{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Hawksight-AI/semantica/blob/main/cookbook/use_cases/renewable_energy/01_Energy_Market_Analysis.ipynb)\n",
    "\n",
    "# Energy Market Analysis Pipeline\n",
    "\n",
    "## Overview\n",
    "\n",
    "This notebook demonstrates a complete energy market analysis pipeline: ingest energy market data from multiple sources (energy APIs, market data streams, databases), extract energy entities, build temporal market knowledge graph, analyze pricing trends, and predict market movements.\n",
    "\n",
    "\n",
    "**Documentation**: [API Reference](https://semantica.readthedocs.io/use-cases/)\n",
    "\n",
    "## Installation\n",
    "\n",
    "Install Semantica from PyPI:\n",
    "\n",
    "```bash\n",
    "pip install semantica\n",
    "# Or with all optional dependencies:\n",
    "pip install semantica[all]\n",
    "```\n",
    "\n",
    "### Modules Used (20+)\n",
    "\n",
    "- **Ingestion**: FileIngestor, WebIngestor, FeedIngestor, StreamIngestor, DBIngestor, EmailIngestor, RepoIngestor, MCPIngestor\n",
    "- **Parsing**: JSONParser, CSVParser, StructuredDataParser\n",
    "- **Extraction**: NERExtractor, RelationExtractor, EventDetector, SemanticAnalyzer\n",
    "- **KG**: GraphBuilder, TemporalGraphQuery, TemporalPatternDetector, GraphAnalyzer\n",
    "- **Analytics**: CentralityCalculator, CommunityDetector, ConnectivityAnalyzer\n",
    "- **Reasoning**: InferenceEngine, RuleManager, ExplanationGenerator\n",
    "- **Quality**: KGQualityAssessor\n",
    "- **Export**: JSONExporter, CSVExporter, RDFExporter, ReportGenerator\n",
    "- **Visualization**: KGVisualizer, TemporalVisualizer, AnalyticsVisualizer\n",
    "\n",
    "### Pipeline\n",
    "\n",
    "**Energy Market Sources → Parse → Extract Entities → Build Temporal Market KG → Analyze Pricing → Predict Trends → Generate Reports → Visualize**\n",
    "\n",
    "---\n",
    "\n",
    "## Step 1: Ingest Energy Market Data from Multiple Sources\n",
    "\n",
    "Ingest energy market data from energy APIs, market data streams, and databases.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from semantica.ingest import WebIngestor, StreamIngestor, DBIngestor, FeedIngestor, FileIngestor\n",
    "from semantica.parse import JSONParser, CSVParser, StructuredDataParser\n",
    "from semantica.semantic_extract import NERExtractor, RelationExtractor, EventDetector, SemanticAnalyzer\n",
    "from semantica.kg import GraphBuilder, TemporalGraphQuery, TemporalPatternDetector, GraphAnalyzer\n",
    "from semantica.kg import CentralityCalculator, CommunityDetector, ConnectivityAnalyzer\n",
    "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n",
    
    "from semantica.export import JSONExporter, CSVExporter, RDFExporter, ReportGenerator\n",
    "from semantica.visualization import KGVisualizer, TemporalVisualizer, AnalyticsVisualizer\n",
    "import tempfile\n",
    "import os\n",
    "import json\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "web_ingestor = WebIngestor()\n",
    "stream_ingestor = StreamIngestor()\n",
    "db_ingestor = DBIngestor()\n",
    "feed_ingestor = FeedIngestor()\n",
    "file_ingestor = FileIngestor()\n",
    "\n",
    "json_parser = JSONParser()\n",
    "csv_parser = CSVParser()\n",
    "structured_parser = StructuredDataParser()\n",
    "\n",
    "# Real energy market data sources\n",
    "energy_apis = [\n",
    "    \"https://api.eia.gov/v2/electricity/retail-sales/data/\",  # EIA Energy Information Administration\n",
    "    \"https://www.energy.gov/data\",  # US Energy Department Data\n",
    "    \"https://api.github.com/repos/energy-data/aggregator\"  # Energy data aggregator\n",
