, fetchFieldSemanticType } from '../app/dfSlice'; import { AppDispatch } from '../app/store'; import { loadTable } from '../app/tableThunks'; import { DataSourceConfig, DictTable } from '../components/ComponentType'; import { createTableFromFromObjectArray, createTableFromText, loadTextDataWrapper, loadBinaryDataWrapper } from '../data/utils'; import { DataLoadingChat } from './DataLoadingChat'; import { DatasetSelectionView, DatasetMetadata } from './TableSelectionView'; import { getUrls, fetchWithIdentity } from '../app/utils'; import { DBManagerPane } from './DBTableManager'; import { MultiTablePreview } from './MultiTablePreview'; import { ToggleButton, ToggleButtonGroup, FormControlLabel, Switch, } from '@mui/material'; import FolderOpenIcon from '@mui/icons-material/FolderOpen'; import CloudIcon from '@mui/icons-material/Cloud'; import OpenInNewIcon from '@mui/icons-material/OpenInNew'; import LanguageIcon from '@mui/icons-material/Language'; export type UploadTabType = 'menu' | 'upload' | 'paste' | 'url' | 'database' | 'extract' | 'explore'; interface TabPanelProps { children?: React.ReactNode; index: UploadTabType; value: UploadTabType; } function TabPanel(props: TabPanelProps) { const { children, value, index, ...other } = props; return ( <div role="tabpanel" hidden={value !== index} id={`data-upload-tabpanel-${index}`} aria-labelledby={`data-upload-tab-${index}`} style={{ height: '100%', overflow: 'auto', boxSizing: 'border-box' }} {...other} > {value === index && children} </div> ); } // Data source menu card component interface DataSourceCardProps { icon: React.ReactNode; title: string; description: string; onClick: () => void; disabled?: boolean; } const DataSourceCard: React.FC<DataSourceCardProps> = ({ icon, title, description, onClick, disabled = false, }) => { const theme = useTheme(); const card = ( <Paper elevation={0} onClick={disabled ? undefined : onClick} sx={{ p: 1.5, cursor: disabled ? 'not-allowed' : 'pointer', border: `1px solid ${borderColor.divider}`, borderRadius: radius.sm, opacity: disabled ? 0.5 : 1, transition: transition.fast, display: 'flex', alignItems: 'center', gap: 1.5, '&:hover': disabled ? {} : { borderColor: 'primary.main', backgroundColor: alpha(theme.palette.primary.main, 0.04), } }} > <Box sx={{ color: disabled ? 'text.disabled' : 'primary.main', display: 'flex', alignItems: 'center', justifyContent: 'center', width: 32, height: 32, borderRadius: 1, backgroundColor: alpha(theme.palette.primary.main, 0.08), flexShrink: 0, '& .MuiSvgIcon-root': { fontSize: 18 } }}> {icon} </Box> <Box sx={{ flex: 1, minWidth: 0 }}> <Typography variant="body2" sx={{ fontWeight: 500, color: disabled ? 'text.disabled' : 'text.primary', }} > {title} </Typography> <Typography variant="caption" sx={{ color: disabled ? 'text.disabled' : 'text.secondary', display: '-webkit-box', WebkitLineClamp: 2, WebkitBoxOrient: 'vertical', overflow: 'hidden', lineHeight: 1.3, mt: 0.25, }} > {description} </Typography> </Box> </Paper> ); return card; }; const getUniqueTableName = (baseName: string, existingNames: Set<string>): string => { let uniqueName = baseName; let counter = 1; while (existingNames.has(uniqueName)) { uniqueName = `${baseName}_${counter}`; counter++; } return uniqueName; }; // Reusable Data Load Menu Component export interface DataLoadMenuProps { onSelectTab: (tab: UploadTabType) => void; serverConfig?: { DISABLE_DATABASE?: boolean }; variant?: 'dialog' | 'page'; // 'dialog' uses smaller cards, 'page' uses larger cards } export const DataLoadMenu: React.FC<DataLoadMenuProps> = ({ onSelectTab, serverConfig = { DISABLE_DATABASE: false }, variant = 'dialog' }) => { const theme = useTheme(); // Data source configurations const regularDataSources = [ { value: 'explore' as UploadTabType, title: 'Sample Datasets', description: 'Explore and load curated example datasets', icon: <ExploreIcon />, disabled: false }, { value: 'upload' as UploadTabType, title: 'Upload File', description: 'Upload local files (CSV, TSV, JSON, Excel)', icon: <UploadFileIcon />, disabled: false }, { value: 'paste' as UploadTabType, title: 'Paste Data', description: 'Paste tabular data directly from clipboard', icon: <ContentPasteIcon />, disabled: false }, { value: 'extract' as UploadTabType, title: 'Extract Unstructured Data', description: 'Extract tables from images or text using AI', icon: <ImageSearchIcon />, disabled: false }, ]; const liveDataSources = [ { value: 'url' as UploadTabType, title: 'Load from URL', description: 'Load data from a URL with optional auto-refresh', icon: <LinkIcon />, disabled: false }, { value: 'database' as UploadTabType, title: 'Database', description: 'Connect to databases or data services', icon: <StorageIcon />, disabled: false }, ]; if (variant === 'page') { // Page variant: 3-column grid, first column for liveDataSources, second 2 columns for regularDataSources return ( <Box sx={{ width: '100%', display: 'grid', gridTemplateColumns: 'minmax(0, 1fr) repeat(2, minmax(0, 1fr))', gridTemplateRows: 'auto repeat(2, auto)', gap: 1.5, rowGap: 2, mx: 0, textAlign: 'left', }}> {/* Section Titles */} <Typography variant="body2" color="text.secondary" sx={{ gridColumn: 1, gridRow: 1, textAlign: 'left', letterSpacing: '0.02em', display: 'flex', alignItems: 'center', gap: 1, position: 'relative', zIndex: 1, marginRight: 3, // Extra space between first column and other columns }} > <StreamIcon sx={{ fontSize: 14, animation: 'pulse 2s infinite', '@keyframes pulse': { '0%': { opacity: 1, color: 'primary.main' }, '50%': { opacity: 0.5, color: 'primary.light' }, '100%': { opacity: 1, color: 'primary.main' }, }, }} /> Connect to live data sources </Typography> <Typography variant="body2" color="text.secondary" sx={{ gridColumn: '2 / 3', gridRow: 1, textAlign: 'left', letterSpacing: '0.02em' }} > Load local data </Typography> {/* Background for Live Data Column */} <Box sx={{ gridColumn: 1, gridRow: '1 / -1', backgroundColor: alpha(theme.palette.primary.main, 0.03), borderRadius: 1, position: 'relative', zIndex: 0, // Extend into gaps to create continuous background marginTop: '-16px', // Extend into row gaps (2 * 8px = 16px) marginBottom: '-16px', marginLeft: '-12px', // Extend into left column gap (1.5 * 8px = 12px) marginRight: '12px', // Extra space between first column and other columns (3 * 8px = 24px total) paddingTop: '16px', paddingBottom: '16px', paddingLeft: '12px', paddingRight: '12px', }} /> {/* Live Data Sources - fill last column, 2 rows */} {liveDataSources.map((source, index) => ( <Box key={source.value} sx={{ gridColumn: 1, gridRow: index + 2, // Start from row 2 (after title row) position: 'relative', zIndex: 1, marginRight: 3, // Extra space between first column and other columns }} > <DataSourceCard icon={source.icon} title={source.title} description={source.description} onClick={() => onSelectTab(source.value)} disabled={source.disabled} /> </Box> ))} {/* Regular Data Sources - fill first 2 columns, 2 rows */} {regularDataSources.map((source, index) => ( <Box key={source.value} sx={{ gridColumn: (index % 2) + 2, gridRow: Math.floor(index / 2) + 2, // Start from row 2 (after title row) }} > <DataSourceCard icon={source.icon} title={source.title} description={source.description} onClick={() => onSelectTab(source.value)} disabled={source.disabled} /> </Box> ))} </Box> ); } // Dialog variant: original two-section layout return ( <Box sx={{ width: '100%', maxWidth: 860, display: 'flex', flexDirection: 'column', gap: 2, mx: 0, textAlign: 'left', }}> {/* Local Data Sources */} <Typography variant="body2" color="text.secondary" sx={{ textAlign: 'left', mb: 1, mt: 1, opacity: 0.6, fontSize: '0.75rem', letterSpacing: '0.02em' }} > Local data </Typography> <Box sx={{ display: 'grid', gridTemplateColumns: 'repeat(2, minmax(0, 1fr))', gap: 1.5, mb: 0, }}> {regularDataSources.map((source) => ( <DataSourceCard key={source.value} icon={source.icon} title={source.title} description={source.description} onClick={() => onSelectTab(source.value)} disabled={source.disabled} /> ))} </Box> {/* Live Data Sources */} <Typography variant="body2" color="text.secondary" sx={{ textAlign: 'left', my: 1, opacity: 0.6, fontSize: '0.75rem', letterSpacing: '0.02em', display: 'flex', alignItems: 'center', gap: 1, }} > <StreamIcon sx={{ fontSize: 14, animation: 'pulse 2s infinite', '@keyframes pulse': { '0%': { opacity: 1, color: 'primary.main' }, '50%': { opacity: 0.5, color: 'primary.light' }, '100%': { opacity: 1, color: 'primary.main' }, }, }} /> Or connect to a data source (with optional auto-refresh) </Typography> <Box sx={{ display: 'grid', gridTemplateColumns: 'repeat(2, minmax(0, 1fr))', gap: 1.5, }}> {liveDataSources.map((source) => ( <DataSourceCard key={source.value} icon={source.icon} title={source.title} description={source.description} onClick={() => onSelectTab(source.value)} disabled={source.disabled} /> ))} </Box> </Box> ); }; export interface UnifiedDataUploadDialogProps { open: boolean; onClose: () => void; initialTab?: UploadTabType; } export const UnifiedDataUploadDialog: React.FC<UnifiedDataUploadDialogProps> = ({ open, onClose, initialTab = 'menu', }) => { const theme = useTheme(); const dispatch = useDispatch<AppDispatch>(); const existingTables = useSelector((state: DataFormulatorState) => state.tables); const serverConfig = useSelector((state: DataFormulatorState) => state.serverConfig); const dataCleanBlocks = useSelector((state: DataFormulatorState) => state.dataCleanBlocks); const frontendRowLimit = useSelector((state: DataFormulatorState) => state.config?.frontendRowLimit ?? 50000); const existingNames = new Set(existingTables.map(t => t.id)); const [activeTab, setActiveTab] = useState<UploadTabType>(initialTab === 'menu' ? 'menu' : initialTab); const fileInputRef = useRef<HTMLInputElement>(null); const urlInputRef = useRef<HTMLInputElement>(null); // Store on server toggle (forced off when DISABLE_DATABASE) const diskPersistenceDisabled = serverConfig.DISABLE_DATABASE; const [storeOnServer, setStoreOnServer] = useState<boolean>(!diskPersistenceDisabled); // When serverConfig loads and database is enabled, default to store on server useEffect(() => { if (!diskPersistenceDisabled) { setStoreOnServer(true); } }, [diskPersistenceDisabled]); // Paste tab state const [pasteContent, setPasteContent] = useState<string>(""); const [isLargeContent, setIsLargeContent] = useState<boolean>(false); const [showFullContent, setShowFullContent] = useState<boolean>(false); const [isOverSizeLimit, setIsOverSizeLimit] = useState<boolean>(false); // File preview state const [filePreviewTables, setFilePreviewTables] = useState<DictTable[] | null>(null); const [filePreviewLoading, setFilePreviewLoading] = useState<boolean>(false); const [filePreviewError, setFilePreviewError] = useState<string | null>(null); const [filePreviewFiles, setFilePreviewFiles] = useState<File[]>([]); const [filePreviewActiveIndex, setFilePreviewActiveIndex] = useState<number>(0); // URL tab state (separate from file upload) const [tableURL, setTableURL] = useState<string>(""); const [urlAutoRefresh, setUrlAutoRefresh] = useState<boolean>(false); const [urlRefreshInterval, setUrlRefreshInterval] = useState<number>(60); // default 60 seconds const [urlPreviewTables, setUrlPreviewTables] = useState<DictTable[] | null>(null); const [urlPreviewLoading, setUrlPreviewLoading] = useState<boolean>(false); const [urlPreviewError, setUrlPreviewError] = useState<string | null>(null); const [urlPreviewActiveIndex, setUrlPreviewActiveIndex] = useState<number>(0); // Example URLs state const [exampleUrls, setExampleUrls] = useState<Array<{ label: string; url: string; refreshSeconds: number; resetUrl?: string }>>([]); // Sample datasets state const [datasetPreviews, setDatasetPreviews] = useState<DatasetMetadata[]>([]); // Loading state for dataset loading const [datasetLoading, setDatasetLoading] = useState<boolean>(false); const [datasetLoadingLabel, setDatasetLoadingLabel] = useState<string>(''); // Constants const MAX_DISPLAY_LINES = 20; const LARGE_CONTENT_THRESHOLD = 50000; const MAX_CONTENT_SIZE = 2 * 1024 * 1024; // Update active tab when initialTab changes useEffect(() => { if (open) { setActiveTab(initialTab === 'menu' ? 