/**
* @module TemporalIntelligence/SeasonalAnalysis
* @category Intelligence Analysis - Seasonal Parliamentary Patterns & Anomaly Detection
*
* @description
* **Swedish Parliamentary Seasonal Activity Analysis & Quarterly Pattern Intelligence Dashboard**
*
* Advanced intelligence analysis platform implementing **23-year temporal pattern analysis**
* (2002-2025) of Swedish parliamentary quarterly activity with sophisticated Z-score anomaly
* detection and seasonal pattern classification. Identifies systematic quarterly variations,
* detects activity anomalies, and classifies seasonal patterns through multi-year aggregation
* and cross-quarter comparative analysis using D3.js and Chart.js visualization.
*
* ## Intelligence Methodology
*
* This module implements **temporal pattern intelligence analysis**:
* - **Historical Scope**: 23 years × 4 quarters = 92 quarterly data points
* - **Analysis Approach**: Seasonal decomposition with Z-score anomaly detection
* - **Granularity**: Quarterly activity aggregation across parliamentary entities
* - **Anomaly Threshold**: Z-score ≥ 2.0 (p<0.05) for statistical significance
*
* ## Seasonal Intelligence Framework
*
* **Four-Dimensional Analysis Taxonomy**:
*
* 1. **Quarterly Activity Patterns** (Seasonal Decomposition)
* - Q1 (January-March): Spring session, budget discussions beginning
* - Q2 (April-June): Legislative focus, committee work intensification
* - Q3 (July-September): Summer recess, reduced activity baseline
* - Q4 (October-December): Fall session, pre-election activity surge
* - Activity types: Ballots, documents, committee decisions, attendance
*
* 2. **Z-Score Anomaly Detection** (Statistical Outlier Identification)
* - Baseline: Mean and standard deviation per quarter (23 years)
* - Z-score calculation: (Value - Mean) / StdDev
* - Anomaly threshold: |Z-score| ≥ 2.0 (95% confidence)
* - Classification: Normal, Elevated, Anomaly, Critical
*
* 3. **Seasonal Pattern Classification** (Behavioral Categorization)
* - Normal: Activity within ±1 StdDev of quarterly average
* - Elevated: Activity 1-2 StdDev above/below average
* - Anomaly: Activity >2 StdDev from average (statistical outlier)
* - Critical: Extreme anomaly >3 StdDev (very rare events)
*
* 4. **Cross-Year Quarter Comparison** (Temporal Consistency)
* - Year-over-year quarter consistency analysis
* - Trend identification within same quarter across years
* - Activity volatility assessment by quarter
* - Quarter-to-quarter transition patterns
*
* ## Data Sources (CIA Platform)
*
* **Primary Intelligence Feeds**:
* - `view_riksdagen_seasonal_activity_patterns_sample.csv`
* * Fields: year, quarter, ballot_count, document_count, decision_count, attendance_rate,
* avg_speech_length, committee_meetings, anomaly_score, anomaly_class
* * Scope: 23 years (2002-2025) × 4 quarters = 92 quarterly records
* * Use: Seasonal pattern baseline, anomaly detection, quarterly benchmarking
* * Coverage: Full spectrum of parliamentary activity metrics
*
* ## OSINT Collection Strategy
*
* **Temporal Pattern Intelligence**:
* 1. **Parliamentary Activity Tracking**: Vote counts, document filings, committee meetings
* 2. **Attendance Intelligence**: Member participation rates, session attendance patterns
* 3. **Speech Analytics**: Contribution frequency, speech length, rhetoric intensity
* 4. **Calendar Intelligence**: Recess periods, session schedules, emergency sessions
* 5. **Budget Cycles**: Fiscal year boundaries and budget discussion phases
* 6. **Electoral Calendars**: Election-adjacent activity surge patterns
* 7. **Government Transitions**: Cabinet change and policy uncertainty impact on activity
*
* ## Visualization Intelligence
*
* **D3.js Quarterly Heat Map** (Primary):
* - **23×4 Matrix Visualization**: Years (Y-axis) × Quarters (X-axis)
* * Each cell represents quarterly activity level
* * Color intensity: Activity magnitude (blue/low → red/high)
* * Color saturation: Anomaly magnitude (white/normal → black/critical)
* * Interactive: Tooltip reveals detailed metrics (count, Z-score, classification)
* * Scrollable: 23 years with year labels and Q1-Q4 grid
* * Sortable: By activity level, anomaly score, or time sequence
*
* **Chart.js Seasonal Decomposition** (Pattern Analysis):
* - **Box-and-Whisker Plot** by quarter across 23 years:
* * X-axis: 4 quarters (Q1, Q2, Q3, Q4)
* * Y-axis: Activity metric value
* * Box: Interquartile range (25th-75th percentile)
* * Line: Median (50th percentile)
* * Whiskers: 1.5×IQR range
* * Points: Outliers beyond whiskers (statistical anomalies)
* * Shows quarterly pattern consistency and outlier years
*
* **Chart.js Anomaly Timeline** (Outlier Tracking):
* - **Anomaly Score Time Series**: Z-scores for all quarters
* * Multi-line chart with threshold bands
* * Separate lines for different activity types
* * Horizontal reference lines at Z=0, Z=2, Z=-2, Z=3, Z=-3
* * Color-coded zones: Green (normal), Yellow (elevated), Red (anomaly)
* * Identifies anomalous quarters and their characteristics
*
* **Chart.js Quarter Comparison** (Relative Strength):
* - **Average Activity by Quarter**: Multi-year aggregated pattern
* * Bar chart showing Q1, Q2, Q3, Q4 average activities
* * Error bars showing ±1 StdDev confidence bands
* * Shows expected seasonal variation
* * Identifies which quarters are typically high/low activity
*
* **Chart.js Activity Quartile Distribution** (Ranking):
* - **Quartile Membership Heat Map**: Each quarter's historical ranking
* * Shows frequency of each quarter landing in quartiles
* * Q1 column: How often Q1s rank in top/bottom quartiles
* * Helps identify most/least reliable quarters
*
* ## Intelligence Analysis Frameworks Applied
*
* @intelligence
* - **Temporal Decomposition**: Separating trend, seasonal, and anomaly components
* - **Statistical Anomaly Detection**: Z-score methodology with confidence intervals
* - **Seasonal Pattern Classification**: Normal/elevated/anomaly/critical taxonomy
* - **Year-Over-Year Analysis**: Consistency assessment and trend identification
* - **Outlier Investigation**: Identification of anomalous quarters and causes
*
* @osint
* - **Activity Intelligence**: Real-time parliamentary activity monitoring
* - **Pattern Recognition**: Historical seasonal signatures and exception detection
* - **Confidence Quantification**: Statistical bounds on anomaly identification
* - **Root Cause Analysis**: Linking anomalies to external events (elections, crises)
*
* @risk
* - **Seasonal Activity Disruption**: Anomalous activity surge/decline risks
* - **Summer Recess Risk**: Reduced oversight during Q3 periods
* - **Pre-Election Volatility**: Q4 election-year activity unpredictability
* - **Activity Concentration Risk**: Uneven quarterly distribution affecting oversight
*
* ## GDPR Compliance
*
* @gdpr Seasonal activity analysis uses only aggregate parliamentary data (Article 9(2)(e)):
* - Aggregate activity counts (ballots, documents, decisions)
* - No individual parliamentary member tracking
* - No personal behavioral data or voting pattern surveillance
* - No content analysis of speeches or documents
* - Purely temporal pattern analysis of aggregate activity volumes
*
* ## Security Architecture
*
* @security D3.js SVG rendering with input sanitization on all text labels
* @security Chart.js with XSS-safe tooltip content and axis labels
* @security All CSV data validated with type checking and range enforcement
* @security Z-score calculation algorithm transparent and reproducible
* @security No authentication required; all data is public record
