From 070bb8261e9accfe6775728a7a9f9b6917f198d5 Mon Sep 17 00:00:00 2001 From: Anthony Volk Date: Wed, 8 Apr 2026 22:26:42 +0200 Subject: [PATCH 01/15] Add interactive pipeline diagram app with @pipeline_node decorators --- .../demos/pipeline-demos/.gitignore | 41 + .../demos/pipeline-demos/AGENTS.md | 5 + .../demos/pipeline-demos/CLAUDE.md | 1 + .../demos/pipeline-demos/README.md | 36 + .../pipeline-demos/app/[country]/page.tsx | 27 + .../app/[country]/stage/[stageId]/page.tsx | 25 + .../demos/pipeline-demos/app/colors.ts | 25 + .../pipeline-demos/app/components/ElkEdge.tsx | 96 + .../app/components/NodeDetailPanel.tsx | 113 + .../app/components/PipelineDiagram.tsx | 276 + .../app/components/PipelineNode.tsx | 62 + .../pipeline-demos/app/components/Sidebar.tsx | 173 + .../demos/pipeline-demos/app/favicon.ico | Bin 0 -> 25931 bytes .../demos/pipeline-demos/app/globals.css | 103 + .../demos/pipeline-demos/app/layout.tsx | 29 + .../demos/pipeline-demos/app/page.tsx | 5 + .../demos/pipeline-demos/app/pipeline.json | 1922 +++++ .../demos/pipeline-demos/eslint.config.mjs | 18 + .../demos/pipeline-demos/next.config.ts | 7 + .../demos/pipeline-demos/package-lock.json | 6825 +++++++++++++++++ .../demos/pipeline-demos/package.json | 29 + .../demos/pipeline-demos/postcss.config.mjs | 7 + .../demos/pipeline-demos/public/file.svg | 1 + .../demos/pipeline-demos/public/globe.svg | 1 + .../demos/pipeline-demos/public/next.svg | 1 + .../demos/pipeline-demos/public/vercel.svg | 1 + .../demos/pipeline-demos/public/window.svg | 1 + .../demos/pipeline-demos/tsconfig.json | 34 + docs/pipeline-diagrams/pipeline.json | 1922 +++++ pipeline_stages.yaml | 883 +++ policyengine_us_data/datasets/cps/cps.py | 92 + policyengine_us_data/datasets/puf/puf.py | 24 + policyengine_us_data/pipeline_metadata.py | 40 + policyengine_us_data/pipeline_schema.py | 117 + scripts/extract_pipeline.py | 248 + 35 files changed, 13190 insertions(+) create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/.gitignore create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/AGENTS.md create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/CLAUDE.md create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/README.md create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/app/[country]/page.tsx create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/app/[country]/stage/[stageId]/page.tsx create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/app/colors.ts create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/app/components/ElkEdge.tsx create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/app/components/NodeDetailPanel.tsx create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/app/components/PipelineDiagram.tsx create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/app/components/PipelineNode.tsx create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/app/components/Sidebar.tsx create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/app/favicon.ico create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/app/globals.css create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/app/layout.tsx create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/app/page.tsx create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/app/pipeline.json create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/eslint.config.mjs create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/next.config.ts create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/package-lock.json create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/package.json create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/postcss.config.mjs create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/public/file.svg create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/public/globe.svg create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/public/next.svg create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/public/vercel.svg create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/public/window.svg create mode 100644 docs/pipeline-diagrams/demos/pipeline-demos/tsconfig.json create mode 100644 docs/pipeline-diagrams/pipeline.json create mode 100644 pipeline_stages.yaml create mode 100644 policyengine_us_data/pipeline_metadata.py create mode 100644 policyengine_us_data/pipeline_schema.py create mode 100644 scripts/extract_pipeline.py diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/.gitignore b/docs/pipeline-diagrams/demos/pipeline-demos/.gitignore new file mode 100644 index 00000000..5ef6a520 --- /dev/null +++ b/docs/pipeline-diagrams/demos/pipeline-demos/.gitignore @@ -0,0 +1,41 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.* +.yarn/* +!.yarn/patches +!.yarn/plugins +!.yarn/releases +!.yarn/versions + +# testing +/coverage + +# next.js +/.next/ +/out/ + +# production +/build + +# misc +.DS_Store +*.pem + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* +.pnpm-debug.log* + +# env files (can opt-in for committing if needed) +.env* + +# vercel +.vercel + +# typescript +*.tsbuildinfo +next-env.d.ts diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/AGENTS.md b/docs/pipeline-diagrams/demos/pipeline-demos/AGENTS.md new file mode 100644 index 00000000..8bd0e390 --- /dev/null +++ b/docs/pipeline-diagrams/demos/pipeline-demos/AGENTS.md @@ -0,0 +1,5 @@ + +# This is NOT the Next.js you know + +This version has breaking changes — APIs, conventions, and file structure may all differ from your training data. Read the relevant guide in `node_modules/next/dist/docs/` before writing any code. Heed deprecation notices. + diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/CLAUDE.md b/docs/pipeline-diagrams/demos/pipeline-demos/CLAUDE.md new file mode 100644 index 00000000..43c994c2 --- /dev/null +++ b/docs/pipeline-diagrams/demos/pipeline-demos/CLAUDE.md @@ -0,0 +1 @@ +@AGENTS.md diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/README.md b/docs/pipeline-diagrams/demos/pipeline-demos/README.md new file mode 100644 index 00000000..e215bc4c --- /dev/null +++ b/docs/pipeline-diagrams/demos/pipeline-demos/README.md @@ -0,0 +1,36 @@ +This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app). + +## Getting Started + +First, run the development server: + +```bash +npm run dev +# or +yarn dev +# or +pnpm dev +# or +bun dev +``` + +Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. + +You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file. + +This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel. + +## Learn More + +To learn more about Next.js, take a look at the following resources: + +- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API. +- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial. + +You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome! + +## Deploy on Vercel + +The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js. + +Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details. diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/[country]/page.tsx b/docs/pipeline-diagrams/demos/pipeline-demos/app/[country]/page.tsx new file mode 100644 index 00000000..14f5d798 --- /dev/null +++ b/docs/pipeline-diagrams/demos/pipeline-demos/app/[country]/page.tsx @@ -0,0 +1,27 @@ +"use client"; + +import PipelineDiagram from "../components/PipelineDiagram"; +import pipelineData from "../pipeline.json"; + +// Overview: show all stages as a high-level flow +const overviewStage = { + id: -1, + label: "Overview", + title: "Pipeline Overview — Cross-Stage Data Flow", + description: "High-level view of all pipeline stages", + nodes: pipelineData.stages.map((s) => ({ + id: `stage_${s.id}`, + label: `${s.label}: ${s.description}`, + node_type: "process", + description: s.description, + })), + edges: pipelineData.stages.slice(0, -1).map((s, i) => ({ + source: `stage_${s.id}`, + target: `stage_${pipelineData.stages[i + 1].id}`, + edge_type: "data_flow", + })), +}; + +export default function CountryOverview() { + return ; +} diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/[country]/stage/[stageId]/page.tsx b/docs/pipeline-diagrams/demos/pipeline-demos/app/[country]/stage/[stageId]/page.tsx new file mode 100644 index 00000000..23e5824e --- /dev/null +++ b/docs/pipeline-diagrams/demos/pipeline-demos/app/[country]/stage/[stageId]/page.tsx @@ -0,0 +1,25 @@ +"use client"; + +import { use } from "react"; +import PipelineDiagram from "../../../components/PipelineDiagram"; +import pipelineData from "../../../pipeline.json"; + +export default function StagePage({ + params, +}: { + params: Promise<{ country: string; stageId: string }>; +}) { + const { stageId } = use(params); + const stageIdNum = parseInt(stageId, 10); + const stage = pipelineData.stages.find((s) => s.id === stageIdNum); + + if (!stage) { + return ( +
+ Stage {stageId} not found. +
+ ); + } + + return ; +} diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/colors.ts b/docs/pipeline-diagrams/demos/pipeline-demos/app/colors.ts new file mode 100644 index 00000000..abfdebaf --- /dev/null +++ b/docs/pipeline-diagrams/demos/pipeline-demos/app/colors.ts @@ -0,0 +1,25 @@ +// Node type → visual properties (matching pipeline_schema.py NODE_COLORS) +export const NODE_COLORS: Record = { + input: { fill: "#dbeafe", border: "#3b82f6" }, + output: { fill: "#dcfce7", border: "#22c55e" }, + process: { fill: "#ffedd5", border: "#f97316" }, + utility: { fill: "#f3e8ff", border: "#a855f7" }, + external: { fill: "#fef9c3", border: "#eab308" }, + us_specific: { fill: "#fce7f3", border: "#ec4899" }, + uk_specific: { fill: "#ccfbf1", border: "#14b8a6" }, + missing: { fill: "#fee2e2", border: "#ef4444" }, + absent: { fill: "#f3f4f6", border: "#d1d5db" }, +}; + +// Edge type → visual properties (matching pipeline_schema.py EDGE_STYLES) +export const EDGE_STYLES: Record< + string, + { color: string; style: string; width: number } +> = { + data_flow: { color: "#334155", style: "solid", width: 2 }, + produces_artifact: { color: "#16a34a", style: "solid", width: 2 }, + uses_utility: { color: "#7c3aed", style: "dashed", width: 1.5 }, + external_source: { color: "#b45309", style: "dotted", width: 1.5 }, + runs_on_infra: { color: "#dc2626", style: "dashed", width: 1.5 }, + informational: { color: "#9ca3af", style: "dotted", width: 1 }, +}; diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/components/ElkEdge.tsx b/docs/pipeline-diagrams/demos/pipeline-demos/app/components/ElkEdge.tsx new file mode 100644 index 00000000..199570eb --- /dev/null +++ b/docs/pipeline-diagrams/demos/pipeline-demos/app/components/ElkEdge.tsx @@ -0,0 +1,96 @@ +"use client"; + +import { EdgeLabelRenderer, getSmoothStepPath, type EdgeProps } from "@xyflow/react"; + +/** + * Custom edge that renders using ELK's computed bend points when available, + * falling back to smoothstep for edges without routes. + */ +export default function ElkEdge({ + id, + sourceX, + sourceY, + targetX, + targetY, + sourcePosition, + targetPosition, + style = {}, + markerEnd, + label, + data, +}: EdgeProps) { + let edgePath: string; + let labelX: number; + let labelY: number; + + if (data?.elkRoute) { + const { startPoint, endPoint, bendPoints = [] } = data.elkRoute as { + startPoint: { x: number; y: number }; + endPoint: { x: number; y: number }; + bendPoints?: { x: number; y: number }[]; + }; + const points = [startPoint, ...bendPoints, endPoint]; + + edgePath = points + .map((p, i) => `${i === 0 ? "M" : "L"} ${p.x} ${p.y}`) + .join(" "); + + // Label at midpoint of the path + const mid = Math.floor(points.length / 2); + labelX = points[mid].x; + labelY = points[mid].y; + } else { + // Fallback to smoothstep + const [path, lx, ly] = getSmoothStepPath({ + sourceX, + sourceY, + targetX, + targetY, + sourcePosition, + targetPosition, + }); + edgePath = path; + labelX = lx; + labelY = ly; + } + + return ( + <> + + {label && ( + +
+ + {label} + +
+ + )} + + ); +} diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/components/NodeDetailPanel.tsx b/docs/pipeline-diagrams/demos/pipeline-demos/app/components/NodeDetailPanel.tsx new file mode 100644 index 00000000..182b308a --- /dev/null +++ b/docs/pipeline-diagrams/demos/pipeline-demos/app/components/NodeDetailPanel.tsx @@ -0,0 +1,113 @@ +"use client"; + +import { NODE_COLORS } from "../colors"; + +const TYPE_LABELS: Record = { + input: "Input data", + output: "Output artifact", + process: "Processing step", + utility: "Utility module", + external: "External service", + us_specific: "US-specific", + uk_specific: "UK-specific", + missing: "Planned", + absent: "Not applicable", +}; + +interface NodeDetailProps { + data: { + label: string; + nodeType: string; + description?: string; + details?: string; + source_file?: string; + } | null; + onClose: () => void; +} + +export default function NodeDetailPanel({ data, onClose }: NodeDetailProps) { + if (!data) return null; + + const colors = NODE_COLORS[data.nodeType] || NODE_COLORS.process; + const typeLabel = TYPE_LABELS[data.nodeType] || data.nodeType; + + return ( +
+ {/* Colored top border */} +
+ +
+
+
+

+ {data.label} +

+ + {typeLabel} + +
+ +
+ + {data.description && ( +

+ {data.description} +

+ )} + + {data.details && ( +
+ {data.details} +
+ )} + + {data.source_file && ( +
+ + + + + + + {data.source_file} + +
+ )} +
+
