manual_edit_oe.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from optical_element_io import *\n",
    "from column_io import OpticalColumn\n",
    "from calculate_optical_properties import calc_properties_optics\n",
    "from automation import optimize_many_shapes\n",
    "from automation_library import calculate_c3\n",
    "from importlib import reload\n",
    "from misc_library import choose_logger\n",
    "import logging\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading file /home/trh/data/mirror/open_end/5elec/30kV/openendmir5elec00_30kV.dat \n",
      "with title: mirror3.dat - test mirror 3\n",
      "Warning! This data file seems to have curvature coordinates. Setting so=True.\n"
     ]
    }
   ],
   "source": [
    "# obj = StrongMagLens('/home/trh/data/std_obj/30kV/obj01.dat')\n",
    "# obj.write('/home/trh/data/std_obj/30kV/obj01_test.dat')\n",
    "oe = ElecLens('/home/trh/data/mirror/open_end/5elec/30kV/openendmir5elec00_30kV.dat')\n",
    "oe.mirror_type(mirror=True,curved_mirror=True)\n",
    "oe.write('/home/trh/data/mirror/open_end/5elec//30kV/openendmir5elec00_30kV.dat')\n",
    "# col = OpticalColumn(oe)\n",
    "# col.write_opt_img_cond_file('/home/trh/data/mirror/open_end/5elec/30kV/test_col00.dat',oe_pos=-8,img_pos=1,energy=30000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1  39  51  89  91 113 117 145 149 177 181 251]\n",
      "[ 1 11 21 31 41]\n",
      "[array([39, 51]), array([89, 91]), array([113, 117]), array([145, 149]), array([177, 181])]\n",
      "[array([ 1, 21]), array([ 1, 21]), array([ 1, 31]), array([ 1, 31]), array([ 1, 31])]\n",
      "[array([ 1, 41]), array([  1,  39,  51,  89,  91, 113, 117, 145, 149, 177, 251]), array([ 1, 41])]\n"
     ]
    }
   ],
   "source": [
    "print(oe.z_indices)\n",
    "print(oe.r_indices)\n",
    "print(oe.electrode_z_indices)\n",
    "print(oe.electrode_r_indices)\n",
    "# print(oe.electrode_unit_potentials)\n",
    "print(oe.boundary_indices)\n",
    "# print(oe.boundary_unit_potentials)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "oe.z_indices+= 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for indices in oe.electrode_z_indices:\n",
    "    indices += 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "oe.boundary_indices[1] += 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "oe.z_indices[:3] = [1, 20, 39]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "oe.boundary_indices[1][0] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "oe.z+=25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "oe.r[:,0] = [40,20,15,5,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "oe.z[:,1] = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "oe.write('/home/trh/data/mirror/open_end/5elec/30kV/openendmir5elec00_30kV.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "optical_element_io:calc_field:846: \n",
      "\n",
      "     *******************************************************************\n",
      "     *                                                                 *\n",
      "     *                       SOFEM-FIELD+RAYTRACE                      *\n",
      "     *                                                                 *\n",
      "     *       For Analysing Magnetic and Electrostatic Lenses and       *\n",
      "     *     Deflectors Using the Second-Order Finite Element Method     *\n",
      "     *                                                                 *\n",
      "     *                         CL Version 1.1                          *\n",
      "     *    Copyright (C) Munro's Electron Beam Software Ltd  Jul 2020   *\n",