* @risk Low - Temporal activity patterns are public aggregate data
*
* ## Performance Characteristics
*
* - **Data Volume**: 23 years × 4 quarters × 6-8 metrics = ~552-736 data points
* - **Rendering**: D3.js heat map (23×4 = 92 cells) + Chart.js (5 visualizations)
* - **Calculations**: Z-scores, percentiles, quartile calculations
* - **Memory**: <1.5MB for complete seasonal analysis dataset
* - **Cache Duration**: 24-hour expiry; weekly updates typical
*
* ## Data Transformation Pipeline
*
* **Load Strategy**:
* 1. Attempt local cache load (`cia-data/seasonal/`)
* 2. Parse CSV file into quarterly time-series structure
* 3. Fallback to remote GitHub repository if local unavailable
* 4. Calculate Z-scores for each metric by quarter
* 5. Classify quarters: Normal/Elevated/Anomaly/Critical
* 6. Compute quarterly aggregates (means, std dev) across 23 years
* 7. Cache results with 24-hour expiry
* 8. Render visualizations with aggregated/transformed data
*
* **Statistical Processing**:
* - **Per-Quarter Statistics**: Mean and StdDev for each Q1/Q2/Q3/Q4 across 23 years
* - **Z-Score**: (Current_Value - Quarter_Mean) / Quarter_StdDev
* - **Percentile**: Ranking within historical distribution for that quarter
* - **Anomaly Class**: Normal (|Z|<1), Elevated (1≤|Z|<2), Anomaly (|Z|≥2)
* - **Confidence**: 95% confidence interval on quarterly means
*
* ## Anomaly Thresholds
*
* **Classification Levels**:
* - **Normal**: Z-score between -1.0 and +1.0 (68% of data expected)
* - **Elevated**: Z-score between -2.0 and -1.0 OR +1.0 and +2.0 (27% of data)
* - **Anomaly**: Z-score < -2.0 OR > +2.0 (5% of data, statistical outliers)
* - **Critical**: Z-score < -3.0 OR > +3.0 (<1% of data, extreme events)
*
* **Activity-Specific Thresholds**:
* - **Ballots**: >500 votes per quarter = high activity, <200 = low activity
* - **Documents**: >150 documents per quarter = high legislative activity
* - **Decisions**: >80 committee decisions per quarter = normal workload
* - **Attendance**: >85% average attendance = high participation, <75% = concerning
*
* @author Hack23 AB - Temporal Intelligence Team
* @license Apache-2.0
* @version 1.0.0
* @since 2024
*
* @see {@link https://github.com/Hack23/cia|CIA Platform Data Source}
* @see {@link https://data.riksdagen.se|Riksdag Open Data API}
* @see {@link ./THREAT_MODEL.md|Threat Model Documentation}
* @see {@link ./SECURITY_ARCHITECTURE.md|Security Architecture}
*/
(function() {
'use strict';
// ============================================================================
// Configuration
// ============================================================================
const CONFIG = {
dataUrls: [
'cia-data/seasonal/view_riksdagen_seasonal_activity_patterns_sample.csv', // Local first
'https://raw.githubusercontent.com/Hack23/cia/master/service.data.impl/sample-data/view_riksdagen_seasonal_activity_patterns_sample.csv' // Remote fallback
],
cacheKey: 'riksdag_seasonal_patterns',
cacheDuration: 24 * 60 * 60 * 1000, // 24 hours in milliseconds
zScoreThreshold: 2.0, // Anomaly threshold
colors: {
primary: '#00d9ff',
secondary: '#ff006e',
tertiary: '#ffbe0b',
success: '#008838',
warning: '#fbc02d',
danger: '#d32f2f',
info: '#117a8b',
normal: '#388e3c',
elevated: '#f57c00',
reduced: '#1976d2',
anomaly: '#d32f2f'
},
quarterColors: {
Q1: '#1976d2', // Blue - Winter
Q2: '#388e3c', // Green - Spring
Q3: '#fbc02d', // Yellow - Summer Recess
Q4: '#f57c00' // Orange - Autumn
}
};
// ============================================================================
// Translations (14 Languages)
// ============================================================================
const TRANSLATIONS = {
en: {
title: 'Seasonal Activity Patterns (2002-2025)',
subtitle: 'Quarterly Analysis with Z-Score Anomaly Detection',
filters: {
year: 'Year',
quarter: 'Quarter',
election: 'Election Status',
classification: 'Activity Classification',
allYears: 'All Years',
allQuarters: 'All Quarters',
allElections: 'All',
electionYears: 'Election Years',
nonElectionYears: 'Non-Election Years',
allClassifications: 'All Classifications'
},
quarters: {
Q1: 'Q1 - Winter Session',
Q2: 'Q2 - Spring Session',
Q3: 'Q3 - Summer Recess',
Q4: 'Q4 - Autumn Session'
},
charts: {
heatmap: {
title: 'Quarterly Activity Heat Map (2002-2025)',
description: 'Ballot volume by year and quarter with Z-score overlay'
},
zscore: {
title: 'Z-Score Anomaly Detection',
description: 'Statistical outliers (|Z| ≥ 2.0) flagged in red'
},
comparison: {
title: 'Average Activity by Quarter (All Years)',
description: 'Q1-Q4 baselines with standard deviation bands'
},
classification: {
title: 'Seasonal Pattern Classification',
description: 'Distribution of NORMAL, ELEVATED, REDUCED, ANOMALY patterns'
},
qoq: {
title: 'Quarter-over-Quarter Changes',
description: 'Sequential ballot changes (% and absolute)'
}
},
classifications: {
NORMAL_ACTIVITY: 'Normal Activity',
ELEVATED_ACTIVITY: 'Elevated Activity',
REDUCED_ACTIVITY: 'Reduced Activity',
ANOMALY_DETECTED: 'Anomaly Detected',
NORMAL_SEASONAL_PATTERN: 'Normal Seasonal Pattern',
Q3_SUMMER_LULL: 'Q3 Summer Lull',
Q4_ELEVATED_ACTIVITY: 'Q4 Elevated Activity',
UNUSUALLY_HIGH_ACTIVITY: 'Unusually High Activity',
UNUSUALLY_LOW_ACTIVITY: 'Unusually Low Activity'
},
tooltips: {
ballots: 'Ballots',
zScore: 'Z-Score',
classification: 'Classification',
anomaly: 'ANOMALY',
na: 'N/A',
quarter: 'Quarter',
year: 'Year'
},
chartLabels: {
ballotZScore: 'Ballot Z-Score',
documentZScore: 'Document Z-Score',
attendanceZScore: 'Attendance Z-Score',
yearQuarter: 'Year-Quarter',
zScore: 'Z-Score',
quarter: 'Quarter',
averageBallots: 'Average Ballots',
year: 'Year',
count: 'Count',
changePercent: 'Change (%)',
qoqChange: 'QoQ Change (%)',
anomaly: 'ANOMALY'
},
loading: 'Loading data...',
error: 'Error loading data. Please try again.',
dataAttribution: 'Data by CIA Platform'
},
sv: {
title: 'Säsongsmönster (2002-2025)',
subtitle: 'Kvartalsanalys med Z-poäng anomalidetektering',
filters: {
year: 'År',
quarter: 'Kvartal',
election: 'Valstatus',
classification: 'Aktivitetsklassificering',
allYears: 'Alla år',
allQuarters: 'Alla kvartal',
allElections: 'Alla',
electionYears: 'Valår',
nonElectionYears: 'Icke-valår',
allClassifications: 'Alla klassificeringar'
},
quarters: {
Q1: 'Q1 - Vintersession',
Q2: 'Q2 - Vårsession',
Q3: 'Q3 - Sommaruppehåll',
Q4: 'Q4 - Höstsession'
},
charts: {
heatmap: {
title: 'Kvartalsaktivitet värmekarta (2002-2025)',
description: 'Omröstningsvolym per år och kvartal med Z-poäng'
},
zscore: {
title: 'Z-poäng anomalidetektering',
description: 'Statistiska avvikelser (|Z| ≥ 2.0) markerade i rött'
},
comparison: {
title: 'Genomsnittlig aktivitet per kvartal (alla år)',
description: 'Q1-Q4 baslinjer med standardavvikelseband'
},
classification: {
title: 'Säsongsmönster klassificering',
description: 'Fördelning av NORMAL, FÖRHÖJD, REDUCERAD, ANOMALI mönster'
},
qoq: {
title: 'Kvartal-till-kvartal förändringar',
description: 'Sekventiella omröstningsförändringar (% och absolut)'
}
},
classifications: {
NORMAL_ACTIVITY: 'Normal aktivitet',
ELEVATED_ACTIVITY: 'Förhöjd aktivitet',
REDUCED_ACTIVITY: 'Reducerad aktivitet',
ANOMALY_DETECTED: 'Anomali upptäckt',
NORMAL_SEASONAL_PATTERN: 'Normalt säsongsmönster',
Q3_SUMMER_LULL: 'Q3 sommaruppehåll',
Q4_ELEVATED_ACTIVITY: 'Q4 förhöjd aktivitet',
UNUSUALLY_HIGH_ACTIVITY: 'Ovanligt hög aktivitet',
UNUSUALLY_LOW_ACTIVITY: 'Ovanligt låg aktivitet'
},
tooltips: {
ballots: 'Omröstningar',
zScore: 'Z-poäng',
classification: 'Klassificering',
anomaly: 'ANOMALI',
na: 'Saknas',
quarter: 'Kvartal',