+ ); +} diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/components/PipelineDiagram.tsx b/docs/pipeline-diagrams/demos/pipeline-demos/app/components/PipelineDiagram.tsx new file mode 100644 index 00000000..196bed6d --- /dev/null +++ b/docs/pipeline-diagrams/demos/pipeline-demos/app/components/PipelineDiagram.tsx @@ -0,0 +1,276 @@ +"use client"; + +import { useCallback, useEffect, useRef, useState } from "react"; +import { + ReactFlow, + Background, + Controls, + MiniMap, + ReactFlowProvider, + useReactFlow, + useNodesState, + useEdgesState, + type Node, + type Edge, + BackgroundVariant, +} from "@xyflow/react"; +import "@xyflow/react/dist/style.css"; +import ELK from "elkjs/lib/elk.bundled.js"; +import { NODE_COLORS, EDGE_STYLES } from "../colors"; +import PipelineNode from "./PipelineNode"; +import ElkEdge from "./ElkEdge"; +import NodeDetailPanel from "./NodeDetailPanel"; + +const elk = new ELK(); +const nodeTypes = { pipeline: PipelineNode }; +const edgeTypes = { elk: ElkEdge }; + +const ELK_OPTIONS: Record = { + "elk.algorithm": "layered", + "elk.direction": "DOWN", + "elk.edgeRouting": "ORTHOGONAL", + "elk.spacing.nodeNode": "60", + "elk.layered.spacing.nodeNodeBetweenLayers": "80", + "elk.layered.spacing.edgeNodeBetweenLayers": "30", + "elk.spacing.edgeEdge": "20", + "elk.spacing.edgeNode": "30", + "elk.layered.nodePlacement.strategy": "NETWORK_SIMPLEX", + "elk.layered.crossingMinimization.strategy": "LAYER_SWEEP", +}; + +interface StageData { + id: number; + label: string; + title: string; + description: string; + nodes: any[]; + edges: any[]; +} + +/** + * Estimate node size to match the CSS: + * min-w-[180px] max-w-[280px], px-3 py-2, 12px label + 10px description + */ +function estimateNodeSize(node: any) { + let height = 16; // py-2 top + height += 18; // label line + if (node.description) height += 16; // description line + height += 14; // py-2 bottom + return { width: 240, height: Math.max(58, height) }; +} + +/** + * Assign sourceHandle/targetHandle based on relative node positions. + */ +function assignHandles( + sourcePos: { x: number; y: number }, + targetPos: { x: number; y: number } +) { + const dx = targetPos.x - sourcePos.x; + const dy = targetPos.y - sourcePos.y; + + if (Math.abs(dx) >= Math.abs(dy)) { + return dx >= 0 + ? { sourceHandle: "sr", targetHandle: "tl" } + : { sourceHandle: "sl", targetHandle: "tr" }; + } + return dy >= 0 + ? { sourceHandle: "sb", targetHandle: "tt" } + : { sourceHandle: "st", targetHandle: "tb" }; +} + +async function runElkLayout( + pipelineNodes: any[], + pipelineEdges: any[] +): Promise<{ nodes: Node[]; edges: Edge[] }> { + const graph = { + id: "root", + layoutOptions: ELK_OPTIONS, + children: pipelineNodes.map((n: any) => { + const size = estimateNodeSize(n); + return { id: n.id, width: size.width, height: size.height }; + }), + edges: pipelineEdges.map((e: any, i: number) => ({ + id: `e-${i}`, + sources: [e.source], + targets: [e.target], + })), + }; + + const result = await elk.layout(graph); + + // Build position map + const positionMap: Record = {}; + for (const child of result.children || []) { + positionMap[child.id] = { x: child.x || 0, y: child.y || 0 }; + } + + // Build edge route map from ELK sections + const routeMap: Record = {}; + for (const edge of (result.edges || []) as any[]) { + if (edge.sections && edge.sections.length > 0) { + routeMap[edge.id] = edge.sections[0]; + } + } + + // Position nodes + const nodes: Node[] = pipelineNodes.map((n: any) => ({ + id: n.id, + type: "pipeline", + position: positionMap[n.id] || { x: 0, y: 0 }, + data: { + label: n.label || n.id, + nodeType: n.node_type || "process", + description: n.description || "", + details: n.details || "", + source_file: n.source_file || "", + }, + })); + + // Enrich edges with handles + ELK routes + const edges: Edge[] = pipelineEdges.map((e: any, i: number) => { + const edgeId = `e-${i}`; + const srcPos = positionMap[e.source] || { x: 0, y: 0 }; + const tgtPos = positionMap[e.target] || { x: 0, y: 0 }; + const handles = assignHandles(srcPos, tgtPos); + const route = routeMap[edgeId]; + const edgeStyle = EDGE_STYLES[e.edge_type] || EDGE_STYLES.data_flow; + + return { + id: edgeId, + source: e.source, + target: e.target, + ...handles, + type: "elk", + label: e.label || undefined, + data: { + elkRoute: route || null, + }, + style: { + stroke: edgeStyle.color, + strokeWidth: edgeStyle.width, + strokeDasharray: + edgeStyle.style === "dashed" ? "6 3" : edgeStyle.style === "dotted" ? "2 2" : undefined, + }, + }; + }); + + return { nodes, edges }; +} + +function DiagramInner({ stage }: { stage: StageData }) { + const [nodes, setNodes, onNodesChange] = useNodesState([]); + const [edges, setEdges, onEdgesChange] = useEdgesState([]); + const [selectedNode, setSelectedNode] = useState(null); + const [layoutDone, setLayoutDone] = useState(false); + const { fitView } = useReactFlow(); + const hasFit = useRef(false); + + useEffect(() => { + if (!stage?.nodes?.length) return; + hasFit.current = false; + setLayoutDone(false); + runElkLayout(stage.nodes, stage.edges).then(({ nodes, edges }) => { + setNodes(nodes); + setEdges(edges); + setLayoutDone(true); + }); + }, [stage]); + + useEffect(() => { + if (layoutDone && !hasFit.current) { + hasFit.current = true; + requestAnimationFrame(() => { + fitView({ padding: 0.15 }); + }); + } + }, [layoutDone, fitView]); + + const onNodeClick = useCallback((_: any, node: Node) => { + setSelectedNode(node.data); + }, []); + + const onPaneClick = useCallback(() => { + setSelectedNode(null); + }, []); + + if (!stage?.nodes?.length) { + return ( +
+
+
+ No diagram data available +
+
+
+ ); + } + + return ( +
+ {/* Stage info */} +
+

+ {stage.title} +

+

+ {stage.description} +

+
+ + {stage.nodes.length} nodes + + · + + {stage.edges.length} edges + +
+
+ + + + + NODE_COLORS[n.data?.nodeType as string]?.border || "#9CA3AF"} + maskColor="rgba(0,0,0,0.04)" + style={{ width: 140, height: 90 }} + /> + + + setSelectedNode(null)} /> +
+ ); +} + +export default function PipelineDiagram({ stage }: { stage: StageData }) { + return ( + + + + ); +} diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/components/PipelineNode.tsx b/docs/pipeline-diagrams/demos/pipeline-demos/app/components/PipelineNode.tsx new file mode 100644 index 00000000..21d3bb7e --- /dev/null +++ b/docs/pipeline-diagrams/demos/pipeline-demos/app/components/PipelineNode.tsx @@ -0,0 +1,62 @@ +"use client"; + +import { Handle, Position } from "@xyflow/react"; +import { NODE_COLORS } from "../colors"; + +const handleClass = "!w-2 !h-2 !border-none"; + +/** + * 8 handles — source + target on each of 4 sides. + * Handle ID convention: + * "sr" = source-right, "tl" = target-left (horizontal flow →) + * "sb" = source-bottom, "tt" = target-top (vertical flow ↓) + * "st" = source-top, "tb" = target-bottom (upward flow ↑) + * "sl" = source-left, "tr" = target-right (leftward flow ←) + */ +function AllHandles({ color }: { color: string }) { + const s = { background: color }; + return ( + <> + + + + + + + + + + ); +} + +export default function PipelineNode({ data }: { data: any }) { + const colors = NODE_COLORS[data.nodeType] || NODE_COLORS.process; + const isDashed = data.nodeType === "missing" || data.nodeType === "absent"; + + return ( +
+
+ {data.label} +
+ {data.description && ( +
+ {data.description} +
+ )} + +
+ ); +} diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/components/Sidebar.tsx b/docs/pipeline-diagrams/demos/pipeline-demos/app/components/Sidebar.tsx new file mode 100644 index 00000000..91df39c7 --- /dev/null +++ b/docs/pipeline-diagrams/demos/pipeline-demos/app/components/Sidebar.tsx @@ -0,0 +1,173 @@ +"use client"; + +import Link from "next/link"; +import { usePathname } from "next/navigation"; +import { NODE_COLORS, EDGE_STYLES } from "../colors"; +import pipelineData from "../pipeline.json"; + +const NODE_LEGEND = [ + { type: "input", label: "Input" }, + { type: "output", label: "Output" }, + { type: "process", label: "Process" }, + { type: "utility", label: "Utility" }, + { type: "external", label: "External" }, + { type: "us_specific", label: "US-specific" }, + { type: "missing", label: "Missing" }, + { type: "absent", label: "Absent" }, +]; + +const EDGE_LEGEND = [ + { type: "data_flow", label: "Data flow" }, + { type: "produces_artifact", label: "Produces artifact" }, + { type: "uses_utility", label: "Uses utility" }, + { type: "external_source", label: "External source" }, + { type: "runs_on_infra", label: "Runs on infra" }, + { type: "informational", label: "Informational" }, +]; + +export default function Sidebar() { + const pathname = usePathname(); + const country = "us"; + const stages = pipelineData.stages; + + return ( + + ); +} diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/favicon.ico b/docs/pipeline-diagrams/demos/pipeline-demos/app/favicon.ico new file mode 100644 index 0000000000000000000000000000000000000000..718d6fea4835ec2d246af9800eddb7ffb276240c GIT binary patch literal 25931 zcmeHv30#a{`}aL_*G&7qml|y<+KVaDM2m#dVr!KsA!#An?kSQM(q<_dDNCpjEux83 zLb9Z^XxbDl(w>%i@8hT6>)&Gu{h#Oeyszu?xtw#Zb1mO{pgX9699l+Qppw7jXaYf~-84xW z)w4x8?=youko|}Vr~(D$UXIbiXABHh`p1?nn8Po~fxRJv}|0e(BPs|G`(TT%kKVJAdg5*Z|x0leQq0 zkdUBvb#>9F()jo|T~kx@OM8$9wzs~t2l;K=woNssA3l6|sx2r3+kdfVW@e^8e*E}v 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@@ -0,0 +1,103 @@ +@import "tailwindcss"; + +@theme inline { + --color-pe-primary-50: #E6FFFA; + --color-pe-primary-400: #38B2AC; + --color-pe-primary-500: #319795; + --color-pe-primary-600: #2C7A7B; + --color-pe-primary-700: #285E61; + --color-pe-primary-800: #234E52; + --color-pe-gray-50: #F9FAFB; + --color-pe-gray-100: #F2F4F7; + --color-pe-gray-200: #E2E8F0; + --color-pe-gray-500: #6B7280; + --color-pe-gray-600: #4B5563; + --color-pe-gray-700: #344054; +} + +:root { + /* PolicyEngine design tokens */ + --pe-primary-50: #E6FFFA; + --pe-primary-400: #38B2AC; + --pe-primary-500: #319795; + --pe-primary-600: #2C7A7B; + --pe-primary-700: #285E61; + --pe-primary-800: #234E52; + + --pe-gray-50: #F9FAFB; + --pe-gray-100: #F2F4F7; + --pe-gray-200: #E2E8F0; + --pe-gray-500: #6B7280; + --pe-gray-600: #4B5563; + --pe-gray-700: #344054; + + --pe-text-primary: #000000; + --pe-text-secondary: #5A5A5A; + --pe-text-tertiary: #9CA3AF; + + --pe-bg-primary: #FFFFFF; + --pe-bg-secondary: #F5F9FF; + --pe-bg-tertiary: #F1F5F9; + + --pe-blue-500: #0EA5E9; + --pe-success: #22C55E; + --pe-error: #EF4444; + --pe-warning: #FEC601; + + --pe-font-primary: 'Inter', system-ui, sans-serif; + --pe-font-mono: 'JetBrains Mono', monospace; + + --pe-radius-sm: 4px; + --pe-radius-md: 6px; + --pe-radius-lg: 8px; +} + +* { box-sizing: border-box; } + +body { + background: var(--pe-bg-primary); + color: var(--pe-text-primary); + font-family: var(--pe-font-primary); + -webkit-font-smoothing: antialiased; +} + +/* React Flow overrides */ +.react-flow__background { + background-color: var(--pe-bg-secondary) !important; +} + +.react-flow__controls { + box-shadow: 0 1px 3px rgba(0,0,0,0.06) !important; + border: 1px solid var(--pe-gray-200) !important; + border-radius: var(--pe-radius-lg) !important; + overflow: hidden !important; +} + +.react-flow__controls-button { + border-bottom: 1px solid var(--pe-gray-100) !important; + background: white !important; + width: 32px !important; + height: 32px !important; +} + +.react-flow__controls-button:hover { + background: var(--pe-gray-50) !important; +} + +.react-flow__minimap { + border-radius: var(--pe-radius-lg) !important; + box-shadow: 0 1px 3px rgba(0,0,0,0.06) !important; + border: 1px solid var(--pe-gray-200) !important; + background: white !important; +} + +.react-flow__edge-text { + font-family: var(--pe-font-primary) !important; + font-size: 10px !important; +} + +/* Sidebar scrollbar */ +aside::-webkit-scrollbar { width: 3px; } +aside::-webkit-scrollbar-track { background: transparent; } +aside::-webkit-scrollbar-thumb { background: rgba(0,0,0,0.08); border-radius: 3px; } +aside::-webkit-scrollbar-thumb:hover { background: rgba(0,0,0,0.15); } diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/layout.tsx b/docs/pipeline-diagrams/demos/pipeline-demos/app/layout.tsx new file mode 100644 index 00000000..e1769cd7 --- /dev/null +++ b/docs/pipeline-diagrams/demos/pipeline-demos/app/layout.tsx @@ -0,0 +1,29 @@ +import type { Metadata } from "next"; +import "./globals.css"; +import Sidebar from "./components/Sidebar"; + +export const metadata: Metadata = { + title: "Pipeline explorer — PolicyEngine US Data", + description: "Interactive pipeline documentation for the US data build process", +}; + +export default function RootLayout({ + children, +}: Readonly<{ + children: React.ReactNode; +}>) { + return ( + + + + + + +
{children}
