Testing

numgrids uses pytest as its test runner. All tests live in the tests/ directory at the repository root.

Running the Full Test Suite

python -m pytest tests

To see verbose output with individual test names:

python -m pytest tests -v

Running Specific Tests

Run a single test file:

python -m pytest tests/test_diff.py

Run a single test class or function:

python -m pytest tests/test_diff.py::TestDiffSomeClass
python -m pytest tests/test_diff.py::TestDiffSomeClass::test_specific_case

Test Organization

The test suite is organized with roughly one test file per module:

Test file	What it covers
test_grids.py	Grid creation and properties
test_diff.py	Differentiation operators
test_boundary.py	Boundary conditions (Dirichlet, Neumann, Robin)
test_axes.py	Axis types (uniform, logarithmic, etc.)
test_curvilinear.py	Curvilinear grids (spherical, cylindrical, polar)
test_amr.py	Adaptive mesh refinement
test_integration.py	Numerical integration
test_interpol.py	Interpolation
test_io.py	Save / load functionality
test_api.py	Public API surface
test_plots.py	Plotting utilities

Writing New Tests

When adding or modifying functionality, always include corresponding tests.

Test structure

Tests are written using Python’s unittest.TestCase base class:

import unittest
import numpy as np
from numpy.testing import assert_array_almost_equal

from numgrids import Grid


class TestMyFeature(unittest.TestCase):

    def test_something(self):
        grid = Grid((-1, 1, 50))
        # ... set up the problem ...
        result = compute_something(grid)
        expected = analytical_solution(grid)
        assert_array_almost_equal(result, expected, decimal=6)

Numerical assertions

Because numgrids deals with numerical computations, most assertions compare arrays with a finite tolerance. Prefer helpers from numpy.testing:

• numpy.testing.assert_array_almost_equal – element-wise comparison up to a number of decimal places.

• numpy.testing.assert_allclose – relative and absolute tolerance comparison.

Mathematical correctness

Tests should verify results against known analytical solutions whenever possible. For example, a test for a second-order derivative operator might compare its output to the exact second derivative of a polynomial on the same grid. This ensures that the numerical discretization converges to the correct mathematical result.