From b37f1582739525084a827cf2e8f296bb79b0dc77 Mon Sep 17 00:00:00 2001
From: Guenther Obermair <guenther.obermair@tuwien.ac.at>
Date: Tue, 2 Jul 2024 13:36:38 +0200
Subject: [PATCH] Add: auto_refinement variable

---
 examples/show_swept.py    |  1 +
 splinepy/helpme/create.py | 91 ++++++++++++++++++++++-----------------
 2 files changed, 52 insertions(+), 40 deletions(-)

diff --git a/examples/show_swept.py b/examples/show_swept.py
index 9e3a82e50..e4007169e 100644
--- a/examples/show_swept.py
+++ b/examples/show_swept.py
@@ -120,6 +120,7 @@
         trajectory=trajectory,
         cross_section=cross_section,
         cross_section_normal=cs_nv,
+        auto_refinement=True,
     )
 
     ### VISUALIZATION ###
diff --git a/splinepy/helpme/create.py b/splinepy/helpme/create.py
index 2f90750c5..ed0fa2639 100644
--- a/splinepy/helpme/create.py
+++ b/splinepy/helpme/create.py
@@ -302,6 +302,7 @@ def swept(
     cross_section,
     trajectory,
     cross_section_normal=None,
+    auto_refinement=False,
 ):
     """
     Sweeps a cross-section along a trajectory. The cross-section
@@ -320,6 +321,9 @@ def swept(
     cross_section_normal : np.array
       Normal vector of the cross-section
       Default is [0, 0, 1]
+    auto_refinement : bool
+      If True, the trajectory will be refined at points of
+      highest curvature. Default is False.
 
     Returns
     -------
@@ -340,6 +344,8 @@ def swept(
         raise NotImplementedError("Trajectory must be 1D")
     if not len(cross_section_normal) == 3:
         raise ValueError("Cross section normal must be 3D")
+    if not isinstance(auto_refinement, bool):
+        raise ValueError("auto_refinement must be a boolean")
 
     # setting default value for cross_section_normal
     if cross_section_normal is None:
@@ -349,6 +355,10 @@ def swept(
     trajectory = trajectory.create.embedded(3)
     cross_section = cross_section.create.embedded(3)
 
+    # initialize parameter values
+    par_value = trajectory.greville_abscissae()
+    par_value = par_value.reshape(-1, 1)
+
     def transformation_matrices(traj, par_value):
 
         ### TRANSFORMATION MATRICES ###
@@ -454,49 +464,50 @@ def transformation_matrices(traj, par_value):
 
     ### REFINEMENT OF TRAJECTORY ###
 
-    par_value = trajectory.greville_abscissae()
-    par_value = par_value.reshape(-1, 1)
+    if auto_refinement:
+        ## inserts knots in trajectory area with highest curvature #
 
-    ### insert knots in trajectory area with highest curvature ###
-    curv = []
-    for i in par_value:
-        # calculate curvature of trajectory at parametric value i
-        curv.append(round(_np.linalg.norm(trajectory.derivative([i], [2])), 2))
-    # evaluate the par_values-vector indices of the maximum curvature points
-    max_curv = max(curv)
-    max_indices = [i for i, x in enumerate(curv) if x == max_curv]
-    # prepare matrix for the insertion
-    insertion_values = []
-    # compute the new insertion values
-    par_values = par_value.ravel()
-
-    for maxi in max_indices:
-        if maxi == 0:
-            insertion_values.append(
-                (par_values[maxi] + par_values[maxi + 1]) / 2
-            )
-        elif maxi == len(par_values) - 1:
-            insertion_values.append(
-                (par_values[maxi] + par_values[maxi - 1]) / 2
-            )
-        else:
-            insertion_values.append(
-                (par_values[maxi] + par_values[maxi - 1]) / 2
-            )
-            insertion_values.append(
-                (par_values[maxi] + par_values[maxi + 1]) / 2
+        curv = []
+        for i in par_value:
+            # calculate curvature of trajectory at parametric value i
+            curv.append(
+                round(_np.linalg.norm(trajectory.derivative([i], [2])), 2)
             )
+        # evaluate the par_values-vector indices of the maximum curvature points
+        max_curv = max(curv)
+        max_indices = [i for i, x in enumerate(curv) if x == max_curv]
+        # prepare matrix for the insertion
+        insertion_values = []
+        # compute the new insertion values
+        par_values = par_value.ravel()
+
+        for maxi in max_indices:
+            if maxi == 0:
+                insertion_values.append(
+                    (par_values[maxi] + par_values[maxi + 1]) / 2
+                )
+            elif maxi == len(par_values) - 1:
+                insertion_values.append(
+                    (par_values[maxi] + par_values[maxi - 1]) / 2
+                )
+            else:
+                insertion_values.append(
+                    (par_values[maxi] + par_values[maxi - 1]) / 2
+                )
+                insertion_values.append(
+                    (par_values[maxi] + par_values[maxi + 1]) / 2
+                )
 
-    # insert knots into the trajectory's knot vector
-    insertion_values = _np.unique(insertion_values)
-    trajectory.uniform_refine_fixed_knots(0, len(insertion_values))
-    # add insertion values to the existing parameter values
-    par_value = _np.concatenate(
-        (par_value.reshape(-1), _np.asanyarray(insertion_values))
-    )
-    # sort parameter values
-    par_value = _np.sort(par_value)
-    par_value = par_value.reshape(-1, 1)
+        # insert knots into the trajectory's knot vector
+        insertion_values = _np.unique(insertion_values)
+        trajectory.uniform_refine_fixed_knots(0, len(insertion_values))
+        # add insertion values to the existing parameter values
+        par_value = _np.concatenate(
+            (par_value.reshape(-1), _np.asanyarray(insertion_values))
+        )
+        # sort parameter values
+        par_value = _np.sort(par_value)
+        par_value = par_value.reshape(-1, 1)
 
     ### SWEEPING PROCESS ###