+ + + ); +} diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/page.tsx b/docs/pipeline-diagrams/demos/pipeline-demos/app/page.tsx new file mode 100644 index 00000000..8da13a42 --- /dev/null +++ b/docs/pipeline-diagrams/demos/pipeline-demos/app/page.tsx @@ -0,0 +1,5 @@ +import { redirect } from "next/navigation"; + +export default function Home() { + redirect("/us"); +} diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/pipeline.json b/docs/pipeline-diagrams/demos/pipeline-demos/app/pipeline.json new file mode 100644 index 00000000..df31f401 --- /dev/null +++ b/docs/pipeline-diagrams/demos/pipeline-demos/app/pipeline.json @@ -0,0 +1,1922 @@ +{ + "stages": [ + { + "id": 0, + "label": "Stage 0", + "title": "Stage 0: Raw Data Download", + "description": "Download raw survey data from Census, IRS, Federal Reserve, and HuggingFace", + "country": "us", + "nodes": [ + { + "id": "cps_url", + "label": "Census CPS ASEC", + "node_type": "input", + "description": "ZIP with CSV files from www2.census.gov" + }, + { + "id": "acs_url", + "label": "Census ACS PUMS", + "node_type": "input", + "description": "Person + Household CSVs, 1-Year estimates" + }, + { + "id": "scf_url", + "label": "Federal Reserve SCF", + "node_type": "input", + "description": "Stata format (.dta), Survey of Consumer Finances" + }, + { + "id": "hf_private", + "label": "HuggingFace Private Repo", + "node_type": "external", + "description": "policyengine/irs-soi-puf \u2014 requires HUGGING_FACE_TOKEN" + }, + { + "id": "hf_public", + "label": "HuggingFace Public Repo", + "node_type": "external", + "description": "policyengine/policyengine-us-data \u2014 SIPP, block distributions, policy_data.db" + }, + { + "id": "download_http", + "label": "HTTP Download + ZIP Extract", + "node_type": "process", + "description": "requests.get() with streaming, tqdm progress bar" + }, + { + "id": "download_hf", + "label": "HuggingFace Hub Download", + "node_type": "process", + "description": "hf_hub_download(), token-authenticated for private repos" + }, + { + "id": "csv_parse", + "label": "CSV/Stata Parsing", + "node_type": "process", + "description": "pandas read_csv/read_stata, entity table construction" + }, + { + "id": "out_cps_raw", + "label": "census_cps_2024.h5", + "node_type": "output", + "description": "5 entity tables: Person, Family, Household, Tax Unit, SPM Unit" + }, + { + "id": "out_acs_raw", + "label": "census_acs_2022.h5", + "node_type": "output", + "description": "Person + Household tables" + }, + { + "id": "out_puf_raw", + "label": "irs_puf_2015.h5", + "node_type": "output", + "description": "PUF + Demographics tables" + }, + { + "id": "out_soi", + "label": "soi.csv", + "node_type": "output", + "description": "SOI aggregate statistics" + }, + { + "id": "out_scf", + "label": "SCF raw data", + "node_type": "output", + "description": "Stata \u2192 DataFrame, auto loans + net worth" + }, + { + "id": "out_sipp", + "label": "pu2023_slim.csv", + "node_type": "output", + "description": "SIPP microdata (pipe-delimited)" + }, + { + "id": "out_block", + "label": "block_cd_distributions.csv.gz", + "node_type": "output", + "description": "Census block populations, block-to-CD crosswalk" + }, + { + "id": "out_pop", + "label": "np2023_d5_mid.csv", + "node_type": "output", + "description": "Census population projections" + }, + { + "id": "out_calibration_db", + "label": "policy_data.db", + "node_type": "output", + "description": "SQLite calibration targets database" + }, + { + "id": "util_storage", + "label": "STORAGE_FOLDER", + "node_type": "utility", + "description": "policyengine_us_data/storage/ \u2014 all downloads cached here" + } + ], + "edges": [ + { + "source": "cps_url", + "target": "download_http", + "edge_type": "external_source", + "label": "CPS ASEC ZIP" + }, + { + "source": "acs_url", + "target": "download_http", + "edge_type": "external_source", + "label": "ACS PUMS CSV" + }, + { + "source": "scf_url", + "target": "download_http", + "edge_type": "external_source", + "label": "SCF .dta" + }, + { + "source": "hf_private", + "target": "download_hf", + "edge_type": "external_source", + "label": "PUF, demographics, SOI, pop" + }, + { + "source": "hf_public", + "target": "download_hf", + "edge_type": "external_source", + "label": "SIPP, block, policy_data.db" + }, + { + "source": "download_http", + "target": "csv_parse", + "edge_type": "data_flow", + "label": "raw files" + }, + { + "source": "download_hf", + "target": "csv_parse", + "edge_type": "data_flow", + "label": "raw files" + }, + { + "source": "csv_parse", + "target": "out_cps_raw", + "edge_type": "produces_artifact", + "label": "census_cps_2024.h5" + }, + { + "source": "csv_parse", + "target": "out_acs_raw", + "edge_type": "produces_artifact", + "label": "census_acs_2022.h5" + }, + { + "source": "csv_parse", + "target": "out_puf_raw", + "edge_type": "produces_artifact", + "label": "irs_puf_2015.h5" + }, + { + "source": "csv_parse", + "target": "out_soi", + "edge_type": "produces_artifact", + "label": "soi.csv" + }, + { + "source": "download_http", + "target": "out_scf", + "edge_type": "produces_artifact", + "label": "SCF raw data" + }, + { + "source": "download_hf", + "target": "out_sipp", + "edge_type": "produces_artifact", + "label": "pu2023_slim.csv" + }, + { + "source": "download_hf", + "target": "out_block", + "edge_type": "produces_artifact", + "label": "block_cd_distributions.csv.gz" + }, + { + "source": "download_hf", + "target": "out_pop", + "edge_type": "produces_artifact", + "label": "np2023_d5_mid.csv" + }, + { + "source": "download_hf", + "target": "out_calibration_db", + "edge_type": "produces_artifact", + "label": "policy_data.db" + } + ] + }, + { + "id": 1, + "label": "Stage 1", + "title": "Stage 1: Base Dataset Construction", + "description": "Build CPS 2024 and PUF 2024 from raw survey data", + "country": "us", + "nodes": [ + { + "id": "in_census_cps", + "label": "census_cps_2024.h5", + "node_type": "input", + "description": "Raw CPS ASEC from Stage 0" + }, + { + "id": "in_census_cps_prev", + "label": "census_cps_2023.h5", + "node_type": "input", + "description": "Previous year CPS for income matching" + }, + { + "id": "in_acs", + "label": "ACS 2022", + "node_type": "input", + "description": "Training data for rent QRF" + }, + { + "id": "in_sipp", + "label": "SIPP 2023", + "node_type": "input", + "description": "Training data for tips QRF" + }, + { + "id": "in_scf", + "label": "SCF 2022", + "node_type": "input", + "description": "Training data for auto loans QRF" + }, + { + "id": "in_uprating", + "label": "uprating_factors.csv", + "node_type": "input", + "description": "PE uprating factors table" + }, + { + "id": "out_cps", + "label": "cps_2024.h5", + "node_type": "output", + "description": "Dataset.ARRAYS format, ~65K households (half-sample)" + }, + { + "id": "out_puf", + "label": "puf_2024.h5", + "node_type": "output", + "description": "Dataset.ARRAYS format" + }, + { + "id": "in_irs_puf", + "label": "irs_puf_2015.h5", + "node_type": "input", + "description": "Raw IRS PUF from Stage 0" + }, + { + "id": "in_demographics", + "label": "demographics_2015.csv", + "node_type": "input", + "description": "PUF demographics from HuggingFace" + }, + { + "id": "util_seeded_rng", + "label": "seeded_rng()", + "node_type": "utility", + "description": "Deterministic per-variable RNG" + }, + { + "id": "util_qrf", + "label": "microimpute QRF", + "node_type": "utility", + "description": "fit_predict() for sequential imputation" + }, + { + "id": "util_retirement_limits", + "label": "get_retirement_limits()", + "node_type": "utility", + "description": "IRS contribution limits" + }, + { + "id": "add_rent", + "label": "Rent Imputation (QRF)", + "node_type": "process", + "description": "Impute rent and real estate taxes using QRF from ACS 2022", + "details": "10K sampled household heads as training data", + "source_file": "policyengine_us_data/datasets/cps/cps.py" + }, + { + "id": "impute_puf_demographics", + "label": "Impute PUF Demographics", + "node_type": "process", + "description": "QRF imputation for age, gender, and earnings split", + "source_file": "policyengine_us_data/datasets/puf/puf.py" + }, + { + "id": "add_ssn_card_type", + "label": "SSN Card Type", + "node_type": "us_specific", + "description": "US immigration classification from 14 ASEC conditions", + "details": "Undocumented target: 13M; SSN card types 0-3", + "source_file": "policyengine_us_data/datasets/cps/cps.py" + }, + { + "id": "add_personal_variables", + "label": "Add Personal Variables", + "node_type": "process", + "description": "Age, sex, disability, occupation, overtime flags", + "details": "80+ ages randomized to 80-84; 12 overtime occupation flags", + "source_file": "policyengine_us_data/datasets/cps/cps.py" + }, + { + "id": "add_id_variables", + "label": "Add ID Variables", + "node_type": "process", + "description": "Create person_id, household_id, tax_unit_id, spm_unit_id, marital_unit_id", + "source_file": "policyengine_us_data/datasets/cps/cps.py" + }, + { + "id": "add_tips", + "label": "Tips Imputation (QRF)", + "node_type": "process", + "description": "Impute tip income and liquid assets from SIPP 2023", + "details": "Models cached as pickle; QRF fit_predict()", + "source_file": "policyengine_us_data/datasets/cps/cps.py" + }, + { + "id": "add_auto_loan", + "label": "Auto Loan / Net Worth (QRF)", + "node_type": "process", + "description": "Impute auto loan balance, interest, and net worth from SCF 2022", + "source_file": "policyengine_us_data/datasets/cps/cps.py" + }, + { + "id": "simulate_qbi", + "label": "QBI Simulation", + "node_type": "process", + "description": "Simulate W-2 wages, UBIA, and SSTB for Section 199A", + "source_file": "policyengine_us_data/datasets/puf/puf.py" + }, + { + "id": "add_spm_variables", + "label": "SPM Variables", + "node_type": "process", + "description": "SPM thresholds and transfers (SNAP, housing, energy subsidies)", + "source_file": "policyengine_us_data/datasets/cps/cps.py" + }, + { + "id": "preprocess_puf", + "label": "Preprocess PUF", + "node_type": "process", + "description": "Rename 60+ IRS variables to PolicyEngine names", + "details": "E00200 \u2192 employment_income, etc.", + "source_file": "policyengine_us_data/datasets/puf/puf.py" + }, + { + "id": "add_personal_income_variables", + "label": "Add Income Variables", + "node_type": "process", + "description": "30+ income types with splits", + "details": "SS classified by reason codes; retirement split by account type; capital gains 88% LT / 12% ST", + "source_file": "policyengine_us_data/datasets/cps/cps.py" + }, + { + "id": "add_previous_year_income", + "label": "Previous Year Income", + "node_type": "process", + "description": "Cross-year PERIDNUM linking for prior-year income", + "source_file": "policyengine_us_data/datasets/cps/cps.py" + }, + { + "id": "add_household_variables", + "label": "Household Variables", + "node_type": "process", + "description": "State FIPS, county FIPS, NYC flag from county codes", + "source_file": "policyengine_us_data/datasets/cps/cps.py" + }, + { + "id": "add_takeup", + "label": "Benefit Takeup", + "node_type": "us_specific", + "description": "Stochastic takeup for 9 benefit programs", + "details": "SNAP, ACA, Medicaid, EITC, SSI, TANF, WIC, Head Start; state-specific Medicaid rates", + "source_file": "policyengine_us_data/datasets/cps/cps.py" + }, + { + "id": "downsample", + "label": "Downsampling", + "node_type": "process", + "description": "frac=0.5 for CPS_2024 using Microsimulation.subsample()", + "source_file": "policyengine_us_data/datasets/cps/cps.py" + } + ], + "edges": [ + { + "source": "in_census_cps", + "target": "add_id_variables", + "edge_type": "data_flow", + "label": "raw CPS tables" + }, + { + "source": "add_id_variables", + "target": "add_personal_variables", + "edge_type": "data_flow" + }, + { + "source": "add_personal_variables", + "target": "add_personal_income_variables", + "edge_type": "data_flow" + }, + { + "source": "add_personal_income_variables", + "target": "add_previous_year_income", + "edge_type": "data_flow" + }, + { + "source": "in_census_cps_prev", + "target": "add_previous_year_income", + "edge_type": "data_flow", + "label": "prior year PERIDNUM" + }, + { + "source": "add_previous_year_income", + "target": "add_ssn_card_type", + "edge_type": "data_flow" + }, + { + "source": "add_ssn_card_type", + "target": "add_spm_variables", + "edge_type": "data_flow" + }, + { + "source": "add_spm_variables", + "target": "add_household_variables", + "edge_type": "data_flow" + }, + { + "source": "add_household_variables", + "target": "add_rent", + "edge_type": "data_flow" + }, + { + "source": "in_acs", + "target": "add_rent", + "edge_type": "external_source", + "label": "ACS training data" + }, + { + "source": "add_rent", + "target": "add_tips", + "edge_type": "data_flow" + }, + { + "source": "in_sipp", + "target": "add_tips", + "edge_type": "external_source", + "label": "SIPP training data" + }, + { + "source": "add_tips", + "target": "add_auto_loan", + "edge_type": "data_flow" + }, + { + "source": "in_scf", + "target": "add_auto_loan", + "edge_type": "external_source", + "label": "SCF training data" + }, + { + "source": "add_auto_loan", + "target": "add_takeup", + "edge_type": "data_flow" + }, + { + "source": "add_takeup", + "target": "downsample", + "edge_type": "data_flow" + }, + { + "source": "downsample", + "target": "out_cps", + "edge_type": "produces_artifact", + "label": "cps_2024.h5" + }, + { + "source": "in_irs_puf", + "target": "preprocess_puf", + "edge_type": "data_flow", + "label": "raw PUF records" + }, + { + "source": "preprocess_puf", + "target": "simulate_qbi", + "edge_type": "data_flow" + }, + { + "source": "simulate_qbi", + "target": "impute_puf_demographics", + "edge_type": "data_flow" + }, + { + "source": "in_demographics", + "target": "impute_puf_demographics", + "edge_type": "data_flow", + "label": "demographics_2015.csv" + }, + { + "source": "impute_puf_demographics", + "target": "out_puf", + "edge_type": "produces_artifact", + "label": "puf_2024.h5" + }, + { + "source": "in_uprating", + "target": "out_puf", + "edge_type": "data_flow", + "label": "SOI growth rates" + }, + { + "source": "util_seeded_rng", + "target": "add_takeup", + "edge_type": "uses_utility" + }, + { + "source": "util_qrf", + "target": "add_rent", + "edge_type": "uses_utility" + }, + { + "source": "util_qrf", + "target": "add_tips", + "edge_type": "uses_utility" + }, + { + "source": "util_qrf", + "target": "add_auto_loan", + "edge_type": "uses_utility" + }, + { + "source": "util_retirement_limits", + "target": "add_personal_income_variables", + "edge_type": "uses_utility" + }, + { + "source": "util_qrf", + "target": "impute_puf_demographics", + "edge_type": "uses_utility" + } + ] + }, + { + "id": 2, + "label": "Stage 2", + "title": "Stage 2: Extended CPS (PUF Clone)", + "description": "Merge CPS + PUF via cloning, impute 64 income vars + 51 override vars via QRF", + "country": "us", + "nodes": [ + { + "id": "in_cps_s2", + "label": "CPS_2024_Full", + "node_type": "input", + "description": "From Stage 1 (full sample)" + }, + { + "id": "in_puf_s2", + "label": "PUF_2024", + "node_type": "input", + "description": "From Stage 1" + }, + { + "id": "in_blocks_s2", + "label": "block_cd_distributions.csv.gz", + "node_type": "input", + "description": "Census block populations" + }, + { + "id": "geo_assign_s2", + "label": "Geography Assignment", + "node_type": "process", + "description": "assign_random_geography() \u2014 