Bugfix in regular_circle_polyline documentation

.github/workflows/linux_build.yml

@@ -31,7 +31,7 @@ jobs:
         run: |
           python -m pip install "numpy<=1.96.0"
           python -m pip install scipy
-          python -m pip install scikit-image
+          python -m pip install scikit-image==0.24.0
           sudo apt-get install \
               libmpfr-dev \
               libgmp-dev \

.github/workflows/macos_build.yml

@@ -27,7 +27,7 @@ jobs:
         run: |
           ${{ steps.installpython.outputs.python-path }} -m pip install "numpy<=1.96.0"
           ${{ steps.installpython.outputs.python-path }} -m pip install scipy
-          ${{ steps.installpython.outputs.python-path }} -m pip install scikit-image
+          ${{ steps.installpython.outputs.python-path }} -m pip install scikit-image==0.24.0
 
       - name: Install c++ bindings
         shell: bash -l {0}

.github/workflows/windows_build.yml

@@ -31,7 +31,7 @@ jobs:
         run: |
           python -m pip install "numpy<=1.96.0"
           python -m pip install scipy
-          python -m pip install scikit-image
+          python -m pip install scikit-image==0.24.0
 
       # - name: Install dependencies (Windows)
       #   shell: bash -l {0}

src/gpytoolbox/approximate_hausdorff_distance.py

@@ -69,7 +69,7 @@ def approximate_hausdorff_distance(v1,f1,v2,f2,use_cpp=True):
     current_best_guess_hd = 0.0
     for i in range(v1.shape[0]):
         # print("Vertex %d of %d" % (i+1,v1.shape[0]))
-        current_best_guess_dviB = np.Inf # current best guess for d(vi,B)
+        current_best_guess_dviB = np.inf # current best guess for d(vi,B)
 
         queue = [0]
         while (len(queue)>0 and current_best_guess_dviB>current_best_guess_hd):
@@ -108,7 +108,7 @@ def approximate_hausdorff_distance(v1,f1,v2,f2,use_cpp=True):
     # Now we do the other side, i.e., max(d(vB,A))
     for i in range(v2.shape[0]):
         # print("Vertex %d of %d" % (i+1,v2.shape[0]))
-        current_best_guess_dviA = np.Inf
+        current_best_guess_dviA = np.inf
         queue = [0]
         while (len(queue)>0 and current_best_guess_dviA>current_best_guess_hd):
             q2 = queue.pop()

src/gpytoolbox/minimum_distance.py

@@ -43,7 +43,7 @@ def minimum_distance(v1,f1,v2,f2):
 
     first_queue_pair = [0,0]
     queue = [first_queue_pair]
-    current_best_guess = np.Inf
+    current_best_guess = np.inf
     while len(queue)>0:
         q1, q2 = queue.pop()
         # print("-----------")

src/gpytoolbox/ray_polyline_intersect.py

@@ -23,7 +23,7 @@ def ray_polyline_intersect(position, direction, polyline_vertices, max_distance
     Returns
     -------
     x : numpy double array
-        Vector of intersection point coordinates (np.Inf if no intersection)
+        Vector of intersection point coordinates (np.inf if no intersection)
     n : numpy double array
         Vector of polyline normal at intersection (zeros if no intersection)
     ind : int 
@@ -53,7 +53,7 @@ def ray_polyline_intersect(position, direction, polyline_vertices, max_distance
     if (EC is None):
         EC = edge_indices(polyline_vertices.shape[0],closed=True)
     ind = -1
-    x = np.array([np.Inf, np.Inf])
+    x = np.array([np.inf, np.inf])
     n = np.array([0.0, 0.0])
     distance_to_hit = max_distance 
     for i in range(0,EC.shape[0]):

src/gpytoolbox/ray_triangle_intersect.py

@@ -60,8 +60,8 @@ def ray_triangle_intersect(origin,dir, v0, v1, v2, return_negative=False):
     det = np.dot(v0v1,pvec)
 
     if (np.abs(det) < 1e-6):
-        t =  np.Inf
-        hit = np.Inf*np.ones(3)
+        t =  np.inf
+        hit = np.inf*np.ones(3)
         is_hit = False
         return t,is_hit, hit
 
