{
    "cells": [
        {
            "cell_type": "markdown",
            "id": "9f678c19-03c2-4780-98f4-ffe53885d101",
            "metadata": {},
            "source": [
                "# NURBS - Freeform telescope\n",
                "\n",
                "Author: Matteo Taccola"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "b1d0e97e-f7cd-4359-a12d-b3e7e8290393",
            "metadata": {},
            "source": [
                "Example of telescope definition based on NURBS freeform surfaces and first optimization"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "id": "7eefdffa-52fb-4a35-a952-c83903b1cc9f",
            "metadata": {},
            "outputs": [],
            "source": [
                "import numpy as np\n",
                "from optiland import optic, analysis, optimization"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "id": "f4a7a099-d16e-4447-9e39-43ea3631e278",
            "metadata": {},
            "outputs": [],
            "source": [
                "from optiland.geometries import NurbsGeometry\n",
                "from optiland.coordinate_system import CoordinateSystem\n",
                "from optiland.materials import IdealMaterial, Material\n",
                "from optiland.surfaces import Surface"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "1db832a2-7eaf-4e13-a3ca-a54adae75674",
            "metadata": {},
            "source": [
                "First optical parameters derived from the optimization of an obstructed telescope"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "id": "7cd7c583-385c-4c5d-9578-5c5559d0db96",
            "metadata": {},
            "outputs": [],
            "source": [
                "radius1 = 99.7616\n",
                "radius2 = 300.247\n",
                "radius3 = -220.462\n",
                "conic1 = 3.15161e-06\n",
                "conic2 = -1.27041e-05\n",
                "conic3 = -3.57947e-05"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "id": "f6e1623f-1345-4977-b257-d4935576d1ae",
            "metadata": {},
            "outputs": [],
            "source": [
                "field_max = 2"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "276d0efd-7b67-4582-b217-ce643ac637e1",
            "metadata": {},
            "source": [
                "Distances between optical elements"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "id": "dcdbe2a4-2dc8-4d89-81ce-56c167c53ae2",
            "metadata": {},
            "outputs": [],
            "source": [
                "d12 = 100.0\n",
                "d23 = 100.0\n",
                "d3f = 110.0"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "a0390c16-5628-4b5c-a4e0-a7c3b7cd3c33",
            "metadata": {},
            "source": [
                "Mirrors and focal plane tilts"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "id": "ac0cd635-782a-48c2-8390-2930df89d698",
            "metadata": {},
            "outputs": [],
            "source": [
                "rx1 = np.radians(22.5)\n",
                "drx2 = np.radians(22.5)\n",
                "drx3 = np.radians(22.5)\n",
                "rx4 = np.radians(-45)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "e5c14a18-3d20-49a2-ad11-242654cad77e",
            "metadata": {},
            "source": [
                "Dimensions to perform fitting of conic surfaces with NURBS"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "id": "09f058b7-734c-4838-8101-750f4459919e",
            "metadata": {},
            "outputs": [],
            "source": [
                "norm_x_1 = 10.\n",
                "norm_y_1 = 10.\n",
                "norm_x_2 = 30.\n",
                "norm_y_2 = 30.\n",
                "norm_x_3 = 30.\n",
                "norm_y_3 = 30."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "id": "e2400387-3cbf-4298-a512-58c59c731c10",
            "metadata": {},
            "outputs": [],
            "source": [
                "nurbs_tel = optic.Optic()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "id": "09661c53-dd5b-45c4-a424-7f9e3a47be07",
            "metadata": {},
            "outputs": [],
            "source": [
                "material_pre = IdealMaterial(n=1.0)  # air\n",
                "material_post = IdealMaterial(n=1.0)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "id": "83b77f2e-22db-48b0-ae73-552bf8a4f30e",
            "metadata": {},
            "outputs": [],
            "source": [
                "cs1 = CoordinateSystem(x=0, y=0, z=0, rx=rx1, ry=0, rz=0, reference_cs=None)\n",
                "y2 = d12*np.sin(2.0*rx1)\n",
                "z2 = -d12*np.cos(2.0*rx1)\n",
                "rx2 = 2.0*rx1 + drx2\n",
                "cs2 = CoordinateSystem(x=0, y=y2, z=z2, rx=rx2, ry=0, rz=0, reference_cs=None)\n",
                "y3 = y2-d23*np.sin(2.0*(drx2+rx1))\n",
                "z3 = z2+d23*np.cos(2.0*(drx2+rx1))\n",
                "rx3 = 2.0*rx1 + 2.0*drx2 + drx3\n",
                "cs3 = CoordinateSystem(x=0, y=y3, z=z3, rx=rx3, ry=0, rz=0, reference_cs=None)\n",
                "y4 = y3 + d3f*np.sin(2.0*rx1)\n",
                "z4 = z3 + d3f*np.cos(2.0*rx1)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "id": "6308e67e-9ea8-4ec5-95ba-b8e78dc06554",
            "metadata": {},
            "outputs": [],
            "source": [
                "# if control points are passed radius and conic are ignored. The surface is built as NURBS from control points and weights \n",
                "nurbs_geo1 = NurbsGeometry(\n",
                "    coordinate_system=cs1,\n",
                "    radius = radius1,\n",
                "    conic=conic1,\n",
                "    nurbs_norm_x = norm_x_1,\n",
                "    nurbs_norm_y = norm_y_1,\n",
                ")       \n",
                "nurbs_geo1.fit_surface()\n",
                "nurbs_geo2 = NurbsGeometry(\n",
                "    coordinate_system=cs2,\n",
                "    radius = radius2,\n",
                "    conic=conic2,\n",
                "    nurbs_norm_x = norm_x_2,\n",
                "    nurbs_norm_y = norm_y_2,    \n",
                ") \n",
                "nurbs_geo2.fit_surface()\n",
                "nurbs_geo3 = NurbsGeometry(\n",
                "    coordinate_system=cs3,\n",
                "    radius = radius3,\n",
                "    conic=conic3,\n",
                "    nurbs_norm_x = norm_x_3,\n",
                "    nurbs_norm_y = norm_y_3,\n",
                ")        \n",
                "nurbs_geo3.fit_surface()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "id": "48fea766-2899-42fd-bcc3-78424982e48d",
            "metadata": {},
            "outputs": [],
            "source": [
                "new_surface1 = Surface(\n",
                "    geometry=nurbs_geo1,\n",
                "    material_pre=material_pre,\n",
                "    material_post=material_post,\n",
                "    is_stop = True\n",
                ")\n",
                "new_surface2 = Surface(\n",
                "    geometry=nurbs_geo2,\n",
                "    material_pre=material_pre,\n",
                "    material_post=material_post,\n",
                ")\n",
                "new_surface3 = Surface(\n",
                "    geometry=nurbs_geo3,\n",
                "    material_pre=material_pre,\n",
                "    material_post=material_post,\n",
                ")\n",
                "\n",
                "# Set interaction model to reflective for all surfaces\n",
                "new_surface1.interaction_model.is_reflective = True\n",
                "new_surface2.interaction_model.is_reflective = True\n",
                "new_surface3.interaction_model.is_reflective = True"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "id": "ab676a58-d403-4226-bbca-5c905f58e562",
            "metadata": {},
            "outputs": [],
            "source": [
                "# add surfaces\n",
                "nurbs_tel.surfaces.add(index=0, radius=np.inf, thickness=np.inf)\n",
                "nurbs_tel.surfaces.add(\n",
                "    index=1,\n",
                "    new_surface=new_surface1,\n",
                ")\n",
                "nurbs_tel.surfaces.add(\n",
                "    index=2,\n",
                "    new_surface=new_surface2,\n",
                ")\n",
                "nurbs_tel.surfaces.add(\n",
                "    index=3,\n",
                "    new_surface=new_surface3,\n",
                ")\n",
                "nurbs_tel.surfaces.add(index=4, y=y4, z=z4, rx=rx4)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "id": "103d78b7-6465-4d56-b2da-77b639d50cbf",
            "metadata": {},
            "outputs": [],
            "source": [
                "nurbs_tel.set_aperture(aperture_type='imageFNO', value=5)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "id": "53ba8d69-6883-4de3-82e5-587f96a653b2",
            "metadata": {},
            "outputs": [],
            "source": [
                "# add field\n",
                "field_max = 2.\n",
                "nurbs_tel.fields.set_type(field_type=\"angle\")\n",
                "nurbs_tel.fields.add(y=0)\n",
                "nurbs_tel.fields.add(y=field_max)\n",
                "nurbs_tel.fields.add(x=-field_max, y=0)\n",
                "nurbs_tel.fields.add(x=field_max, y=0)\n",
                "\n",
                "# add wavelength\n",
                "wave = 0.55\n",
                "nurbs_tel.wavelengths.add(value=wave, is_primary=True)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "id": "88389837-3f88-45a4-9169-75c5233d9812",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1000x400 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "_ = nurbs_tel.draw()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "id": "b611722d-a07e-44bc-886b-adc88c922948",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1200x800 with 4 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "spot = analysis.SpotDiagram(nurbs_tel,num_rings=18)\n",