population-weighted block draw" + }, + { + "id": "record_double", + "label": "Record Doubling", + "node_type": "process", + "description": "puf_clone_dataset() \u2014 CPS half keeps originals, PUF half starts with zero weight" + }, + { + "id": "qrf_pass1", + "label": "QRF Pass 1: Full Imputation", + "node_type": "process", + "description": "64 income variables \u2014 training on PUF ~20K records, 7 demographic predictors" + }, + { + "id": "qrf_pass2", + "label": "QRF Pass 2: Override Imputation", + "node_type": "process", + "description": "51 variables (both halves) \u2014 partnership, S-corp, charitable, mortgage, credits" + }, + { + "id": "retire_impute", + "label": "Retirement Contribution Imputation", + "node_type": "process", + "description": "401k, IRA, SE pension \u2014 IRS limits + catch-up applied" + }, + { + "id": "weeks_impute", + "label": "Weeks Unemployed Imputation", + "node_type": "process", + "description": "QRF on CPS weeks_unemployed \u2014 clips [0, 52], zero if no UC" + }, + { + "id": "ss_reconcile", + "label": "SS Sub-component Reconciliation", + "node_type": "process", + "description": "Retirement/Disability/Survivors/Dependents \u2014 scaled to match PUF total" + }, + { + "id": "cps_only", + "label": "CPS-Only Variable Re-imputation", + "node_type": "process", + "description": "80 variables for PUF half \u2014 transfers, SPM, medical, hours, retirement" + }, + { + "id": "formula_drop", + "label": "Formula Variable Dropping", + "node_type": "process", + "description": "Remove PE-computed variables, rename employment_income \u2192 employment_income_before_lsr" + }, + { + "id": "out_ext", + "label": "extended_cps_2024.h5", + "node_type": "output", + "description": "~260K households (doubled), CPS half + PUF half" + }, + { + "id": "util_puf_clone", + "label": "puf_clone_dataset()", + "node_type": "utility", + "description": "calibration/puf_impute.py" + }, + { + "id": "util_qrf_s2", + "label": "microimpute QRF", + "node_type": "utility", + "description": "fit_predict() for sequential imputation" + } + ], + "edges": [ + { + "source": "in_cps_s2", + "target": "geo_assign_s2", + "edge_type": "data_flow", + "label": "CPS records" + }, + { + "source": "in_blocks_s2", + "target": "geo_assign_s2", + "edge_type": "data_flow", + "label": "block populations" + }, + { + "source": "in_puf_s2", + 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...nextVitals, + ...nextTs, + // Override default ignores of eslint-config-next. + globalIgnores([ + // Default ignores of eslint-config-next: + ".next/**", + "out/**", + "build/**", + "next-env.d.ts", + ]), +]); + +export default eslintConfig; diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/next.config.ts b/docs/pipeline-diagrams/demos/pipeline-demos/next.config.ts new file mode 100644 index 00000000..e9ffa308 --- /dev/null +++ b/docs/pipeline-diagrams/demos/pipeline-demos/next.config.ts @@ -0,0 +1,7 @@ +import type { NextConfig } from "next"; + +const nextConfig: NextConfig = { + /* config options here */ +}; + +export default nextConfig; diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/package-lock.json b/docs/pipeline-diagrams/demos/pipeline-demos/package-lock.json new file mode 100644 index 00000000..1ac321f6 --- /dev/null +++ b/docs/pipeline-diagrams/demos/pipeline-demos/package-lock.json @@ -0,0 +1,6825 @@ +{ + "name": "pipeline-demos", + "version": "0.1.0", + 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+ } + }, + "include": [ + "next-env.d.ts", + "**/*.ts", + "**/*.tsx", + ".next/types/**/*.ts", + ".next/dev/types/**/*.ts", + "**/*.mts" + ], + "exclude": ["node_modules"] +} diff --git a/docs/pipeline-diagrams/pipeline.json b/docs/pipeline-diagrams/pipeline.json new file mode 100644 index 00000000..df31f401 --- /dev/null +++ b/docs/pipeline-diagrams/pipeline.json @@ -0,0 +1,1922 @@ +{ + "stages": [ + { + "id": 0, + "label": "Stage 0", + "title": "Stage 0: Raw Data Download", + "description": "Download raw survey data from Census, IRS, Federal Reserve, and HuggingFace", + "country": "us", + "nodes": [ + { + "id": "cps_url", + "label": "Census CPS ASEC", + "node_type": "input", + "description": "ZIP with CSV files from www2.census.gov" + }, + { + "id": "acs_url", + "label": "Census ACS PUMS", + "node_type": "input", + "description": "Person + Household CSVs, 1-Year estimates" + }, + { + "id": "scf_url", + "label": "Federal Reserve SCF", + "node_type": "input", + "description": "Stata format (.dta), Survey of Consumer Finances" + }, + { + "id": "hf_private", + "label": "HuggingFace Private Repo", + "node_type": "external", + "description": "policyengine/irs-soi-puf \u2014 requires HUGGING_FACE_TOKEN" + }, + { + "id": "hf_public", + "label": "HuggingFace Public Repo", + "node_type": "external", + "description": "policyengine/policyengine-us-data \u2014 SIPP, block distributions, policy_data.db" + }, + { + "id": "download_http", + "label": "HTTP Download + ZIP Extract", + "node_type": "process", + "description": "requests.get() with streaming, tqdm progress bar" + }, + { + "id": "download_hf", + "label": "HuggingFace Hub Download", + "node_type": "process", + "description": "hf_hub_download(), token-authenticated for private repos" + }, + { + "id": "csv_parse", + "label": "CSV/Stata Parsing", + "node_type": "process", + "description": "pandas read_csv/read_stata, entity table construction" + }, + { + "id": "out_cps_raw", + "label": "census_cps_2024.h5", + "node_type": "output", + "description": "5 entity tables: Person, Family, Household, Tax Unit, SPM Unit" + }, + { + "id": "out_acs_raw", + "label": "census_acs_2022.h5", + "node_type": "output", + "description": "Person + Household tables" + }, + { + "id": "out_puf_raw", + "label": "irs_puf_2015.h5", + "node_type": "output", + "description": "PUF + Demographics tables" + }, + { + "id": "out_soi", + "label": "soi.csv", + "node_type": "output", + "description": "SOI aggregate statistics" + }, + { + "id": "out_scf", + "label": "SCF raw data", + "node_type": "output", + "description": "Stata \u2192 DataFrame, auto loans + net worth" + }, + { + "id": "out_sipp", + "label": "pu2023_slim.csv", + "node_type": "output", + "description": "SIPP microdata (pipe-delimited)" + }, + { + "id": "out_block", + "label": "block_cd_distributions.csv.gz", + "node_type": "output", + "description": "Census block populations, block-to-CD crosswalk" + }, + { + "id": "out_pop", + "label": "np2023_d5_mid.csv", + "node_type": 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repos" + - id: csv_parse + label: "CSV/Stata Parsing" + node_type: process + description: "pandas read_csv/read_stata, entity table construction" + - id: out_cps_raw + label: "census_cps_2024.h5" + node_type: output + description: "5 entity tables: Person, Family, Household, Tax Unit, SPM Unit" + - id: out_acs_raw + label: "census_acs_2022.h5" + node_type: output + description: "Person + Household tables" + - id: out_puf_raw + label: "irs_puf_2015.h5" + node_type: output + description: "PUF + Demographics tables" + - id: out_soi + label: "soi.csv" + node_type: output + description: "SOI aggregate statistics" + - id: out_scf + label: "SCF raw data" + node_type: output + description: "Stata → DataFrame, auto loans + net worth" + - id: out_sipp + label: "pu2023_slim.csv" + node_type: output + description: "SIPP microdata (pipe-delimited)" + - id: out_block + label: "block_cd_distributions.csv.gz" + node_type: output + description: "Census block populations, block-to-CD crosswalk" + - id: out_pop + label: "np2023_d5_mid.csv" + node_type: output + description: "Census population projections" + - id: out_calibration_db + label: "policy_data.db" + node_type: output + description: "SQLite calibration targets database" + - id: util_storage + label: "STORAGE_FOLDER" + node_type: utility + description: "policyengine_us_data/storage/ — all downloads cached here" + edges: + - source: cps_url + target: download_http + edge_type: external_source + label: "CPS ASEC ZIP" + - source: acs_url + target: download_http + edge_type: external_source + label: "ACS PUMS CSV" + - source: scf_url + target: download_http + edge_type: external_source + label: "SCF .dta" + - source: hf_private + target: download_hf + edge_type: external_source + label: "PUF, demographics, SOI, pop" + - source: hf_public + target: download_hf + edge_type: external_source + label: "SIPP, block, policy_data.db" + - source: download_http + target: csv_parse + edge_type: data_flow + label: "raw files" + - source: download_hf + target: csv_parse + edge_type: data_flow + label: "raw files" + - source: csv_parse + target: out_cps_raw + edge_type: produces_artifact + label: "census_cps_2024.h5" + - source: csv_parse + target: out_acs_raw + edge_type: produces_artifact + label: "census_acs_2022.h5" + - source: csv_parse + target: out_puf_raw + edge_type: produces_artifact + label: "irs_puf_2015.h5" + - source: csv_parse + target: out_soi + edge_type: produces_artifact + label: "soi.csv" + - source: download_http + target: out_scf + edge_type: produces_artifact + label: "SCF raw data" + - source: download_hf + target: out_sipp + edge_type: produces_artifact + label: "pu2023_slim.csv" + - source: download_hf + target: out_block + edge_type: produces_artifact + label: "block_cd_distributions.csv.gz" + - source: download_hf + target: out_pop + edge_type: produces_artifact + label: "np2023_d5_mid.csv" + - source: download_hf + target: out_calibration_db + edge_type: produces_artifact + label: "policy_data.db" + + # ══════════════════════════════════════════════════════════════ + # Stage 1: Base Dataset Construction + # ══════════════════════════════════════════════════════════════ + - id: 1 + label: "Stage 1" + title: "Stage 1: Base Dataset Construction" + description: "Build CPS 2024 and PUF 2024 from raw survey data" + country: us + extra_nodes: + - id: in_census_cps + label: "census_cps_2024.h5" + node_type: input + description: "Raw CPS ASEC from Stage 0" + - id: in_census_cps_prev + label: "census_cps_2023.h5" + node_type: input + description: "Previous year CPS for income matching" + - id: in_acs + label: "ACS 2022" + node_type: input + description: "Training data for rent QRF" + - id: in_sipp + label: "SIPP 2023" + node_type: input + description: "Training data for tips QRF" + - id: in_scf + label: "SCF 2022" + node_type: input + description: "Training data for auto loans QRF" + - id: in_uprating + label: "uprating_factors.csv" + node_type: input + description: "PE uprating factors table" + - id: out_cps + label: "cps_2024.h5" + node_type: output + description: "Dataset.ARRAYS format, ~65K households (half-sample)" + - id: out_puf + label: "puf_2024.h5" + node_type: output + description: "Dataset.ARRAYS format" + - id: in_irs_puf + label: "irs_puf_2015.h5" + node_type: input + description: "Raw IRS PUF from Stage 0" + - id: in_demographics + label: "demographics_2015.csv" + node_type: input + description: "PUF demographics from HuggingFace" + - id: util_seeded_rng + label: "seeded_rng()" + node_type: utility + description: "Deterministic per-variable RNG" + - id: util_qrf + label: "microimpute QRF" + node_type: utility + description: "fit_predict() for sequential imputation" + - id: util_retirement_limits + label: "get_retirement_limits()" + node_type: utility + description: "IRS contribution limits" + edges: + # CPS processing chain + - { source: in_census_cps, target: add_id_variables, edge_type: data_flow, label: "raw CPS tables" } + - { source: add_id_variables, target: add_personal_variables, edge_type: data_flow } + - { source: add_personal_variables, target: add_personal_income_variables, edge_type: data_flow } + - { source: add_personal_income_variables, target: add_previous_year_income, edge_type: data_flow } + - { source: in_census_cps_prev, target: add_previous_year_income, edge_type: data_flow, label: "prior year PERIDNUM" } + - { source: add_previous_year_income, target: add_ssn_card_type, edge_type: data_flow } + - { source: add_ssn_card_type, target: add_spm_variables, edge_type: data_flow } + - { source: add_spm_variables, target: add_household_variables, edge_type: data_flow } + - { source: add_household_variables, target: add_rent, edge_type: data_flow } + - { source: in_acs, target: add_rent, edge_type: external_source, label: "ACS training data" } + - { source: add_rent, target: add_tips, edge_type: data_flow } + - { source: in_sipp, target: add_tips, edge_type: external_source, label: "SIPP training data" } + - { source: add_tips, target: add_auto_loan, edge_type: data_flow } + - { source: in_scf, target: add_auto_loan, edge_type: external_source, label: "SCF training data" } + - { source: add_auto_loan, target: add_takeup, edge_type: data_flow } + - { source: add_takeup, target: downsample, edge_type: data_flow } + - { source: downsample, target: out_cps, edge_type: produces_artifact, label: "cps_2024.h5" } + # PUF processing chain + - { source: in_irs_puf, target: preprocess_puf, edge_type: data_flow, label: "raw PUF records" } + - { source: preprocess_puf, target: simulate_qbi, edge_type: data_flow } + - { source: simulate_qbi, target: impute_puf_demographics, edge_type: data_flow } + - { source: in_demographics, target: impute_puf_demographics, edge_type: data_flow, label: "demographics_2015.csv" } + - { source: impute_puf_demographics, target: out_puf, edge_type: produces_artifact, label: "puf_2024.h5" } + - { source: in_uprating, target: out_puf, edge_type: data_flow, label: "SOI growth rates" } + # Utility edges + - { source: util_seeded_rng, target: add_takeup, edge_type: uses_utility } + - { source: util_qrf, target: add_rent, edge_type: uses_utility } + - { source: util_qrf, target: add_tips, edge_type: uses_utility } + - { source: util_qrf, target: add_auto_loan, edge_type: uses_utility } + - { source: util_retirement_limits, target: add_personal_income_variables, edge_type: uses_utility } + - { source: util_qrf, target: impute_puf_demographics, edge_type: uses_utility } + + # ══════════════════════════════════════════════════════════════ + # Stage 2: Extended CPS (PUF Clone + QRF Imputation) + # ══════════════════════════════════════════════════════════════ + - id: 2 + label: "Stage 2" + title: "Stage 2: Extended CPS (PUF Clone)" + description: "Merge CPS + PUF via cloning, impute 64 income vars + 51 override vars via QRF" + country: us + extra_nodes: + - id: in_cps_s2 + label: "CPS_2024_Full" + node_type: input + description: "From Stage 1 (full sample)" + - id: in_puf_s2 + label: "PUF_2024" + node_type: input + description: "From Stage 1" + - id: in_blocks_s2 + label: "block_cd_distributions.csv.gz" + node_type: input + description: "Census block populations" + - id: geo_assign_s2 + label: "Geography Assignment" + node_type: process + description: "assign_random_geography() — population-weighted block draw" + - id: record_double + label: "Record Doubling" + node_type: process + description: "puf_clone_dataset() — CPS half keeps originals, PUF half starts with zero weight" + - id: qrf_pass1 + label: "QRF Pass 1: Full Imputation" + node_type: process + description: "64 income variables — training on PUF ~20K records, 7 demographic predictors" + - id: qrf_pass2 + label: "QRF Pass 2: Override Imputation" + node_type: process + description: "51 variables (both halves) — partnership, S-corp, charitable, mortgage, credits" + - id: retire_impute + label: "Retirement Contribution Imputation" + node_type: process + description: "401k, IRA, SE pension — IRS limits + catch-up applied" + - id: weeks_impute + label: "Weeks Unemployed Imputation" + node_type: process + description: "QRF on CPS weeks_unemployed — clips [0, 52], zero if no UC" + - id: ss_reconcile + label: "SS Sub-component Reconciliation" + node_type: process + description: "Retirement/Disability/Survivors/Dependents — scaled to match PUF total" + - id: cps_only + label: "CPS-Only Variable Re-imputation" + node_type: process + description: "80 variables for PUF half — transfers, SPM, medical, hours, retirement" + - id: formula_drop + label: "Formula Variable Dropping" + node_type: process + description: "Remove PE-computed variables, rename employment_income → employment_income_before_lsr" + - id: out_ext + label: "extended_cps_2024.h5" + node_type: output + description: "~260K households (doubled), CPS half + PUF half" + - id: util_puf_clone + label: "puf_clone_dataset()" + node_type: utility + description: "calibration/puf_impute.py" + - id: util_qrf_s2 + label: "microimpute QRF" + node_type: utility + description: "fit_predict() for sequential imputation" + edges: + - { source: in_cps_s2, target: geo_assign_s2, edge_type: data_flow, label: "CPS records" } + - { source: in_blocks_s2, target: geo_assign_s2, edge_type: data_flow, label: "block populations" } + - { source: in_puf_s2, target: record_double, edge_type: data_flow, label: "PUF records" } + - { source: in_cps_s2, target: record_double, edge_type: data_flow, label: "CPS records" } + - { source: geo_assign_s2, target: record_double, edge_type: data_flow } + - { source: record_double, target: qrf_pass1, edge_type: data_flow } + - { source: qrf_pass1, target: qrf_pass2, edge_type: data_flow } + - { source: qrf_pass2, target: retire_impute, edge_type: data_flow } + - { source: qrf_pass2, target: weeks_impute, edge_type: data_flow } + - { source: retire_impute, target: ss_reconcile, edge_type: data_flow } + - { source: weeks_impute, target: ss_reconcile, edge_type: data_flow } + - { source: ss_reconcile, target: cps_only, edge_type: data_flow } + - { source: cps_only, target: formula_drop, edge_type: data_flow } + - { source: formula_drop, target: out_ext, edge_type: produces_artifact } + - { source: util_puf_clone, target: record_double, edge_type: uses_utility } + - { source: util_qrf_s2, target: qrf_pass1, edge_type: uses_utility } + - { source: util_qrf_s2, target: qrf_pass2, edge_type: uses_utility } + - { source: util_qrf_s2, target: cps_only, edge_type: uses_utility } + + # ══════════════════════════════════════════════════════════════ + # Stage 3: Stratified CPS + Enhanced CPS Reweighting + # ══════════════════════════════════════════════════════════════ + - id: 3 + label: "Stage 3" + title: "Stage 3: Stratified CPS + Enhanced CPS Reweighting" + description: "Stratify by income (keep top 1%), reweight to match calibration targets" + country: us + extra_nodes: + - id: in_ext_cps + label: "extended_cps_2024.h5" + node_type: input + description: "From Stage 2 (~260K HH)" + - id: calc_agi + label: "Calculate AGI" + node_type: process + description: "Microsimulation.calculate() — adjusted_gross_income mapped to household" + - id: strat_top + label: "Retain Top 1% by AGI" + node_type: process + description: "All high-income households kept — preserves tail representation" + - id: strat_sample + label: "Uniform Sample Remaining 99%" + node_type: process + description: "Target: ~12,000 households — optional 1.5x oversample of bottom 25%" + - id: out_strat + label: "stratified_extended_cps_2024.h5" + node_type: output + description: "~12K households — input to Stages 4-6" + - id: in_ext_half + label: "ExtendedCPS_2024_Half" + node_type: input + description: "Half-sample from Stage 2" + - id: build_loss + label: "build_loss_matrix()" + node_type: process + description: "Hundreds of calibration targets — returns (matrix, target_vector)" + - id: t_soi + label: "IRS SOI" + node_type: external + description: "AGI, income types, filer counts by AGI band / filing status" + - id: t_census + label: "Census Population" + node_type: external + description: "86 single-year age groups from np2023_d5_mid.csv" + - id: t_cbo + label: "CBO Budget" + node_type: external + description: "Income tax, SNAP, SS, SSI, UC" + - id: t_state + label: "State Targets" + node_type: us_specific + description: "50 states + DC — population, AGI, ACA, SNAP, age groups" + - id: t_jct + label: "JCT Tax Expenditures" + node_type: external + description: "SALT, medical, charitable — counterfactual simulations" + - id: reweight + label: "reweight()" + node_type: process + description: "PyTorch Adam + L0 HardConcrete — 500 epochs, lr=0.2" + - id: weight_validate + label: "Weight Validation" + node_type: process + description: "No NaN, no negatives — 100M < total HH < 200M" + - id: out_enhanced + label: "enhanced_cps_2024.h5" + node_type: output + description: "Reweighted production simulation dataset" + - id: util_loss + label: "build_loss_matrix()" + node_type: utility + description: "utils/loss.py" + - id: util_l0_s3 + label: "HardConcrete L0" + node_type: utility + description: "utils/l0.py" + edges: + - { source: in_ext_cps, target: calc_agi, edge_type: data_flow } + - { source: calc_agi, target: strat_top, edge_type: data_flow } + - { source: strat_top, target: strat_sample, edge_type: data_flow } + - { source: strat_top, target: out_strat, edge_type: data_flow, label: "top 1%" } + - { source: strat_sample, target: out_strat, edge_type: data_flow, label: "sampled 99%" } + - { source: in_ext_half, target: build_loss, edge_type: data_flow } + - { source: t_soi, target: build_loss, edge_type: external_source } + - { source: t_census, target: build_loss, edge_type: external_source } + - { source: t_cbo, target: build_loss, edge_type: external_source } + - { source: t_jct, target: build_loss, edge_type: external_source } + - { source: t_state, target: build_loss, edge_type: external_source } + - { source: build_loss, target: reweight, edge_type: data_flow, label: "(matrix, targets)" } + - { source: reweight, target: weight_validate, edge_type: data_flow } + - { source: weight_validate, target: out_enhanced, edge_type: produces_artifact } + - { source: util_loss, target: build_loss, edge_type: uses_utility } + - { source: util_l0_s3, target: reweight, edge_type: uses_utility } + + # ══════════════════════════════════════════════════════════════ + # Stage 4: Source Imputation (ACS + SIPP + SCF) + # ══════════════════════════════════════════════════════════════ + - id: 4 + label: "Stage 4" + title: "Stage 4: Source Imputation (ACS + SIPP + SCF)" + description: "Impute wealth/assets from external surveys onto stratified CPS via QRF" + country: us + extra_nodes: + - id: in_strat_s4 + label: "stratified_extended_cps_2024.h5" + node_type: input + description: "From Stage 3 (~12K HH)" + - id: in_acs_s4 + label: "ACS_2022" + node_type: input + description: "American Community Survey — has state_fips predictor" + - id: in_sipp_s4 + label: "SIPP 2023" + node_type: external + description: "pu2023_slim.csv from HuggingFace" + - id: in_scf_s4 + label: "SCF_2022" + node_type: input + description: "Survey of Consumer Finances — 50% random subsample" + - id: geo_assign_s4 + label: "Geography Assignment" + node_type: process + description: "assign_random_geography(n_clones=1) — block draw + state FIPS" + - id: acs_qrf + label: "ACS QRF Imputation" + node_type: process + description: "10 predictors incl. state_fips — imputes rent, real_estate_taxes" + - id: sipp_tips_qrf + label: "SIPP Tips QRF" + node_type: process + description: "4 predictors, NO state — imputes tip_income" + - id: sipp_assets_qrf + label: "SIPP Assets QRF" + node_type: process + description: "5 predictors — imputes bank_account_assets, stock_assets, bond_assets" + - id: scf_qrf + label: "SCF QRF Imputation" + node_type: process + description: "8 predictors — imputes net_worth, auto_loan_balance, auto_loan_interest" + - id: out_imputed + label: "source_imputed_stratified_extended_cps.h5" + node_type: output + description: "Enriched with ACS/SIPP/SCF vars — uploaded to HuggingFace" + - id: util_clone_assign + label: "clone_and_assign.py" + node_type: utility + description: "Geography assignment" + - id: util_qrf_s4 + label: "microimpute QRF" + node_type: utility + description: "fit_predict() for sequential imputation" + edges: + - { source: in_strat_s4, target: geo_assign_s4, edge_type: data_flow } + - { source: geo_assign_s4, target: acs_qrf, edge_type: data_flow, label: "state_fips" } + - { source: in_acs_s4, target: acs_qrf, edge_type: data_flow } + - { source: in_sipp_s4, target: sipp_tips_qrf, edge_type: external_source } + - { source: in_sipp_s4, target: sipp_assets_qrf, edge_type: external_source } + - { source: in_scf_s4, target: scf_qrf, edge_type: external_source } + - { source: acs_qrf, target: sipp_tips_qrf, edge_type: data_flow, label: "chain" } + - { source: sipp_tips_qrf, target: sipp_assets_qrf, edge_type: data_flow, label: "chain" } + - { source: sipp_assets_qrf, target: scf_qrf, edge_type: data_flow, label: "chain" } + - { source: scf_qrf, target: out_imputed, edge_type: produces_artifact } + - { source: util_clone_assign, target: geo_assign_s4, edge_type: uses_utility } + - { source: util_qrf_s4, target: acs_qrf, edge_type: uses_utility } + - { source: util_qrf_s4, target: sipp_tips_qrf, edge_type: uses_utility } + - { source: util_qrf_s4, target: sipp_assets_qrf, edge_type: uses_utility } + - { source: util_qrf_s4, target: scf_qrf, edge_type: uses_utility } + + # ══════════════════════════════════════════════════════════════ + # Stage 5: Matrix Build (Calibration Target Construction) + # ══════════════════════════════════════════════════════════════ + - id: 5 + label: "Stage 5" + title: "Stage 5: Matrix Build (Calibration Target Construction)" + description: "Build sparse calibration matrix (targets × households × clones)" + country: us + extra_nodes: + - id: in_cps_s5 + label: "source_imputed_stratified_extended_cps.h5" + node_type: input + description: "From Stage 4 (~12K HH)" + - id: in_db_s5 + label: "policy_data.db" + node_type: external + description: "SQLite calibration database — 10-step ETL" + - id: in_config_s5 + label: "target_config.yaml" + node_type: input + description: "Active target include list" + - id: in_blocks_s5 + label: "block_cd_distributions.csv.gz" + node_type: input + description: "Census block populations" + - id: target_resolve + label: "Target Resolution" + node_type: process + description: "SQL query to target_overview — ~8,000 rows total" + - id: target_uprate + label: "Target Uprating" + node_type: process + description: "CPI-U for dollars, pop growth for counts" + - id: geo_build + label: "Geography Index Build" + node_type: process + description: "state_to_cols, cd_to_cols maps" + - id: constraint_resolve + label: "Constraint Resolution" + node_type: process + description: "Non-geographic constraints from DB (age, medicaid, filer)" + - id: state_precomp + label: "Per-State Precomputation" + node_type: us_specific + description: "51 fresh Microsimulations — most expensive step" + - id: clone_assembly + label: "Clone Assembly" + node_type: process + description: "430 clones × 12K records = 5.16M columns" + - id: takeup_rerand + label: "Block-Level Takeup Re-randomization" + node_type: process + description: "Seeded on (block_geoid, hh_id) — ensures calibration consistency" + - id: sparse_build + label: "Sparse Matrix Construction" + node_type: process + description: "COO triples → CSR matrix — shape (n_targets, 5.16M), ~0.02% nonzero" + - id: out_pkg + label: "calibration_package.pkl" + node_type: output + description: "X_sparse CSR matrix, targets_df, initial_weights, metadata" + - id: util_sql + label: "sqlalchemy" + node_type: utility + description: "Database queries" + - id: util_pool + label: "ProcessPoolExecutor" + node_type: utility + description: "Parallel state computation" + - id: util_takeup_s5 + label: "compute_block_takeup_for_entities()" + node_type: utility + description: "utils/takeup.py" + - id: util_scipy + label: "scipy.sparse" + node_type: utility + description: "CSR/COO matrix construction" + edges: + - { source: in_cps_s5, target: target_resolve, edge_type: data_flow } + - { source: in_db_s5, target: target_resolve, edge_type: external_source, label: "SQL targets" } + - { source: in_config_s5, target: target_resolve, edge_type: data_flow, label: "include list" } + - { source: target_resolve, target: target_uprate, edge_type: data_flow } + - { source: target_uprate, target: geo_build, edge_type: data_flow } + - { source: geo_build, target: constraint_resolve, edge_type: data_flow } + - { source: constraint_resolve, target: state_precomp, edge_type: data_flow } + - { source: in_cps_s5, target: state_precomp, edge_type: data_flow, label: "household data" } + - { source: state_precomp, target: clone_assembly, edge_type: data_flow } + - { source: in_blocks_s5, target: clone_assembly, edge_type: data_flow, label: "block populations" } + - { source: clone_assembly, target: takeup_rerand, edge_type: data_flow } + - { source: takeup_rerand, target: sparse_build, edge_type: data_flow } + - { source: sparse_build, target: out_pkg, edge_type: produces_artifact } + - { source: util_sql, target: target_resolve, edge_type: uses_utility } + - { source: util_pool, target: state_precomp, edge_type: uses_utility } + - { source: util_takeup_s5, target: takeup_rerand, edge_type: uses_utility } + - { source: util_scipy, target: sparse_build, edge_type: uses_utility } + + # ══════════════════════════════════════════════════════════════ + # Stage 6: Weight Fitting (L0 Calibration) + # ══════════════════════════════════════════════════════════════ + - id: 6 + label: "Stage 6" + title: "Stage 6: Weight Fitting (L0 Calibration)" + description: "Fit log-weights using L0 HardConcrete gates on GPU" + country: us + extra_nodes: + - id: in_pkg_s6 + label: "calibration_package.pkl" + node_type: input + description: "From Stage 5 — X_sparse, targets_df, initial_weights" + - id: modal_gpu + label: "Modal GPU Container" + node_type: external + description: "T4 / A10 / A100 / H100 — 32GB RAM, 8 CPU" + - id: init_weights + label: "Compute Initial Weights" + node_type: process + description: "Population-proportional per CD" + - id: create_model + label: "Create SparseCalibrationWeights" + node_type: process + description: "n_features = 5.16M, init_keep_prob = 0.999" + - id: fit_model + label: "model.fit()" + node_type: process + description: "Adam optimizer — loss = RSE + λ₀·L0(w) + λ₂·‖w‖²" + - id: extract_weights + label: "Extract Weights" + node_type: process + description: "Deterministic gate threshold — produces exact zeros" + - id: out_weights + label: "calibration_weights.npy" + node_type: output + description: "Shape: (n_records × n_clones) — most entries zero" + - id: out_geo_s6 + label: "geography.npz" + node_type: output + description: "block_geoid, cd_geoid, county_fips, state_fips" + - id: out_diag + label: "unified_diagnostics.csv" + node_type: output + description: "Per-target error analysis" + - id: out_config_s6 + label: "unified_run_config.json" + node_type: output + description: "Hyperparameters + SHA256 checksums" + - id: util_l0 + label: "l0-python" + node_type: utility + description: "SparseCalibrationWeights — HardConcrete gates" + - id: util_pytorch + label: "PyTorch" + node_type: utility + description: "Adam optimizer, autograd, CUDA" + edges: + - { source: in_pkg_s6, target: init_weights, edge_type: data_flow } + - { source: init_weights, target: create_model, edge_type: data_flow } + - { source: create_model, target: fit_model, edge_type: data_flow } + - { source: modal_gpu, target: fit_model, edge_type: runs_on_infra, label: "runs on" } + - { source: fit_model, target: extract_weights, edge_type: data_flow } + - { source: extract_weights, target: out_weights, edge_type: produces_artifact } + - { source: extract_weights, target: out_geo_s6, edge_type: produces_artifact } + - { source: fit_model, target: out_diag, edge_type: produces_artifact } + - { source: fit_model, target: out_config_s6, edge_type: produces_artifact } + - { source: util_l0, target: create_model, edge_type: uses_utility } + - { source: util_pytorch, target: fit_model, edge_type: uses_utility } + + # ══════════════════════════════════════════════════════════════ + # Stage 7: Local Area H5 Build + # ══════════════════════════════════════════════════════════════ + - id: 7 + label: "Stage 7" + title: "Stage 7: Local Area H5 Build" + description: "Build 51 state + 435 district + 1 city H5 files on Modal workers" + country: us + extra_nodes: + - id: in_weights_s7 + label: "calibration_weights.npy" + node_type: input + description: "From Stage 6" + - id: in_dataset_s7 + label: "source_imputed_stratified_extended_cps.h5" + node_type: input + description: "From Stage 4" + - id: in_db_s7 + label: "policy_data.db" + node_type: external + description: "CD list for partitioning" + - id: modal_coord + label: "Modal Coordinator" + node_type: external + description: "coordinate_publish() — 8GB RAM, 24hr timeout" + - id: partition + label: "Partition Work" + node_type: process + description: "50 workers — weights: state by CD count, city=11, district=1" + - id: phase1 + label: "Phase 1: States" + node_type: us_specific + description: "51 state H5 files (50 + DC) — workers in parallel" + - id: phase2 + label: "Phase 2: Districts" + node_type: us_specific + description: "~435 congressional district H5 files — workers in parallel" + - id: phase3 + label: "Phase 3: Cities" + node_type: us_specific + description: "NYC (5 counties, 13 CDs) — city probability filtering" + - id: build_h5 + label: "build_h5() — Core Logic" + node_type: process + description: "18-step H5 construction — load, reshape, filter, clone, derive geo, SPM, takeup, write" + - id: worker_s7 + label: "Modal Worker Container" + node_type: external + description: "16GB RAM, 1 CPU each, 8-hour timeout" + - id: geo_derive + label: "derive_geography_from_blocks()" + node_type: process + description: "15 geographic variables from block GEOID" + - id: spm_recalc + label: "SPM Threshold Recalculation" + node_type: process + description: "Local median rents, family composition, tenure type" + - id: takeup_apply + label: "Takeup Re-application" + node_type: process + description: "9 takeup variables — block-level seeded draws" + - id: out_states + label: "states/*.h5" + node_type: output + description: "51 files — AL.h5 through WY.h5" + - id: out_districts + label: "districts/*.h5" + node_type: output + description: "~435 files — NC-01.h5, CA-52.h5, ..." + - id: out_cities + label: "cities/*.h5" + node_type: output + description: "1 file — NYC.h5" + - id: out_manifest + label: "manifest.json" + node_type: output + description: "SHA256 checksums for all H5 files" + - id: util_build_h5 + label: "publish_local_area.build_h5()" + node_type: utility + description: "calibration/publish_local_area.py" + - id: util_takeup_s7 + label: "apply_block_takeup_to_arrays()" + node_type: utility + description: "utils/takeup.py" + edges: + - { source: in_weights_s7, target: partition, edge_type: data_flow } + - { source: in_dataset_s7, target: partition, edge_type: data_flow } + - { source: in_db_s7, target: partition, edge_type: external_source, label: "CD list" } + - { source: partition, target: phase1, edge_type: data_flow } + - { source: phase1, target: phase2, edge_type: data_flow } + - { source: phase2, target: phase3, edge_type: data_flow } + - { source: phase1, target: build_h5, edge_type: data_flow, label: "calls" } + - { source: phase2, target: build_h5, edge_type: data_flow, label: "calls" } + - { source: phase3, target: build_h5, edge_type: data_flow, label: "calls" } + - { source: build_h5, target: geo_derive, edge_type: data_flow } + - { source: geo_derive, target: spm_recalc, edge_type: data_flow } + - { source: spm_recalc, target: takeup_apply, edge_type: data_flow } + - { source: modal_coord, target: worker_s7, edge_type: runs_on_infra, label: "orchestrates" } + - { source: worker_s7, target: build_h5, edge_type: runs_on_infra, label: "runs" } + - { source: phase1, target: out_states, edge_type: produces_artifact } + - { source: phase2, target: out_districts, edge_type: produces_artifact } + - { source: phase3, target: out_cities, edge_type: produces_artifact } + - { source: build_h5, target: out_manifest, edge_type: produces_artifact } + - { source: util_build_h5, target: build_h5, edge_type: uses_utility } + - { source: util_takeup_s7, target: takeup_apply, edge_type: uses_utility } + + # ══════════════════════════════════════════════════════════════ + # Stage 8: Validation & Promotion + # ══════════════════════════════════════════════════════════════ + - id: 8 + label: "Stage 8" + title: "Stage 8: Validation & Promotion" + description: "7-layer validation, staging upload, atomic promotion to production" + country: us + extra_nodes: + - id: in_h5s + label: "51 state + 435 district + 1 city H5s" + node_type: input + description: "From Stage 7 (Modal volume)" + - id: in_db_s8 + label: "policy_data.db" + node_type: external + description: "Validation targets" + - id: v1 + label: "Layer 1: Manifest Verification" + node_type: process + description: "SHA256 checksums per file — catches corruption" + - id: v2 + label: "Layer 2: Structural Sanity" + node_type: process + description: "Weight non-negativity, entity ID uniqueness, no NaN/Inf, mapping integrity" + - id: v3 + label: "Layer 3: Target-Based Validation" + node_type: process + description: "Full Microsimulation per area — compare sim values to targets" + - id: v4 + label: "Layer 4: Smoke Test" + node_type: process + description: "Sum 20 key vars across 51 states — GDP ~$29T, pop ~335M" + - id: v5 + label: "Layer 5: National H5 Validation" + node_type: process + description: ">30% deviation flagged — hardcoded reference values" + - id: v6 + label: "Layer 6: Pre-Upload Validation" + node_type: process + description: "File size minimums (100MB/50MB), H5 structure, Microsim aggregates" + - id: v7 + label: "Layer 7: Package Validation" + node_type: process + description: "Matrix achievability, target ratio analysis, zero-row detection" + - id: staging_upload + label: "Upload to Staging" + node_type: process + description: "upload_to_staging_hf() — batches of 50 files/commit" + - id: atomic_promote + label: "Atomic Promotion" + node_type: process + description: "promote_staging_to_production_hf() — single CommitOperationCopy commit" + - id: gcs_upload + label: "GCS Parallel Upload" + node_type: external + description: "gs://policyengine-us-data/ — version metadata on each blob" + - id: staging_cleanup + label: "Staging Cleanup" + node_type: process + description: "cleanup_staging_hf() — removes staging/ files" + - id: out_hf_prod + label: "HuggingFace Production" + node_type: external + description: "policyengine/policyengine-us-data — final published datasets" + - id: out_gcs + label: "Google Cloud Storage" + node_type: external + description: "gs://policyengine-us-data/ — CDN/backup" + - id: util_manifest_s8 + label: "manifest.py" + node_type: utility + description: "generate_manifest(), verify_manifest()" + - id: util_sanity + label: "sanity_checks.py" + node_type: utility + description: "run_sanity_checks()" + - id: util_validate + label: "validate_staging.py" + node_type: utility + description: "Full target comparison" + - id: util_upload + label: "data_upload.py" + node_type: utility + description: "staging/promote/cleanup" + edges: + - { source: in_h5s, target: v1, edge_type: data_flow } + - { source: in_db_s8, target: v3, edge_type: external_source, label: "targets" } + - { source: v1, target: v2, edge_type: data_flow } + - { source: v2, target: v3, edge_type: data_flow } + - { source: v3, target: v4, edge_type: data_flow } + - { source: v4, target: v5, edge_type: data_flow } + - { source: v5, target: v6, edge_type: data_flow } + - { source: v6, target: v7, edge_type: data_flow } + - { source: v7, target: staging_upload, edge_type: data_flow, label: "all pass" } + - { source: staging_upload, target: atomic_promote, edge_type: data_flow } + - { source: atomic_promote, target: gcs_upload, edge_type: data_flow } + - { source: gcs_upload, target: staging_cleanup, edge_type: data_flow } + - { source: atomic_promote, target: out_hf_prod, edge_type: produces_artifact } + - { source: gcs_upload, target: out_gcs, edge_type: produces_artifact } + - { source: util_manifest_s8, target: v1, edge_type: uses_utility } + - { source: util_sanity, target: v2, edge_type: uses_utility } + - { source: util_validate, target: v3, edge_type: uses_utility } + - { source: util_upload, target: staging_upload, edge_type: uses_utility } diff --git a/policyengine_us_data/datasets/cps/cps.py b/policyengine_us_data/datasets/cps/cps.py index e227760a..4782b946 100644 --- a/policyengine_us_data/datasets/cps/cps.py +++ b/policyengine_us_data/datasets/cps/cps.py @@ -34,6 +34,8 @@ assign_takeup_with_reported_anchors, reported_subsidized_marketplace_by_tax_unit, ) +from policyengine_us_data.pipeline_metadata import pipeline_node +from policyengine_us_data.pipeline_schema import PipelineNode CURRENT_HEALTH_COVERAGE_REPORTED_VAR_MAP = { @@ -156,6 +158,13 @@ def generate(self): if self.frac is not None and self.frac < 1.0: self.downsample(frac=self.frac) + @pipeline_node(PipelineNode( + id="downsample", + label="Downsampling", + node_type="process", + description="frac=0.5 for CPS_2024 using Microsimulation.subsample()", + source_file="policyengine_us_data/datasets/cps/cps.py", + )) def downsample(self, frac: float): from policyengine_us import Microsimulation @@ -171,6 +180,14 @@ def downsample(self, frac: float): ) +@pipeline_node(PipelineNode( + id="add_rent", + label="Rent Imputation (QRF)", + node_type="process", + description="Impute rent and real estate taxes using QRF from ACS 2022", + details="10K sampled household heads as training data", + source_file="policyengine_us_data/datasets/cps/cps.py", +)) def add_rent(self, cps: h5py.File, person: DataFrame, household: DataFrame): cps["tenure_type"] = household.H_TENURE.map( { @@ -243,6 +260,14 @@ def add_rent(self, cps: h5py.File, person: DataFrame, household: DataFrame): cps["real_estate_taxes"][mask] = imputed_values["real_estate_taxes"] +@pipeline_node(PipelineNode( + id="add_takeup", + label="Benefit Takeup", + node_type="us_specific", + description="Stochastic takeup for 9 benefit programs", + details="SNAP, ACA, Medicaid, EITC, SSI, TANF, WIC, Head Start; state-specific Medicaid rates", + source_file="policyengine_us_data/datasets/cps/cps.py", +)) def add_takeup(self): data = self.load_dataset() @@ -414,6 +439,13 @@ def uprate_cps_data(data, from_period, to_period): return data +@pipeline_node(PipelineNode( + id="add_id_variables", + label="Add ID Variables", + node_type="process", + description="Create person_id, household_id, tax_unit_id, spm_unit_id, marital_unit_id", + source_file="policyengine_us_data/datasets/cps/cps.py", +)) def add_id_variables( cps: h5py.File, person: DataFrame, @@ -463,6 +495,14 @@ def add_id_variables( cps["marital_unit_id"] = marital_unit_id.drop_duplicates().values +@pipeline_node(PipelineNode( + id="add_personal_variables", + label="Add Personal Variables", + node_type="process", + description="Age, sex, disability, occupation, overtime flags", + details="80+ ages randomized to 80-84; 12 overtime occupation flags", + source_file="policyengine_us_data/datasets/cps/cps.py", +)) def add_personal_variables(cps: h5py.File, person: DataFrame) -> None: """Add personal demographic variables. @@ -575,6 +615,14 @@ def children_per_parent(col: str) -> pd.DataFrame: add_overtime_occupation(cps, person) +@pipeline_node(PipelineNode( + id="add_personal_income_variables", + label="Add Income Variables", + node_type="process", + description="30+ income types with splits", + details="SS classified by reason codes; retirement split by account type; capital gains 88% LT / 12% ST", + source_file="policyengine_us_data/datasets/cps/cps.py", +)) def add_personal_income_variables(cps: h5py.File, person: DataFrame, year: int): """Add income variables. @@ -832,6 +880,13 @@ def add_personal_income_variables(cps: h5py.File, person: DataFrame, year: int): cps[f"{var}_would_be_qualified"] = rng.random(len(person)) < prob +@pipeline_node(PipelineNode( + id="add_spm_variables", + label="SPM Variables", + node_type="process", + description="SPM thresholds and transfers (SNAP, housing, energy subsidies)", + source_file="policyengine_us_data/datasets/cps/cps.py", +)) def add_spm_variables(self, cps: h5py.File, spm_unit: DataFrame) -> None: from policyengine_us_data.utils.spm import ( calculate_spm_thresholds_with_geoadj, @@ -881,6 +936,13 @@ def add_spm_variables(self, cps: h5py.File, spm_unit: DataFrame) -> None: cps["reduced_price_school_meals_reported"] = cps["free_school_meals_reported"] * 0 +@pipeline_node(PipelineNode( + id="add_household_variables", + label="Household Variables", + node_type="process", + description="State FIPS, county FIPS, NYC flag from county codes", + source_file="policyengine_us_data/datasets/cps/cps.py", +)) def add_household_variables(cps: h5py.File, household: DataFrame) -> None: cps["state_fips"] = household.GESTFIPS cps["county_fips"] = household.GTCO @@ -904,6 +966,13 @@ def add_household_variables(cps: h5py.File, household: DataFrame) -> None: cps["in_nyc"] = np.isin(state_county_fips, nyc_full_county_fips) +@pipeline_node(PipelineNode( + id="add_previous_year_income", + label="Previous Year Income", + node_type="process", + description="Cross-year PERIDNUM linking for prior-year income", + source_file="policyengine_us_data/datasets/cps/cps.py", +)) def add_previous_year_income(self, cps: h5py.File) -> None: if self.previous_year_raw_cps is None: logging.info( @@ -964,6 +1033,14 @@ def add_previous_year_income(self, cps: h5py.File) -> None: ].values +@pipeline_node(PipelineNode( + id="add_ssn_card_type", + label="SSN Card Type", + node_type="us_specific", + description="US immigration classification from 14 ASEC conditions", + details="Undocumented target: 13M; SSN card types 0-3", + source_file="policyengine_us_data/datasets/cps/cps.py", +)) def add_ssn_card_type( cps: h5py.File, person: pd.DataFrame, @@ -1846,6 +1923,14 @@ def _update_documentation_with_numbers(log_df, docs_dir): print(f"Documentation updated with population numbers: {doc_path}") +@pipeline_node(PipelineNode( + id="add_tips", + label="Tips Imputation (QRF)", + node_type="process", + description="Impute tip income and liquid assets from SIPP 2023", + details="Models cached as pickle; QRF fit_predict()", + source_file="policyengine_us_data/datasets/cps/cps.py", +)) def add_tips(self, cps: h5py.File): self.save_dataset(cps) from policyengine_us import Microsimulation @@ -2030,6 +2115,13 @@ def add_overtime_occupation(cps: h5py.File, person: DataFrame) -> None: ) +@pipeline_node(PipelineNode( + id="add_auto_loan", + label="Auto Loan / Net Worth (QRF)", + node_type="process", + description="Impute auto loan balance, interest, and net worth from SCF 2022", + source_file="policyengine_us_data/datasets/cps/cps.py", +)) def add_auto_loan_interest_and_net_worth(self, cps: h5py.File) -> None: """ "Add auto loan balance, interest and net_worth variable.""" self.save_dataset(cps) diff --git a/policyengine_us_data/datasets/puf/puf.py b/policyengine_us_data/datasets/puf/puf.py index bde0f33f..759de8fe 100644 --- a/policyengine_us_data/datasets/puf/puf.py +++ b/policyengine_us_data/datasets/puf/puf.py @@ -20,6 +20,8 @@ from policyengine_us_data.utils.uprating import ( create_policyengine_uprating_factors_table, ) +from policyengine_us_data.pipeline_metadata import pipeline_node +from policyengine_us_data.pipeline_schema import PipelineNode rng = np.random.default_rng(seed=64) @@ -57,6 +59,13 @@ def conditionally_sample_lognormal(flag, target_mean, log_sigma, rng): ) +@pipeline_node(PipelineNode( + id="simulate_qbi", + label="QBI Simulation", + node_type="process", + description="Simulate W-2 wages, UBIA, and SSTB for Section 199A", + source_file="policyengine_us_data/datasets/puf/puf.py", +)) def simulate_w2_and_ubia_from_puf(puf, *, seed=None, diagnostics=True): """ Simulate two Section 199A guard-rail quantities for every record @@ -206,6 +215,13 @@ def impute_pension_contributions_to_puf(puf_df): return predictions["pre_tax_contributions"] +@pipeline_node(PipelineNode( + id="impute_puf_demographics", + label="Impute PUF Demographics", + node_type="process", + description="QRF imputation for age, gender, and earnings split", + source_file="policyengine_us_data/datasets/puf/puf.py", +)) def impute_missing_demographics( puf: pd.DataFrame, demographics: pd.DataFrame ) -> pd.DataFrame: @@ -313,6 +329,14 @@ def decode_age_dependent(age_range: int) -> int: return rng.integers(low=lower, high=upper, endpoint=False) +@pipeline_node(PipelineNode( + id="preprocess_puf", + label="Preprocess PUF", + node_type="process", + description="Rename 60+ IRS variables to PolicyEngine names", + details="E00200 → employment_income, etc.", + source_file="policyengine_us_data/datasets/puf/puf.py", +)) def preprocess_puf(puf: pd.DataFrame) -> pd.DataFrame: # Add variable renames puf.S006 = puf.S006 / 100 diff --git a/policyengine_us_data/pipeline_metadata.py b/policyengine_us_data/pipeline_metadata.py new file mode 100644 index 00000000..9e17d0b1 --- /dev/null +++ b/policyengine_us_data/pipeline_metadata.py @@ -0,0 +1,40 @@ +"""Pipeline metadata decorators. + +Attach pipeline documentation to functions without changing their behavior. +The Griffe extraction script reads these decorators statically (via AST) +to generate pipeline.json for visualization. + +Usage: + from policyengine_us_data.pipeline_metadata import pipeline_node + from policyengine_us_data.pipeline_schema import PipelineNode + + @pipeline_node(PipelineNode( + id="add_rent", + label="Rent Imputation (QRF)", + node_type="process", + description="Impute rent and real estate taxes using QRF from ACS", + )) + def add_rent(self, cps, person, household): + ... +""" + +from policyengine_us_data.pipeline_schema import PipelineNode + + +def pipeline_node(node: PipelineNode): + """Decorator that attaches pipeline node metadata to a function. + + Does not modify the function's behavior. The metadata is stored + as func._pipeline_node and read by the Griffe extension during + static analysis. + + Args: + node: A PipelineNode describing this function's role in the + pipeline — its type, description, and visual properties. + """ + + def wrapper(func): + func._pipeline_node = node + return func + + return wrapper diff --git a/policyengine_us_data/pipeline_schema.py b/policyengine_us_data/pipeline_schema.py new file mode 100644 index 00000000..9192b6d7 --- /dev/null +++ b/policyengine_us_data/pipeline_schema.py @@ -0,0 +1,117 @@ +"""Pipeline documentation schema. + +Defines typed dataclasses for pipeline nodes, edges, and stages. +Used by @pipeline_node decorators to annotate source code and by +the Griffe extraction script to generate pipeline.json. + +Node and edge types match the visual taxonomy in +docs/pipeline-diagrams/CLAUDE.md and components/legends.js. +""" + +from dataclasses import dataclass, field +from typing import Literal + +NodeType = Literal[ + "input", + "output", + "process", + "utility", + "external", + "us_specific", + "uk_specific", + "missing", + "absent", +] + +EdgeType = Literal[ + "data_flow", + "produces_artifact", + "uses_utility", + "external_source", + "runs_on_infra", + "informational", +] + +# Node type → fill color, border color (matching legends.js) +NODE_COLORS = { + "input": {"fill": "#dbeafe", "border": "#3b82f6"}, + "output": {"fill": "#dcfce7", "border": "#22c55e"}, + "process": {"fill": "#ffedd5", "border": "#f97316"}, + "utility": {"fill": "#f3e8ff", "border": "#a855f7"}, + "external": {"fill": "#fef9c3", "border": "#eab308"}, + "us_specific": {"fill": "#fce7f3", "border": "#ec4899"}, + "uk_specific": {"fill": "#ccfbf1", "border": "#14b8a6"}, + "missing": {"fill": "#fee2e2", "border": "#ef4444"}, + "absent": {"fill": "#f3f4f6", "border": "#d1d5db"}, +} + +# Edge type → color, style, width (matching legends.js) +EDGE_STYLES = { + "data_flow": {"color": "#334155", "style": "solid", "width": 2}, + "produces_artifact": {"color": "#16a34a", "style": "solid", "width": 2}, + "uses_utility": {"color": "#7c3aed", "style": "dashed", "width": 1.5}, + "external_source": {"color": "#b45309", "style": "dotted", "width": 1.5}, + "runs_on_infra": {"color": "#dc2626", "style": "dashed", "width": 1.5}, + "informational": {"color": "#9ca3af", "style": "dotted", "width": 1}, +} + + +@dataclass +class PipelineNode: + """A node in a pipeline stage diagram. + + Args: + id: Unique identifier (snake_case, e.g., "add_rent"). + label: Display name (e.g., "Rent Imputation (QRF)"). + node_type: Visual category from NodeType. + description: One-line summary shown in tooltips. + details: Multi-line implementation notes shown in detail panel. + source_file: Python file path relative to repo root. + """ + + id: str + label: str + node_type: NodeType + description: str = "" + details: str = "" + source_file: str = "" + + +@dataclass +class PipelineEdge: + """A directed edge between two pipeline nodes. + + Args: + source: Source node ID. + target: Target node ID. + edge_type: Visual category from EdgeType. + label: Artifact name or data description on the edge. + """ + + source: str + target: str + edge_type: EdgeType = "data_flow" + label: str = "" + + +@dataclass +class PipelineStage: + """A stage in the pipeline (e.g., Stage 1: Base Dataset Construction). + + Args: + id: Stage number (0-8). + label: Short label (e.g., "Stage 1"). + title: Full title (e.g., "Stage 1: Base Dataset Construction"). + description: What this stage does. + country: Which country pipeline ("us" or "uk"). + nodes: Nodes in this stage. + edges: Edges within this stage. + """ + + id: int + label: str + title: str + description: str + country: str = "us" + nodes: list[PipelineNode] = field(default_factory=list) + edges: list[PipelineEdge] = field(default_factory=list) diff --git a/scripts/extract_pipeline.py b/scripts/extract_pipeline.py new file mode 100644 index 00000000..5dbee5bf --- /dev/null +++ b/scripts/extract_pipeline.py @@ -0,0 +1,248 @@ +"""Extract pipeline metadata from decorated source code and YAML manifest. + +Reads @pipeline_node decorators from Python source files via AST (no imports), +merges with pipeline_stages.yaml for stage groupings and edges, validates +the schema, and exports pipeline.json for React visualization. + +Usage: + python scripts/extract_pipeline.py + python scripts/extract_pipeline.py --output docs/pipeline-diagrams/pipeline.json +""" + +import ast +import json +import sys +from dataclasses import asdict +from datetime import datetime, timezone +from pathlib import Path + +import yaml + +REPO_ROOT = Path(__file__).resolve().parent.parent +STAGES_YAML = REPO_ROOT / "pipeline_stages.yaml" +DEFAULT_OUTPUT = REPO_ROOT / "docs" / "pipeline-diagrams" / "pipeline.json" + +# Valid node and edge types (from pipeline_schema.py) +VALID_NODE_TYPES = { + "input", "output", "process", "utility", "external", + "us_specific", "uk_specific", "missing", "absent", +} +VALID_EDGE_TYPES = { + "data_flow", "produces_artifact", "uses_utility", + "external_source", "runs_on_infra", "informational", +} + + +def extract_pipeline_nodes_from_file(filepath: Path) -> list[dict]: + """Parse a Python file's AST and extract @pipeline_node decorator data. + + Returns a list of node dicts extracted from PipelineNode(...) calls + in decorators, without importing the module. + """ + try: + source = filepath.read_text() + tree = ast.parse(source, filename=str(filepath)) + except SyntaxError as e: + print(f" WARNING: Syntax error in {filepath}: {e}", file=sys.stderr) + return [] + + nodes = [] + for node in