add stubs for glam_fit

src/python/photospline.pyi

@@ -4,28 +4,50 @@ import numpy.typing as npt
 
 class SplineTable:
     coefficients: npt.NDArray[np.float32]
-    extents: tuple[tuple[float,float], ...]
+    extents: tuple[tuple[float, float], ...]
     knots: tuple[npt.NDArray[np.float32], ...]
     ndim: int
     order: tuple[int, ...]
 
     def __init__(self, path: str): ...
-
     @classmethod
-    def stack(cls, tables: Sequence[SplineTable], coordinates: Sequence[float], stackOrder: int = 2) -> SplineTable:
-        """
-        """
+    def stack(
+        cls,
+        tables: Sequence[SplineTable],
+        coordinates: Sequence[float],
+        stackOrder: int = 2,
+    ) -> SplineTable:
+        """ """
         ...
-
     def aux_value(self, *args, **kwargs) -> Any: ...
     def convolve(self, *args, **kwargs) -> Any: ...
     def deriv(self, *args, **kwargs) -> Any: ...
     def evaluate(self, *args, **kwargs) -> Any: ...
     def evaluate_gradient(self, *args, **kwargs) -> Any: ...
     def evaluate_simple(self, *args, **kwargs) -> Any: ...
+    def grideval(self, *args, **kwargs) -> Any: ...
     def permute_dimensions(self, *args, **kwargs) -> Any: ...
     def search_centers(self, *args, **kwargs) -> Any: ...
     def write(self, *args, **kwargs) -> Any: ...
     def __call__(self, *args, **kwargs) -> Any: ...
 
-def bspline(*args, **kwargs) -> Any: ...
+class ndsparse:
+    def __init__(self, rows: int, ndim: int) -> None: ...
+    @classmethod
+    def from_data(
+        cls, values: npt.ArrayLike, weights: None | npt.ArrayLike = None
+    ) -> tuple[ndsparse, ndsparse]: ...
+    def insert(self, value: float, indices: Sequence[int]) -> None: ...
+
+def bspline(knots: npt.ArrayLike, x: float, index: int, order: int) -> float: ...
+def glam_fit(
+    data: ndsparse,
+    weights: ndsparse,
+    coordinates: Sequence[Sequence[float]],
+    knots: Sequence[Sequence[float]],
+    order: Sequence[int],
+    smoothing: Sequence[float],
+    penaltyOrder: Sequence[int],
+    monodim: None | int = None,
+    verbose: int = 1,
+) -> SplineTable: ...

test/test_fit.py

@@ -27,8 +27,8 @@ def pad_knots(knots, order=2):
 order = [2,2,3]
 smooth = 1
 
-data, w = photospline.ndsparse.from_data(z, w)
-spline = photospline.glam_fit(data, w, centers, knots, order, [smooth]*3, [2]*3, monodim=2, verbose=False)
+data, ws = photospline.ndsparse.from_data(z, w)
+spline = photospline.glam_fit(data, ws, centers, knots, order, [smooth]*3, [2]*3, monodim=2, verbose=False)
 y = spline.grideval(centers)
 
 residual = ((z-y)**2).sum()