      "     *                                                                 *\n",
      "     *******************************************************************\n",
      "\n",
      "\n",
      "      SOMLENSS Module    For Computing Vector Potential Distribution\n",
      "      ----------------   -------------------------------------------\n",
      "                         in Saturated Magnetic Lenses\n",
      "                         ----------------------------\n",
      "                         Using the Second-Order Finite Element Method\n",
      "                         --------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Opening files:\n",
      "     obj01_test.dat    ( input) \n",
      "     obj01_test.axb    (output) \n",
      "     obj01_test.pot    (output) \n",
      "     obj01_test.cct    (output) \n",
      "\n",
      "Title:  MAGNETIC SATURATED CIRCUIT (SYMMETRIC LENS)\n",
      "\n",
      "\n",
      "Allocating memory for arrays\n",
      "\n",
      "\n",
      "  Number of        Largest     Max change       Gaussian\n",
      " iterations       residual    in vec potl    Elimination \n",
      "  completed     (Ampturns)     (Webers/m)      Block No.\n",
      "\n",
      "          1     6.3614e+03     3.6951e-01         Finish\n",
      "          2     8.0788e+04     3.1616e-01         Finish\n",
      "          3     3.6930e+03     7.2130e-03         Finish\n",
      "          4     8.7590e+02     2.5417e-03         Finish\n",
      "          5     8.5387e+01     1.0505e-03         Finish\n",
      "          6     1.2326e+01     6.9536e-04         Finish\n",
      "          7     2.3750e+00     1.3658e-04         Finish\n",
      "          8     1.3925e-01     8.6464e-06         Finish\n",
      "          9     2.3596e-03     5.2705e-08         Finish\n",
      "\n",
      " Adequate convergence achieved\n",
      "\n",
      "Writing out the computed results\n",
      "\n",
      "calculate_optical_properties:calc_properties_optics:168: \n"
     ]
    }
   ],
   "source": [
    "oe.calc_field()\n",
    "calc_properties_optics(col)\n",
    "col.read_optical_properties()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.97258\n"
     ]
    }
   ],
   "source": [
    "print(col.c3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column_io:calc_rays:111: \n",
      "\n",
      "     *******************************************************************\n",
      "     *                                                                 *\n",
      "     *                       SOFEM-FIELD+RAYTRACE                      *\n",
      "     *                                                                 *\n",
      "     *       For Analysing Magnetic and Electrostatic Lenses and       *\n",
      "     *     Deflectors Using the Second-Order Finite Element Method     *\n",
      "     *                                                                 *\n",
      "     *                         CL Version 1.1                          *\n",
      "     *    Copyright (C) Munro's Electron Beam Software Ltd  Jul 2020   *\n",
      "     *                                                                 *\n",
      "     *******************************************************************\n",
      "\n",
      "\n",
      "SORAY Module    For Computing Trajectories in Electrostatic and Magnetic Lenses\n",
      "-------------   ---------------------------------------------------------------\n",
      "                With Rotational (z,r) Symmetry, Using the Second-Order FEM\n",
      "                ----------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "     Opening file:  opt_col01_rays.fcs  for  text output\n",