'menu' : initialTab); } }, [initialTab, open]); // Load sample datasets useEffect(() => { if (open && activeTab === 'explore') { fetchWithIdentity(`${getUrls().EXAMPLE_DATASETS}`) .then((response) => response.json()) .then((result) => { let datasets: DatasetMetadata[] = result.map((info: any) => { let tables = info["tables"].map((table: any) => { if (table["format"] == "json") { return { table_name: table["name"], url: table["url"], format: table["format"], sample: table["sample"], } } else if (table["format"] == "csv" || table["format"] == "tsv") { const delimiter = table["format"] === "csv" ? "," : "\t"; const rows = table["sample"] .split("\n") .map((row: string) => row.split(delimiter)); if (rows.length > 0) { const headers = rows[0]; const dataRows = rows.slice(1); const sampleData = dataRows.map((row: string[]) => { const obj: any = {}; headers.forEach((header: string, index: number) => { obj[header] = row[index] || ''; }); return obj; }); return { table_name: table["name"], url: table["url"], format: table["format"], sample: sampleData, }; } return { table_name: table["name"], url: table["url"], format: table["format"], sample: [], }; } }) return { tables: tables, name: info["name"], source: info["source"], live: info["live"], refreshIntervalSeconds: info["refreshIntervalSeconds"] } }).filter((t: DatasetMetadata | undefined) => t != undefined); setDatasetPreviews(datasets); }); } else if (open && activeTab === 'url') { fetchWithIdentity(`${window.location.origin}/api/demo-stream/info`) .then(res => res.json()) .then(data => { const demoExamples = data.demo_examples .map((ex: any) => ({ label: ex.name, url: ex.url, refreshSeconds: ex.refresh_seconds || 60, resetUrl: ex.reset_url || undefined, })); setExampleUrls(demoExamples); }) .catch((err) => { console.error('Failed to load examples:', err); }) .finally(() => { }); } }, [open, activeTab]); const handleClose = useCallback(() => { // Reset state when closing setPasteContent(""); setIsLargeContent(false); setIsOverSizeLimit(false); setShowFullContent(false); setFilePreviewTables(null); setFilePreviewLoading(false); setFilePreviewError(null); setFilePreviewFiles([]); // Reset URL tab state setTableURL(""); setUrlAutoRefresh(false); setUrlRefreshInterval(60); setUrlPreviewTables(null); setUrlPreviewLoading(false); setUrlPreviewError(null); setUrlPreviewActiveIndex(0); setExampleUrls([]); onClose(); }, [onClose]); // File upload handler const handleFileUpload = (event: React.ChangeEvent<HTMLInputElement>): void => { const files = event.target.files; if (files && files.length > 0) { const selectedFiles = Array.from(files); setFilePreviewFiles(selectedFiles); setFilePreviewError(null); setFilePreviewTables(null); setFilePreviewLoading(true); const MAX_FILE_SIZE = 5 * 1024 * 1024; const previewTables: DictTable[] = []; const errors: string[] = []; const processFiles = async () => { for (const file of selectedFiles) { const uniqueName = getUniqueTableName(file.name, existingNames); const isTextFile = file.type === 'text/csv' || file.type === 'text/tab-separated-values' || file.type === 'application/json' || file.name.endsWith('.csv') || file.name.endsWith('.tsv') || file.name.endsWith('.json'); const isExcelFile = file.type === 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet' || file.type === 'application/vnd.ms-excel' || file.name.endsWith('.xlsx') || file.name.endsWith('.xls'); if (file.size > MAX_FILE_SIZE && isTextFile) { errors.push(`File ${file.name} is too large (${(file.size / (1024 * 1024)).toFixed(2)}MB). Use Database for large files.`); continue; } if (isTextFile) { try { const text = await file.text(); const table = loadTextDataWrapper(uniqueName, text, file.type); if (table) { previewTables.push(table); } else { errors.push(`Failed to parse ${file.name}.`); } } catch { errors.push(`Failed to read ${file.name}.`); } continue; } if (isExcelFile) { try { const arrayBuffer = await file.arrayBuffer(); const tables = await loadBinaryDataWrapper(uniqueName, arrayBuffer); if (tables.length > 0) { previewTables.push(...tables); } else { errors.push(`Failed to parse Excel file ${file.name}.`); } } catch { errors.push(`Failed to parse Excel file ${file.name}.`); } continue; } errors.push(`Unsupported file format: ${file.name}.`); } setFilePreviewTables(previewTables.length > 0 ? previewTables : null); setFilePreviewError(errors.length > 0 ? errors.join(' ') : null); setFilePreviewLoading(false); }; processFiles(); } if (fileInputRef.current) { fileInputRef.current.value = ''; } }; // Reset activeIndex when tables change useEffect(() => { if (filePreviewTables && filePreviewTables.length > 0) { if (filePreviewActiveIndex >= filePreviewTables.length) { setFilePreviewActiveIndex(filePreviewTables.length - 1); } } else { setFilePreviewActiveIndex(0); } }, [filePreviewTables, filePreviewActiveIndex]); const handleFileLoadSingleTable = (): void => { if (!filePreviewTables || filePreviewTables.length === 0) { return; } const table = filePreviewTables[filePreviewActiveIndex]; if (table) { const sourceConfig: DataSourceConfig = { type: 'file', fileName: filePreviewFiles[0]?.name }; const tableWithSource = { ...table, source: sourceConfig }; dispatch(loadTable({ table: tableWithSource, storeOnServer, file: storeOnServer ? filePreviewFiles[filePreviewActiveIndex] || filePreviewFiles[0] : undefined, })); handleClose(); } }; const handleFileLoadAllTables = (): void => { if (!filePreviewTables || filePreviewTables.length === 0) { return; } for (let i = 0; i < filePreviewTables.length; i++) { const table = filePreviewTables[i]; const sourceConfig: DataSourceConfig = { type: 'file', fileName: filePreviewFiles[i]?.name || filePreviewFiles[0]?.name }; const tableWithSource = { ...table, source: sourceConfig }; dispatch(loadTable({ table: tableWithSource, storeOnServer, file: storeOnServer ? filePreviewFiles[i] || filePreviewFiles[0] : undefined, })); } handleClose(); }; const handleRemoveFilePreviewTable = (index: number): void => { setFilePreviewTables((prev) => { if (!prev) return prev; const next = prev.filter((_, i) => i !== index); return next.length > 0 ? next : null; }); }; // Paste content handler const handleContentChange = useCallback((event: React.ChangeEvent<HTMLInputElement>) => { const newContent = event.target.value; setPasteContent(newContent); const contentSizeBytes = new Blob([newContent]).size; const isOverLimit = contentSizeBytes > MAX_CONTENT_SIZE; setIsOverSizeLimit(isOverLimit); const isLarge = newContent.length > LARGE_CONTENT_THRESHOLD; setIsLargeContent(isLarge); // If switching from large to small content, ensure full content is shown if (!isLarge) { setShowFullContent(true); } }, []); const toggleFullContent = useCallback(() => { setShowFullContent(!showFullContent); }, [showFullContent]); const handlePasteSubmit = (): void => { let table: undefined | DictTable = undefined; const defaultName = (() => { const hashStr = pasteContent.substring(0, 100) + Date.now(); const hashCode = hashStr.split('').reduce((acc, char) => { return ((acc << 5) - acc) + char.charCodeAt(0) | 0; }, 0); const shortHash = Math.abs(hashCode).toString(36).substring(0, 4); return `data-${shortHash}`; })(); const uniqueName = getUniqueTableName(defaultName, existingNames); try { let content = JSON.parse(pasteContent); table = createTableFromFromObjectArray(uniqueName, content, true); } catch (error) { table = createTableFromText(uniqueName, pasteContent); } if (table) { // Add source info for paste data const tableWithSource = { ...table, source: { type: 'paste' as const } }; dispatch(loadTable({ table: tableWithSource, storeOnServer })); handleClose(); } }; const handleURLPreview = (urlToUse: string): void => { if (!urlToUse) { return; } setUrlPreviewLoading(true); setUrlPreviewError(null); setUrlPreviewTables(null); // Support relative URLs by constructing full URL let fullUrl = urlToUse; if (urlToUse.startsWith('/')) { fullUrl = window.location.origin + urlToUse; } let parts = urlToUse.split('/'); const baseName = parts[parts.length - 1]?.split('?')[0] || 'dataset'; const tableName = getUniqueTableName(baseName.replace(/\.[^.]+$/, ''), existingNames); fetch(fullUrl) .then(res => { if (!res.ok) { throw new Error(`HTTP ${res.status}: ${res.statusText}`); } return res.text(); }) .then(content => { let table: undefined | DictTable = undefined; try { // Try parsing as JSON first let jsonContent = JSON.parse(content); if (!Array.isArray(jsonContent)) { throw new Error('JSON content must be an array of objects.'); } table = createTableFromFromObjectArray(tableName, jsonContent, true); } catch (jsonError) { // If JSON parsing fails, try JSONL (JSON Lines) format try { const lines = content.trim().split('\n').filter(line => line.trim() !== ''); const jsonlObjects = lines.map(line => { try { return JSON.parse(line); } catch (e) { throw new Error(`Invalid JSONL line: ${line.substring(0, 50)}...`); } }); if (jsonlObjects.length > 0 && typeof jsonlObjects[0] === 'object' && jsonlObjects[0] !== null) { table = createTableFromFromObjectArray(tableName, jsonlObjects, true); } else { throw new Error('JSONL must contain objects.'); } } catch (jsonlError) { // If JSONL parsing fails, try CSV/TSV table = createTableFromText(tableName, content); } } if (table) { setUrlPreviewTables([table]); } else { setUrlPreviewError('Unable to parse data from the provided URL. Please ensure the URL points to CSV, JSON, or JSONL data.'); } }) .catch((err) => { setUrlPreviewError(`Failed to fetch data: ${err.message}. Please ensure the URL points to CSV, JSON, or JSONL data.`); }) .finally(() => { setUrlPreviewLoading(false); }); }; // URL tab load handlers const handleURLLoadSingleTable = (): void => { if (!urlPreviewTables || urlPreviewTables.length === 0) { return; } const table = urlPreviewTables[urlPreviewActiveIndex]; if (table) { let sourceConfig: DataSourceConfig; if (urlAutoRefresh) { sourceConfig = { type: 'stream', url: tableURL, autoRefresh: true, refreshIntervalSeconds: urlRefreshInterval, lastRefreshed: Date.now() }; } else { sourceConfig = { type: 'url', url: tableURL }; } const tableWithSource = { ...table, source: sourceConfig }; dispatch(loadTable({ table: tableWithSource, storeOnServer })); handleClose(); } }; const handleURLLoadAllTables = (): void => { if (!urlPreviewTables || urlPreviewTables.length === 0) { return; } for (let i = 0; i < urlPreviewTables.length; i++) { const table = urlPreviewTables[i]; let sourceConfig: DataSourceConfig; if (urlAutoRefresh) { sourceConfig = { type: 'stream', url: tableURL, autoRefresh: true, refreshIntervalSeconds: urlRefreshInterval, lastRefreshed: Date.now() }; } else { sourceConfig = { type: 'url', url: tableURL }; } const tableWithSource = { ...table, source: sourceConfig }; dispatch(loadTable({ table: tableWithSource, storeOnServer })); } handleClose(); }; const handleRemoveUrlPreviewTable = (index: number): void => { setUrlPreviewTables((prev) => { if (!prev) return prev; const next = prev.filter((_, i) => i !== index); return next.length > 0 ? next : null; }); }; // URL validation - allow common data file extensions and API endpoints const hasValidUrl = (tableURL || '').trim() !== '' && ( (tableURL || '').startsWith('http://') || (tableURL || '').startsWith('https://') || (tableURL || '').startsWith('/') ); const hasMultipleFileTables = (filePreviewTables?.length || 0) > 1; const hasMultipleUrlTables = (urlPreviewTables?.length || 0) > 1; const showFilePreview = filePreviewLoading || !!filePreviewError || (filePreviewTables && filePreviewTables.length > 0); const showUrlPreview = urlPreviewLoading || !!urlPreviewError || (urlPreviewTables && urlPreviewTables.length > 0); const hasPasteContent = (pasteContent || '').trim() !== ''; // Get current tab title for header const getCurrentTabTitle = () => { const tabTitles: Record<UploadTabType, string> = { 'menu': 'Load Data', 'explore': 'Sample Datasets', 'upload': 'Upload File', 'paste': 'Paste Data', 'extract': 'Extract from Documents', 'url': 'Load from URL', 'database': 'Database', }; return tabTitles[activeTab] || 'Add Data'; }; return ( <Dialog open={open} onClose={handleClose} maxWidth={false} sx={{ '& .MuiDialog-paper': { width: 1100, maxWidth: '95vw', height: 600, maxHeight: '90vh', display: 'flex', flexDirection: 'column', transition: 'width 0.2s ease', } }} > <DialogTitle sx={{ display: 'flex', alignItems: 'center', gap: 1, pb: 1 }}> {activeTab !== 'menu' && ( <IconButton size="small" onClick={() => setActiveTab('menu')} sx={{ mr: 0.5 }} > <ArrowBackIcon fontSize="small" /> </IconButton> )} <Typography variant="h6" component="span"> {activeTab === 'menu' ? 'Load Data' : getCurrentTabTitle()} </Typography> {activeTab === 'extract' && dataCleanBlocks.length > 0 && ( <Tooltip title="Reset extraction"> <IconButton size="small" color='warning' sx={{ '&:hover': { transform: 'rotate(180deg)', transition: 'transform 0.4s cubic-bezier(0.4, 0, 0.2, 1)' } }} onClick={() => dispatch(dfActions.resetDataCleanBlocks())} > <RestartAltIcon fontSize="small" /> </IconButton> </Tooltip> )} {activeTab !== 'menu' && ( <Box sx={{ ml: 'auto', mr: 0, display: 'flex', alignItems: 'center', gap: 0.5 }}> <Typography variant="caption" sx={{ fontSize: '0.7rem', color: 'text.secondary', mr: 0.5 }}> Load data in </Typography> <ToggleButtonGroup value={storeOnServer ? 'disk' : 'browser'} exclusive onChange={(_, val) => { if (val) setStoreOnServer(val === 'disk'); }} size="small" sx={{ height: 26, '& .MuiToggleButton-root': { textTransform: 'none', fontSize: '0.7rem', px: 1, py: 0 } }} > <ToggleButton value="browser"> <Tooltip title={`Data stays in browser only (limited to ${frontendRowLimit.toLocaleString()} rows)`} placement="bottom"> <Box sx={{ display: 'flex', alignItems: 'center', gap: 0.5 }}> <LanguageIcon sx={{ fontSize: 14 }} /> Browser </Box> </Tooltip> </ToggleButton> <ToggleButton value="disk" disabled={diskPersistenceDisabled}> <Tooltip title={diskPersistenceDisabled ? 