    "]\n",
    "\n",
    "energy_feeds = [\n",
    "    \"https://www.energy.gov/rss\",  # US Energy Department RSS\n",
    "    \"https://feeds.reuters.com/reuters/businessNews\"  # Reuters Business (energy news)\n",
    "]\n",
    "\n",
    "# Real streaming sources for energy market data\n",
    "stream_sources = [\n",
    "    {\n",
    "        \"type\": \"kafka\",\n",
    "        \"topic\": \"energy_market\",\n",
    "        \"bootstrap_servers\": [\"localhost:9092\"],\n",
    "        \"consumer_config\": {\"group_id\": \"energy_analysis\"}\n",
    "    }\n",
    "]\n",
    "\n",
    "# Real database connection for energy market data\n",
    "db_connection_string = \"postgresql://user:password@localhost:5432/energy_market_db\"\n",
    "db_query = \"SELECT energy_type, price, volume, timestamp, region FROM energy_prices WHERE timestamp > NOW() - INTERVAL '24 hours' ORDER BY timestamp DESC\"\n",
    "\n",
    "temp_dir = tempfile.mkdtemp()\n",
    "\n",
    "# Sample energy market data (real-world structure)\n",
    "energy_market_file = os.path.join(temp_dir, \"energy_market.json\")\n",
    "energy_market_data = [\n",
    "    {\n",
    "        \"energy_type\": \"Solar\",\n",
    "        \"price\": 0.045,\n",
    "        \"volume\": 1000000,\n",
    "        \"timestamp\": (datetime.now() - timedelta(hours=2)).isoformat(),\n",
    "        \"region\": \"California\"\n",
    "    },\n",
    "    {\n",
    "        \"energy_type\": \"Wind\",\n",
    "        \"price\": 0.035,\n",
    "        \"volume\": 800000,\n",
    "        \"timestamp\": (datetime.now() - timedelta(hours=1)).isoformat(),\n",
    "        \"region\": \"Texas\"\n",
    "    },\n",
    "    {\n",
    "        \"energy_type\": \"Hydroelectric\",\n",
    "        \"price\": 0.040,\n",
    "        \"volume\": 600000,\n",
    "        \"timestamp\": datetime.now().isoformat(),\n",
    "        \"region\": \"Pacific Northwest\"\n",
    "    }\n",
    "]\n",
    "\n",
    "with open(energy_market_file, 'w') as f:\n",
    "    json.dump(energy_market_data, f, indent=2)\n",
    "\n",
    "file_objects = file_ingestor.ingest_file(energy_market_file, read_content=True)\n",
    "parsed_data = structured_parser.parse_json(energy_market_file)\n",
    "\n",
    "# Ingest from energy APIs\n",
    "energy_api_list = []\n",
    "for api_url in energy_apis[:1]:\n",
    "    api_content = web_ingestor.ingest_url(api_url)\n",
    "    if api_content:\n",
    "        energy_api_list.append(api_content)\n",
    "        print(f\"  Ingested energy API: {api_url}\")\n",
    "\n",
    "# Ingest from energy feeds\n",
    "energy_feed_list = []\n",
    "for feed_url in energy_feeds:\n",
    "    feed_data = feed_ingestor.ingest_feed(feed_url)\n",
    "    if feed_data:\n",
    "        energy_feed_list.append(feed_data)\n",
    "        print(f\"  Ingested feed: {feed_url}\")\n",
    "\n",
    "print(f\"\\n📊 Energy Market Ingestion Summary:\")\n",
    "print(f\"  Energy market files: {len([file_objects]) if file_objects else 0}\")\n",
    "print(f\"  Energy APIs: {len(energy_api_list)}\")\n",
    "print(f\"  Energy feeds: {len(energy_feed_list)}\")\n",
    "print(f\"  Streaming sources: {len(stream_sources)}\")\n",
    "print(f\"  Database sources: 1\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Extract Energy Entities and Build Temporal Market Knowledge Graph\n",
    "\n",
    "Extract energy entities and build temporal market knowledge graph.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ner_extractor = NERExtractor()\n",
    "relation_extractor = RelationExtractor()\n",
    "event_detector = EventDetector()\n",
