Merge pull request #96 from espdev/mypy

Bump version, bump minimal Python version to 3.10, fix type annotations and mypy checks

Author: espdev

.github/workflows/main.yaml

@@ -27,7 +27,7 @@ jobs:
       max-parallel: 8
       matrix:
         platform: [ "ubuntu-latest", "macos-latest", "windows-latest" ]
-        python-version: [ "3.9", "3.10", "3.11", "3.12", "3.13" ]
+        python-version: [ "3.10", "3.11", "3.12", "3.13" ]
         poetry-version: [ "1.8.5" ]
 
     steps:
@@ -58,7 +58,7 @@ jobs:
       - name: Install the project dependencies
         run: poetry install -E docs
 
-      - name: Run static analysis and linters
+      - name: Run static analysis, linters and mypy
         run: poetry run poe check
 
       - name: Run tests

.github/workflows/release.yaml

@@ -26,7 +26,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.12"
 
       - name: Install Poetry
         uses: abatilo/actions-poetry@v4

.pre-commit-config.yaml

@@ -16,3 +16,9 @@ repos:
         language: system
         types: [ python ]
         require_serial: true
+      - id: check-types
+        name: check-types
+        entry: poetry run poe check-types-pre-commit
+        language: system
+        types: [ python ]
+        require_serial: true

CHANGELOG.md

@@ -1,5 +1,11 @@
 # Changelog
 
+## v1.3.0 (14.04.2025)
+
+* Bump minimal Python version to 3.10
+* Fix type annotations
+* Fix checking types by mypy
+
 ## v1.2.1 (10.04.2025)
 
 * Update dependencies

csaps/__init__.py

@@ -6,7 +6,7 @@
 from csaps._shortcut import AutoSmoothingResult, csaps
 from csaps._sspndg import NdGridCubicSmoothingSpline, NdGridSplinePPForm
 from csaps._sspumv import CubicSmoothingSpline, SplinePPForm
-from csaps._types import MultivariateDataType, NdGridDataType, UnivariateDataType
+from csaps._types import MultivariateDataType, SequenceUnivariateDataType, UnivariateDataType
 from csaps._version import __version__
 
 __all__ = [
@@ -23,6 +23,6 @@
     # Type-hints
     'UnivariateDataType',
     'MultivariateDataType',
-    'NdGridDataType',
+    'SequenceUnivariateDataType',
     '__version__',
 ]

csaps/_base.py

@@ -7,7 +7,7 @@
 
 import numpy as np
 
-from ._types import TData, TExtrapolate, TNu, TProps, TSmooth, TSpline, TXi
+from ._types import TData, TExtrapolate, TNu, TProps, TSmooth, TSpline, TXi, FloatNDArrayType
 
 
 class ISplinePPForm(abc.ABC, Generic[TData, TProps]):
@@ -22,7 +22,7 @@ def breaks(self) -> TData:
 
         Returns
         -------
-        breaks : Union[np.ndarray, ty.Tuple[np.ndarray, ...]]
+        breaks : Union[np.ndarray, Tuple[np.ndarray, ...]]
             Breaks data
         """
 
@@ -44,7 +44,7 @@ def order(self) -> TProps:
 
         Returns
         -------
-        order : ty.Union[int, ty.Tuple[int, ...]]
+        order : Union[int, Tuple[int, ...]]
             The spline order
         """
 
@@ -55,7 +55,7 @@ def pieces(self) -> TProps:
 
         Returns
         -------
-        pieces : ty.Union[int, ty.Tuple[int, ...]]
+        pieces : Union[int, Tuple[int, ...]]
             The spline pieces data
         """
 
@@ -98,5 +98,10 @@ def spline(self) -> TSpline:
         """Returns spline representation in PP-form"""
 
     @abc.abstractmethod
-    def __call__(self, xi: TXi, nu: Optional[TNu] = None, extrapolate: Optional[TExtrapolate] = None) -> np.ndarray:
+    def __call__(
+        self,
+        xi: TXi,
+        nu: Optional[TNu] = None,
+        extrapolate: Optional[TExtrapolate] = None,
+    ) -> FloatNDArrayType:
         """Evaluates spline on the data sites"""

csaps/_reshape.py

@@ -111,8 +111,11 @@ def umv_coeffs_to_canonical(arr: np.ndarray, pieces: int):
 
     """
 
-    ndim = arr.shape[0]
-    order = arr.shape[1] // pieces
+    ndim: int = arr.shape[0]
+    order: int = arr.shape[1] // pieces
+
+    shape: tuple[int, ...]
+    strides: tuple[int, ...]
 
     if ndim == 1:
         shape = (order, pieces)

csaps/_shortcut.py

@@ -5,10 +5,12 @@
 from typing import NamedTuple, Optional, Sequence, Union, overload
 from collections import abc as c_abc
 
+import numpy as np
+
 from ._base import ISmoothingSpline
-from ._sspndg import NdGridCubicSmoothingSpline, ndgrid_prepare_data_vectors
+from ._sspndg import NdGridCubicSmoothingSpline
 from ._sspumv import CubicSmoothingSpline
-from ._types import MultivariateDataType, NdGridDataType, UnivariateDataType
+from ._types import MultivariateDataType, SequenceUnivariateDataType, UnivariateDataType
 
 
 class AutoSmoothingResult(NamedTuple):
@@ -24,6 +26,8 @@ class AutoSmoothingResult(NamedTuple):
 # **************************************
 # csaps signatures
 #
+
+
 @overload
 def csaps(
     xdata: UnivariateDataType,
@@ -66,10 +70,10 @@ def csaps(
 
 @overload
 def csaps(
-    xdata: NdGridDataType,
+    xdata: SequenceUnivariateDataType,
     ydata: MultivariateDataType,
     *,
-    weights: Optional[NdGridDataType] = None,
+    weights: Optional[SequenceUnivariateDataType] = None,
     smooth: Optional[Sequence[float]] = None,
     axis: Optional[int] = None,
     normalizedsmooth: bool = False,
@@ -79,11 +83,11 @@ def csaps(
 
 @overload
 def csaps(
-    xdata: NdGridDataType,
+    xdata: SequenceUnivariateDataType,
     ydata: MultivariateDataType,
-    xidata: NdGridDataType,
+    xidata: SequenceUnivariateDataType,
     *,
-    weights: Optional[NdGridDataType] = None,
+    weights: Optional[SequenceUnivariateDataType] = None,
     axis: Optional[int] = None,
     normalizedsmooth: bool = False,
 ) -> AutoSmoothingResult:  # pragma: no cover
@@ -92,33 +96,32 @@ def csaps(
 
 @overload
 def csaps(
-    xdata: NdGridDataType,
+    xdata: SequenceUnivariateDataType,
     ydata: MultivariateDataType,
-    xidata: NdGridDataType,
+    xidata: SequenceUnivariateDataType,
     *,
     smooth: Sequence[float],
-    weights: Optional[NdGridDataType] = None,
+    weights: Optional[SequenceUnivariateDataType] = None,
     axis: Optional[int] = None,
     normalizedsmooth: bool = False,
 ) -> MultivariateDataType:  # pragma: no cover
     ...
 
 
-#
-# csaps signatures
 # **************************************
+# csaps implementation
 
 
 def csaps(
-    xdata: Union[UnivariateDataType, NdGridDataType],
-    ydata: MultivariateDataType,
-    xidata: Optional[Union[UnivariateDataType, NdGridDataType]] = None,
+    xdata,
+    ydata,
+    xidata=None,
     *,
-    weights: Optional[Union[UnivariateDataType, NdGridDataType]] = None,
-    smooth: Optional[Union[float, Sequence[float]]] = None,
-    axis: Optional[int] = None,
-    normalizedsmooth: bool = False,
-) -> Union[MultivariateDataType, ISmoothingSpline, AutoSmoothingResult]:
+    weights=None,
+    smooth=None,
+    axis=None,
+    normalizedsmooth=False,
+):
     """Smooths the univariate/multivariate/gridded data or computes the corresponding splines
 
     This function might be used as the main API for smoothing any data.
@@ -220,32 +223,27 @@ def csaps(
 
     """
 
+    umv = True
     if isinstance(xdata, c_abc.Sequence):
-        try:
-            ndgrid_prepare_data_vectors(xdata, 'xdata')
-        except ValueError:
-            umv = True
-        else:
+        if len(xdata) and isinstance(xdata[0], (np.ndarray, c_abc.Sequence)):
             umv = False
-    else:
-        umv = True
 
     if umv:
         axis = -1 if axis is None else axis
         sp = CubicSmoothingSpline(
-            xdata,
-            ydata,
+            xdata=xdata,
+            ydata=ydata,
             weights=weights,
             smooth=smooth,
             axis=axis,
             normalizedsmooth=normalizedsmooth,
         )
     else:
         sp = NdGridCubicSmoothingSpline(
-            xdata,
-            ydata,
-            weights,
-            smooth,
+            xdata=xdata,
+            ydata=ydata,
+            weights=weights,
+            smooth=smooth,
             normalizedsmooth=normalizedsmooth,
         )
 

csaps/_sspndg.py

@@ -2,9 +2,8 @@
 ND-Gridded cubic smoothing spline implementation
 """
 