'Install Data Formulator locally to unlock analysis for large datasets' : serverConfig.WORKSPACE_BACKEND === 'azure_blob' ? 'Data stored in Azure Blob Storage (supports large tables)' : 'Data stored in workspace on disk (supports large tables)'} placement="bottom"> <Box sx={{ display: 'flex', alignItems: 'center', gap: 0.5 }}> {serverConfig.WORKSPACE_BACKEND === 'azure_blob' ? <><CloudIcon sx={{ fontSize: 14 }} /> Azure</> : <><FolderOpenIcon sx={{ fontSize: 14 }} /> Disk</>} </Box> </Tooltip> </ToggleButton> </ToggleButtonGroup> {storeOnServer && !diskPersistenceDisabled && serverConfig.DATA_FORMULATOR_HOME && serverConfig.WORKSPACE_BACKEND !== 'azure_blob' && ( <Tooltip title={`Open workspace: ${serverConfig.DATA_FORMULATOR_HOME}`} placement="bottom"> <IconButton size="small" onClick={() => { fetchWithIdentity(getUrls().OPEN_WORKSPACE, { method: 'POST' }).catch(() => {}); }} sx={{ p: 0.5 }} > <OpenInNewIcon sx={{ fontSize: 14, color: 'text.secondary' }} /> </IconButton> </Tooltip> )} </Box> )} <IconButton sx={{ marginLeft: activeTab === 'menu' ? 'auto' : undefined }} size="small" onClick={handleClose} aria-label="close" > <CloseIcon fontSize="small" /> </IconButton> </DialogTitle> <DialogContent sx={{ flex: 1, overflow: 'hidden', p: 0 }}> {/* Main Menu */} <TabPanel value={activeTab} index="menu"> <Box sx={{ p: 2, boxSizing: 'border-box', width: '100%', height: '100%', display: 'flex', justifyContent: 'center', alignItems: 'center' }}> <DataLoadMenu onSelectTab={(tab) => setActiveTab(tab)} serverConfig={serverConfig} variant="dialog" /> </Box> </TabPanel> {/* Upload File Tab */} <TabPanel value={activeTab} index="upload"> <Box sx={{ display: 'flex', flexDirection: 'column', height: '100%', boxSizing: 'border-box', gap: 2, p: 2, justifyContent: showFilePreview ? 'flex-start' : 'center', }}> <Box sx={{ width: '100%', maxWidth: showFilePreview ? '60%' : 760, alignSelf: 'center', display: 'flex', flexDirection: 'column', gap: 2 }}> <Input inputProps={{ accept: '.csv,.tsv,.json,.xlsx,.xls', multiple: true, }} id="unified-upload-data-file" type="file" sx={{ display: 'none' }} inputRef={fileInputRef} onChange={handleFileUpload} /> {/* File Upload Section - only show drop zone when file upload is enabled */} {!serverConfig.DISABLE_FILE_UPLOAD ? ( <Box sx={{ border: '2px dashed', borderColor: borderColor.divider, borderRadius: radius.md, p: showFilePreview ? 2 : 3, textAlign: 'center', cursor: 'pointer', transition: transition.normal, '&:hover': { borderColor: 'primary.main', backgroundColor: alpha(theme.palette.primary.main, 0.04), } }} onClick={() => fileInputRef.current?.click()} > <UploadFileIcon sx={{ fontSize: showFilePreview ? 28 : 36, color: 'text.secondary', mb: 1 }} /> <Typography variant={showFilePreview ? "body2" : "subtitle1"} gutterBottom> Drag & drop file here </Typography> <Typography variant="body2" color="text.secondary" sx={{ fontSize: showFilePreview ? '0.75rem' : '0.875rem' }}> or <Link component="button" sx={{ textDecoration: 'underline', cursor: 'pointer' }}>Browse</Link> </Typography> {!showFilePreview && ( <Typography variant="caption" color="text.secondary" sx={{ mt: 1, display: 'block' }}> Supported: CSV, TSV, JSON, Excel (xlsx, xls) </Typography> )} </Box> ) : ( <Box sx={{ textAlign: 'center', py: 4, px: 2 }}> <Typography color="text.secondary" sx={{ mb: 2 }}> File upload is disabled in this environment. </Typography> <Typography variant="body2" color="text.secondary"> Use "Load from URL" to load data from a remote source. </Typography> </Box> )} </Box> {showFilePreview && ( <Box sx={{ width: '90%', alignSelf: 'center' }}> <MultiTablePreview loading={filePreviewLoading} error={filePreviewError} tables={filePreviewTables} emptyLabel="Select a file to preview." onRemoveTable={handleRemoveFilePreviewTable} activeIndex={filePreviewActiveIndex} onActiveIndexChange={setFilePreviewActiveIndex} /> </Box> )} {filePreviewTables && filePreviewTables.length > 0 && ( <Box sx={{ display: 'flex', justifyContent: 'center', gap: 1, alignItems: 'center' }}> <Button variant="outlined" onClick={handleFileLoadSingleTable} disabled={filePreviewLoading} sx={{ textTransform: 'none', width: 240 }} > Load Table </Button> {hasMultipleFileTables && ( <Button variant="contained" onClick={handleFileLoadAllTables} disabled={filePreviewLoading} sx={{ textTransform: 'none', width: 240 }} > Load All Tables </Button> )} </Box> )} </Box> </TabPanel> {/* URL Tab */} <TabPanel value={activeTab} index="url"> <Box sx={{ display: 'flex', flexDirection: 'column', height: '100%', boxSizing: 'border-box', gap: 2, p: 2, justifyContent: showUrlPreview ? 'flex-start' : 'center', }}> <Box sx={{ width: '100%', maxWidth: showUrlPreview ? '80%' : 760, alignSelf: 'center', display: 'flex', flexDirection: 'column', gap: 2 }}> {/* URL Input */} <Box sx={{ display: 'flex', flexDirection: 'column', gap: 0.5 }}> <Box sx={{ display: 'flex', alignItems: 'center', gap: 1 }}> <TextField fullWidth placeholder="Enter URL: https://example.com/data.json or /api/data" value={tableURL || ''} onChange={(e) => setTableURL((e.target.value || '').trim())} inputRef={urlInputRef} error={tableURL !== "" && !hasValidUrl} helperText={tableURL !== "" && !hasValidUrl ? "Enter a valid URL starting with http://, https://, or /" : undefined} size="small" sx={{ flex: 1, '& .MuiInputBase-input': { fontSize: '0.875rem', }, '& .MuiInputBase-input::placeholder': { fontSize: '0.875rem', }, }} /> <Button variant="outlined" size="small" onClick={() => handleURLPreview(tableURL || '')} disabled={!hasValidUrl || urlPreviewLoading} sx={{ textTransform: 'none', whiteSpace: 'nowrap' }} > Preview </Button> </Box> <Typography variant="caption" color="text.secondary" sx={{ fontSize: '0.75rem', ml: 0.5 }}> The URL must point to data in CSV, JSON, or JSONL format </Typography> </Box> {/* Watch/Auto-refresh options - always visible */} <Paper variant="outlined" sx={{ p: 2, borderRadius: 1 }}> <Box sx={{ display: 'flex', flexDirection: 'row', gap: 1, alignItems: 'center', height: 24 }}> <FormControlLabel control={ <Switch checked={urlAutoRefresh} onChange={(e) => setUrlAutoRefresh(e.target.checked)} size="small" /> } label={ <Typography component="span" variant="body2" sx={{ fontWeight: 500 }}> Watch Mode </Typography> } /> {urlAutoRefresh ? ( <Box sx={{ display: 'flex', alignItems: 'center', gap: 1, }}> <Typography variant="body2" color="text.secondary" sx={{ fontSize: '0.8rem' }}> check data updates every </Typography> {[ { seconds: 5, label: '5s' }, { seconds: 15, label: '15s' }, { seconds: 30, label: '30s' }, { seconds: 60, label: '1m' }, { seconds: 300, label: '5m' }, { seconds: 600, label: '10m' }, { seconds: 1800, label: '30m' }, { seconds: 3600, label: '1h' }, { seconds: 86400, label: '24h' }, ].map((opt) => ( <Chip key={opt.seconds} label={opt.label} size="small" variant={urlRefreshInterval === opt.seconds ? 'filled' : 'outlined'} color={urlRefreshInterval === opt.seconds ? 'primary' : 'default'} onClick={() => setUrlRefreshInterval(opt.seconds)} sx={{ cursor: 'pointer', fontSize: '0.7rem', height: 24, }} /> ))} </Box> ) : <Typography component="span" variant="caption" color="text.secondary"> automatically check and refresh data from the URL at regular intervals </Typography>} </Box> </Paper> {/* Example APIs - Compact List */} {(!urlPreviewTables || urlPreviewTables.length === 0) && !urlPreviewLoading && ( <Box sx={{ display: 'flex', flexDirection: 'column', gap: 0.5 }}> <Typography variant="caption" color="text.secondary" sx={{ mb: 0.5 }}> Try examples: </Typography> <Box component="ul" sx={{ listStyle: 'none', padding: 0, margin: 0, display: 'flex', flexDirection: 'column', gap: 0.25, }}> {exampleUrls.map((example) => ( <Box component="li" key={example.url} onClick={() => { console.log('example', example); if (example.url) { setTableURL(example.url); setUrlAutoRefresh(true); setUrlRefreshInterval(example.refreshSeconds || 60); handleURLPreview(example.url); } }} sx={{ cursor: 'pointer', '&::before': { content: '"• "', color: 'text.secondary', marginRight: 0.5, } }} > <Typography component="span" variant="caption" sx={{ fontSize: '0.75rem', color: 'primary.main', textDecoration: 'none', '&:hover': { textDecoration: 'underline', } }} > {example.label} </Typography> {example.resetUrl && ( <Typography component="span" variant="caption" onClick={(e) => { e.stopPropagation(); fetchWithIdentity(`${window.location.origin}${example.resetUrl}`, { method: 'POST' }) .then(() => { console.log('Reset successful'); }) .catch(err => console.error('Reset failed:', err)); }} sx={{ fontSize: '0.7rem', color: 'text.secondary', ml: 1, cursor: 'pointer', display: 'inline-flex', alignItems: 'center', gap: 0.25, '&:hover': { color: 'warning.main' }, }} > <RestartAltIcon sx={{ fontSize: 12 }} /> reset </Typography> )} </Box> ))} </Box> </Box> )} </Box> {showUrlPreview && ( <Box sx={{ width: '90%', alignSelf: 'center' }}> <MultiTablePreview loading={urlPreviewLoading} error={urlPreviewError} tables={urlPreviewTables} emptyLabel="Enter a URL and click Preview to see data." onRemoveTable={handleRemoveUrlPreviewTable} activeIndex={urlPreviewActiveIndex} onActiveIndexChange={setUrlPreviewActiveIndex} /> </Box> )} {urlPreviewTables && urlPreviewTables.length > 0 && ( <Box sx={{ display: 'flex', justifyContent: 'center', gap: 1, alignItems: 'center' }}> {urlAutoRefresh && ( <Typography variant="caption" color="success.main" sx={{ mr: 1 }}> <StreamIcon sx={{ fontSize: 14, verticalAlign: 'middle', mr: 0.5 }} /> Watch mode: {urlRefreshInterval < 60 ? `${urlRefreshInterval}s` : `${Math.floor(urlRefreshInterval / 60)}m`} </Typography> )} <Button variant="contained" onClick={handleURLLoadSingleTable} disabled={urlPreviewLoading} sx={{ textTransform: 'none', width: 240 }} > Load Table </Button> {hasMultipleUrlTables && ( <Button variant="contained" size="small" onClick={handleURLLoadAllTables} disabled={urlPreviewLoading} sx={{ textTransform: 'none' }} > Load All Tables </Button> )} </Box> )} </Box> </TabPanel> {/* Paste Data Tab */} <TabPanel value={activeTab} index="paste"> <Box sx={{ display: 'flex', flexDirection: 'column', height: '100%', boxSizing: 'border-box', p: 2, justifyContent: hasPasteContent ? 'flex-start' : 'center', alignItems: hasPasteContent ? 'stretch' : 'center', }}> {isOverSizeLimit && ( <Box sx={{ display: 'flex', alignItems: 'center', mb: 1, p: 1, backgroundColor: 'rgba(244, 67, 54, 0.1)', borderRadius: 1, border: '1px solid rgba(244, 67, 54, 0.3)' }}> <Typography variant="caption" sx={{ flex: 1, color: 'error.main', fontWeight: 500 }}> ⚠️ Content exceeds {(MAX_CONTENT_SIZE / (1024 * 1024)).toFixed(0)}MB size limit. Current size: {(new Blob([pasteContent]).size / (1024 * 1024)).toFixed(2)}MB. Please use the DATABASE tab for large datasets. </Typography> </Box> )} {isLargeContent && !isOverSizeLimit && ( <Box sx={{ display: 'flex', alignItems: 'center', mb: 1, p: 1, backgroundColor: 'rgba(255, 193, 7, 0.1)', borderRadius: 1 }}> <Typography variant="caption" sx={{ flex: 1 }}> Large content detected ({Math.round(pasteContent.length / 1000)}KB). {showFullContent ? 'Showing full content (may be slow)' : 'Showing preview for performance'} </Typography> <Button size="small" variant="outlined" onClick={toggleFullContent} sx={{ textTransform: 'none', minWidth: 'auto' }} > {showFullContent ? 'Show Preview' : 'Show Full'} </Button> </Box> )} <Box sx={{ width: '100%', maxWidth: hasPasteContent ? 'none' : 720 }}> <TextField autoFocus multiline fullWidth value={pasteContent} onChange={handleContentChange} placeholder="Paste your data here (CSV, TSV, or JSON format)" InputProps={{ readOnly: isLargeContent && !showFullContent, }} sx={{ flex: hasPasteContent ? 1 : 'none', '& .MuiInputBase-root': { height: hasPasteContent ? '100%' : 220, alignItems: 'flex-start', }, '& .MuiInputBase-input': { fontSize: 12, fontFamily: 'monospace', height: hasPasteContent ? '100% !important' : 'auto !important', overflow: 'auto !important', }, '& .MuiInputBase-input[readonly]': { cursor: 'not-allowed', } }} /> {/* Show preview indicator when in preview mode */} {isLargeContent && !showFullContent && ( <Box sx={{ mt: 0.5, px: 1, py: 0.5, backgroundColor: alpha(theme.palette.info.main, 0.08), borderRadius: 0.5, border: `1px solid ${alpha(theme.palette.info.main, 0.2)}` }}> <Typography variant="caption" color="text.secondary" sx={{ fontSize: '0.7rem' }}> Preview mode: Editing disabled. Click "Show Full" to enable editing. </Typography> </Box> )} </Box> <Box sx={{ display: 'flex', justifyContent: 'flex-end', mt: 2, gap: 1 }}> <Button variant="contained" onClick={handlePasteSubmit} disabled={(pasteContent || '').trim() === '' || isOverSizeLimit} sx={{ textTransform: 'none' }} > Upload Data </Button> </Box> </Box> </TabPanel> {/* Database Tab */} <TabPanel value={activeTab} index="database"> <DBManagerPane onClose={handleClose} storeOnServer={storeOnServer} /> </TabPanel> {/* Extract Data Tab */} <TabPanel value={activeTab} index="extract"> <DataLoadingChat storeOnServer={storeOnServer} /> </TabPanel> {/* Explore Sample Datasets Tab */} <TabPanel value={activeTab} index="explore"> <Box sx={{ p: 2, height: '100%', boxSizing: 'border-box', display: 'flex', flexDirection: 'column' }}> <DatasetSelectionView datasets={datasetPreviews} hideRowNum handleSelectDataset={async (dataset) => { // Check if this is a live dataset const isLiveDataset = dataset.live === true; setDatasetLoading(true); setDatasetLoadingLabel(`Loading ${dataset.name}...