HydroGenerate Test Suite

HydroGenerate Test Suite#

This page documents the purpose and coverage of the automated tests for HydroGenerate, located in tests/test_hg.py.

You can run all tests locally or in CI with:

pytest -q

test_basic_hydropower_calculation
Confirms that the calculate_hp_potential() function produces the correct rated power for a simple BASIC hydropower configuration.
test_units_roundtrip_basic
Ensures hydropower results remain consistent when switching between US and SI units.

test_diversion_calculate_potential
Runs an end-to-end diversion test using a short flow DataFrame, verifying that power, design flow, and revenue are computed correctly.
test_diversion_
Ensures turbine efficiency values remain between 0 and 1, and headloss is computed correctly.
test_percent_exceedance_design_flow_uses_P70_when_pe_30
Verifies that a percent exceedance of 30 selects the 70th percentile of flow as the design flow.
test_min_turbineflow_default_10_percent_clamps_to_zero
Confirms that the minimum turbine flow defaults to 10% of the design flow and that smaller flows are clamped to zero.

test_headloss_methods_run
Ensures both Darcy–Weisbach and Hazen–Williams methods run successfully.
test_darcy_headloss_increases_with_length_si
Verifies that longer penstock lengths produce higher headloss and lower rated power.
test_hazen_williams_infers_diameter_when_missing
Checks that Hazen–Williams can infer penstock diameter when not provided.

test_turbine_type_selector_inside_regions
Confirms correct turbine type (Kaplan, Francis, Turgo, Crossflow) is chosen for valid flow–head pairs.
test_generator_efficiency_default_applied
Ensures the default generator efficiency (0.98) is applied and a valid runner diameter is assigned.

test_hydrokinetic_mode_simple_si
Verifies that hydrokinetic power > 0 when blade area and velocity are given and that costs are skipped.

test_costs_present_for_diversion_canalconduit_us
Checks that the ORNL Baseline Cost Model sets ICC (initial capital cost) and annual O&M for canal conduit projects.
test_costs_present_for_diversion_npd_alias
Verifies that the “NPD” alias for non-powered dams produces valid cost outputs.
test_annual_om_is_capped_at_2p5_percent_of_icc_when_applicable
Confirms that annual O&M ≤ 2.5% of ICC, per model guidance.
test_costs_not_computed_for_hydrokinetic
Ensures hydrokinetic projects skip cost modeling.

test_revenue_capacity_factor_path_sets_days_and_energy
Checks that providing a capacity factor sets the correct number of operation days and computes annual energy correctly.
test_revenue_days_path_sets_capacity_factor_and_energy
Validates the reverse case: providing days sets the capacity factor and energy outputs correctly.
test_revenue_outputs_present_and_valid
Ensures DataFrame-based revenue calculations produce valid, non-negative energy and revenue values.
test_revenue_dataframe_path_uses_default_price_when_none
Verifies that default wholesale electricity price (0.0582 $/kWh) is applied when none is provided.
test_dataframe_revenue_groupby_yields_reasonable_capacity_factor
Confirms that the computed capacity factor is clamped between 0 and 1.

test_missing_flow_column_raises
Ensures a missing flow_column parameter raises a ValueError.
test_invalid_units_raise
Confirms invalid unit strings raise an error.
test_invalid_hydropower_type_raises
Ensures unsupported hydropower types are rejected.
test_diversion_requires_penstock_length_when_headloss_calc_true
Verifies that penstock length is required if headloss calculation is enabled.

All DataFrames use a dateTime index.
Frequency codes use lowercase ("h") to avoid pandas deprecation warnings.
Floating-point comparisons use pytest.approx() for stability.
Run the tests regularly to ensure physics, units, and economics remain consistent.

From the repository root:

pip install -e HydroGenerate
pip install pytest
pytest -q