-from typing import Optional, Sequence, Tuple, Union
+from typing import Optional, Sequence, Tuple, Union, cast
 import collections.abc as c_abc
-from numbers import Number
 
 import numpy as np
 from scipy.interpolate import NdPPoly, PPoly
@@ -18,24 +17,28 @@
     umv_coeffs_to_flatten,
 )
 from ._sspumv import CubicSmoothingSpline
-from ._types import NdGridDataType, UnivariateDataType
+from ._types import SequenceUnivariateDataType, FloatNDArrayType, Float1DArrayTupe
 
 
-def ndgrid_prepare_data_vectors(data, name, min_size: int = 2) -> Tuple[np.ndarray, ...]:
+def ndgrid_prepare_data_vectors(
+    data: SequenceUnivariateDataType,
+    name: str,
+    min_size: int = 2,
+) -> Tuple[Float1DArrayTupe, ...]:
     if not isinstance(data, c_abc.Sequence):
         raise TypeError(f"'{name}' must be a sequence of 1-d array-like (vectors) or scalars.")
 
-    data = list(data)
+    data_: list[Float1DArrayTupe] = []
 
     for axis, d in enumerate(data):
         d = np.asarray(d, dtype=np.float64)
         if d.ndim > 1:
             raise ValueError(f"All '{name}' elements must be a vector for axis {axis}.")
         if d.size < min_size:
             raise ValueError(f"'{name}' must contain at least {min_size} data points for axis {axis}.")
-        data[axis] = d
+        data_.append(d)
 
-    return tuple(data)
+    return tuple(data_)
 
 
 class NdGridSplinePPForm(ISplinePPForm[Tuple[np.ndarray, ...], Tuple[int, ...]], NdPPoly):
@@ -76,9 +79,9 @@ def ndim(self) -> int:
     def shape(self) -> Tuple[int, ...]:
         return tuple(len(xi) for xi in self.x)
 
-    def __call__(
+    def __call__(  # type: ignore[override]
         self,
-        x: Sequence[UnivariateDataType],
+        x: SequenceUnivariateDataType,
         nu: Optional[Tuple[int, ...]] = None,
         extrapolate: Optional[bool] = None,
     ) -> np.ndarray:
@@ -87,8 +90,8 @@ def __call__(
         Parameters
         ----------
 
-        x : tuple of 1-d array-like
-            The tuple of point values for each dimension to evaluate the spline at.
+        x : Sequence of 1-d array-like
+            The sequence of point values for each dimension to evaluate the spline at.
 
         nu : [*Optional*] tuple of int
             Orders of derivatives to evaluate. Each must be non-negative.
@@ -158,7 +161,7 @@ class NdGridCubicSmoothingSpline(
     ISmoothingSpline[
         NdGridSplinePPForm,
         Tuple[float, ...],
-        NdGridDataType,
+        SequenceUnivariateDataType,
         Tuple[int, ...],
         bool,
     ]
@@ -204,31 +207,29 @@ class NdGridCubicSmoothingSpline(
 
     def __init__(
         self,
-        xdata: NdGridDataType,
+        xdata: SequenceUnivariateDataType,
         ydata: np.ndarray,
-        weights: Optional[Union[UnivariateDataType, NdGridDataType]] = None,
+        weights: Optional[SequenceUnivariateDataType] = None,
         smooth: Optional[Union[float, Sequence[Optional[float]]]] = None,
         normalizedsmooth: bool = False,
     ) -> None:
         x, y, w, s = self._prepare_data(xdata, ydata, weights, smooth)
-        coeffs, smooth = self._make_spline(x, y, w, s, normalizedsmooth)
-
-        self._spline = NdGridSplinePPForm.construct_fast(coeffs, x)
-        self._smooth = smooth
+        coeffs, self._smooth = self._make_spline(x, y, w, s, normalizedsmooth)
+        self._spline = cast(NdGridSplinePPForm, NdGridSplinePPForm.construct_fast(coeffs, x))
 
     def __call__(
         self,
-        x: Union[NdGridDataType, Sequence[Number]],
+        x: SequenceUnivariateDataType,
         nu: Optional[Tuple[int, ...]] = None,
         extrapolate: Optional[bool] = None,
-    ) -> np.ndarray:
+    ) -> FloatNDArrayType:
         """Evaluate the spline for given data
 
         Parameters
         ----------
 
-        x : tuple of 1-d array-like
-            The tuple of point values for each dimension to evaluate the spline at.
+        x : Sequence of 1-d array-like
+            The sequence of point values for each dimension to evaluate the spline at.
 
         nu : [*Optional*] tuple of int
             Orders of derivatives to evaluate. Each must be non-negative.