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But the user needs the Full Analysis to reflect input changes immediately and sync with the comparison cooling technology selection, while still using the exact values the user entered in the calculator. The constraint is tricky—I can't modify the Input or Basic Result views, yet the Full Analysis needs to be reactive. Since the Full Analysis only appears after unlocking (which requires Calculate + email), I need to find a way to make it derive from the current calculator state rather than just the last calculated snapshot. The user said not to change the input view, so I need to keep the Calculate button behavior. But there's a problem: `handleCalculate` sets `unlocked` to false, which would hide the Full Analysis whenever inputs change and recalculate. I need to rethink this — maybe `unlocked` should stay true after the first calculation, or the Full Analysis should remain visible even when recalculating with new inputs. Actually, the core request is really about replacing the Full Analysis view itself with the new spec. Since FullDashboard receives `result` as props and derives everything from those values using reusable calculation functions, it's already fully dynamic to the inputs — nothing is hardcoded. So I'll focus on rebuilding FullDashboard to match the A1-A5 specification with the new deterministic model, including the shared cooling comparison selector and the radar chart. Now I'm realizing the user mentions keeping an "existing 4-point radar chart" and using "existing reference content," but I don't see either of those in the current version of full-dashboard.tsx. These might have existed in an earlier version that got reverted, so I need to clarify what exactly should be there or build it fresh according to the spec. Since there's no reference data currently in the project, I'll create a single, reusable reference dataset with industry benchmarks for electricity pricing and PUE/utilization metrics, then apply it consistently across the relevant sections. This gives me a clear source of truth rather than inventing data multiple times. Now I'm mapping out the calculation model for the Full Analysis... Defining the stranded capacity formula... Adding cost efficiency scores... Now I'm mapping the capacity flow across three layers—facility, IT, and workload—where each stage loses a portion of the stranded capacity, so I need to track the cumulative losses to show what actually reaches the workload level. I'm laying out the dashboard in a grid with the scenario selector positioned above the second row, letting users compare the current cooling type against Air, Hybrid, or Liquid options. Each card will display the capacity bars and flow arrows showing power loss at each stage. A4 will contain both a capacity comparison visualization and a radar chart overlaying the current versus comparison scenarios across four axes. A3 focuses on the financial side—showing the estimated annual loss with its range, the projected savings from switching technologies, and industry context around electricity pricing. I'm setting up a centralized calculations module in `lib/full-analysis.ts` to avoid duplicating logic across components, then importing those functions into the dashboard. For language, I'll use the exact Spanish labels from the spec where specified ("Ver cómo se calculó", "Pérdida financiera anual estimada", "Ahorro proyectado si cambiás de tecnología", etc.) while keeping English for section titles that were defined in English. For state management, I'll derive base inputs from the result prop and use useState to track the comparisonCoolingType separately, keeping everything in one calculatorState object. Now I'm defining the reference data structure for industry context, starting with regional electricity price references that I can reuse throughout. I'm organizing three reference cards horizontally — one for electricity pricing across regions, another for utilization benchmarks like facility capacity and PUE metrics, and a third for stranded MW costs — each displaying key figures relevant to the cooling analysis. Now I'm setting up the cost per megawatt references at $200k, $250k, and $300k to align with the model calculations. I'll build out the analysis library with cooling type definitions and their corresponding efficiency factors, then update the dashboard component