    "semantic_analyzer = SemanticAnalyzer()\n",
    "\n",
    "energy_entities = []\n",
    "energy_relationships = []\n",
    "\n",
    "# Extract from energy market data\n",
    "if parsed_data and parsed_data.data:\n",
    "    for entry in parsed_data.data if isinstance(parsed_data.data, list) else [parsed_data.data]:\n",
    "        if isinstance(entry, dict):\n",
    "            energy_type = entry.get(\"energy_type\", \"\")\n",
    "            region = entry.get(\"region\", \"\")\n",
    "            timestamp = entry.get(\"timestamp\", \"\")\n",
    "            \n",
    "            energy_entities.append({\n",
    "                \"id\": f\"{energy_type}_{region}_{timestamp}\",\n",
    "                \"type\": \"Energy_Price\",\n",
    "                \"name\": f\"{energy_type} in {region}\",\n",
    "                \"properties\": {\n",
    "                    \"energy_type\": energy_type,\n",
    "                    \"price\": entry.get(\"price\", 0),\n",
    "                    \"volume\": entry.get(\"volume\", 0),\n",
    "                    \"region\": region,\n",
    "                    \"timestamp\": timestamp\n",
    "                }\n",
    "            })\n",
    "            \n",
    "            energy_entities.append({\n",
    "                \"id\": energy_type,\n",
    "                \"type\": \"Energy_Source\",\n",
    "                \"name\": energy_type,\n",
    "                \"properties\": {}\n",
    "            })\n",
    "            \n",
    "            energy_entities.append({\n",
    "                \"id\": region,\n",
    "                \"type\": \"Region\",\n",
    "                \"name\": region,\n",
    "                \"properties\": {}\n",
    "            })\n",
    "            \n",
    "            energy_relationships.append({\n",
    "                \"source\": energy_type,\n",
    "                \"target\": f\"{energy_type}_{region}_{timestamp}\",\n",
    "                \"type\": \"has_price_in\",\n",
    "                \"properties\": {\"timestamp\": timestamp}\n",
    "            })\n",
    "            \n",
    "            energy_relationships.append({\n",
    "                \"source\": f\"{energy_type}_{region}_{timestamp}\",\n",
    "                \"target\": region,\n",
    "                \"type\": \"in_region\",\n",
    "                \"properties\": {}\n",
    "            })\n",
    "\n",
    "builder = GraphBuilder()\n",
    "temporal_query = TemporalGraphQuery()\n",
    "temporal_pattern_detector = TemporalPatternDetector()\n",
    "graph_analyzer = GraphAnalyzer()\n",
    "\n",
    "energy_market_kg = builder.build(energy_entities, energy_relationships)\n",
    "\n",
    "metrics = graph_analyzer.compute_metrics(energy_market_kg)\n",
    "\n",
    "print(f\"Extracted {len(energy_entities)} energy entities\")\n",
    "print(f\"Extracted {len(energy_relationships)} relationships\")\n",
    "print(f\"Built temporal energy market knowledge graph with {len(energy_market_kg.get('entities', []))} entities\")\n",
    "print(f\"Graph density: {metrics.get('density', 0):.3f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Analyze Energy Pricing Trends\n",
    "\n",
    "Analyze energy pricing trends using temporal queries and pattern detection.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "centrality_calculator = CentralityCalculator()\n",
    "community_detector = CommunityDetector()\n",
    "connectivity_analyzer = ConnectivityAnalyzer()\n",
    "\n",
    "start_time = (datetime.now() - timedelta(hours=24)).isoformat()\n",
    "end_time = datetime.now().isoformat()\n",
    "\n",
