removing unused resample_distance routine from parametric volumes

Author: iraikov

linear_volume.py

@@ -135,85 +135,6 @@ def _create_vol(self, obs_u, obs_v, obs_l, xyz, **kwargs):
         self._vvol = vvol
         self._lvol = lvol
 
-    def _resample_distance_strategy(self):
-        from scipy.spatial import cKDTree
-
-        u, v, l = self.u, self.v, self.l
-        um, vm, lm = np.meshgrid(u, v, l, indexing='ij')
-        uvl = np.array([um.ravel(), vm.ravel(), lm.ravel()]).T
-        x, y, z = self._xvol(uvl), self._yvol(uvl), self._zvol(uvl)
-        xyz = np.asarray([x, y, z], dtype='float32').T
-        tree = cKDTree(xyz)
-
-        distance_dictionary = {}
-        distances = []
-        N = xyz.shape[0]
-        min_distance, min_index = 1.0e9, -1
-        for n in range(N):
-            d, i = tree.query(xyz[n], k=2)
-            d, i = d[-1], i[-1]
-            distance_dictionary[tuple(xyz[n])] = [d, n, i]
-            distances.append(d)
-            if d < min_distance:
-                min_distance, min_index = d, (n, i)
-
-        f = open('distances_curv.txt', 'w')
-        for distance in distances:
-            f.write(str(distance) + '\n')
-        f.close()
-
-        f = open('add_points.txt', 'w')
-        for n in range(N):
-            ndistance, self_index, neighbor_index = distance_dictionary[tuple(xyz[n])]
-            nearest_neighbor = xyz[neighbor_index]
-            rel = old_div(np.abs(ndistance - min_distance), min_distance)
-            points_to_add = 2 ** (rel + 2)
-            distance_dictionary[tuple(xyz[n])].append(points_to_add)
-            f.write(str(int(points_to_add)) + '\n')
-        f.close()
-
-        def sample_from_sphere(R, xyz):
-            phi = np.random.uniform(0, 2. * np.pi)
-            costheta = np.random.uniform(-1, 1)
-            u = np.random.uniform(0, 1)
-            theta = np.arccos(costheta)
-            r = sphere_radius * (u ** (1. / 3.))
-            xnew, ynew, znew = xyz[0] + r * np.sin(theta) * np.cos(phi), xyz[1] + r * np.sin(theta) * np.sin(phi), xyz[
-                2] + r * np.cos(theta)
-
-            xyz_new = np.asarray([xnew, ynew, znew], dtype='float32').reshape(1, 3)
-            u, v, l = self._uvol(xyz_new), self._vvol(xyz_new), self._lvol(xyz_new)
-            u, v, l = u[0], v[0], l[0]
-            if (u < np.min(self.u) or u > np.max(self.u)):
-                return None, None
-            if (v < np.min(self.v) or v > np.max(self.v)):
-                return None, None
-            if (l < np.min(self.l) or l > np.max(self.l)):
-                return None, None
-            return xyz_new, np.array([u, v, l], dtype='float32').reshape(1, 3)
-
-        for xyz_key in list(distance_dictionary.keys()):
-            ndistance, _, _, points_to_add = distance_dictionary[xyz_key]
-
-            sphere_centroid, sphere_radius = xyz_key, ndistance / 2.
-            for i in range(int(points_to_add)):
-                xyz_new = None
-                while xyz_new is None:
-                    xyz_new, uvl_new = sample_from_sphere(sphere_radius, sphere_centroid)
-                xyz = np.concatenate((xyz, xyz_new))
-                uvl = np.concatenate((uvl, uvl_new))
-
-        f = open('distance_curv_added.txt', 'w')
-        tree_added = cKDTree(xyz)
-
-        N2 = xyz.shape[0]
-        for n in range(N2):
-            d, i = tree_added.query(xyz[n], k=2)
-            d, i = d[-1], i[-1]
-            f.write(str(d) + '\n')
-        f.close()
-        return xyz, uvl
-
     def _resample_uv(self, ures, vres):
         """Helper function to re-sample to u and v parameters
         at the specified resolution

rbf_volume.py

@@ -140,85 +140,6 @@ def _create_vol(self, obs_u, obs_v, obs_l, xyz, **kwargs):
         self._vvol = vvol
         self._lvol = lvol
 
