Documentation Index
Fetch the complete documentation index at: https://docs.mangrovesystems.com/llms.txt
Use this file to discover all available pages before exploring further.
What you’ll learn in this lesson:
- Design comprehensive test datasets for your project
- Calculate expected outputs before running tests
- Test edge cases: missing data, extreme values, and boundary conditions
- Use Analytics and Data Inputs views to validate results
Designing test datasets
A good test dataset covers three scenarios: happy path, edge cases, and error cases.- Happy path
- Edge cases
- Error cases
Normal operations with typical values. This is your baseline — if this doesn’t work, nothing will.Example for Mangrove Biochar (January 2024):
| Event Type | Count | Typical Values |
|---|---|---|
| Feedstock Receipt | 5 | 8-15 t wet mass, 10-20% moisture |
| Lab Analysis | 3 | 75-85% carbon, 0.2-0.5 H:C ratio |
| Biochar Delivery | 4 | 2-6 t biochar |
| Energy Consumption | 1 | 10,000 kWh, 500 gal propane |
| Transportation | 4 | 100-250 km per delivery |
Calculating expected outputs
Before running the pipeline, calculate the expected output for each test scenario by hand or in a spreadsheet. This is critical — without expected values, you can’t tell if the output is correct.Set up a spreadsheet
Create a spreadsheet with columns for each input value and each intermediate/final calculation. Include all 5 steps from your Module 2 model plus the LCA calculations from Module 3.
Enter your test data
Fill in the test values for each event. Calculate each intermediate value step by step, exactly as your model should.
Calculate batch-level outputs
For each batch (delivery), compute: gross carbon, allocated emissions (proportional + direct), and net carbon.
| Delivery | Mass (t) | Carbon % | Gross tCO2e | Alloc. Elec | Alloc. Propane | Transport | Net tCO2e |
|---|---|---|---|---|---|---|---|
| DEL-001 | 3 | 80% | 8.81 | 1.25 | 0.61 | 0.11 | 6.84 |
| DEL-002 | 5 | 78% | 14.31 | 2.09 | 1.02 | 0.14 | 11.06 |
| DEL-003 | 4 | 82% | 12.04 | 1.67 | 0.82 | 0.13 | 9.42 |
| DEL-004 | 2 | 79% | 5.80 | 0.83 | 0.41 | 0.09 | 4.47 |
| Total | 14 | 40.96 | 5.84 | 2.86 | 0.47 | 31.79 |
The numbers above are illustrative — your actual values will depend on your model structure and emission factors. The point is to have a reference table before you run the pipeline, so you can compare against the results.
Validating results in Mangrove
After running Generate Batches, use these views to validate:Data Inputs view
Check that all events were created correctly:- Event count matches expected
- Datapoint values are correct
- Evidence is attached where required
- Date ranges are within the accounting period
Batch outputs
For each batch, verify:- Gross carbon matches your spreadsheet
- Allocated emissions are proportional to mass
- Transport emissions are assigned to the correct batch
- Net carbon = gross - total emissions
Ledger balances
Check that each ledger reconciles:- Credits (incoming batches) match expected values
- Debits (allocations to downstream ledgers) are correct
- Running balance makes physical sense
Systematic edge case testing
For each edge case, follow this pattern: Edge cases to test for Mangrove Biochar:| Edge Case | Expected Behavior |
|---|---|
| Moisture = 50% | Dry mass is halved; all downstream values proportionally reduced |
| H:C ratio = 0.75 | Permanence factor = 0.7 (lowest tier); net carbon is lower |
| Mass = 0.5 t | Very small batch; emissions may be a large proportion of gross carbon |
| Distance = 0 km | Transport emissions = 0; only shared emissions allocated |
| Carbon content = 95% | High carbon; verify no overflow or rounding errors |
Check your understanding
Why should you calculate expected outputs before running the pipeline?
Why should you calculate expected outputs before running the pipeline?
Without pre-calculated expected values, you have no way to verify if the pipeline output is correct — only that it runs without errors. A pipeline can produce wrong numbers silently. Pre-calculated values give you a reference to compare against, catching formula errors, unit mismatches, and configuration problems.
What are the 3 types of test scenarios you should create?
What are the 3 types of test scenarios you should create?
Happy path (normal operations with typical values), edge cases (unusual but valid values that test boundaries), and error cases (invalid data that should be caught by validation). Together these cover the range of inputs your project might encounter in production.
Next, learn about common problems and how to fix them in Lesson 5.3: Common Integration Pitfalls.