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[wpimath] Only print Sleipnir diagnostics on test failure (#8382)

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Bazel and CMake's CTest already do this by default since they run tests
in parallel, but Gradle doesn't.
This commit is contained in:
Tyler Veness
2025-11-15 17:04:09 -08:00
committed by GitHub
parent 34a72b71e1
commit b63743645f
1 changed files with 37 additions and 1 deletions
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38
wpimath/src/test/native/cpp/optimization/NonlinearProblemTest.cpp
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@@ -2,9 +2,9 @@
// Open Source Software; you can modify and/or share it under the terms of
// the WPILib BSD license file in the root directory of this project.
#include <concepts>
#include <ranges>
#include <fmt/base.h>
#include <gtest/gtest.h>
#include <sleipnir/autodiff/expression_type.hpp>
#include <sleipnir/optimization/problem.hpp>
@@ -17,6 +17,8 @@ auto Range(T start, T end, T step) {
}
TEST(ProblemTest, Quartic) {
testing::internal::CaptureStdout();
slp::Problem problem;
auto x = problem.decision_variable();
@@ -33,9 +35,15 @@ TEST(ProblemTest, Quartic) {
EXPECT_EQ(problem.solve({.diagnostics = true}), slp::ExitStatus::SUCCESS);
EXPECT_NEAR(x.value(), 1.0, 1e-6);
if (auto output = testing::internal::GetCapturedStdout(); HasFailure()) {
fmt::println("{}", output);
}
}
TEST(ProblemTest, RosenbrockWithCubicAndLineConstraint) {
testing::internal::CaptureStdout();
// https://en.wikipedia.org/wiki/Test_functions_for_optimization#Test_functions_for_constrained_optimization
for (auto x0 : Range(-1.5, 1.5, 0.1)) {
for (auto y0 : Range(-0.5, 2.5, 0.1)) {
@@ -68,9 +76,15 @@ TEST(ProblemTest, RosenbrockWithCubicAndLineConstraint) {
EXPECT_TRUE((near(0.0, y.value(), 1e-2) || near(1.0, y.value(), 1e-2)));
}
}
if (auto output = testing::internal::GetCapturedStdout(); HasFailure()) {
fmt::println("{}", output);
}
}
TEST(ProblemTest, RosenbrockWithDiskConstraint) {
testing::internal::CaptureStdout();
// https://en.wikipedia.org/wiki/Test_functions_for_optimization#Test_functions_for_constrained_optimization
for (auto x0 : Range(-1.5, 1.5, 0.1)) {
for (auto y0 : Range(-1.5, 1.5, 0.1)) {
@@ -96,9 +110,15 @@ TEST(ProblemTest, RosenbrockWithDiskConstraint) {
EXPECT_NEAR(y.value(), 1.0, 1e-3);
}
}
if (auto output = testing::internal::GetCapturedStdout(); HasFailure()) {
fmt::println("{}", output);
}
}
TEST(ProblemTest, Minimum2DDistanceWithLinearConstraint) {
testing::internal::CaptureStdout();
slp::Problem problem;
auto x = problem.decision_variable();
@@ -126,9 +146,15 @@ TEST(ProblemTest, Minimum2DDistanceWithLinearConstraint) {
EXPECT_NEAR(x.value(), 2.5, 1e-2);
EXPECT_NEAR(y.value(), 2.5, 1e-2);
if (auto output = testing::internal::GetCapturedStdout(); HasFailure()) {
fmt::println("{}", output);
}
}
TEST(ProblemTest, ConflictingBounds) {
testing::internal::CaptureStdout();
slp::Problem problem;
auto x = problem.decision_variable();
@@ -147,9 +173,15 @@ TEST(ProblemTest, ConflictingBounds) {
EXPECT_EQ(problem.solve({.diagnostics = true}),
slp::ExitStatus::GLOBALLY_INFEASIBLE);
if (auto output = testing::internal::GetCapturedStdout(); HasFailure()) {
fmt::println("{}", output);
}
}
TEST(ProblemTest, WachterAndBieglerLineSearchFailure) {
testing::internal::CaptureStdout();
// See example 19.2 of [1]
//
// [1] Nocedal, J. and Wright, S. "Numerical Optimization", 2nd. ed., Ch. 19.
@@ -183,4 +215,8 @@ TEST(ProblemTest, WachterAndBieglerLineSearchFailure) {
// EXPECT_EQ(x.value(), 1.0);
// EXPECT_EQ(s1.value(), 0.0);
// EXPECT_EQ(s2.value(), 0.5);
if (auto output = testing::internal::GetCapturedStdout(); HasFailure()) {
fmt::println("{}", output);
}
}