37.4. Workflows

37.4.1. Overview

The memories-dev framework provides a flexible workflow system for integrating data acquisition, processing, analysis, and visualization components.

37.4.2. Workflow Components

37.4.2.1. Data Sources

from memories.workflow import WorkflowBuilder
from memories.data_acquisition import DataManager

# Create workflow
workflow = WorkflowBuilder()

# Add data source
workflow.add_data_source(
    source=DataManager(),
    name="satellite_data",
    config={
        "type": "sentinel-2",
        "bands": ["B02", "B03", "B04", "B08"],
        "cloud_cover_max": 20
    }
)

37.4.2.2. Processors

from memories.processors import ImageProcessor

# Add processor
workflow.add_processor(
    processor=ImageProcessor(),
    name="ndvi_calculator",
    input_key="satellite_data",
    output_key="ndvi_data",
    config={
        "operation": "ndvi",
        "red_band": "B04",
        "nir_band": "B08"
    }
)

37.4.2.3. Analyzers

from memories.analysis import ChangeDetector

# Add analyzer
workflow.add_analyzer(
    analyzer=ChangeDetector(),
    name="change_analysis",
    input_key="ndvi_data",
    output_key="change_metrics",
    config={
        "method": "threshold",
        "threshold": 0.2
    }
)

37.4.2.4. Visualizers

from memories.visualization import MapVisualizer

# Add visualizer
workflow.add_visualizer(
    visualizer=MapVisualizer(),
    name="change_map",
    input_key="change_metrics",
    config={
        "style": "change_detection",
        "colormap": "RdYlGn"
    }
)

37.4.3. Building Workflows

37.4.3.1. Sequential Workflow

# Build sequential workflow
workflow = (
    WorkflowBuilder()
    .add_data_source(...)
    .add_processor(...)
    .add_analyzer(...)
    .add_visualizer(...)
    .build()
)

# Execute workflow
results = await workflow.execute()

37.4.3.2. Parallel Workflow

# Build parallel workflow
workflow = (
    WorkflowBuilder()
    .add_data_source("source1", ...)
    .add_data_source("source2", ...)
    .add_parallel_processors([
        ("processor1", ...),
        ("processor2", ...)
    ])
    .add_analyzer(...)
    .build()
)

# Execute workflow
results = await workflow.execute()

37.4.3.3. Conditional Workflow

def quality_check(data):
    return data["quality_score"] > 0.8

# Build conditional workflow
workflow = (
    WorkflowBuilder()
    .add_data_source(...)
    .add_condition(
        check=quality_check,
        if_true=processor1,
        if_false=processor2
    )
    .add_analyzer(...)
    .build()
)

37.4.4. Workflow Management

37.4.4.1. Monitoring

# Add monitoring
workflow.add_monitor(
    metrics=["execution_time", "memory_usage"],
    log_level="INFO"
)

# Get monitoring data
stats = workflow.get_statistics()

37.4.4.2. Error Handling

# Configure error handling
workflow.set_error_handler(
    on_error="retry",
    max_retries=3,
    retry_delay=5
)

try:
    results = await workflow.execute()
except WorkflowError as e:
    print(f"Workflow failed: {e}")

37.4.5. Best Practices

Design Principles - Keep workflows modular - Use clear naming conventions - Document component interactions - Handle errors gracefully
Performance - Optimize data flow - Use parallel processing when appropriate - Implement caching strategies - Monitor resource usage
Maintenance - Version control workflows - Test workflow components - Document dependencies - Regular validation

37.4.6. See Also

/api_reference/workflow
/examples/workflows
/deployment/scaling