@@ -70,17 +70,17 @@ def ray_triangle_intersect(origin,dir, v0, v1, v2, return_negative=False):
     u = np.dot(tvec,pvec) * invDet
 
     if (u < 0 or u > 1):
-        t =  np.Inf
-        hit = np.Inf*np.ones(3)
+        t =  np.inf
+        hit = np.inf*np.ones(3)
         is_hit = False
         return t,is_hit, hit
 
     qvec = np.cross(tvec,v0v1)
     v = np.dot(dir,qvec)*invDet
 
     if ((v < 0) or ((u + v) > 1)):
-        t =  np.Inf
-        hit = np.Inf*np.ones(3)
+        t =  np.inf
+        hit = np.inf*np.ones(3)
         is_hit = False
         return t,is_hit, hit
 
@@ -89,8 +89,8 @@ def ray_triangle_intersect(origin,dir, v0, v1, v2, return_negative=False):
     is_hit = True
     if (not return_negative):
         if(t<0):
-            t =  np.Inf
-            hit = np.Inf*np.ones(3)
+            t =  np.inf
+            hit = np.inf*np.ones(3)
             is_hit = False
     return t,is_hit, hit
 

src/gpytoolbox/reach_for_the_arcs.py

@@ -21,7 +21,7 @@ def reach_for_the_arcs(U, S,
     local_search_iters=20,
     local_search_t=0.01,
     tol=1e-4,
-    clamp_value=np.Inf,
+    clamp_value=np.inf,
     force_cpu=False,
     parallel=False,
     verbose=False):
@@ -68,7 +68,7 @@ def reach_for_the_arcs(U, S,
         locally make feasible step
     tol : float, optional (default 1e-4)
         tolerance for determining whether a point is inside a sphere
-    clamp_value : float, optional (default np.Inf)
+    clamp_value : float, optional (default np.inf)
         value to which the SDF is clamped for clamped SDF reconstruction
     force_cpu : bool, optional (default False)
         whether to force rasterization onto the CPU
@@ -259,7 +259,7 @@ def _sdf_to_point_cloud(U, S,
     batch_size=10000,
     num_rasterization_spheres=0,
     tol=1e-4,
-    clamp_value=np.Inf,
+    clamp_value=np.inf,
     force_cpu=False,
     parallel=False,
     verbose=False):
@@ -291,7 +291,7 @@ def _sdf_to_point_cloud(U, S,
         Set to zero to use all spheres.
     tol : float, optional (default 1e-4)
         tolerance for determining whether a point is inside a sphere
-    clamp_value : float, optional (default np.Inf)
+    clamp_value : float, optional (default np.inf)
         value to which the SDF is clamped for clamped SDF reconstruction
     parallel : bool, optional (default False)
         whether to parallelize the algorithm or not
@@ -461,7 +461,7 @@ def _locally_make_feasible(U, S, P,
     local_search_iters=20,
     batch_size=10000,
     tol=1e-4,
-    clamp_value=np.Inf,
+    clamp_value=np.inf,
     parallel=False,
     verbose=False):
     """Given a number of SDF samples and points, tries to make each point
@@ -488,7 +488,7 @@ def _locally_make_feasible(U, S, P,
         how many points in one batch. Set to 0 to disable batching.
     tol : float, optional (default 1e-4)
         tolerance for determining whether a point is inside a sphere
-    clamp_value : float, optional (default np.Inf)
+    clamp_value : float, optional (default np.inf)
         value to which the SDF is clamped for clamped SDF reconstruction
     parallel : bool, optional (default False)
         whether to parallelize the algorithm or not
@@ -557,7 +557,7 @@ def _fine_tune_point_cloud(U, S, P, N, f,
     local_search_iters=20,
     local_search_t=0.01,
     tol=1e-4,
-    clamp_value=np.Inf,
+    clamp_value=np.inf,
     parallel=False,
     verbose=False):
     """Improve the point cloud with respect to the SDF such that the
@@ -602,7 +602,7 @@ def _fine_tune_point_cloud(U, S, P, N, f,
         locally make feasible step
     tol : float, optional (default 1e-4)
         tolerance for determining whether a point is inside a sphere
-    clamp_value : float, optional (default np.Inf)
+    clamp_value : float, optional (default np.inf)
         value to which the SDF is clamped for clamped SDF reconstruction
     parallel : bool, optional (default False)
         whether to parallelize the algorithm or not

