Add doctests and acceptance tests to CI pipeline and fix broken doctests

.github/workflows/run_unit_tests.yml

@@ -34,8 +34,8 @@ jobs:
         run: poetry install -v
       - name: Display installed dependency versions
         run: poetry run pip list
-      - name: Run unit tests and code coverage
-        run: poetry run pytest ./tests/unit -v --cov=nitypes --junitxml=test_results/nitypes-${{ matrix.os }}-py${{ matrix.python-version }}.xml
+      - name: Run unit/acceptance/doc tests and code coverage
+        run: poetry run pytest -v --cov=nitypes --junitxml=test_results/nitypes-${{ matrix.os }}-py${{ matrix.python-version }}.xml
       - name: Run benchmarks
         run: poetry run pytest ./tests/benchmark -v --junitxml=test_results/nitypes-benchmarks-${{ matrix.os }}-py${{ matrix.python-version }}.xml
       - name: Upload test results

src/nitypes/_arguments.py

@@ -14,10 +14,6 @@
 )
 from nitypes._numpy import isdtype as _np_isdtype
 
-# Some of these doctests use types introduced in NumPy 2.0 (np.long and np.ulong) or highlight
-# formatting differences between NumPy 1.x and 2.x (e.g. dtype=int32, 1.23 vs. np.float64(1.23)).
-__doctest_requires__ = {("arg_to_float", "is_dtype", "validate_dtype"): ["numpy>=2.0"]}
-
 
 def arg_to_float(
     arg_description: str, value: SupportsFloat | None, default_value: float | None = None
@@ -143,6 +139,17 @@ def is_dtype(dtype: npt.DTypeLike, supported_dtypes: tuple[npt.DTypeLike, ...])
 
     Unlike :any:`numpy.isdtype`, this function supports structured data types.
 
+    # Some of these doctests use types introduced in NumPy 2.0 (np.long and np.ulong) or highlight
+    # formatting differences between NumPy 1.x and 2.x (e.g. dtype=int32, 1.23 vs.
+    # np.float64(1.23)).
+    # We use inline version checks instead of __doctest_requires__ due to a pytest-doctestplus 1.6.0
+    # bug that doesn't properly parse version requirements like "numpy>=2.0".
+    # This check may exist in multiple places in the code. If you are making changes, you
+    # probably need them in every location.
+    # TODO: Remove these version checks when NumPy < 2.0 compatibility is no longer required.
+    >>> import numpy as np; import pytest
+    >>> version_tuple = tuple(map(int, np.__version__.split('.')[:2]))
+    >>> if version_tuple < (2, 0): pytest.skip("requires numpy>=2.0")
     >>> is_dtype(np.float64, (np.float64, np.intc, np.long,))
     True
     >>> is_dtype("float64", (np.float64, np.intc, np.long,))
@@ -172,6 +179,17 @@ def is_dtype(dtype: npt.DTypeLike, supported_dtypes: tuple[npt.DTypeLike, ...])
 def validate_dtype(dtype: npt.DTypeLike, supported_dtypes: tuple[npt.DTypeLike, ...]) -> None:
     """Validate a dtype-like object against a tuple of supported dtype-like objects.
 
+    # Some of these doctests use types introduced in NumPy 2.0 (np.long and np.ulong) or highlight
+    # formatting differences between NumPy 1.x and 2.x (e.g. dtype=int32, 1.23 vs.
+    # np.float64(1.23)).
+    # We use inline version checks instead of __doctest_requires__ due to a pytest-doctestplus 1.6.0
+    # bug that doesn't properly parse version requirements like "numpy>=2.0".
+    # This check may exist in multiple places in the code. If you are making changes, you
+    # probably need them in every location.
+    # TODO: Remove these version checks when NumPy < 2.0 compatibility is no longer required.
+    >>> import numpy as np; import pytest
+    >>> version_tuple = tuple(map(int, np.__version__.split('.')[:2]))
+    >>> if version_tuple < (2, 0): pytest.skip("requires numpy>=2.0")
     >>> validate_dtype(np.float64, (np.float64, np.intc, np.long,))
     >>> validate_dtype("float64", (np.float64, np.intc, np.long,))
     >>> validate_dtype(np.float64, (np.byte, np.short, np.intc, np.int_, np.long, np.longlong))

src/nitypes/complex/__init__.py

@@ -27,7 +27,15 @@
 
 You can construct an array of complex integers from a sequence of tuples using :func:`numpy.array`:
 
->>> import numpy as np
+# Some of these doctests use types introduced in NumPy 2.0 (np.long and np.ulong) or highlight
+# formatting differences between NumPy 1.x and 2.x (e.g. dtype=int32, 1.23 vs. np.float64(1.23)).
+# We use inline version checks instead of __doctest_requires__ due to a pytest-doctestplus 1.6.0
+# bug that doesn't properly parse version requirements like "numpy>=2.0".
+# This check may exist in multiple places in the code. If you are making changes, you
+# probably need them in every location.
+# TODO: Remove these version checks when NumPy < 2.0 compatibility is no longer required.
+>>> import numpy as np; import pytest
+>>> if tuple(map(int, np.__version__.split('.')[:2])) < (2, 0): pytest.skip("requires numpy>=2.0")
 >>> np.array([(1, 2), (3, 4)], dtype=ComplexInt32DType)
 array([(1, 2), (3, 4)], dtype=[('real', '<i2'), ('imag', '<i2')])
 
@@ -127,4 +135,3 @@
 from nitypes.complex._dtypes import ComplexInt32Base, ComplexInt32DType
 
 __all__ = ["convert_complex", "ComplexInt32DType", "ComplexInt32Base"]
-__doctest_requires__ = {".": ["numpy>=2.0"]}

src/nitypes/waveform/_digital/_port.py

@@ -147,7 +147,7 @@ def _mask_to_column_indices(
     [8]
     >>> _mask_to_column_indices(0xDEADBEEF, 32, "little")
     [0, 1, 2, 3, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16, 18, 19, 21, 23, 25, 26, 27, 28, 30, 31]
-    >>> _mask_to_column_indices(-1, 8)
+    >>> _mask_to_column_indices(-1, 8, "little")
     Traceback (most recent call last):
     ...
     ValueError: The mask must be a non-negative integer.

src/nitypes/waveform/_digital/_waveform.py

@@ -273,10 +273,10 @@ class DigitalWaveform(Generic[TDigitalState]):
     and the digital state from the actual and expected waveforms:
 
     >>> result.failures[0]  # doctest: +NORMALIZE_WHITESPACE
-    DigitalWaveformFailure(sample_index=0, expected_sample_index=0, signal_index=0, column_index=1,
+    DigitalWaveformFailure(sample_index=0, expected_sample_index=0, signal_index=0,
     actual_state=<DigitalState.FORCE_UP: 1>, expected_state=<DigitalState.COMPARE_LOW: 3>)
     >>> result.failures[1]  # doctest: +NORMALIZE_WHITESPACE
-    DigitalWaveformFailure(sample_index=1, expected_sample_index=1, signal_index=0, column_index=1,
+    DigitalWaveformFailure(sample_index=1, expected_sample_index=1, signal_index=0,
     actual_state=<DigitalState.FORCE_UP: 1>, expected_state=<DigitalState.COMPARE_LOW: 3>)
 
     Timing information