                "_ = spot.view()"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "bbda7c59-a64d-434d-9a2e-d77a49c082c4",
            "metadata": {},
            "source": [
                "Telescope has severe aberrations due to tilts of the optical elements"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "id": "3a948b41-fa55-49c2-b53c-db699b95e429",
            "metadata": {},
            "outputs": [],
            "source": [
                "n_control_points_u = nurbs_geo1.P_size_u\n",
                "n_control_points_v = nurbs_geo1.P_size_v"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "00b0cd81-685e-4fac-abcd-40aefcfea76d",
            "metadata": {},
            "source": [
                "First optimization trial.\n",
                "The variables are the positions and weigths of the control points and position/tilt of the focal plane"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "id": "d87f45a1-86c0-4826-8bee-318106765c65",
            "metadata": {},
            "outputs": [],
            "source": [
                "problem = optimization.OptimizationProblem()\n",
                "\n",
                "for field in nurbs_tel.fields.get_field_coords():\n",
                "    input_data = {\n",
                "        \"optic\": nurbs_tel,\n",
                "        \"surface_number\": -1,\n",
                "        \"Hx\": field[0],\n",
                "        \"Hy\": field[1],\n",
                "        \"num_rays\": 16,\n",
                "        \"wavelength\": wave,\n",
                "        \"distribution\": \"uniform\",\n",
                "    }\n",
                "    problem.add_operand(\n",
                "        operand_type=\"rms_spot_size\",\n",
                "        target=0,\n",
                "        weight=1,\n",
                "        input_data=input_data,\n",
                "    )\n",
                "#variables are the coordinates and weights of the control points \n",
                "for i in range(3):\n",
                "    for j in range(n_control_points_u):\n",
                "        for k in range(n_control_points_v):\n",
                "            problem.add_variable(nurbs_tel, \"nurbs_control_point\", surface_number=1, coeff_index = [i,j,k], min_val=-100, max_val=100)\n",
                "            problem.add_variable(nurbs_tel, \"nurbs_control_point\", surface_number=2, coeff_index = [i,j,k], min_val=-100, max_val=100)\n",
                "            problem.add_variable(nurbs_tel, \"nurbs_control_point\", surface_number=3, coeff_index = [i,j,k], min_val=-100, max_val=100)\n",
                "\n",
                "for j in range(n_control_points_u):\n",
                "    for k in range(n_control_points_v):\n",
                "        problem.add_variable(nurbs_tel, \"nurbs_weight\", surface_number=1, coeff_index = [j,k], min_val=0, max_val=10)\n",
                "        problem.add_variable(nurbs_tel, \"nurbs_weight\", surface_number=2, coeff_index = [j,k], min_val=0, max_val=10)\n",
                "        problem.add_variable(nurbs_tel, \"nurbs_weight\", surface_number=3, coeff_index = [j,k], min_val=0, max_val=10)\n",
                "\n",
                "problem.add_variable(\n",
                "    nurbs_tel,\n",
                "    \"decenter\",\n",
                "    axis=\"y\",\n",
                "    surface_number=4,\n",
                "    min_val=y4-5.,\n",
                "    max_val=y4+5.,\n",
                ")\n",
                "problem.add_variable(\n",
                "    nurbs_tel,\n",
                "    \"decenter\",\n",
                "    axis=\"z\",\n",
                "    surface_number=4,\n",
                "    min_val=z4-5.,\n",
                "    max_val=z4+5.,\n",
                ")\n",
                "\n",
                "# tilts\n",
                "problem.add_variable(\n",
                "    nurbs_tel,\n",
                "    \"tilt\",\n",
                "    axis=\"x\",\n",
                "    surface_number=4,\n",
                "    min_val=rx4-np.radians(5.0),\n",
                "    max_val=rx4+np.radians(5.0),\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "id": "eae7b6d2-5c6b-4885-af36-035651077558",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "╒════╤════════════════════════╤═══════════════════╕\n",
                        "│    │   Merit Function Value │   Improvement (%) │\n",
                        "╞════╪════════════════════════╪═══════════════════╡\n",
                        "│  0 │                5.77534 │                 0 │\n",
                        "╘════╧════════════════════════╧═══════════════════╛\n",
                        "╒════╤════════════════╤══════════╤══════════════╤══════════════╤══════════╤═════════╤═════════╤════════════════╕\n",
