Apply suggestions from code review

Co-authored-by: Patrick Schuenke <37338697+schuenke@users.noreply.github.com>

src/mrpro/data/KTrajectory.py

@@ -92,29 +92,33 @@ def from_tensor(
         axes_order
             Order of the axes in the tensor. Our convention usually is zyx order.
         repeat_detection_tolerance
-            detects if broadcasting can be used, i.e. if dimensions are repeated.
-            Set to None to disable.
+            Tolerance for detecting repeated dimensions (broadcasting).
+            If trajectory points differ by less than this value, they are considered
+            identical. Set to None to disable this feature.
         grid_detection_tolerance
-            tolerance to detect if trajectory points are on integer grid positions
-        encoding_matrix
-            if an encoding matrix is supplied, the trajectory is rescaled to fit
-            within the matrix. Otherwise, it is left as-is.
+            Tolerance for detecting whether trajectory points align with integer 
+            grid positions. This tolerance is applied after rescaling if 
+            `scaling_matrix` is provided.
+        scaling_matrix
+            If a scaling matrix is provided, the trajectory is rescaled to fit within 
+            the dimensions of the matrix. If not provided, the trajectory remains unchanged.
+
         """
         ks = tensor.unbind(dim=stack_dim)
         kz, ky, kx = (ks[axes_order.index(axis)] for axis in 'zyx')
 
-        def normalize(k: torch.Tensor, encoding_size: int) -> torch.Tensor:
+        def rescale(k: torch.Tensor, size: float) -> torch.Tensor:
             max_abs_range = 2 * k.abs().max()
-            if encoding_size == 1 or max_abs_range < 1e-6:
+            if size < 2 or max_abs_range < 1e-6:
                 # a single encoding point should be at zero
                 # avoid division by zero
                 return torch.zeros_like(k)
-            return k * (encoding_size / max_abs_range)
+            return k * (size / max_abs_range)
 
-        if encoding_matrix is not None:
-            kz = normalize(kz, encoding_matrix.z)
-            ky = normalize(ky, encoding_matrix.y)
-            kx = normalize(kx, encoding_matrix.x)
+        if scaling_matrix is not None:
+            kz = rescale(kz, scaling_matrix.z)
+            ky = rescale(ky, scaling_matrix.y)
+            kx = rescale(kx, scaling_matrix.x)
 
         return cls(
             kz,