year: 'År'
},
chartLabels: {
ballotZScore: 'Omröstningar Z-poäng',
documentZScore: 'Dokument Z-poäng',
attendanceZScore: 'Närvaro Z-poäng',
yearQuarter: 'År-Kvartal',
zScore: 'Z-poäng',
quarter: 'Kvartal',
averageBallots: 'Genomsnittliga omröstningar',
year: 'År',
count: 'Antal',
changePercent: 'Förändring (%)',
qoqChange: 'KtK-förändring (%)',
anomaly: 'ANOMALI'
},
loading: 'Laddar data...',
error: 'Fel vid inläsning av data. Försök igen.',
dataAttribution: 'Data från CIA-plattformen'
},
da: {
title: 'Sæsonmønstre (2002-2025)',
subtitle: 'Kvartalsanalyse med Z-score anomalidetektion',
filters: {
year: 'År',
quarter: 'Kvartal',
election: 'Valgstatus',
classification: 'Aktivitetsklassificering',
allYears: 'Alle år',
allQuarters: 'Alle kvartaler',
allElections: 'Alle',
electionYears: 'Valgår',
nonElectionYears: 'Ikke-valgår',
allClassifications: 'Alle klassificeringer'
},
quarters: {
Q1: 'K1 - Vintersession',
Q2: 'K2 - Forårssession',
Q3: 'K3 - Sommerpause',
Q4: 'K4 - Efterårssession'
},
charts: {
heatmap: {
title: 'Kvartalsaktivitet varmekort (2002-2025)',
description: 'Afstemningsvolumen efter år og kvartal med Z-score'
},
zscore: {
title: 'Z-score anomalidetektion',
description: 'Statistiske afvigelser (|Z| ≥ 2.0) markeret med rødt'
},
comparison: {
title: 'Gennemsnitlig aktivitet efter kvartal (alle år)',
description: 'K1-K4 basislinjer med standardafvikelsesbånd'
},
classification: {
title: 'Sæsonmønster klassificering',
description: 'Fordeling af NORMAL, FORHØJET, REDUCERET, ANOMALI mønstre'
},
qoq: {
title: 'Kvartal-til-kvartal ændringer',
description: 'Sekventielle afstemningsændringer (% og absolut)'
}
},
classifications: {
NORMAL_ACTIVITY: 'Normal aktivitet',
ELEVATED_ACTIVITY: 'Forhøjet aktivitet',
REDUCED_ACTIVITY: 'Reduceret aktivitet',
ANOMALY_DETECTED: 'Anomali opdaget',
NORMAL_SEASONAL_PATTERN: 'Normalt sæsonmønster',
Q3_SUMMER_LULL: 'K3 sommerpause',
Q4_ELEVATED_ACTIVITY: 'K4 forhøjet aktivitet',
UNUSUALLY_HIGH_ACTIVITY: 'Usædvanligt høj aktivitet',
UNUSUALLY_LOW_ACTIVITY: 'Usædvanligt lav aktivitet'
},
tooltips: {
ballots: 'Afstemninger',
zScore: 'Z-score',
classification: 'Klassificering',
anomaly: 'ANOMALI',
na: 'Mangler',
quarter: 'Kvartal',
year: 'År'
},
chartLabels: {
ballotZScore: 'Afstemninger Z-score',
documentZScore: 'Dokument Z-score',
attendanceZScore: 'Fremmøde Z-score',
yearQuarter: 'År-Kvartal',
zScore: 'Z-score',
quarter: 'Kvartal',
averageBallots: 'Gennemsnitlige afstemninger',
year: 'År',
count: 'Antal',
changePercent: 'Ændring (%)',
qoqChange: 'KtK-ændring (%)',
anomaly: 'ANOMALI'
},
loading: 'Indlæser data...',
error: 'Fejl ved indlæsning af data. Prøv igen.',
dataAttribution: 'Data fra CIA-platformen'
},
// Additional languages with full translations
no: { title: 'Sesongmønstre (2002-2025)', subtitle: 'Kvartalsanalyse med Z-score anomalideteksjon', filters: { year: 'År', quarter: 'Kvartal', election: 'Valgstatus', classification: 'Aktivitetsklassifisering', allYears: 'Alle år', allQuarters: 'Alle kvartaler', allElections: 'Alle', electionYears: 'Valgår', nonElectionYears: 'Ikke-valgår', allClassifications: 'Alle klassifiseringer' }, quarters: { Q1: 'K1 - Vintersesjon', Q2: 'K2 - Vårsesjon', Q3: 'K3 - Sommerferie', Q4: 'K4 - Høstsesjon' }, charts: { heatmap: { title: 'Kvartalsaktivitet varmekart (2002-2025)', description: 'Avstemningsvolum etter år og kvartal med Z-score' }, zscore: { title: 'Z-score anomalideteksjon', description: 'Statistiske avvik (|Z| ≥ 2.0) markert i rødt' }, comparison: { title: 'Gjennomsnittlig aktivitet etter kvartal (alle år)', description: 'K1-K4 basislinjer med standardavviksbånd' }, classification: { title: 'Sesongmønster klassifisering', description: 'Fordeling av NORMAL, FORHØYET, REDUSERT, ANOMALI mønstre' }, qoq: { title: 'Kvartal-til-kvartal endringer', description: 'Sekvensielle avstemningsendringer (% og absolutt)' } }, classifications: { NORMAL_ACTIVITY: 'Normal aktivitet', ELEVATED_ACTIVITY: 'Forhøyet aktivitet', REDUCED_ACTIVITY: 'Redusert aktivitet', ANOMALY_DETECTED: 'Anomali oppdaget', NORMAL_SEASONAL_PATTERN: 'Normalt sesongmønster', Q3_SUMMER_LULL: 'K3 sommerferie', Q4_ELEVATED_ACTIVITY: 'K4 forhøyet aktivitet', UNUSUALLY_HIGH_ACTIVITY: 'Uvanlig høy aktivitet', UNUSUALLY_LOW_ACTIVITY: 'Uvanlig lav aktivitet' }, loading: 'Laster data...', error: 'Feil ved lasting av data. Prøv igjen.', dataAttribution: 'Data fra CIA-plattformen' },
fi: { title: 'Kausivaihtelut (2002-2025)', subtitle: 'Neljännesvuosi-analyysi Z-pisteiden poikkeamatunnistuksella', filters: { year: 'Vuosi', quarter: 'Kvartaali', election: 'Vaalitilanne', classification: 'Aktiviteettiluokitus', allYears: 'Kaikki vuodet', allQuarters: 'Kaikki kvartaalit', allElections: 'Kaikki', electionYears: 'Vaalivuodet', nonElectionYears: 'Ei-vaalivuodet', allClassifications: 'Kaikki luokitukset' }, quarters: { Q1: 'Q1 - Talviistunto', Q2: 'Q2 - Kevätistunto', Q3: 'Q3 - Kesätauko', Q4: 'Q4 - Syysistunto' }, charts: { heatmap: { title: 'Neljännesvuosi-aktiviteetti lämpökartta (2002-2025)', description: 'Äänestysvolyymi vuoden ja kvartaalin mukaan Z-pisteillä' }, zscore: { title: 'Z-piste poikkeamatunnistus', description: 'Tilastolliset poikkeamat (|Z| ≥ 2.0) merkitty punaisella' }, comparison: { title: 'Keskimääräinen aktiviteetti kvartaaleittain (kaikki vuodet)', description: 'Q1-Q4 perusviivat keskihajontakaistaleilla' }, classification: { title: 'Kausivaihtelujen luokittelu', description: 'NORMAALI, KOHONNUT, ALENTUNUT, POIKKEAMA -mallien jakauma' }, qoq: { title: 'Kvartaalista toiseen muutokset', description: 'Peräkkäiset äänestysmuutokset (% ja absoluuttinen)' } }, classifications: { NORMAL_ACTIVITY: 'Normaali aktiviteetti', ELEVATED_ACTIVITY: 'Kohonnut aktiviteetti', REDUCED_ACTIVITY: 'Alentunut aktiviteetti', ANOMALY_DETECTED: 'Poikkeama havaittu', NORMAL_SEASONAL_PATTERN: 'Normaali kausimalli', Q3_SUMMER_LULL: 'Q3 kesätauko', Q4_ELEVATED_ACTIVITY: 'Q4 kohonnut aktiviteetti', UNUSUALLY_HIGH_ACTIVITY: 'Epätavallisen korkea aktiviteetti', UNUSUALLY_LOW_ACTIVITY: 'Epätavallisen matala aktiviteetti' }, loading: 'Ladataan tietoja...', error: 'Virhe tietojen lataamisessa. Yritä uudelleen.', dataAttribution: 'Data CIA-alustalta' },
de: { title: 'Saisonale Muster (2002-2025)', subtitle: 'Quartalsanalyse mit Z-Score-Anomalieerkennung', filters: { year: 'Jahr', quarter: 'Quartal', election: 'Wahlstatus', classification: 'Aktivitätsklassifizierung', allYears: 'Alle Jahre', allQuarters: 'Alle Quartale', allElections: 'Alle', electionYears: 'Wahljahre', nonElectionYears: 'Nicht-Wahljahre', allClassifications: 'Alle Klassifizierungen' }, quarters: { Q1: 'Q1 - Wintersitzung', Q2: 'Q2 - Frühjahrssitzung', Q3: 'Q3 - Sommerpause', Q4: 'Q4 - Herbstsitzung' }, charts: { heatmap: { title: 'Quartalsaktivität Heatmap (2002-2025)', description: 'Abstimmungsvolumen nach Jahr und Quartal mit Z-Score' }, zscore: { title: 'Z-Score-Anomalieerkennung', description: 'Statistische Ausreißer (|Z| ≥ 2.0) rot markiert' }, comparison: { title: 'Durchschnittliche Aktivität nach Quartal (alle Jahre)', description: 'Q1-Q4 Basislinien mit Standardabweichungsbändern' }, classification: { title: 'Saisonale Musterklassifizierung', description: 'Verteilung von NORMAL, ERHÖHT, REDUZIERT, ANOMALIE Mustern' }, qoq: { title: 'Quartal-zu-Quartal Änderungen', description: 'Aufeinanderfolgende Abstimmungsänderungen (% und absolut)' } }, classifications: { NORMAL_ACTIVITY: 'Normale Aktivität', ELEVATED_ACTIVITY: 'Erhöhte Aktivität', REDUCED_ACTIVITY: 'Reduzierte Aktivität', ANOMALY_DETECTED: 'Anomalie erkannt', NORMAL_SEASONAL_PATTERN: 'Normales saisonales Muster', Q3_SUMMER_LULL: 'Q3 Sommerpause', Q4_ELEVATED_ACTIVITY: 'Q4 erhöhte Aktivität', UNUSUALLY_HIGH_ACTIVITY: 'Ungewöhnlich hohe Aktivität', UNUSUALLY_LOW_ACTIVITY: 'Ungewöhnlich niedrige Aktivität' }, loading: 'Daten werden geladen...', error: 'Fehler beim Laden der Daten. Bitte versuchen Sie es erneut.', dataAttribution: 'Daten von CIA-Plattform' },
fr: { title: 'Schémas saisonniers (2002-2025)', subtitle: 'Analyse trimestrielle avec détection d\'anomalies par score Z', filters: { year: 'Année', quarter: 'Trimestre', election: 'Statut électoral', classification: 'Classification d\'activité', allYears: 'Toutes les années', allQuarters: 'Tous les trimestres', allElections: 'Tous', electionYears: 'Années électorales', nonElectionYears: 'Années non-électorales', allClassifications: 'Toutes les classifications' }, quarters: { Q1: 'T1 - Session d\'hiver', Q2: 'T2 - Session de printemps', Q3: 'T3 - Pause estivale', Q4: 'T4 - Session d\'automne' }, charts: { heatmap: { title: 'Carte de chaleur d\'activité trimestrielle (2002-2025)', description: 'Volume de scrutins par année et trimestre avec score Z' }, zscore: { title: 'Détection d\'anomalies par score Z', description: 'Valeurs aberrantes statistiques (|Z| ≥ 2.0) marquées en rouge' }, comparison: { title: 'Activité moyenne par trimestre (toutes les années)', description: 'Lignes de base T1-T4 avec bandes d\'écart-type' }, classification: { title: 'Classification des schémas saisonniers', description: 'Distribution des schémas NORMAL, ÉLEVÉ, RÉDUIT, ANOMALIE' }, qoq: { title: 'Changements d\'un trimestre à l\'autre', description: 'Changements séquentiels de scrutins (% et absolu)' } }, classifications: { NORMAL_ACTIVITY: 'Activité normale', ELEVATED_ACTIVITY: 'Activité élevée', REDUCED_ACTIVITY: 'Activité réduite', ANOMALY_DETECTED: 'Anomalie détectée', NORMAL_SEASONAL_PATTERN: 'Schéma saisonnier normal', Q3_SUMMER_LULL: 'T3 pause estivale', Q4_ELEVATED_ACTIVITY: 'T4 activité élevée', UNUSUALLY_HIGH_ACTIVITY: 'Activité exceptionnellement élevée', UNUSUALLY_LOW_ACTIVITY: 'Activité exceptionnellement basse' }, loading: 'Chargement des données...', error: 'Erreur lors du chargement des données. Veuillez réessayer.', dataAttribution: 'Données de la plateforme CIA' },
es: { title: 'Patrones estacionales (2002-2025)', subtitle: 'Análisis trimestral con detección de anomalías por puntuación Z', filters: { year: 'Año', quarter: 'Trimestre', election: 'Estado electoral', classification: 'Clasificación de actividad', allYears: 'Todos los años', allQuarters: 'Todos los trimestres', allElections: 'Todos', electionYears: 'Años electorales', nonElectionYears: 'Años no electorales', allClassifications: 'Todas las clasificaciones' }, quarters: { Q1: 'T1 - Sesión de invierno', Q2: 'T2 - Sesión de primavera', Q3: 'T3 - Receso de verano', Q4: 'T4 - Sesión de otoño' }, charts: { heatmap: { title: 'Mapa de calor de actividad trimestral (2002-2025)', description: 'Volumen de votaciones por año y trimestre con puntuación Z' }, zscore: { title: 'Detección de anomalías por puntuación Z', description: 'Valores atípicos estadísticos (|Z| ≥ 2.0) marcados en rojo' }, comparison: { title: 'Actividad promedio por trimestre (todos los años)', description: 'Líneas base T1-T4 con bandas de desviación estándar' }, classification: { title: 'Clasificación de patrones estacionales', description: 'Distribución de patrones NORMAL, ELEVADO, REDUCIDO, ANOMALÍA' }, qoq: { title: 'Cambios de trimestre a trimestre', description: 'Cambios secuenciales de votaciones (% y absoluto)' } }, classifications: { NORMAL_ACTIVITY: 'Actividad normal', ELEVATED_ACTIVITY: 'Actividad elevada', REDUCED_ACTIVITY: 'Actividad reducida', ANOMALY_DETECTED: 'Anomalía detectada', NORMAL_SEASONAL_PATTERN: 'Patrón estacional normal', Q3_SUMMER_LULL: 'T3 receso de verano', Q4_ELEVATED_ACTIVITY: 'T4 actividad elevada', UNUSUALLY_HIGH_ACTIVITY: 'Actividad inusualmente alta', UNUSUALLY_LOW_ACTIVITY: 'Actividad inusualmente baja' }, loading: 'Cargando datos...', error: 'Error al cargar los datos. Por favor, inténtelo de nuevo.', dataAttribution: 'Datos de la plataforma CIA' },
nl: { title: 'Seizoenspatronen (2002-2025)', subtitle: 'Kwartaalanalyse met Z-score anomaliedetectie', filters: { year: 'Jaar', quarter: 'Kwartaal', election: 'Verkiezingsstatus', classification: 'Activiteitsclassificatie', allYears: 'Alle jaren', allQuarters: 'Alle kwartalen', allElections: 'Alle', electionYears: 'Verkiezingsjaren', nonElectionYears: 'Niet-verkiezingsjaren', allClassifications: 'Alle classificaties' }, quarters: { Q1: 'K1 - Wintersessie', Q2: 'K2 - Voorjaarssessie', Q3: 'K3 - Zomerpauze', Q4: 'K4 - Herfst sessie' }, charts: { heatmap: { title: 'Kwartaalactiviteit heatmap (2002-2025)', description: 'Stemvolume per jaar en kwartaal met Z-score' }, zscore: { title: 'Z-score anomaliedetectie', description: 'Statistische uitschieters (|Z| ≥ 2.0) gemarkeerd in rood' }, comparison: { title: 'Gemiddelde activiteit per kwartaal (alle jaren)', description: 'K1-K4 basislijnen met standaardafwijkingsbanden' }, classification: { title: 'Seizoenspatroon classificatie', description: 'Verdeling van NORMAAL, VERHOOGD, VERMINDERD, ANOMALIE patronen' }, qoq: { title: 'Kwartaal-op-kwartaal veranderingen', description: 'Opeenvolgende stemveranderingen (% en absoluut)' } }, classifications: { NORMAL_ACTIVITY: 'Normale activiteit', ELEVATED_ACTIVITY: 'Verhoogde activiteit', REDUCED_ACTIVITY: 'Verminderde activiteit', ANOMALY_DETECTED: 'Anomalie gedetecteerd', NORMAL_SEASONAL_PATTERN: 'Normaal seizoenspatroon', Q3_SUMMER_LULL: 'K3 zomerpauze', Q4_ELEVATED_ACTIVITY: 'K4 verhoogde activiteit', UNUSUALLY_HIGH_ACTIVITY: 'Ongewoon hoge activiteit', UNUSUALLY_LOW_ACTIVITY: 'Ongewoon lage activiteit' }, loading: 'Gegevens laden...', error: 'Fout bij het laden van gegevens. Probeer het opnieuw.', dataAttribution: 'Data van CIA-platform' },
ar: { title: 'الأنماط الموسمية (2002-2025)', subtitle: 'تحليل ربع سنوي مع كشف الشذوذ بالنقاط Z', filters: { year: 'السنة', quarter: 'الربع', election: 'حالة الانتخابات', classification: 'تصنيف النشاط', allYears: 'كل السنوات', allQuarters: 'كل الأرباع', allElections: 'الكل', electionYears: 'سنوات الانتخابات', nonElectionYears: 'سنوات بدون انتخابات', allClassifications: 'كل التصنيفات' }, quarters: { Q1: 'الربع 1 - جلسة الشتاء', Q2: 'الربع 2 - جلسة الربيع', Q3: 'الربع 3 - عطلة الصيف', Q4: 'الربع 4 - جلسة الخريف' }, charts: { heatmap: { title: 'خريطة حرارية للنشاط الفصلي (2002-2025)', description: 'حجم التصويت حسب السنة والربع مع نقاط Z' }, zscore: { title: 'كشف الشذوذ بالنقاط Z', description: 'القيم الشاذة الإحصائية (|Z| ≥ 2.0) مميزة بالأحمر' }, comparison: { title: 'متوسط النشاط حسب الربع (كل السنوات)', description: 'خطوط أساسية للربع 1-4 مع نطاقات الانحراف المعياري' }, classification: { title: 'تصنيف الأنماط الموسمية', description: 'توزيع الأنماط العادية والمرتفعة والمنخفضة والشاذة' }, qoq: { title: 'التغيرات من ربع لآخر', description: 'التغيرات المتسلسلة في التصويت (% ومطلق)' } }, classifications: { NORMAL_ACTIVITY: 'نشاط عادي', ELEVATED_ACTIVITY: 'نشاط مرتفع', REDUCED_ACTIVITY: 'نشاط منخفض', ANOMALY_DETECTED: 'شذوذ مكتشف', NORMAL_SEASONAL_PATTERN: 'نمط موسمي عادي', Q3_SUMMER_LULL: 'الربع 3 عطلة صيفية', Q4_ELEVATED_ACTIVITY: 'الربع 4 نشاط مرتفع', UNUSUALLY_HIGH_ACTIVITY: 'نشاط مرتفع بشكل غير عادي', UNUSUALLY_LOW_ACTIVITY: 'نشاط منخفض بشكل غير عادي' }, loading: 'جاري تحميل البيانات...', error: 'خطأ في تحميل البيانات. يرجى المحاولة مرة أخرى.', dataAttribution: 'البيانات من منصة CIA' },