                        "│    │ Operand Type   │   Target │ Min. Bound   │ Max. Bound   │   Weight │   Value │   Delta │   Contrib. [%] │\n",
                        "╞════╪════════════════╪══════════╪══════════════╪══════════════╪══════════╪═════════╪═════════╪════════════════╡\n",
                        "│  0 │ rms spot size  │        0 │              │              │        1 │   1.224 │   1.224 │          25.92 │\n",
                        "│  1 │ rms spot size  │        0 │              │              │        1 │   1.121 │   1.121 │          21.75 │\n",
                        "│  2 │ rms spot size  │        0 │              │              │        1 │   1.229 │   1.229 │          26.16 │\n",
                        "│  3 │ rms spot size  │        0 │              │              │        1 │   1.229 │   1.229 │          26.16 │\n",
                        "╘════╧════════════════╧══════════╧══════════════╧══════════════╧══════════╧═════════╧═════════╧════════════════╛\n",
                        "╒═════╤═════════════════════╤═══════════╤════════════╤══════════════╤══════════════╕\n",
                        "│     │ Variable Type       │   Surface │      Value │   Min. Bound │   Max. Bound │\n",
                        "╞═════╪═════════════════════╪═══════════╪════════════╪══════════════╪══════════════╡\n",
                        "│   0 │ nurbs_control_point │         1 │ -10        │  -100        │   100        │\n",
                        "│   1 │ nurbs_control_point │         2 │ -30        │  -100        │   100        │\n",
                        "│   2 │ nurbs_control_point │         3 │ -30        │  -100        │   100        │\n",
                        "│   3 │ nurbs_control_point │         1 │ -10        │  -100        │   100        │\n",
                        "│   4 │ nurbs_control_point │         2 │ -30        │  -100        │   100        │\n",
                        "│   5 │ nurbs_control_point │         3 │ -30        │  -100        │   100        │\n",
                        "│   6 │ nurbs_control_point │         1 │ -10        │  -100        │   100        │\n",
                        "│   7 │ nurbs_control_point │         2 │ -30        │  -100        │   100        │\n",
                        "│   8 │ nurbs_control_point │         3 │ -30        │  -100        │   100        │\n",
                        "│   9 │ nurbs_control_point │         1 │ -10        │  -100        │   100        │\n",
                        "│  10 │ nurbs_control_point │         2 │ -30        │  -100        │   100        │\n",
                        "│  11 │ nurbs_control_point │         3 │ -30        │  -100        │   100        │\n",
                        "│  12 │ nurbs_control_point │         1 │  -3.35587  │  -100        │   100        │\n",
                        "│  13 │ nurbs_control_point │         2 │ -10.0672   │  -100        │   100        │\n",
                        "│  14 │ nurbs_control_point │         3 │ -10.1256   │  -100        │   100        │\n",
                        "│  15 │ nurbs_control_point │         1 │  -3.35587  │  -100        │   100        │\n",
                        "│  16 │ nurbs_control_point │         2 │ -10.0672   │  -100        │   100        │\n",
                        "│  17 │ nurbs_control_point │         3 │ -10.1256   │  -100        │   100        │\n",
                        "│  18 │ nurbs_control_point │         1 │  -3.35587  │  -100        │   100        │\n",
                        "│  19 │ nurbs_control_point │         2 │ -10.0672   │  -100        │   100        │\n",
                        "│  20 │ nurbs_control_point │         3 │ -10.1256   │  -100        │   100        │\n",
                        "│  21 │ nurbs_control_point │         1 │  -3.35587  │  -100        │   100        │\n",
                        "│  22 │ nurbs_control_point │         2 │ -10.0672   │  -100        │   100        │\n",
                        "│  23 │ nurbs_control_point │         3 │ -10.1256   │  -100        │   100        │\n",
                        "│  24 │ nurbs_control_point │         1 │   3.35587  │  -100        │   100        │\n",
                        "│  25 │ nurbs_control_point │         2 │  10.0672   │  -100        │   100        │\n",
                        "│  26 │ nurbs_control_point │         3 │  10.1256   │  -100        │   100        │\n",
                        "│  27 │ nurbs_control_point │         1 │   3.35587  │  -100        │   100        │\n",
                        "│  28 │ nurbs_control_point │         2 │  10.0672   │  -100        │   100        │\n",
                        "│  29 │ nurbs_control_point │         3 │  10.1256   │  -100        │   100        │\n",
                        "│  30 │ nurbs_control_point │         1 │   3.35587  │  -100        │   100        │\n",
                        "│  31 │ nurbs_control_point │         2 │  10.0672   │  -100        │   100        │\n",
                        "│  32 │ nurbs_control_point │         3 │  10.1256   │  -100        │   100        │\n",
                        "│  33 │ nurbs_control_point │         1 │   3.35587  │  -100        │   100        │\n",
                        "│  34 │ nurbs_control_point │         2 │  10.0672   │  -100        │   100        │\n",
                        "│  35 │ nurbs_control_point │         3 │  10.1256   │  -100        │   100        │\n",
                        "│  36 │ nurbs_control_point │         1 │  10        │  -100        │   100        │\n",
                        "│  37 │ nurbs_control_point │         2 │  30        │  -100        │   100        │\n",
                        "│  38 │ nurbs_control_point │         3 │  30        │  -100        │   100        │\n",
                        "│  39 │ nurbs_control_point │         1 │  10        │  -100        │   100        │\n",
                        "│  40 │ nurbs_control_point │         2 │  30        │  -100        │   100        │\n",
                        "│  41 │ nurbs_control_point │         3 │  30        │  -100        │   100        │\n",
                        "│  42 │ nurbs_control_point │         1 │  10        │  -100        │   100        │\n",
                        "│  43 │ nurbs_control_point │         2 │  30        │  -100        │   100        │\n",
                        "│  44 │ nurbs_control_point │         3 │  30        │  -100        │   100        │\n",
                        "│  45 │ nurbs_control_point │         1 │  10        │  -100        │   100        │\n",
                        "│  46 │ nurbs_control_point │         2 │  30        │  -100        │   100        │\n",
                        "│  47 │ nurbs_control_point │         3 │  30        │  -100        │   100        │\n",
                        "│  48 │ nurbs_control_point │         1 │ -10        │  -100        │   100        │\n",
                        "│  49 │ nurbs_control_point │         2 │ -30        │  -100        │   100        │\n",
                        "│  50 │ nurbs_control_point │         3 │ -30        │  -100        │   100        │\n",
                        "│  51 │ nurbs_control_point │         1 │  -3.35587  │  -100        │   100        │\n",
                        "│  52 │ nurbs_control_point │         2 │ -10.0672   │  -100        │   100        │\n",
                        "│  53 │ nurbs_control_point │         3 │ -10.1256   │  -100        │   100        │\n",
                        "│  54 │ nurbs_control_point │         1 │   3.35587  │  -100        │   100        │\n",
                        "│  55 │ nurbs_control_point │         2 │  10.0672   │  -100        │   100        │\n",
                        "│  56 │ nurbs_control_point │         3 │  10.1256   │  -100        │   100        │\n",
                        "│  57 │ nurbs_control_point │         1 │  10        │  -100        │   100        │\n",
                        "│  58 │ nurbs_control_point │         2 │  30        │  -100        │   100        │\n",
                        "│  59 │ nurbs_control_point │         3 │  30        │  -100        │   100        │\n",
                        "│  60 │ nurbs_control_point │         1 │ -10        │  -100        │   100        │\n",
                        "│  61 │ nurbs_control_point │         2 │ -30        │  -100        │   100        │\n",
                        "│  62 │ nurbs_control_point │         3 │ -30        │  -100        │   100        │\n",
                        "│  63 │ nurbs_control_point │         1 │  -3.35587  │  -100        │   100        │\n",
                        "│  64 │ nurbs_control_point │         2 │ -10.0672   │  -100        │   100        │\n",
                        "│  65 │ nurbs_control_point │         3 │ -10.1256   │  -100        │   100        │\n",
                        "│  66 │ nurbs_control_point │         1 │   3.35587  │  -100        │   100        │\n",
                        "│  67 │ nurbs_control_point │         2 │  10.0672   │  -100        │   100        │\n",
                        "│  68 │ nurbs_control_point │         3 │  10.1256   │  -100        │   100        │\n",
                        "│  69 │ nurbs_control_point │         1 │  10        │  -100        │   100        │\n",
                        "│  70 │ nurbs_control_point │         2 │  30        │  -100        │   100        │\n",
                        "│  71 │ nurbs_control_point │         3 │  30        │  -100        │   100        │\n",
                        "│  72 │ nurbs_control_point │         1 │ -10        │  -100        │   100        │\n",
                        "│  73 │ nurbs_control_point │         2 │ -30        │  -100        │   100        │\n",
                        "│  74 │ nurbs_control_point │         3 │ -30        │  -100        │   100        │\n",
                        "│  75 │ nurbs_control_point │         1 │  -3.35587  │  -100        │   100        │\n",
                        "│  76 │ nurbs_control_point │         2 │ -10.0672   │  -100        │   100        │\n",
                        "│  77 │ nurbs_control_point │         3 │ -10.1256   │  -100        │   100        │\n",
                        "│  78 │ nurbs_control_point │         1 │   3.35587  │  -100        │   100        │\n",
                        "│  79 │ nurbs_control_point │         2 │  10.0672   │  -100        │   100        │\n",
                        "│  80 │ nurbs_control_point │         3 │  10.1256   │  -100        │   100        │\n",
                        "│  81 │ nurbs_control_point │         1 │  10        │  -100        │   100        │\n",
                        "│  82 │ nurbs_control_point │         2 │  30        │  -100        │   100        │\n",
                        "│  83 │ nurbs_control_point │         3 │  30        │  -100        │   100        │\n",
                        "│  84 │ nurbs_control_point │         1 │ -10        │  -100        │   100        │\n",
                        "│  85 │ nurbs_control_point │         2 │ -30        │  -100        │   100        │\n",
                        "│  86 │ nurbs_control_point │         3 │ -30        │  -100        │   100        │\n",
                        "│  87 │ nurbs_control_point │         1 │  -3.35587  │  -100        │   100        │\n",
                        "│  88 │ nurbs_control_point │         2 │ -10.0672   │  -100        │   100        │\n",
                        "│  89 │ nurbs_control_point │         3 │ -10.1256   │  -100        │   100        │\n",
                        "│  90 │ nurbs_control_point │         1 │   3.35587  │  -100        │   100        │\n",
                        "│  91 │ nurbs_control_point │         2 │  10.0672   │  -100        │   100        │\n",
                        "│  92 │ nurbs_control_point │         3 │  10.1256   │  -100        │   100        │\n",
                        "│  93 │ nurbs_control_point │         1 │  10        │  -100        │   100        │\n",
                        "│  94 │ nurbs_control_point │         2 │  30        │  -100        │   100        │\n",
                        "│  95 │ nurbs_control_point │         3 │  30        │  -100        │   100        │\n",
                        "│  96 │ nurbs_control_point │         1 │   1.00748  │  -100        │   100        │\n",
                        "│  97 │ nurbs_control_point │         2 │   3.01265  │  -100        │   100        │\n",
                        "│  98 │ nurbs_control_point │         3 │  -4.12085  │  -100        │   100        │\n",
                        "│  99 │ nurbs_control_point │         1 │   0.334195 │  -100        │   100        │\n",
                        "│ 100 │ nurbs_control_point │         2 │   0.999372 │  -100        │   100        │\n",
                        "│ 101 │ nurbs_control_point │         3 │  -1.36121  │  -100        │   100        │\n",
                        "│ 102 │ nurbs_control_point │         1 │   0.334195 │  -100        │   100        │\n",
                        "│ 103 │ nurbs_control_point │         2 │   0.999372 │  -100        │   100        │\n",
                        "│ 104 │ nurbs_control_point │         3 │  -1.36121  │  -100        │   100        │\n",
                        "│ 105 │ nurbs_control_point │         1 │   1.00748  │  -100        │   100        │\n",
                        "│ 106 │ nurbs_control_point │         2 │   3.01265  │  -100        │   100        │\n",
                        "│ 107 │ nurbs_control_point │         3 │  -4.12085  │  -100        │   100        │\n",
                        "│ 108 │ nurbs_control_point │         1 │   0.334195 │  -100        │   100        │\n",
                        "│ 109 │ nurbs_control_point │         2 │   0.999372 │  -100        │   100        │\n",
                        "│ 110 │ nurbs_control_point │         3 │  -1.36121  │  -100        │   100        │\n",
                        "│ 111 │ nurbs_control_point │         1 │  -0.334536 │  -100        │   100        │\n",
                        "│ 112 │ nurbs_control_point │         2 │  -1.00039  │  -100        │   100        │\n",
                        "│ 113 │ nurbs_control_point │         3 │   1.3638   │  -100        │   100        │\n",
                        "│ 114 │ nurbs_control_point │         1 │  -0.334536 │  -100        │   100        │\n",
                        "│ 115 │ nurbs_control_point │         2 │  -1.00039  │  -100        │   100        │\n",
                        "│ 116 │ nurbs_control_point │         3 │   1.3638   │  -100        │   100        │\n",
                        "│ 117 │ nurbs_control_point │         1 │   0.334195 │  -100        │   100        │\n",
                        "│ 118 │ nurbs_control_point │         2 │   0.999372 │  -100        │   100        │\n",
                        "│ 119 │ nurbs_control_point │         3 │  -1.36121  │  -100        │   100        │\n",
                        "│ 120 │ nurbs_control_point │         1 │   0.334195 │  -100        │   100        │\n",
                        "│ 121 │ nurbs_control_point │         2 │   0.999372 │  -100        │   100        │\n",
                        "│ 122 │ nurbs_control_point │         3 │  -1.36121  │  -100        │   100        │\n",
                        "│ 123 │ nurbs_control_point │         1 │  -0.334536 │  -100        │   100        │\n",
                        "│ 124 │ nurbs_control_point │         2 │  -1.00039  │  -100        │   100        │\n",
                        "│ 125 │ nurbs_control_point │         3 │   1.3638   │  -100        │   100        │\n",
                        "│ 126 │ nurbs_control_point │         1 │  -0.334536 │  -100        │   100        │\n",
                        "│ 127 │ nurbs_control_point │         2 │  -1.00039  │  -100        │   100        │\n",
                        "│ 128 │ nurbs_control_point │         3 │   1.3638   │  -100        │   100        │\n",
                        "│ 129 │ nurbs_control_point │         1 │   0.334195 │  -100        │   100        │\n",
                        "│ 130 │ nurbs_control_point │         2 │   0.999372 │  -100        │   100        │\n",
                        "│ 131 │ nurbs_control_point │         3 │  -1.36121  │  -100        │   100        │\n",
                        "│ 132 │ nurbs_control_point │         1 │   1.00748  │  -100        │   100        │\n",
                        "│ 133 │ nurbs_control_point │         2 │   3.01265  │  -100        │   100        │\n",
                        "│ 134 │ nurbs_control_point │         3 │  -4.12085  │  -100        │   100        │\n",
                        "│ 135 │ nurbs_control_point │         1 │   0.334195 │  -100        │   100        │\n",
                        "│ 136 │ nurbs_control_point │         2 │   0.999372 │  -100        │   100        │\n",
                        "│ 137 │ nurbs_control_point │         3 │  -1.36121  │  -100        │   100        │\n",
                        "│ 138 │ nurbs_control_point │         1 │   0.334195 │  -100        │   100        │\n",
                        "│ 139 │ nurbs_control_point │         2 │   0.999372 │  -100        │   100        │\n",
                        "│ 140 │ nurbs_control_point │         3 │  -1.36121  │  -100        │   100        │\n",
                        "│ 141 │ nurbs_control_point │         1 │   1.00748  │  -100        │   100        │\n",
                        "│ 142 │ nurbs_control_point │         2 │   3.01265  │  -100        │   100        │\n",
                        "│ 143 │ nurbs_control_point │         3 │  -4.12085  │  -100        │   100        │\n",
                        "│ 144 │ nurbs_weight        │         1 │   1        │     0        │    10        │\n",
                        "│ 145 │ nurbs_weight        │         2 │   1        │     0        │    10        │\n",
                        "│ 146 │ nurbs_weight        │         3 │   1        │     0        │    10        │\n",
                        "│ 147 │ nurbs_weight        │         1 │   1        │     0        │    10        │\n",
                        "│ 148 │ nurbs_weight        │         2 │   1        │     0        │    10        │\n",
                        "│ 149 │ nurbs_weight        │         3 │   1        │     0        │    10        │\n",
                        "│ 150 │ nurbs_weight        │         1 │   1        │     0        │    10        │\n",
                        "│ 151 │ nurbs_weight        │         2 │   1        │     0        │    10        │\n",
                        "│ 152 │ nurbs_weight        │         3 │   1        │     0        │    10        │\n",
                        "│ 153 │ nurbs_weight        │         1 │   1        │     0        │    10        │\n",
                        "│ 154 │ nurbs_weight        │         2 │   1        │     0        │    10        │\n",
                        "│ 155 │ nurbs_weight        │         3 │   1        │     0        │    10        │\n",
                        "│ 156 │ nurbs_weight        │         1 │   1        │     0        │    10        │\n",
                        "│ 157 │ nurbs_weight        │         2 │   1        │     0        │    10        │\n",
                        "│ 158 │ nurbs_weight        │         3 │   1        │     0        │    10        │\n",
                        "│ 159 │ nurbs_weight        │         1 │   1        │     0        │    10        │\n",
                        "│ 160 │ nurbs_weight        │         2 │   1        │     0        │    10        │\n",
                        "│ 161 │ nurbs_weight        │         3 │   1        │     0        │    10        │\n",
                        "│ 162 │ nurbs_weight        │         1 │   1        │     0        │    10        │\n",
                        "│ 163 │ nurbs_weight        │         2 │   1        │     0        │    10        │\n",
                        "│ 164 │ nurbs_weight        │         3 │   1        │     0        │    10        │\n",
                        "│ 165 │ nurbs_weight        │         1 │   1        │     0        │    10        │\n",
                        "│ 166 │ nurbs_weight        │         2 │   1        │     0        │    10        │\n",
                        "│ 167 │ nurbs_weight        │         3 │   1        │     0        │    10        │\n",
                        "│ 168 │ nurbs_weight        │         1 │   1        │     0        │    10        │\n",
                        "│ 169 │ nurbs_weight        │         2 │   1        │     0        │    10        │\n",
                        "│ 170 │ nurbs_weight        │         3 │   1        │     0        │    10        │\n",
                        "│ 171 │ nurbs_weight        │         1 │   1        │     0        │    10        │\n",
                        "│ 172 │ nurbs_weight        │         2 │   1        │     0        │    10        │\n",
                        "│ 173 │ nurbs_weight        │         3 │   1        │     0        │    10        │\n",
                        "│ 174 │ nurbs_weight        │         1 │   1        │     0        │    10        │\n",
                        "│ 175 │ nurbs_weight        │         2 │   1        │     0        │    10        │\n",
                        "│ 176 │ nurbs_weight        │         3 │   1        │     0        │    10        │\n",
                        "│ 177 │ nurbs_weight        │         1 │   1        │     0        │    10        │\n",
                        "│ 178 │ nurbs_weight        │         2 │   1        │     0        │    10        │\n",
                        "│ 179 │ nurbs_weight        │         3 │   1        │     0        │    10        │\n",
                        "│ 180 │ nurbs_weight        │         1 │   1        │     0        │    10        │\n",
                        "│ 181 │ nurbs_weight        │         2 │   1        │     0        │    10        │\n",
                        "│ 182 │ nurbs_weight        │         3 │   1        │     0        │    10        │\n",
                        "│ 183 │ nurbs_weight        │         1 │   1        │     0        │    10        │\n",
                        "│ 184 │ nurbs_weight        │         2 │   1        │     0        │    10        │\n",
                        "│ 185 │ nurbs_weight        │         3 │   1        │     0        │    10        │\n",
                        "│ 186 │ nurbs_weight        │         1 │   1        │     0        │    10        │\n",
                        "│ 187 │ nurbs_weight        │         2 │   1        │     0        │    10        │\n",
                        "│ 188 │ nurbs_weight        │         3 │   1        │     0        │    10        │\n",
                        "│ 189 │ nurbs_weight        │         1 │   1        │     0        │    10        │\n",
                        "│ 190 │ nurbs_weight        │         2 │   1        │     0        │    10        │\n",
                        "│ 191 │ nurbs_weight        │         3 │   1        │     0        │    10        │\n",
                        "│ 192 │ decenter            │         4 │  48.4924   │    43.4924   │    53.4924   │\n",
                        "│ 193 │ decenter            │         4 │   7.07107  │     2.07107  │    12.0711   │\n",
                        "│ 194 │ tilt                │         4 │  -0.785398 │    -0.872665 │    -0.698132 │\n",
                        "╘═════╧═════════════════════╧═══════════╧════════════╧══════════════╧══════════════╛\n"
                    ]
                }
            ],
            "source": [
                "problem.info()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "952bea4f-cb22-4533-9726-b509f0384ce6",
            "metadata": {},
            "outputs": [],
            "source": [
                "optimizer = optimization.OptimizerGeneric(problem)\n",
                "res = optimizer.optimize()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "2cad1813-8233-4492-9ee3-1467f2fe642f",
            "metadata": {},
            "outputs": [],
            "source": [
                "problem.info()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "e2724818-3416-4574-858c-5a4e55659ab7",
            "metadata": {},
            "outputs": [],
            "source": [
                "spot = analysis.SpotDiagram(nurbs_tel, num_rings=18)\n",
                "_ = spot.view() "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "66601209-b690-46f5-8113-371f2b720ecd",
            "metadata": {},
            "outputs": [],
            "source": [
                "_ = nurbs_tel.draw()"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "optiland (3.13.2)",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
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