using the forest-green and gold color scheme with the existing design tokens. For the radar chart, I'm constructing a custom SVG with four axes arranged in a diamond pattern to compare current versus comparison states. Defining cooling factors... Finishing the annual loss calculation function that multiplies stranded MW by cost factors across min, average, and max scenarios, then defining functions to break down costs by facility, IT, and workload layers, plus a cooling comparison function and radar data generator. I'm also double-checking that the stranded percent calculation stays within bounds—at zero utilization with air cooling it maxes out at 60%, which is acceptable. Now I'm setting up the radar chart axes with efficiency scores for each cooling type and calculating capacity recovery as the inverse of stranded percentage, so higher utilization and better cooling types push the recovery metric upward. Defining radar data structure... For the savings calculation in section A3, I'm taking the difference between current and comparison loss averages to show projected savings, which should be positive when switching to a more efficient cooling type like liquid. Then I'm building the scenario selector text that dynamically displays the current setup parameters and offers buttons to compare against alternative cooling types. I'll set the default comparison option intelligently—if the current cooling type is air, default to liquid; otherwise default to air. For the collapsible explanation section, I'm using a toggle button to reveal how the efficiency percentage is calculated, showing the transformation from 100% to the delivered percentage and walking through the formula with actual values. Now I'm building out the utility functions for formatting currency and megawatts, then creating the full analysis module. I'm structuring the capacity flow visualization to track the cascade of losses: starting with facility capacity, subtracting facility losses, then IT losses, then workload losses to arrive at the final delivered value. I'll display this as a series of cards showing each stage of the flow. Writing the dashboard layout...…44:oa,115:T65c3,44:o65c3,[V0_FILE]typescriptreact:file="/components/full-dashboard.tsx" "use client" import { useMemo, useState } from "react" import type { CalculatorResult } from "@/components/capacity-calculator" import { type CoolingType, COOLING_ORDER, COOLING_LABELS, calculateStrandedPercent, calculateStrandedMW, calculateAnnualLoss, calculateCapacityFlow, calculateCoolingComparison, calculateRadarData, INDUSTRY_CONTEXT, formatUsd, formatMW, } from "@/lib/full-analysis" import { Building2, Server, Cpu, ArrowDown, FileDown, Link2, Share2, Check, ChevronDown, TrendingDown, Factory, Zap, Coins, } from "lucide-react" const LAYER_ICONS = { facility: Building2, it: Server, workload: Cpu } as const export function FullDashboard({ result }: { result: CalculatorResult }) { // Full Analysis input values come from the shared calculator result. const facilityMW = result.facilitySize const utilization = result.utilization const coolingType = result.coolingType as CoolingType // Shared comparison selector state (drives BOTH A4 and A3). const [comparisonCoolingType, setComparisonCoolingType] = useState(() => coolingType === "liquid" ? "air" : "liquid", ) // Everything below derives from the single source of truth model. const strandedPercent = calculateStrandedPercent(utilization, coolingType) const strandedMW = calculateStrandedMW(facilityMW, utilization, coolingType) const annualLoss = calculateAnnualLoss(strandedMW) const flow = useMemo( () => calculateCapacityFlow(facilityMW, utilization, coolingType), [facilityMW, utilization, coolingType], ) const comparison = useMemo( () => calculateCoolingComparison(facilityMW, utilization, coolingType, comparisonCoolingType), [facilityMW, utilization, coolingType, comparisonCoolingType], ) const radarCurrent = useMemo(() => calculateRadarData(utilization, coolingType), [utilization, coolingType]) const radarComparison = useMemo( () => calculateRadarData(utilization, comparisonCoolingType), [utilization, comparisonCoolingType], ) const [showCalc, setShowCalc] = useState(false) const [copied, setCopied] = useState(false) function handleCopy() { setCopied(true) setTimeout(() => setCopied(false), 2000) } const deliveredPercent = Math.max(0, 100 - strandedPercent) return (

Full Analysis

Unlocked

{/* ===== ROW 1: A1 summary | A2 capacity flow ===== */}

{/* A1 — SUMMARY */}

Summary

{COOLING_LABELS[coolingType]} · {formatMW(facilityMW)} · {utilization}% utilization

{strandedPercent.toFixed(0)} %

Stranded capacity

{strandedMW.toFixed(1)} MW

Lost capacity

{formatUsd(annualLoss.average)}

Estimated annual financial loss

Range {formatUsd(annualLoss.min)} – {formatUsd(annualLoss.max)} / year

Delivered Stranded

{deliveredPercent.toFixed(0)}% {strandedPercent.toFixed(0)}%

{/* A2 — CAPACITY FLOW */}

Capacity Flow — where your capacity is lost

{/* Flow (main) */}

{flow.map((layer, i) => { const Icon = LAYER_ICONS[layer.key] const passesPct = facilityMW > 0 ? (layer.passes / facilityMW) * 100 : 0 const lostPct = facilityMW > 0 ? (layer.lostHere / facilityMW) * 100 : 0 return (

{layer.label}

{layer.sub}

{layer.incoming.toFixed(1)}{" "} MW

{layer.lostHere > 0 && (

−{formatMW(layer.lostHere)} lost

)}

{lostPct > 0 && (

)}

{i < flow.length - 1 && ( )}

) })}

{/* Industry Context — reference data */}

Reference data

{item.label} {item.value}

{/* Collapsible calculation explanation */}

{showCalc && (

Esta capa: 100% → {deliveredPercent.toFixed(0)}%

Stranded % = (100 − utilización) × factor de refrigeración ={" "} (100 − {utilization}) × {(strandedPercent / (100 - utilization || 1)).toFixed(2)} {" "} = {strandedPercent.toFixed(0)}%

Stranded MW = {formatMW(facilityMW)} × {strandedPercent.toFixed(0)}% ={" "} {formatMW(strandedMW)}

Pérdida anual = {formatMW(strandedMW)} × $250k/MW ={" "} {formatUsd(annualLoss.average)}

)}

{/* ===== SHARED SCENARIO SELECTOR (drives A4 + A3) ===== */}

Comparar escenario actual{" "} ({COOLING_LABELS[coolingType]}, {formatMW(facilityMW)}, {utilization}%) {" "} contra

{COOLING_ORDER.map((type) => { const active = comparisonCoolingType === type return ( ) })}

{/* ===== ROW 2: A4 comparison + radar | A3 financial ===== */}

{/* A4 — CAPACITY COMPARISON */}

Capacity Comparison

{COOLING_LABELS[coolingType]} vs {COOLING_LABELS[comparisonCoolingType]} — same facility and utilization.

{/* Comparison bars */}

Capacity recovered by switching

{formatMW(Math.max(0, comparison.recoveredMW))}

{/* A4 RADAR — exactly 4 axes */}

{/* A3 — FINANCIAL ANALYSIS */}

{formatUsd(annualLoss.average)}

Range {formatUsd(annualLoss.min)} – {formatUsd(annualLoss.max)} / year

{comparison.projectedSavings > 0 ? formatUsd(comparison.projectedSavings) : formatUsd(0)} / year

Switching {COOLING_LABELS[coolingType]} → {COOLING_LABELS[comparisonCoolingType]}

{/* Contexto de industria — precio de electricidad de referencia */}

Precio de electricidad de referencia

{item.region} {item.price}

{/* ===== A5 — REFERENCES (horizontal) ===== */}

References

{INDUSTRY_CONTEXT.electricityPrices.map((item) => ( ))} {INDUSTRY_CONTEXT.utilizationBenchmark.map((item) => ( ))} {INDUSTRY_CONTEXT.strandedCostPerMW.map((item) => ( ))}

{/* Actions */}

) } function ComparisonBar({ label, strandedMW, strandedPercent, facilityMW, tone, }: { label: string strandedMW: number strandedPercent: number facilityMW: number tone: "current" | "comparison" }) { const deliveredPct = facilityMW > 0 ? Math.max(0, 100 - strandedPercent) : 0 const strandedPct = facilityMW > 0 ? strandedPercent : 0 const strandedColor = tone === "current" ? "bg-accent" : "bg-accent/60" return (

{label} {strandedPercent.toFixed(0)}% · {formatMW(strandedMW)}

) } function ReferenceCard({ icon: Icon, title, children, }: { icon: React.ElementType title: string children: React.ReactNode }) { return (

{title}

{children}

) } function ReferenceRow({ label, value }: { label: string; value: string }) { return (

{label} {value}

Full Analysis

Unlocked

{/* ===== ROW 1: A1 summary | A2 capacity flow ===== */}

{/* A1 — SUMMARY */}

Summary

{COOLING_LABELS[coolingType]} · {formatMW(facilityMW)} · {utilization}% utilization