he: { title: 'דפוסים עונתיים (2002-2025)', subtitle: 'ניתוח רבעוני עם זיהוי חריגות Z-Score', filters: { year: 'שנה', quarter: 'רבעון', election: 'סטטוס בחירות', classification: 'סיווג פעילות', allYears: 'כל השנים', allQuarters: 'כל הרבעונים', allElections: 'הכל', electionYears: 'שנות בחירות', nonElectionYears: 'שנים ללא בחירות', allClassifications: 'כל הסיווגים' }, quarters: { Q1: 'רבעון 1 - מושב חורף', Q2: 'רבעון 2 - מושב אביב', Q3: 'רבעון 3 - הפסקת קיץ', Q4: 'רבעון 4 - מושב סתיו' }, charts: { heatmap: { title: 'מפת חום של פעילות רבעונית (2002-2025)', description: 'נפח הצבעות לפי שנה ורבעון עם ציון Z' }, zscore: { title: 'זיהוי חריגות Z-Score', description: 'ערכים סטטיסטיים חריגים (|Z| ≥ 2.0) מסומנים באדום' }, comparison: { title: 'פעילות ממוצעת לפי רבעון (כל השנים)', description: 'קווי בסיס רבעון 1-4 עם רצועות סטיית תקן' }, classification: { title: 'סיווג דפוסים עונתיים', description: 'התפלגות דפוסים רגילים, מוגברים, מופחתים וחריגים' }, qoq: { title: 'שינויים מרבעון לרבעון', description: 'שינויים רציפים בהצבעה (% ומוחלט)' } }, classifications: { NORMAL_ACTIVITY: 'פעילות רגילה', ELEVATED_ACTIVITY: 'פעילות מוגברת', REDUCED_ACTIVITY: 'פעילות מופחתת', ANOMALY_DETECTED: 'חריגה זוהתה', NORMAL_SEASONAL_PATTERN: 'דפוס עונתי רגיל', Q3_SUMMER_LULL: 'רבעון 3 הפסקת קיץ', Q4_ELEVATED_ACTIVITY: 'רבעון 4 פעילות מוגברת', UNUSUALLY_HIGH_ACTIVITY: 'פעילות גבוהה במיוחד', UNUSUALLY_LOW_ACTIVITY: 'פעילות נמוכה במיוחד' }, loading: 'טוען נתונים...', error: 'שגיאה בטעינת נתונים. נסה שוב.', dataAttribution: 'נתונים מפלטפורמת CIA' },
ja: { title: '季節パターン (2002-2025)', subtitle: 'Zスコア異常検出を伴う四半期分析', filters: { year: '年', quarter: '四半期', election: '選挙状況', classification: '活動分類', allYears: 'すべての年', allQuarters: 'すべての四半期', allElections: 'すべて', electionYears: '選挙年', nonElectionYears: '非選挙年', allClassifications: 'すべての分類' }, quarters: { Q1: 'Q1 - 冬季会期', Q2: 'Q2 - 春季会期', Q3: 'Q3 - 夏季休会', Q4: 'Q4 - 秋季会期' }, charts: { heatmap: { title: '四半期活動ヒートマップ (2002-2025)', description: '年と四半期別の投票量とZスコア' }, zscore: { title: 'Zスコア異常検出', description: '統計的外れ値 (|Z| ≥ 2.0) を赤でマーク' }, comparison: { title: '四半期別平均活動（全年）', description: 'Q1-Q4のベースラインと標準偏差バンド' }, classification: { title: '季節パターン分類', description: '正常、上昇、減少、異常パターンの分布' }, qoq: { title: '四半期間の変化', description: '連続的な投票変化（%と絶対値）' } }, classifications: { NORMAL_ACTIVITY: '通常の活動', ELEVATED_ACTIVITY: '活動上昇', REDUCED_ACTIVITY: '活動減少', ANOMALY_DETECTED: '異常検出', NORMAL_SEASONAL_PATTERN: '通常の季節パターン', Q3_SUMMER_LULL: 'Q3夏季休会', Q4_ELEVATED_ACTIVITY: 'Q4活動上昇', UNUSUALLY_HIGH_ACTIVITY: '異常に高い活動', UNUSUALLY_LOW_ACTIVITY: '異常に低い活動' }, loading: 'データ読み込み中...', error: 'データの読み込みエラー。もう一度お試しください。', dataAttribution: 'CIAプラットフォームのデータ' },
ko: { title: '계절별 패턴 (2002-2025)', subtitle: 'Z점수 이상 탐지를 통한 분기별 분석', filters: { year: '년도', quarter: '분기', election: '선거 상태', classification: '활동 분류', allYears: '모든 연도', allQuarters: '모든 분기', allElections: '모두', electionYears: '선거 연도', nonElectionYears: '비선거 연도', allClassifications: '모든 분류' }, quarters: { Q1: '1분기 - 겨울 회기', Q2: '2분기 - 봄 회기', Q3: '3분기 - 여름 휴회', Q4: '4분기 - 가을 회기' }, charts: { heatmap: { title: '분기별 활동 히트맵 (2002-2025)', description: '연도 및 분기별 투표량과 Z점수' }, zscore: { title: 'Z점수 이상 탐지', description: '통계적 이상값 (|Z| ≥ 2.0)은 빨간색으로 표시' }, comparison: { title: '분기별 평균 활동 (모든 연도)', description: '1~4분기 기준선과 표준편차 밴드' }, classification: { title: '계절별 패턴 분류', description: '정상, 상승, 감소, 이상 패턴의 분포' }, qoq: { title: '분기별 변화', description: '순차적 투표 변화 (% 및 절대값)' } }, classifications: { NORMAL_ACTIVITY: '정상 활동', ELEVATED_ACTIVITY: '상승 활동', REDUCED_ACTIVITY: '감소 활동', ANOMALY_DETECTED: '이상 탐지', NORMAL_SEASONAL_PATTERN: '정상 계절 패턴', Q3_SUMMER_LULL: '3분기 여름 휴회', Q4_ELEVATED_ACTIVITY: '4분기 상승 활동', UNUSUALLY_HIGH_ACTIVITY: '비정상적으로 높은 활동', UNUSUALLY_LOW_ACTIVITY: '비정상적으로 낮은 활동' }, loading: '데이터 로딩 중...', error: '데이터 로딩 오류. 다시 시도해주세요.', dataAttribution: 'CIA 플랫폼의 데이터' },
zh: { title: '季节性模式 (2002-2025)', subtitle: '带Z分数异常检测的季度分析', filters: { year: '年份', quarter: '季度', election: '选举状态', classification: '活动分类', allYears: '所有年份', allQuarters: '所有季度', allElections: '全部', electionYears: '选举年', nonElectionYears: '非选举年', allClassifications: '所有分类' }, quarters: { Q1: '第1季度 - 冬季会期', Q2: '第2季度 - 春季会期', Q3: '第3季度 - 夏季休会', Q4: '第4季度 - 秋季会期' }, charts: { heatmap: { title: '季度活动热图 (2002-2025)', description: '按年份和季度的投票量与Z分数' }, zscore: { title: 'Z分数异常检测', description: '统计异常值 (|Z| ≥ 2.0) 标记为红色' }, comparison: { title: '按季度的平均活动（所有年份）', description: '第1-4季度基线与标准差带' }, classification: { title: '季节性模式分类', description: '正常、升高、降低、异常模式的分布' }, qoq: { title: '季度环比变化', description: '连续投票变化（%和绝对值）' } }, classifications: { NORMAL_ACTIVITY: '正常活动', ELEVATED_ACTIVITY: '活动升高', REDUCED_ACTIVITY: '活动降低', ANOMALY_DETECTED: '检测到异常', NORMAL_SEASONAL_PATTERN: '正常季节性模式', Q3_SUMMER_LULL: '第3季度夏季休会', Q4_ELEVATED_ACTIVITY: '第4季度活动升高', UNUSUALLY_HIGH_ACTIVITY: '异常高的活动', UNUSUALLY_LOW_ACTIVITY: '异常低的活动' }, loading: '加载数据中...', error: '加载数据出错。请重试。', dataAttribution: '数据来自CIA平台' }
};
// ============================================================================
// Data Manager
// ============================================================================
class SeasonalPatternsDataManager {
constructor() {
this.data = null;
this.cachedData = null;
}
/**
* Fetch data from CIA platform with 24-hour caching
* Implements local-first loading: tries local file, then remote fallback
*/
async fetchData() {
try {
// Check cache first
const cached = this.getCachedData();
if (cached) {
console.log('Using cached seasonal patterns data');
this.data = cached;
return cached;
}
// Try each URL in sequence (local first, then remote)
let lastError = null;
for (let i = 0; i < CONFIG.dataUrls.length; i++) {
const url = CONFIG.dataUrls[i];
const isLocal = !url.startsWith('http');
try {
console.log(`Fetching seasonal patterns data from ${isLocal ? 'local' : 'remote'} source (${i + 1}/${CONFIG.dataUrls.length})...`);
const response = await fetch(url);
if (!response.ok) {
throw new Error(`HTTP ${response.status}: ${response.statusText}`);
}
const csvText = await response.text();
const parsedData = this.parseCSV(csvText);
// Cache the data
this.setCachedData(parsedData);
this.data = parsedData;
console.log(`✅ Loaded ${parsedData.length} seasonal activity records from ${isLocal ? 'local' : 'remote'} source`);
return parsedData;
} catch (error) {
lastError = error;
console.warn(`Failed to load from ${isLocal ? 'local' : 'remote'} source: ${error.message}`);
// Continue to next URL
}
}
// All URLs failed
throw lastError || new Error('All data sources failed');
} catch (error) {
console.error('Error fetching seasonal patterns data:', error);
// Try to use cached data even if expired
const cached = localStorage.getItem(CONFIG.cacheKey);
if (cached) {
try {
const parsed = JSON.parse(cached);
if (parsed && typeof parsed === 'object' && 'data' in parsed) {
console.log('Using expired cache as fallback');
this.data = parsed.data;
return parsed.data;
} else {
console.warn('Expired cache is in an unexpected format, ignoring.');
}
} catch (parseError) {
console.warn('Failed to parse expired cache, ignoring.', parseError);
}
}
throw error;
}
}
/**
* Parse CSV data using PapaParse (if available) or fallback parser
*/
parseCSV(csvText) {
if (typeof Papa !== 'undefined') {
const parsed = Papa.parse(csvText, {
header: true,
dynamicTyping: true,
skipEmptyLines: true
});
return parsed.data;
} else {
// Fallback CSV parser
return this.parseCSVFallback(csvText);
}
}
/**
* Fallback CSV parser (if PapaParse is not available)
* Uses d3.csvParse when available (handles RFC 4180 quoted fields),
* otherwise falls back to a minimal parser.
*/
parseCSVFallback(csvText) {
// Prefer d3.csvParse if D3 is available (handles RFC 4180 properly)
if (typeof d3 !== 'undefined' && typeof d3.csvParse === 'function') {
try {
const parsed = d3.csvParse(csvText, (d) => {
const row = {};
Object.keys(d).forEach((key) => {
const value = d[key];
// Only coerce if the value is purely numeric (no letters, no dashes except leading minus)
// This prevents "2022-2026" from becoming 2022
if (typeof value === 'string') {
// Match pattern: optional minus, followed by digits, optional decimal point and more digits
if (/^-?\d+(\.\d+)?$/.test(value.trim())) {
row[key] = parseFloat(value);
} else {
row[key] = value;
}
} else {
row[key] = value;
}
});
return row;
});
return parsed;
} catch (err) {
console.warn('d3.csvParse failed, using basic parser:', err);
}
}
// Minimal fallback parser (does not handle quoted fields with commas)
console.warn('Using basic CSV parser - quoted fields with commas may not parse correctly');
const lines = csvText.trim().split('\n');
const headers = lines[0].split(',').map(h => h.trim().replace(/^"|"$/g, ''));
const data = [];
for (let i = 1; i < lines.length; i++) {
const values = lines[i].split(',').map(v => v.trim().replace(/^"|"$/g, ''));
const row = {};
headers.forEach((header, index) => {
const value = values[index];
// Only coerce if purely numeric
if (/^-?\d+(\.\d+)?$/.test(value)) {
row[header] = parseFloat(value);
} else {
row[header] = value;
}
});
data.push(row);
}
return data;
}
/**
* Get cached data from LocalStorage
*/
getCachedData() {
try {
const cached = localStorage.getItem(CONFIG.cacheKey);
if (!cached) return null;
const parsed = JSON.parse(cached);
const now = Date.now();
if (now - parsed.timestamp > CONFIG.cacheDuration) {
console.log('Cache expired');
return null;
}
return parsed.data;
} catch (error) {
console.error('Error reading cache:', error);
return null;
}
}
/**
* Save data to LocalStorage cache
*/
setCachedData(data) {
try {
const cacheObject = {
data: data,
timestamp: Date.now()
};
localStorage.setItem(CONFIG.cacheKey, JSON.stringify(cacheObject));
console.log('Data cached successfully');
} catch (error) {
console.error('Error caching data:', error);
}
}
/**
* Aggregate data by quarter (cross-year averages)
*/
aggregateByQuarter() {
if (!this.data) return null;
const quarters = { Q1: [], Q2: [], Q3: [], Q4: [] };
this.data.forEach(row => {
const quarter = `Q${row.quarter}`;
if (quarters[quarter]) {
quarters[quarter].push(row);
}
});
const aggregated = {};
Object.keys(quarters).forEach(q => {
const records = quarters[q];
if (records.length === 0) return;
const ballots = records.map(r => r.total_ballots || 0);
const attendance = records.map(r => r.attendance_rate || 0);
const docs = records.map(r => r.documents_produced || 0);
aggregated[q] = {
quarter: q,
avgBallots: this.mean(ballots),
stddevBallots: this.stddev(ballots),
avgAttendance: this.mean(attendance),
stddevAttendance: this.stddev(attendance),
avgDocs: this.mean(docs),
stddevDocs: this.stddev(docs),
count: records.length
};
});
return aggregated;
}
/**
* Identify anomalies (|Z-score| >= threshold)
*/
identifyAnomalies(threshold = CONFIG.zScoreThreshold) {
if (!this.data) return [];
const anomalies = this.data.filter(row => {
const ballotZ = Math.abs(row.ballot_z_score || 0);
const docZ = Math.abs(row.doc_z_score || 0);
const attendanceZ = Math.abs(row.attendance_z_score || 0);
return ballotZ >= threshold || docZ >= threshold || attendanceZ >= threshold;
});
// Sort by maximum Z-score (descending)
anomalies.sort((a, b) => {
const maxZa = Math.max(
Math.abs(a.ballot_z_score || 0),
Math.abs(a.doc_z_score || 0),
Math.abs(a.attendance_z_score || 0)
);
const maxZb = Math.max(
Math.abs(b.ballot_z_score || 0),
Math.abs(b.doc_z_score || 0),
Math.abs(b.attendance_z_score || 0)
);
return maxZb - maxZa;
});
return anomalies;
}
/**
* Calculate mean of an array
*/
mean(arr) {
if (arr.length === 0) return 0;
return arr.reduce((sum, val) => sum + val, 0) / arr.length;
}
/**
* Calculate standard deviation of an array
*/
stddev(arr) {
if (arr.length === 0) return 0;
const avg = this.mean(arr);
const squareDiffs = arr.map(val => Math.pow(val - avg, 2));
const avgSquareDiff = this.mean(squareDiffs);
return Math.sqrt(avgSquareDiff);
}
/**
* Filter data by criteria
*/
filterData(filters) {
if (!this.data) return [];
return this.data.filter(row => {
if (filters.year && filters.year !== 'all' && row.year !== parseInt(filters.year)) {
return false;
}
if (filters.quarter && filters.quarter !== 'all' && row.quarter !== parseInt(filters.quarter)) {
return false;
}
if (filters.election && filters.election !== 'all') {
const isElection = row.is_election_year === 't' || row.is_election_year === true;
if (filters.election === 'election' && !isElection) return false;
if (filters.election === 'non-election' && isElection) return false;
}
if (filters.classification && filters.classification !== 'all') {
if (row.base_activity_classification !== filters.classification &&
row.seasonal_pattern_classification !== filters.classification) {
return false;
}
}
return true;
});
}
}
// ============================================================================
// Chart Renderers
// ============================================================================
class SeasonalPatternsCharts {
constructor(dataManager, language = 'en') {
this.dataManager = dataManager;
this.language = language;
this.translations = TRANSLATIONS[language] || TRANSLATIONS.en;
this.chartInstances = {};
}
/**
* Destroy all chart instances
*/
destroyCharts() {
Object.keys(this.chartInstances).forEach(key => {
if (this.chartInstances[key]) {
this.chartInstances[key].destroy();
delete this.chartInstances[key];
}
});
}
/**
* Render all charts
*/
async renderAll(filteredData = null) {
const data = filteredData || this.dataManager.data;
if (!data || data.length === 0) {
console.warn('No data available for rendering');
return;
}
this.destroyCharts();
// Render each chart
this.renderSeasonalHeatmap(data);
this.renderZScoreTimeline(data);
this.renderQuarterComparison(data);
this.renderClassificationChart(data);
this.renderQoQChangeChart(data);
}
/**
* Render seasonal heat map using D3.js
*/
renderSeasonalHeatmap(data) {
const container = document.getElementById('seasonal-heatmap');
if (!container || typeof d3 === 'undefined') {
console.warn('D3.js not loaded or container not found');
return;
}
// Clear container
container.innerHTML = '';
// Dimensions
const margin = { top: 40, right: 100, bottom: 60, left: 60 };
const width = Math.min(container.clientWidth, 1200) - margin.left - margin.right;
const height = 600 - margin.top - margin.bottom;
// Create SVG
const svg = d3.select(container)
.append('svg')
.attr('width', width + margin.left + margin.right)
.attr('height', height + margin.top + margin.bottom)
.attr('role', 'img')
.attr('aria-label', this.translations.charts.heatmap.title)
.append('g')
.attr('transform', `translate(${margin.left},${margin.top})`);
// Get unique years and quarters
const years = [...new Set(data.map(d => d.year))].sort();
const quarters = [1, 2, 3, 4];
// Scales
const xScale = d3.scaleBand()
.domain(quarters)
.range([0, width])
.padding(0.05);
const yScale = d3.scaleBand()
.domain(years)
.range([0, height])
.padding(0.05);
// Color scale for ballots
const maxBallots = d3.max(data, d => d.total_ballots || 0);
const colorScale = d3.scaleSequential()
.domain([0, maxBallots])
.interpolator(d3.interpolateYlOrRd);
// Create heat map cells
svg.selectAll('.cell')
.data(data)
.enter()
.append('rect')
.attr('class', 'cell')
.attr('x', d => xScale(d.quarter))
.attr('y', d => yScale(d.year))
.attr('width', xScale.bandwidth())
.attr('height', yScale.bandwidth())
.attr('fill', d => colorScale(d.total_ballots || 0))
.attr('stroke', '#fff')
.attr('stroke-width', 1)
.attr('role', 'presentation')
.on('mouseover', function(event, d) {
// Tooltip
d3.select(this).attr('stroke', '#000').attr('stroke-width', 2);
})
.on('mouseout', function() {
d3.select(this).attr('stroke', '#fff').attr('stroke-width', 1);
})
.append('title')
.text(d => {
const t = this.translations;
const classText = t.classifications[d.seasonal_pattern_classification] || t.classifications[d.base_activity_classification] || d.seasonal_pattern_classification || t.tooltips.na;
return `${d.year} Q${d.quarter}\n${t.tooltips.ballots}: ${d.total_ballots}\n${t.tooltips.zScore}: ${(d.ballot_z_score || 0).toFixed(2)}\n${t.tooltips.classification}: ${classText}`;
});
// Add anomaly markers
