fix objects not being completely stateless

Author: Vlad-Kor

model/cylinder/cylinder.py

@@ -14,9 +14,7 @@
 class Cylinder(Manifold):
 	def __init__(self, resolution=100, r=1):
 		self.xyz = self.generate_xyz(resolution, 0.998, r_height=1)
-		self.mesh = np.array([])
-		self.samples = np.array([])
-		self.samples_2d = np.array([])
+
 		self.distributions = DistributionLoader(CylinderDistribution, "model.distributions.cylinder").get_distributions()
 
 		self.r = r
@@ -66,13 +64,16 @@ def update_sample(self, selected_distribution, selected_sampling_method, sample_
 		new_sample = sampling_method.sample(sample_options, distribution_options)
 
 		if (new_sample is None) or new_sample.size == 0:
-			self.samples = np.empty((0, 3), dtype=float)
-			return
+			samples = np.empty((0, 3), dtype=float)
+			samples_2d = np.empty((0, 2), dtype=float)
+			return (samples, samples_2d)
 
 		x, y, z = self.p_z_to_xyz(new_sample[:,0], new_sample[:,1], self.r)
 
-		self.samples = np.column_stack((x, y, z))
-		self.samples_2d = new_sample
+		samples = np.column_stack((x, y, z))
+		samples_2d = new_sample
+
+		return (samples, samples_2d)
 
 
 	@staticmethod
@@ -145,16 +146,6 @@ def _init_mesh(self, resolution=3000):
 		simplices_merged = np.vstack((simplices, simplices_2))
 		simplices_merged = np.unique(simplices_merged, axis=0)
 
-		# throw away points at bottom so they dont stretch across
-		# eps = 1e-2
-		# ok_mask_no_across = (pz[:, 1] > 0 + eps) & (pz[:, 1] < (2 * np.pi - eps))
-		# ok_idx_no_across = np.where(ok_mask_no_across)[0]
-		# good_vertices_no_across = np.isin(simplices_merged, ok_idx_no_across)
-		# good_triangles_no_across = good_vertices_no_across.all(axis=1)
-		# simplices_merged = simplices_merged[good_triangles_no_across]
-
-
-
 		def cf(xi, yi, zi, zmin=np.min(z), zmax=np.max(z)):
 			if zi > zmax:
 				zi = np.nextafter(zmax, zmin)

model/manifold.py

@@ -24,7 +24,8 @@ def plot_settings_2d(self, value):
 	def generate_xyz(self, *args, **kwargs):
 		pass
 
-	# updates self.sample based on selected distribution and sample options
+	# returns samples based on selected distribution and sample options
+	# samples are returned as a tuple (saples_xyz, samples_2d | None)
 	# also converts the sample output type to xyz coordinates
 	@abstractmethod
 	def update_sample(self,  selected_distribution, sample_options):

model/sphere/sphere.py

@@ -12,8 +12,7 @@
 class Sphere(Manifold):
 	def __init__(self, resolution=200, radius=0.999):
 		self.xyz = self.generate_xyz(resolution, radius)
-		self.mesh = np.array([])
-		self.samples = np.array([])
+
 		self.distributions = DistributionLoader(SphereDistribution, "model.distributions.sphere").get_distributions()
 
 
@@ -35,7 +34,7 @@ def generate_xyz(self, resolution=50, radius=1):
 	def update_sample(self, selected_distribution, selected_sampling_method, sample_options, distribution_options):
 		dist = self.distributions[selected_distribution]
 		sampling_method = dist.sampling_method_dict[selected_sampling_method]
-		self.samples = sampling_method.sample(sample_options, distribution_options)
+		return (sampling_method.sample(sample_options, distribution_options), None)
 
 
 	def generate_mesh(self, pdf, alpha=1):

model/torus/torus.py

@@ -12,9 +12,7 @@
 class Torus(Manifold):
 	def __init__(self, resolution=100, r=1, R=3):
 		self.xyz = self.generate_xyz(resolution, r - 0.01, R - 0.01) # slightly smaller to avoid artifacts from mesh
-		self.mesh = np.array([])
-		self.samples = np.array([])
-		self.samples_2d = np.array([])
+
 		self.distributions = DistributionLoader(TorusDistribution, "model.distributions.torus").get_distributions()
 
