Disabled external gits

2022-04-07 18:46:57 +02:00
parent 88cb3426ad
commit 15e7120d6d
5316 changed files with 4563444 additions and 6 deletions
--- a/cs457-gc/assignment_3_3/data/Model01.obj
+++ b/cs457-gc/assignment_3_3/data/Model01.obj
--- a/cs457-gc/assignment_3_3/data/Model01_indexes.txt
+++ b/cs457-gc/assignment_3_3/data/Model01_indexes.txt
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--- a/cs457-gc/assignment_3_3/data/Model01_rhino.txt
+++ b/cs457-gc/assignment_3_3/data/Model01_rhino.txt
--- a/cs457-gc/assignment_3_3/data/final.obj
+++ b/cs457-gc/assignment_3_3/data/final.obj
--- a/cs457-gc/assignment_3_3/data/final_cutting.svg
+++ b/cs457-gc/assignment_3_3/data/final_cutting.svg
--- a/cs457-gc/assignment_3_3/data/final_indexes.txt
+++ b/cs457-gc/assignment_3_3/data/final_indexes.txt
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--- a/cs457-gc/assignment_3_3/data/final_rhino.txt
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--- a/cs457-gc/assignment_3_3/data/mesh.obj
+++ b/cs457-gc/assignment_3_3/data/mesh.obj
--- a/cs457-gc/assignment_3_3/mesh.blend
+++ b/cs457-gc/assignment_3_3/mesh.blend
--- a/cs457-gc/assignment_3_3/mesh.blend1
+++ b/cs457-gc/assignment_3_3/mesh.blend1
--- a/cs457-gc/assignment_3_3/mesh.mtl
+++ b/cs457-gc/assignment_3_3/mesh.mtl
@@ -0,0 +1,2 @@
+# Blender MTL File: 'mesh.blend'
+# Material Count: 0
--- a/cs457-gc/assignment_3_3/notebook/.ipynb_checkpoints/design_tool-checkpoint.ipynb
+++ b/cs457-gc/assignment_3_3/notebook/.ipynb_checkpoints/design_tool-checkpoint.ipynb
@@ -0,0 +1,457 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Interactive Design Tool for Asymptotic Grid-Shells "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import igl\n",
+    "import pyvista as pv\n",
+    "import numpy as np\n",
+    "\n",
+    "import sys\n",
+    "sys.path.append('../src')\n",
+    "\n",
+    "import importlib, fabrication_helper, tracer_tool\n",
+    "importlib.reload(fabrication_helper)\n",
+    "importlib.reload(tracer_tool)\n",
+    "from tracer_tool import AsymptoticTracer\n",
+    "from fabrication_helper import FabricationHelper"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Mesh\n",
+    "Import your own mesh as an .obj.\n",
+    "<br>Note that the filename should not contain the file extension because it will be used as the basis for saving further data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = \"../data/Model01\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize Interface\n",
+    "Don't modify this part. All parameters can be tuned with the interface."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fabrication parameters in cm\n",
+    "strips_scale = 3\n",
+    "strips_width = 2\n",
+    "strips_spacing = 0.2\n",
+    "strips_thickness = 0.3\n",
+    "board_width = 60\n",
+    "board_length = 100\n",
+    "# Creation parameters\n",
+    "sampling_distance = 1.0\n",
+    "num_neighbors = 4\n",
+    "iter_sampling = False\n",
+    "# Visualisation\n",
+    "strips_actor = []\n",
+    "points_actor = []\n",
+    "labels_actor = []\n",
+    "samples_actor = {}\n",
+    "pathA_actor = {}\n",
+    "pathA_indexes = {}\n",
+    "pathB_actor = {}\n",
+    "pathB_indexes = {}\n",
+    "intersections = np.empty((0,3), float)\n",
+    "visual_scaling = 0.2\n",
+    "\n",
+    "# Initialise tracer\n",
+    "mesh = pv.read(filename + \".obj\")\n",
+    "v,f = igl.read_triangle_mesh(filename + \".obj\")\n",
+    "tracer = AsymptoticTracer(filename + \".obj\")\n",
+    "helper = FabricationHelper(strips_width, strips_thickness, strips_spacing, strips_scale)\n",
+    "\n",
+    "plot = pv.Plotter(notebook=0)\n",
+    "\n",
+    "def add_pathA(pid):\n",
+    "    points, samples = tracer.generate_asymptotic_path(pid, True, num_neighbors, sampling_distance) \n",
+    "\n",
+    "    if len(points)> 1:\n",
+    "        pathA_actor[pid] = plot.add_mesh(pv.Spline(points, 400), color='white', line_width=10, pickable=False)\n",
+    "        pathA_indexes[pid] = tracer.num_pathsA()-1\n",
+    "        tracer.samples_indexes[0].append(pid)\n",
+    "    return samples\n",
+    "\n",
+    "def add_pathB(pid):\n",
+    "    points, samples = tracer.generate_asymptotic_path(pid, False, num_neighbors, sampling_distance) \n",
+    "    if len(points)> 1:\n",
+    "        pathB_actor[pid] = plot.add_mesh(pv.Spline(points, 400), color='yellow', line_width=10, pickable=False)\n",
+    "        pathB_indexes[pid] = tracer.num_pathsB()-1\n",
+    "        tracer.samples_indexes[1].append(pid) \n",
+    "    return samples\n",
+    "\n",
+    "def remove_pathA(pid):\n",
+    "    plot.remove_actor(pathA_actor[pid])\n",
+    "    tracer.delete_path(pathA_indexes[pid], True)\n",
+    "    del pathA_actor[pid]\n",
+    "    #Update indexes\n",
+    "    update_indexes(pid, True)\n",
+    "\n",
+    "def remove_pathB(pid):\n",
+    "    plot.remove_actor(pathB_actor[pid])\n",
+    "    tracer.delete_path(pathB_indexes[pid], False)\n",
+    "    del pathB_actor[pid]\n",
+    "    #Update indexes\n",
+    "    update_indexes(pid, False)\n",
+    "\n",
+    "def add_or_delete_sample_point(pid):\n",
+    "    orig = v[pid]\n",
+    "\n",
+    "    if pid not in pathB_actor.keys() and pid not in pathA_actor.keys():\n",
+    "        if pid in samples_actor.keys():\n",
+    "            plot.remove_actor(samples_actor[pid])\n",
+    "            del samples_actor[pid]\n",
+    "            clean_intersections()\n",
+    "    else:\n",
+    "        if pid not in samples_actor.keys():\n",
+    "            color = 'blue'\n",
+    "            if tracer.flagA and not tracer.flagB:\n",
+    "                color = 'white'\n",
+    "            elif tracer.flagB and not tracer.flagA:\n",
+    "                color = 'yellow'\n",
+    "\n",
+    "            samples_actor[pid] = plot.add_points(np.array(orig), color=color, render_points_as_spheres=True, point_size=20.0, pickable=False)\n",
+    "            clean_intersections()\n",
+    "        else:\n",
+    "            plot.remove_actor(samples_actor[pid])\n",
+    "            color = 'blue'\n",
+    "            if pid not in pathB_actor.keys() and pid in pathA_actor.keys():\n",
+    "                color = 'white'\n",
+    "            elif pid in pathB_actor.keys() and pid not in pathA_actor.keys():\n",
+    "                color = 'yellow'\n",
+    "            samples_actor[pid] = plot.add_points(np.array(orig), color=color, render_points_as_spheres=True, point_size=20.0, pickable=False)\n",
+    "            clean_intersections()\n",
+    "\n",
+    "def update_indexes(path_index, first_principal_direction):\n",
+    "    if first_principal_direction:\n",
+    "        if path_index in pathA_indexes.keys():\n",
+    "            del pathA_indexes[path_index]\n",
+    "            idx = 0\n",
+    "            for key in pathA_indexes:\n",
+    "                pathA_indexes[key] = idx\n",
+    "                idx+=1\n",
+    "    else:\n",
+    "        if path_index in pathB_indexes.keys():\n",
+    "            del pathB_indexes[path_index]\n",
+    "            idx = 0\n",
+    "            for key in pathB_indexes:\n",
+    "                pathB_indexes[key] = idx\n",
+    "                idx+=1\n",
+    "\n",
+    "def callback_first_family(value):\n",
+    "    tracer.flagA = value\n",
+    "\n",
+    "def callback_second_family(value):\n",
+    "    tracer.flagB = value\n",
+    "\n",
+    "def clean_intersections():\n",
+    "    if len(points_actor)>0:\n",
+    "        callback_remove_labels()\n",
+    "        plot.remove_actor(points_actor)\n",
+    "        points_actor.clear()\n",
+    "        labels_actor.clear()\n",
+    "        tracer.flag_intersections = False\n",
+    "\n",
+    "def callback_intersection():\n",
+    "    clean_intersections() \n",
+    "    global intersections\n",
+    "    intersections = np.empty((0,3), float)\n",
+    "    intersections = np.append(intersections, tracer.generate_intersection_network(), axis=0)\n",
+    "    if len(tracer.intersection_points)>0:\n",
+    "        points_actor.append(plot.add_points(tracer.intersection_points, color='red',point_size=13.0, pickable=False)) \n",
+    "\n",
+    "def callback_flatten():\n",
+    "    if not tracer.flag_intersections:\n",
+    "        callback_intersection()\n",
+    "    \n",
+    "    helper.generate_flatten_network(tracer)\n",
+    "    \n",
+    "    strips_num = helper.strips_numA if helper.strips_numA > helper.strips_numB else helper.strips_numB\n",
+    "    plot.remove_actor(strips_actor)\n",
+    "    strips_actor.clear()\n",
+    "    if strips_num:\n",
+    "        for i in range(strips_num):\n",
+    "            if i<helper.strips_numA:\n",
+    "                points = helper.paths_flatten[0][i] * visual_scaling\n",
+    "                strips_actor.append(plot.add_lines(lines_from_points(points), color='white', width=3))\n",
+    "            if i<helper.strips_numB:\n",
+    "                points = helper.paths_flatten[1][i] * visual_scaling\n",
+    "                strips_actor.append(plot.add_lines(lines_from_points(points), color='yellow', width=3))\n",
+    "        # Board boundary\n",
+    "        points = np.array([[0.,0.,0.],[board_length*visual_scaling,0.,0.],[board_length*visual_scaling, board_width*visual_scaling,0.],[0., board_width*visual_scaling,0],[0.,0.,0.]])\n",
+    "        strips_actor.append(plot.add_lines(lines_from_points(points), color='red', width=3))\n",
+    "    \n",
+    "def callback_save_indexes():\n",
+    "    file = open(filename + \"_indexes.txt\", 'w')\n",
+    "    for i in range(len(tracer.samples_indexes)):\n",
+    "        for idx in tracer.samples_indexes[i]:\n",
+    "            if i==0:  \n",
+    "                file.write(\"A\"+str(idx))\n",
+    "            elif i==1:\n",
+    "                file.write(\"B\"+str(idx))\n",
+    "            file.write('\\n')\n",
+    "    file.close()\n",
+    "\n",
+    "def callback_load_indexes():\n",
+    "    indexes = np.loadtxt(filename + \"_indexes.txt\", dtype=str)\n",
+    "    old_flagA = tracer.flagA\n",
+    "    old_flagB = tracer.flagB\n",
+    "\n",
+    "    for data in indexes:\n",
+    "        pid = int(data[1:])\n",
+    "\n",
+    "        tracer.flagA = True if data[0] == \"A\" else False\n",
+    "        tracer.flagB = True if data[0] == \"B\" else False\n",
+    "\n",
+    "        callback_picking(mesh,pid)\n",
+    "\n",
+    "    tracer.flagA = old_flagA\n",
+    "    tracer.flagB = old_flagB\n",
+    "\n",
+    "def lines_from_points(points):\n",
+    "    cells = np.empty((len(points)-1, 2), dtype=np.int_)\n",
+    "    cells[:,0] = np.arange(0, len(points)-1, dtype=np.int_)\n",
+    "    cells[:,1] = np.arange(1, len(points), dtype=np.int_)\n",
+    "    cells = cells.flatten()\n",
+    "    return np.array([points[i] for i in cells])\n",
+    "\n",
+    "def callback_picking(mesh, pid, iterative_sampling=None):\n",
+    "\n",
+    "    if(iterative_sampling==None):\n",
+    "        iterative_sampling = iter_sampling\n",
+    "\n",
+    "    old_flagA = tracer.flagA\n",
+    "    old_flagB = tracer.flagB\n",
+    "\n",
+    "    # Generate first family of asymptotic curves\n",
+    "    if tracer.flagA:\n",
+    "        if pid in pathA_actor.keys():\n",
+    "            remove_pathA(pid)\n",
+    "        else:\n",
+    "            samples = add_pathA(pid)\n",
+    "\n",
+    "            if iterative_sampling:\n",
+    "                for pt in samples:\n",
+    "                    idx = tracer.mesh.kd_tree.query(pt)[1]\n",
+    "                    tracer.flagA = False\n",
+    "                    tracer.flagB = True\n",
+    "                    callback_picking(mesh, idx, iterative_sampling=False)\n",
+    "\n",
+    "    tracer.flagA = old_flagA\n",
+    "    tracer.flagB = old_flagB\n",
+    "\n",
+    "    # Generate second family of asymptotic curves\n",
+    "    if tracer.flagB:\n",
+    "        if pid in pathB_actor.keys():\n",
+    "            remove_pathB(pid)\n",
+    "        else:\n",
+    "            samples = add_pathB(pid)\n",
+    "\n",
+    "            if iterative_sampling:\n",
+    "                for pt in samples:\n",
+    "                    idx = tracer.mesh.kd_tree.query(pt)[1]\n",
+    "                    tracer.flagA = True\n",
+    "                    tracer.flagB = False\n",
+    "                    callback_picking(mesh, idx, iterative_sampling=False)\n",
+    "\n",
+    "    tracer.flagA = old_flagA\n",
+    "    tracer.flagB = old_flagB\n",
+    "    \n",
+    "    add_or_delete_sample_point(pid)\n",
+    "    \n",
+    "def callback_save_svg():\n",
+    "    cutting_color = \"red\"\n",
+    "    engraving_color = \"black\"\n",
+    "    font_size = 0.4\n",
+    "    if helper.flag_flatten==False:\n",
+    "        helper.generate_flatten_network()\n",
+    "    helper.generate_svg_file(filename + \"_cutting.svg\", font_size, cutting_color, engraving_color, board_length, board_width)\n",
+    "\n",
+    "def callback_width(value):\n",
+    "    helper.strips_width = value\n",
+    "    callback_flatten()\n",
+    "\n",
+    "def callback_board_width(value):\n",
+    "    global board_width\n",
+    "    board_width = value\n",
+    "    callback_flatten()\n",
+    "\n",
+    "def callback_thickness(value):\n",
+    "    helper.strips_thickness = value\n",
+    "    callback_flatten()\n",
+    "\n",
+    "def callback_length(value):\n",
+    "    helper.scale_length = value\n",
+    "    callback_flatten()\n",
+    "\n",
+    "def callback_spacing(value):\n",
+    "    helper.strips_spacing = value\n",
+    "    callback_flatten()\n",
+    "\n",
+    "def callback_board_length(value):\n",
+    "    global board_length\n",
+    "    board_length = value\n",
+    "    callback_flatten()\n",
+    "\n",
+    "def callback_remove_labels():\n",
+    "    plot.remove_actor(labels_actor)\n",
+    "    labels_actor.clear()\n",
+    "\n",
+    "def callback_add_all_labels():\n",
+    "    callback_add_labels(True, True)\n",
+    "\n",
+    "def callback_add_labelsA():\n",
+    "    callback_add_labels(True, False)\n",
+    "\n",
+    "def callback_add_labelsB():\n",
+    "    callback_add_labels(False, True)\n",
+    "    \n",
+    "def callback_add_labels(labelsA, labelsB):\n",
