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Recipes Guide

Recipes are predefined workflows that combine multiple analysis steps. This guide covers how to discover, run, and monitor recipe executions.

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What are Recipes?

Recipes are automated workflows that:
  • Combine multiple analysis steps
  • Standardize analysis processes
  • Enable batch processing
  • Produce consistent results

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Types of Recipes

  • Scan Recipes - Processing workflows for scan data
  • Analysis Recipes - Analysis workflows (porosity, wall thickness, etc.)

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Listing Available Recipes

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Organization Recipe Versions

  • Python
from api_client import LumafieldAPIClient

client = LumafieldAPIClient(api_url, email, password)

# List recipe versions for an organization
organization_id = "your-org-uuid"
recipes = client.list_organization_recipe_versions(organization_id)

for recipe in recipes['results']:
    print(f"Recipe: {recipe['name']}")
    print(f"Version: {recipe['version']}")

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Scan Recipe Revisions

  • Python
# List scan recipe revisions
scan_recipes = client.list_organization_scan_recipe_revisions(organization_id)

for recipe in scan_recipes['results']:
    print(f"Scan Recipe: {recipe['name']}")

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Running Recipes

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Run Recipe on Project

  • Python
project_id = "your-project-uuid"
recipe_pk = "recipe-uuid"

# Run recipe
invocation = client.run_project_recipe(project_id, recipe_pk)
print(f"Recipe invocation started: {invocation['id']}")

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Monitoring Recipe Execution

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Get Recipe Invocations

  • Python
# Get all recipe invocations for a project
invocations = client.get_project_recipe_invocations(project_id)

for invocation in invocations['results']:
    print(f"Recipe: {invocation['recipe']['name']}")
    print(f"State: {invocation['state']}")
    print(f"Created: {invocation['created_at']}")

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Check Invocation Status

  • Python
invocation_id = "invocation-uuid"

# Get invocation details
invocation = client.get_recipe_invocation(invocation_id)

print(f"State: {invocation['state']}")
print(f"Progress: {invocation.get('progress', 'N/A')}")

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Getting Recipe Results

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Download Recipe Results

  • Python
# Get recipe results
results = client.get_recipe_invocation_results(invocation_id)

# Results include downloadable files
if results.get('downloadableResults'):
    download_url = results['downloadableResults']
    # Download the results archive

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Recipe Result Formats

Recipe results typically include:
  • CSV files - Tabular analysis data
  • JSON files - Structured metadata
  • PLY files - 3D visualization meshes
  • ZIP archives - Pre-packaged result sets

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Best Practices

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1. Check Recipe Availability

Verify recipes are available before running: 
recipes = client.list_organization_recipe_versions(org_id)
recipe_names = [r['name'] for r in recipes['results']]

if 'Porosity Analysis' not in recipe_names:
    print("Porosity Analysis recipe not available")

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2. Monitor Execution

Implement proper polling: 
def wait_for_recipe(invocation_id, max_wait=3600):
    start = time.time()
    while time.time() - start < max_wait:
        invocation = client.get_recipe_invocation(invocation_id)
        if invocation['state'] == 'done':
            return invocation
        elif invocation['state'] == 'failed':
            raise Exception("Recipe execution failed")
        time.sleep(30)
    raise TimeoutError("Recipe timeout")

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3. Handle Results

Download and process results efficiently: 
results = client.get_recipe_invocation_results(invocation_id)

# Check available files
for result in results.get('results', []):
    files = result.get('dataObjectFileNames', [])
    for filename in files:
        # Download individual files
        download_info = client.get_export(
            id=result['dataObjectId'],
            file_name=filename
        )

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Next Steps