Make strict mypy and the test suite pass on every CI platform

src/vane/io/openfast_compat.py

@@ -502,7 +502,7 @@ def _unique_index(names: list[str], requested: str) -> int:
     return matches[0]
 
 
-def _require_zero(values: np.ndarray, atol: float, label: str) -> None:
+def _require_zero(values: npt.NDArray[np.float64], atol: float, label: str) -> None:
     if not np.all(np.abs(values) <= atol):
         maximum = float(np.max(np.abs(values)))
         msg = f"{label} is not zero within atol={atol:g} (max={maximum:g})"

src/vane/modal/averaging.py

@@ -213,7 +213,7 @@ def interval_weights(azimuths: npt.ArrayLike) -> npt.NDArray[np.float64]:
         )
         raise ValueError(msg)
     sorted_weights = 0.5 * (np.roll(gaps, 1) + gaps) / _TWO_PI
-    weights = np.empty_like(sorted_weights)
+    weights: npt.NDArray[np.float64] = np.empty_like(sorted_weights)
     weights[order] = sorted_weights
     return weights
 
@@ -957,7 +957,10 @@ def azimuth_tangent(
             stacklevel=2,
         )
     vector = basis.right[:, index] / basis.right[position, index]
-    return np.asarray(vector.real, dtype=np.float64)
+    tangent = np.array(vector.real, dtype=np.float64)
+    # z / z need not round to exactly one on every platform.
+    tangent[position] = 1.0
+    return tangent
 
 
 def pin_azimuth(

src/vane/modal/averaging_error_estimate.py

@@ -1571,7 +1571,7 @@ def azimuth_count_table(
     basis = eigen_basis(model.mean)
     labels = _resolve_branches(basis, branches)
     tails: dict[str, AliasTail] = {}
-    scale = max(float(np.linalg.norm(model.mean)), np.finfo(np.float64).tiny)
+    scale = max(float(np.linalg.norm(model.mean)), float(np.finfo(np.float64).tiny))
     if extrapolate:
         for label, j in labels.items():
             orders, sensitivity = _sensitivities(model, basis, j, b)

src/vane/modal/floquet.py

@@ -151,7 +151,10 @@ def _fourier_coefficients(
     coeffs: dict[int, npt.NDArray[np.complex128]] = {}
     for n in range(-n_max, n_max + 1):
         phase = np.exp(-1j * n * angles)
-        coeffs[n] = np.tensordot(phase, matrices, axes=(0, 0)) / n_azimuth
+        coeffs[n] = np.asarray(
+            np.tensordot(phase, matrices, axes=(0, 0)) / n_azimuth,
+            dtype=np.complex128,
+        )
     return coeffs
 
 

src/vane/modal/identifier.py

@@ -951,7 +951,9 @@ def _best_path(
     parent: list[npt.NDArray[np.int_]] = []
     for op in range(n_op):
         start = np.where(used[op], _NEG_INF, 0.0)
-        link_parent = np.full(used[op].shape[0], -1, dtype=np.int_)
+        link_parent: npt.NDArray[np.int_] = np.full(
+            used[op].shape[0], -1, dtype=np.int_
+        )
         if op > 0 and best[op - 1].size and start.size:
             candidate = best[op - 1][:, np.newaxis] + affinities[op - 1]
             from_prev = candidate.max(axis=0) if candidate.size else start

src/vane/modal/labeler.py

@@ -716,7 +716,7 @@ def _number_tower_modes(
     """
     frequencies = np.asarray(solution.natural_frequencies_hz, dtype=np.float64)
     eigenvalues = np.asarray(solution.eigenvalues, dtype=np.complex128)
-    flags = np.zeros(len(labels), dtype=np.bool_)
+    flags: npt.NDArray[np.bool_] = np.zeros(len(labels), dtype=np.bool_)
     if np.shape(solution.is_degenerate) == flags.shape:
         flags = np.asarray(solution.is_degenerate, dtype=np.bool_)
     if frequencies.shape != flags.shape or eigenvalues.shape != flags.shape:
@@ -1201,7 +1201,9 @@ def label_solution(
     mbc_coordinates = solution.dof_mbc_coordinates
     # A degenerate mode's eigenvector is an arbitrary rotation within its subspace,
     # so its multi-blade (whirl) type is not defined; it gets no suffix.
-    degenerate = np.zeros(solution.mode_shapes.shape[1], dtype=np.bool_)
+    degenerate: npt.NDArray[np.bool_] = np.zeros(
+        solution.mode_shapes.shape[1], dtype=np.bool_
+    )
     if np.shape(solution.is_degenerate) == degenerate.shape:
         degenerate = np.asarray(solution.is_degenerate, dtype=np.bool_)
     columns = [

