{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "249e042e-710f-4cef-8574-c402b3a06a97",
   "metadata": {},
   "source": [
    "# Topo - VTK 4x4 transform matrix from control points\n",
    "O. Kaufmann, 2021."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff99e3f8-3aac-4904-8ec5-3051cfe9f28f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import TwoByTwoLayout\n",
    "import bootsoff.topo.geometry as btg\n",
    "import pyvista as pv\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f6fc0a-5aa8-4435-aa79-093db7bd599e",
   "metadata": {},
   "source": [
    "## Coordinates of the control points in the origin space \n",
    "at least 4 non co-planar points are required, if more than 4 are given, a least-squares approach is used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df858c46-d083-4a58-ac49-75be9fad05bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "p0 = np.array([0., 0., 0.])\n",
    "p1 = np.array([0., 4., 0.])\n",
    "p2 = np.array([1., 0., 0.])\n",
    "p3 = np.array([0., 0., 2.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "88beccc2-f439-437b-9f56-364ea66ab701",
   "metadata": {},
   "outputs": [],
   "source": [
    "origin_points = [p0, p1, p2, p3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ead8d8aa-4794-414e-8a19-b1a013694633",
   "metadata": {},
   "source": [
    "## Corresponding coordinates of the control points in the destination space "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "52a96d19-3a12-400a-8be7-38100fdfeeff",
   "metadata": {},
   "outputs": [],
   "source": [
    "P0 = np.array([2., 0., 0.])\n",
    "P1 = np.array([2., 0., 1.])\n",
    "P2 = np.array([3., 0., 0.])\n",
    "P3 = np.array([2., -4., 3.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bf0abbd3-d038-4de6-b095-2b0cde606b05",
   "metadata": {},
   "outputs": [],
   "source": [
    "destination_points = [P0, P1, P2, P3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a253b424-a4c6-4880-8120-87cf21164e7e",
   "metadata": {},
   "source": [
    "## Convert control points to vtk objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4083c669-d6b1-44b0-8943-586788209bc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[33m2021-10-03 19:03:20.624 (   0.205s) [        99D48740]vtkDataSetAttributes.cx:1285  WARN| vtkPointData (0x5586cc42cb40): Can not set attribute Scalars. Only vtkDataArray subclasses can be set as active attributes.\u001b[0m\n",
      "\u001b[0m\u001b[33m2021-10-03 19:03:20.625 (   0.206s) [        99D48740]vtkDataSetAttributes.cx:1285  WARN| vtkPointData (0x5586cc58c6e0): Can not set attribute Scalars. Only vtkDataArray subclasses can be set as active attributes.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "origin = pv.PolyData(origin_points)\n",
    "origin['Labels'] = [f'p{i}' for i in range(4)]\n",
    "destination = pv.PolyData(destination_points)\n",
    "destination['Labels'] = [f'P{i}' for i in range(4)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e96ccc9-1071-4c0f-8cd9-970aefe2b3cd",
   "metadata": {},
   "source": [
    "## Create plotters for the origin and destination spaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8044e978-c8cb-4ad7-9c84-0f45c0ed0d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "origin_plotter = pv.Plotter()\n",
    "origin_plotter.add_point_labels(origin, 'Labels', point_size=12, font_size=12, point_color='green')\n",
    "#origin_plotter.add_points(origin, point_size=12, color='green')\n",
    "origin_plotter.add_axes_at_origin(labels_off=True)\n",
    "origin_plotter.camera_position = 'iso'\n",
    "# origin_plotter.show_grid()\n",
    "origin_scene = origin_plotter.show(jupyter_backend='ipyvtklink', return_viewer=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9c63bbba-3ea9-42d3-93d0-4eca718f8741",
   "metadata": {},
   "outputs": [],
   "source": [
    "destination_plotter = pv.Plotter()\n",
    "destination_plotter.add_point_labels(destination, 'Labels', point_size=12, font_size=12, point_color='orange')\n",
    "destination_plotter.add_axes_at_origin(labels_off=True)\n",
    "destination_plotter.camera_position = 'iso'\n",
    "# destination_plotter.show_grid()\n",
    "destination_scene = destination_plotter.show(jupyter_backend='ipyvtklink', return_viewer=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11f9f246-6926-4d37-9439-2ca067b60150",
   "metadata": {},
   "source": [
    "## Compute the 4x4 transform matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "511ae7bd-8c70-47f3-8626-ee34c1131a2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "transform_matrix, _ = btg.vtk_transform_matrix_from_control_points(origin_points, destination_points)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d98d0d13-b60a-4d06-b042-a735b118224f",
   "metadata": {},
   "source": [
    "## Use the 4x4 transform to transform origin points into destination points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c5edbc84-2c6a-447d-ab94-ef70b22da4cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[33m2021-10-03 19:03:21.102 (   0.683s) [        99D48740]vtkDataSetAttributes.cx:1285  WARN| vtkPointData (0x5586cc42cb40): Can not set attribute Scalars. Only vtkDataArray subclasses can be set as active attributes.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "origin_copy = origin.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "38326194-e9ff-4e00-9286-3c56f5e4eaa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "origin_copy.clear_arrays() # must remove field before applying transform\n",