+    "    callback_remove_labels()\n",
+    "\n",
+    "    if len(tracer.paths_indexes[0])>0 and labelsA:\n",
+    "        labels = np.core.defchararray.add('A', np.arange(len(tracer.paths_indexes[0])).astype(str))\n",
+    "        indexes = [idx[:1][0] for idx in tracer.paths_indexes[0]]\n",
+    "        labels_actor.append(plot.add_point_labels(tracer.paths[0][indexes], labels, font_size=22, always_visible=True, show_points=False))\n",
+    "\n",
+    "        indexes = np.unique(np.array([item for sublist in tracer.intersections[0] for item in sublist[:,2]], int).flatten())\n",
+    "        labels = np.core.defchararray.add('c', indexes.astype(str))\n",
+    "        labels_actor.append(plot.add_point_labels(tracer.intersection_points[indexes], labels, bold=False, font_size=18, always_visible=True, show_points=False))\n",
+    "\n",
+    "    if len(tracer.paths_indexes[1])>0 and labelsB:\n",
+    "        labels = np.core.defchararray.add('B', np.arange(len(tracer.paths_indexes[1])).astype(str))\n",
+    "        indexes = [idx[:1][0] for idx in tracer.paths_indexes[1]]\n",
+    "        labels_actor.append(plot.add_point_labels(tracer.paths[1][indexes], labels, font_size=22, always_visible=True, show_points=False))\n",
+    "\n",
+    "        indexes = np.unique(np.array([item for sublist in tracer.intersections[1] for item in sublist[:,2]], int).flatten())\n",
+    "        labels = np.core.defchararray.add('c', indexes.astype(str))\n",
+    "        labels_actor.append(plot.add_point_labels(tracer.intersection_points[indexes], labels, bold=False, font_size=18, always_visible=True, show_points=False))\n",
+    "\n",
+    "def callback_save_network():\n",
+    "    file = open(filename + \"_rhino.txt\", 'w')\n",
+    "    for i in range(2):\n",
+    "        label = \"A\"\n",
+    "        if i==1:\n",
+    "            label =\"B\"\n",
+    "\n",
+    "        # positions\n",
+    "        for j in range(len(tracer.paths_indexes[i])):\n",
+    "            path = tracer.paths_indexes[i][j]\n",
+    "            for idx in path:\n",
+    "                pt = tracer.paths[i][idx]\n",
+    "                file.write(label + str(j)+ \"_\" +str(pt[0]) + \",\" + str(pt[1]) + \",\" +str(pt[2]) + \"\\n\")\n",
+    "\n",
+    "    # Intersections    \n",
+    "    for i in range( len(tracer.intersection_points) ):\n",
+    "        pt = tracer.intersection_points[i]\n",
+    "        file.write(\"C\" + str(i) + \"_\" +str(pt[0]) + \",\" + str(pt[1]) + \",\" +str(pt[2]) + \"\\n\")\n",
+    "    file.close()\n",
+    "\n",
+    "def callback_sampling_distance(value):\n",
+    "    global sampling_distance\n",
+    "    sampling_distance = value\n",
+    "\n",
+    "def callback_iterative_sampling(value):\n",
+    "    global iter_sampling\n",
+    "    iter_sampling = value\n",
+    "\n",
+    "plot.add_mesh(mesh, show_edges=True)\n",
+    "plot.add_axes()\n",
+    "msg = \"Press <K> for saving indexes, <L> for loading indexes or <O> to save the curve network model.\\n\"\n",
+    "msg += \"Press <I> for computing intersections, <J> for generating the flatten strips and <H> for saving the laser-cutting file.\\n\"\n",
+    "msg += \"Press <M> for hiding labels, <N> for showing all labels, <U> for showing A labels or <Y> for showing B labels.\\n\"\n",
+    "plot.add_text(msg, position='lower_right', font_size=12, color=None, font=None, shadow=False, name=None, viewport=False)\n",
+    "plot.add_checkbox_button_widget(callback_first_family, value=tracer.flagA, position=(10, 200.0), size=40, border_size=1, color_on='white', color_off='grey', background_color='red')\n",
+    "plot.add_checkbox_button_widget(callback_second_family, value=tracer.flagB, position=(10, 300.0), size=40, border_size=1, color_on='yellow', color_off='grey', background_color='red')\n",
+    "plot.add_checkbox_button_widget(callback_iterative_sampling, value=iter_sampling, position=(10, 400.0), size=40, border_size=1, color_on='green', color_off='grey', background_color='red')\n",
+    "plot.add_text(\"First Family\", position=(80.0, 200.0), font_size=12, color=None, font=None, shadow=False, name=None, viewport=False)\n",
+    "plot.add_text(\"Second Family\", position=(80, 300.0), font_size=12, color=None, font=None, shadow=False, name=None, viewport=False)\n",
+    "plot.add_text(\"Iterative sampling\", position=(80, 400.0), font_size=12, color=None, font=None, shadow=False, name=None, viewport=False)\n",
+    "plot.add_key_event('i', callback_intersection)\n",
+    "plot.add_key_event('j', callback_flatten)\n",
+    "plot.add_key_event('k', callback_save_indexes)\n",
+    "plot.add_key_event('l', callback_load_indexes)\n",
+    "plot.add_key_event('h', callback_save_svg)\n",
+    "plot.add_key_event('m', callback_remove_labels)\n",
+    "plot.add_key_event('n', callback_add_all_labels)\n",
+    "plot.add_key_event('u', callback_add_labelsA)\n",
+    "plot.add_key_event('y', callback_add_labelsB)\n",
+    "plot.add_key_event('o', callback_save_network)\n",
+    "plot.enable_point_picking(callback=callback_picking, show_message=True, color='pink', point_size=10, use_mesh=True, show_point=True)\n",
+    "plot.add_slider_widget(callback_width, [0.1, 5.0], value=strips_width, title=\"Strip Width (cm)\", pointa=(.83, .15), pointb=(.98, .15), title_height=0.02, fmt=\"%0.1f\", style='modern')\n",
+    "plot.add_slider_widget(callback_thickness, [0.1, 1], value=strips_thickness, title=\"Strip Thickness (cm)\", pointa=(.67, .15), pointb=(.82, .15), title_height=0.02, fmt=\"%0.2f\", style='modern')\n",
+    "plot.add_slider_widget(callback_length, [1, 10], value=strips_scale, title=\"Scale Strip Length\", pointa=(.51, .15), pointb=(.66, .15), title_height=0.02, fmt=\"%0.1f\", style='modern')\n",
+    "plot.add_slider_widget(callback_spacing, [0., 0.5], value=strips_spacing, title=\"Strip spacing\", pointa=(.51, .88), pointb=(.66, .88), title_height=0.02, fmt=\"%0.1f\", style='modern')\n",
+    "plot.add_slider_widget(callback_board_width, [10, 100], value=board_width, title=\"Board width (cm)\", pointa=(.67, .88), pointb=(.82, .88), title_height=0.02, fmt=\"%0.0f\", style='modern')\n",
+    "plot.add_slider_widget(callback_board_length, [10, 100], value=board_length, title=\"Board length (cm)\", pointa=(.83, .88), pointb=(.98, .88), title_height=0.02, fmt=\"%0.0f\", style='modern')\n",
+    "plot.add_slider_widget(callback_sampling_distance, [0.1, 2.0], value=sampling_distance, title=\"Sampling distance\", pointa=(.005, .88), pointb=(.16, .88), title_height=0.02, fmt=\"%0.1f\", style='modern')\n",
+    "plot.show(\"Asymptotic GridShell\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "294c9a689e437aa1430c5ffd3595515046a5cee981399d48a2291c079c814bc8"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
--- a/cs457-gc/assignment_3_3/notebook/.ipynb_checkpoints/tracing_test-checkpoint.ipynb
+++ b/cs457-gc/assignment_3_3/notebook/.ipynb_checkpoints/tracing_test-checkpoint.ipynb
@@ -0,0 +1,171 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from meshplot import plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../src')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import importlib, tracer_helper, tracer_utils\n",
+    "importlib.reload(tracer_helper)\n",
+    "importlib.reload(tracer_utils)\n",
+    "from tracer_utils import asymptotic_path\n",
+    "from tracer_helper import Mesh"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Example\n",
+    "Build mesh from file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f19ce42be2f6405796de381ba4b89b42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Renderer(camera=PerspectiveCamera(aspect=1.5, children=(DirectionalLight(color='white', intensity=0.6, positio…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mesh = Mesh(\"../data/Model01.obj\")\n",
+    "p = plot(mesh.V, mesh.F, shading={\"wireframe\": True,\"width\": 900, \"height\": 600}, return_plot=True, c=np.array([0,0.7,1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Trace first asymptotic curve\n",
+    "Use principal directions V1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set tracing parameters\n",
+    "num_steps = 1000\n",
+    "step_size = 1e-6\n",
+    "num_neighbors = 4\n",
+    "first_principal_direction = False\n",
+    "\n",
+    "# Pre-selection of vertices\n",
+    "idxA = [167,173,178,146,159,291,49,40,254,57,15,65,268,341,283]\n",
+    "# Tracing\n",
+    "for idx in idxA:\n",
+    "    P, A, PP = asymptotic_path(idx, mesh, num_steps, step_size, first_principal_direction, num_neighbors)\n",
+    "\n",
+    "    # Plot starting vertex\n",
+    "    p.add_points(np.array([mesh.V[idx]]), shading={\"point_size\": 0.7, \"point_color\": \"black\"})\n",
+    "    # Plot edge-points shaping the asymptotic path\n",
+    "    p.add_points(P, shading={\"point_size\": 0.2,\"point_color\": \"white\"})\n",
+    "    # Plot asymptotic curve\n",
+    "    if(len(P)>1): \n",
+    "        p.add_lines(P[:-1], P[1:], shading={\"line_color\": \"white\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Trace second asymptotic curve\n",
+    "Use principal directions V2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set tracing parameters\n",
+    "num_steps = 1000\n",
+    "step_size = 1e-6\n",
+    "num_neighbors = 4\n",
+    "first_principal_direction = True\n",
+    "\n",
+    "# Pre-selection of vertices\n",
+    "idxB = [119,203,188,129,95,66,308,298,290,282,335,143,81,73,33]\n",
+    "# Tracing\n",
+    "for idx in idxB:\n",
+    "    P, A, PP = asymptotic_path(idx, mesh, num_steps, step_size, first_principal_direction, num_neighbors)\n",
+    "    \n",
+    "    # Plot starting vertex\n",
+    "    p.add_points(np.array([mesh.V[idx]]), shading={\"point_size\": 0.7, \"point_color\": \"black\"})\n",
+    "    # Plot edge-points shaping the asymptotic path\n",
+    "    p.add_points(P, shading={\"point_size\": 0.2,\"point_color\": \"yellow\"})\n",
+    "    # Plot asymptotic curve\n",
+    "    if(len(P)>1): \n",
+    "        p.add_lines(P[:-1], P[1:], shading={\"line_color\": \"yellow\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "d74ce2d711eca57e47550fdd427f707c5faa08517318f0967f4aae699c902c0e"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
--- a/cs457-gc/assignment_3_3/notebook/design_tool.ipynb
+++ b/cs457-gc/assignment_3_3/notebook/design_tool.ipynb
@@ -0,0 +1,509 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Interactive Design Tool for Asymptotic Grid-Shells "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import igl\n",
+    "import pyvista as pv\n",
+    "import numpy as np\n",
+    "\n",
+    "import sys\n",
+    "sys.path.append('../src')\n",
+    "\n",
+    "import importlib, fabrication_helper, tracer_tool\n",
+    "importlib.reload(fabrication_helper)\n",
+    "importlib.reload(tracer_tool)\n",
+    "from tracer_tool import AsymptoticTracer\n",
+    "from fabrication_helper import FabricationHelper"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Mesh\n",
+    "Import your own mesh as an .obj.\n",
+    "<br>Note that the filename should not contain the file extension because it will be used as the basis for saving further data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = \"../data/final\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize Interface\n",
+    "Don't modify this part. All parameters can be tuned with the interface."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hit Boundary\n",
+      "Duplicate Point\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Duplicate Point\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Duplicate Point\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n",
+      "Hit Boundary\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Fabrication parameters in cm\n",
+    "strips_scale = 3\n",
+    "strips_width = 2\n",
+    "strips_spacing = 0.2\n",
+    "strips_thickness = 0.3\n",
+    "board_width = 60\n",
+    "board_length = 100\n",
+    "# Creation parameters\n",
+    "sampling_distance = 1.0\n",
+    "num_neighbors = 4\n",
+    "iter_sampling = False\n",
+    "# Visualisation\n",
+    "strips_actor = []\n",
+    "points_actor = []\n",
+    "labels_actor = []\n",
+    "samples_actor = {}\n",
+    "pathA_actor = {}\n",
+    "pathA_indexes = {}\n",
+    "pathB_actor = {}\n",
+    "pathB_indexes = {}\n",
+    "intersections = np.empty((0,3), float)\n",
+    "visual_scaling = 0.2\n",
+    "\n",
+    "# Initialise tracer\n",
+    "mesh = pv.read(filename + \".obj\")\n",
+    "v,f = igl.read_triangle_mesh(filename + \".obj\")\n",
+    "tracer = AsymptoticTracer(filename + \".obj\")\n",
+    "helper = FabricationHelper(strips_width, strips_thickness, strips_spacing, strips_scale)\n",
+    "\n",
+    "plot = pv.Plotter(notebook=0)\n",
+    "\n",
+    "def add_pathA(pid):\n",
+    "    points, samples = tracer.generate_asymptotic_path(pid, True, num_neighbors, sampling_distance) \n",
+    "\n",
+    "    if len(points)> 1:\n",
+    "        pathA_actor[pid] = plot.add_mesh(pv.Spline(points, 400), color='white', line_width=10, pickable=False)\n",
+    "        pathA_indexes[pid] = tracer.num_pathsA()-1\n",
+    "        tracer.samples_indexes[0].append(pid)\n",
+    "    return samples\n",
+    "\n",
+    "def add_pathB(pid):\n",
+    "    points, samples = tracer.generate_asymptotic_path(pid, False, num_neighbors, sampling_distance) \n",
+    "    if len(points)> 1:\n",
+    "        pathB_actor[pid] = plot.add_mesh(pv.Spline(points, 400), color='yellow', line_width=10, pickable=False)\n",
+    "        pathB_indexes[pid] = tracer.num_pathsB()-1\n",
+    "        tracer.samples_indexes[1].append(pid) \n",
+    "    return samples\n",
+    "\n",
+    "def remove_pathA(pid):\n",
+    "    plot.remove_actor(pathA_actor[pid])\n",
+    "    tracer.delete_path(pathA_indexes[pid], True)\n",
+    "    del pathA_actor[pid]\n",
+    "    #Update indexes\n",
+    "    update_indexes(pid, True)\n",
+    "\n",
+    "def remove_pathB(pid):\n",
+    "    plot.remove_actor(pathB_actor[pid])\n",
+    "    tracer.delete_path(pathB_indexes[pid], False)\n",
+    "    del pathB_actor[pid]\n",
+    "    #Update indexes\n",
+    "    update_indexes(pid, False)\n",
+    "\n",
+    "def add_or_delete_sample_point(pid):\n",
+    "    orig = v[pid]\n",