 		self.r = r
@@ -59,13 +57,15 @@ def update_sample(self, selected_distribution, selected_sampling_method, sample_
 		new_sample = sampling_method.sample(sample_options, distribution_options)
 
 		if (new_sample is None) or new_sample.size == 0:
-			self.samples = np.empty((0, 3), dtype=float)
-			return
+			samples = np.empty((0, 3), dtype=float)
+			samples_2d = np.empty((0, 2), dtype=float)
+			return (samples, samples_2d)
 
 		x, y, z = self.t_p_to_xyz(new_sample[:,0], new_sample[:,1], self.r, self.R)
 
-		self.samples = np.column_stack((x, y, z))
-		self.samples_2d = new_sample
+		samples = np.column_stack((x, y, z))
+		samples_2d = new_sample
+		return (samples, samples_2d)
 
 
 	@staticmethod

renderer/Object3DAnd2DRenderer.py

@@ -194,13 +194,13 @@ def update_plot_sample_2d(self, values_dist, ids_dist, values_samp, ids_samp, se
 			opt.update_state(new_state)
 
 		# samples 
-		self.object.update_sample(selected_distribution, selected_sampling, sampling_options, dist_options)
+		samples, samples_2d = self.object.update_sample(selected_distribution, selected_sampling, sampling_options, dist_options)
 
 		patched_figure = Patch()
-		tp = self.object.samples_2d
+		tp = samples_2d
 
 		# marker size scaling
-		sample_count = self.object.samples.shape[0]
+		sample_count = samples.shape[0]
 		if sample_count == 0:
 			marker_size = 0 # no samples, no size
 		else:

renderer/Object3DRenderer.py

@@ -27,15 +27,8 @@ def __init__(self, object_3D, id, register_3d_callbacks=True):
 				"scale": 4,
 			}
 		}
-		if self.object.samples.size and self.object.samples.shape[0] != 0:
-			sample_count = self.object.samples.shape[0]
-			if sample_count == 0:
-				marker_size = 4 # default
-			else:
-				marker_size = (10 * (sample_count / 100) ** (-0.35)) / self.device_pixel_ratio
-				marker_size = np.minimum(10,marker_size)
-		else:
-			marker_size = 4
+		
+		marker_size = 4 # initial size, will be updated based on samples
 
 		self.fig = go.Figure(
 			data=[
@@ -48,9 +41,9 @@ def __init__(self, object_3D, id, register_3d_callbacks=True):
 				),
 				go.Scatter3d(
 					name="Samples",
-					x=self.object.samples[:, 0] if self.object.samples.size else [],
-					y=self.object.samples[:, 1] if self.object.samples.size else [],
-					z=self.object.samples[:, 2] if self.object.samples.size else [],
+					x=[],
+					y=[],
+					z=[],
 					mode="markers",
 					marker=dict(
 						size=marker_size,
@@ -284,21 +277,21 @@ def update_plot_sample(self, values_dist, ids_dist, values_samp, ids_samp, selec
 			opt.update_state(new_state)
 
 		# samples 
-		self.object.update_sample(selected_distribution, selected_sampling, sampling_options, dist_options)
+		samples, _ = self.object.update_sample(selected_distribution, selected_sampling, sampling_options, dist_options)
 
 
 		patched_figure = Patch()
 
 
-		patched_figure["data"][1].x = self.object.samples[:, 0]
-		patched_figure["data"][1].y = self.object.samples[:, 1]
-		patched_figure["data"][1].z = self.object.samples[:, 2]
+		patched_figure["data"][1].x = samples[:, 0]
+		patched_figure["data"][1].y = samples[:, 1]
+		patched_figure["data"][1].z = samples[:, 2]
 
 		# set size based on number of samples
-		if self.object.samples.size == 0:
+		if samples.size == 0:
 			marker_size = 0 
 		else:
-			sample_count = self.object.samples.shape[0]
+			sample_count = samples.shape[0]
 			marker_size = (10 * (sample_count / 100) ** (-0.35)) / dpr
 			marker_size = np.minimum(10,marker_size)