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// SPDX-License-Identifier: Apache-2.0
/**
* @module Generators/Builders/SharedBuilders
* @description Shared helper functions used across multiple analysis builder
* domains. Includes outcome matrix construction, AI marker impact assessments,
* coalition metrics, pipeline builders, trend analytics, and SWOT dimension helpers.
*/
import type {
DeepAnalysis,
StakeholderOutcomeMatrix,
AnalysisStakeholderType,
CoalitionMetrics,
LegislativePipeline,
TrendAnalytics,
DashboardPanel,
DashboardBuilderStrings,
WeekAheadData,
VotingPattern,
SwotDimension,
SwotItem,
StakeholderMetric,
} from '../../types/index.js';
import { buildStakeholderOutcomeMatrix } from '../../utils/intelligence-analysis.js';
import { AI_MARKER } from '../../constants/analysis-constants.js';
// ─── Style constants ─────────────────────────────────────────────────────────
export const EP_BLUE_TRANSPARENT = 'rgba(0,51,153,0.1)';
export const EP_BLUE_BORDER = '#003399';
export const CIVIL_SOCIETY = 'Civil Society';
/**
* Build the stakeholder outcome matrix for a list of key actions.
* Used by all 5 analysis builders to populate the outcome matrix.
*
* @param actions - Readonly array of (action, scores) pairs to include in the matrix
* @returns Stakeholder outcome matrix rows
*/
export function buildOutcomeMatrix(
actions: readonly {
readonly action: string;
readonly scores: Readonly<Partial<Record<AnalysisStakeholderType, number>>>;
readonly confidence: 'high' | 'medium' | 'low';
}[]
): StakeholderOutcomeMatrix[] {
return actions.map(({ action, scores, confidence }) =>
buildStakeholderOutcomeMatrix(action, scores, confidence)
);
}
/**
* Build an AI_MARKER impact assessment placeholder.
* All five dimensions are marked for AI completion.
*
* @returns Impact assessment with AI_MARKER placeholders
*/
export function buildAiMarkerImpactAssessment(): DeepAnalysis['impactAssessment'] {
return {
political: AI_MARKER,
economic: AI_MARKER,
social: AI_MARKER,
legal: AI_MARKER,
geopolitical: AI_MARKER,
};
}
/**
* Build coalition metrics from voting patterns data.
* Derives alignment scores and shift indicators for the coalition radar chart.
*
* @param patterns - Voting pattern data
* @returns Coalition metrics object or null if no real patterns
*/
export function buildCoalitionMetricsFromPatterns(
patterns: readonly VotingPattern[]
): CoalitionMetrics | null {
const realPatterns = patterns.filter((p) => !/placeholder/i.test(p.group));
if (realPatterns.length === 0) return null;
const avgCohesion = realPatterns.reduce((sum, p) => sum + p.cohesion, 0) / realPatterns.length;
const alignmentScore = Math.round(avgCohesion * 100);
// Detect shift from cohesion spread
const maxCohesion = Math.max(...realPatterns.map((p) => p.cohesion));
const minCohesion = Math.min(...realPatterns.map((p) => p.cohesion));
const spread = maxCohesion - minCohesion;
const shiftIndicator: CoalitionMetrics['shiftIndicator'] =
spread > 0.3 ? 'weakening' : avgCohesion > 0.7 ? 'strengthening' : 'stable';
return {
alignmentScore,
votingBlocs: realPatterns.slice(0, 6).map((p) => ({
group: p.group,
alignmentScore: Math.round(p.cohesion * 100),
})),
shiftIndicator,
};
}
/**
* Build legislative pipeline data from WeekAheadData.
*
* @param weekData - Aggregated week/month data
* @returns Legislative pipeline object
*/
export function buildPipelineFromWeekData(weekData: WeekAheadData): LegislativePipeline {
const bottlenecked = weekData.pipeline.filter((p) => p.bottleneck === true).length;
const total = weekData.pipeline.length;
const onTrack = total - bottlenecked;
const healthScore = total > 0 ? Math.round((onTrack / total) * 100) : 100;
return {
healthScore,
onTrack,
delayed: bottlenecked,
blocked: 0,
fastTracked: 0,
total,
};
}
/**
* Build trend analytics from feed data counts using the provided periods as-is.
*
* @param counts - Array of activity counts per period in chronological order
* @param period - Trend period label
* @returns Trend analytics object or null if no data
*/
export function buildTrendFromCounts(
counts: readonly number[],
period: TrendAnalytics['period']
): TrendAnalytics | null {
if (counts.length === 0 || counts.every((c) => c === 0)) return null;
const periodLabels = counts.map((_, i) => {
if (period === 'weekly') return `W${i + 1}`;
if (period === 'monthly') return `M${i + 1}`;
return `Q${i + 1}`;
});
const metrics = counts.map((value, i) => ({ period: periodLabels[i] ?? `${i + 1}`, value }));
const last = counts.at(-1) ?? 0;
const prev = counts.at(-2) ?? last;
const change = prev > 0 ? ((last - prev) / prev) * 100 : 0;
const direction: TrendAnalytics['direction'] =
change > 5 ? 'improving' : change < -5 ? 'declining' : 'stable';
return {
period,
metrics,
direction,
weekOverWeekChange: period === 'weekly' ? Math.round(change * 10) / 10 : undefined,
monthOverMonthChange: period === 'monthly' ? Math.round(change * 10) / 10 : undefined,
};
}
/**
* Build stakeholder metrics from voting patterns.