+    "\n",
+    "    if pid not in pathB_actor.keys() and pid not in pathA_actor.keys():\n",
+    "        if pid in samples_actor.keys():\n",
+    "            plot.remove_actor(samples_actor[pid])\n",
+    "            del samples_actor[pid]\n",
+    "            clean_intersections()\n",
+    "    else:\n",
+    "        if pid not in samples_actor.keys():\n",
+    "            color = 'blue'\n",
+    "            if tracer.flagA and not tracer.flagB:\n",
+    "                color = 'white'\n",
+    "            elif tracer.flagB and not tracer.flagA:\n",
+    "                color = 'yellow'\n",
+    "\n",
+    "            samples_actor[pid] = plot.add_points(np.array(orig), color=color, render_points_as_spheres=True, point_size=20.0, pickable=False)\n",
+    "            clean_intersections()\n",
+    "        else:\n",
+    "            plot.remove_actor(samples_actor[pid])\n",
+    "            color = 'blue'\n",
+    "            if pid not in pathB_actor.keys() and pid in pathA_actor.keys():\n",
+    "                color = 'white'\n",
+    "            elif pid in pathB_actor.keys() and pid not in pathA_actor.keys():\n",
+    "                color = 'yellow'\n",
+    "            samples_actor[pid] = plot.add_points(np.array(orig), color=color, render_points_as_spheres=True, point_size=20.0, pickable=False)\n",
+    "            clean_intersections()\n",
+    "\n",
+    "def update_indexes(path_index, first_principal_direction):\n",
+    "    if first_principal_direction:\n",
+    "        if path_index in pathA_indexes.keys():\n",
+    "            del pathA_indexes[path_index]\n",
+    "            idx = 0\n",
+    "            for key in pathA_indexes:\n",
+    "                pathA_indexes[key] = idx\n",
+    "                idx+=1\n",
+    "    else:\n",
+    "        if path_index in pathB_indexes.keys():\n",
+    "            del pathB_indexes[path_index]\n",
+    "            idx = 0\n",
+    "            for key in pathB_indexes:\n",
+    "                pathB_indexes[key] = idx\n",
+    "                idx+=1\n",
+    "\n",
+    "def callback_first_family(value):\n",
+    "    tracer.flagA = value\n",
+    "\n",
+    "def callback_second_family(value):\n",
+    "    tracer.flagB = value\n",
+    "\n",
+    "def clean_intersections():\n",
+    "    if len(points_actor)>0:\n",
+    "        callback_remove_labels()\n",
+    "        plot.remove_actor(points_actor)\n",
+    "        points_actor.clear()\n",
+    "        labels_actor.clear()\n",
+    "        tracer.flag_intersections = False\n",
+    "\n",
+    "def callback_intersection():\n",
+    "    clean_intersections() \n",
+    "    global intersections\n",
+    "    intersections = np.empty((0,3), float)\n",
+    "    intersections = np.append(intersections, tracer.generate_intersection_network(), axis=0)\n",
+    "    if len(tracer.intersection_points)>0:\n",
+    "        points_actor.append(plot.add_points(tracer.intersection_points, color='red',point_size=13.0, pickable=False)) \n",
+    "\n",
+    "def callback_flatten():\n",
+    "    if not tracer.flag_intersections:\n",
+    "        callback_intersection()\n",
+    "    \n",
+    "    helper.generate_flatten_network(tracer)\n",
+    "    \n",
+    "    strips_num = helper.strips_numA if helper.strips_numA > helper.strips_numB else helper.strips_numB\n",
+    "    plot.remove_actor(strips_actor)\n",
+    "    strips_actor.clear()\n",
+    "    if strips_num:\n",
+    "        for i in range(strips_num):\n",
+    "            if i<helper.strips_numA:\n",
+    "                points = helper.paths_flatten[0][i] * visual_scaling\n",
+    "                strips_actor.append(plot.add_lines(lines_from_points(points), color='white', width=3))\n",
+    "            if i<helper.strips_numB:\n",
+    "                points = helper.paths_flatten[1][i] * visual_scaling\n",
+    "                strips_actor.append(plot.add_lines(lines_from_points(points), color='yellow', width=3))\n",
+    "        # Board boundary\n",
+    "        points = np.array([[0.,0.,0.],[board_length*visual_scaling,0.,0.],[board_length*visual_scaling, board_width*visual_scaling,0.],[0., board_width*visual_scaling,0],[0.,0.,0.]])\n",
+    "        strips_actor.append(plot.add_lines(lines_from_points(points), color='red', width=3))\n",
+    "    \n",
+    "def callback_save_indexes():\n",
+    "    file = open(filename + \"_indexes.txt\", 'w')\n",
+    "    for i in range(len(tracer.samples_indexes)):\n",
+    "        for idx in tracer.samples_indexes[i]:\n",
+    "            if i==0:  \n",
+    "                file.write(\"A\"+str(idx))\n",
+    "            elif i==1:\n",
+    "                file.write(\"B\"+str(idx))\n",
+    "            file.write('\\n')\n",
+    "    file.close()\n",
+    "\n",
+    "def callback_load_indexes():\n",
+    "    indexes = np.loadtxt(filename + \"_indexes.txt\", dtype=str)\n",
+    "    old_flagA = tracer.flagA\n",
+    "    old_flagB = tracer.flagB\n",
+    "\n",
+    "    for data in indexes:\n",
+    "        pid = int(data[1:])\n",
+    "\n",
+    "        tracer.flagA = True if data[0] == \"A\" else False\n",
+    "        tracer.flagB = True if data[0] == \"B\" else False\n",
+    "\n",
+    "        callback_picking(mesh,pid)\n",
+    "\n",
+    "    tracer.flagA = old_flagA\n",
+    "    tracer.flagB = old_flagB\n",
+    "\n",
+    "def lines_from_points(points):\n",
+    "    cells = np.empty((len(points)-1, 2), dtype=np.int_)\n",
+    "    cells[:,0] = np.arange(0, len(points)-1, dtype=np.int_)\n",
+    "    cells[:,1] = np.arange(1, len(points), dtype=np.int_)\n",
+    "    cells = cells.flatten()\n",
+    "    return np.array([points[i] for i in cells])\n",
+    "\n",
+    "def callback_picking(mesh, pid, iterative_sampling=None):\n",
+    "\n",
+    "    if(iterative_sampling==None):\n",
+    "        iterative_sampling = iter_sampling\n",
+    "\n",
+    "    old_flagA = tracer.flagA\n",
+    "    old_flagB = tracer.flagB\n",
+    "\n",
+    "    # Generate first family of asymptotic curves\n",
+    "    if tracer.flagA:\n",
+    "        if pid in pathA_actor.keys():\n",
+    "            remove_pathA(pid)\n",
+    "        else:\n",
+    "            samples = add_pathA(pid)\n",
+    "\n",
+    "            if iterative_sampling:\n",
+    "                for pt in samples:\n",
+    "                    idx = tracer.mesh.kd_tree.query(pt)[1]\n",
+    "                    tracer.flagA = False\n",
+    "                    tracer.flagB = True\n",
+    "                    callback_picking(mesh, idx, iterative_sampling=False)\n",
+    "\n",
+    "    tracer.flagA = old_flagA\n",
+    "    tracer.flagB = old_flagB\n",
+    "\n",
+    "    # Generate second family of asymptotic curves\n",
+    "    if tracer.flagB:\n",
+    "        if pid in pathB_actor.keys():\n",
+    "            remove_pathB(pid)\n",
+    "        else:\n",
+    "            samples = add_pathB(pid)\n",
+    "\n",
+    "            if iterative_sampling:\n",
+    "                for pt in samples:\n",
+    "                    idx = tracer.mesh.kd_tree.query(pt)[1]\n",
+    "                    tracer.flagA = True\n",
+    "                    tracer.flagB = False\n",
+    "                    callback_picking(mesh, idx, iterative_sampling=False)\n",
+    "\n",
+    "    tracer.flagA = old_flagA\n",
+    "    tracer.flagB = old_flagB\n",
+    "    \n",
+    "    add_or_delete_sample_point(pid)\n",
+    "    \n",
+    "def callback_save_svg():\n",
+    "    cutting_color = \"red\"\n",
+    "    engraving_color = \"black\"\n",
+    "    font_size = 0.4\n",
+    "    if helper.flag_flatten==False:\n",
+    "        helper.generate_flatten_network()\n",
+    "    helper.generate_svg_file(filename + \"_cutting.svg\", font_size, cutting_color, engraving_color, board_length, board_width)\n",
+    "\n",
+    "def callback_width(value):\n",
+    "    helper.strips_width = value\n",
+    "    callback_flatten()\n",
+    "\n",
+    "def callback_board_width(value):\n",
+    "    global board_width\n",
+    "    board_width = value\n",
+    "    callback_flatten()\n",
+    "\n",
+    "def callback_thickness(value):\n",
+    "    helper.strips_thickness = value\n",
+    "    callback_flatten()\n",
+    "\n",
+    "def callback_length(value):\n",
+    "    helper.scale_length = value\n",
+    "    callback_flatten()\n",
+    "\n",
+    "def callback_spacing(value):\n",
+    "    helper.strips_spacing = value\n",
+    "    callback_flatten()\n",
+    "\n",
+    "def callback_board_length(value):\n",
+    "    global board_length\n",
+    "    board_length = value\n",
+    "    callback_flatten()\n",
+    "\n",
+    "def callback_remove_labels():\n",
+    "    plot.remove_actor(labels_actor)\n",
+    "    labels_actor.clear()\n",
+    "\n",
+    "def callback_add_all_labels():\n",
+    "    callback_add_labels(True, True)\n",
+    "\n",
+    "def callback_add_labelsA():\n",
+    "    callback_add_labels(True, False)\n",
+    "\n",
+    "def callback_add_labelsB():\n",
+    "    callback_add_labels(False, True)\n",
+    "    \n",
+    "def callback_add_labels(labelsA, labelsB):\n",
+    "    callback_remove_labels()\n",
+    "\n",
+    "    if len(tracer.paths_indexes[0])>0 and labelsA:\n",
+    "        labels = np.core.defchararray.add('A', np.arange(len(tracer.paths_indexes[0])).astype(str))\n",
+    "        indexes = [idx[:1][0] for idx in tracer.paths_indexes[0]]\n",
+    "        labels_actor.append(plot.add_point_labels(tracer.paths[0][indexes], labels, font_size=22, always_visible=True, show_points=False))\n",
+    "\n",
+    "        indexes = np.unique(np.array([item for sublist in tracer.intersections[0] for item in sublist[:,2]], int).flatten())\n",
+    "        labels = np.core.defchararray.add('c', indexes.astype(str))\n",
+    "        labels_actor.append(plot.add_point_labels(tracer.intersection_points[indexes], labels, bold=False, font_size=18, always_visible=True, show_points=False))\n",
+    "\n",
+    "    if len(tracer.paths_indexes[1])>0 and labelsB:\n",
+    "        labels = np.core.defchararray.add('B', np.arange(len(tracer.paths_indexes[1])).astype(str))\n",
+    "        indexes = [idx[:1][0] for idx in tracer.paths_indexes[1]]\n",
+    "        labels_actor.append(plot.add_point_labels(tracer.paths[1][indexes], labels, font_size=22, always_visible=True, show_points=False))\n",
+    "\n",
+    "        indexes = np.unique(np.array([item for sublist in tracer.intersections[1] for item in sublist[:,2]], int).flatten())\n",
+    "        labels = np.core.defchararray.add('c', indexes.astype(str))\n",
+    "        labels_actor.append(plot.add_point_labels(tracer.intersection_points[indexes], labels, bold=False, font_size=18, always_visible=True, show_points=False))\n",
+    "\n",
+    "def callback_save_network():\n",
+    "    file = open(filename + \"_rhino.txt\", 'w')\n",
+    "    for i in range(2):\n",
+    "        label = \"A\"\n",
+    "        if i==1:\n",
+    "            label =\"B\"\n",
+    "\n",
+    "        # positions\n",
+    "        for j in range(len(tracer.paths_indexes[i])):\n",
+    "            path = tracer.paths_indexes[i][j]\n",
+    "            for idx in path:\n",
+    "                pt = tracer.paths[i][idx]\n",
+    "                file.write(label + str(j)+ \"_\" +str(pt[0]) + \",\" + str(pt[1]) + \",\" +str(pt[2]) + \"\\n\")\n",
+    "\n",
+    "    # Intersections    \n",
+    "    for i in range( len(tracer.intersection_points) ):\n",
+    "        pt = tracer.intersection_points[i]\n",
+    "        file.write(\"C\" + str(i) + \"_\" +str(pt[0]) + \",\" + str(pt[1]) + \",\" +str(pt[2]) + \"\\n\")\n",
+    "    file.close()\n",
+    "\n",
+    "def callback_sampling_distance(value):\n",
+    "    global sampling_distance\n",
+    "    sampling_distance = value\n",
+    "\n",
+    "def callback_iterative_sampling(value):\n",
+    "    global iter_sampling\n",
+    "    iter_sampling = value\n",
+    "\n",
+    "plot.add_mesh(mesh, show_edges=True)\n",
+    "plot.add_axes()\n",
+    "msg = \"Press <K> for saving indexes, <L> for loading indexes or <O> to save the curve network model.\\n\"\n",
+    "msg += \"Press <I> for computing intersections, <J> for generating the flatten strips and <H> for saving the laser-cutting file.\\n\"\n",
+    "msg += \"Press <M> for hiding labels, <N> for showing all labels, <U> for showing A labels or <Y> for showing B labels.\\n\"\n",
+    "plot.add_text(msg, position='lower_right', font_size=12, color=None, font=None, shadow=False, name=None, viewport=False)\n",
+    "plot.add_checkbox_button_widget(callback_first_family, value=tracer.flagA, position=(10, 200.0), size=40, border_size=1, color_on='white', color_off='grey', background_color='red')\n",
+    "plot.add_checkbox_button_widget(callback_second_family, value=tracer.flagB, position=(10, 300.0), size=40, border_size=1, color_on='yellow', color_off='grey', background_color='red')\n",
+    "plot.add_checkbox_button_widget(callback_iterative_sampling, value=iter_sampling, position=(10, 400.0), size=40, border_size=1, color_on='green', color_off='grey', background_color='red')\n",
+    "plot.add_text(\"First Family\", position=(80.0, 200.0), font_size=12, color=None, font=None, shadow=False, name=None, viewport=False)\n",
+    "plot.add_text(\"Second Family\", position=(80, 300.0), font_size=12, color=None, font=None, shadow=False, name=None, viewport=False)\n",
+    "plot.add_text(\"Iterative sampling\", position=(80, 400.0), font_size=12, color=None, font=None, shadow=False, name=None, viewport=False)\n",
+    "plot.add_key_event('i', callback_intersection)\n",
+    "plot.add_key_event('j', callback_flatten)\n",
+    "plot.add_key_event('k', callback_save_indexes)\n",
+    "plot.add_key_event('l', callback_load_indexes)\n",
+    "plot.add_key_event('h', callback_save_svg)\n",
+    "plot.add_key_event('m', callback_remove_labels)\n",
+    "plot.add_key_event('n', callback_add_all_labels)\n",
+    "plot.add_key_event('u', callback_add_labelsA)\n",
+    "plot.add_key_event('y', callback_add_labelsB)\n",
+    "plot.add_key_event('o', callback_save_network)\n",
+    "plot.enable_point_picking(callback=callback_picking, show_message=True, color='pink', point_size=10, use_mesh=True, show_point=True)\n",
+    "plot.add_slider_widget(callback_width, [0.1, 5.0], value=strips_width, title=\"Strip Width (cm)\", pointa=(.83, .15), pointb=(.98, .15), title_height=0.02, fmt=\"%0.1f\", style='modern')\n",
+    "plot.add_slider_widget(callback_thickness, [0.1, 1], value=strips_thickness, title=\"Strip Thickness (cm)\", pointa=(.67, .15), pointb=(.82, .15), title_height=0.02, fmt=\"%0.2f\", style='modern')\n",
+    "plot.add_slider_widget(callback_length, [1, 10], value=strips_scale, title=\"Scale Strip Length\", pointa=(.51, .15), pointb=(.66, .15), title_height=0.02, fmt=\"%0.1f\", style='modern')\n",
+    "plot.add_slider_widget(callback_spacing, [0., 0.5], value=strips_spacing, title=\"Strip spacing\", pointa=(.51, .88), pointb=(.66, .88), title_height=0.02, fmt=\"%0.1f\", style='modern')\n",
+    "plot.add_slider_widget(callback_board_width, [10, 100], value=board_width, title=\"Board width (cm)\", pointa=(.67, .88), pointb=(.82, .88), title_height=0.02, fmt=\"%0.0f\", style='modern')\n",
+    "plot.add_slider_widget(callback_board_length, [10, 100], value=board_length, title=\"Board length (cm)\", pointa=(.83, .88), pointb=(.98, .88), title_height=0.02, fmt=\"%0.0f\", style='modern')\n",
+    "plot.add_slider_widget(callback_sampling_distance, [0.1, 2.0], value=sampling_distance, title=\"Sampling distance\", pointa=(.005, .88), pointb=(.16, .88), title_height=0.02, fmt=\"%0.1f\", style='modern')\n",
+    "plot.show(\"Asymptotic GridShell\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "294c9a689e437aa1430c5ffd3595515046a5cee981399d48a2291c079c814bc8"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
--- a/cs457-gc/assignment_3_3/notebook/tracing_test.ipynb
+++ b/cs457-gc/assignment_3_3/notebook/tracing_test.ipynb
@@ -0,0 +1,171 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from meshplot import plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../src')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import importlib, tracer_helper, tracer_utils\n",
+    "importlib.reload(tracer_helper)\n",
+    "importlib.reload(tracer_utils)\n",
+    "from tracer_utils import asymptotic_path\n",
+    "from tracer_helper import Mesh"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Example\n",
+    "Build mesh from file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f19ce42be2f6405796de381ba4b89b42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Renderer(camera=PerspectiveCamera(aspect=1.5, children=(DirectionalLight(color='white', intensity=0.6, positio…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mesh = Mesh(\"../data/Model01.obj\")\n",
+    "p = plot(mesh.V, mesh.F, shading={\"wireframe\": True,\"width\": 900, \"height\": 600}, return_plot=True, c=np.array([0,0.7,1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Trace first asymptotic curve\n",
+    "Use principal directions V1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set tracing parameters\n",
+    "num_steps = 1000\n",
+    "step_size = 1e-6\n",
+    "num_neighbors = 4\n",
+    "first_principal_direction = False\n",
+    "\n",
+    "# Pre-selection of vertices\n",
+    "idxA = [167,173,178,146,159,291,49,40,254,57,15,65,268,341,283]\n",
+    "# Tracing\n",
+    "for idx in idxA:\n",
+    "    P, A, PP = asymptotic_path(idx, mesh, num_steps, step_size, first_principal_direction, num_neighbors)\n",
+    "\n",
+    "    # Plot starting vertex\n",
+    "    p.add_points(np.array([mesh.V[idx]]), shading={\"point_size\": 0.7, \"point_color\": \"black\"})\n",
+    "    # Plot edge-points shaping the asymptotic path\n",
+    "    p.add_points(P, shading={\"point_size\": 0.2,\"point_color\": \"white\"})\n",
+    "    # Plot asymptotic curve\n",
+    "    if(len(P)>1): \n",
+    "        p.add_lines(P[:-1], P[1:], shading={\"line_color\": \"white\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Trace second asymptotic curve\n",
+    "Use principal directions V2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set tracing parameters\n",
+    "num_steps = 1000\n",
+    "step_size = 1e-6\n",
+    "num_neighbors = 4\n",
+    "first_principal_direction = True\n",
+    "\n",
+    "# Pre-selection of vertices\n",
+    "idxB = [119,203,188,129,95,66,308,298,290,282,335,143,81,73,33]\n",
+    "# Tracing\n",
+    "for idx in idxB:\n",
+    "    P, A, PP = asymptotic_path(idx, mesh, num_steps, step_size, first_principal_direction, num_neighbors)\n",
+    "    \n",
+    "    # Plot starting vertex\n",
+    "    p.add_points(np.array([mesh.V[idx]]), shading={\"point_size\": 0.7, \"point_color\": \"black\"})\n",
+    "    # Plot edge-points shaping the asymptotic path\n",
+    "    p.add_points(P, shading={\"point_size\": 0.2,\"point_color\": \"yellow\"})\n",
+    "    # Plot asymptotic curve\n",
+    "    if(len(P)>1): \n",
+    "        p.add_lines(P[:-1], P[1:], shading={\"line_color\": \"yellow\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "d74ce2d711eca57e47550fdd427f707c5faa08517318f0967f4aae699c902c0e"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
--- a/cs457-gc/assignment_3_3/src/fabrication_helper.py
+++ b/cs457-gc/assignment_3_3/src/fabrication_helper.py
@@ -0,0 +1,186 @@
+import numpy as np
+import numpy.linalg as la
+import svgwrite
+from tracer_tool import AsymptoticTracer
+
+class FabricationHelper:
+
+    def __init__(self, width, thickness, spacing, scale_length=1.0):
+        self.strips_width = width
+        self.strips_spacing = spacing
+        self.strips_thickness = thickness
+        self.scale_length = scale_length
+        self.flag_flatten = False
+
+    def generate_flatten_path(self, pathIndex, second_direction=False, position=np.array([0,0,0])):
+
+        length_direction = np.array([1,0,0])
+        width_direction = np.array([0,1,0]) * self.strips_width * 0.5
+        if second_direction:
+           width_direction *= -1
+        
+        self.intersection_points[second_direction][pathIndex] = np.empty((0,3), float)
+        self.intersection_labels[second_direction][pathIndex] = []
+
+        intersections = self.intersections[second_direction][pathIndex]
+        paths_lengths = self.paths_lengths[second_direction][pathIndex]
+        length = np.trace(np.diag(paths_lengths)) * self.scale_length
+
+        path_forwards = np.empty((0,3), float)
+        path_backwards = np.empty((0,3), float)
+        path_intersection = np.empty((0,3), float)
+
+        # Add start point
+        start_position_forward = position + width_direction
+        start_position_backward = position - width_direction
+        end_position_forward = position + width_direction + length_direction * length
+        end_position_backward = position - width_direction + length_direction * length
+        path_forwards = np.append(path_forwards, np.array([start_position_forward]),axis=0)
+        path_backwards = np.append(path_backwards, np.array([start_position_backward]),axis=0)
+
+        # Connections
+        if len(intersections)>0:
+            for i in range(len(intersections)):
+
+                intersection_event = intersections[i]
+                # Store label
+                self.intersection_labels[second_direction][pathIndex].append(int(intersection_event[2]))
+
+                # Add intersection points
+                edge_index = int(intersection_event[0])
+                
+                partial_length = (np.trace(np.diag(paths_lengths[:edge_index])) + paths_lengths[edge_index] * intersection_event[1]) * self.scale_length
+
+                p = position + length_direction * partial_length
+
+                path_intersection = np.append(path_intersection, np.array([p]), axis=0)
+
+                p += width_direction
+
+                vec_offset = length_direction * self.strips_thickness * 0.5
+
+                if partial_length - self.strips_thickness * 0.5 <= 0:
+                    p0 = position
+                    p1 = p + vec_offset - width_direction
+                    p2 = p + vec_offset
+                    path_forwards = np.array([p0, p1, p2])
+                elif partial_length + self.strips_thickness * 0.5 >= length:
+                    p0 = p - vec_offset
+                    p1 = p - vec_offset - width_direction
+                    p2 = position + length_direction * length
+                    path_forwards = np.append(path_forwards, np.array([p0, p1, p2]), axis=0)
+                else:
+                    p0 = p - vec_offset
+                    p1 = p - vec_offset - width_direction
+                    p2 = p + vec_offset - width_direction
+                    p3 = p + vec_offset
+                    path_forwards = np.append(path_forwards, np.array([p0, p1, p2, p3]), axis=0)
+
+            if end_position_forward[0]>path_forwards[len(path_forwards)-1][0]:
+                path_forwards = np.append(path_forwards, np.array([end_position_forward]), axis=0)
+            path_backwards = np.append(path_backwards, np.array([end_position_backward]), axis=0)
+        else:
+            path_forwards = np.append(path_forwards, np.array([end_position_forward]), axis=0)
+            path_backwards = np.append(path_backwards, np.array([end_position_backward]), axis=0)
+
+        path = path_backwards[::-1]
+
+        # Combine paths
+        path = np.append(path, path_forwards, axis=0)
+        path = np.append(path, np.array([path[0]]), axis=0)
+        self.paths_flatten[second_direction][pathIndex] = path
+        self.intersection_points[second_direction][pathIndex] = path_intersection
+
+        return path, length
+
+    def generate_flatten_network(self, tracer):
+        self.paths_flatten = tracer.paths_flatten
+        self.paths_lengths = tracer.paths_lengths
+        self.intersection_points = tracer.paths_labels
+        self.intersection_labels = [[] for i in range(2) ]
+        self.intersections = tracer.intersections
+        self.strips_numA = len(tracer.paths_indexes[0])
+        self.strips_numB = len(tracer.paths_indexes[1])
+        # init labels 
+        self.intersection_labels[0] = [[] for i in range(self.strips_numA)]
+        self.intersection_labels[1] = [[] for i in range(self.strips_numB)]
+
+        strips_num = self.strips_numA if self.strips_numA > self.strips_numB else self.strips_numB
+
+        # Generate flat strips
+        strips_added = 0
+
+        # Fabrication parameters
+        self.board_width = self.strips_width * (self.strips_numA+self.strips_numB) + self.strips_spacing * (self.strips_numA+self.strips_numB+1)
+        self.board_length = 0
+
+        for i in range(strips_num):
+            if i< self.strips_numA:
+                position = np.array([ self.strips_spacing, self.strips_width * 0.5 + self.strips_spacing + (self.strips_width + self.strips_spacing) * strips_added, 0 ] )
+                points, path_len = self.generate_flatten_path(i, False, position)
+                strips_added +=1
+
+                if path_len>self.board_length:
+                    self.board_length = path_len
+            
+            if i< self.strips_numB:
+                position = np.array([ self.strips_spacing, self.strips_width * 0.5 + self.strips_spacing + (self.strips_width + self.strips_spacing) * strips_added, 0 ] )
+                points, path_len = self.generate_flatten_path(i, True, position)
+                strips_added +=1
+
+                if path_len>self.board_length:
+                    self.board_length = path_len
+
+        self.flag_flatten = True
+
+    def generate_svg_file(self, filename, font_size=1, cutting_color="red", engraving_color="black", length=None, width=None):
+        
+        if length!=None:
+            self.board_length = length
+        if width!=None:
+            self.board_width = width
+
+        dwg = svgwrite.Drawing(filename,size=(str(self.board_length), str(self.board_width)), fill="none", stroke_width=".005cm")
+
+        strips_numA = len(self.paths_flatten[0])
+        strips_numB = len(self.paths_flatten[1])
+        strips_num = strips_numA if strips_numA > strips_numB else strips_numB
+
+        for i in range(strips_num):
+            if i< strips_numA:
+                pts = self.paths_flatten[0][i][:,[0,1]]
+                dwg.add(dwg.polyline(pts, stroke=cutting_color))
+                p0 = self.paths_flatten[0][i][0]
+                p1 = self.paths_flatten[0][i][len(self.paths_flatten[0][i])-2]
+                p2 = self.paths_flatten[0][i][len(self.paths_flatten[0][i])-3]
+                vecX = p1-p2
+                vecY = (p1-p0) * 0.5
+                vecX /= la.norm(vecX)
+                pos = p0 + vecY - vecX * self.strips_width * 0.9
+                dwg.add(dwg.text('A'+str(i), insert=pos, fill=engraving_color, font_family="Code Light" ,font_size=str(font_size)))
+                pts = self.intersection_points[0][i]
+                labels = self.intersection_labels[0][i]
+                for j in range(len(pts)):
+                    pos = pts[j] - vecY * 0.5
+                    dwg.add(dwg.text('c' + str(labels[j]), insert=pos, fill=engraving_color, font_family="Code Light" ,font_size=str(font_size * 0.7)))
+                    
+            if i< strips_numB:
+                pts = self.paths_flatten[1][i][:,[0,1]]
+                dwg.add(dwg.polyline(pts, stroke=cutting_color))
+                p0 = self.paths_flatten[1][i][0]
+                p1 = self.paths_flatten[1][i][len(self.paths_flatten[1][i])-2]
+                p2 = self.paths_flatten[1][i][len(self.paths_flatten[1][i])-3]
+                vecX = p1-p2
+                vecY = (p1-p0) * 0.5
+                vecX /= la.norm(vecX)
+                pos = p0 + vecY - vecX * self.strips_width * 0.9
+                dwg.add(dwg.text('B'+str(i), insert=pos, fill=engraving_color, font_family="Code Light" ,font_size=str(font_size)))
+                pts = self.intersection_points[1][i]
+                labels = self.intersection_labels[1][i]
+                for j in range(len(pts)):
+                    pos = pts[j] - vecY * 0.5
+                    dwg.add(dwg.text('c' + str(labels[j]), insert=pos, fill=engraving_color, font_family="Code Light" ,font_size=str(font_size * 0.7)))
+        #Board boundary
+        points = np.array([[0.,0.],[self.board_length,0.],[self.board_length, self.board_width],[0., self.board_width],[0.,0.]])
+        dwg.add(dwg.polyline(points, stroke=engraving_color))       
+        dwg.save()
--- a/cs457-gc/assignment_3_3/src/fitting.py
+++ b/cs457-gc/assignment_3_3/src/fitting.py
@@ -0,0 +1,219 @@
+import numpy as np
+
+from scipy import sparse
+
+from scipy.sparse import linalg
+
+import igl
+
+from smooth_surfaces import *
+
+from utils import *
+
+
+# -----------------------------------------------------------------------------
+# UTILITIES
+# -----------------------------------------------------------------------------
+
+def compute_orthogonal_frames(N):
+    """Computes an orthonormal frame {e1, e2, e3} at vertices x_i.
+
+    Parameters:
+    - N : np.array (|n|, 3)
+        The i-th row contains a vector in direction e3 at x_i.
+
+    Returns:
+    - e1: np.array (|n|, 3)
+        The i-th row contains the axis e1 at vertex x_i.
+    - e2: np.array (|n|, 3)
+        The i-th row contains the axis e2 at vertex x_i.
+    - e3: np.array (|n|, 3)
+        The i-th row contains the axis e3 at vertex x_i.
+    """
+    e3 = N / np.linalg.norm(N, axis=1, keepdims=True)
+    e1 = np.zeros(e3.shape)
+    e1[:, 0] = - e3[:, 1]
+    e1[:, 1] = e3[:, 0]
+    e1[np.where((e1[:, 0] == 0) & (e1[:, 1] == 0))[0], 1] = 1
+    e1 = e1 / np.linalg.norm(e1, axis=1, keepdims=True)
+    e2 = np.cross(e3, e1)
+    return e1, e2, e3
+
+
+def vertex_double_rings(F):
+    """Computes double rings of mesh vertices.
+
+    Parameters:
+    - F : np.array (|F|, 3)
+        The array of triangle faces.
+
+    Returns:
+    - v_i : np.array (n, )
+        The indices of the central vertices i.
+    - v_j : np.array (n, )
+        The indices of the vertices j connected to vertex i by at most two
+        edges, such that v_j[k] belongs to the double ring of v_i[k].
+    """
+    M = igl.adjacency_matrix(F)
+    vi, vj = M.nonzero()
+    N = M[vj]
+    vii, vjj = N.nonzero()
+    L = sparse.coo_matrix((np.ones(len(vii)), (vii, np.arange(len(vii)))))
+    k = np.array(L.sum(axis=1)).flatten().astype('i')
+    vii = np.repeat(vi, k)
+    M = sparse.coo_matrix((np.ones(len(vii)), (vii, vjj)), shape=M.shape)
+    M = M.tolil()
+    M.setdiag(0)
+    return M.nonzero()
+
+
+# -----------------------------------------------------------------------------
+# OSCULATING PARABOLOID
+# -----------------------------------------------------------------------------
+
+def compute_osculating_paraboloids(V, F, e1, e2, e3):
+    """Computes the coefficients of the osculating paraboloid at vertices x_i
+    in local orthonormal coordinates with base {x_i; e1, e2, e3}, with
+    eta(x,y) = a x^2 + b y^2 + c xy + d x + e y
+    through least squares fitting. Try to vectorize this function.
+
+    Parameters:
+    - V : np.array (|V|, 3)
+        The array of vertices positions.
+        Contains the global coordinates of the vertex x_i in i-th row
+    - F : np.array (|F|, 3)
+        The array of triangle faces.
+    - e1: np.array (|V|, 3)
+        The i-th row contains the axis e1 at vertex x_i.
+    - e2: np.array (|V|, 3)
+        The i-th row contains the axis e2 at vertex x_i.
+    - e3: np.array (|V|, 3)
+        The i-th row contains the axis e3 at vertex x_i.
+
+    Returns:
+    - a : np.array (|V|, 5)
+        The paraboloid coefficients. i-th row contains the coefficients
+        [a, b, c, d, e] of the paraboloid at x_i.
+    """
+    # we compute the indices for the double ring vj at each vertex vi
+    vi, vj = vertex_double_rings(F)
+    Pj = V[vj] - V[vi]
+    x = np.einsum('ij,ij->i',Pj, e1[vi])
+    y = np.einsum('ij,ij->i',Pj, e2[vi])
+    z = np.einsum('ij,ij->i',Pj, e3[vi])
+
+    i = np.arange(vj.shape[0])
+    i = np.hstack((i,i,i,i,i))
+    
+    j = 5*vi
+    j = np.hstack((j,j+1,j+2,j+3,j+4))
+
+    data = np.hstack((x**2, y**2, x*y, x,y))
+    X = sparse.coo_matrix((data,(i,j)), shape=(vj.shape[0],5*len(V)))
+    X = X.tocsr()
+    A = X.T.dot(X)
+    b = X.T.dot(z)
+    a = sparse.linalg.spsolve(A,b)
+    a = np.reshape(a,(len(V),5))
+    return a
+
+
+def compute_osculating_paraboloid_first_derivatives(a):
+    """Computes the first derivatives of the osculating paraboloid at vertices x_i
+    in local orthonormal coordinates with base {x_i; e1, e2, e3}, with
+    eta(x,y) = a x^2 + b y^2 + c xy + d x + e y,
+    evaluated at the point x_i. Try to vectorize this function.
+
+    Parameters:
+    - a : np.array (|V|, 5)
+        The paraboloid coefficients. i-th row contains the coefficients
+        [a, b, c, d, e] of the paraboloid at x_i.
+
+    Returns:
+    - x_x : np.array (|V|, 3)
+        The first derivatives x_x, where the i-th row contains the local (x,y,z)
+        coordinates of the vector x_x(x_i).
+    - x_y : np.array (|V|, 3)
+        The second derivatives x_y, where the i-th row contains the local (x,y,z)
+        coordinates of the vector x_y(x_i).
+    """
+    x_x = np.column_stack((np.ones(len(a)), np.zeros(len(a)), a[:,3]))
+    x_y = np.column_stack((np.zeros(len(a)), np.ones(len(a)), a[:,4]))
+    return x_x, x_y
+
+
+def compute_osculating_paraboloid_second_derivatives(a):
+    """Computes the second derivatives of the osculating paraboloid at vertices x_i
+    in local orthonormal coordinates with base {x_i; e1, e2, e3}, with
+    eta(x,y) = a x^2 + b y^2 + c xy + d x + e y,
+    evaluated at the point x_i. Try to vectorize this function.
+
+    Parameters:
+    - a : np.array (|V|, 5)
+        The paraboloid coefficients. i-th row contains the coefficients
+        [a, b, c, d, e] of the paraboloid at x_i.
+
+    Returns:
+    - x_xx : np.array (|V|, 3)
+        The second derivatives x_xx, where the i-th row contains the local (x,y,z)
+        coordinates of the vector x_xx(x_i).
+    - x_xy : np.array (|V|, 3)
+        The second derivatives x_xy, where the i-th row contains the local (x,y,z)
+        coordinates of the vector x_xy(x_i).
+    - x_yy : np.array (|V|, 3)
+        The second derivatives x_yy, where the i-th row contains the local (x,y,z)
+        coordinates of the vector x_yy(x_i).
+    """
+    x_xx = np.column_stack((np.zeros(len(a)), np.zeros(len(a)), 2*a[:,0]))
+    x_xy = np.column_stack((np.zeros(len(a)), np.zeros(len(a)), a[:,2]))
+    x_yy = np.column_stack((np.zeros(len(a)), np.zeros(len(a)), 2*a[:,1]))
+    return x_xx, x_xy, x_yy
+
+
+def compute_mesh_principal_curvatures(V, F):
+    """Computes the principal curvatures at mesh vertices v_i through quadratic
+    fitting.
+
+    Parameters:
+    - V : np.array (|V|, 3)
+        The array of vertices positions.
+        Contains the global coordinates of the vertex x_i in i-th row
+    - F : np.array (|F|, 3)
+        The array of triangle faces.
+
+    Returns:
+    - k_1 : np.array (n)
+        The min principal curvature. i-th element contains the curvature
+        at vertex x_i.
+    - k_2 : np.array (n)
+        The max principal curvature. i-th element contains the curvature
+        at vertex x_i.
+    - d_1 : np.array (n, 3)
+        The unitized principal curvature direction corresponding to k_1.
+        The i-th row contains the global coordinates of d_1(x_i).
+    - d_2 : np.array (n, 3)
+        The unitized principal curvature direction corresponding to k_2.
+        The i-th row contains the global coordinates of d_2(x_i).
+    """
+    # we compute a vertex normal with libigl and use it as local axis e3:
+    N = igl.per_vertex_normals(V, F)
+    # then we compute the local axes:
+    e1, e2, e3 = compute_orthogonal_frames(N)
+
+    a = compute_osculating_paraboloids(V,F,e1,e2,e3)
+    x_x, x_y = compute_osculating_paraboloid_first_derivatives(a) 
+    x_xx, x_xy, x_yy = compute_osculating_paraboloid_second_derivatives(a)
+    n = compute_surface_normal(x_x, x_y)
+    I = compute_first_fundamental_form(x_x, x_y)
+    II = compute_second_fundamental_form(x_xx, x_xy, x_yy, n)
+    S = compute_shape_operator(I, II)
+    k_1,k_2, d_1, d_2 = compute_principal_curvatures(S, x_x, x_y)
+
+    d_1 = np.einsum('i,ij->ij', d_1[:,0], e1) +\
+          np.einsum('i,ij->ij', d_1[:,1], e2) +\
+          np.einsum('i,ij->ij', d_1[:,2], e3)
+
+    d_2 = np.einsum('i,ij->ij', d_2[:,0], e1) +\
+          np.einsum('i,ij->ij', d_2[:,1], e2) +\
+          np.einsum('i,ij->ij', d_2[:,2], e3)
+    return k_1, k_2, d_1, d_2
--- a/cs457-gc/assignment_3_3/src/smooth_surfaces.py
+++ b/cs457-gc/assignment_3_3/src/smooth_surfaces.py
@@ -0,0 +1,423 @@
+
+import numpy as np
+
+
+# -----------------------------------------------------------------------------
+#  DERIVATIVES OF PARABOLOID
+# -----------------------------------------------------------------------------
+
+def compute_paraboloid_points(P, a, b, c, d, e):
+    """Computes the points of the paraboloid x(u,v) = (u, v, z(u,v)) with
+    z(u,v) = a*u^2 + b*v^2 + c*u*v + d*u + e*v.
+    Try to vectorize this function.
+
+    Parameters:
+    - P : np.array (n, 2)
+        Contains in i-th row the (u, v) coordinates of the i-th parameter point p_i.
+    - a, b, c, d, e : float
+        The parameters of the paraboloid.
+
+    Returns:
+    - x : np.array (n, 3)
+        The points x(P), where the i-th row contains the (x,y,z) coordinates
+        of the point x(p_i)
+    """
+    n = a*P[:,0]**2 + \
+        b*P[:,1]**2 + \
+        c*P[:,0]*P[:,1] + \
+        d*P[:,0] + \
+        e*P[:,1]
+        
+    x = np.dstack((P[:,0],P[:,1],n))[0]
+    return x
+
+
+def compute_paraboloid_first_derivatives(P, a, b, c, d, e):
+    """Computes the first derivatives of the paraboloid x(u,v) = (u, v, z(u,v))
+    with z(u,v) = a*u^2 + b*v^2 + c*u*v + d*u + e*v.
+    Try to vectorize this function.
+
+    Parameters:
+    - P : np.array (n, 2)
+        Contains in i-th row the (u, v) coordinates of the i-th parameter point p_i.
+    - a, b, c, d, e : float
+        The parameters of the paraboloid.
+
+    Returns:
+    - x_u : np.array (n, 3)
+        The vectors x_u(P), where the i-th row contains the (x,y,z) coordinates
+        of the vector x_u(p_i).
+    - x_v : np.array (n, 3)
+        The vectors x_v(P), where the i-th row contains the (x,y,z) coordinates
+        of the vector x_v(p_i).
+    """
+    nu = 2*a*P[:,0] + \
+        c*P[:,1] + \
+        d
+    nv = 2*b*P[:,1] + \
+        c*P[:,0] + \
+        e
+    
+    z = np.zeros(P.shape[0])
+    o = np.ones(P.shape[0])
+
+    x_u = np.dstack((o,z,nu))[0]
+    x_v = np.dstack((z,o,nv))[0]
+    return x_u, x_v
+
+
+def compute_paraboloid_second_derivatives(P, a, b, c, d, e):
+    """Computes the second derivatives of the paraboloid x(u,v) = (u, v, z(u,v))
+    with z(u,v) = a*u^2 + b*v^2 + c*u*v + d*u + e*v.
+    Try to vectorize this function.
+
+    Parameters:
+    - P : np.array (n, 2)
+        Contains in i-th row the (u, v) coordinates of the i-th parameter point p_i.
+    - a, b, c, d, e : float
+        The parameters of the paraboloid.
+
+    Returns:
+    - x_uu : np.array (n, 3)
+        The vectors x_uu(P), where the i-th row contains the (x,y,z) coordinates
+        of the vector x_uu(p_i).
+    - x_uv : np.array (n, 3)
+        The vectors x_uv(P), where the i-th row contains the (x,y,z) coordinates
+        of the vector x_uv(p_i).
+    - x_vv : np.array (n, 3)
+        The vectors x_vv(P), where the i-th row contains the (x,y,z) coordinates
+        of the vector x_vv(p_i).
+    """
+    nuu = np.repeat(2*a, P.shape[0])
+    nuv = np.repeat( c,  P.shape[0])
+    nvv = np.repeat(2*b, P.shape[0])
+            
+    z = np.zeros(P.shape[0])
+        
+    x_uu = np.dstack((z,z,nuu))[0]
+    x_uv = np.dstack((z,z,nuv))[0]
+    x_vv = np.dstack((z,z,nvv))[0]
+    
+    return x_uu, x_uv, x_vv
+
+
+# -----------------------------------------------------------------------------
+#  DERIVATIVES OF TORUS
+# -----------------------------------------------------------------------------
+
+def compute_torus_points(P, R, r):
+    """Computes the second derivatives of a torus.
+    Try to vectorize this function.
+
+    Parameters:
+    - P : np.array (n, 2)
+        Contains in i-th row the (u, v) coordinates of the i-th parameter point p_i.
+    - R : float
+        The radius of revolution.
+    - r : float
+        The radius of the cross section.
+    Returns:
+    - x : np.array (n, 3)
+        The points x(P), where the i-th row contains the (x,y,z) coordinates
+        of the point x(p_i)
+    """
+    cosu = np.cos(P[:,0])
+    sinu = np.sin(P[:,0])
+    cosv = np.cos(P[:,1])
+    sinv = np.sin(P[:,1])
+    fx = (r * cosu+ R) * cosv
+    fy = (r * cosu+ R) * sinv
+    fz = r * sinu
+        
+    x = np.dstack((fx, fy, fz))[0]
+    return x
+
+
+def compute_torus_first_derivatives(P, R, r):
+    """Computes the second derivatives of a torus.
+    Try to vectorize this function.
+
+    Parameters:
+    - P : np.array (n, 2)
+        Contains in i-th row the (u, v) coordinates of the i-th parameter point p_i.
+    - R : float
+        The radius of revolution.
+    - r : float
+        The radius of the cross section.
+    Returns:
+    - x_u : np.array (n, 3)
+        The vectors x_u(P), where the i-th row contains the (x,y,z) coordinates
+        of the vector x_u(p_i).
+    - x_v : np.array (n, 3)
+        The vectors x_v(P), where the i-th row contains the (x,y,z) coordinates
+        of the vector x_v(p_i).
+    """
+    cosu = np.cos(P[:,0])
+    sinu = np.sin(P[:,0])
+    cosv = np.cos(P[:,1])
+    sinv = np.sin(P[:,1])
+
+    z = np.zeros(P.shape[0])
+
+    fx_u = - r * sinu * cosv
+    fy_u = - r * sinu * sinv
+    fz_u = r * cosu
+
+    fx_v = -(r * cosu+ R) * sinv
+    fy_v = (r * cosu+ R) * cosv
+    fz_v = z
+        
+    x_u = np.dstack((fx_u, fy_u, fz_u))[0]
+    x_v = np.dstack((fx_v, fy_v, fz_v))[0]
+    return x_u, x_v
+
+
+def compute_torus_second_derivatives(P, R, r):
+    """Computes the second derivatives of a torus.
+    Try to vectorize this function.
+
+    Parameters:
+    - P : np.array (n, 2)
+        Contains in i-th row the (u, v) coordinates of the i-th parameter point p_i.
+    - R : float
+        The radius of revolution.
+    - r : float
+        The radius of the cross section.
+
+    Returns:
+    - x_uu : np.array (n, 3)
+        The vectors x_uu(P), where the i-th row contains the (x,y,z) coordinates
+        of the vector x_uu(p_i).
+    - x_uv : np.array (n, 3)
+        The vectors x_uv(P), where the i-th row contains the (x,y,z) coordinates
+        of the vector x_uv(p_i).
+    - x_vv : np.array (n, 3)
+        The vectors x_vv(P), where the i-th row contains the (x,y,z) coordinates
+        of the vector x_vv(p_i).
+    """
+    cosu = np.cos(P[:,0])
+    sinu = np.sin(P[:,0])
+    cosv = np.cos(P[:,1])
+    sinv = np.sin(P[:,1])
+
+    z = np.zeros(P.shape[0])
+
+    fx_uu = - r * cosu * cosv
+    fy_uu = - r * cosu * sinv
+    fz_uu = - r * sinu
+
+    fx_uv = (r * sinu) * sinv
+    fy_uv = -(r * sinu) * cosv
+    fz_uv = z
+
+    fx_vv = -(r * cosu+ R) * cosv
+    fy_vv = -(r * cosu+ R) * sinv
+    fz_vv = z
+        
+    x_uu = np.dstack((fx_uu, fy_uu, fz_uu))[0]
+    x_uv = np.dstack((fx_uv, fy_uv, fz_uv))[0]
+    x_vv = np.dstack((fx_vv, fy_vv, fz_vv))[0]
+    return x_uu, x_uv, x_vv
+
+
+# -----------------------------------------------------------------------------
+#  SHAPE OPERATOR
+# -----------------------------------------------------------------------------
+
+def compute_first_fundamental_form(x_u, x_v):
+    """Computes the first fundamental form I.
+    Try to vectorize this function.
+
+    Parameters:
+    - x_u : np.array (n, 3)
+        The i-th row contains the (x,y,z) coordinates of the vector x_u(p_i).
+    - x_v : np.array (n, 3)
+        The i-th row contains the (x,y,z) coordinates of the vector x_v(p_i).
+
+    Returns:
+    - I : np.array (n, 2, 2)
+        The first fundamental forms.
+        The (i, j, k) position contains the (j, k) element of the first
+        fundamental form I(p_i).
+    """
+    e00 = np.einsum('lm,ml->m',x_u.T,x_u)
+    e01 = np.einsum('lm,ml->m',x_u.T,x_v)
+    e10 = np.einsum('lm,ml->m',x_v.T,x_u)
+    e11 = np.einsum('lm,ml->m',x_v.T,x_v)
+    I = np.dstack((e00,e01,e10,e11))[0].reshape((x_u.shape[0],2,2))
+    return I
+
+
+def compute_surface_normal(x_u, x_v):
+    """Computes the surface normal n.
+    Try to vectorize this function.
+
+    Parameters:
+    - x_u : np.array (n, 3)
+        The i-th row contains the (x,y,z) coordinates of the vector x_u(p_i).
+    - x_v : np.array (n, 3)
+        The i-th row contains the (x,y,z) coordinates of the vector x_v(p_i).
+
+    Returns:
+    - n : np.array (n, 3)
+        The surface normals.
+        The i-th row contains the (x,y,z) coordinates of the vector n(p_i).
+    """
+    exp = np.cross(x_u, x_v)
+    div = np.linalg.norm(exp, axis=1)
+    n = exp/div[:,None]
+    return n
+
+
+def compute_second_fundamental_form(x_uu, x_uv, x_vv, n):
+    """Computes the second fundamental form II.
+    Try to vectorize this function.
+
+    Parameters:
+    - x_uu : np.array (n, 3)
+        The i-th row contains the (x,y,z) coordinates of the vector x_uu(p_i).
+    - x_uv : np.array (n, 3)
+        The i-th row contains the (x,y,z) coordinates of the vector x_uv(p_i).
+    - x_vv : np.array (n, 3)
+        The i-th row contains the (x,y,z) coordinates of the vector x_vv(p_i).
+    - n : np.array (n, 3)
+        The surface normals.
+        The i-th row contains the (x,y,z) coordinates of the vector n(p_i).
+
+    Returns:
+    - II : np.array (n, 2, 2)
+        The second fundamental forms.
+        The (i, j, k) position contains the (j, k) element of the second
+        fundamental form II(p_i).
+    """
+    e00 = np.einsum('lm,ml->m',n.T, x_uu)
+    e01 = np.einsum('lm,ml->m',n.T, x_uv)
+    e10 = np.einsum('lm,ml->m',n.T, x_uv)
+    e11 = np.einsum('lm,ml->m',n.T, x_vv)
+    II = np.dstack((e00,e01,e10,e11))[0].reshape((n.shape[0],2,2))
+    return II
+
+
+def compute_shape_operator(I, II):
+    """Computes the shape operator S.
+    Try to vectorize this function.
+
+    Parameters:
+    - I : np.array (n, 2, 2)
+        The first fundamental forms.
+        The (i, j, k) position contains the (j, k) element of the first
+        fundamental form I(p_i).
+    - II : np.array (n, 2, 2)
+        The second fundamental forms.
+        The (i, j, k) position contains the (j, k) element of the second
+        fundamental form II(p_i).
+
+    Returns:
+    - S : np.array (n, 2, 2)
+        The shape operators.
+        The (i, j, k) position contains the (j, k) element of the shape
+        operator S(p_i).
+    """
+    Iinv = np.linalg.inv(I)
+    S = Iinv @ II
+    return S
+
+
+# -----------------------------------------------------------------------------
+#  PRINCIPAL CURVATURES
+# -----------------------------------------------------------------------------
+
+def compute_principal_curvatures(S, x_u, x_v):
+    """Computes principal curvatures and corresponding principal directions.
+    Try to vectorize this function.
+
+    Parameters:
+    - S : np.array (n, 2, 2)
+        The shape operators.
+        The (i, j, k) position contains the (j, k) element of the shape
+        operator S(p_i).
+    - x_u : np.array (n, 3)
+        The i-th row contains the (x,y,z) coordinates of the vector x_u(p_i).
+    - x_v : np.array (n, 3)
+        The i-th row contains the (x,y,z) coordinates of the vector x_v(p_i).
+
+    Returns:
+    - k_1 : np.array (n)
+        The min principal curvature. i-th element contains the curvature k_1(p_i).
+    - k_2 : np.array (n)
+        The max principal curvature. i-th element contains the curvature k_2(p_i).
+    - e_1 : np.array (n, 3)
+        The unitized principal curvature direction corresponding to k_1.
+        The i-th row contains the (x,y,z) coordinates of e_1(p_i).
+    - e_2 : np.array (n, 3)
+        The unitized principal curvature direction corresponding to k_2.
+        The i-th row contains the (x,y,z) coordinates of e_2(p_i).
+    """
+    # this section computes the ordered eigenvalues and eigenvectors of S where
+    # k_1[i] = min eigenvalue at p_i, k_2[i] = max eigenvalue at p_i,
+    # bar_e_1[i] = [u, v] components of the eigenvector of k_1,
+    # bar_e_2[i] = [u, v] components of the eigenvector of k_2
+    eig = np.linalg.eig(S)
+    index = np.argsort(eig[0], axis=1)
+    k_1 = eig[0][np.arange(len(S)), index[:, 0]]
+    k_2 = eig[0][np.arange(len(S)), index[:, 1]]
+    bar_e_1 = eig[1][np.arange(len(S)), :, index[:, 0]]
+    bar_e_2 = eig[1][np.arange(len(S)), :, index[:, 1]]
+
+    # Compute the normalized 3D vectors e_1, e_2
+    J = np.concatenate((x_u,x_v), axis=1).reshape((S.shape[0],2,3))
+    Je1 = np.einsum('lnm,ln->lm',J, bar_e_1)
+    Je2 = np.einsum('lnm,ln->lm',J, bar_e_2)
+    e_1 = Je1/np.linalg.norm(Je1, axis=1)[:,None]
+    e_2 = Je2/np.linalg.norm(Je2, axis=1)[:,None]
+    return k_1, k_2, e_1, e_2
+
+
+# -----------------------------------------------------------------------------
+#  ASYMPTOTIC DIRECTIONS
+# -----------------------------------------------------------------------------
+
+def compute_asymptotic_directions(k_1, k_2, e_1, e_2):
+    """Computes principal curvatures and corresponding principal directions.
+    Try to vectorize this function.
+
+    Parameters:
+    - k_1 : np.array (n)
+        The min principal curvature. i-th element contains the curvature k_1(p_i).
+    - k_2 : np.array (n)
+        The max principal curvature. i-th element contains the curvature k_2(p_i).
+    - e_1 : np.array (n, 3)
+        The unitized principal curvature direction corresponding to k_1.
+        The i-th row contains the (x,y,z) coordinates of e_1(p_i).
+    - e_2 : np.array (n, 3)
+        The unitized principal curvature direction corresponding to k_2.
+        The i-th row contains the (x,y,z) coordinates of e_2(p_i).
+
+    Returns:
+    - a_1 : np.array (n, 3)
+        The first unitized asymptotic direction. The i-th row contains the
+        (x,y,z) coordinates of a_2(p_i) if it exists, (0, 0, 0) otherwise.
+    - a_2 : np.array (n, 3)
+        The second unitized asymptotic direction. The i-th row contains the
+        (x,y,z) coordinates of a_2(p_i) if it exists, (0, 0, 0) otherwise.
+    """
+    a_1 = np.zeros((k_1.shape[0],3))
+    a_2 = np.zeros((k_1.shape[0],3))
+    k1k2 = k_1*k_2
+    v1 = np.where(k1k2<=0)
+    v2 = np.where(k1k2<0)
+
+    f = np.zeros((k_1.shape[0]))
+    ff = np.zeros((k_1.shape[0]))
+    v3 = np.where((k_1-k_2)!=0)
+
+    f[v3] = (k_1[v3]/((k_1 - k_2)[v3]))
+    ff[v1] = f[v1]
+
+    cosT = np.sqrt(1-f)
+    sinT = np.sqrt(f)
+
+    a_1[v1] =(e_1*cosT[:,None] + e_2*sinT[:,None])[v1]
+    a_2[v2] =(e_1*cosT[:,None] - e_2*sinT[:,None])[v2]
+    return a_1, a_2
+
--- a/cs457-gc/assignment_3_3/src/tracer_helper.py
+++ b/cs457-gc/assignment_3_3/src/tracer_helper.py
@@ -0,0 +1,52 @@
+import igl
+import numpy as np
+import numpy.linalg as la
+import scipy.spatial as ds
+from meshplot import plot
+
+from fitting import compute_mesh_principal_curvatures
+
+class Mesh():
+    def __init__(self, *args):
+        '''
+        Inputs:
+        - filename : str
+            Path to mesh file.
+        '''
+        if isinstance(args[0], str):
+            # Build vertices and faces from file
+            self.V, self.F = igl.read_triangle_mesh(args[0])
+            self.build()
+        elif len(args)==2:
+            self.V = args[0]
+            self.F = args[1]
+            self.build()
+
+    def build(self):
+        # Calculate principal curvatures
+        self.K1, self.K2, self.V1, self.V2  = compute_mesh_principal_curvatures(self.V, self.F)
+
+        # Calculate normals
+        self.N = igl.per_vertex_normals(self.V, self.F)
+
+        # Build topology
+        self.edge_vertices, self.face_edges, self.edge_faces = igl.edge_topology(self.V,self.F)
+
+        # Build KDTree for topologic queries
+        self.kd_tree = ds.KDTree(self.V)
+    
+    def get_closest_vertices(self, point, numNeighbors):
+        return self.kd_tree.query(point,numNeighbors) 
+
+    def get_closest_mesh_point(self, point):
+        D,F,P = igl.point_mesh_squared_distance(point, self.V, self.F)
+        return F, P
+
+def rotate_vector(v, ang, v1, v2, n):
+    v1 /= la.norm(v1, axis = -1)
+    v2 /= la.norm(v2, axis = -1)
+    sign = np.sign(np.dot(np.cross(v1, v2), n.transpose()))
+    v2 *= sign * -1
+
+    v_arr = igl.rotate_vectors(np.array(v, ndmin=2), np.array([ang], ndmin=2), np.array(v1, ndmin=2), np.array(v2, ndmin=2))
+    return v_arr  
--- a/cs457-gc/assignment_3_3/src/tracer_tool.py
+++ b/cs457-gc/assignment_3_3/src/tracer_tool.py
@@ -0,0 +1,165 @@
+import igl
+import scipy.spatial as ds
+import numpy as np
+import math
+import numpy.linalg as la
+from tracer_helper import Mesh, rotate_vector
+from tracer_utils import intersection_event, asymptotic_path
+
+class AsymptoticTracer:
+
+    def __init__(self, filename, step_size=0.01, num_steps=200):
+        self.mesh = Mesh(filename)
+        self.step_size = step_size
+        self.num_steps = num_steps
+        # Variables for visualisation
+        self.flagA = False
+        self.flagB = False
+        self.flag_intersections = False
+        # Main data
+        self.paths = [np.empty((0,3), float) for i in range(2)] 
+        self.paths_indexes = [[] for i in range(2)] 
+        self.paths_flatten = [[] for i in range(2)] 
+        self.paths_lengths = [[] for i in range(2)] 
+        self.paths_labels = [[] for i in range(2)]
+        self.intersections = [[] for i in range(2)] 
+        self.samples_indexes = [[] for i in range(2)]
+        self.intersection_points = np.empty((0,3), float)
+    
+    def delete_path(self, path_index, first_principal_direction=True):
+        self.paths_indexes[1-first_principal_direction].pop(path_index)
+        self.paths_flatten[1-first_principal_direction].pop(path_index)
+        self.paths_lengths[1-first_principal_direction].pop(path_index)
+        self.paths_labels[1-first_principal_direction].pop(path_index)
+        self.intersections[1-first_principal_direction].pop(path_index)
+        self.samples_indexes[1-first_principal_direction].pop(path_index)
+
+    def num_pathsA(self):
+        return len(self.paths_indexes[0])
+
+    def num_pathsB(self):
+        return len(self.paths_indexes[1])
+
+    def generate_asymptotic_path(self, vertex_idx, first_principal_direction, num_neighbors, sampling_dist):
+    
+        P,A,PP = asymptotic_path(vertex_idx, self.mesh, self.num_steps, self.step_size, first_principal_direction, num_neighbors, sampling_dist)
+
+        if len(P)>0:
+            start_vertex = len(self.paths[1-first_principal_direction])
+            self.paths[1-first_principal_direction] = np.append(self.paths[1-first_principal_direction], P, axis=0)
+            self.paths_indexes[1-first_principal_direction].append(np.arange(start_vertex, start_vertex+len(P)))
+            # Initialize data for flattening
+            self.intersections[1-first_principal_direction].append(np.empty((0,3), float))
+            self.paths_lengths[1-first_principal_direction].append(np.zeros(len(P)-1))
+            self.paths_flatten[1-first_principal_direction].append(np.empty((0,2), float))
+            self.paths_labels[1-first_principal_direction].append(np.empty((0,2), float))
+            self.flag_intersections = False
+        
+        return P, PP
+
+    def generate_intersection_network(self):
+        treeA = ds.KDTree(self.paths[0])
+        treeB = ds.KDTree(self.paths[1])
+
+        strips_numA = len(self.paths_indexes[0])
+        strips_numB = len(self.paths_indexes[1])
+        strips_num = strips_numA if strips_numA > strips_numB else strips_numB
+
+        self.intersection_points = np.empty((0,3), float)
+
+        for i in range(strips_num):
+            if i< strips_numA:
+                self.generate_intersection_path(i, treeB, False)
+            if i< strips_numB:
+                self.generate_intersection_path(i, treeA, True)
+
+        self.flag_intersections = True
+        return self.intersection_points
+
+    def generate_intersection_path(self, pathIndex, tree, second_direction=False):
+
+        self.intersections[second_direction][pathIndex] = np.empty((0,3), float)
+        path_indexesA = self.paths_indexes[second_direction][pathIndex]
+        self.paths_lengths[second_direction][pathIndex] = np.zeros(len(path_indexesA)-1)
+
+        for i in range (len(path_indexesA)-1):
+            
+            # Get the segment on the first path
+            origA = self.paths[second_direction][path_indexesA[i]]
+            endA = self.paths[second_direction][path_indexesA[i+1]]
+            vecA = endA - origA
+                
+            # Store the length of the edge
+            self.paths_lengths[second_direction][pathIndex][i] = la.norm(vecA)
+
+            for j in range(len(self.paths_indexes[1-second_direction])):
+
+                # Get the KDTree
+                path_indexesB = self.paths_indexes[1-second_direction][j]
+
+                # Query for closest nodes on the second path (Find closest nodes for both ends)
+                dist, closest_idxB = tree.query([origA,endA], 1)
+
+                # Find intersections in both end-nodes
+                count = len(path_indexesB)
+
+                for idx in closest_idxB: 
+                    origB = tree.data[idx]
+
+                    # Check for intersection events with the node following the closest node on the second path
+                    # Break if an intersection is found
+                    if idx in path_indexesB:
+
+                        local_idx = np.where(path_indexesB==idx)[0][0]
+
+                        vecB = None
+                        
+                        if local_idx<=count-2:
+                            vecB = tree.data[path_indexesB[local_idx+1]] - origB
+                            t, u, inter = intersection_event(origA, vecA, origB, vecB)
+                            if inter==0 and t>=0 and t<=1 and u>=0 and u<=1:
+                                # Store edge index and the parameter where the intersection occured. 
+                                p = origA + vecA * t
+
+                                distance = (self.intersection_points[:,0]-p[0])**2 + (self.intersection_points[:,1]-p[1])**2 + (self.intersection_points[:,2]-p[2])**2
+                                closest_intersection = np.where(distance<1e-3)[0]
+
+                                if len(closest_intersection)==0: # No duplicate intersection
+                                    label = len(self.intersection_points)
+                                    self.intersections[second_direction][pathIndex] = np.append(self.intersections[second_direction][pathIndex],np.array([[i,t,label]]), axis=0)                     
+                                    self.intersection_points = np.append(self.intersection_points, np.array([p]), axis=0)
+                                    break
+                                else: # Duplicated intersection
+                                    label = closest_intersection[0]
+                                    self.intersections[second_direction][pathIndex] = np.append(self.intersections[second_direction][pathIndex],np.array([[i,t,label]]), axis=0)    
+                                    break                 
+
+                        
+                        # If no intersection is found, check with the node preceding the closest node on the second path
+                        # Break if an intersection is found
+                        if local_idx >= 1:
+                            vecB = tree.data[path_indexesB[local_idx-1]] - origB
+                            t, u, inter = intersection_event(origA, vecA, origB, vecB)
+                            if inter==0 and t>=0 and t<=1 and u>=0 and u<=1:
+                                # Store edge index and the parameter where the intersection occured.
+                                p = origA + vecA * t
+
+                                distance = (self.intersection_points[:,0]-p[0])**2 + (self.intersection_points[:,1]-p[1])**2 + (self.intersection_points[:,2]-p[2])**2
+                                closest_intersection = np.where(distance<1e-3)[0]
+
+                                if len(closest_intersection)==0: # No duplicate intersection
+                                    label = len(self.intersection_points)
+                                    self.intersections[second_direction][pathIndex] = np.append(self.intersections[second_direction][pathIndex],np.array([[i,t,label]]), axis=0)                     
+                                    self.intersection_points = np.append(self.intersection_points, np.array([p]), axis=0)
+                                    break
+                                else: # Duplicated intersection
+                                    label = closest_intersection[0]
+                                    self.intersections[second_direction][pathIndex] = np.append(self.intersections[second_direction][pathIndex],np.array([[i,t,label]]), axis=0)    
+                                    break  
+
+
+
+
+
+
+    
--- a/cs457-gc/assignment_3_3/src/tracer_utils.py
+++ b/cs457-gc/assignment_3_3/src/tracer_utils.py
@@ -0,0 +1,324 @@
+import igl
+import numpy as np
+import math
+import numpy.linalg as la
+from tracer_helper import rotate_vector
+
+def asymptotic_path(idx, mesh, num_steps, step_size, first_principal_direction, num_neighbors, sampling_dist=0):
+    '''Computes both tracing direction (backward and forward) following an asymptotic path.
+    Try to compute an asymptotic path with both tracing directions 
+
+    Inputs:
+    - idx : int
+        The index of the vertex on the mesh to start tracing.
+    - mesh : Mesh
+        The mesh for tracing.
+    - num_steps : int
+        The number of tracing steps.
+    - step_size : int
+        The size of the projection at each tracing step.
+    - first_principal_direction : bool
+        Indicator for using the first principal curvature to do the tracing.
+    - num_neighbors : int
+        Number of closest vertices to consider for avering principal curvatures.
+    - sampling_distance : float
+        The distance to sample points on the path (For the design interface - Don't need to be define).
+            
+    Outputs:
+    - P : np.array (n, 3)
+        The ordered set of unique points representing a full asymptotic path.
+    - A : np.array (n,)
+        The ordered set of deviated angles in degrees calculated for the asymptotic directions.
+    - PP : np.array (m,3)
+        The ordered set of points laying on the path spaced with a given distance (For the design interface).
+    '''
+        
+    Pf, Af, PPf = trace(idx, mesh, num_steps, step_size, first_principal_direction, False, num_neighbors, sampling_dist)
+    Pb, Ab, PPb = trace(idx, mesh, num_steps, step_size, first_principal_direction, True, num_neighbors, sampling_dist)
+
+    P = np.concatenate((np.flip(Pb[1:],0), Pf), axis=0)
+    A = np.concatenate((np.flip(Ab[1:],0), Af), axis=0)
+    PP = np.concatenate((np.flip(PPb[1:],0), PPf), axis=0)
+
+    return P, A, PP
+
+def trace(idx, mesh, num_steps, step_size, first_principal_direction, trace_backwards, num_neighbors, sampling_dist=0):
+    '''Computes one tracing direction following an asymptotic path.
+    Try to compute the points on the asymptotic path.
+
+    Inputs:
+    - idx : int
+        The index of the vertex on the mesh to start tracing.
+    - mesh : Mesh
+        The mesh for tracing.
+    - num_steps : int
+        The number of tracing steps.
+    - step_size : int
+        The size of the projection at each tracing step.
+    - first_principal_direction : bool
+        Indicator for using the first principal curvature to do the tracing.
+    - trace_backwards : bool
+        Indicator for mirroring the deviated angle
+    - num_neighbors : int
+        Number of closest vertices to consider for avering principal curvatures.
+    - sampling_distance : float
+        The distance to sample points on the path (For the design interface - Don't need to be define).
+            
+    Outputs:
+    - P : np.array (n, 3)
+        The ordered set of points representing one tracing direction.
+    - A : np.array (n,)
+        The ordered set of deviated angles calculated for the asymptotic directions.
+    - PP : np.array (m,3)
+        The ordered set of points laying on the path spaced with a given distance (For the design interface).
+    '''
+
+    P = np.empty((0, 3), float)
+    PP = np.empty((0,3), float)
+    A = np.array([],float)
+
+    #Get the data of the first vertex in the path
+    pt = mesh.V[idx]
+
+    # Store partial distance (For the design interface)
+    partial_dist = 0
+    prev_dir = None
+
+    while len(P) < num_steps:
+
+        # Add the current point to the path
+        P = np.append(P, np.array([pt]), axis=0)
+
+        # Get the averaged principal curvature directions & values
+        k1_aver, k2_aver, v1_aver, v2_aver, n_aver = averaged_principal_curvatures(pt, mesh, num_neighbors)
+
+
+        # Calculate deviation angle (theta) based on principal curvature values
+        theta = 2* np.arctan(
+            np.sqrt(
+                (
+                    2*np.sqrt(k2_aver*(k2_aver - k1_aver))
+                        + k1_aver - 2*k2_aver
+                ) / k1_aver
+            )
+        )
+        
+        # Store theta
+        A = np.append(A, np.array([theta]), axis=0)
+
+        # Mirror the angle for tracing backwards. Use trace_backwards indicator
+        if trace_backwards:
+            theta -= math.pi
+
+        
+        if(prev_dir is not None and np.dot(prev_dir,v1_aver)<0):
+            v1_aver = -v1_aver
+        if(prev_dir is not None and np.dot(prev_dir,v2_aver)<0):
+            v2_aver = -v2_aver
+
+        # Rotate principal curvature direction to get asymptotic direction. Use first_principal_direction indicator
+        a_dir = None
+        if first_principal_direction:
+            a_dir = rotate_vector(v1_aver, theta, v1_aver, v2_aver, n_aver)
+        else:
+            a_dir = rotate_vector(v1_aver, -theta, v1_aver, v2_aver, n_aver)
+
+        # Check for anticlastic surface-regions
+        if (k1_aver * k2_aver) > 0:
+            print("Anticlastic")
+            break
+
+        # Check for valid asymptotic direction and unitize
+        if(np.linalg.norm(a_dir)<0):
+            print("Invalid Asymptotic Direction")
+            break
+        a_dir = a_dir / np.linalg.norm(a_dir)
+
+        # Prevent the tracer to go in the opposite direction
+        if prev_dir is not None and np.dot(prev_dir, a_dir)<0:
+            a_dir = prev_dir
+        else:
+            prev_dir = a_dir
+
+        # Scale the asymptotic direction to the given step-size
+        a_dir = a_dir * step_size
+
+        # Compute edge-point
+        edge_point, is_boundary_edge = find_edge_point(mesh, pt, a_dir)
+
+        # Check for boundaries
+        if is_boundary_edge:
+            P = np.append(P, np.array([edge_point]), axis=0) 
+            print("Hit Boundary")
+            break
+            
+        # Check for duplicated points
+        if (np.any(np.linalg.norm(P-edge_point,axis=1)<1e-6)):
+            print("Duplicate Point")
+            break
+
+        # Store sampling points (For the design interface)
+        if sampling_dist>0:
+            partial_dist += la.norm(edge_point-pt)
+            if partial_dist >= sampling_dist :
+                partial_dist = 0
+                PP = np.append(PP, np.array([edge_point]), axis=0)
+
+        pt = edge_point
+
+    return P, A, PP
+    
+def averaged_principal_curvatures(pt, mesh, num_neighbors=2, eps=1e-6):
+    '''Computes inverse weighted distance average of principal curvatures of a given mesh-point
+       on the basis of the two closest vertices.
+    Try to compute values, directions and normal at the given query point.
+
+    Inputs:
+    - pt : np.array (3,)
+        The query point position.
+    - mesh : Mesh
+        The mesh for searching nearest vertices.
+    - num_neighbors : int
+        Number of closest vertices to consider for avering.
+    - eps : float
+        The distance tolerance to consider whether the given point and a mesh-vertex are coincident.
+            
+    Outputs:
+    - k_1 : np.array (n)
+        The min principal curvature average at the given query point.
+    - k_2 : np.array (n)
+        The max principal curvature average at the given query point.
+    - v1_aver : np.array (3,)
+        The unitized min principal curvature direction average at the given query point.
+    - v2_aver : np.array (3,)
+        The unitized max principal curvature direction average at the given query point.
+    - n_aver : np.array (3,)
+        The unitized normal average at the given query point.
+    '''
+
+    # Get the closest vertices and distances to the query point
+    # Use these data to compute principal curvature weighted averages.
+    dist, neighbors = mesh.get_closest_vertices(pt, num_neighbors)
+
+    if(np.any(dist <= eps)):
+        n = neighbors[dist <= eps][0]
+        return mesh.K1[n], mesh.K2[n], mesh.V1[n], mesh.V2[n], mesh.N[n]
+    
+    sidx = np.argsort(dist)
+    dist,neighbors = dist[sidx][:2], neighbors[sidx][:2]
+
+    w_dist = 1/dist
+    
+    k1_aver = np.average(mesh.K1[neighbors],weights=w_dist)
+    k2_aver = np.average(mesh.K2[neighbors],weights=w_dist)
+    v1_aver = np.average(mesh.V1[neighbors],weights=w_dist, axis=0)
+    v2_aver = np.average(mesh.V2[neighbors],weights=w_dist, axis=0)
+    n_aver = np.average(mesh.N[neighbors],weights=w_dist, axis=0)
+
+    v1_aver /= np.linalg.norm(v1_aver)
+    v2_aver /= np.linalg.norm(v2_aver)
+    n_aver /= np.linalg.norm(n_aver)
+
+    # print(v1_aver, neighbors, mesh.V1[neighbors])
+
+    return k1_aver, k2_aver, v1_aver, v2_aver, n_aver
+
+def find_edge_point(mesh, a_orig, a_dir):
+    '''Computes the point where a mesh-edge intersects with the asymptotic projection.
+    Try to compute the edge-point resulting from this intersection.
+
+    Inputs:
+    - mesh : Mesh
+        The mesh for searching edge intersections.
+    - a_orig : np.array (3,)
+        The start position of the asymptotic projection.
+    - a_dic : np.array (3,)
+        The direction of the asymptotic projection.
+            
+    Outputs:
+    - edge_point : np.array (3,)
+        The position of the edge-point.
+    - is_boundary_point : bool
+        Indicator for whether the edge-point is at the boundary of the mesh.
+    '''
+
+    # Get the closest face-index and mesh-point (point laying on the mesh)
+    proj_pt = a_orig+a_dir
+    face_index, mesh_point = mesh.get_closest_mesh_point(proj_pt)
+
+    # Update the projection vector with the position of the mesh-point
+    a_dir = mesh_point - a_orig
+
+    # If the mesh-point is equal to the starting point, return flag for boundary vertex.
+    if la.norm(a_dir)==0:
+        return mesh_point, True
+
+    # Unitize projection vector
+    a_dir /= la.norm(a_dir)
+
+    # Initialize variables
+    edge_point = mesh_point
+    is_boundary_point = False
+    prev_projection_param = 0
+ 
+    # Find the required edge-point by computing intersections between the edge-segments of the face and the asymptotic-segment. 
+    # Different intersection events need to be considered. 
+    edges = mesh.face_edges[face_index]
+    for e_idx in edges:
+
+        e = mesh.edge_vertices[e_idx]
+        e_orig = mesh.V[e[0]]
+        e_dir = mesh.V[e[1]]-e_orig
+        is_boundary_edge = np.any(mesh.edge_faces[e_idx]== -1)
+
+        edge_param, projection_param, intersection = intersection_event(e_orig, e_dir, a_orig, a_dir)
+
+        # Find the edge-point
+        if intersection == 1:
+            continue
+        elif projection_param>prev_projection_param:
+            is_boundary_point = is_boundary_edge
+            prev_projection_param = projection_param
+            edge_point = e_orig + edge_param * e_dir
+            
+
+    return edge_point, is_boundary_point
+
+def intersection_event(a_orig, a_dir, b_orig, b_dir, eps=1e-6):
+    '''Computes the intersection event between segments A and B.
+    Try to compute the intersection event.
+
+    Inputs:
+    - a_orig : np.array (3,)
+        The start position of segment A.
+    - a_dic : np.array (3,)
+        The direction of the segment A.
+    - b_orig : np.array (3,)
+        The start position of segment B.
+    - b_dic : np.array (3,)
+        The direction of the segment B.
+    - eps : float
+        The tolerance for determining intersections.
+            
+    Outputs:
+    - t : float
+        The parameter on segment A where the intersection occurred.
+    - u : float
+        The parameter on segment B where the intersection occurred.
+    - E  : int
+        Indicator for the type of intersection event. 
+        Returns 0 for all intersection events. 
+        Returns 1 for collinearity.
+    '''
+
+    d_dir = a_dir - b_dir
+    d_orig = a_orig - b_orig
+    d = -np.cross(a_dir, b_dir)
+    dv = np.linalg.norm(d)**2
+    if(dv<=eps):
+        return  None, None, 1
+
+    t = np.dot(np.cross(d_orig, b_dir), d)/ dv
+    u = np.dot(np.cross(d_orig, a_dir), d)/ dv
+    E = (t<0 or t>1) or (u<0 or u>1)
+    return t,u,E
--- a/cs457-gc/assignment_3_3/src/utils.py
+++ b/cs457-gc/assignment_3_3/src/utils.py
@@ -0,0 +1 @@
+#This file exists only for backward compatibility.
--- a/cs457-gc/assignment_3_3/test/test.py
+++ b/cs457-gc/assignment_3_3/test/test.py
@@ -0,0 +1,101 @@
+import time
+import pytest
+import json
+import sys
+import igl
+import numpy as np
+sys.path.append('../')
+sys.path.append('../src')
+import json
+from tracer_utils import intersection_event, find_edge_point,averaged_principal_curvatures, trace
+from tracer_helper import Mesh
+
+eps=1e-6
+
+with open('test_data.json', 'r') as infile:
+    homework_data = json.load(infile)
+
+@pytest.mark.timeout(0.5)
+@pytest.mark.parametrize("data", homework_data[0])
+def test_intersection_event(data):
+    ground_truth_t1, ground_truth_u1, ground_truth_E1, a1_orig, a1_dir, b1_orig, b1_dir, epsilon = data
+    a1_orig = np.array(a1_orig, dtype = float)
+    a1_dir = np.array(a1_dir, dtype = float)
+    b1_orig = np.array(b1_orig, dtype = float)
+    b1_dir = np.array(b1_dir, dtype = float)
+    student_t1, student_u1, student_E1 = intersection_event(a1_orig, a1_dir, b1_orig, b1_dir, epsilon)
+    if (ground_truth_t1 == None):
+        assert ground_truth_t1 == student_t1
+        assert ground_truth_u1 == student_u1
+        assert ground_truth_E1 == student_E1
+    else:
+        assert np.linalg.norm(ground_truth_t1 - student_t1) < eps
+        assert np.linalg.norm(ground_truth_u1 - student_u1) < eps
+        assert np.linalg.norm(ground_truth_E1 - student_E1) < eps
+
+@pytest.mark.timeout(0.5)
+def test_find_edge_point():
+    ground_truth_edge_point, ground_truth_is_boundary_point, v, f, a3_orig, a3_dir = homework_data[1]
+    ground_truth_edge_point = np.array(ground_truth_edge_point, dtype=float)
+    v = np.array(v, dtype=float)
+    f = np.array(f, dtype=int)
+    a3_orig = np.array(a3_orig, dtype=float)
+    a3_dir = np.array(a3_dir, dtype=float)
+
+    m = Mesh(v, f)
+    student_edge_point, student_is_boundary_point = find_edge_point(m, a3_orig, a3_dir)
+    assert np.linalg.norm(ground_truth_edge_point - student_edge_point) < eps
+    assert ground_truth_is_boundary_point == student_is_boundary_point 
+
+@pytest.mark.timeout(0.5)
+def test_averaged_principal_curvatures():
+    ground_truth_k1,ground_truth_k2,ground_truth_v1,ground_truth_v2,ground_truth_n, a4_orig, v, f, num_neighbors, epsilon = homework_data[2]
+    ground_truth_k1 = np.array(ground_truth_k1, dtype=float)
+    ground_truth_k2 = np.array(ground_truth_k2, dtype=float)
+    ground_truth_v1 = np.array(ground_truth_v1, dtype=float)
+    ground_truth_v2 = np.array(ground_truth_v2, dtype=float)
+    ground_truth_n = np.array(ground_truth_n, dtype=float)
+    a4_orig = np.array(a4_orig, dtype=float)
+    v = np.array(v, dtype=float)
+    f = np.array(f, dtype=int)
+    m = Mesh(v, f)
+
+    student_k1,student_k2,student_v1,student_v2,student_n = averaged_principal_curvatures(a4_orig, m, num_neighbors, epsilon)
+
+    assert np.linalg.norm(ground_truth_k1 - student_k1) < eps
+    assert np.linalg.norm(ground_truth_k2 - student_k2) < eps
+    assert np.linalg.norm(ground_truth_v1 - student_v1) < eps
+    assert np.linalg.norm(ground_truth_v2 - student_v2) < eps
+    assert np.linalg.norm(ground_truth_n - student_n) < eps
+
+@pytest.mark.timeout(0.5)
+def test_trace_1():
+    ground_truth_P1, ground_truth_A1, ground_truth_PP1, vertex_idx, v, f, num_steps, epsilon, backward_trace, num_neighbors = homework_data[3]
+    ground_truth_P1 = np.array(ground_truth_P1, dtype=float)
+    ground_truth_A1 = np.array(ground_truth_A1, dtype=float)
+    ground_truth_PP1 = np.empty(ground_truth_PP1, dtype=float)
+    v = np.array(v, dtype=float)
+    f = np.array(f, dtype=int)
+    m = Mesh(v, f)
+    student_P1, student_A1, student_PP1 = trace(vertex_idx, m, num_steps, epsilon, True, backward_trace, num_neighbors)
+
+    assert np.linalg.norm(ground_truth_P1 - student_P1) < eps
+    assert np.linalg.norm(ground_truth_A1 - student_A1) < eps
+    assert np.linalg.norm(ground_truth_PP1 - student_PP1) < eps
+
+
+@pytest.mark.timeout(0.5)
+def test_trace_2():
+    ground_truth_P1, ground_truth_A1, ground_truth_PP1, vertex_idx, v, f, num_steps, epsilon, backward_trace, num_neighbors = homework_data[4]
+    ground_truth_P1 = np.array(ground_truth_P1, dtype=float)
+    ground_truth_A1 = np.array(ground_truth_A1, dtype=float)
+    ground_truth_PP1 = np.empty(ground_truth_PP1, dtype=float)
+    v = np.array(v, dtype=float)
+    f = np.array(f, dtype=int)
+    m = Mesh(v, f)
+    student_P1, student_A1, student_PP1 = trace(vertex_idx, m, num_steps, epsilon, False, backward_trace, num_neighbors)
+    assert np.linalg.norm(ground_truth_P1 - student_P1) < eps
+    assert np.linalg.norm(ground_truth_A1 - student_A1) < eps
+    assert np.linalg.norm(ground_truth_PP1 - student_PP1) < eps
+
+
--- a/cs457-gc/assignment_3_3/test/test_data.json
+++ b/cs457-gc/assignment_3_3/test/test_data.json
				`@@ -0,0 +1 @@`
				`#This file exists only for backward compatibility.`