`); try { const loadPromises = dataset.tables.map(async (table) => { // For live datasets with relative URLs, construct full URL let fullUrl = table.url; if (table.url.startsWith('/')) { fullUrl = window.location.origin + table.url; } const res = await fetch(fullUrl); const textData = await res.text(); let tableName = table.url.split("/").pop()?.split(".")[0]?.split("?")[0] || 'table-' + Date.now().toString().substring(0, 8); let dictTable; if (table.format == "csv") { dictTable = createTableFromText(tableName, textData); } else if (table.format == "json") { dictTable = createTableFromFromObjectArray(tableName, JSON.parse(textData), true); } if (dictTable) { // For live datasets, set up as stream source with auto-refresh if (isLiveDataset) { dictTable.source = { type: 'stream', url: fullUrl, autoRefresh: true, refreshIntervalSeconds: dataset.refreshIntervalSeconds || 60, lastRefreshed: Date.now() }; } else { // Regular example data dictTable.source = { type: 'example', url: table.url }; } await dispatch(loadTable({ table: dictTable, storeOnServer })); } }); await Promise.all(loadPromises); } catch (error) { console.error('Failed to load dataset:', error); } finally { setDatasetLoading(false); setDatasetLoadingLabel(''); } handleClose(); }} /> </Box> </TabPanel> </DialogContent> {/* Loading overlay for dataset loading */} <Backdrop open={datasetLoading} sx={{ position: 'absolute', zIndex: (theme) => theme.zIndex.drawer + 1, backgroundColor: 'rgba(255, 255, 255, 0.85)', backdropFilter: 'blur(4px)', display: 'flex', flexDirection: 'column', gap: 2, }} > <CircularProgress size={36} /> <Typography variant="body2" color="text.secondary"> {datasetLoadingLabel || 'Loading data...'} </Typography> <Button variant="text" size="small" onClick={() => { setDatasetLoading(false); setDatasetLoadingLabel(''); }} sx={{ mt: 1, textTransform: 'none', color: 'text.secondary' }} > Cancel </Button> </Backdrop> </Dialog> ); }; export default UnifiedDataUploadDialog; ================================================ FILE: src/views/ViewUtils.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React from "react"; import ts from "typescript"; import { runCodeOnInputListsInVM } from "../app/utils"; import { FieldItem } from "../components/ComponentType"; import { Type } from "../data/types"; import { BooleanIcon, NumericalIcon, StringIcon, DateIcon, UnknownIcon } from '../icons'; import AutoFixHighIcon from '@mui/icons-material/AutoFixHigh'; import BarChartIcon from '@mui/icons-material/BarChart'; import CommitIcon from '@mui/icons-material/Commit'; import { DictTable } from '../components/ComponentType'; export const groupConceptItems = (conceptShelfItems: FieldItem[], tables: DictTable[]) => { // group concepts based on which source table they belongs to return conceptShelfItems.map(f => { let group = "" if (f.source == "original") { group = tables.find(t => t.id == f.tableRef)?.displayId || f.tableRef; } else if (f.source == "custom") { group = "new fields" } return {group, field: f} }); } // TODO: fix Unknown icon export const getIconFromType = (t: Type | undefined): JSX.Element => { switch (t) { case Type.Boolean: return <BooleanIcon fontSize="inherit" />; case Type.Date: return <DateIcon fontSize="inherit" />; case Type.Integer: case Type.Number: return <NumericalIcon fontSize="inherit" />; case Type.String: return <StringIcon fontSize="inherit" />; case Type.Auto: return <AutoFixHighIcon fontSize="inherit" />; } return <CommitIcon sx={{opacity: 0.3}} fontSize="inherit" />; }; export const getIconFromDtype = (t: "quantitative" | "nominal" | "ordinal" | "temporal" | "auto"): JSX.Element => { switch (t) { case "quantitative": return <NumericalIcon fontSize="inherit" />; case "nominal": return <StringIcon fontSize="inherit" />; case "ordinal": return <BarChartIcon fontSize="inherit" />; case "temporal": return <DateIcon fontSize="inherit" />; case "auto": return <AutoFixHighIcon fontSize="inherit" />; } return <CommitIcon sx={{opacity: 0.3}} fontSize="inherit" />; }; ================================================ FILE: src/views/VisualizationView.tsx ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import React, { FC, useCallback, useEffect, useMemo, useRef, useState } from 'react'; import { Box, Button, Divider, Icon, IconButton, Stack, Tooltip, Typography, ListItemIcon, ListItemText, MenuItem, LinearProgress, Card, ListSubheader, Menu, CardContent, Slider, Dialog, DialogContent, TextField, CircularProgress, Popover, Snackbar, Alert, Collapse, Fade, Grow, } from '@mui/material'; import _ from 'lodash'; import { borderColor } from '../app/tokens'; import ButtonGroup from '@mui/material/ButtonGroup'; import '../scss/VisualizationView.scss'; import { useDispatch, useSelector } from 'react-redux'; import { DataFormulatorState, dfActions, fetchChartInsight } from '../app/dfSlice'; import { assembleVegaChart, extractFieldsFromEncodingMap, getUrls, prepVisTable, fetchWithIdentity } from '../app/utils'; import { Chart, EncodingItem, EncodingMap, FieldItem, computeInsightKey } from '../components/ComponentType'; import { DictTable } from "../components/ComponentType"; import AddchartIcon from '@mui/icons-material/Addchart'; import DeleteIcon from '@mui/icons-material/Delete'; import StarIcon from '@mui/icons-material/Star'; import TerminalIcon from '@mui/icons-material/Terminal'; import StarBorderIcon from '@mui/icons-material/StarBorder'; import QuestionAnswerIcon from '@mui/icons-material/QuestionAnswer'; import CloseIcon from '@mui/icons-material/Close'; import ContentCopyIcon from '@mui/icons-material/ContentCopy'; import ZoomInIcon from '@mui/icons-material/ZoomIn'; import ZoomOutIcon from '@mui/icons-material/ZoomOut'; import InfoIcon from '@mui/icons-material/Info'; import CasinoIcon from '@mui/icons-material/Casino'; import SaveAltIcon from '@mui/icons-material/SaveAlt'; import OpenInNewIcon from '@mui/icons-material/OpenInNew'; import { CHART_TEMPLATES, getChartTemplate } from '../components/ChartTemplates'; import Prism from 'prismjs' import 'prismjs/components/prism-python' // Language import 'prismjs/components/prism-sql' // Language import 'prismjs/components/prism-markdown' // Language import 'prismjs/components/prism-typescript' // Language import 'prismjs/themes/prism.css'; //Example style, you can use another import { ChatDialog } from './ChatDialog'; import { EncodingShelfThread } from './EncodingShelfThread'; import { CustomReactTable } from './ReactTable'; import { InsightIcon } from '../icons'; import { dfSelectors } from '../app/dfSlice'; import { ChartRecBox } from './ChartRecBox'; import { CodeExplanationCard, ConceptExplCards, extractConceptExplanations } from './ExplComponents'; import CodeIcon from '@mui/icons-material/Code'; export interface VisPanelProps { } export interface VisPanelState { focusedIndex: number; focusUpdated: boolean; viewMode: "gallery" | "carousel"; } export let generateChartSkeleton = (icon: any, width: number = 160, height: number = 160, opacity: number = 0.5) => ( <Box width={width} height={height} sx={{ display: "flex" }}> {icon == undefined ? <AddchartIcon sx={{ color: "lightgray", margin: "auto" }} /> : typeof icon == 'string' ? <Box width="100%" sx={{ display: "flex", opacity: opacity }}> <img height={Math.min(64, height)} width={Math.min(64, width)} style={{ maxHeight: Math.min(height, Math.max(32, 0.5 * height)), maxWidth: Math.min(width, Math.max(32, 0.5 * width)), margin: "auto" }} src={icon} alt="" role="presentation" /> </Box> : <Box width="100%" sx={{ display: "flex", opacity: opacity }}> {React.cloneElement(icon, { style: { maxHeight: Math.min(height, 32), maxWidth: Math.min(width, 32), margin: "auto" } })} </Box>} </Box> ) export let renderTableChart = ( chart: Chart, conceptShelfItems: FieldItem[], extTable: any[], width: number = 120, height: number = 120) => { let fields = Object.entries(chart.encodingMap).filter(([channel, encoding]) => { return encoding.fieldID != undefined; }).map(([channel, encoding]) => conceptShelfItems.find(f => f.id == encoding.fieldID) as FieldItem); if (fields.length == 0) { fields = conceptShelfItems.filter(f => Object.keys(extTable[0]).includes(f.name)); } let rows = extTable.map(row => Object.fromEntries(fields.filter(f => Object.keys(row).includes(f.name)).map(f => [f.name, row[f.name]]))) let colDefs = fields.map(field => { let name = field.name; return { id: name, label: name, minWidth: 30, align: undefined, format: (value: any) => `${value}`, source: field.source } }) return <Box sx={{ position: "relative", display: "flex", flexDirection: "column", margin: 'auto' }}> <CustomReactTable rows={rows} columnDefs={colDefs} rowsPerPageNum={10} maxCellWidth={180} compact /> </Box> } export let getDataTable = (chart: Chart, tables: DictTable[], charts: Chart[], conceptShelfItems: FieldItem[], ignoreTableRef = false) => { // given a chart, determine which table would be used to visualize the chart // return the table directly if (chart.tableRef && !ignoreTableRef) { return tables.find(t => t.id == chart.tableRef) as DictTable; } let activeFields = conceptShelfItems.filter((field) => Array.from(Object.values(chart.encodingMap)).map((enc: EncodingItem) => enc.fieldID).includes(field.id)); let workingTableCandidates = tables.filter(t => { return activeFields.every(f => t.names.includes(f.name)); }); let confirmedTableCandidates = workingTableCandidates.filter(t => !charts.some(c => c.saved && c.tableRef == t.id)); if(confirmedTableCandidates.length > 0) { return confirmedTableCandidates[0]; } else if (workingTableCandidates.length > 0) { return workingTableCandidates[0]; } else { // sort base tables based on how many active fields are covered by existing tables return tables.filter(t => t.derive == undefined).sort((a, b) => activeFields.filter(f => a.names.includes(f.name)).length - activeFields.filter(f => b.names.includes(f.name)).length).reverse()[0]; } } export let CodeBox : FC<{code: string, language: string, fontSize?: number}> = function CodeBox({ code, language, fontSize = 10 }) { useEffect(() => { Prism.highlightAll(); }, [code]); return ( <pre style={{fontSize: fontSize}}> <code className={`language-${language}`} >{code}</code> </pre> ); } export let checkChartAvailabilityOnPreparedData = (chart: Chart, conceptShelfItems: FieldItem[], visTableRows: any[]) => { let visFieldsFinalNames = Object.keys(chart.encodingMap) .filter(key => chart.encodingMap[key as keyof EncodingMap].fieldID != undefined) .map(key => [chart.encodingMap[key as keyof EncodingMap].fieldID, chart.encodingMap[key as keyof EncodingMap].aggregate]) .map(([id, aggregate]) => { let field = conceptShelfItems.find(f => f.id == id); if (field) { if (aggregate) { return aggregate == "count" ? "_count" : `${field.name}_${aggregate}`; } else { return field.name; } } return undefined; }).filter((f): f is string => f != undefined); return visFieldsFinalNames.length > 0 && visTableRows.length > 0 && visFieldsFinalNames.every(name => Object.keys(visTableRows[0]).includes(name)); } export let checkChartAvailability = (chart: Chart, conceptShelfItems: FieldItem[], visTableRows: any[]) => { let visFieldIds = Object.keys(chart.encodingMap) .filter(key => chart.encodingMap[key as keyof EncodingMap].fieldID != undefined) .map(key => chart.encodingMap[key as keyof EncodingMap].fieldID); let visFields = conceptShelfItems.filter(f => visFieldIds.includes(f.id)); return visFields.length > 0 && visTableRows.length > 0 && visFields.every(f => Object.keys(visTableRows[0]).includes(f.name)); } export let SampleSizeEditor: FC<{ initialSize: number; totalSize: number; onSampleSizeChange: (newSize: number) => void; }> = function SampleSizeEditor({ initialSize, totalSize, onSampleSizeChange }) { const [localSampleSize, setLocalSampleSize] = useState<number>(initialSize); const [anchorEl, setAnchorEl] = useState<null | HTMLElement>(null); const open = Boolean(anchorEl); useEffect(() => { setLocalSampleSize(initialSize); }, [initialSize]) let maxSliderSize = Math.min(totalSize, 30000); const handleClick = (event: React.MouseEvent<HTMLElement>) => { setAnchorEl(event.currentTarget); }; const handleClose = () => { setAnchorEl(null); }; return <Box component="span" sx={{ display: 'flex', flexDirection: 'row', alignItems: 