      "\n",
      "Allocating memory for arrays\n",
      "\n",
      "Reading finite element mesh of magnetic lens\n",
      "\n",
      "Reading magnetic potential distributions\n",
      "\n",
      "Reading initial ray conditions\n",
      "Reading initial ray conditions in old format\n",
      "\n",
      "Computing ray number: 2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "col.write_raytrace_file(\"/home/trh/data/std_oe/30kV/opt_col01.dat\",lens_type='magnetic',initial_direction=0,\n",
    "                        minimum_rays=True,source_pos=-8,screen_pos=40,energy=30000,\n",
    "                        lens_excitation=40000)\n",
    "                        # lens_excitation=col.lens_curr)\n",
    "col.calc_rays()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAFNCAYAAAApcSVtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAukUlEQVR4nO3de3wU9b3/8dcnCSTcQoAAAQIEEaqIVm0Ez7FaxCJIFbRatfXC0Sq21Sq1/s7xdo733tS2tnpUVBRtK6X1VFEpKCjeRVERELQg94AQruEakuz398cMuGASlrA7M7v7fvaxzex3Z2c+WZc3X77znRlzziEiItGVE3YBIiLSOAW1iEjEKahFRCJOQS0iEnEKahGRiFNQi4hEnIJaRCTiFNSSVcxsqZntMLOtZvaFmT1hZq3DrkukMQpqyUZnOOdaA0cDxwA3hFuOSOMU1JK1nHNfAFPxAhszu97MPjezLWY238zO8tubm9kGMzty93vNrJOZbTezjmZWbGYvmNkmf703zEx/tiRp9GWSrGVmpcBpwCK/6XPgRKAtcBvwJzPr4pzbBUwALox7+/eB6c65SuDnwEqgI9AZuBHQtRkkaRTUko2eNbMtwApgLXALgHPub865Vc65mHPur8BCYID/nvHA983M/OcXAU/5yzVAF6Cnc67GOfeG00V0JIkU1JKNznTOtQEGAYcBxQBmdrGZzfaHMDYB/Xe/5pybCWwHBpnZYcChwCR/e3fj9cpfMrPFZnZ9kL+MZD4FtWQt59xrwBPAPWbWE3gEuAro4JwrAuYBFveW8XjDHxcBf3fO7fS3s8U593Pn3CHACOBaMzslsF9EMl5e2AWIhOz3wFKgCG9cuRLAzC7B61HH+xPwMbAFL6zx1z0d+BRvjHszUAfEUlq1ZBX1qCWr+QcDnwT+B7gXeAdYAxwJvLXPuiuAD/EC/Y24l/oA04Ct/vv/1zn3asqLl6xhOuYhkjgzGwescs7dHHYtkj009CGSIDMrA76Ld5KMSGA09CGSADO7A+/g4t3OuSVh1yPZRUMfIiIRpx61iEjEKahFRCIurQ8mFhcXu7KysrDLEBE5aB988ME651zH+l5L66AuKytj1qxZYZchInLQzGxZQ69p6ENEJOIU1CIiEaegFhGJOAW1iEjEKahFRCJOQS0iEnEKahGRiEtZUJtZgZm9Z2Yfm9knZnab397LzGaa2SIz+6uZNffb8/3ni/zXy1JVm4hIOkllj7oaGOyc+zpwNDDMzI4Hfg38zjl3KLAR+KG//g+BjX777/z1RESyXsqC2nm2+k+b+Q8HDAb+7rePB870l0f6z/FfPyXujs8iIlkrpaeQm1ku8AHeHZsfwLun3CbnXK2/ykqgm7/cDVgB4JyrNbPNQAdg3T7bHA2MBujRo0eT6pphM5r0PhGJvkFuUNglJF1Kg9o5VwccbWZFwD+Aw5KwzbHAWIDy8vImX0w7E/9jimS7TO2EBTLrwzm3CXgV+DegyMx2/wVRClT4yxVAdwD/9bbA+iDqExGJslTO+ujo96QxsxbAEGABXmCf4682CnjOX57kP8d//RWn28+IiKR06KMLMN4fp84BJjrnXjCz+cAEM7sT+Ah4zF//MeApM1sEbADOT2FtIiJpI2VB7ZybQz13a3bOLQYG1NO+E/hequoREUlXOjNRRCTiFNQiIhGnoBYRiTgFtYhIxCmoRUQiTkEtIhJxCmoRkYhTUIuIRJyCWkQk4hTUIiIRp6AWEYk4BbWISMQpqEVEIk5BLSIScQpqEZGIU1CLiEScglpEJOIU1CIiEaegFhGJOAW1iEjEKahFRCJOQS0iEnEKahGRiFNQi4hEnIJaRCTiFNQiIhGnoBYRiTgFtYhIxCmoRUQiTkEtIhJxCmoRkYhTUIuIRFzKgtrMupvZq2Y238w+MbNr/PZbzazCzGb7j+Fx77nBzBaZ2WdmNjRVtYmIpJO8FG67Fvi5c+5DM2sDfGBmL/uv/c45d0/8ymbWDzgfOALoCkwzs77OuboU1igiEnkp61E751Y75z70l7cAC4BujbxlJDDBOVftnFsCLAIGpKo+EZF0EcgYtZmVAccAM/2mq8xsjpmNM7N2fls3YEXc21bSeLCLiGSFlAe1mbUGngHGOOeqgAeB3sDRwGrg3gPc3mgzm2VmsyorK5NdrohI5KQ0qM2sGV5I/9k5938Azrk1zrk651wMeIQvhzcqgO5xby/12/binBvrnCt3zpV37NgxleWLiERCKmd9GPAYsMA599u49i5xq50FzPOXJwHnm1m+mfUC+gDvpao+EZF0kcpZHycAFwFzzWy233Yj8H0zOxpwwFLgCgDn3CdmNhGYjzdj5ErN+BARSWFQO+feBKyelyY38p67gLtSVZOISDrSmYkiIhGnoBYRiTgFtYhIxCmoRUQiTkEtIhJxCmoRkYhTUIuIRJyCWkQk4hTUIiIRp6AWEYk4BbWISMQpqEVEIk5BLSIScQpqEZGIU1CLiEScglpEJOIU1CIiEaegFhGJOAW1iEjEKahFRCJOQS0iEnEKahGRiFNQi4hEnIJaRCTiFNQiIhGnoBYRiTgFtYhIxCmoRUQiTkEtIhJxCmoRkYhTUIuIRJyCWkQk4lIW1GbW3cxeNbP5ZvaJmV3jt7c3s5fNbKH/s53fbmb2BzNbZGZzzOzYVNUmIpJOUtmjrgV+7pzrBxwPXGlm/YDrgenOuT7AdP85wGlAH/8xGngwhbWJiKSNlAW1c261c+5Df3kLsADoBowExvurjQfO9JdHAk86z7tAkZl1SVV9IiLpIpAxajMrA44BZgKdnXOr/Ze+ADr7y92AFXFvW+m3iYhktZQHtZm1Bp4BxjjnquJfc845wB3g9kab2Swzm1VZWZnESkVEoikvlRs3s2Z4If1n59z/+c1rzKyLc261P7Sx1m+vALrHvb3Ub9uLc24sMBagvLz8gEJeRA5OLBZjx44dbNu27YAeS5