    "# Query pricing trends\n",
    "pricing_trends = temporal_query.query_time_range(\n",
    "    graph=energy_market_kg,\n",
    "    query=\"Find energy pricing trends in the last 24 hours\",\n",
    "    start_time=start_time,\n",
    "    end_time=end_time\n",
    ")\n",
    "\n",
    "# Detect temporal patterns\n",
    "temporal_patterns = temporal_pattern_detector.detect_temporal_patterns(\n",
    "    energy_market_kg,\n",
    "    pattern_type=\"trend\",\n",
    "    min_frequency=1\n",
    ")\n",
    "\n",
    "# Analyze pricing by energy type\n",
    "pricing_analysis = {}\n",
    "if parsed_data and parsed_data.data:\n",
    "    for entry in parsed_data.data if isinstance(parsed_data.data, list) else [parsed_data.data]:\n",
    "        if isinstance(entry, dict):\n",
    "            energy_type = entry.get(\"energy_type\", \"\")\n",
    "            price = entry.get(\"price\", 0)\n",
    "            \n",
    "            if energy_type not in pricing_analysis:\n",
    "                pricing_analysis[energy_type] = {\n",
    "                    \"prices\": [],\n",
    "                    \"volumes\": [],\n",
    "                    \"regions\": []\n",
    "                }\n",
    "            \n",
    "            pricing_analysis[energy_type][\"prices\"].append(price)\n",
    "            pricing_analysis[energy_type][\"volumes\"].append(entry.get(\"volume\", 0))\n",
    "            pricing_analysis[energy_type][\"regions\"].append(entry.get(\"region\", \"\"))\n",
    "\n",
    "# Calculate average prices\n",
    "for energy_type, data in pricing_analysis.items():\n",
    "    if data[\"prices\"]:\n",
    "        pricing_analysis[energy_type][\"avg_price\"] = sum(data[\"prices\"]) / len(data[\"prices\"])\n",
    "        pricing_analysis[energy_type][\"total_volume\"] = sum(data[\"volumes\"])\n",
    "\n",
    "centrality_result = centrality_calculator.calculate_degree_centrality(market_kg)\n",
    "centrality_scores = centrality_result.get('centrality', {})\n",
    "communities = community_detector.detect_communities(energy_market_kg)\n",
    "connectivity = connectivity_analyzer.analyze_connectivity(energy_market_kg)\n",
    "\n",
    "print(f\"Pricing analysis complete\")\n",
    "print(f\"  Temporal patterns: {len(temporal_patterns)}\")\n",
    "print(f\"  Energy types analyzed: {len(pricing_analysis)}\")\n",
    "for energy_type, data in pricing_analysis.items():\n",
    "    print(f\"    {energy_type}: Avg Price ${data.get('avg_price', 0):.4f}/kWh, Total Volume {data.get('total_volume', 0):,}\")\n",
    "print(f\"  Central energy sources: {len([e for e, score in centrality_scores.items() if score > 0])}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Predict Energy Market Trends\n",
    "\n",
    "Predict energy market trends using inference engine.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inference_engine = InferenceEngine()\n",
    "rule_manager = RuleManager()\n",
    "explanation_generator = ExplanationGenerator()\n",
    "\n",
    "# Energy market trend prediction rules\n",
    "inference_engine.add_rule(\"IF price < 0.04 AND volume > 500000 THEN competitive_pricing\")\n",
    "inference_engine.add_rule(\"IF price > 0.05 AND volume < 500000 THEN high_pricing\")\n",
    "inference_engine.add_rule(\"IF multiple regions show same energy_type THEN market_trend\")\n",
    "\n",
    "# Add facts from energy market data\n",
    "if parsed_data and parsed_data.data:\n",
    "    for entry in parsed_data.data if isinstance(parsed_data.data, list) else [parsed_data.data]:\n",
    "        if isinstance(entry, dict):\n",
    "            inference_engine.add_fact({\n",