'center' }}> <Button onClick={handleClick} sx={{ textTransform: 'none', fontSize: '12px' }} > {localSampleSize} / {totalSize} </Button> <Popover open={open} anchorEl={anchorEl} onClose={handleClose} anchorOrigin={{ vertical: 'bottom', horizontal: 'left', }} transformOrigin={{ vertical: 'top', horizontal: 'left', }} > <Box sx={{ p: 2, width: 300 }}> <Typography fontSize="small" gutterBottom> Adjust sample size: {localSampleSize} / {totalSize} rows </Typography> <Box sx={{ display: 'flex', flexDirection: 'row', alignItems: 'center' }}> <Typography variant="caption" color="text.secondary" sx={{ mr: 1 }}>100</Typography> <Slider size="small" min={100} max={maxSliderSize} sx={{ mr: 1 }} value={localSampleSize} onChange={(_, value) => setLocalSampleSize(value as number)} valueLabelDisplay="auto" aria-label="Sample size" /> <Typography variant="caption" color="text.secondary" sx={{ ml: 1 }}>{maxSliderSize}</Typography> <Button sx={{ textTransform: 'none', ml: 2, fontSize: '12px' }} onClick={() => { onSampleSizeChange(localSampleSize); setAnchorEl(null); }}> Resample </Button> </Box> </Box> </Popover> </Box> } /** * Module-level caches that persist across component remounts. * - displayRowsCache: avoids re-fetching server data when switching back to a chart * - displaySvgCache: avoids re-running toSVG when chart+data haven't changed */ const displayRowsCache = new Map<string, { rows: any[], totalCount: number }>(); const displaySvgCache = new Map<string, { specKey: string; svg: string; spec: any }>(); // Simple component that only handles Vega chart rendering — now uses headless toSVG() const VegaChartRenderer: FC<{ chart: Chart; conceptShelfItems: FieldItem[]; visTableRows: any[]; tableMetadata: any; chartWidth: number; chartHeight: number; scaleFactor: number; maxStretchFactor?: number; chartUnavailable: boolean; onSpecReady?: (spec: any | null) => void; }> = React.memo(({ chart, conceptShelfItems, visTableRows, tableMetadata, chartWidth, chartHeight, scaleFactor, maxStretchFactor, chartUnavailable, onSpecReady }) => { // Initialize from display SVG cache for instant display on chart switch const svgCached = displaySvgCache.get(chart.id); const [svgContent, setSvgContent] = useState<string | null>(svgCached?.svg ?? null); const [assembledSpec, setAssembledSpec] = useState<any>(svgCached?.spec ?? null); useEffect(() => { if (chart.chartType === "Auto" || chart.chartType === "Table" || chartUnavailable) { setSvgContent(null); setAssembledSpec(null); return; } // Skip rendering when we have no data yet (data is being fetched) if (visTableRows.length === 0) { return; } const spec = assembleVegaChart( chart.chartType, chart.encodingMap, conceptShelfItems, visTableRows, tableMetadata, chartWidth, chartHeight, true, chart.config, scaleFactor, maxStretchFactor, ); if (!spec || spec === "Table") { setSvgContent(null); setAssembledSpec(null); onSpecReady?.(null); return; } spec['background'] = 'white'; // Check display SVG cache — skip toSVG entirely if spec matches const specKey = JSON.stringify(spec); const cached = displaySvgCache.get(chart.id); if (cached && cached.specKey === specKey) { setSvgContent(cached.svg); setAssembledSpec(cached.spec); onSpecReady?.(cached.spec); return; } setAssembledSpec(spec); onSpecReady?.(spec); // Headless render via Vega: compile VL → parse → View → toSVG() let cancelled = false; (async () => { try { const { compile: vlCompile } = await import('vega-lite'); const vega = await import('vega'); const vgSpec = vlCompile(spec as any).spec; const runtime = vega.parse(vgSpec); const view = new vega.View(runtime, { renderer: 'none' }); await view.runAsync(); const svg = await view.toSVG(); view.finalize(); if (!cancelled) { setSvgContent(svg); // Cache the rendered SVG for instant reuse on revisit displaySvgCache.set(chart.id, { specKey, svg, spec }); } } catch (err) { console.warn('VegaChartRenderer: SVG render failed', err); if (!cancelled) { setSvgContent(null); } } })(); return () => { cancelled = true; }; }, [chart.id, chart.chartType, chart.encodingMap, chart.config, conceptShelfItems, visTableRows, tableMetadata, chartWidth, chartHeight, scaleFactor, maxStretchFactor, chartUnavailable]); const handleSavePng = useCallback(async () => { if (!assembledSpec) return; try { const { compile: vlCompile } = await import('vega-lite'); const vega = await import('vega'); const vgSpec = vlCompile(assembledSpec as any).spec; const runtime = vega.parse(vgSpec); const view = new vega.View(runtime, { renderer: 'none' }); await view.runAsync(); const pngUrl = await view.toImageURL('png', 2); view.finalize(); // Trigger download const link = document.createElement('a'); link.download = `${chart.chartType}-${chart.id}.png`; link.href = pngUrl; document.body.appendChild(link); link.click(); document.body.removeChild(link); } catch (err) { console.error('Save PNG failed:', err); } }, [assembledSpec, chart.chartType, chart.id]); const handleOpenInVegaEditor = useCallback(() => { if (!assembledSpec) return; // Use postMessage to pass spec to Vega Editor (same approach as vega-embed) const editorUrl = 'https://vega.github.io/editor/'; const editor = window.open(editorUrl); if (!editor) return; const wait = 10_000; const step = 250; const { origin } = new URL(editorUrl); let count = Math.floor(wait / step); function listen(evt: MessageEvent) { if (evt.source === editor) { count = 0; window.removeEventListener('message', listen, false); } } window.addEventListener('message', listen, false); function send() { if (count <= 0) return; editor!.postMessage({ spec: JSON.stringify(assembledSpec, null, 2), mode: 'vega-lite', }, origin); setTimeout(send, step); count -= 1; } setTimeout(send, step); }, [assembledSpec]); if (chart.chartType === "Auto") { return <Box sx={{ position: "relative", display: "flex", flexDirection: "column", margin: 'auto', color: 'darkgray' }}> <InsightIcon fontSize="large"/> </Box> } if (chart.chartType === "Table") { return visTableRows.length > 0 ? renderTableChart(chart, conceptShelfItems, visTableRows) : <Box sx={{ height: '100%', display: 'flex', alignItems: 'center', justifyContent: 'center' }} > <InsightIcon fontSize="large"/> </Box>; } const chartTemplate = getChartTemplate(chart.chartType); if (!checkChartAvailabilityOnPreparedData(chart, conceptShelfItems, visTableRows)) { return <Box sx={{ height: '100%', display: 'flex', alignItems: 'center', justifyContent: 'center' }} > {generateChartSkeleton(chartTemplate?.icon, 48, 48)} </Box> } return ( <Box sx={{ mx: 2, display: 'flex', flexDirection: 'column', alignItems: 'center', maxWidth: '100%', overflow: 'hidden' }}> {svgContent ? ( <Box dangerouslySetInnerHTML={{ __html: svgContent }} sx={{ maxWidth: '100%', '& svg': { display: 'block', maxWidth: '100%', height: 'auto' }, }} /> ) : ( <Box sx={{ width: chartWidth, height: chartHeight, display: 'flex', alignItems: 'center', justifyContent: 'center' }}> {generateChartSkeleton(chartTemplate?.icon, 48, 48, 0.3)} </Box> )} </Box> ); }); export const ChartEditorFC: FC<{}> = function ChartEditorFC({}) { const config = useSelector((state: DataFormulatorState) => state.config); const serverConfig = useSelector((state: DataFormulatorState) => state.serverConfig); const componentRef = useRef<HTMLHeadingElement>(null); // Add ref for the container box that holds all exploration components const explanationComponentsRef = useRef<HTMLDivElement>(null); let tables = useSelector((state: DataFormulatorState) => state.tables); let charts = useSelector(dfSelectors.getAllCharts); let focusedId = useSelector((state: DataFormulatorState) => state.focusedId); let focusedChartId = focusedId?.type === 'chart' ? focusedId.chartId : undefined; let chartSynthesisInProgress = useSelector((state: DataFormulatorState) => state.chartSynthesisInProgress) || []; let synthesisRunning = focusedChartId ? chartSynthesisInProgress.includes(focusedChartId) : false; let handleDeleteChart = () => { focusedChartId && dispatch(dfActions.deleteChartById(focusedChartId)) } // Track the assembled Vega-Lite spec from the renderer so we can open it in the Vega Editor const [renderedSpec, setRenderedSpec] = useState<any | null>(null); const handleSpecReady = useCallback((spec: any | null) => { setRenderedSpec(spec); }, []); const handleOpenInVegaEditor = useCallback(() => { if (!renderedSpec) return; const editorUrl = 'https://vega.github.io/editor/'; const editor = window.open(editorUrl); if (!editor) return; const wait = 10_000; const step = 250; const { origin } = new URL(editorUrl); let count = Math.floor(wait / step); function listen(evt: MessageEvent) { if (evt.source === editor) { count = 0; window.removeEventListener('message', listen, false); } } window.addEventListener('message', listen, false); function send() { if (count <= 0) return; editor!.postMessage({ spec: JSON.stringify(renderedSpec, null, 2), mode: 'vega-lite' }, origin); setTimeout(send, step); count -= 1; } setTimeout(send, step); }, [renderedSpec]); let focusedChart = charts.find(c => c.id == focusedChartId) as Chart; let trigger = focusedChart.source == "trigger" ? tables.find(t => t.derive?.trigger?.chart?.id == focusedChartId)?.derive?.trigger : undefined; const dispatch = useDispatch(); const conceptShelfItems = useSelector((state: DataFormulatorState) => state.conceptShelfItems); const [codeViewOpen, setCodeViewOpen] = useState<boolean>(false); const [conceptExplanationsOpen, setConceptExplanationsOpen] = useState<boolean>(false); const [insightViewOpen, setInsightViewOpen] = useState<boolean>(false); const [chatDialogOpen, setChatDialogOpen] = useState<boolean>(false); const [localScaleFactor, setLocalScaleFactor] = useState<number>(1); // Reset local UI state when focused chart changes useEffect(() => { setCodeViewOpen(false); setConceptExplanationsOpen(false); setInsightViewOpen(false); setChatDialogOpen(false); setLocalScaleFactor(1); }, [focusedChartId]); // Combined useEffect to scroll to exploration components when any of them open useEffect(() => { if ((conceptExplanationsOpen || codeViewOpen || insightViewOpen) && explanationComponentsRef.current) { setTimeout(() => { explanationComponentsRef.current?.scrollIntoView({ behavior: 'smooth', block: 'start' }); }, 200); // Small delay to ensure the component is rendered } }, [conceptExplanationsOpen, codeViewOpen, insightViewOpen]); let table = getDataTable(focusedChart, tables, charts, conceptShelfItems); let visFieldIds = Object.keys(focusedChart.encodingMap).filter(key => focusedChart.encodingMap[key as keyof EncodingMap].fieldID != undefined).map(key => focusedChart.encodingMap[key as keyof EncodingMap].fieldID); let visFields = conceptShelfItems.filter(f => visFieldIds.includes(f.id)); let dataFieldsAllAvailable = visFields.every(f => table.names.includes(f.name)); // Create a stable identifier for data requirements (fields + aggregations) const dataRequirements = useMemo(() => { let { aggregateFields, groupByFields } = extractFieldsFromEncodingMap(focusedChart.encodingMap, conceptShelfItems); let sortedFields = [...aggregateFields.map(f => `${f[0]}_${f[1]}`), ...groupByFields].sort(); return JSON.stringify({ tableId: table.id, sortedFields }); }, [focusedChart.encodingMap, conceptShelfItems, table.id]); let setSystemMessage = (content: string, severity: "error" | "warning" | "info" | "success") => { dispatch(dfActions.addMessages({ "timestamp": Date.now(), "component": "Chart Builder", "type": severity, "value": content })); } let createVisTableRowsLocal = (rows: any[]) => { if (visFields.length == 0) { return rows; } let filteredRows = rows.map(row => Object.fromEntries(visFields.filter(f => table.names.includes(f.name)).map(f => [f.name, row[f.name]]))); let visTable = prepVisTable(filteredRows, conceptShelfItems, focusedChart.encodingMap); if (visTable.length > serverConfig.MAX_DISPLAY_ROWS) { let rowSample = _.sampleSize(visTable, serverConfig.MAX_DISPLAY_ROWS); visTable = rowSample; } visTable = structuredClone(visTable); return visTable; } const [visTableRows, setVisTableRows] = useState<any[]>(() => createVisTableRowsLocal(table.rows)); const [visTableTotalRowCount, setVisTableTotalRowCount] = useState<number>(table.virtual?.rowCount || table.rows.length); let { aggregateFields, groupByFields } = extractFieldsFromEncodingMap(focusedChart.encodingMap, conceptShelfItems); let sortedVisDataFields = [...aggregateFields.map(f => `${f[0]}_${f[1]}`), ...groupByFields].sort(); // Track which chart+table+requiredFields the current data belongs to (prevents showing stale data during transitions) const computeVersionId = () => { const contentSuffix = table.contentHash ? `-${table.contentHash.slice(0, 8)}` : `-${table.rows.length}`; return `${table.id}-${sortedVisDataFields.join("_")}${contentSuffix}`; }; const [dataVersion, setDataVersion] = useState<string>(computeVersionId()); const currentRequestRef = useRef<string>(''); // Check if current data is stale (belongs to different table/fields — ignoring contentHash for stale check // since we want to show the previous render while new data is loading) const baseVersionId = `${table.id}-${sortedVisDataFields.join("_")}`; const isDataStale = !dataVersion.startsWith(baseVersionId); // Use empty data if stale to avoid showing incorrect data during transitions const activeVisTableRows = isDataStale ? [] : visTableRows; const activeVisTableTotalRowCount = isDataStale ? 0 : visTableTotalRowCount; async function fetchDisplayRows(sampleSize?: number) { if (sampleSize == undefined) { sampleSize = 1000; } // If all rows are already in browser memory, sample locally (no server call needed). // This covers non-virtual tables and virtual tables whose rows have been fully loaded. const allRowsInMemory = !table.virtual || table.rows.length >= (table.virtual.rowCount || 0); if (!allRowsInMemory) { // Generate unique request ID to track this specific request const requestId = `${focusedChart.id}-${table.id}-${Date.now()}`; currentRequestRef.current = requestId; let { aggregateFields, groupByFields } = extractFieldsFromEncodingMap(focusedChart.encodingMap, conceptShelfItems); fetchWithIdentity(getUrls().SAMPLE_TABLE, { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ table: table.id, size: sampleSize, method: 'random', select_fields: groupByFields, aggregate_fields_and_functions: aggregateFields, }), }) .then(response => response.json()) .then(data => { // Only update if this is still the current request (not stale) if (currentRequestRef.current === requestId) { const versionId = computeVersionId(); if (data.status == "success") { setVisTableRows(data.rows); setVisTableTotalRowCount(data.total_row_count); setDataVersion(versionId); // Cache for instant reuse on chart revisit displayRowsCache.set(versionId, { rows: data.rows, totalCount: data.total_row_count }); } else { setVisTableRows([]); setVisTableTotalRowCount(0); setDataVersion(versionId); setSystemMessage(data.message, "error"); } } // Else: this response is stale, ignore it }) .catch(error => { // Only show error if this is still the current request if (currentRequestRef.current === requestId) { console.error('Error sampling table:', error); } }); } else { // All rows available locally — use in-memory data // When sample size covers all rows, preserve original order // (_.sampleSize shuffles, which destroys data-driven sort order) const rowsToUse = sampleSize >= table.rows.length ? table.rows : _.sampleSize(table.rows, sampleSize); const clonedRows = structuredClone(rowsToUse); const versionId = computeVersionId(); setVisTableRows(clonedRows); setVisTableTotalRowCount(table.rows.length); setDataVersion(versionId); // Cache for instant reuse on chart revisit displayRowsCache.set(versionId, { rows: clonedRows, totalCount: table.rows.length }); } } useEffect(() => { const allRowsInMemory = !table.virtual || table.rows.length >= (table.virtual.rowCount || 0); if (!allRowsInMemory && visFields.length > 0 && dataFieldsAllAvailable) { fetchDisplayRows(); } }, []) useEffect(() => { // Include contentHash in versionId so the cache invalidates when streaming/refreshed data changes const contentSuffix = table.contentHash ? `-${table.contentHash.slice(0, 8)}` : `-${table.rows.length}`; const versionId = `${table.id}-${sortedVisDataFields.join("_")}${contentSuffix}`; if (visFields.length > 0 && dataFieldsAllAvailable) { // Check cache first — avoid server round-trip on chart revisit const cached = displayRowsCache.get(versionId); if (cached) { setVisTableRows(cached.rows); setVisTableTotalRowCount(cached.totalCount); setDataVersion(versionId); } else { fetchDisplayRows(); } } else { // If no fields, just use the table rows directly setVisTableRows(table.rows); setVisTableTotalRowCount(table.virtual?.rowCount || table.rows.length); setDataVersion(versionId); } }, [dataRequirements, table.rows]) let encodingShelfEmpty = useMemo(() => { return Object.keys(focusedChart.encodingMap).every(key => focusedChart.encodingMap[key as keyof EncodingMap].fieldID == undefined && focusedChart.encodingMap[key as keyof EncodingMap].aggregate == undefined); }, [focusedChart.encodingMap]); // Calculate chart availability in the parent const chartUnavailable = useMemo(() => { if (focusedChart.chartType === "Auto" || focusedChart.chartType === "Table") { return false; } // Check if fields exist in table and table has rows return !(dataFieldsAllAvailable && table.rows.length > 0); }, [focusedChart.chartType, dataFieldsAllAvailable, table.rows.length]); let resultTable = tables.find(t => t.id == trigger?.resultTableId); // Chart insight const chartInsightInProgress = useSelector((state: DataFormulatorState) => state.chartInsightInProgress) || []; const insightLoading = chartInsightInProgress.includes(focusedChart.id); const currentInsightKey = computeInsightKey(focusedChart); const insightFresh = focusedChart.insight?.key === currentInsightKey; const actionBtnSx = { padding: '4px', borderRadius: '6px', color: 'text.secondary', transition: 'all 0.15s ease', '&:hover': { backgroundColor: 'rgba(25, 118, 210, 0.08)', color: 'primary.main', }, '&.Mui-disabled': { color: 'action.disabled', }, }; let saveButton = ( <Tooltip key="save-copy-tooltip" title={focusedChart.saved ? "Not anymore" : "I like it!"}> <span> <IconButton key="unsave-btn" size="small" sx={actionBtnSx} onClick={() => { if (!chartUnavailable) { dispatch(dfActions.saveUnsaveChart(focusedChart.id)); } }}> {focusedChart.saved ? <StarIcon sx={{ fontSize: 18, color: "goldenrod" }} /> : <StarBorderIcon sx={{ fontSize: 18 }} />} </IconButton> </span> </Tooltip> ); let deleteButton = ( <Tooltip title="Delete" key="delete-btn-tooltip"> <span> <IconButton size="small" disabled={trigger != undefined} sx={{ ...actionBtnSx, color: 'error.main', '&:hover': { backgroundColor: 'rgba(211, 47, 47, 0.08)', color: 'error.main' } }} onClick={() => { handleDeleteChart() }}> <DeleteIcon sx={{ fontSize: 18 }} /> </IconButton> </span> </Tooltip> ); let transformCode = ""; if (table.derive?.code) { transformCode = `${table.derive.code}` } // Check if concepts are available const availableConcepts = extractConceptExplanations(table); const hasConcepts = availableConcepts.length > 0; let derivedTableItems = (resultTable?.derive || table.derive) ? [ <Box key="explanation-toggle-group" sx={{ display: 'flex', alignItems: 'center', mx: 0.5, backgroundColor: 'rgba(0, 0, 0, 0.02)', borderRadius: 1, padding: '2px', border: `1px solid ${borderColor.component}` }}> <ButtonGroup key="explanation-button-group" size="small" sx={{ '& .MuiButton-root': { textTransform: 'none', fontSize: '0.7rem', fontWeight: 500, border: 'none', borderRadius: '3px', padding: '2px 6px', minWidth: 'auto', color: 'text.secondary', '&:hover': { backgroundColor: 'rgba(25, 118, 210, 0.08)', } } }} > <Button key="chat-dialog-btn" onClick={() => { setChatDialogOpen(!chatDialogOpen) }} sx={{ backgroundColor: chatDialogOpen ? 'rgba(25, 118, 210, 0.2)' : 'transparent', color: chatDialogOpen ? 'primary.main' : 'text.secondary', fontWeight: chatDialogOpen ? 600 : 500, '&:hover': { backgroundColor: chatDialogOpen ? 'rgba(25, 118, 210, 0.25)' : 'rgba(25, 118, 210, 0.08)', }, }} > <QuestionAnswerIcon sx={{ fontSize: '14px', mr: 0.5 }} /> log </Button> <Button key="code-btn" onClick={() => { if (codeViewOpen) { setCodeViewOpen(false); } else { setCodeViewOpen(true); setConceptExplanationsOpen(false); setInsightViewOpen(false); } }} sx={{ backgroundColor: codeViewOpen ? 'rgba(25, 118, 210, 0.2)' : 'transparent', color: codeViewOpen ? 'primary.main' : 'text.secondary', fontWeight: codeViewOpen ? 600 : 500, '&:hover': { backgroundColor: codeViewOpen ? 'rgba(25, 118, 210, 0.25)' : 'rgba(25, 118, 210, 0.08)', } }} > <TerminalIcon sx={{ fontSize: '14px', mr: 0.5 }} /> code </Button> {hasConcepts && ( <Button key="concepts-btn" onClick={() => { if (conceptExplanationsOpen) { setConceptExplanationsOpen(false); } else { setConceptExplanationsOpen(true); setCodeViewOpen(false); setInsightViewOpen(false); } }} sx={{ backgroundColor: conceptExplanationsOpen ? 'rgba(25, 118, 210, 0.2)' : 'transparent', color: conceptExplanationsOpen ? 'primary.main' : 'text.secondary', fontWeight: conceptExplanationsOpen ? 600 : 500, '&:hover': { backgroundColor: conceptExplanationsOpen ? 'rgba(25, 118, 210, 0.25)' : 'rgba(25, 118, 210, 0.08)', } }} > <InfoIcon sx={{ fontSize: '14px', mr: 0.5 }} /> concepts </Button> )} </ButtonGroup> </Box>, <ChatDialog key="chat-dialog-button" open={chatDialogOpen} handleCloseDialog={() => { setChatDialogOpen(false) }} code={transformCode} dialog={resultTable?.derive?.dialog || table.derive?.dialog as any[]} /> ] : []; let insightButton = !chartUnavailable && focusedChart.chartType !== "Table" ? ( <Box key="insight-toggle-group" sx={{ display: 'flex', alignItems: 'center', mx: 0.5, backgroundColor: 'rgba(0, 0, 0, 0.02)', borderRadius: 1, padding: '2px', border: `1px solid ${borderColor.component}` }}> <Button key="insight-btn" size="small" onClick={() => { if (insightViewOpen) { setInsightViewOpen(false); } else { setInsightViewOpen(true); setCodeViewOpen(false); setConceptExplanationsOpen(false); if (!insightFresh && !insightLoading) { dispatch(fetchChartInsight({ chartId: focusedChart.id, tableId: table.id }) as any); } } }} sx={{ textTransform: 'none', fontSize: '0.7rem', fontWeight: insightViewOpen ? 600 : 500, minWidth: 'auto', padding: '2px 6px', borderRadius: '3px', backgroundColor: insightViewOpen ? 'rgba(25, 118, 210, 0.2)' : 'transparent', color: insightViewOpen ? 'primary.main' : 'text.secondary', '&:hover': { backgroundColor: insightViewOpen ? 'rgba(25, 118, 210, 0.25)' : 'rgba(25, 118, 210, 0.08)', } }} > <InsightIcon sx={{ fontSize: '14px', mr: 0.5 }} /> {insightLoading ? <CircularProgress size={10} sx={{ ml: 0.5 }} /> : 'insight'} </Button> </Box> ) : null; let vegaEditorButton = ( <Tooltip key="vega-editor-tooltip" title="Open in Vega Editor"> <span> <IconButton key="vega-editor-btn" size="small" sx={actionBtnSx} disabled={!renderedSpec || focusedChart.chartType === "Table" || focusedChart.chartType === "Auto"} onClick={handleOpenInVegaEditor}> <OpenInNewIcon sx={{ fontSize: 18 }} /> </IconButton> </span> </Tooltip> ); let chartActionButtons = [ ...derivedTableItems, insightButton, saveButton, vegaEditorButton, deleteButton, ] let chartMessage = ""; if (focusedChart.chartType == "Table") { chartMessage = "Tell me what you want to visualize!"; } else if (focusedChart.chartType == "Auto") { chartMessage = "Say something to get chart recommendations!"; } else if (encodingShelfEmpty) { chartMessage = "Put data fields to chart builder or describe what you want!"; } else if (chartUnavailable) { chartMessage = "Formulate data to create the visualization!"; } else if (chartSynthesisInProgress.includes(focusedChart.id)) { chartMessage = "Synthesis in progress..."; } else if (table.derive) { chartMessage = "AI generated results can be inaccurate, inspect it!"; } let chartActionItems = isDataStale ? [] : ( <Box sx={{display: "flex", flexDirection: "column", flex: 1, my: 1}}> {(table.virtual ? activeVisTableTotalRowCount > serverConfig.MAX_DISPLAY_ROWS : table.rows.length > serverConfig.MAX_DISPLAY_ROWS) && !(chartUnavailable || encodingShelfEmpty) ? ( <Box sx={{ display: 'flex', flexDirection: "row", margin: "auto", justifyContent: 'center', alignItems: 'center'}}> <Typography component="span" fontSize="small" color="text.secondary" sx={{textAlign:'center'}}> visualizing </Typography> <SampleSizeEditor initialSize={activeVisTableRows.length} totalSize={activeVisTableTotalRowCount} onSampleSizeChange={(newSize) => { fetchDisplayRows(newSize); }} /> <Typography component="span" fontSize="small" color="text.secondary" sx={{textAlign:'center'}}> sample rows </Typography> <Tooltip title="sample again!"> <IconButton size="small" color="primary" onClick={() => { fetchDisplayRows(activeVisTableRows.length); }}> <CasinoIcon sx={{ fontSize: '14px', transition: 'transform 0.5s ease-in-out', '&:hover': { transform: 'rotate(180deg)' } }}/> </IconButton> </Tooltip> </Box> ) : ""} <Typography component="span" fontSize="small" color="text.secondary" sx={{textAlign:'center'}}> {chartMessage} </Typography> </Box> ) let focusedComponent = []; let focusedElement = <Fade key={`fade-${focusedChart.id}-${dataVersion}-${focusedChart.chartType}-${JSON.stringify(focusedChart.encodingMap)}`} in={!isDataStale} timeout={600}> <Box sx={{display: "flex", flexDirection: "column", flexShrink: 0, justifyContent: 'center', justifyItems: 'center', maxWidth: '100%'}} className="chart-box"> {insightFresh && focusedChart.insight?.title && ( <Typography fontSize="small" sx={{ fontWeight: (focusedChart.encodingMap.column?.fieldID || focusedChart.encodingMap.row?.fieldID) ? 