YsYd26dQwbNowePXrQvXt3evToQWlpKfn5+WH/WpGXsqA2MwMeAxY4534b99IkYBTwK//nc3HtV5nZBGAgsDluiEREElRbW3vAQdrQY/v27Xs937FjBwUFBbRq1SrhR/v27SkuLqayspJt27YxdepUVqxYwYoVK6ioqKBdu3Z7wnt3gMf/LCkpITc3N+yPNVSp7FGfAFwEzDWz2X7bjXgBPdHMfggsA871X5sMDAcWAduBS1JYm0honHPs2rUraWG676O2tpaWLVsmHKSFhYV06dIloXVbtmxJTk7yRkzr6upYs2YNy5cv3xPey5cv5+23396zvGHDBjp27Ihzjr///e/8+7//e9L2ny5SFtTOuTcBa+DlU+pZ3wFXpqoekYOxfv16brvtNs4666ykhKmZHVCvtLi4mJ49eya0bkFBAd4/aKMvNzeXrl270rVrV44//vh616murmbJkiVMmDCB73//+xxxxBHcfvvtlJeXB1xteFI6Ri2SCZxzjBkzhj/96U/Mnj2b1q1b1xuQXbt2TTh4mzdvHvavlTby8/M57LDDuPXWW7nhhht47LHHOPPMMykvL+f222/nqKOOCrvElFNQizQiFotx5ZVXsmDBAgAmTJhA165dQ64qe+Xn5/OTn/yESy65hIcffpihQ4dy4okncuutt9KvX7+wy0sZXetDpAG1tbWMGjWK+fPn88orr9C8eXPef//9sMsSoEWLFowZM4ZFixZRXl7OoEGDuPDCC1nJyrBLSwkFtUg9qqurOffcc1m3bh3//Oc/KSwspE2bNnz88cdhlyZxWrVqxX/+53+yaNEiDjvsMC7iorBLSgkFtcg+tm/fzogRI8jJyeHZZ5+lZcuWABQXF/PZZ5+FXJ3Up7CwkJtvvjnsMlJGQS0SZ/PmzQwdOpSSkhImTJiw18kYpaWlLF26NLziJGspqEV869at45RTTuGoo47i8ccfJy9v72PthxxyCKtWrQqpOslmCmoRYPXq1XzrW99iyJAh3H///fWe1NGvXz82bNgQQnWS7RTUkvWWLl3KiSeeyIUXXsgvf/nLBk8WOfroo9m2bVvA1YkoqCXLffbZZ5x00klcc8013HDDDY2ue+yxx1JXV8euXbsCqk7Eo6CWrPXxxx9z8sknc9ttt/HTn/50v+sXFhaSk5OjKXoSOAW1ZKV3332XU089lfvuu49LLkn8+l8tW7bkww8/TGFlIl+lU8gl67zyyiucf/75PPHEEwwfPvyA3tuuXTvmzZuXospE6qeglqzy4osvcskllzBx4kQGDRp0wO/v3LkzixYtSn5hIo3Q0IdkjYkTJ3LppZfy/PPPNymkAXr27MnKlZl5PYlU2LRkCbN//3teOfFEZhUX8/Y117B48mTqdED2gKhHLVlh3Lhx3Hzzzbz88ssHdVnMvn378sYbbySxsswQq61lxWuvsWryZKrfe48WCxfSbd062tbVkdu6Nc2Li6kpKCDnb38j93//l521tSxr2ZL1paXEjjyStieeSNmIERT16hX2rxJJCmrJeJdeeimTJk3i7bffpm/fvge1ra9//etUVVXtf8UMtvWLL1gyaRIbZ8yAjz+m3fLllG3dSm5uLjnFxdCnD7GLLqJu2DBafetbHJn31ZjZsmoVdc8/j3vtNezjj8mdOpXcMWNYmZvLqg4d2N6nD/kDBtB5yBB6nnIKuf71u10sRnVVFTs3bqR682aqN21i1+bN1GzZQk1VFZl6KwEFtWS0Xbt2MXXqVB577LGDDmmA8vJyqqurk1BZ9LlYjFUzZ1IxeTLb33mH/E8/pWTtWjrX1JDXogXm94brRo+mbsQISnv1ojTBbbfp2pUjr7gCrrhiT1ustpYNr79ObMoUmDmTnKefJu+PfyRWW8t2vLDKx7ttlAGWk7PnQW4u5OWRqRehVVBLRhs/fjz9+/dn5MiRSdler169cM6xcuVKSksTjaXoq66qYvHzz7Nu+nRiH31E4dKllG3eTDMzctq1g969iY0cSd2pp9J86FAOLyhIeg05eXn0HDyYnoMH79W+4o03yG3enKLevaGoiPy8PBq8b3ma3ILsQCmoJWPV1NTwy1/+kieffDJp28zJydlzA4F0DerKefNY/vzzbHnzTZrNn0+n1aspra4mLz+fnC5dqOvXj9i551J7xhl06t+fTiHX2/3EE0OuIHwJBbWZdcK7q3hXYAcwD5jlnIulsDaRg/KXv/yFnj178s1vfjOp223Tpg1z5szhrLPOSup2k612506WTp3KmpdeombWLFp//jndN24k3zly2rbFysqInXwytaecgn3nO/QpKqJP2EVLvRoNajM7GbgeaA98BKwFCoAzgd5m9nfgXudcdh9dkcipq6vjF7/4BQ8++GDSt92xY8fI3UBg87JlLHn2WTa//jo5c+fSoaKCsu3byW3WjJxOneCww4j9+MfUDh9Op4EDOaaeqwNKdO2vRz0cuNw5t3zfF8wsDzgdGAI8k4LaRJps4sSJFBcXc/LJJyd922HeQGDPNLgpU6ieOdObBldZuWcanPXogSsvp+6664iNGEGvkhI04S39NRrUzrn/18hrtcCzyS5I5GDFYjHuuusu7rnnngYvWXowevfuzdSpU5O+3X1tW7uWJZMmseHVV71pcMuWfXUa3A9+QO2wYbQ6+eR6p8FJZkh0jLoIuBgoi3+Pc+7qlFQlchD+8Y9/0KJFC4YOHZqS7R9++OE8/fTTSduei8VY/f77rHzxRW8a3IIFe6bB5e6eBte/P3WXXUbtGWdQ2rt3wtPgJDMk+lfwZOBdYC6gA4gSWc457rzzTm699daU9KYBjjnmmCbfQKC6qoolL77IuunTqfvggz3T4PJ2T4M75BBiI0ZQO2QIzYYO5XD/xrqS3RIN6gLn3LUprUQkCV544QVisRgjRoxI2T5230Bg586dFDQyn7jyk0++nAb3ySd7psHl5udjJSW4fv2oO+ccas44g05HHRX6NDiJrkSD+ikzuxx4AdhzWpZzTjeQk8hwznHHHXdw8803p6w3DdC6des9NxAYOHCgNw3upZdYM3XqV6bB5RYWetPgBg2i9tvf1jQ4aZJEg3oXcDdwE+D8NgcckoqiRJripZdeYuvWrZx99tkp39dvgaqhQ5lfU0PZ9u3k7Z4G97WvUXfFFdSefjqdBg7kaE2DkyRINKh/DhzqnFuXymJEDsaoUaP4r//6r3rvIJ5s3waWFBVRd+ONxEaMoKykhLKU71WyVaJBvQjYnspCRA6WmXH66acHsq8d7dpRctllHDl6dCD7k+yWaFBvA2ab2avsPUat6XkSGTt37qRDhw6B7MucA81bloAk+m/EZ4G7gLeBD+IeDTKzcWa21szmxbXdamYVZjbbfwyPe+0GM1tkZp+ZWWomwErGcs5RVVVFmzZtAtmfOYfl5gayL5GEugTOufFN2PYTwP3Avpcu+51z7p74BjPrB5wPHIF34adpZtbXOVfXhP1KFtq6dSsFBQU0a9YskP2Zc941kEUCkFCP2sxON7OPzGyDmVWZ2RYza/RCTM6514FEp++NBCY456qdc0vwxsQHJPheEaqqqmjbtm1g+1OPWoKU6NDH74FRQAfnXKFzro1zrrCJ+7zKzOb4QyPt/LZuwIq4dVb6bSIJ2bx5M4WFTf1KHjgFtQQp0aBeAcxzzrn9rtm4B4HewNHAauDeA92AmY02s1lmNquysvIgy5FMsXnzZvWoJWMletj6P4HJZvYae8/6+O2B7Mw5t2b3spk9gnemI0AF0D1u1VK/rb5tjAXGApSXlx/sXxySIcIY+tAYtQQl0R71XXjzqAuANnGPA2JmXeKenoV3pxiAScD5ZpZvZr2APsB7B7p9yV4a+pBMlmiPuqtzrv+BbNjMngYGAcVmthK4BRhkZkfjnX6+FLgCwDn3iZlNBOYDtcCVmvEhByLooY8czaOWACV8mVMzO9U591KiG3bOfb+e5scaWf8uvJ67yAHT0IdkskSHPn4MTDGzHYlOzxMJUuAHE0FDHxKYRE94CeZ0L5Em2rx5M2VlZYHtTz1qCVKjPWozK9vP62ZmuiuQhC7ooY8cHUyUAO2vR323meUAz+Fd26MSb+bHocDJwCl4BwlXprJIkf0JZehDBxMlIPu7C/n3/OtwXABcCnTBm6a3AO8+inc553amvEqR/Qhjep6GPiQo++0SOOfm493ZRSSywpie5xTUEhD9200yQuDT89DQhwRH3zTJCBr6kEy233nU/syO7vtbTyRMgQ99ADnqUUtA9hvU/hXzJgdQi0iTVFdXU1dXR4sWLQLbp671IUFK9MzED83suJRWItJEu8enzSywfapHLUFK9Js2ELjAzJbh3ejW8DrbR6WsMpEEBT0+DRqjlmAlGtS62axEVtAzPsD/p6h61BKQRK/1sSzVhYg0VdAHEkGXOZVg6ZsmaS+MoY8cgIDueC6ioJa0F0qPGtSjlsDomyZpL5Qxag19SID0TZO0F8qsD9CsDwlMovOoRSIrrKEPzaOWoCioJe2FNT1PQS1B0TdN0l5osz4U1BIQfdMk7WnWh2Q6fdMk7enMRMl0+qZJ2lOPWjKdDiZK2gt6jNrFYuQAlqM/PhIMfdMk7QU99OFiMWIoqCU4+qZJWovFYmzdupU2bdoEt8/aWmKB7U1EY9SS5rZs2ULLli3JDfAswVhtbWD7EgEFtaS5MA4kKqglaApqSWthTM1TUEvQFNSS1sI4K1FBLUFTUEta09CHZIOUzfows3FmttbM5sW1tTezl81sof+znd9uZvYHM1tkZnPM7NhU1SWZJYyhD1dXhwt0j5LtUjk97wlg2D5t1wPTnXN9gOn+c4DTgD7+YzTwYArrkgwSVo86ZhboPiW7pSyonXOvAxv2aR4JjPeXxwNnxrU/6TzvAkVm1iVVtUnmCGuMWvOoJUhBn/DS2Tm32l/+AujsL3cDVsStt9JvE2lUWLM+FNQSpNDOTHTOOTjwoT4zG21ms8xsVmVlZQoqk3QSxtCHq6vDaehDAhR0UK/ZPaTh/1zrt1cA3ePWK/XbvsI5N9Y5V+6cK+/YsWNKi5XoC2Poo27XLvWoJVBBB/UkYJS/PAp4Lq79Yn/2x/HA5rghEpEGhdWj1sFECVLK5lGb2dPAIKDYzFYCtwC/Aiaa2Q+BZcC5/uqTgeHAImA7cEmq6pLMojMTJRukLKidc99v4KVT6lnXAVemqhbJXGEMfbi6OlCPWgKkMxMlrenMRMkGCmpJa2GdmagetQRJNw6QtOWcC+2EF51CLkFSj1rS1s6dOzEzCgoKAt2v5lFL0BTUkrbCGPYADX1I8DT0IWkrjAOJoKEPCZ561JK2whifBg19SPAU1JK2NPQh2UJBLWkrrKEPV1cX+D4luymoJW2FOfShHrUESUEtaSvMg4kKagmSglrSlsaoJVtoep6kLc36kGyhoJa0FebBRAW1BElDH5K2NPQh2UJBLWkr1Ol5CmoJkIJa0pam50m2UFBL2tLQh2QLBbWkLQ19SLZQUEva0tCHZAtNz5O0VFtby44dO2jdunXg+3Y6M1ECph61pKUtW7bQpk0bcnKC72u4ujpcCPuV7KVvm6SlsIY9AHYsWEDHTZvgvfdAV9KTACioJS2FdSARYPshh7AsPx8uvRQ6d4bzz4fHH4dVq0KpRzKfglrSUlhT8wDanX021x92GMybB7Nnw6mnwpQpcOSR3uO66+Dll2HnzlDqk8yjoJa0FObQR9u2bdm8ebP3pLTU61n/9a+wdi08+igUFsKtt0KnTnDaafD738OCBeB0p0VpGgW1pKUwhz72Cup4ubkwcCD8z//AW2/B8uVw2WUwfz4MGwZlZXD55fDMM7BpU9BlSxpTUEtaCnPoo6ioqP6g/uqKcPbZMHYsLF0KU6dC//7w2GPQoweccALcfjvMnKmDktIoTc+TtDRr1qzEwjIFWrZsSU1NDTU1NTRr1iyxN5nBYYd5j2uu8cav33jDC+/LLvMORH772zB0qDfmXVqa2l9C0op61JKWBgwYwOuvv85tt91GLBYLdN9mRmFh4cH9RVFQAEOGwD33wNy5MGeONzzy0ktw9NFez/vnP/ee79iRtNolPSmoJS1dccUVzJkzh+nTpzNixAg2BTzm27Zt2+Tus1s3uOQSmDAB1qyBceO8oZPbb/emAA4bBr/7nTferYOSWUdBLWmrpKSE6dOn07t3b4477jjmzp0b2L4THqduitxcGDAA/vu/4c03vYOSo0fDp5/C8OHe+PZll8Hf/gYbN6amBomUUILazJaa2Vwzm21ms/y29mb2spkt9H+2C6M2SS/NmjXjvvvu45ZbbmHw4MFMmDAhkP02OPMjFYqK4LvfhYcfhiVLYNo0OOooeOIJ6NkT/u3fvOmA77yjg5IZKswe9cnOuaOdc+X+8+uB6c65PsB0/7lIQi688EKmTZvGTTfdxLXXXktNTU1K9xdoUMczg699Da6+Gl58ESor4c47Yft2+NGPoGNH+N73vPncK1YEX5+kRJSGPkYC4/3l8cCZ4ZUi6ejrX/8677//PgsWLGDIkCGsWbMmZfsKLaj3lZ8Pp5wCv/kNfPwxfPIJnH46TJ8OxxwD/frBz37mnTmpg5JpK6ygdsBLZvaBmY322zo751b7y18AncMpTdJZ+/bteeGFFzjppJM47rjjmDlzZkr2k/SDicnSpQuMGgVPP+0dlBw/Hjp0gLvu8s6UPPVUuPdeL9B1UDJthBXU33TOHQucBlxpZifFv+icc3hh/hVmNtrMZpnZrMrKygBKlXSTm5vL7bffzv33388ZZ5zB2LFjcUkOpZQeTEyW3Fw47ji4+WZvzvbKlfDjH8PChV6vu3t3+OEPYeJE2LAh7GqlEaEEtXOuwv+5FvgHMABYY2ZdAPyfaxt471jnXLlzrrxjx45BlSxpaMSIEbz55pvcd999XH755exM4kWSIjP0cSDatoWzzoKHHoLFi+GVV7w5208+6Z3efvzxcMst8PbbUFsbdrUSJ/CgNrNWZtZm9zJwKjAPmASM8lcbBTwXdG2Sefr27cvMmTOpqqrixBNPZPny5UnZbloGdTwz6NsXfvpTeOEF76DkL37hnTH5k594wyTnnAOPPOJND5RQhdGj7gy8aWYfA+8BLzrnpgC/AoaY2ULg2/5zkYPWunVr/vrXv3LeeecxcOBAXnnllYPeZtoH9b7y82HwYPj1r71Lt37yCYwYATNmQHk5HH44jBkD//ynN8NEAhX4tT6cc4uBr9fTvh44Jeh6JDuYGddddx3HHnssF1xwAddeey3XXXcd1sR7HxYVFUXzYGKydOkCF1/sPWIx+Ogj77okv/oVnHuuN0wydKj36N9f95BMsShNzxNJucGDBzNz5kz++Mc/