    "transformed = origin_copy.transform(transform_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d83ef9db-a64e-40cd-8212-2123ee076c94",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[33m2021-10-03 19:03:21.115 (   0.696s) [        99D48740]vtkDataSetAttributes.cx:1285  WARN| vtkPointData (0x5586cd4fd720): Can not set attribute Scalars. Only vtkDataArray subclasses can be set as active attributes.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "transformed['Labels'] = [f'P{i}' for i in range(4)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3be90ba7-912e-4021-9c3f-ed6b173d9a4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[33m2021-10-03 19:03:21.122 (   0.703s) [        99D48740]vtkDataSetAttributes.cx:1285  WARN| vtkPointData (0x5586cd4fd720): Can not set attribute Scalars. Only vtkDataArray subclasses can be set as active attributes.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>PolyData</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>4</td></tr>\n",
       "<tr><td>N Points</td><td>4</td></tr>\n",
       "<tr><td>X Bounds</td><td>2.000e+00, 3.000e+00</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-4.000e+00, 1.449e-16</td></tr>\n",
       "<tr><td>Z Bounds</td><td>-3.886e-16, 3.000e+00</td></tr>\n",
       "<tr><td>N Arrays</td><td>1</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td><b>Labels</b></td><td>Points</td><td><U2</td><td>1</td><td>nan</td><td>nan</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "PolyData (0x7f2c0c148dc0)\n",
       "  N Cells:\t4\n",
       "  N Points:\t4\n",
       "  X Bounds:\t2.000e+00, 3.000e+00\n",
       "  Y Bounds:\t-4.000e+00, 1.449e-16\n",
       "  Z Bounds:\t-3.886e-16, 3.000e+00\n",
       "  N Arrays:\t1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b7a7b87-d6a6-4e3d-96b8-703e5a92d183",
   "metadata": {},
   "source": [
    "## Create a plotter for transformed points and display origin (top left), destination (top right) end transformed (bottom right) control points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c145dffa-f3c1-4f73-ae35-991e5af1a873",
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed_plotter = pv.Plotter()\n",
    "transformed_plotter.add_point_labels(transformed, 'Labels', point_size=12, font_size=12, point_color='red')\n",
    "#transformed_plotter.add_points(transformed, point_size=12, color='green')\n",
    "transformed_plotter.add_axes_at_origin(labels_off=True)\n",
    "transformed_plotter.camera_position = 'iso'\n",
    "#transformed_plotter.show_grid()\n",
    "transformed_scene = transformed_plotter.show(jupyter_backend='ipyvtklink', return_viewer=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ae89b3f3-3362-4db3-b6dd-b8ed88c871cd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2729b997369b48c8b6ab96466de0a63d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "TwoByTwoLayout(children=(ViewInteractiveWidget(height=768, layout=Layout(grid_area='top-left', height='auto', …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TwoByTwoLayout(top_left=origin_scene, top_right=destination_scene, bottom_right=transformed_scene)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3351d218-07e5-48d9-80e9-9a00a4d316e4",
   "metadata": {
    "nbsphinx-thumbnail": {
     "tooltip": "This tooltip message was defined in cell metadata"
    },
    "tags": []
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768 at 0x7F2C8C3BB580>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "static_plotter = pv.Plotter()\n",
    "static_plotter.add_point_labels(transformed, 'Labels', point_size=12, font_size=12, point_color='red')\n",
    "static_plotter.add_point_labels(origin, 'Labels', point_size=12, font_size=12, point_color='green')\n",
    "static_plotter.add_axes_at_origin(labels_off=True)\n",
    "static_plotter.camera_position = 'iso'\n",
    "static_plotter.show_grid()\n",
    "static_plotter.show(jupyter_backend='static')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8c394444-df2b-483a-aee9-586ad333c08f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[ 2.00000000e+00, -1.69891899e-16, -3.88578059e-16]]),\n",
       " array([[ 2.00000000e+00, -1.05807032e-15,  1.00000000e+00]]),\n",
       " array([[ 3.00000000e+00,  1.44942688e-16, -2.61544505e-16]]),\n",
       " array([[ 2., -4.,  3.]])]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[transformed.cell_points(i) for i in range(transformed.n_cells)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "07708992-18db-4758-97e7-bbd6c53edd3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[2., 0., 0.]]),\n",
       " array([[2., 0., 1.]]),\n",
       " array([[3., 0., 0.]]),\n",
       " array([[ 2., -4.,  3.]])]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[destination.cell_points(i) for i in range(destination.n_cells)]"
   ]
  }
 ],
 "metadata": {
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}