*
* @param patterns - Voting patterns
* @param anomalyCount - Number of anomalies
* @returns Stakeholder metric array
*/
export function buildStakeholderMetricsFromVoting(
patterns: readonly VotingPattern[],
anomalyCount: number
): StakeholderMetric[] {
const realPatterns = patterns.filter((p) => !/placeholder/i.test(p.group));
const metrics: StakeholderMetric[] = realPatterns.slice(0, 4).map((p) => ({
stakeholder: p.group,
impactScore: Math.round(p.cohesion * 100),
impactDirection: (p.cohesion > 0.7 ? 'positive' : p.cohesion < 0.4 ? 'negative' : 'neutral') as
| 'positive'
| 'negative'
| 'neutral',
}));
if (anomalyCount > 0) {
metrics.push({
stakeholder: 'Coalition stability',
impactScore: Math.max(0, 100 - anomalyCount * 15),
impactDirection: anomalyCount > 3 ? 'negative' : 'neutral',
});
}
return metrics;
}
/**
* Build stakeholder metrics for legislative pipeline actors.
*
* @param pipeline - Legislative pipeline data
* @returns Stakeholder metric array
*/
export function buildStakeholderMetricsFromPipeline(
pipeline: LegislativePipeline | null
): StakeholderMetric[] {
if (!pipeline || pipeline.total === 0) return [];
return [
{
stakeholder: 'Legislators',
impactScore: pipeline.healthScore,
impactDirection:
pipeline.healthScore > 70 ? 'positive' : pipeline.healthScore < 40 ? 'negative' : 'neutral',
},
{
stakeholder: 'Pending proposals',
impactScore: pipeline.total > 0 ? Math.round((pipeline.blocked / pipeline.total) * 100) : 0,
impactDirection: pipeline.blocked > 0 ? 'negative' : 'neutral',
description:
pipeline.blocked > 0
? `${pipeline.blocked} blocked procedure${pipeline.blocked > 1 ? 's' : ''}`
: undefined,
},
];
}
/**
* Build a stakeholder panel from stakeholder metric array.
*
* @param d - Localized strings
* @param stakeholderMetrics - Stakeholder metric data
* @returns Panel object or null
*/
export function buildStakeholderPanel(
d: DashboardBuilderStrings,
stakeholderMetrics: readonly StakeholderMetric[]
): DashboardPanel | null {
if (stakeholderMetrics.length === 0) return null;
return {
title: d.stakeholderImpact,
metrics: stakeholderMetrics.map((s) => ({
label: s.stakeholder,
value: `${s.impactScore}/100`,
trend: (s.impactDirection === 'positive'
? 'up'
: s.impactDirection === 'negative'
? 'down'
: 'stable') as 'up' | 'down' | 'stable',
})),
};
}
/**
* Resolve a direction label from trend direction.
*
* @param d - Localized strings
* @param direction - Trend direction
* @returns Localized direction label
*/
export function resolveTrendDirectionLabel(
d: DashboardBuilderStrings,
direction: TrendAnalytics['direction']
): string {
if (direction === 'improving') return d.trendImproving;
if (direction === 'declining') return d.trendDeclining;
return d.trendStableLabel;
}
/**
* Build a generic trend panel from a trend object.
*
* @param d - Localized strings
* @param trend - Trend analytics
* @param labels - Labels for x-axis
* @param datasetLabel - Label for the dataset
* @returns Panel object or null
*/
export function buildGenericTrendPanel(
d: DashboardBuilderStrings,
trend: TrendAnalytics | null,
labels: string[],
datasetLabel: string
): DashboardPanel | null {
if (!trend) return null;
return {
title: d.trendAnalysis,
metrics: [
{
label: d.trendAnalysis,
value: resolveTrendDirectionLabel(d, trend.direction),
},
],
chart: {
type: 'line' as const,
title: d.activityTrendChart,
data: {
labels,
datasets: [
{
label: datasetLabel,
data: trend.metrics.map((m) => m.value),
borderColor: EP_BLUE_BORDER,
backgroundColor: EP_BLUE_TRANSPARENT,
},
],
},
},
};
}
/**
* Build a category distribution panel showing counts per category as a bar chart.
* Unlike `buildGenericTrendPanel`, this does not compute direction or week-over-week
* change metrics, which are only meaningful for chronological time-series data.
*
* @param d - Localized strings
* @param labels - Category labels for x-axis
* @param counts - Counts per category (must align with labels)
* @param datasetLabel - Label for the dataset
* @param title - Panel title
* @returns Panel object or null if all counts are zero
*/
export function buildCategoryDistributionPanel(
d: DashboardBuilderStrings,
labels: readonly string[],
counts: readonly number[],
datasetLabel: string,
title: string
): DashboardPanel | null {
if (counts.length === 0 || counts.every((c) => c === 0)) return null;
return {
title,
metrics: [
{
label: d.trendAnalysis,
value: `${counts.reduce((a, b) => a + b, 0)} total`,
},
],
chart: {
type: 'bar' as const,
title,
data: {
labels: [...labels],
datasets: [
{
label: datasetLabel,
data: [...counts],
borderColor: EP_BLUE_BORDER,
backgroundColor: EP_BLUE_TRANSPARENT,
},
],
},
},
};
}
/**
* Build a dimension object from sets of pre-computed SWOT items.
*
* @param name - Dimension name
* @param strengths - Strength items for this dimension
* @param weaknesses - Weakness items for this dimension
* @param opportunities - Opportunity items for this dimension
* @param threats - Threat items for this dimension
* @returns Typed SwotDimension
*/
export function makeDimension(
name: SwotDimension['name'],
strengths: readonly SwotItem[],
weaknesses: readonly SwotItem[],
opportunities: readonly SwotItem[],
threats: readonly SwotItem[]
): SwotDimension {
return { name, strengths, weaknesses, opportunities, threats };
}
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