remove a potential source of non-determinism

i observed occasional test failures for the samples with a single point, but maybe it was simply due to the problem being ill-posed and different correct solutions being returned. hopefully this fixes it, but i am not sure

src/depiction/calibration/models/linear_model.py

@@ -46,20 +46,18 @@ def zero(cls) -> LinearModel:
         return cls([0, 0])
 
     @classmethod
-    def fit_lsq(cls, x_arr: NDArray[float], y_arr: NDArray[float], min_points: int | None = None) -> LinearModel:
+    def fit_lsq(cls, x_arr: NDArray[float], y_arr: NDArray[float], min_points: int = 2) -> LinearModel:
         """Fits a linear model to the given data using least squares regression."""
-        if min_points is not None and x_arr.size < min_points:
+        if x_arr.size < min_points:
             return cls.fit_constant(y_arr=y_arr)
         model = sklearn.linear_model.LinearRegression()
         model.fit(x_arr[:, np.newaxis], y_arr[:, np.newaxis])
         return LinearModel(coef=[model.intercept_[0], model.coef_[0, 0]])
 
     @classmethod
-    def fit_siegelslopes(
-        cls, x_arr: NDArray[float], y_arr: NDArray[float], min_points: int | None = None
-    ) -> LinearModel:
+    def fit_siegelslopes(cls, x_arr: NDArray[float], y_arr: NDArray[float], min_points: int = 2) -> LinearModel:
         """Fits a linear model to the given data using robust Siegel-Slopes regression."""
-        if min_points is not None and x_arr.size < min_points:
+        if x_arr.size < min_points:
             return cls.fit_constant(y_arr)
         slope, intercept = scipy.stats.siegelslopes(y=y_arr, x=x_arr)
         return LinearModel(coef=[intercept, slope])