MmTIkCZvJ+N61I3JyYFvfANuvBFeew0qKuDKK+Hzz2HkSO8CUrtPf1+/PuxqM5KCWrJO9+7dyc/PZ8yYMU3eRlYF9b4KC+HMM+HBB72wnjHDC/I//xl69dr79HcdlEwKXeZUss6MGTNo3rw53/nOd5q8jawO6nhm0KeP97jqKqiu9maNTJ3qHahcuhROPvnLYZKysrArTkvqUUvWefjhh/nRj37U5PFpSJN51GHIz/eC+Ve/8sa1FyzwpgS+8YbX0959+vvEid4p7jrpJiHqUUtWWbt2LVOmTOGhhx46qO0UFBRQXV3Neeedx6GHHkq3bt3o2rUr3bp1o1u3bnTu3Jnc3NwkVZ3GSkrgoou8RyzmzSiZOtUbGlm2DPLyvKsB7vvo3t37WVrqhX+8bdu8Gy1UVHg/45czlIJassrjjz/Od7/7XYqKig56W2ZG7969adGiBXPnzmXKlCmsWrWKiooK1q9fT8eOHfcK7/qW27Zte1A9+7SSkwPHHus9brjBa6uq8nrWy5d/+Xj55S+XV62C9u29kG/VyjtYuWuXd/3url2//Fla6vXYJ04M93dMEQW1ZI1YLMbYsWP5y1/+kpTtFRQUcMQRR3DBBRd85bWamhrWrFlDRUXFnvCuqKjg1Vdf3bO8atUqamtr94R2fUHetWtXunbtSv6+vcpMUVgIRxzhPepTVwdffOFd2vWww7yhk7ZtG5wOOOMHXRiUumpDo6CWrDFt2jQKCwsZMGBAUrbXokULli5dWu9rzZo1o7S0lNL93Ptw69atewX5qlWrWLJkCW+++eae9i+++ILCwsIGg3x3W3FxMTk5GXbYKTfX6zWPGrX/dTOYglqyxsMPP8wVV1yRtKGGwsJCVhzkNZ9bt25N37596du3b4PrxGIx1q1b95Xe+fvvv79XW1VVFSUlJQ0G+e7l1q1bH1TNEjwFtWSFVatW8corr/DEE08kbZvt27enoqIiadtrSE5ODp06daJTp04cc8wxDa5XXV3N6tWr9+qdV1RUMGfOnD1tFRUVNGvWrNEg330wNOE7rEvKKaglK4wbN45zzz2XNm3aJG2bHTt2ZO3aei/yGIr8/HzKysooa2SusnOOzZs37xXkq1atYsGCBUybNm1PW2VlJR06dNjvwdB27dplz8HQECmoJePV1dUxduxYnnsuuRdkLCkpYfHixUndZqqZGUVFRRQVFXFEQwfw8D6z+g6GvvHGG3u17dy5M6GDoS1atAjwt8w8CmrJeFOmTKGkpKTRYYOm6N69e8ae9JKbm7snZBuzbdu2rwy3rFixgpkzZ+5pW716Na1ataJjx45s3bqV1157jd69ewf0m2QGBbVkvN1nIiZbWVkZ27ZtS/p200mrVq049NBDOfTQQxtcxznH+vXrWbZsGePGjWPgwIHcdNNNXH311TopKEEZNpdHZG8rVqzgrbfe4rzzzkv6tvv06ZPUu8ZkKjOjuLiYb3zjGzzwwAO88847PPvss3zzm99k/vz5YZeXFhTUktEeffRRfvCDH9CqVaukb7tv377U1dUlfbuZrk+fPrz66qtcfPHFnHTSSdx5553U1NSEXVakKaglY9XW1vLoo49yxRVXpGT7nTp1AojUzI90kZOTw49//GM+/PBD3nrrLQYMGMBHH30UdlmRpaCWjPXCCy/Qq1cv+vfvn5Lt5+TkkJuby6effpqS7WeDHj16MHnyZMaMGcPQoUO58cYbNZxUDwW1ZKyHHnooZb3p3QoKCli4cGFK95HpzIxRo0YxZ84cPvvsM4455hjefvvtsMuKFAW1ZKTFixfzwQcfcM4556R0Py1btmTZsmUp3Ue2KCkp4ZlnnuGOO+7g7LPP5pprrsn6WTW7KaglIz3yyCNcdNFFKT/Rom3btgd9vQ/Z2znnnMO8efPYsGEDRx55JNOnTw+7pNApqCXj7Nq1i8cffzzlwx7gXe9j9erVKd9PtunQoQNPPfUU999/P5dccgmXX355xp5clAgFtWSc5557jsMPP5yvfe1rKd9Xp06dqKysTPl+stXw4cOZN28eeXl59O/fn+effz7skkKhoJaME8RBxN26dOnCxo0bA9lXtiosLOTBBx/kqaee4mc/+xkXXHAB69atC7usQCmoJaP861//Yt68eZx11lmB7K9Hjx5UVVUFsq9sN2jQIObMmUNJSQl9+/bllltuCbukwOhaH5JRrr76ag499FAeeOAB2rZt2+CjoKAgKZfn7NWrF9u3b09C5ZKIli1bcu+99zJv3jx27NgRdjmBUVBLRjnyyCOpqalhxYoVzJs3j82bN3/lsWnTJpxzFBUVNRrm8Y/61i0oKKBv375UV1eH/WtnlVgsxkcffcQjjzwSdimBUVBLRrn77rsTWm/nzp31hnh8mC9fvrzR182MNm3aEIvFOO6442jfvj1FRUW0a9eOdu3aNbjcrl072rZtqyvHNdGHH35IcXExPXr0CLuUwCioJSsVFBRQUFBA586dm/R+5xw7d+5k48aNjBs3jsGDB1NVVcXGjRvZuHEjmzZtYt26dSxcuJBNmzbt1b5x40aqqqpo3bp1vSGeSNhn7F3JEzBlyhSGDRsWdhmBUlCLNIGZ0aJFC1q0aMHNN998wO+PxWJ7BXt8iO9eXrVqVb3tGzduJC8vL+He+76vtW7dOq1vnzV16tQmfebpTEEtEoKcnJw9t8Tq1avXAb3XOcf27dv3CvB9A33ZsmXMnj273qCvrq7es+8D7ckXFhaSlxdebGzatInZs2dz0kknhVZDGBTUImnGzGjVqhWtWrWiW7duB/z+Xbt2sWnTpgaDvrKykoULFzbY0y8oKKCwsJDWrVsn7ZHoHc+nT5/OCSeckHX3YFRQi2SZ5s2b06lTpz3X0z4QtbW1bN++ne3bt7N169b9PtavX5/Qerm5uQkF+owZMzjttNNS8KlEW+SC2syGAfcBucCjzrlfhVySiPjy8vIoLCyksLAwadt0zlFdXZ1QoJeWljJ48OCk7TtdRCqozSwXeAAYAqwE3jezSc453VhNJEOZ2Z5ZOMXFxY2ue9111wVUVbREKqiBAcAi59xiADObAIwEFNQiwo7qWv7f2MmA2/M/9vyEin4refoPlfu8Drgv1425vd/zlW25L/dnmPf//iwZw8B5bXu97j/HjBxyeGTMuUn9vaMW1N2A+Iv7rgQGxq9gZqOB0cBBTXifYTOa/F4RCcf65tU8cOPIhlc4Fwj7GlmxHB4hs4N6v5xzY4GxAOXl5W4/q9drkBuUzJJEJCA1tXVMnvcB4Pdjzfb8jO/l5uT47fv0hnevl2NfvrfebRl7euHO72Lv6Xc7hxl7lnf3zOOXky1qQV0BdI97Xuq3iYjQLC+X044+NuwyAhe1y5y+D/Qxs15m1hw4H5gUck0iIqGKVI/aOVdrZlcBU/Gm541zzn0SclkiIqGKVFADOOcmA5PDrkNEJCqiNvQhIiL7UFCLiEScglpEJOIU1CIiEaegFhGJOAW1iEjEKahFRCLOnEvNuelBMLNKYFkT3loMrEtyOcmWDjVCetSpGpNDNSZHQzX2dM51rO8NaR3UTWVms5xz5WHX0Zh0qBHSo07VmByqMTmaUqOGPkREIk5BLSIScdka1GPDLiAB6VAjpEedqjE5VGNyHHCNWTlGLSKSTrK1Ry0ikjYyPqjN7A4zm2Nms83sJTPr6rebmf3BzBb5rx8b955RZrbQf4wKoMa7zexTv45/mFmR315mZjv82meb2UNx7/mGmc316/+D7b7fUMA1+q/d4NfxmZkNjWsf5rctMrPrU1mfv7/vmdknZhYzs/K49ih9jvXW6L8Wic+xnppvNbOKuM9v+P5qDkPYn1NDzGyp/x2bbWaz/Lb2ZvaynzEvm1m7RjfinMvoB1AYt3w18JC/PBz4J97d0Y4HZvrt7YHF/s92/nK7FNd4KpDnL/8a+LW/XAbMa+A97/l1m/97nBZSjf2Aj4F8oBfwOd5NH3L95UOA5v46/VJc4+HA14AZQHlce5Q+x4ZqjMznWE/NtwLX1dNeb81B1hZXS+ifUyO1LQWK92n7DXC9v3z97j9PDT0yvkftnKuKe9oK9tx9ciTwpPO8CxSZWRdgKPCyc26Dc24j8DIwLMU1vuScq/Wfvot3r8gG+XUWOufedd5/6SeBM0OqcSQwwTlX7ZxbAiwCBviPRc65xc65XcAEf91U1rjAOfdZouuH9Dk2VGNkPscD0FDNYYjy51SfkcB4f3k8+/neZXxQA5jZXWa2ArgA+B+/uRuwIm61lX5bQ+1BuRSvZ7dbLzP7yMxeM7MT/bZufl27hVljVD/HfUXxc4wX9c/xKn/Ya1zcP9OjUlvUatmXA14ysw/MbLTf1tk5t9pf/gLo3NgGIncrrqYws2lAST0v3eSce845dxNwk5ndAFwF3BJogey/Rn+dm4Ba4M/+a6uBHs659Wb2DeBZMzsiYjUGKpEa6xG5zzFqGqsZeBC4Ay9w7gDuxfvLWhLzTedchZl1Al42s0/jX3TOOTNrdPpdRgS1c+7bCa76Z7z7Md4CVADd414r9dsqgEH7tM9IdY1m9h/A6cAp/j/Dcc5VA9X+8gdm9jnQ168xfnhkd+2B10jDnyONtKesxgbeE6nPsQGBfo77SrRmM3sEeMF/2ljNQYtSLXtxzlX4P9ea2T/whmnWmFkX59xqfwhu7f42ktEPoE/c8k+Bv/vL32Hvg4nv+e3tgSV4BxLb+cvtU1zjMGA+0HGf9o74B2fwDpJU7K6Frx4EGx5SjUew9wGlxXgHdvL85V58eXDniID+m89g7wN1kfkcG6kxcp9jXG1d4pZ/hjcu3WDNQdYWV1fon1MDdbUC2sQtv+3/WbqbvQ8m/qbR7YT9iwTwQT0DzAPmAM8D3fx2Ax7AO1I8d58/NJfiHRhZBFwSQI2L8MbXZvuP3TNTzgY+8ds+BM6Ie0+5/3t9DtyPf/JS0DX6r93k1/EZcbMm8GbW/Mt/7aYAPsez8MYmq4E1wNQIfo711hilz7Gemp/y/4zMASbtE9z11hzGI+zPqYGaDvH/0vjY/w7e5Ld3AKYDC4Fp7KczqDMTRUQiLitmfYiIpDMFtYhIxCmoRUQiTkEtIhJxCmoRkYhTUIuIRJyCWgQws7+b2SEp2O49ZjY42duV7KKglqznX/cj1zm3OAWb/yPemWciTaagloxmZj+Ku+D9EjN7tZ7VLgCei3vPVv9GCZ+Y2TQzG2BmM8xssZmN8Nf5DzN71r/o+1Izu8rMrvWv0PeumbUHcM4tAzqYWX0XPBJJiIJaMppz7iHn3NHAcXinbv+2ntVOAD6Ie94KeMU5dwSwBbgTGIJ3+vftcev1B77rb/suYLtz7hjgHeDiuPU+9Pch0iQZcfU8kQTchxe+z9fzWhegMu75LmCKvzwXqHbO1ZjZXLy7xez2qnNuC7DFzDbjXUtm93uOiltvLdD14H8FyVYKasl4/uVZe+Jdi7w+O4CCuOc17suL4MT48hKpMTOL/zNTHbcci3seY+8/WwX+PkSaREMfktH8GwVcB1zonIs1sNoC4NAUltEX7wp9Ik2ioJZMdxXeNcZf9Q8oPlrPOi+y980iksbMmuH9JTArFduX7KDLnErWM7MWwKvACc65uiRv+yzgWOfcfydzu5Jd1KOWrOec24F3e7ZU3Aw1D+8egyJNph61iEjEqUctIhJxCmoRkYhTUIuIRJyCWkQk4hTUIiIR9/8BIbx4PWsZbEUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col.plot_rays(coarse_mesh=False,boundary_mesh=True)#,savefile='/home/trh/data/std_oe/30kV/oe_rays.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col.plot_rays(ylim=[-0.25,0.25],xlim=[-9,45],equal_aspect=False,coarse_mesh=False,boundary_mesh=True,mirror=True) #,savefile='/home/trh/data/std_oe/30kV/only_rays.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method write_raytrace_file in module column_io:\n",
      "\n",
      "write_raytrace_file(mircondfilename, source_pos=90, source_size=200, semiangle=10, energy=200000, initial_direction=180, lens_type='Electrostatic', lens_pos=0, lens_excitation=None, excitation_flag=None, potentials=None, screen_pos=95, relativity=False, cyl_symm=True, r_samples=3, alpha_samples=3, minimum_rays=False, precision=8, n_equipotentials=50) method of column_io.OpticalColumn instance\n",
      "    Creates an input file for SORAY.exe. Primarily for visualizing columns\n",
      "    implemented in MIRROR. All physical parameters have same name, units\n",
      "    and default as in write_mir_img_cond_file(), even when SORAY does not\n",
      "    share units with MIRROR.\n",
      "    \n",
      "    Parameters:\n",
      "        mircondfilename : path\n",
      "            full filename to write imaging conditions file to.\n",
      "    \n",
      "    Optional parameters:\n",
      "        source_pos : float\n",
      "            Source z position (mm). The \"source\" is the starting \n",
      "            position of rays for auto-focusing. \n",
      "            Default 90.\n",
      "        source_size: float\n",
      "            Source size (microns). Not related to resolution; just the\n",
      "            starting size of the ray bundle.\n",
      "            Default 200.\n",
      "        semiangle : float\n",
      "            Semiangle (mrad) for emission from source.\n",
      "            Default 10.\n",
      "        energy : float\n",
      "            Beam kinetic energy (eV). Default 200000.\n",
      "        initial_direction : float\n",
      "            Polar angle (deg) of initial direction. 0 for forwards \n",
      "            propagation or 180 for reverse. \n",
      "            Default 180.\n",
      "        lens_type : str \n",
      "            Type of lens: 'Electrostatic' or 'Magnetic'. \n",
      "            Unused for multi-element columns.\n",
      "            Default 'Electrostatic'.\n",
      "        lens_pos : float\n",
      "            Lens z position (mm). \n",
      "            Unused for multi-element columns.\n",
      "            Default 10. **\n",
      "        lens_excitation : float\n",
      "            Specifies excitation strength of magnetic round lens or\n",
      "            magnetic or electric multipole. Units are A-turns for magnetic \n",
      "            or volts for electric. \n",
      "            Use None if unused or for multi-element columns.\n",
      "            Default None.\n",
      "        excitation_flag : string\n",
      "            Flag associated with lens_excitation.\n",
      "            Flag options are f for fixed, vn for variable, where\n",
      "            n is an integer grouping lenses varied together (e.g. v1)\n",
      "            during autofocusing, or d for dynamic. The purpose of the \n",
      "            dynamic option is unclear. \n",
      "            Use None if unused or for multi-element columns.\n",
      "            Default None.\n",
      "        potentials : MirPotentials instance\n",
      "            The MirPotentials class is used to sensibly store and format the \n",
      "            string used for specifying the potentials of several electrodes.\n",
      "            Use None if unused or for multi-element columns.\n",
      "            Default None. **\n",
      "        screen_pos : float\n",
      "            Screen plane z position (mm). End position of rays for auto-\n",
      "            focusing. Default 95.\n",
      "        relativity : bool\n",
      "            Determines whether relativistic effects are included. \n",
      "            Default False.\n",
      "        cyl_symm : bool\n",
      "            Determines whether to use 2D initial positions for rays.\n",
      "            Default True.\n",
      "        r_samples : int\n",
      "            Number of samples of initial positions in the radial direction.\n",
      "            Default 3.\n",
      "        alpha_samples : int\n",
      "            Number of samples of initial polar angles. Default 3.\n",
      "        minimum_rays : bool\n",
      "            If True, overrides alpha_samples and r_samples and produces \n",
      "            exactly two rays.\n",
      "            default False.\n",
      "        precision : int\n",
      "            Sets precision with which to print ray-related floating point\n",
      "            numbers. \n",
      "            default 8 here to allow tiny source_pos - img_pos difference.\n",
      "        n_equipotentials : int\n",
      "            Number of equally-spaced equipotentials to plot.\n",
      "            Default 50.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(col.write_raytrace_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}