diff --git a/ntrfc/data/t106_cascadecase_how5/Readme.md b/ntrfc/data/t106_cascadecase_how5/Readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..9288ee3802e1322f5971a06aa29b9ad82b1ef9e0
--- /dev/null
+++ b/ntrfc/data/t106_cascadecase_how5/Readme.md
@@ -0,0 +1,7 @@
+data is distributed by
+
+https://how5.cenaero.be/content/cs2-t106-lpt-cascades
+
+data description
+
+https://how5.cenaero.be/sites/how5.cenaero.be/files/CS2_DNSLEST106_0.pdf
diff --git a/ntrfc/data/t106_cascadecase_how5/profilepoints.txt b/ntrfc/data/t106_cascadecase_how5/profilepoints.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d3bd1e670d036b479f31c6ca5e97786031856f11
--- /dev/null
+++ b/ntrfc/data/t106_cascadecase_how5/profilepoints.txt
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diff --git a/ntrfc/turbo/profile_tele_extraction.py b/ntrfc/turbo/profile_tele_extraction.py
index 27b5ccb4d255064994152c89adbc0ab806fe6f78..10f894450e352691adad1b8638e0e45bea3c54f5 100644
--- a/ntrfc/turbo/profile_tele_extraction.py
+++ b/ntrfc/turbo/profile_tele_extraction.py
@@ -5,7 +5,7 @@ from scipy.interpolate import splprep, splev
from scipy.spatial import KDTree
from ntrfc.geometry.line import lines_from_points, polyline_from_points
from ntrfc.geometry.plane import inside_poly
-from ntrfc.math.vectorcalc import findNearest, vecDir, compute_minmax_distance_in_pointcloud
+from ntrfc.math.vectorcalc import findNearest, vecDir, compute_minmax_distance_in_pointcloud, randomUnitVec
def clean_sites(sites, boundary, tolerance_factor =3e-2):
@@ -39,8 +39,6 @@ def clean_sites(sites, boundary, tolerance_factor =3e-2):
radii.append(r)
print(f"ratio of cleaned sites: {len(cleaned)/len(sites)}")
- cleaned = np.array(cleaned)
-
return np.array(cleaned), np.array(radii)
@@ -50,14 +48,18 @@ def extract_vk_hk(sortedPoly, verbose=False):
points_2d_closed_refined = pointcloud_to_profile(points)
- x_center, y_center = voronoi_skeleton(points_2d_closed_refined)
+ sites_raw_clean, radii= voronoi_skeleton_sites(points_2d_closed_refined)
+ tck, u = splprep(sites_raw_clean.T, u=None, s=0.000005, per=0, k=3)
+ res = 10000
+ u_new = np.linspace(u.min(), u.max(), res)
+ x_center, y_center = splev(u_new, tck, der=0)
le_ind, te_ind = skeletonline_completion(x_center, y_center, points)
return le_ind, te_ind
-def voronoi_skeleton(points_2d_closed_refined):
+def voronoi_skeleton_sites(points_2d_closed_refined):
vor = Voronoi(points_2d_closed_refined)
voronoi_sites_inside = vor.vertices[inside_poly(points_2d_closed_refined, vor.vertices)]
@@ -65,12 +67,8 @@ def voronoi_skeleton(points_2d_closed_refined):
sites_inside_sorted = voronoi_sites_inside[sort_indices]
clean_sites_inside, radii = clean_sites(sites_inside_sorted, points_2d_closed_refined)
- tck, u = splprep(clean_sites_inside.T, u=None, s=0.000005, per=0, k=3)
- res = 10000
- u_new = np.linspace(u.min(), u.max(), res)
- x_center, y_center = splev(u_new, tck, der=0)
+ return clean_sites_inside, radii
- return x_center, y_center
def skeletonline_completion(x_center, y_center, points):
@@ -104,7 +102,7 @@ def skeletonline_completion(x_center, y_center, points):
def pointcloud_to_profile(points):
points_2d_closed = np.vstack((points[:, :2], points[:, :2][0]))
tck, u = splprep(points_2d_closed.T, u=None, s=0.0, per=1, k=3)
- res = 10000
+ res = 30000
u_new = np.linspace(u.min(), u.max(), res)
x_new, y_new = splev(u_new, tck, der=0)
points_2d_closed_refined = np.stack([x_new, y_new]).T