{strandedPercent.toFixed(0)} %

Stranded capacity

{strandedMW.toFixed(1)} MW

Lost capacity

{formatUsd(annualLoss.average)}

Estimated annual financial loss

Range {formatUsd(annualLoss.min)} – {formatUsd(annualLoss.max)} / year

Delivered Stranded

{deliveredPercent.toFixed(0)}% {strandedPercent.toFixed(0)}%

{/* A2 — CAPACITY FLOW */}

Capacity Flow — where your capacity is lost

{/* Flow (main) */}

{layer.label}

{layer.sub}

{layer.incoming.toFixed(1)}{" "} MW

{layer.lostHere > 0 && (

−{formatMW(layer.lostHere)} lost

)}

{lostPct > 0 && (

)}

{i < flow.length - 1 && ( )}

) })}

{/* Industry Context — reference data */}

Reference data

{item.label} {item.value}

{/* Collapsible calculation explanation */}

{showCalc && (

Esta capa: 100% → {deliveredPercent.toFixed(0)}%

Stranded % = (100 − utilización) × factor de refrigeración ={" "} (100 − {utilization}) × {(strandedPercent / (100 - utilization || 1)).toFixed(2)} {" "} = {strandedPercent.toFixed(0)}%

Stranded MW = {formatMW(facilityMW)} × {strandedPercent.toFixed(0)}% ={" "} {formatMW(strandedMW)}

Pérdida anual = {formatMW(strandedMW)} × $250k/MW ={" "} {formatUsd(annualLoss.average)}

)}

{/* ===== SHARED SCENARIO SELECTOR (drives A4 + A3) ===== */}

Comparar escenario actual{" "} ({COOLING_LABELS[coolingType]}, {formatMW(facilityMW)}, {utilization}%) {" "} contra

{COOLING_ORDER.map((type) => { const active = comparisonCoolingType === type return ( ) })}

{/* ===== ROW 2: A4 comparison + radar | A3 financial ===== */}

{/* A4 — CAPACITY COMPARISON */}

Capacity Comparison

{COOLING_LABELS[coolingType]} vs {COOLING_LABELS[comparisonCoolingType]} — same facility and utilization.

{/* Comparison bars */}

Capacity recovered by switching

{formatMW(Math.max(0, comparison.recoveredMW))}

{/* A4 RADAR — exactly 4 axes */}

{/* A3 — FINANCIAL ANALYSIS */}

{formatUsd(annualLoss.average)}

Range {formatUsd(annualLoss.min)} – {formatUsd(annualLoss.max)} / year

{comparison.projectedSavings > 0 ? formatUsd(comparison.projectedSavings) : formatUsd(0)} / year

Switching {COOLING_LABELS[coolingType]} → {COOLING_LABELS[comparisonCoolingType]}

{/* Contexto de industria — precio de electricidad de referencia */}

Precio de electricidad de referencia

{item.region} {item.price}

{/* ===== A5 — REFERENCES (horizontal) ===== */}

References

{INDUSTRY_CONTEXT.electricityPrices.map((item) => ( ))} {INDUSTRY_CONTEXT.utilizationBenchmark.map((item) => ( ))} {INDUSTRY_CONTEXT.strandedCostPerMW.map((item) => ( ))}

{/* Actions */}

{label} {strandedPercent.toFixed(0)}% · {formatMW(strandedMW)}

) } function ReferenceCard({ icon: Icon, title, children, }: { icon: React.ElementType title: string children: React.ReactNode }) { return (

{title}

{children}

) } function ReferenceRow({ label, value }: { label: string; value: string }) { return (

{label} {value}

) } // 4-axis radar chart (diamond). Two series overlaid. function RadarChart({ current, comparison, }: { current: { axis: string; value: number }[] comparison: { axis: string; value: number }[] }) { const size = 200 const center = size / 2 const maxRadius = 78 // 4 axes: top, right, bottom, left const angles = [-90, 0, 90, 180] function pointFor(value: number, angleDeg: number) { const r = (Math.max(0, Math.min(100, value)) / 100) * maxRadius const rad = (angleDeg * Math.PI) / 180 return { x: center + r * Math.cos(rad), y: center + r * Math.sin(rad) } } function polygon(series: { value: number }[]) { return series.map((s, i) => { const p = pointFor(s.value, angles[i]) return `${p.x},${p.y}` }).join(" ") } const gridLevels = [0.25, 0.5, 0.75, 1] return ( ) 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