    "                \"energy_type\": entry.get(\"energy_type\", \"\"),\n",
    "                \"price\": entry.get(\"price\", 0),\n",
    "                \"volume\": entry.get(\"volume\", 0),\n",
    "                \"region\": entry.get(\"region\", \"\")\n",
    "            })\n",
    "\n",
    "trend_predictions = inference_engine.forward_chain()\n",
    "\n",
    "# Generate trend predictions\n",
    "market_predictions = []\n",
    "for energy_type, data in pricing_analysis.items():\n",
    "    avg_price = data.get(\"avg_price\", 0)\n",
    "    total_volume = data.get(\"total_volume\", 0)\n",
    "    \n",
    "    prediction = {\n",
    "        \"energy_type\": energy_type,\n",
    "        \"current_avg_price\": avg_price,\n",
    "        \"total_volume\": total_volume,\n",
    "        \"trend\": \"increasing\" if avg_price > 0.04 else \"stable\" if avg_price > 0.035 else \"decreasing\",\n",
    "        \"market_share\": total_volume / sum(p.get(\"total_volume\", 0) for p in pricing_analysis.values()) if sum(p.get(\"total_volume\", 0) for p in pricing_analysis.values()) > 0 else 0\n",
    "    }\n",
    "    market_predictions.append(prediction)\n",
    "\n",
    "print(f\"Generated {len(trend_predictions)} trend predictions\")\n",
    "print(f\"Market predictions for {len(market_predictions)} energy types:\")\n",
    "for prediction in market_predictions:\n",
    "    print(f\"  {prediction['energy_type']}: {prediction['trend']} trend, Market Share: {prediction['market_share']*100:.1f}%\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Generate Reports and Visualize\n",
    "\n",
    "Generate energy market analysis reports and visualize results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "quality_assessor = KGQualityAssessor()\n",
    "json_exporter = JSONExporter()\n",
    "csv_exporter = CSVExporter()\n",
    "rdf_exporter = RDFExporter()\n",
    "report_generator = ReportGenerator()\n",
    "\n",
    "quality_score = quality_assessor.assess_overall_quality(energy_market_kg)\n",
    "\n",
    "json_exporter.export_knowledge_graph(energy_market_kg, os.path.join(temp_dir, \"energy_market_kg.json\"))\n",
    "csv_exporter.export_entities(energy_entities, os.path.join(temp_dir, \"energy_entities.csv\"))\n",
    "rdf_exporter.export_knowledge_graph(energy_market_kg, os.path.join(temp_dir, \"energy_market_kg.rdf\"))\n",
    "\n",
    "report_data = {\n",
    "    \"summary\": f\"Energy market analysis identified {len(trend_predictions)} trends and {len(market_predictions)} market predictions\",\n",
    "    \"energy_types_analyzed\": len(pricing_analysis),\n",
    "    \"patterns\": len(temporal_patterns),\n",
    "    \"predictions\": len(market_predictions),\n",
    "    \"quality_score\": quality_score.get('overall_score', 0)\n",
    "}\n",
    "\n",
    "report = report_generator.generate_report(report_data, format=\"markdown\")\n",
    "\n",
    "kg_visualizer = KGVisualizer()\n",
    "temporal_visualizer = TemporalVisualizer()\n",
    "analytics_visualizer = AnalyticsVisualizer()\n",
    "\n",
    "kg_viz = kg_visualizer.visualize_network(energy_market_kg, output=\"interactive\")\n",
    "temporal_viz = temporal_visualizer.visualize_timeline(energy_market_kg, output=\"interactive\")\n",
    "analytics_viz = analytics_visualizer.visualize_analytics(energy_market_kg, output=\"interactive\")\n",
    "\n",
    "print(f\"Total modules used: 20+\")\n",
    "print(f\"Pipeline complete: Energy Market Sources → Parse → Extract → Build Temporal KG → Analyze Pricing → Predict Trends → Reports → Visualize\")\n"
   ]
  }
 ],
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