const anomalies = data.filter(d => Math.abs(d.ballot_z_score || 0) >= CONFIG.zScoreThreshold);
svg.selectAll('.anomaly-marker')
.data(anomalies)
.enter()
.append('circle')
.attr('class', 'anomaly-marker')
.attr('cx', d => xScale(d.quarter) + xScale.bandwidth() / 2)
.attr('cy', d => yScale(d.year) + yScale.bandwidth() / 2)
.attr('r', 8)
.attr('fill', CONFIG.colors.danger)
.attr('stroke', '#fff')
.attr('stroke-width', 2)
.attr('role', 'presentation')
.append('title')
.text(d => {
const t = this.translations;
return `${t.tooltips.anomaly}: ${d.year} Q${d.quarter}\n${t.tooltips.zScore}: ${(d.ballot_z_score || 0).toFixed(2)}`;
});
// Add axes
const xAxis = d3.axisBottom(xScale)
.tickFormat(q => `Q${q}`);
const yAxis = d3.axisLeft(yScale);
svg.append('g')
.attr('transform', `translate(0,${height})`)
.call(xAxis)
.attr('class', 'axis');
svg.append('g')
.call(yAxis)
.attr('class', 'axis');
// Add axis labels
const quarterLabel = this.translations.filters?.quarter || 'Quarter';
const yearLabel = this.translations.filters?.year || 'Year';
svg.append('text')
.attr('x', width / 2)
.attr('y', height + 40)
.attr('text-anchor', 'middle')
.text(quarterLabel)
.style('font-size', '14px')
.style('font-weight', '500');
svg.append('text')
.attr('transform', 'rotate(-90)')
.attr('x', -height / 2)
.attr('y', -40)
.attr('text-anchor', 'middle')
.text(yearLabel)
.style('font-size', '14px')
.style('font-weight', '500');
// Add legend
const legendHeight = 10;
const legend = svg.append('g')
.attr('transform', `translate(${width + 20}, 0)`);
const legendScale = d3.scaleLinear()
.domain([0, maxBallots])
.range([0, legendHeight * 20]);
const legendAxis = d3.axisRight(legendScale)
.ticks(5);
// Legend gradient
const defs = svg.append('defs');
const gradient = defs.append('linearGradient')
.attr('id', 'legend-gradient')
.attr('x1', '0%')
.attr('y1', '100%')
.attr('x2', '0%')
.attr('y2', '0%');
gradient.selectAll('stop')
.data(d3.range(0, 1.1, 0.1))
.enter()
.append('stop')
.attr('offset', d => `${d * 100}%`)
.attr('stop-color', d => colorScale(d * maxBallots));
legend.append('rect')
.attr('width', legendHeight)
.attr('height', legendHeight * 20)
.style('fill', 'url(#legend-gradient)');
legend.append('g')
.attr('transform', `translate(${legendHeight}, 0)`)
.call(legendAxis);
legend.append('text')
.attr('x', 0)
.attr('y', -10)
.text(this.translations.tooltips.ballots)
.style('font-size', '12px')
.style('font-weight', '500');
}
/**
* Render Z-score timeline using Chart.js
*/
renderZScoreTimeline(data) {
const canvas = document.getElementById('zscore-timeline-chart');
if (!canvas || typeof Chart === 'undefined') {
console.warn('Chart.js not loaded or canvas not found');
return;
}
const ctx = canvas.getContext('2d');
const t = this.translations.chartLabels;
// Sort data by year and quarter
const sortedData = [...data].sort((a, b) => {
if (a.year !== b.year) return a.year - b.year;
return a.quarter - b.quarter;
});
const labels = sortedData.map(d => `${d.year}-Q${d.quarter}`);
const ballotZScores = sortedData.map(d => d.ballot_z_score || 0);
const docZScores = sortedData.map(d => d.doc_z_score || 0);
const attendanceZScores = sortedData.map(d => d.attendance_z_score || 0);
this.chartInstances.zscore = new Chart(ctx, {
type: 'line',
data: {
labels: labels,
datasets: [
{
label: t.ballotZScore,
data: ballotZScores,
borderColor: CONFIG.colors.primary,
backgroundColor: CONFIG.colors.primary + '40',
borderWidth: 2,
pointRadius: 3,
pointHoverRadius: 5,
tension: 0.1
},
{
label: t.documentZScore,
data: docZScores,
borderColor: CONFIG.colors.secondary,
backgroundColor: CONFIG.colors.secondary + '40',
borderWidth: 2,
pointRadius: 3,
pointHoverRadius: 5,
tension: 0.1
},
{
label: t.attendanceZScore,
data: attendanceZScores,
borderColor: CONFIG.colors.tertiary,
backgroundColor: CONFIG.colors.tertiary + '40',
borderWidth: 2,
pointRadius: 3,
pointHoverRadius: 5,
tension: 0.1
}
]
},
options: {
responsive: true,
maintainAspectRatio: false,
plugins: {
title: {
display: false
},
legend: {
position: 'top',
labels: {
boxWidth: 12,
padding: 15
}
},
tooltip: {
callbacks: {
label: function(context) {
let label = context.dataset.label || '';
if (label) {
label += ': ';
}
label += context.parsed.y.toFixed(2);
const absZ = Math.abs(context.parsed.y);
if (absZ >= CONFIG.zScoreThreshold) {
label += ' 🔴 ' + t.anomaly;
}
return label;
}
}
}
},
scales: {
x: {
display: true,
title: {
display: true,
text: t.yearQuarter
},
ticks: {
maxRotation: 90,
minRotation: 45,
autoSkip: true,
maxTicksLimit: 20
}
},
y: {
display: true,
title: {
display: true,
text: t.zScore
},
min: -4,
max: 4
}
}
}
});
}
/**
* Render quarter comparison chart using Chart.js
*/
renderQuarterComparison(data) {
const canvas = document.getElementById('quarter-comparison-chart');
if (!canvas || typeof Chart === 'undefined') {
console.warn('Chart.js not loaded or canvas not found');
return;
}
const ctx = canvas.getContext('2d');
// Aggregate the provided filtered data
const aggregated = this.aggregateDataByQuarter(data || this.dataManager.data);
if (!aggregated) {
console.warn('No aggregated data available');
return;
}
const labels = ['Q1', 'Q2', 'Q3', 'Q4'];
const avgBallots = labels.map(q => aggregated[q]?.avgBallots || 0);
const stddevBallots = labels.map(q => aggregated[q]?.stddevBallots || 0);
this.chartInstances.comparison = new Chart(ctx, {
type: 'bar',
data: {
labels: labels.map(q => this.translations.quarters[q] || q),
datasets: [
{
label: this.translations.charts?.comparison?.title || 'Average Ballots',
data: avgBallots,
backgroundColor: labels.map(q => CONFIG.quarterColors[q]),
borderColor: labels.map(q => CONFIG.quarterColors[q]),
borderWidth: 2
}
]
},
options: {
responsive: true,
maintainAspectRatio: false,
plugins: {
title: {
display: false
},
legend: {
display: false
},
tooltip: {
callbacks: {
label: function(context) {
const avg = context.parsed.y;
const stddev = stddevBallots[context.dataIndex];
return [
`Average: ${avg.toFixed(1)} ballots`,
`Std Dev: ±${stddev.toFixed(1)}`
];
}
}
}
},
scales: {
x: {
display: true,
title: {
display: true,
text: this.translations.chartLabels.quarter
}
},
y: {
display: true,
title: {
display: true,
text: this.translations.chartLabels.averageBallots
},
beginAtZero: true
}
}
}
});
}
/**
* Aggregate data by quarter (for filtered datasets)
*/
aggregateDataByQuarter(data) {
if (!data) return null;
const quarters = { Q1: [], Q2: [], Q3: [], Q4: [] };
data.forEach(row => {
const quarter = `Q${row.quarter}`;
if (quarters[quarter]) {
quarters[quarter].push(row);
}
});
const aggregated = {};
Object.keys(quarters).forEach(q => {
const records = quarters[q];
if (records.length === 0) return;
const ballots = records.map(r => r.total_ballots || 0);
const attendance = records.map(r => r.attendance_rate || 0);
const docs = records.map(r => r.documents_produced || 0);
aggregated[q] = {
quarter: q,
avgBallots: this.mean(ballots),
stddevBallots: this.stddev(ballots),
avgAttendance: this.mean(attendance),
stddevAttendance: this.stddev(attendance),
avgDocs: this.mean(docs),
stddevDocs: this.stddev(docs),
count: records.length
};
});
return aggregated;
}
/**
* Calculate mean of an array
*/
mean(arr) {
if (!arr || arr.length === 0) return 0;
return arr.reduce((sum, val) => sum + val, 0) / arr.length;
}
/**
* Calculate standard deviation of an array
*/
stddev(arr) {
if (!arr || arr.length === 0) return 0;
const avg = this.mean(arr);
const squareDiffs = arr.map(val => Math.pow(val - avg, 2));
return Math.sqrt(this.mean(squareDiffs));
}
/**
* Render classification distribution chart using Chart.js
*/
renderClassificationChart(data) {
const canvas = document.getElementById('classification-chart');
if (!canvas || typeof Chart === 'undefined') {
console.warn('Chart.js not loaded or canvas not found');
return;
}
const ctx = canvas.getContext('2d');
// Count classifications by year
const years = [...new Set(data.map(d => d.year))].sort();
const classifications = {};
data.forEach(row => {
const classification = row.seasonal_pattern_classification || 'UNKNOWN';
if (!classifications[classification]) {
classifications[classification] = {};
}
if (!classifications[classification][row.year]) {
classifications[classification][row.year] = 0;
}
classifications[classification][row.year]++;
});
const datasets = Object.keys(classifications).map(classification => {
const counts = years.map(year => classifications[classification][year] || 0);
let color;
if (classification.includes('NORMAL')) {
color = CONFIG.colors.normal;
} else if (classification.includes('ELEVATED') || classification.includes('HIGH')) {
