# Example: Execute FME workspace from Nika
from nika_fme import FMEConnector

# Initialize FME connection
fme = FMEConnector(
    server_url="https://your-fme-server.com",
    username="your-username",
    password="your-password"
)

# Execute a workspace
result = fme.run_workspace(
    workspace_name="data_processing.fmw",
    parameters={
        "input_file": "path/to/input/data.shp",
        "output_format": "geojson",
        "coordinate_system": "EPSG:4326"
    }
)

# Process results in Nika
if result.success:
    processed_data = result.output_data
    # Continue with Nika analysis

# Example: Complex ETL pipeline with FME and Nika
import nika_fme as fme
import nika_analytics as analytics

# Define ETL pipeline
def complex_etl_pipeline():
    # Step 1: Data extraction and cleaning with FME
    fme_result = fme.run_workspace(
        workspace_name="data_cleaning.fmw",
        parameters={
            "source_database": "postgresql://host/db",
            "validation_rules": "quality_checks.json",
            "output_format": "parquet"
        }
    )
    
    # Step 2: Load cleaned data into Nika
    if fme_result.success:
        cleaned_data = analytics.load_data(fme_result.output_path)
        
        # Step 3: Perform advanced analytics in Nika
        analysis_result = analytics.spatial_analysis(
            data=cleaned_data,
            analysis_type="hotspot_analysis",
            parameters={"bandwidth": 1000}
        )
        
        # Step 4: Export results back through FME
        fme.run_workspace(
            workspace_name="result_export.fmw",
            parameters={
                "input_data": analysis_result,
                "output_formats": ["shapefile", "geojson", "kml"]
            }
        )

# Example: Legacy system migration
def migrate_legacy_data():
    # Extract from legacy system
    fme.run_workspace("legacy_extract.fmw", {
        "legacy_database": "oracle://legacy_system",
        "tables": ["parcels", "buildings", "infrastructure"]
    })
    
    # Transform data structure
    fme.run_workspace("data_transform.fmw", {
        "coordinate_system": "EPSG:4326",
        "attribute_mapping": "field_mapping.json",
        "data_validation": "validation_rules.json"
    })
    
    # Load into modern system
    fme.run_workspace("modern_load.fmw", {
        "target_database": "postgresql://modern_system",
        "spatial_indexing": True
    })

# Example: Real-time sensor data processing
def real_time_processing():
    # Set up continuous monitoring
    fme.run_workspace("sensor_monitor.fmw", {
        "sensor_endpoints": ["sensor1", "sensor2", "sensor3"],
        "polling_interval": 60,  # seconds
        "data_format": "json"
    })
    
    # Process incoming data
    def process_sensor_data(data):
        # FME processing
        processed = fme.run_workspace("sensor_process.fmw", {
            "input_data": data,
            "calibration": "sensor_calibration.json"
        })
        
        # Nika analytics
        if processed.success:
            analytics.real_time_analysis(processed.output_data)

# Example: Integrating diverse data sources
def integrate_diverse_sources():
    sources = [
        {"type": "cad", "path": "engineering_drawings.dwg"},
        {"type": "gis", "path": "parcel_data.shp"},
        {"type": "database", "path": "postgresql://host/asset_db"},
        {"type": "api", "path": "https://api.weather.com/current"}
    ]
    
    for source in sources:
        # Use FME to handle format conversion
        fme.run_workspace("format_converter.fmw", {
            "input_source": source,
            "output_format": "geojson",
            "coordinate_system": "EPSG:4326"
        })
    
    # Integrate all data in Nika
    analytics.integrate_multiple_sources()

# Example: Advanced spatial processing
def advanced_spatial_processing():
    # Buffer analysis
    fme.run_workspace("spatial_analysis.fmw", {
        "analysis_type": "buffer",
        "buffer_distance": 100,
        "buffer_units": "meters"
    })
    
    # Spatial joins
    fme.run_workspace("spatial_joins.fmw", {
        "join_type": "intersect",
        "target_layer": "parcels",
        "join_layer": "flood_zones"
    })
    
    # Topology validation
    fme.run_workspace("topology_check.fmw", {
        "validation_rules": ["no_gaps", "no_overlaps"],
        "tolerance": 0.001
    })

# Example: Comprehensive data validation
def data_quality_pipeline():
    # Schema validation
    fme.run_workspace("schema_validation.fmw", {
        "schema_definition": "expected_schema.json",
        "strict_mode": True
    })
    
    # Spatial validation
    fme.run_workspace("spatial_validation.fmw", {
        "geometry_checks": ["valid_geometry", "correct_crs"],
        "attribute_checks": ["no_null_values", "value_ranges"]
    })
    
    # Business rule validation
    fme.run_workspace("business_rules.fmw", {
        "rules_file": "business_rules.json",
        "error_handling": "log_and_continue"
    })