src/vane/run/acceptance.py

@@ -611,7 +611,9 @@ def _component_indices(
     return tuple(indices)
 
 
-def _values(files: Sequence[LinFile], indices: tuple[int, ...]) -> np.ndarray:
+def _values(
+    files: Sequence[LinFile], indices: tuple[int, ...]
+) -> npt.NDArray[np.float64]:
     """Stack the selected state operating-point values by azimuth."""
     if not indices:
         return np.empty((len(files), 0), dtype=np.float64)
@@ -622,7 +624,7 @@ def _values(files: Sequence[LinFile], indices: tuple[int, ...]) -> np.ndarray:
 
 
 def _vector_metrics(
-    values: np.ndarray,
+    values: npt.NDArray[np.float64],
     weights: npt.NDArray[np.float64],
 ) -> tuple[tuple[float, ...] | None, float | None, float | None]:
     """Return component mean, norm of that mean, and maximum instantaneous norm."""

src/vane/sysid/kalman_filter.py

@@ -96,7 +96,7 @@ class FilterResult:
 
 def _as_available(
     available: npt.NDArray[np.bool_] | None, n_outputs: int
-) -> np.ndarray:
+) -> npt.NDArray[np.bool_]:
     """Return a boolean output mask of length ``n_outputs`` (all-True if ``None``)."""
     if available is None:
         return np.ones(n_outputs, dtype=bool)
@@ -242,7 +242,7 @@ def __init__(
         self.state_names = list(model.state_names)
         n = self.a.shape[0]
         if initial_state is None:
-            self.x = np.zeros(n, dtype=np.float64)
+            self.x: npt.NDArray[np.float64] = np.zeros(n, dtype=np.float64)
         else:
             self.x = np.asarray(initial_state, dtype=np.float64).ravel()
             if self.x.shape != (n,):
@@ -648,7 +648,7 @@ def __init__(
             )
             raise ValueError(msg)
         if initial_state is None:
-            self.x = np.zeros(n, dtype=np.float64)
+            self.x: npt.NDArray[np.float64] = np.zeros(n, dtype=np.float64)
         else:
             self.x = np.asarray(initial_state, dtype=np.float64).ravel()
             if self.x.shape != (n,):

src/vane/sysid/observability.py

@@ -647,7 +647,7 @@ def observability_scores(
             np.trace(_gramian_from_information(a, info_i, system.dt, horizon))
         )
 
-    per_mode = np.empty(0, dtype=np.float64)
+    per_mode: npt.NDArray[np.float64] = np.empty(0, dtype=np.float64)
     if mode_states is not None:
         information = _output_information(c, measurement_noise)
         gramian = _gramian_from_information(a, information, system.dt, horizon)

src/vane/sysid/periodic.py

@@ -12,7 +12,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, cast
 
 import numpy as np
 
@@ -131,7 +131,7 @@ def discretized(self, dt: float) -> PeriodicStateSpace:
 
 
 def _shape(matrix: npt.NDArray[np.float64] | None) -> tuple[int, int] | None:
-    return None if matrix is None else matrix.shape
+    return None if matrix is None else cast("tuple[int, int]", matrix.shape)
 
 
 def _blend(

src/vane/sysid/physical_lpv.py

@@ -375,7 +375,8 @@ def _dropped_coupling(
     chosen = set(x_idx)
     dropped = [j for j in range(a.shape[0]) if j not in chosen]
     a_ratio, a_cols = _relative_block_norms(a[list(x_idx), :], dropped)
-    c_ratio, c_cols = 0.0, np.zeros(len(dropped), dtype=np.float64)
+    c_ratio = 0.0
+    c_cols: npt.NDArray[np.float64] = np.zeros(len(dropped), dtype=np.float64)
     if c is not None and y_idx:
         c_ratio, c_cols = _relative_block_norms(c[list(y_idx), :], dropped)
     shares = np.maximum(a_cols, c_cols)