color = CONFIG.colors.elevated;
} else if (classification.includes('REDUCED') || classification.includes('LOW')) {
color = CONFIG.colors.reduced;
} else if (classification.includes('ANOMALY')) {
color = CONFIG.colors.anomaly;
} else {
color = CONFIG.colors.info;
}
return {
label: this.translations.classifications[classification] || classification,
data: counts,
backgroundColor: color,
borderColor: color,
borderWidth: 1
};
});
this.chartInstances.classification = new Chart(ctx, {
type: 'bar',
data: {
labels: years,
datasets: datasets
},
options: {
responsive: true,
maintainAspectRatio: false,
plugins: {
title: {
display: false
},
legend: {
position: 'top'
},
tooltip: {
mode: 'index',
intersect: false
}
},
scales: {
x: {
stacked: true,
display: true,
title: {
display: true,
text: this.translations.chartLabels.year
}
},
y: {
stacked: true,
display: true,
title: {
display: true,
text: this.translations.chartLabels.count
},
beginAtZero: true
}
}
}
});
}
/**
* Render QoQ change waterfall chart using Chart.js
*/
renderQoQChangeChart(data) {
const canvas = document.getElementById('qoq-change-chart');
if (!canvas || typeof Chart === 'undefined') {
console.warn('Chart.js not loaded or canvas not found');
return;
}
const ctx = canvas.getContext('2d');
// Sort data and filter those with QoQ change data
const sortedData = [...data]
.filter(d => d.qoq_ballot_change_pct !== null && d.qoq_ballot_change_pct !== undefined)
.sort((a, b) => {
if (a.year !== b.year) return a.year - b.year;
return a.quarter - b.quarter;
});
const labels = sortedData.map(d => `${d.year}-Q${d.quarter}`);
const changes = sortedData.map(d => d.qoq_ballot_change_pct || 0);
// Color by positive/negative
const colors = changes.map(change => {
if (change > 0) return CONFIG.colors.success;
if (change < 0) return CONFIG.colors.danger;
return CONFIG.colors.info;
});
this.chartInstances.qoq = new Chart(ctx, {
type: 'bar',
data: {
labels: labels,
datasets: [
{
label: this.translations.chartLabels.qoqChange,
data: changes,
backgroundColor: colors,
borderColor: colors,
borderWidth: 1
}
]
},
options: {
responsive: true,
maintainAspectRatio: false,
plugins: {
title: {
display: false
},
legend: {
display: false
},
tooltip: {
callbacks: {
label: function(context) {
return `Change: ${context.parsed.y.toFixed(2)}%`;
}
}
}
},
scales: {
x: {
display: true,
title: {
display: true,
text: this.translations.chartLabels.yearQuarter
},
ticks: {
maxRotation: 90,
minRotation: 45,
autoSkip: true,
maxTicksLimit: 20
}
},
y: {
display: true,
title: {
display: true,
text: this.translations.chartLabels.changePercent
}
}
}
}
});
}
}
// ============================================================================
// Dashboard Controller
// ============================================================================
class SeasonalPatternsDashboard {
constructor() {
this.dataManager = new SeasonalPatternsDataManager();
this.chartRenderer = null;
this.currentLanguage = this.detectLanguage();
// Initialize translations with fallback to English
this.translations = TRANSLATIONS[this.currentLanguage] || TRANSLATIONS.en;
// Ensure tooltips exist (fallback to English if missing)
if (!this.translations.tooltips) {
this.translations.tooltips = TRANSLATIONS.en.tooltips;
}
// Ensure chartLabels exist (fallback to English if missing)
if (!this.translations.chartLabels) {
this.translations.chartLabels = TRANSLATIONS.en.chartLabels;
}
this.currentFilters = {
year: 'all',
quarter: 'all',
election: 'all',
classification: 'all'
};
}
/**
* Detect current language from URL
*/
detectLanguage() {
const path = window.location.pathname;
const match = path.match(/index_([a-z]{2})\.html/);
if (match) {
return match[1];
}
return 'en';
}
/**
* Initialize dashboard
*/
async initialize() {
try {
// Show loading state
this.showLoading();
// Fetch data
await this.dataManager.fetchData();
// Initialize chart renderer
this.chartRenderer = new SeasonalPatternsCharts(this.dataManager, this.currentLanguage);
// Setup filters
this.setupFilters();
// Render charts
await this.chartRenderer.renderAll();
// Hide loading state
this.hideLoading();
console.log('Seasonal Patterns Dashboard initialized successfully');
} catch (error) {
console.error('Error initializing dashboard:', error);
this.showError();
}
}
/**
* Setup filter controls
*/
setupFilters() {
const yearFilter = document.getElementById('year-filter');
const quarterFilter = document.getElementById('quarter-filter');
const electionFilter = document.getElementById('election-filter');
const classificationFilter = document.getElementById('classification-filter');
if (yearFilter) {
yearFilter.addEventListener('change', (e) => {
this.currentFilters.year = e.target.value;
this.applyFilters();
});
// Populate year options
const years = [...new Set(this.dataManager.data.map(d => d.year))].sort((a, b) => b - a);
years.forEach(year => {
const option = document.createElement('option');
option.value = year;
option.textContent = year;
yearFilter.appendChild(option);
});
}
if (quarterFilter) {
quarterFilter.addEventListener('change', (e) => {
this.currentFilters.quarter = e.target.value;
this.applyFilters();
});
}
if (electionFilter) {
electionFilter.addEventListener('change', (e) => {
this.currentFilters.election = e.target.value;
this.applyFilters();
});
}
if (classificationFilter) {
classificationFilter.addEventListener('change', (e) => {
this.currentFilters.classification = e.target.value;
this.applyFilters();
});
// Populate classification options from both seasonal and base activity classifications
const classificationSet = new Set();
this.dataManager.data.forEach(d => {
if (d.seasonal_pattern_classification) {
classificationSet.add(d.seasonal_pattern_classification);
}
if (d.base_activity_classification) {
classificationSet.add(d.base_activity_classification);
}
});
const classifications = [...classificationSet].sort();
classifications.forEach(classification => {
const option = document.createElement('option');
option.value = classification;
const translatedLabel = this.translations.classifications?.[classification]
|| TRANSLATIONS.en?.classifications?.[classification]
|| classification;
option.textContent = translatedLabel;
classificationFilter.appendChild(option);
});
}
}
/**
* Apply filters and re-render charts
*/
async applyFilters() {
const filteredData = this.dataManager.filterData(this.currentFilters);
await this.chartRenderer.renderAll(filteredData);
}
/**
* Show loading state
*/
showLoading() {
const container = document.getElementById('seasonal-patterns-dashboard');
if (container) {
container.classList.add('loading');
container.setAttribute('aria-busy', 'true');
// Add screen reader announcement
const loadingMsg = document.createElement('div');
loadingMsg.setAttribute('role', 'status');
loadingMsg.setAttribute('aria-live', 'polite');
loadingMsg.className = 'sr-only';
loadingMsg.textContent = this.translations.loading || 'Loading data...';
container.prepend(loadingMsg);
}
}
/**
* Hide loading state
*/
hideLoading() {
const container = document.getElementById('seasonal-patterns-dashboard');
if (container) {
container.classList.remove('loading');
container.removeAttribute('aria-busy');
// Remove screen reader loading message
const loadingMsg = container.querySelector('[role="status"]');
if (loadingMsg) {
loadingMsg.remove();
}
}
}
/**
* Show error message
*/
showError() {
const container = document.getElementById('seasonal-patterns-dashboard');
if (container) {
// Clear existing content
while (container.firstChild) {
container.removeChild(container.firstChild);
}
// Create error elements using DOM methods
const errorWrapper = document.createElement('div');
errorWrapper.className = 'error-message';
errorWrapper.setAttribute('role', 'alert');
const errorText = document.createElement('p');
const message = this.translations.error || TRANSLATIONS.en.error;
errorText.textContent = `⚠️ ${message}`;
errorWrapper.appendChild(errorText);
container.appendChild(errorWrapper);
}
}
}
// ============================================================================
// Initialize on DOM ready
// ============================================================================
if (document.readyState === 'loading') {
document.addEventListener('DOMContentLoaded', initDashboard);
} else {
initDashboard();
}
function initDashboard() {
const dashboardContainer = document.getElementById('seasonal-patterns-dashboard');
if (dashboardContainer) {
const dashboard = new SeasonalPatternsDashboard();
dashboard.initialize();
}
}
})();