common/lib/deflectometry: Test docs

opencsp/common/lib/deflectometry/ParamsSlopeSolver.py

@@ -2,4 +2,6 @@
 
 
 class ParamsSlopeSolver(ABC):
+    """Abstract ParamsSlopeSolver class"""
+
     pass

opencsp/common/lib/deflectometry/Surface2DParabolic.py

@@ -9,6 +9,8 @@
 
 
 class Surface2DParabolic(Surface2DAbstract):
+    """Representation of 2D fit parabolic surface."""
+
     def __init__(self, initial_focal_lengths_xy: tuple[float, float], robust_least_squares: bool, downsample: int):
         """
         Representation of 2D fit parabolic surface.

opencsp/common/lib/deflectometry/Surface2DPlano.py

@@ -9,6 +9,8 @@
 
 
 class Surface2DPlano(Surface2DAbstract):
+    """Representation of 2D plano surface."""
+
     def __init__(self, robust_least_squares: bool, downsample: int):
         """
         Representation of 2D plano surface.

opencsp/common/lib/deflectometry/slope_fitting_2d.py

@@ -74,6 +74,36 @@ def calc_slopes(v_surf_int_pts_optic: Vxyz, v_optic_cam_optic: Vxyz, v_screen_po
 def fit_slope_robust_ls(
     slope_fit_poly_order: int, slope: np.ndarray, weights: np.ndarray, v_surf_int_pts_optic: Vxyz
 ) -> np.ndarray:
+    """
+    Fits a slope using robust least squares fitting with weighted residuals.
+
+    This function performs a robust least squares fit to the provided slope data,
+    adjusting weights iteratively based on the residuals to minimize the influence
+    of outliers.
+
+    Parameters
+    ----------
+    slope_fit_poly_order : int
+        The order of the polynomial used for fitting the slope.
+    slope : np.ndarray
+        A 1D array of slope measurements.
+    weights : np.ndarray
+        A 1D array of weights corresponding to the slope measurements.
+    v_surf_int_pts_optic : Vxyz
+        An object containing the x and y coordinates of the surface intersection points.
+
+    Returns
+    -------
+    np.ndarray
+        The coefficients of the fitted slope.
+
+    Raises
+    ------
+    ValueError
+        If the lengths of the input arrays do not match or if the fitting process does not converge
+        within the maximum number of iterations.
+    """
+    # "ChatGPT 4o" assisted with generating this docstring.
     # Check lengths match
     if slope.size != weights.size or slope.size != len(v_surf_int_pts_optic):
         raise ValueError(
@@ -129,9 +159,26 @@ def fit_slope_robust_ls(
 
 def fit_slope_ls(slope_fit_poly_order: int, slope: np.ndarray, v_surf_int_pts_optic: Vxyz) -> np.ndarray:
     """
-    Returns best fit slope coefficients to measured slope points using least
-    squared fitting.
+    Fits a slope using ordinary least squares fitting.
+
+    This function computes the best fit slope coefficients for the provided slope data
+    using the least squares method.
+
+    Parameters
+    ----------
+    slope_fit_poly_order : int
+        The order of the polynomial used for fitting the slope.
+    slope : np.ndarray
+        A 1D array of slope measurements.
+    v_surf_int_pts_optic : Vxyz
+        An object containing the x and y coordinates of the surface intersection points.
+
+    Returns
+    -------
+    np.ndarray
+        The coefficients of the fitted slope.
     """
+    # "ChatGPT 4o" assisted with generating this docstring.
     # Create terms
     terms = poly_terms(slope_fit_poly_order, v_surf_int_pts_optic.x, v_surf_int_pts_optic.y)