-    def _resample_distance_strategy(self):
-        from scipy.spatial import cKDTree
-
-        u, v, l = self.u, self.v, self.l
-        um, vm, lm = np.meshgrid(u, v, l, indexing='ij')
-        uvl = np.array([um.ravel(), vm.ravel(), lm.ravel()]).T
-        x, y, z = self._xvol(uvl), self._yvol(uvl), self._zvol(uvl)
-        xyz = np.asarray([x, y, z], dtype='float32').T
-        tree = cKDTree(xyz)
-
-        distance_dictionary = {}
-        distances = []
-        N = xyz.shape[0]
-        min_distance, min_index = 1.0e9, -1
-        for n in range(N):
-            d, i = tree.query(xyz[n], k=2)
-            d, i = d[-1], i[-1]
-            distance_dictionary[tuple(xyz[n])] = [d, n, i]
-            distances.append(d)
-            if d < min_distance:
-                min_distance, min_index = d, (n, i)
-
-        f = open('distances_curv.txt', 'w')
-        for distance in distances:
-            f.write(str(distance) + '\n')
-        f.close()
-
-        f = open('add_points.txt', 'w')
-        for n in range(N):
-            ndistance, self_index, neighbor_index = distance_dictionary[tuple(xyz[n])]
-            nearest_neighbor = xyz[neighbor_index]
-            rel = np.abs(ndistance - min_distance) / min_distance
-            points_to_add = 2 ** (rel + 2)
-            distance_dictionary[tuple(xyz[n])].append(points_to_add)
-            f.write(str(int(points_to_add)) + '\n')
-        f.close()
-
-        def sample_from_sphere(R, xyz):
-            phi = np.random.uniform(0, 2. * np.pi)
-            costheta = np.random.uniform(-1, 1)
-            u = np.random.uniform(0, 1)
-            theta = np.arccos(costheta)
-            r = sphere_radius * (u ** (1. / 3.))
-            xnew, ynew, znew = xyz[0] + r * np.sin(theta) * np.cos(phi), xyz[1] + r * np.sin(theta) * np.sin(phi), xyz[
-                2] + r * np.cos(theta)
-
-            xyz_new = np.asarray([xnew, ynew, znew], dtype='float32').reshape(1, 3)
-            u, v, l = self._uvol(xyz_new), self._vvol(xyz_new), self._lvol(xyz_new)
-            u, v, l = u[0], v[0], l[0]
-            if (u < np.min(self.u) or u > np.max(self.u)):
-                return None, None
-            if (v < np.min(self.v) or v > np.max(self.v)):
-                return None, None
-            if (l < np.min(self.l) or l > np.max(self.l)):
-                return None, None
-            return xyz_new, np.array([u, v, l], dtype='float32').reshape(1, 3)
-
-        for xyz_key in list(distance_dictionary.keys()):
-            ndistance, _, _, points_to_add = distance_dictionary[xyz_key]
-
-            sphere_centroid, sphere_radius = xyz_key, ndistance / 2.
-            for i in range(int(points_to_add)):
-                xyz_new = None
-                while xyz_new is None:
-                    xyz_new, uvl_new = sample_from_sphere(sphere_radius, sphere_centroid)
-                xyz = np.concatenate((xyz, xyz_new))
-                uvl = np.concatenate((uvl, uvl_new))
-
-        f = open('distance_curv_added.txt', 'w')
-        tree_added = cKDTree(xyz)
-
-        N2 = xyz.shape[0]
-        for n in range(N2):
-            d, i = tree_added.query(xyz[n], k=2)
-            d, i = d[-1], i[-1]
-            f.write(str(d) + '\n')
-        f.close()
-        return xyz, uvl
-
     def _resample_uv(self, ures, vres):
         """Helper function to re-sample to u and v parameters
         at the specified resolution