400 : 600, textAlign: 'center', mb: 0.5, color: 'text.secondary', }}> {focusedChart.insight.title} </Typography> )} <Box sx={{m: 'auto', minHeight: 240, maxWidth: '100%', overflow: 'hidden'}}> <VegaChartRenderer key={focusedChart.id} chart={focusedChart} conceptShelfItems={conceptShelfItems} visTableRows={activeVisTableRows} tableMetadata={table.metadata} chartWidth={config.defaultChartWidth} chartHeight={config.defaultChartHeight} scaleFactor={localScaleFactor} maxStretchFactor={config.maxStretchFactor} chartUnavailable={chartUnavailable} onSpecReady={handleSpecReady} /> </Box> {chartActionItems} </Box> </Fade>; focusedComponent = [ <Box key="chart-focused-element" sx={{ width: "100%", minHeight: "calc(100% - 40px)", margin: "auto", mt: 4, mb: 1, display: "flex", flexDirection: "column"}}> {focusedElement} <Box ref={explanationComponentsRef} sx={{width: "100%", mx: "auto"}}> <Collapse in={conceptExplanationsOpen}> <Box sx={{minWidth: 440, maxWidth: 800, padding: "0px 8px", position: 'relative', margin: '8px auto'}}> <ConceptExplCards concepts={extractConceptExplanations(table)} title="Derived Concepts" maxCards={8} /> </Box> </Collapse> <Collapse in={codeViewOpen}> <Box sx={{minWidth: 440, maxWidth: 800, padding: "0px 8px", position: 'relative', margin: '8px auto'}}> <ButtonGroup sx={{position: 'absolute', right: 8, top: 1}}> <IconButton onClick={() => { setCodeViewOpen(false); }} color='primary' aria-label="delete"> <CloseIcon /> </IconButton> </ButtonGroup> {/* <Typography fontSize="small" sx={{color: 'gray'}}>{table.derive?.source} → {table.id}</Typography> */} <CodeExplanationCard title="Data transformation code" icon={<CodeIcon sx={{ fontSize: 16, color: 'primary.main' }} />} > <Box sx={{ maxHeight: '400px', overflow: 'auto', width: '100%', p: 0.5 }} > <CodeBox code={transformCode.trimStart()} language={table.virtual ? "sql" : "python"} /> </Box> </CodeExplanationCard> </Box> </Collapse> <Collapse in={insightViewOpen}> <Box sx={{minWidth: 440, maxWidth: 800, padding: "0px 8px", position: 'relative', margin: '8px auto'}}> <ButtonGroup sx={{position: 'absolute', right: 8, top: 0}}> <IconButton onClick={() => { setInsightViewOpen(false); }} color='primary' aria-label="close"> <CloseIcon /> </IconButton> </ButtonGroup> <CodeExplanationCard title="Chart insight" icon={<InsightIcon sx={{ fontSize: 16, color: 'primary.main' }} />} > {insightLoading ? ( <Box sx={{ display: 'flex', alignItems: 'center', gap: 1, p: 1 }}> <CircularProgress size={16} /> <Typography fontSize="small" color="text.secondary">Analyzing chart...</Typography> </Box> ) : insightFresh && focusedChart.insight ? ( <Box sx={{ p: 0.5 }}> <Typography fontSize="small" sx={{ fontWeight: 600, mb: 0.5 }}> {focusedChart.insight.title} </Typography> {(focusedChart.insight.takeaways || []).map((t, i) => ( <Typography key={i} fontSize="small" color="text.secondary" sx={{ mb: 0.25, display: 'flex', alignItems: 'baseline' }}> <span style={{ marginRight: 4, flexShrink: 0 }}>•</span>{t} </Typography> ))} <Button size="small" sx={{ mt: 1, textTransform: 'none', fontSize: '0.7rem' }} onClick={() => { dispatch(fetchChartInsight({ chartId: focusedChart.id, tableId: table.id }) as any); }} > regenerate </Button> </Box> ) : ( <Box sx={{ p: 0.5 }}> <Typography fontSize="small" color="text.secondary"> No insight available. </Typography> <Button size="small" sx={{ mt: 0.5, textTransform: 'none', fontSize: '0.7rem' }} onClick={() => { dispatch(fetchChartInsight({ chartId: focusedChart.id, tableId: table.id }) as any); }} > generate insight </Button> </Box> )} </CodeExplanationCard> </Box> </Collapse> </Box> <Box key='chart-action-buttons' sx={{ display: 'flex', flexShrink: 0, flexDirection: "row", alignItems: 'center', mx: "auto", py: 0.5, gap: 0.25, }}> {chartActionButtons} </Box> </Box> ] const ENCODING_SHELF_WIDTH = 200; let content = [ <Box key='focused-box' className="vega-focused" sx={{ display: "flex", overflow: 'auto', flexDirection: 'column', position: 'relative', flex: 1, pr: `${ENCODING_SHELF_WIDTH}px` }}> {focusedComponent} </Box>, /* Floating encoding shelf panel */ <Box key='encoding-shelf' sx={{ position: 'absolute', top: 0, right: 0, zIndex: 10, height: '100%', pointerEvents: 'none', }}> <Box sx={{ pointerEvents: 'auto' }}> <EncodingShelfThread chartId={focusedChart.id} /> </Box> </Box> ] let [scaleMin, scaleMax] = [0.2, 2.4] // Memoize chart resizer to avoid re-creating Material-UI components on every render let chartResizer = useMemo(() => <Stack spacing={1} direction="row" sx={{ margin: 1, width: 160, position: "absolute", zIndex: 10, backgroundColor: 'rgba(255, 255, 255, 0.9)', borderRadius: '4px', }} alignItems="center"> <Tooltip key="zoom-out-tooltip" title="zoom out"> <span> <IconButton color="primary" size='small' disabled={localScaleFactor <= scaleMin} onClick={() => { setLocalScaleFactor(s => Math.max(scaleMin, Math.round((s - 0.1) * 10) / 10)); }}> <ZoomOutIcon fontSize="small" /> </IconButton> </span> </Tooltip> <Slider aria-label="chart-resize" size='small' defaultValue={1} step={0.1} min={scaleMin} max={scaleMax} value={localScaleFactor} onChange={(event: Event, newValue: number | number[]) => { setLocalScaleFactor(newValue as number); }} /> <Tooltip key="zoom-in-tooltip" title="zoom in"> <span> <IconButton color="primary" size='small' disabled={localScaleFactor >= scaleMax} onClick={() => { setLocalScaleFactor(s => Math.min(scaleMax, Math.round((s + 0.1) * 10) / 10)); }}> <ZoomInIcon fontSize="small" /> </IconButton> </span> </Tooltip> </Stack>, [localScaleFactor]); return <Box ref={componentRef} sx={{overflow: "hidden", display: 'flex', flex: 1, position: 'relative'}}> {synthesisRunning ? <Box sx={{ position: "absolute", height: "calc(100%)", width: "calc(100%)", zIndex: 1001, backgroundColor: "rgba(243, 243, 243, 0.8)", display: "flex", alignItems: "center" }}> <LinearProgress sx={{ width: "100%", height: "100%", opacity: 0.05 }} /> </Box> : ''} {chartUnavailable ? "" : chartResizer} {content} </Box> } export const VisualizationViewFC: FC<VisPanelProps> = function VisualizationView({ }) { let allCharts = useSelector(dfSelectors.getAllCharts); let focusedId = useSelector((state: DataFormulatorState) => state.focusedId); let focusedChartId = focusedId?.type === 'chart' ? focusedId.chartId : undefined; let focusedTableId = React.useMemo(() => { if (!focusedId) return undefined; if (focusedId.type === 'table') return focusedId.tableId; const chartId = focusedId.chartId; const chart = allCharts.find(c => c.id === chartId); return chart?.tableRef; }, [focusedId, allCharts]); let chartSynthesisInProgress = useSelector((state: DataFormulatorState) => state.chartSynthesisInProgress) || []; const dispatch = useDispatch(); let focusedChart = allCharts.find(c => c.id == focusedChartId) as Chart; let synthesisRunning = focusedChartId ? chartSynthesisInProgress.includes(focusedChartId) : false; // when there is no result and synthesis is running, just show the waiting panel if (!focusedChart || focusedChart?.chartType == "?") { let chartSelectionBox = <Box sx={{ display: "flex", flexDirection: "row", flexWrap: "wrap", gap: 2 }}> {Object.entries(CHART_TEMPLATES) .filter(([category, templates]) => category !== "Custom" && templates.some(t => t.chart !== "Auto")) .map(([category, templates]) => ( <Box key={category} sx={{ display: 'flex', flexDirection: 'column', alignItems: 'center' }}> {templates .filter(t => t.chart !== "Auto") .map((t, index) => ( <Button disabled={synthesisRunning} key={`${category}-${index}-${t.chart}-btn`} sx={{ margin: '1px', padding: '2px', display: 'flex', flexDirection: 'row', textTransform: 'none', justifyContent: 'flex-start', minWidth: 0, width: '100%' }} onClick={() => { let focusedChart = allCharts.find(c => c.id == focusedChartId); if (focusedChart?.chartType == "?") { dispatch(dfActions.updateChartType({ chartType: t.chart, chartId: focusedChartId as string })); } else { dispatch(dfActions.createNewChart({ chartType: t.chart, tableId: focusedTableId as string })); } }} > <Box sx={{ opacity: synthesisRunning ? 0.5 : 1, width: 40, height: 40, display: 'flex', alignItems: 'center', justifyContent: 'center', flexShrink: 0 }}> {typeof t?.icon == 'string' ? <img height="40px" width="40px" src={t?.icon} alt="" role="presentation" /> : t.icon} </Box> <Typography sx={{ ml: '4px', whiteSpace: "nowrap", fontSize: '10px', lineHeight: 1.2 }}>{t?.chart}</Typography> </Button> )) } </Box> )) } </Box> return ( <Box sx={{ margin: "auto" }}> {focusedTableId ? <ChartRecBox sx={{margin: 'auto'}} tableId={focusedTableId as string} placeHolderChartId={focusedChartId as string} /> : null} <Divider sx={{my: 3}} textAlign='left'> <Typography sx={{fontSize: 12, color: "text.secondary"}}> or, start with a chart type </Typography> </Divider> {chartSelectionBox} </Box> ) } let visPanel = <Box sx={{ width: "100%", overflow: "hidden", display: "flex", flexDirection: "row" }}> <Box className="visualization-carousel" sx={{display: "contents"}} > <ChartEditorFC /> </Box> </Box> return visPanel; } ================================================ FILE: src/views/useFormulateData.ts ================================================ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. import { useSelector, useDispatch } from 'react-redux'; import { DataFormulatorState, dfActions, dfSelectors, fetchCodeExpl, fetchChartInsight, fetchFieldSemanticType } from '../app/dfSlice'; import { AppDispatch } from '../app/store'; import { Chart, FieldItem, Trigger, createDictTable, DictTable } from '../components/ComponentType'; import { getUrls, getTriggers, fetchWithIdentity } from '../app/utils'; export type IdeaItem = { text: string; goal: string; difficulty: 'easy' | 'medium' | 'hard'; tag?: string; }; export interface StreamIdeasOptions { actionTableIds: string[]; currentTable: DictTable; onIdeas: (ideas: IdeaItem[]) => void; onThinkingBuffer: (buffer: string) => void; onLoadingChange: (loading: boolean) => void; /** Chart image (PNG data URL) for current visualization context */ currentChartImage?: string | null; /** Sample rows from the current table */ currentDataSample?: any[]; /** Optional start question for idea generation */ startQuestion?: string; /** If true, only blocks with type==="question" are included (default: false) */ filterByType?: boolean; } export interface FormulateDataOptions { instruction: string; mode: 'formulate' | 'ideate'; actionTableIds: string[]; currentTable: DictTable; overrideTableId?: string; currentVisualization?: any; expectedVisualization?: any; /** The chart spec to embed in the trigger for the derived table */ triggerChart: Chart; /** * Component-specific chart creation callback. * Called with the candidate table, refined goal, and resolved concepts. * Should dispatch chart creation actions and return the focused chart ID (or undefined). */ createChart: (params: { candidateTable: DictTable; refinedGoal: any; currentConcepts: FieldItem[]; }) => string | undefined; /** Called before the request is made */ onStarted?: () => void; /** Called on successful formulation */ onSuccess?: (params: { displayInstruction: string; candidateTable: DictTable; focusedChartId?: string }) => void; /** Called on error */ onError?: (error: any) => void; /** Called after the request completes (success or error) */ onFinally?: () => void; } function generateTableId(tables: DictTable[]): string { let tableSuffix = Number.parseInt((Date.now() - Math.floor(Math.random() * 10000)).toString().slice(-6)); let tableId = `table-${tableSuffix}`; while (tables.find(t => t.id === tableId) !== undefined) { tableSuffix += 1; tableId = `table-${tableSuffix}`; } return tableId; } /** * Shared hook for data formulation and idea streaming. * Used by both EncodingShelfCard (chart-aware formulation) and ChartRecBox (NL-driven formulation). */ export function useFormulateData() { const dispatch = useDispatch<AppDispatch>(); const tables = useSelector((state: DataFormulatorState) => state.tables); const config = useSelector((state: DataFormulatorState) => state.config); const agentRules = useSelector((state: DataFormulatorState) => state.agentRules); const conceptShelfItems = useSelector((state: DataFormulatorState) => state.conceptShelfItems); const activeModel = useSelector(dfSelectors.getActiveModel); /** * Build an exploration thread from the current table's derivation chain. */ function buildExplorationThread(currentTable: DictTable): any[] { if (!currentTable.derive || currentTable.anchored) return []; const triggers = getTriggers(currentTable, tables); return triggers.map(trigger => ({ name: trigger.resultTableId, rows: tables.find(t2 => t2.id === trigger.resultTableId)?.rows, description: `Derive from ${tables.find(t2 => t2.id === trigger.resultTableId)?.derive?.source} with instruction: ${trigger.instruction}`, })); } /** * Stream ideas/recommendations from the exploration agent via SSE. */ async function streamIdeas(options: StreamIdeasOptions): Promise<void> { const { actionTableIds, currentTable, onIdeas, onThinkingBuffer, onLoadingChange, currentChartImage, currentDataSample, startQuestion, filterByType = false, } = options; onLoadingChange(true); onThinkingBuffer(""); onIdeas([]); try { const explorationThread = buildExplorationThread(currentTable); const actionTables = actionTableIds.map(id => tables.find(t => t.id === id) as DictTable); const messageBody = JSON.stringify({ token: String(Date.now()), model: activeModel, mode: 'interactive', input_tables: actionTables.map(t => ({ name: t.virtual?.tableId || t.id.replace(/\.