Restore comments in scaler

n3fit/src/n3fit/scaler.py

@@ -23,6 +23,9 @@ def generate_scaler(input_list: List[np.ndarray], interpolation_points: int = No
     input_arr = np.sort(input_arr)
     input_arr_size = input_arr.size
 
+    # Define an evenly spaced grid in the domain [0,1]
+    # force_set_smallest is used to make sure the smallest point included in the scaling is
+    # 1e-9, to prevent trouble when saving it to the LHAPDF grid
     force_set_smallest = input_arr.min() > 1e-9
     if force_set_smallest:
         new_xgrid = np.linspace(
@@ -31,17 +34,23 @@ def generate_scaler(input_list: List[np.ndarray], interpolation_points: int = No
     else:
         new_xgrid = np.linspace(start=0, stop=1.0, endpoint=False, num=input_arr_size)
 
+    # When mapping the FK xgrids onto our new grid, we need to consider degeneracies among
+    # the x-values in the FK grids
     unique, counts = np.unique(input_arr, return_counts=True)
     map_to_complete = []
     for cumsum_ in np.cumsum(counts):
+        # Make sure to include the smallest new_xgrid value, such that we have a point at
+        # x<=1e-9
         map_to_complete.append(new_xgrid[cumsum_ - counts[0]])
     map_to_complete = np.array(map_to_complete)
     map_from_complete = unique
 
+    #  If needed, set feature_scaling(x=1e-9)=0
     if force_set_smallest:
         map_from_complete = np.insert(map_from_complete, 0, 1e-9)
         map_to_complete = np.insert(map_to_complete, 0, 0.0)
 
+    # Select the indices of the points that will be used by the interpolator
     onein = map_from_complete.size / (int(interpolation_points) - 1)
     selected_points = [round(i * onein - 1) for i in range(1, int(interpolation_points))]
     if selected_points[0] != 0: