{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "isdark = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rayoptics.environment import *\n",
    "from rayoptics.elem.elements import Element\n",
    "from rayoptics.raytr.trace import apply_paraxial_vignetting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "opm = OpticalModel()\n",
    "sm  = opm.seq_model\n",
    "osp = opm.optical_spec\n",
    "pm = opm.parax_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#osp.pupil = PupilSpec(osp, key=['object', 'pupil'], value=60.84)\n",
    "osp.pupil = PupilSpec(osp, key=['image', 'f/#'], value=0.98)\n",
    "osp.field_of_view = FieldSpec(osp, key=['object', 'angle'], flds=[0., 19.98])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "osp.spectral_region = WvlSpec([(486.1327, 0.5), (587.5618, 1.0), (656.2725, 0.5)], ref_wl=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "opm.system_spec.title = 'Nikkor Z 58mm f0.95 - WO 2019/229849 Example 1'\n",
    "opm.system_spec.dimensions = 'MM'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "opm.radius_mode = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Surface(lbl='Obj', profile=Spherical(c=0.0), interact_mode=transmit)\n",
      "1 Surface(profile=EvenPolynomial(c=0.009217609320846545, cc=0.0, coefs=[0.0, -3.82177e-07, -6.06486e-11, -3.80172e-15, -1.32266e-18]), interact_mode=transmit)\n",
      "2 Surface(profile=Spherical(c=-0.0011784809380708267), interact_mode=transmit)\n",
      "3 Surface(profile=Spherical(c=0.01989970548435883), interact_mode=transmit)\n",
      "4 Surface(profile=Spherical(c=-0.016469038208168644), interact_mode=transmit)\n",
      "5 Surface(profile=Spherical(c=0.0004004004004004004), interact_mode=transmit)\n",
      "6 Surface(profile=Spherical(c=-0.012946827379950543), interact_mode=transmit)\n",
      "7 Surface(profile=Spherical(c=0.008790204196443482), interact_mode=transmit)\n",
      "8 Surface(profile=Spherical(c=-0.005616084465910367), interact_mode=transmit)\n",
      "9 Surface(profile=Spherical(c=0.014152478806662986), interact_mode=transmit)\n",
      "10 Surface(profile=Spherical(c=-0.000508001016002032), interact_mode=transmit)\n",
      "11 Surface(profile=Spherical(c=0.003452001643152782), interact_mode=transmit)\n",
      "12 Surface(profile=Spherical(c=-0.010300040170156663), interact_mode=transmit)\n",
      "13 Surface(profile=Spherical(c=0.021243149084420276), interact_mode=transmit)\n",
      "14 Surface(profile=Spherical(c=0.0), interact_mode=transmit)\n",
      "15 Surface(profile=Spherical(c=-0.01050089257586895), interact_mode=transmit)\n",
      "16 Surface(profile=Spherical(c=0.024269488399184545), interact_mode=transmit)\n",
      "17 Surface(profile=Spherical(c=-0.003661769660041305), interact_mode=transmit)\n",
      "18 Surface(profile=Spherical(c=0.01312801123757762), interact_mode=transmit)\n",
      "19 Surface(profile=Spherical(c=-0.009844942160964805), interact_mode=transmit)\n",
      "20 Surface(profile=EvenPolynomial(c=0.005677688953488373, cc=0.0, coefs=[0.0, -1.15028e-06, -4.51771e-10, 2.7267e-13, -7.66812e-17]), interact_mode=transmit)\n",
      "21 Surface(profile=Spherical(c=-0.01487630353609735), interact_mode=transmit)\n",
      "22 Surface(profile=Spherical(c=0.01801477211313277), interact_mode=transmit)\n",
      "23 Surface(profile=Spherical(c=0.014003052665481075), interact_mode=transmit)\n",
      "24 Surface(profile=Spherical(c=-0.008693762225603129), interact_mode=transmit)\n",
      "25 Surface(profile=Spherical(c=0.021302430607332296), interact_mode=transmit)\n",
      "26 Surface(profile=Spherical(c=0.018089397803947107), interact_mode=transmit)\n",
      "27 Surface(profile=Spherical(c=-0.006942467769593379), interact_mode=transmit)\n",
      "28 Surface(profile=EvenPolynomial(c=0.0189186121306141, cc=0.0, coefs=[0.0, 3.18645e-06, -1.14718e-08, 7.74567e-11, -2.24225e-13, 3.3479e-16, -1.7047e-19]), interact_mode=transmit)\n",
      "29 Surface(profile=Spherical(c=0.0), interact_mode=transmit)\n",
      "30 Surface(profile=Spherical(c=0.0), interact_mode=transmit)\n",
      "31 Surface(lbl='Img', profile=Spherical(c=0.0), interact_mode=transmit)\n"
     ]
    }
   ],
   "source": [
    "d22 = 2.68\n",
    "sm.gaps[0].thi=1e10\n",
    "sm.add_surface([108.488,7.65,1.902650,35.77])\n",
    "asp1 = EvenPolynomial(r=108.488, cc=0.,\n",
    "                        coefs=[0.,-3.82177E-07,-6.06486E-11,-3.80172E-15,-1.32266E-18])\n",
    "sm.ifcs[sm.cur_surface].profile = asp1\n",
    "sm.add_surface([-848.550,2.80,1.552981,55.07])  \n",
    "sm.add_surface([50.252,18.12])\n",
    "sm.add_surface([-60.720,2.80,1.612660,44.46])\n",
    "sm.add_surface([2497.500,9.15,1.593190,67.90])\n",
    "sm.add_surface([-77.239,0.40])\n",
    "sm.add_surface([113.763,10.95,1.848500,43.79])\n",
    "sm.add_surface([-178.060,0.40])\n",
    "sm.add_surface([70.659,9.74,1.593190,67.90])\n",
    "sm.add_surface([-1968.500,0.20])\n",
    "sm.add_surface([289.687,8.00,1.593190,67.90])\n",
    "sm.add_surface([-97.087,2.80,1.738000,32.33])\n",
    "sm.add_surface([47.074,8.70])\n",
    "sm.add_surface([0, 5.29])\n",
    "sm.set_stop()\n",
    "sm.add_surface([-95.230,2.20,1.612660,44.46])\n",
    "sm.add_surface([41.204,11.55,1.497820,82.57])\n",
    "sm.add_surface([-273.092,0.20])\n",
    "sm.add_surface([76.173,9.50,1.883000,40.69])\n",
    "sm.add_surface([-101.575,0.20])\n",
    "sm.add_surface([176.128,7.45,1.953750,32.33])\n",
    "asp20 = EvenPolynomial(r=176.128, cc=0.,\n",
    "                        coefs=[0.,-1.15028E-06,-4.51771E-10,2.72670E-13,-7.66812E-17])\n",
    "sm.ifcs[sm.cur_surface].profile = asp20\n",
    "sm.add_surface([-67.221,1.80,1.738000,32.33])\n",
    "sm.add_surface([55.510,d22])\n",
    "sm.add_surface([71.413,6.35,1.883000,40.69])\n",
    "sm.add_surface([-115.025,1.81,1.698950,30.13])\n",
    "sm.add_surface([46.943,0.80])\n",
    "sm.add_surface([55.281,9.11,1.883000,40.69])\n",
    "sm.add_surface([-144.041,3.00,1.765538,46.76])\n",
    "sm.add_surface([52.858,14.50])\n",
    "asp28 = EvenPolynomial(r=52.858, cc=0.,\n",
    "                        coefs=[0.,3.18645E-06,-1.14718E-08,7.74567E-11,-2.24225E-13,3.34790E-16,-1.70470E-19])\n",
    "sm.ifcs[sm.cur_surface].profile = asp28\n",
    "sm.add_surface([0,1.60,1.516800,64.14])\n",
    "sm.add_surface([0,1.00])\n",
    "\n",
    "sm.ifcs[1].max_aperture = 66.80 / 2.0\n",
    "sm.ifcs[2].max_aperture = 65.82 / 2.0\n",
    "sm.ifcs[3].max_aperture = 57.94 / 2.0\n",
    "sm.ifcs[4].max_aperture = 58.28 / 2.0\n",
    "sm.ifcs[5].max_aperture = 65.32 / 2.0\n",
    "sm.ifcs[6].max_aperture = 65.32 / 2.0\n",
    "sm.ifcs[7].max_aperture = 70.90 / 2.0\n",
    "sm.ifcs[8].max_aperture = 70.90 / 2.0\n",
    "sm.ifcs[9].max_aperture = 65.00 / 2.0\n",
    "sm.ifcs[10].max_aperture = 65.00 / 2.0\n",
    "sm.ifcs[11].max_aperture = 61.06 / 2.0\n",
    "sm.ifcs[12].max_aperture = 59.42 / 2.0\n",
    "sm.ifcs[13].max_aperture = 50.24 / 2.0\n",
    "sm.ifcs[14].max_aperture = 47.918 / 2.0\n",
    "sm.ifcs[15].max_aperture = 49.92 / 2.0\n",
    "sm.ifcs[16].max_aperture = 49.92 / 2.0\n",
    "sm.ifcs[17].max_aperture = 49.92 / 2.0\n",
    "sm.ifcs[18].max_aperture = 51.12 / 2.0\n",
    "sm.ifcs[19].max_aperture = 51.12 / 2.0\n",
    "sm.ifcs[20].max_aperture = 46.80 / 2.0\n",
    "sm.ifcs[21].max_aperture = 45.36 / 2.0\n",
    "sm.ifcs[22].max_aperture = 39.84 / 2.0\n",
    "sm.ifcs[23].max_aperture = 39.46 / 2.0\n",
    "sm.ifcs[24].max_aperture = 39.46 / 2.0\n",
    "sm.ifcs[25].max_aperture = 39.46 / 2.0\n",
    "sm.ifcs[26].max_aperture = 38.94 / 2.0\n",
    "sm.ifcs[27].max_aperture = 38.28 / 2.0\n",
    "sm.ifcs[28].max_aperture = 38.28 / 2.0\n",
    "sm.ifcs[29].max_aperture = 44.30 / 2.0\n",
    "\n",
    "\n",
    "sm.list_surfaces()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Gap(t=10000000000.0, medium=Air())\n",
      "1 Gap(t=7.65, medium=Glass(nd=1.90265, vd=35.77, mat='', cat=''))\n",
      "2 Gap(t=2.8, medium=Glass(nd=1.552981, vd=55.07, mat='', cat=''))\n",
      "3 Gap(t=18.12, medium=Air())\n",
      "4 Gap(t=2.8, medium=Glass(nd=1.61266, vd=44.46, mat='', cat=''))\n",
      "5 Gap(t=9.15, medium=Glass(nd=1.59319, vd=67.9, mat='', cat=''))\n",
      "6 Gap(t=0.4, medium=Air())\n",
      "7 Gap(t=10.95, medium=Glass(nd=1.8485, vd=43.79, mat='', cat=''))\n",
      "8 Gap(t=0.4, medium=Air())\n",
      "9 Gap(t=9.74, medium=Glass(nd=1.59319, vd=67.9, mat='', cat=''))\n",
      "10 Gap(t=0.2, medium=Air())\n",
      "11 Gap(t=8.0, medium=Glass(nd=1.59319, vd=67.9, mat='', cat=''))\n",
      "12 Gap(t=2.8, medium=Glass(nd=1.738, vd=32.33, mat='', cat=''))\n",
      "13 Gap(t=8.7, medium=Air())\n",
      "14 Gap(t=5.29, medium=Air())\n",
      "15 Gap(t=2.2, medium=Glass(nd=1.61266, vd=44.46, mat='', cat=''))\n",
      "16 Gap(t=11.55, medium=Glass(nd=1.49782, vd=82.57, mat='', cat=''))\n",
      "17 Gap(t=0.2, medium=Air())\n",
      "18 Gap(t=9.5, medium=Glass(nd=1.883, vd=40.69, mat='', cat=''))\n",
      "19 Gap(t=0.2, medium=Air())\n",
      "20 Gap(t=7.45, medium=Glass(nd=1.95375, vd=32.33, mat='', cat=''))\n",
      "21 Gap(t=1.8, medium=Glass(nd=1.738, vd=32.33, mat='', cat=''))\n",
      "22 Gap(t=2.68, medium=Air())\n",
      "23 Gap(t=6.35, medium=Glass(nd=1.883, vd=40.69, mat='', cat=''))\n",
      "24 Gap(t=1.81, medium=Glass(nd=1.69895, vd=30.13, mat='', cat=''))\n",
      "25 Gap(t=0.8, medium=Air())\n",
      "26 Gap(t=9.11, medium=Glass(nd=1.883, vd=40.69, mat='', cat=''))\n",
      "27 Gap(t=3.0, medium=Glass(nd=1.765538, vd=46.76, mat='', cat=''))\n",
      "28 Gap(t=14.5, medium=Air())\n",
      "29 Gap(t=1.6, medium=Glass(nd=1.5168, vd=64.14, mat='', cat=''))\n",
      "30 Gap(t=1.0, medium=Air())\n"
     ]
    }
   ],
   "source": [
    "sm.list_gaps()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "opm.update_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             r            t        medium     mode   zdr      sd\n",
      " Obj:     0.000000  1.00000e+10       air             1  3.6357e+09\n",
      "   1:   108.488000      7.65000   903.358             1      55.609\n",
      "   2:  -848.550000      2.80000   553.551             1      52.287\n",
      "   3:    50.252000      18.1200       air             1      50.758\n",
      "   4:   -60.720000      2.80000   613.445             1      45.517\n",
      "   5:  2497.500000      9.15000   593.679             1      45.812\n",
      "   6:   -77.239000     0.400000       air             1      46.791\n",
      "   7:   113.763000      10.9500   849.438             1      46.715\n",
      "   8:  -178.060000     0.400000       air             1      43.532\n",
      "   9:    70.659000      9.74000   593.679             1      43.234\n",
      "  10: -1968.500000     0.200000       air             1      36.461\n",
      "  11:   289.687000      8.00000   593.679             1      36.237\n",
      "  12:   -97.087000      2.80000   738.323             1      30.247\n",
      "  13:    47.074000      8.70000       air             1      28.398\n",
      "Stop:     0.000000      5.29000       air             1      22.285\n",
      "  15:   -95.230000      2.20000   613.445             1      23.818\n",
      "  16:    41.204000      11.5500   498.826             1      24.423\n",
      "  17:  -273.092000     0.200000       air             1      28.365\n",
      "  18:    76.173000      9.50000   883.407             1      28.457\n",
      "  19:  -101.575000     0.200000       air             1      29.111\n",
      "  20:   176.128000      7.45000   954.323             1      29.086\n",
      "  21:   -67.221000      1.80000   738.323             1      28.015\n",
      "  22:    55.510000      2.68000       air             1      27.630\n",
      "  23:    71.413000      6.35000   883.407             1      27.621\n",
      "  24:  -115.025000      1.81000   699.301             1      26.458\n",
      "  25:    46.943000     0.800000       air             1      26.045\n",
      "  26:    55.281000      9.11000   883.407             1      26.045\n",
      "  27:  -144.041000      3.00000   766.468             1      24.035\n",
      "  28:    52.858000      14.5000       air             1      23.295\n",
      "  29:     0.000000      1.60000   517.641             1      21.877\n",
      "  30:     0.000000      1.00000       air             1      21.774\n",
      " Img:     0.000000      0.00000                       1      21.683\n"
     ]
    }
   ],
   "source": [
    "sm.list_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "efl               59.62\n",
      "ffl                1.66\n",
      "pp1               61.28\n",
      "bfl              0.9925\n",
      "ppk               58.63\n",
      "f/#                0.98\n",
      "m             -0.000125\n",
      "red          -1.677e+08\n",
      "obj_dist          1e+10\n",
      "obj_ang           19.98\n",
      "enp_dist          69.29\n",
      "enp_radius        30.42\n",
      "na obj        3.042e-09\n",
      "n obj                 1\n",
      "img_dist              1\n",
      "img_ht            21.68\n",
      "exp_dist         -51.57\n",
      "exp_radius        26.82\n",
      "na img          -0.4545\n",
      "n img                 1\n",
      "optical invariant        11.06\n"
     ]
    }
   ],
   "source": [
    "pm.first_order_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "opm.ele_model.elements_from_sequence(sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "elmn = [e for e in opm.ele_model.elements if isinstance(e, Element)]\n",
    "elmn[0].sd = 66.8/2\n",
    "elmn[1].sd = 65.82/2\n",
    "elmn[2].sd = 65.32/2\n",
    "elmn[3].sd = 65.32/2\n",
    "elmn[4].sd = 70.90/2\n",
    "elmn[5].sd = 65.0/2\n",
    "elmn[6].sd = 61.06/2\n",
    "elmn[7].sd = 59.42/2\n",
    "elmn[8].sd = 50.24/2\n",
    "elmn[9].sd = 49.92/2\n",
    "elmn[10].sd = 49.92/2\n",
    "elmn[11].sd = 51.12/2\n",
    "elmn[12].sd = 46.8/2\n",
    "elmn[13].sd = 45.36/2\n",
    "elmn[14].sd = 39.46/2\n",
    "elmn[15].sd = 39.46/2\n",
    "elmn[16].sd = 38.94/2\n",
    "elmn[17].sd = 44.3/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: E1 (Element): Element: EvenPolynomial(c=0.009217609320846545, cc=0.0, coefs=[0.0, -3.82177e-07, -6.06486e-11, -3.80172e-15, -1.32266e-18]), Spherical(c=-0.0011784809380708267), t=7.6500, sd=33.4000, glass: 903.358\n",
      "1: E2 (Element): Element: Spherical(c=-0.0011784809380708267), Spherical(c=0.01989970548435883), t=2.8000, sd=32.9100, glass: 553.551\n",
      "2: AirGap E2-E3 (AirGap): Gap(t=18.12, medium=Air())\n",
      "3: E3 (Element): Element: Spherical(c=-0.016469038208168644), Spherical(c=0.0004004004004004004), t=2.8000, sd=32.6600, glass: 613.445\n",
      "4: E4 (Element): Element: Spherical(c=0.0004004004004004004), Spherical(c=-0.012946827379950543), t=9.1500, sd=32.6600, glass: 593.679\n",
      "5: AirGap E4-E5 (AirGap): Gap(t=0.4, medium=Air())\n",
      "6: E5 (Element): Element: Spherical(c=0.008790204196443482), Spherical(c=-0.005616084465910367), t=10.9500, sd=35.4500, glass: 849.438\n",
      "7: AirGap E5-E6 (AirGap): Gap(t=0.4, medium=Air())\n",
      "8: E6 (Element): Element: Spherical(c=0.014152478806662986), Spherical(c=-0.000508001016002032), t=9.7400, sd=32.5000, glass: 593.679\n",
      "9: AirGap E6-E7 (AirGap): Gap(t=0.2, medium=Air())\n",
      "10: E7 (Element): Element: Spherical(c=0.003452001643152782), Spherical(c=-0.010300040170156663), t=8.0000, sd=30.5300, glass: 593.679\n",
      "11: E8 (Element): Element: Spherical(c=-0.010300040170156663), Spherical(c=0.021243149084420276), t=2.8000, sd=29.7100, glass: 738.323\n",
      "12: AirGap E8-Aperture Stop (AirGap): Gap(t=8.7, medium=Air())\n",
      "13: Aperture Stop (DummyInterface): Surface(profile=Spherical(c=0.0), interact_mode=transmit)\n",
      "14: AirGap Aperture Stop-E9 (AirGap): Gap(t=5.29, medium=Air())\n",
      "15: E9 (Element): Element: Spherical(c=-0.01050089257586895), Spherical(c=0.024269488399184545), t=2.2000, sd=25.1200, glass: 613.445\n",
      "16: E10 (Element): Element: Spherical(c=0.024269488399184545), Spherical(c=-0.003661769660041305), t=11.5500, sd=24.9600, glass: 498.826\n",
      "17: AirGap E10-E11 (AirGap): Gap(t=0.2, medium=Air())\n",
      "18: E11 (Element): Element: Spherical(c=0.01312801123757762), Spherical(c=-0.009844942160964805), t=9.5000, sd=24.9600, glass: 883.407\n",
      "19: AirGap E11-E12 (AirGap): Gap(t=0.2, medium=Air())\n",
      "20: E12 (Element): Element: EvenPolynomial(c=0.005677688953488373, cc=0.0, coefs=[0.0, -1.15028e-06, -4.51771e-10, 2.7267e-13, -7.66812e-17]), Spherical(c=-0.01487630353609735), t=7.4500, sd=25.5600, glass: 954.323\n",
      "21: E13 (Element): Element: Spherical(c=-0.01487630353609735), Spherical(c=0.01801477211313277), t=1.8000, sd=23.4000, glass: 738.323\n",
      "22: AirGap E13-E14 (AirGap): Gap(t=2.68, medium=Air())\n",
      "23: E14 (Element): Element: Spherical(c=0.014003052665481075), Spherical(c=-0.008693762225603129), t=6.3500, sd=22.6800, glass: 883.407\n",
      "24: E15 (Element): Element: Spherical(c=-0.008693762225603129), Spherical(c=0.021302430607332296), t=1.8100, sd=19.7300, glass: 699.301\n",
      "25: AirGap E15-E16 (AirGap): Gap(t=0.8, medium=Air())\n",
      "26: E16 (Element): Element: Spherical(c=0.018089397803947107), Spherical(c=-0.006942467769593379), t=9.1100, sd=19.7300, glass: 883.407\n",
      "27: E17 (Element): Element: Spherical(c=-0.006942467769593379), EvenPolynomial(c=0.0189186121306141, cc=0.0, coefs=[0.0, 3.18645e-06, -1.14718e-08, 7.74567e-11, -2.24225e-13, 3.3479e-16, -1.7047e-19]), t=3.0000, sd=19.4700, glass: 766.468\n",
      "28: AirGap E17-E18 (AirGap): Gap(t=14.5, medium=Air())\n",
      "29: E18 (Element): Element: Spherical(c=0.0), Spherical(c=0.0), t=1.6000, sd=22.1500, glass: 517.641\n",
      "30: AirGap E18-Image (AirGap): Gap(t=1.0, medium=Air())\n",
      "31: Image (DummyInterface): Surface(lbl='Img', profile=Spherical(c=0.0), interact_mode=transmit)\n"
     ]
    }
   ],
   "source": [
    "opm.ele_model.list_elements()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Field(x=0.0, y=0.0, wt=1.0), Field(x=0.0, y=19.98, wt=1.0)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "osp.field_of_view.fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field(x=0.0, y=19.98, wt=1.0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "osp.field_of_view.fields[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# manually set vignetting for the full field point\n",
    "osp.field_of_view.fields[1].vly = .7\n",
    "osp.field_of_view.fields[1].vuy = .5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<InteractiveLayout size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#apply_paraxial_vignetting(opm)\n",
    "layout_plt = plt.figure(FigureClass=InteractiveLayout, opt_model=opm, do_draw_rays=True, do_paraxial_layout=False,\n",
    "                        is_dark=isdark).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for i, ifc in enumerate(sm.ifcs): \n",
    "#    sd = abs(pm.ax[i][0]) + abs(pm.pr[i][0]) \n",
    "#    ifc.set_max_aperture(sd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#em = opm.ele_model \n",
    "#for i, e in enumerate(em.elements): \n",
    "#    if isinstance(e, elements.Element): \n",
    "#        sd1 = sm.ifcs[e.s1_indx].max_aperture \n",
    "#        sd2 = sm.ifcs[e.s2_indx].max_aperture \n",
    "#        e.sd = max(sd1, sd2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#layout_plt = plt.figure(FigureClass=InteractiveLayout, \n",
    "#                        opt_model=opm, do_draw_rays=False, do_paraxial_layout=False, is_dark=isdark).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#opm.ele_model.list_elements()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<RayFanFigure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<RayFanFigure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<SpotDiagramFigure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the transverse ray aberrations\n",
    "abr_plt = plt.figure(FigureClass=RayFanFigure, opt_model=opm,\n",
    "          data_type='Ray', scale_type=Fit.All_Same, is_dark=isdark).plot()\n",
    "# Plot the wavefront aberration\n",
    "wav_plt = plt.figure(FigureClass=RayFanFigure, opt_model=opm,\n",
    "          data_type='OPD', scale_type=Fit.All_Same, is_dark=isdark).plot()\n",
    "# Plot spot diagrams\n",
    "spot_plt = plt.figure(FigureClass=SpotDiagramFigure, opt_model=opm,\n",
    "                      scale_type=Fit.User_Scale, user_scale_value=0.1, is_dark=isdark).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}