src/gpytoolbox/reach_for_the_spheres.py

@@ -496,7 +496,7 @@ def reach_for_the_spheres_iteration(state,
         'remesh_iterations':1,
         'batch_size':20000,
         'fix_boundary':False,
-        'clamp':np.Inf, 'pseudosdf_interior':False},
+        'clamp':np.inf, 'pseudosdf_interior':False},
         3: {'max_iter':20000, 'tol':1e-2, 'h':0.2,
         'linesearch':True, 'min_t':1e-6, 'max_t':50.,
         'dt':10.,
@@ -507,7 +507,7 @@ def reach_for_the_spheres_iteration(state,
         'visualize':False,
         'batch_size':20000,
         'fix_boundary':False,
-        'clamp':np.Inf, 'pseudosdf_interior':False}
+        'clamp':np.inf, 'pseudosdf_interior':False}
     }
     if max_iter is None:
         max_iter = default_params[dim]['max_iter']
@@ -566,11 +566,11 @@ def reach_for_the_spheres_iteration(state,
     if state.its is None:
         state.its = 0
     if state.best_performance is None:
-        state.best_performance = np.Inf
+        state.best_performance = np.inf
     if state.convergence_counter is None:
         state.convergence_counter = 0
     if state.best_avg_error is None:
-        state.best_avg_error = np.Inf
+        state.best_avg_error = np.inf
     # if state.use_features is None:
     #     state.use_features = False
     if state.V_last_converged is None:
@@ -687,7 +687,7 @@ def reach_for_the_spheres_iteration(state,
         if state.h>state.min_h:
             state.V_last_converged = state.V.copy()
             state.F_last_converged = state.F.copy()
-            state.best_avg_error = np.Inf
+            state.best_avg_error = np.inf
             state.convergence_counter = 0
         state.h = np.maximum(state.h/2,state.min_h)
     if state.convergence_counter > 100 or F_invalid.shape[0] == 0:
@@ -701,9 +701,9 @@ def reach_for_the_spheres_iteration(state,
             state.U_batch = state.U.copy()
             state.S_batch = state.S.copy()
             state.resample_counter += 1
-            state.best_performance = np.Inf
+            state.best_performance = np.inf
             state.convergence_counter = 0
-            state.best_avg_error = np.Inf
+            state.best_avg_error = np.inf
             if verbose:
                 print(f"Resampled, I now have {state.U.shape[0]} sample points.")
         else:

src/gpytoolbox/regular_circle_polyline.py

@@ -1,7 +1,7 @@
 import numpy as np
 
 def regular_circle_polyline(n):
-    """Triangle mesh of a square
+    """Create a circle polyline
 
     Generates a regular polyline of a circle with radius 1 centered at the origin.
 

src/gpytoolbox/squared_distance.py

@@ -11,15 +11,15 @@ def __init__(self,V,F,ptest):
         self.F = F
         self.dim = V.shape[1]
         self.ptest = ptest
-        self.current_best_guess = np.Inf
+        self.current_best_guess = np.inf
         self.current_best_element = -1
         self.others = []
         self.num_traversal = 0
         self.traversed = []
     # Auxiliary function which finds the distance of point to rectangle
     def sdBox(self,p,center,width):
         q = np.abs(p - center) - 0.5*width
-        maxval = -np.Inf
+        maxval = -np.inf
         for i in range(self.dim):
             maxval = np.maximum(maxval,q[i])
         return np.linalg.norm((np.maximum(q,0.0))) + np.minimum(maxval,0.0)
@@ -183,7 +183,7 @@ def squared_distance(P,V,F=None,use_cpp=False,use_aabb=False,C=None,W=None,CH=No
         # Loop over every element
         t = None
         for j in range(P.shape[0]):
-            min_sqrd_dist = np.Inf
+            min_sqrd_dist = np.inf
             ind = -1
             best_lmb = []
             for i in range(F.shape[0]):

test/test_minimum_distance.py

@@ -17,7 +17,7 @@ def test_cube(self):
                 U2[:,j] += random_displacements[i]
                 dist = gpytoolbox.minimum_distance(V,F,U2,G)
                 # self.assertTrue(np.isclose(dist,0.0,atol=1e-2))
-                dist_gt = np.clip(random_displacements[i]-1,0,np.Inf)
+                dist_gt = np.clip(random_displacements[i]-1,0,np.inf)
                 # print(dist_gt,dist)
                 self.assertTrue(np.isclose(dist,dist_gt,atol=1e-4))
     def test_bunny_faces(self):

test/test_ray_polyline_intersect.py

@@ -29,7 +29,7 @@ def test_rays_against_square_2d(self):
         # There should be no intersection
         x, n, ind = gpytoolbox.ray_polyline_intersect(cam_pos,cam_dir,V)
         # if no intersection, x is infinity and n is zero
-        self.assertTrue((np.isclose(x,np.array([np.Inf,np.Inf]))).all())
+        self.assertTrue((np.isclose(x,np.array([np.inf,np.inf]))).all())
         self.assertTrue((np.isclose(n,np.array([0.0, 0.0]))).all())
         # Index is -1
         self.assertTrue(ind==-1)