src/vane/sysid/rom_family.py

@@ -341,7 +341,7 @@ def build_rom_family(
         if prev_residues is None:
             # Reference: track the top-n_modes (modal_reduction ranks by residue).
             order = list(range(n_modes))
-            mac_row = np.ones(n_modes, dtype=np.float64)
+            mac_row: npt.NDArray[np.float64] = np.ones(n_modes, dtype=np.float64)
         else:
             # Match ALL previously tracked modes jointly against the FULL candidate
             # set by output-residue MAC with the frequency tie-breaker, so a tracked
@@ -710,7 +710,7 @@ def _transfer_eligible(
     n_modes = system.n_states // 2
     c_modal = c[:, 0::2] - 1j * c[:, 1::2]  # (n_outputs, n_modes)
     b_modal = b[0::2] + 1j * b[1::2]  # (n_modes, n_inputs)
-    scores = np.zeros(n_modes, dtype=np.float64)
+    scores: npt.NDArray[np.float64] = np.zeros(n_modes, dtype=np.float64)
     for output, input_channel in pairs:
         row = _resolve_output(system, output)
         column = _resolve_input(system, input_channel)

src/vane/sysid/serialize.py

@@ -21,7 +21,7 @@
 import json
 import os
 from dataclasses import fields, is_dataclass
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import numpy as np
 
@@ -32,6 +32,9 @@
 from vane.sysid.rom_family import RomFamily
 from vane.sysid.state_space import StateSpace
 
+if TYPE_CHECKING:
+    import numpy.typing as npt
+
 __all__ = [
     "LPV_FAMILY_SCHEMA_VERSION",
     "load_lpv_family",
@@ -170,7 +173,7 @@ def save_result(result: _Result, path: str | os.PathLike[str]) -> None:
     if type(result) not in (FilterResult, ScheduledFilterResult):
         msg = f"save_result does not handle {name}"
         raise TypeError(msg)
-    arrays: dict[str, np.ndarray] = {}
+    arrays: dict[str, npt.NDArray[Any]] = {}
     meta_fields: dict[str, Any] = {}
     for field in fields(result):
         value = getattr(result, field.name)
@@ -186,7 +189,7 @@ def save_result(result: _Result, path: str | os.PathLike[str]) -> None:
     }
     # Store the metadata as a 0-d string array so the archive is a single, pickle-free
     # npz (the arrays share the same file under their field names).
-    payload: dict[str, np.ndarray] = {"__meta__": np.asarray(json.dumps(meta))}
+    payload: dict[str, npt.NDArray[Any]] = {"__meta__": np.asarray(json.dumps(meta))}
     payload.update(arrays)
     # mypy reads a splatted dict as a possible ``allow_pickle=`` (bool) keyword; none of
     # our keys is that reserved name, so the arrays are all stored as named arrays. The
@@ -381,7 +384,7 @@ def save_rom_family(family: RomFamily, path: str | os.PathLike[str]) -> None:
     """
     _validate_rom_family(family)
     systems_meta: list[dict[str, Any]] = []
-    payload: dict[str, np.ndarray] = {
+    payload: dict[str, npt.NDArray[Any]] = {
         "operating_points": family.operating_points,
         "frequencies_hz": family.frequencies_hz,
         "damping_ratios": family.damping_ratios,
@@ -459,7 +462,7 @@ def load_rom_family(path: str | os.PathLike[str]) -> RomFamily:
                 raise ValueError(msg)
             prefix = f"system_{index:04d}_"
             required.add(prefix + "a")
-            matrices: dict[str, np.ndarray | None] = {}
+            matrices: dict[str, npt.NDArray[Any] | None] = {}
             for name in ("b", "c", "d"):
                 present = presence[name]
                 if not isinstance(present, bool):
@@ -635,7 +638,7 @@ def save_lpv_family(family: LPVFamily, path: str | os.PathLike[str]) -> None:
         layout (matrix shapes, names and sample time).
     """
     _validate_lpv_family(family)
-    payload: dict[str, np.ndarray] = {
+    payload: dict[str, npt.NDArray[Any]] = {
         "scheduling_points": family.scheduling_points,
         "dropped_coupling_a": family.dropped_coupling_a,
         "dropped_coupling_c": family.dropped_coupling_c,
@@ -719,7 +722,7 @@ def _load_lpv_system(
         msg = f"LPV-family system {index} has invalid matrix metadata"
         raise ValueError(msg)
     prefix = f"system_{index:04d}_"
-    matrices: dict[str, np.ndarray | None] = {}
+    matrices: dict[str, npt.NDArray[Any] | None] = {}
     for name in ("a", "b", "c", "d"):
         present = True if name == "a" else presence[name]
         if not isinstance(present, bool):