ast.walk(tree): + if not isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)): + continue + + for decorator in node.decorator_list: + if not isinstance(decorator, ast.Call): + continue + + # Check if it's @pipeline_node(...) + func = decorator.func + name = "" + if isinstance(func, ast.Name): + name = func.id + elif isinstance(func, ast.Attribute): + name = func.attr + + if name != "pipeline_node": + continue + + # Extract the PipelineNode(...) argument + if not decorator.args: + continue + + arg = decorator.args[0] + if not isinstance(arg, ast.Call): + continue + + # Parse keyword arguments from PipelineNode(...) + node_data = {} + for kw in arg.keywords: + try: + node_data[kw.arg] = ast.literal_eval(kw.value) + except (ValueError, TypeError): + node_data[kw.arg] = str(ast.dump(kw.value)) + + if "id" in node_data: + nodes.append(node_data) + + return nodes + + +def scan_source_files(source_dir: Path) -> dict[str, list[dict]]: + """Scan all Python files under source_dir for @pipeline_node decorators. + + Returns a dict mapping relative file paths to lists of node dicts. + """ + results = {} + for py_file in sorted(source_dir.rglob("*.py")): + rel_path = str(py_file.relative_to(REPO_ROOT)) + nodes = extract_pipeline_nodes_from_file(py_file) + if nodes: + results[rel_path] = nodes + return results + + +def load_stages_yaml() -> dict: + """Load pipeline_stages.yaml manifest.""" + with open(STAGES_YAML) as f: + return yaml.safe_load(f) + + +def build_pipeline_json(output_path: Path = DEFAULT_OUTPUT): + """Build the full pipeline.json from source code + YAML manifest.""" + print("Extracting pipeline metadata...") + + # Step 1: Scan source code for @pipeline_node decorators + source_dir = REPO_ROOT / "policyengine_us_data" + file_nodes = scan_source_files(source_dir) + + total_nodes = sum(len(v) for v in file_nodes.values()) + print(f" Found {total_nodes} @pipeline_node decorators across {len(file_nodes)} files") + + # Build a lookup: node_id → node_data + all_code_nodes = {} + for filepath, nodes in file_nodes.items(): + for node in nodes: + node_id = node["id"] + if node_id in all_code_nodes: + print( + f" WARNING: Duplicate node ID '{node_id}' in {filepath} " + f"(already defined in {all_code_nodes[node_id].get('source_file', '?')})", + file=sys.stderr, + ) + all_code_nodes[node_id] = node + + # Step 2: Load YAML manifest + manifest = load_stages_yaml() + stages_data = manifest.get("stages", []) + + # Step 3: Merge code nodes with YAML stages + stages_output = [] + for stage_def in stages_data: + stage = { + "id": stage_def["id"], + "label": stage_def["label"], + "title": stage_def["title"], + "description": stage_def["description"], + "country": stage_def.get("country", "us"), + "nodes": [], + "edges": [], + } + + # Add extra nodes from YAML (inputs, outputs, utilities) + for extra in stage_def.get("extra_nodes", []): + node_type = extra.get("node_type", "input") + if node_type not in VALID_NODE_TYPES: + print( + f" WARNING: Invalid node_type '{node_type}' for node '{extra['id']}'", + file=sys.stderr, + ) + stage["nodes"].append(extra) + + # Add code-defined nodes that belong to this stage + # (matched by presence in the YAML edges as source or target) + edge_node_ids = set() + for edge in stage_def.get("edges", []): + edge_node_ids.add(edge["source"]) + edge_node_ids.add(edge["target"]) + + for node_id in edge_node_ids: + if node_id in all_code_nodes: + code_node = all_code_nodes[node_id] + # Don't duplicate if already in extra_nodes + existing_ids = {n["id"] for n in stage["nodes"]} + if node_id not in existing_ids: + stage["nodes"].append(code_node) + + # Add edges + for edge in stage_def.get("edges", []): + edge_type = edge.get("edge_type", "data_flow") + if edge_type not in VALID_EDGE_TYPES: + print( + f" WARNING: Invalid edge_type '{edge_type}' for edge " + f"'{edge['source']}' → '{edge['target']}'", + file=sys.stderr, + ) + stage["edges"].append(edge) + + stages_output.append(stage) + + # Step 4: Validate + errors = 0 + for stage in stages_output: + node_ids = {n["id"] for n in stage["nodes"]} + for edge in stage["edges"]: + if edge["source"] not in node_ids: + print( + f" ERROR: Edge source '{edge['source']}' not found in " + f"stage {stage['id']} nodes", + file=sys.stderr, + ) + errors += 1 + if edge["target"] not in node_ids: + print( + f" ERROR: Edge target '{edge['target']}' not found in " + f"stage {stage['id']} nodes", + file=sys.stderr, + ) + errors += 1 + + if errors: + print(f"\n {errors} validation error(s) found", file=sys.stderr) + + # Step 5: Build output + pipeline_json = { + "stages": stages_output, + "metadata": { + "generated_at": datetime.now(timezone.utc).isoformat(), + "total_nodes": sum(len(s["nodes"]) for s in stages_output), + "total_edges": sum(len(s["edges"]) for s in stages_output), + }, + } + + # Step 6: Write + output_path.parent.mkdir(parents=True, exist_ok=True) + with open(output_path, "w") as f: + json.dump(pipeline_json, f, indent=2) + + print(f"\nWrote {output_path}") + print( + f" {len(stages_output)} stages, " + f"{pipeline_json['metadata']['total_nodes']} nodes, " + f"{pipeline_json['metadata']['total_edges']} edges" + ) + + return pipeline_json + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument( + "--output", "-o", + default=str(DEFAULT_OUTPUT), + help="Output path for pipeline.json", + ) + args = parser.parse_args() + build_pipeline_json(Path(args.output)) From 201128965600ec705cc227897a9169d9b12f0c7e Mon Sep 17 00:00:00 2001 From: Anthony Volk Date: Wed, 8 Apr 2026 22:42:30 +0200 Subject: [PATCH 02/15] Add @pipeline_node decorators to Stages 2-8, consolidate app layout --- Makefile | 3 + .../{demos/pipeline-demos => }/.gitignore | 0 .../{demos/pipeline-demos => }/AGENTS.md | 0 .../{demos/pipeline-demos => }/CLAUDE.md | 0 .../{demos/pipeline-demos => }/README.md | 0 .../app/[country]/page.tsx | 0 .../app/[country]/stage/[stageId]/page.tsx | 3 +- .../{demos/pipeline-demos => }/app/colors.ts | 0 .../app/components/ElkEdge.tsx | 0 .../app/components/NodeDetailPanel.tsx | 0 .../app/components/PipelineDiagram.tsx | 7 +- .../app/components/PipelineNode.tsx | 0 .../app/components/Sidebar.tsx | 101 +-- .../pipeline-demos => }/app/favicon.ico | Bin .../pipeline-demos => }/app/globals.css | 0 .../{demos/pipeline-demos => }/app/layout.tsx | 0 .../{demos/pipeline-demos => }/app/page.tsx | 0 .../pipeline-demos => }/app/pipeline.json | 574 +++++++++--------- .../pipeline-demos => }/eslint.config.mjs | 0 .../{demos/pipeline-demos => }/next.config.ts | 0 .../pipeline-demos => }/package-lock.json | 16 - .../{demos/pipeline-demos => }/package.json | 1 - docs/pipeline-diagrams/pipeline.json | 574 +++++++++--------- .../pipeline-demos => }/postcss.config.mjs | 0 .../pipeline-demos => }/public/file.svg | 0 .../pipeline-demos => }/public/globe.svg | 0 .../pipeline-demos => }/public/next.svg | 0 .../pipeline-demos => }/public/vercel.svg | 0 .../pipeline-demos => }/public/window.svg | 0 .../{demos/pipeline-demos => }/tsconfig.json | 0 pipeline_stages.yaml | 180 ++---- .../calibration/block_assignment.py | 9 + .../calibration/clone_and_assign.py | 11 +- .../calibration/create_stratified_cps.py | 9 + .../calibration/promote_local_h5s.py | 16 + .../calibration/publish_local_area.py | 30 + .../calibration/puf_impute.py | 37 ++ .../calibration/sanity_checks.py | 10 + .../calibration/source_impute.py | 23 + .../calibration/unified_calibration.py | 24 + .../calibration/unified_matrix_builder.py | 18 +- .../calibration/validate_staging.py | 11 +- .../datasets/cps/enhanced_cps.py | 11 +- .../datasets/cps/extended_cps.py | 24 +- 44 files changed, 931 insertions(+), 761 deletions(-) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/.gitignore (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/AGENTS.md (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/CLAUDE.md (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/README.md (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/app/[country]/page.tsx (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/app/[country]/stage/[stageId]/page.tsx (82%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/app/colors.ts (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/app/components/ElkEdge.tsx (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/app/components/NodeDetailPanel.tsx (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/app/components/PipelineDiagram.tsx (98%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/app/components/PipelineNode.tsx (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/app/components/Sidebar.tsx (63%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/app/favicon.ico (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/app/globals.css (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/app/layout.tsx (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/app/page.tsx (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/app/pipeline.json (94%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/eslint.config.mjs (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/next.config.ts (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/package-lock.json (99%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/package.json (94%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/postcss.config.mjs (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/public/file.svg (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/public/globe.svg (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/public/next.svg (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/public/vercel.svg (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/public/window.svg (100%) rename docs/pipeline-diagrams/{demos/pipeline-demos => }/tsconfig.json (100%) diff --git a/Makefile b/Makefile index 86f3611a..968ead1f 100644 --- a/Makefile +++ b/Makefile @@ -298,3 +298,6 @@ presentations/nta_2024_11/nta_2024_slides.pdf: presentations/nta_2024_11/main.te cd presentations/nta_2024_11 && \ pdflatex -jobname=nta_2024_slides main && \ pdflatex -jobname=nta_2024_slides main + +diagrams: + cd docs/pipeline-diagrams && npx next dev diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/.gitignore b/docs/pipeline-diagrams/.gitignore similarity index 100% rename from docs/pipeline-diagrams/demos/pipeline-demos/.gitignore rename to docs/pipeline-diagrams/.gitignore diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/AGENTS.md b/docs/pipeline-diagrams/AGENTS.md similarity index 100% rename from docs/pipeline-diagrams/demos/pipeline-demos/AGENTS.md rename to docs/pipeline-diagrams/AGENTS.md diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/CLAUDE.md b/docs/pipeline-diagrams/CLAUDE.md similarity index 100% rename from docs/pipeline-diagrams/demos/pipeline-demos/CLAUDE.md rename to docs/pipeline-diagrams/CLAUDE.md diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/README.md b/docs/pipeline-diagrams/README.md similarity index 100% rename from docs/pipeline-diagrams/demos/pipeline-demos/README.md rename to docs/pipeline-diagrams/README.md diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/[country]/page.tsx b/docs/pipeline-diagrams/app/[country]/page.tsx similarity index 100% rename from docs/pipeline-diagrams/demos/pipeline-demos/app/[country]/page.tsx rename to docs/pipeline-diagrams/app/[country]/page.tsx diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/[country]/stage/[stageId]/page.tsx b/docs/pipeline-diagrams/app/[country]/stage/[stageId]/page.tsx similarity index 82% rename from docs/pipeline-diagrams/demos/pipeline-demos/app/[country]/stage/[stageId]/page.tsx rename to docs/pipeline-diagrams/app/[country]/stage/[stageId]/page.tsx index 23e5824e..62c42beb 100644 --- a/docs/pipeline-diagrams/demos/pipeline-demos/app/[country]/stage/[stageId]/page.tsx +++ b/docs/pipeline-diagrams/app/[country]/stage/[stageId]/page.tsx @@ -10,8 +10,7 @@ export default function StagePage({ params: Promise<{ country: string; stageId: string }>; }) { const { stageId } = use(params); - const stageIdNum = parseInt(stageId, 10); - const stage = pipelineData.stages.find((s) => s.id === stageIdNum); + const stage = pipelineData.stages.find((s) => String(s.id) === stageId); if (!stage) { return ( diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/colors.ts b/docs/pipeline-diagrams/app/colors.ts similarity index 100% rename from docs/pipeline-diagrams/demos/pipeline-demos/app/colors.ts rename to docs/pipeline-diagrams/app/colors.ts diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/components/ElkEdge.tsx b/docs/pipeline-diagrams/app/components/ElkEdge.tsx similarity index 100% rename from docs/pipeline-diagrams/demos/pipeline-demos/app/components/ElkEdge.tsx rename to docs/pipeline-diagrams/app/components/ElkEdge.tsx diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/components/NodeDetailPanel.tsx b/docs/pipeline-diagrams/app/components/NodeDetailPanel.tsx similarity index 100% rename from docs/pipeline-diagrams/demos/pipeline-demos/app/components/NodeDetailPanel.tsx rename to docs/pipeline-diagrams/app/components/NodeDetailPanel.tsx diff --git a/docs/pipeline-diagrams/demos/pipeline-demos/app/components/PipelineDiagram.tsx b/docs/pipeline-diagrams/app/components/PipelineDiagram.tsx similarity index 98% rename from docs/pipeline-diagrams/demos/pipeline-demos/app/components/PipelineDiagram.tsx rename to docs/pipeline-diagrams/app/components/PipelineDiagram.tsx index 196bed6d..9b291d85 100644 --- a/docs/pipeline-diagrams/demos/pipeline-demos/app/components/PipelineDiagram.tsx +++ b/docs/pipeline-diagrams/app/components/PipelineDiagram.tsx @@ -39,7 +39,7 @@ const ELK_OPTIONS: Record = { }; interface StageData { - id: number; + id: number | string; label: string; title: string; description: string; @@ -240,12 +240,13 @@ function DiagramInner({ stage }: { stage: StageData }) { [String(s.id), s])); return (