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Validating NumPy Fast Interpolation Implementation in OpenPilot

This test suite validates the custom interpolation functionality in OpenPilot’s numpy_fast module, specifically focusing on the interp function’s accuracy compared to NumPy’s native interpolation.

Test Coverage Overview

The test coverage focuses on validating the correctness of the custom interpolation implementation against NumPy’s standard interpolation function. Key test cases include:

Array-based interpolation with various input ranges
Single value interpolation across multiple test points
Edge cases including negative values and near-zero inputs
Boundary conditions at array endpoints

Implementation Analysis

The testing approach employs direct comparison between the custom interp function and NumPy’s native interpolation. The test uses both array-based and scalar input testing patterns to ensure comprehensive validation of the interpolation behavior.

The implementation specifically tests cruise control velocity interpolation scenarios with predefined breakpoints and corresponding values.

Technical Details

Testing tools and configuration:

NumPy for reference implementation and array operations
NumPy testing utilities for assertion checks
Custom test vectors including edge cases and typical usage scenarios
Predefined breakpoint arrays (_A_CRUISE_MIN_BP) and corresponding values (_A_CRUISE_MIN_V)

Best Practices Demonstrated

The test implementation showcases several testing best practices:

Comprehensive input range testing
Both bulk and individual value verification
Direct comparison with reference implementation
Clear test case organization and naming
Efficient use of NumPy testing utilities for precise equality checking

commaai/openpilot

common/tests/test_numpy_fast.py

            
import numpy as np

from openpilot.common.numpy_fast import interp


class TestInterp:
  def test_correctness_controls(self):
    _A_CRUISE_MIN_BP = np.asarray([0., 5., 10., 20., 40.])
    _A_CRUISE_MIN_V = np.asarray([-1.0, -.8, -.67, -.5, -.30])
    v_ego_arr = [-1, -1e-12, 0, 4, 5, 6, 7, 10, 11, 15.2, 20, 21, 39,
                 39.999999, 40, 41]

    expected = np.interp(v_ego_arr, _A_CRUISE_MIN_BP, _A_CRUISE_MIN_V)
    actual = interp(v_ego_arr, _A_CRUISE_MIN_BP, _A_CRUISE_MIN_V)

    np.testing.assert_equal(actual, expected)

    for v_ego in v_ego_arr:
      expected = np.interp(v_ego, _A_CRUISE_MIN_BP, _A_CRUISE_MIN_V)
      actual = interp(v_ego, _A_CRUISE_MIN_BP, _A_CRUISE_MIN_V)
      np.testing.assert_equal(actual, expected)