python/ Improve typing.

 * pyproject.toml[tools.mypy]: New.
 * src/moocore/_moocore.py: More type annotations.
  * src/moocore/_utils.py: Likewise.

Author: MLopez-Ibanez

python/pyproject.toml

@@ -189,3 +189,21 @@ lint.pydocstyle.convention = "numpy"
 faulthandler_timeout = 10
 doctest_optionflags = "NUMBER"
 addopts = [ "--import-mode=importlib", "--durations=5", "--durations-min=1.0" ]
+
+[tool.mypy]
+python_version = "3.11"
+# Strictness (recommended for library code)
+disallow_untyped_defs = true
+disallow_incomplete_defs = true
+check_untyped_defs = true
+disallow_untyped_calls = true
+disallow_untyped_decorators = true
+no_implicit_optional = true
+# Warnings
+warn_unused_ignores = true
+warn_return_any = true
+warn_redundant_casts = true
+warn_unused_configs = true
+# Imports
+ignore_missing_imports = true
+files = [ "src/" ]

python/src/moocore/_moocore.py

@@ -49,7 +49,7 @@ class ReadDatasetsError(Exception):
         "ERROR_COLUMNS",
     )
 
-    def __init__(self, error_code):
+    def __init__(self, error_code: int):
         self.error = error_code
         self.message = self._error_strings[abs(error_code)]
         super().__init__(self.message)
@@ -151,7 +151,7 @@ def read_datasets(filename: str | os.PathLike | StringIO) -> np.ndarray:
     return np.frombuffer(data_buf).reshape((-1, ncols_p[0]))
 
 
-def _parse_maximise(maximise, nobj: int):
+def _parse_maximise(maximise, nobj: int) -> np.ndarray:
     """Convert maximise array or single bool to ndarray format."""
     return atleast_1d_of_length_n(maximise, nobj).astype(bool)
 
@@ -160,7 +160,12 @@ def _all_positive(x: ArrayLike) -> bool:
     return x.min() > 0
 
 
-def _unary_refset_common(data, ref, maximise, check_all_positive=False):
+def _unary_refset_common(
+    data: ArrayLike,
+    ref: ArrayLike,
+    maximise: bool | list[bool],
+    check_all_positive: bool = False,
+):
     # Convert to numpy.array in case the user provides a list.  We use
     # np.asarray(dtype=float) to convert it to floating-point, otherwise if a user inputs
     # something like ref = np.array([10, 10]) then numpy would interpret it as
@@ -355,7 +360,9 @@ def epsilon_additive(
     )
 
 
-def epsilon_mult(data, /, ref, *, maximise: bool | list[bool] = False) -> float:
+def epsilon_mult(
+    data: ArrayLike, /, ref: ArrayLike, *, maximise: bool | list[bool] = False
+) -> float:
     """Multiplicative epsilon metric.
 
     .. warning::
@@ -374,7 +381,7 @@ def epsilon_mult(data, /, ref, *, maximise: bool | list[bool] = False) -> float:
     return lib.epsilon_mult(data_p, nobj, npoints, ref_p, ref_size, maximise_p)
 
 
-def _hypervolume(data: ArrayLike, ref: ArrayLike):
+def _hypervolume(data: ArrayLike, ref: ArrayLike) -> float:
     data_p, npoints, nobj = np2d_to_double_array(data)
     ref_buf = ffi.from_buffer("double []", ref)
     hv = lib.fpli_hv(data_p, nobj, npoints, ref_buf)

python/src/moocore/_utils.py

@@ -1,18 +1,23 @@
+from numpy.typing import ArrayLike  # For type hints
+from typing import Any
+
 import numpy as np
 from ._libmoocore import ffi
 
 
-def get1_and_is_copied(x, x_):
+def get1_and_is_copied(x: Any, x_: Any) -> tuple[Any, bool]:
     x_copied = id(x) != id(x_)
     return x, x_copied
 
 
-def asarray_maybe_copy(x, dtype=float):
+def asarray_maybe_copy(
+    x: ArrayLike, dtype: type = float
+) -> tuple[np.ndarray, bool]:
     """Convert to numpy array of dtype=float and detect copies."""
     return get1_and_is_copied(np.asarray(x, dtype=dtype), x)
 
 
-def unique_nosort(array, **kwargs):
+def unique_nosort(array: ArrayLike, **kwargs: Any) -> np.ndarray:
     """Return unique values without sorting them.
 
     See https://github.com/numpy/numpy/issues/7265
@@ -22,7 +27,9 @@ def unique_nosort(array, **kwargs):
     return uniq[index.argsort()]
 
 
-def np2d_to_double_array(x, ctype_shape=("int", "int")):
+def np2d_to_double_array(
+    x: ArrayLike, ctype_shape: tuple[str, str] = ("int", "int")
+) -> tuple[ffi.CData, ffi.CData, ffi.CData]:
     nrows = ffi.cast(ctype_shape[0], x.shape[0])
     ncols = ffi.cast(ctype_shape[1], x.shape[1])
     # FIXME: This may cause an unexpected copy. Make this an assert and force
@@ -32,23 +39,29 @@ def np2d_to_double_array(x, ctype_shape=("int", "int")):
     return x, nrows, ncols
 
 
-def np1d_to_c_array(x, ctype_data, ctype_size):
+def np1d_to_c_array(
+    x: ArrayLike, ctype_data: str, ctype_size: str
+) -> tuple[ffi.CData, ffi.CData]:
     size = ffi.cast(ctype_size, x.shape[0])
     ctype_dtype = np.intc() if ctype_data == "int" else None
     x = np.ascontiguousarray(x, dtype=ctype_dtype)
     x = ffi.from_buffer(ctype_data + "[]", x)
     return x, size
 
 
-def np1d_to_double_array(x, ctype_size="int"):
+def np1d_to_double_array(
+    x: ArrayLike, ctype_size: str = "int"
+) -> tuple[ffi.CData, ffi.CData]:
     return np1d_to_c_array(x, ctype_data="double", ctype_size=ctype_size)
 
 
-def np1d_to_int_array(x, ctype_size="int"):
+def np1d_to_int_array(
+    x: ArrayLike, ctype_size: str = "int"
+) -> tuple[ffi.CData, ffi.CData]:
     return np1d_to_c_array(x, ctype_data="int", ctype_size=ctype_size)
 
 
-def atleast_1d_of_length_n(x, n):
+def atleast_1d_of_length_n(x: ArrayLike, n: int) -> np.ndarray:
     x = np.atleast_1d(x)
     if len(x) == 1:
         return np.full((n), x[0])
@@ -59,7 +72,7 @@ def atleast_1d_of_length_n(x, n):
     )
 
 
-def is_integer_value(n):
+def is_integer_value(n: Any) -> bool:
     if isinstance(n, int):
         return True
     if n is None:
@@ -73,7 +86,7 @@ def is_integer_value(n):
         return False
 
 
-def _get_seed_for_c(seed):
+def _get_seed_for_c(seed: Any) -> ffi.CData:
     if not is_integer_value(seed):
         seed = np.random.default_rng(seed).integers(2**32 - 2, dtype=np.uint32)
     return ffi.cast("uint32_t", seed)