[^/.]+$/, ""), rows: t.rows, attached_metadata: t.attachedMetadata, })), exploration_thread: explorationThread, agent_exploration_rules: agentRules.exploration, ...(currentDataSample ? { current_data_sample: currentDataSample } : {}), ...(currentChartImage ? { current_chart: currentChartImage } : {}), ...(startQuestion ? { start_question: startQuestion } : {}), }); const engine = getUrls().GET_RECOMMENDATION_QUESTIONS; const controller = new AbortController(); const timeoutId = setTimeout(() => controller.abort(), config.formulateTimeoutSeconds * 1000); const response = await fetchWithIdentity(engine, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: messageBody, signal: controller.signal, }); clearTimeout(timeoutId); if (!response.ok) { throw new Error(`HTTP error! status: ${response.status}`); } const reader = response.body?.getReader(); if (!reader) { throw new Error('No response body reader available'); } const decoder = new TextDecoder(); let lines: string[] = []; let buffer = ''; const updateState = (currentLines: string[]) => { const dataBlocks = currentLines .map(line => { try { return JSON.parse(line.trim()); } catch (e) { return null; } }) .filter(block => block != null); const questions = (filterByType ? dataBlocks.filter((block: any) => block.type === "question") : dataBlocks) .map((block: any) => ({ text: block.text, goal: block.goal, difficulty: block.difficulty, tag: block.tag, })); onIdeas(questions); }; try { while (true) { const { done, value } = await reader.read(); if (done) break; buffer += decoder.decode(value, { stream: true }); const newLines = buffer.split('data: ').filter(line => line.trim() !== ""); buffer = newLines.pop() || ''; if (newLines.length > 0) { lines.push(...newLines); updateState(lines); } onThinkingBuffer(buffer.replace(/^data: /, "")); } } finally { reader.releaseLock(); } lines.push(buffer); updateState(lines); if (lines.length === 0) { throw new Error('No valid results returned from agent'); } } catch (error) { dispatch(dfActions.addMessages({ "timestamp": Date.now(), "type": "error", "component": "chart builder", "value": "Failed to get ideas from the exploration agent. Please try again.", "detail": error instanceof Error ? error.message : 'Unknown error', })); } finally { onLoadingChange(false); } } /** * Formulate data: send instruction to derive/refine endpoint and process the result. * Handles request building, dialog continuation, table/concept creation, and error handling. * Chart creation is delegated to the caller via the createChart callback. */ function formulateData(options: FormulateDataOptions): void { const { instruction, mode, actionTableIds, currentTable, overrideTableId, currentVisualization, expectedVisualization, triggerChart, createChart, onStarted, onSuccess, onError, onFinally, } = options; if (actionTableIds.length === 0) return; onStarted?.(); const actionTables = actionTableIds.map(id => tables.find(t => t.id === id) as DictTable); const token = String(Date.now()); // Build input_tables payload (shared across all request variants) const inputTablesPayload = actionTables.map(t => ({ name: t.virtual?.tableId || t.id.replace(/\.[^/.]+$/, ""), rows: t.rows, attached_metadata: t.attachedMetadata, })); // Build base request body let messageBody: any = { token, mode, input_tables: inputTablesPayload, extra_prompt: instruction, model: activeModel, agent_coding_rules: agentRules.coding, ...(currentVisualization ? { current_visualization: currentVisualization } : {}), ...(expectedVisualization ? { expected_visualization: expectedVisualization } : {}), }; let engine = getUrls().DERIVE_DATA; // Handle dialog continuation / refinement if (currentTable.derive?.dialog && !currentTable.anchored) { const sourceTableIds = currentTable.derive.source; const tableIdsChanged = !sourceTableIds.every((id: string) => actionTableIds.includes(id)) || !actionTableIds.every(id => sourceTableIds.includes(id)); if (mode === 'ideate' || tableIdsChanged) { // Start fresh with prior dialog as additional context messageBody.additional_messages = currentTable.derive.dialog; engine = getUrls().DERIVE_DATA; } else { // Refine: continue existing dialog messageBody = { token, mode, input_tables: inputTablesPayload, dialog: currentTable.derive.dialog, latest_data_sample: currentTable.rows.slice(0, 10), new_instruction: instruction, model: activeModel, agent_coding_rules: agentRules.coding, ...(currentVisualization ? { current_visualization: currentVisualization } : {}), ...(expectedVisualization ? { expected_visualization: expectedVisualization } : {}), }; engine = getUrls().REFINE_DATA; } } const controller = new AbortController(); const timeoutId = setTimeout(() => controller.abort(), config.formulateTimeoutSeconds * 1000); fetchWithIdentity(engine, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(messageBody), signal: controller.signal, }) .then((response: Response) => { if (!response.ok) { return response.text().then(text => { try { const errorData = JSON.parse(text); throw new Error(errorData.error_message || errorData.error || `Server error (${response.status})`); } catch (parseError) { if (parseError instanceof SyntaxError) { throw new Error(`Server error (${response.status}): The server returned an unexpected response.`); } throw parseError; } }); } return response.json(); }) .then((data) => { if (data.status === "error" && data.error_message) { dispatch(dfActions.addMessages({ "timestamp": Date.now(), "component": "chart builder", "type": "error", "value": `Data formulation failed: ${data.error_message}`, })); return; } if (!data.results || data.results.length === 0) { dispatch(dfActions.addMessages({ "timestamp": Date.now(), "component": "chart builder", "type": "error", "value": "No result is returned from the data formulation agent. Please try again.", })); return; } if (data["token"] !== token) return; const candidates = data["results"].filter((item: any) => item["status"] === "ok"); if (candidates.length === 0) { dispatch(dfActions.addMessages({ "timestamp": Date.now(), "type": "error", "component": "chart builder", "value": "Data formulation failed, please try again.", "code": data.results[0].code, "detail": data.results[0].content, })); return; } // Process the best candidate const candidate = candidates[0]; const code = candidate["code"]; const codeSignature = candidate["code_signature"]; // HMAC signature from server const rows = candidate["content"]["rows"]; const dialog = candidate["dialog"]; const refinedGoal = candidate['refined_goal']; const displayInstruction = refinedGoal["display_instruction"]; // Determine table ID let candidateTableId: string; if (overrideTableId) { candidateTableId = overrideTableId; } else if (candidate["content"]["virtual"]) { candidateTableId = candidate["content"]["virtual"]["table_name"]; } else { candidateTableId = generateTableId(tables); } // Create trigger const trigger: Trigger = { tableId: currentTable.id, instruction, displayInstruction, chart: triggerChart, resultTableId: candidateTableId, }; // Create candidate table with derive info const candidateTable = createDictTable(candidateTableId, rows, { code, codeSignature, outputVariable: refinedGoal['output_variable'] || 'result_df', source: actionTableIds, dialog, trigger, }); if (candidate["content"]["virtual"]) { candidateTable.virtual = { tableId: candidate["content"]["virtual"]["table_name"], rowCount: candidate["content"]["virtual"]["row_count"], }; } // Bootstrap metadata from agent field_metadata (temporary until fetchFieldSemanticType completes) const fieldMetadata = refinedGoal['field_metadata']; if (fieldMetadata && typeof fieldMetadata === 'object') { for (const [fieldName, meta] of Object.entries(fieldMetadata)) { if (!candidateTable.metadata[fieldName]) continue; if (typeof meta === 'string') { // Plain string format: { "field": "SemanticType" } candidateTable.metadata[fieldName].semanticType = meta; } else if (typeof meta === 'object' && meta !== null) { // Dict format: { "field": { "semantic_type": "...", "unit": "...", ... } } const m = meta as Record<string, any>; if (m['semantic_type']) { candidateTable.metadata[fieldName].semanticType = m['semantic_type']; } if (m['unit']) { candidateTable.metadata[fieldName].unit = m['unit']; } if (m['intrinsic_domain']) { candidateTable.metadata[fieldName].intrinsicDomain = m['intrinsic_domain']; } } } } // Insert or override table if (overrideTableId) { dispatch(dfActions.overrideDerivedTables(candidateTable)); } else { dispatch(dfActions.insertDerivedTables(candidateTable)); } // Add missing concepts const names = candidateTable.names; const missingNames = names.filter((name: string) => !conceptShelfItems.some(field => field.name === name)); const conceptsToAdd = missingNames.map((name: string) => ({ id: `concept-${name}-${Date.now()}`, name, source: "custom", tableRef: "custom", } as FieldItem)); dispatch(dfActions.addConceptItems(conceptsToAdd)); dispatch(fetchFieldSemanticType(candidateTable)); dispatch(fetchCodeExpl(candidateTable)); // Compute current concepts for chart creation const currentConcepts = [...conceptShelfItems.filter(c => names.includes(c.name)), ...conceptsToAdd]; // Delegate chart creation to the caller const focusedChartId = createChart({ candidateTable, refinedGoal, currentConcepts }); // Auto-generate chart insight after rendering if (focusedChartId) { const chartIdForInsight = focusedChartId; setTimeout(() => { dispatch(fetchChartInsight({ chartId: chartIdForInsight, tableId: candidateTable.id }) as any); }, 1500); } onSuccess?.({ displayInstruction, candidateTable, focusedChartId }); }) .catch((error) => { if (error.name === 'AbortError') { dispatch(dfActions.addMessages({ "timestamp": Date.now(), "component": "chart builder", "type": "error", "value": `Data formulation timed out after ${config.formulateTimeoutSeconds} seconds. Consider breaking down the task, using a different model or prompt, or increasing the timeout limit.`, "detail": "Request exceeded timeout limit", })); } else { console.error(error); dispatch(dfActions.addMessages({ "timestamp": Date.now(), "component": "chart builder", "type": "error", "value": "Data formulation failed, please try again.", "detail": error.message, })); } onError?.(error); }) .finally(() => { clearTimeout(timeoutId); onFinally?.(); }); } return { streamIdeas, formulateData }; } ================================================ FILE: tsconfig.json ================================================ { "compilerOptions": { "target": "es5", "lib": [ "dom", "dom.iterable", "esnext", "ESNext.Array", ], "downlevelIteration": true, "allowJs": true, "skipLibCheck": true, "esModuleInterop": true, "allowSyntheticDefaultImports": true, "strict": true, "forceConsistentCasingInFileNames": true, "noFallthroughCasesInSwitch": true, "module": "esnext", "moduleResolution": "node", "resolveJsonModule": true, "isolatedModules": true, "jsx": "react", }, "include": [ "src" ] } ================================================ FILE: vite.config.ts ================================================ import { defineConfig } from 'vite'; import react from '@vitejs/plugin-react-swc'; import path from 'path'; // Get port from environment variable with fallback to 5567 const apiPort = process.env.API_PORT || 5567; export default defineConfig({ plugins: [react()], resolve: { alias: { '@': path.resolve(__dirname, './src'), }, }, css: { preprocessorOptions: { scss: { api: 'modern-compiler', }, }, }, build: { outDir: path.join(__dirname, 'py-src', 'data_formulator', "dist"), rollupOptions: { output: { entryFileNames: `DataFormulator.js`, // specific name for the main JS bundle chunkFileNames: `assets/[name]-[hash].js`, // keep default naming for chunks assetFileNames: `assets/[name]-[hash].[ext]`, // keep default naming for other assets manualChunks: { // Separate vendor chunks for better caching and parallel loading 'vendor-react': ['react', 'react-dom', 'react-redux', 'redux', '@reduxjs/toolkit'], 'vendor-mui': ['@mui/material', '@mui/icons-material', '@mui/lab', '@emotion/react', '@emotion/styled'], 'vendor-vega': ['vega', 'vega-lite', 'vega-embed', 'react-vega'], 'vendor-d3': ['d3'], 'vendor-utils': ['lodash', 'localforage', 'dompurify', 'validator'], 'vendor-editor': ['prismjs', 'prism-react-renderer', 'react-simple-code-editor', 'prettier'], 'vendor-markdown': ['markdown-to-jsx', 'mui-markdown', 'katex', 'react-katex'], 'vendor-misc': ['exceljs', 'html2canvas', 'allotment', 'react-dnd', 'react-dnd-html5-backend', 'react-virtuoso'], } } }, chunkSizeWarningLimit: 1000, // Warn if chunks exceed 1MB }, server: { proxy: { '/api': { target: `http://localhost:${apiPort}`, changeOrigin: true, } } } });