src/vane/sysid/spectral_transfer.py

@@ -248,7 +248,9 @@ def frequency_response(
     b = system.b[:, inputs]
     c = system.c[outputs]
     if system.d is None:
-        d = np.zeros((len(outputs), len(inputs)), dtype=np.float64)
+        d: npt.NDArray[np.float64] = np.zeros(
+            (len(outputs), len(inputs)), dtype=np.float64
+        )
     else:
         d = system.d[np.ix_(outputs, inputs)]
     identity = np.eye(system.n_states)

src/vane/viz/_style.py

@@ -270,7 +270,7 @@ def plot_style() -> Iterator[None]:
     ...     fig, ax = plt.subplots()
     ...     lines = ax.plot([0.0, 1.0], [0.0, 1.0])
     """
-    with mpl.rc_context(rc=style_rc()):
+    with mpl.rc_context(rc=cast("dict[Any, Any]", style_rc())):
         yield
 
 
@@ -316,7 +316,7 @@ def apply_style(target: Figure | Axes | go.Figure | None = None) -> None:
         If ``target`` is none of the supported types.
     """
     if target is None:
-        mpl.rcParams.update(style_rc())
+        mpl.rcParams.update(cast("dict[Any, Any]", style_rc()))
     elif isinstance(target, go.Figure):
         target.update_layout(template=resolve_template(None))
     elif isinstance(target, Figure):
@@ -352,7 +352,7 @@ def static_style(
             else:
                 stack.enter_context(matplotlib.style.context(style))
         if size is not None:
-            stack.enter_context(mpl.rc_context(rc=size.rc()))
+            stack.enter_context(mpl.rc_context(rc=cast("dict[Any, Any]", size.rc())))
         yield
 
 
@@ -521,7 +521,7 @@ def operating_range_label(parameter_name: str) -> str:
 
 def rc_points(key: str) -> float:
     """Return a font-size ``rcParams`` entry in points (``"medium"`` is resolved)."""
-    return float(FontProperties(size=mpl.rcParams[key]).get_size())
+    return float(FontProperties(size=mpl.rcParams[cast("Any", key)]).get_size())
 
 
 def text_color(color: str, shade: float = 0.8) -> str:

src/vane/viz/qoi_plot.py

@@ -62,7 +62,7 @@ def _heatmap_data(
     if min_normalized <= 0.0:
         # The documented escape hatch: keep every mode, including any that are
         # completely dormant (zero contribution to every load).
-        keep = np.arange(matrix.shape[1])
+        keep: npt.NDArray[np.intp] = np.arange(matrix.shape[1])
     else:
         keep = np.flatnonzero(matrix.max(axis=0) > min_normalized)
         if keep.size == 0:

tests/test_run/test_auto_sweep.py

@@ -1103,7 +1103,10 @@ def test_shared_servodyn_allowed_without_override(
         """Without an override ServoDyn is only read, so it may stay shared."""
         from vane.run import auto_sweep as auto_module
         from vane.run import driver as driver_module
+        from vane.run import floating_auto as floating_module
 
+        # The deck names a ``.dll``; make it a host library on every test platform.
+        monkeypatch.setattr(floating_module, "_host_library_suffix", lambda: ".dll")
         shared = tmp_path / "shared"
         shared.mkdir()
         servo = _SERVO_DLL.replace(