TORIC_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "564f6d40",
   "metadata": {},
   "source": [
    "# Notebook for tutorial of `TORIC` setup   \n",
    "This notebook walks you through setup from source eqdsk files , to configuring `TORIC` input files, launching a run and analyzing the results.\n",
    "\n",
    "This notebook assumes it is running on the engaging MIT cluster.\n",
    "\n",
    "In the next cell, you specify variables to configure your run. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "366e38fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jcwright/SPARC/TSC-V2h01\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38633e48",
   "metadata": {},
   "outputs": [],
   "source": [
    "workdir='./run6_test'\n",
    "eqdsk_file='TRANSPrun.geq'\n",
    "\n",
    "profile_file='TRANSPrun.pkl' #or 'select'  or \"Name.equidt\" or \"Name.txt\"\n",
    "#If file ends in pkl assume it is a pkl file with profile info from a prior Transp run that has fields\n",
    "# rho_tor, rho_pol, te, ne, ti,ni\n",
    "\n",
    "#If file ends in equidt assume it is already a prepared TORIC input file of profile info containing species\n",
    "#information as well.\n",
    "\n",
    "#If end is txt assume columnated data of r/a te ne ti in some order (note ni is set by concentration and quasineutrality)\n",
    "\n",
    "#If any other , ignore and provide selection for profiles.\n",
    "\n",
    "\n",
    "nspec = 3 \n",
    "iatm = [2,3,3]\n",
    "iazi = [1,1,2]\n",
    "\n",
    "kdiff_itemp=1\n",
    "kdiff_idens=1\n",
    "\n",
    "kpar_ant = 11.5  #note toric reports kpar_axis, we use kpar_antenna here to set nphi in the input deck\n",
    "toric_mesh = [400, 128] # Psi, theta resolution. Theta must be power of 2.\n",
    "min_conc = 3 #percent #assume third or specify min species index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30f89124",
   "metadata": {},
   "source": [
    "## Requirements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "249a3b3f",
   "metadata": {},
   "source": [
    "Load required python modules:\n",
    "- matplotlib: for plotting\n",
    "- numpy: for array math\n",
    "- plasma, periodictable.elements, f90nml and fortranformat: plotting eqdsk files, preparing toric inputs and plotting outputs\n",
    "- subprocess for running utility to prepare toric input\n",
    "You can source /home/jcwright/bin/m_jupyter_toric to load most of these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8937cc93",
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'plasma' already exists and is not an empty directory.\n",
      "Requirement already satisfied: fortranformat in /nfs/software001/home/software/psfc/local/lib/py36 (1.2.2)\n",
      "Requirement already satisfied: f90nml in /nfs/software001/home/software/psfc/local/lib/py36 (1.4.3)\n",
      "Requirement already satisfied: periodictable in /nfs/software001/home/software/psfc/local/lib/py36 (1.6.1)\n",
      "Requirement already satisfied: numpy in /home/jcwright/.local/lib/python3.6/site-packages (from periodictable) (1.19.5)\n",
      "Requirement already satisfied: pyparsing in /nfs/software001/home/software/psfc/local/lib/py36 (from periodictable) (3.0.7)\n"
     ]
    }
   ],
   "source": [
    "#local installs if needed\n",
    "!git clone https://github.com/jcwright77/plasma.git\n",
    "!python3 -m pip install --user fortranformat\n",
    "!python3 -m pip install --user f90nml\n",
    "!python3 -m pip install --user periodictable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f7a0f669",
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "#Required python modules\n",
    "import sys,os\n",
    "sys.path.insert(0,os.getcwd()+'/plasma')\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy\n",
    "import f90nml\n",
    "from periodictable import elements\n",
    "from IPython.display import Markdown as md\n",
    "import equilibrium_process as eqdsk\n",
    "import subprocess\n",
    "import toric_tools as toric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "36c19aea",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "#Routine to be moved to plasma routines\n",
    "def print_vector(nrep,fstr,a):\n",
    "    \"\"\"\n",
    "    Converts an array of numbers into a string formated by fstr with nrep values per line.\n",
    "    \"\"\"\n",
    "    n=a.size\n",
    "    pa=\"\"\n",
    "    ta=a.reshape((a.size),order='F')\n",
    "    for j in range(0,n,nrep):\n",
    "        pa=pa+ \"\".join(map(lambda f: fstr % f, ta[j:min(j+nrep,n)]))+\"\\n\"\n",
    "    return pa\n",
    "\n",
    "def write_profnt(namelist,equidt,version='profnt2'):\n",
    "    \"\"\"\n",
    "    Inputs:\n",
    "        namelist: as created by f90nml from toric.inp\n",
    "        equidt: python dictionary of profiles to write out\n",
    "            psipro: sqrt(Psipol/Psipol[a])\n",
    "            tbne: [cm-3] on psipro mesh\n",
    "            tbte: [keV]  on psipro mesh\n",
    "            iatm: array of atomic masses/ (C12/12)\n",
    "            iazi: array of atomic numbers\n",
    "            tbni: ion densities on psipro mesh\n",
    "            tbi_provv: ion temperatures on psipro mesh\n",
    "            nspec: number of ion species\n",
    "            \n",
    "         version: Generally format2 is used. File is self describing in number of elements and profiles.\n",
    "    \"\"\"\n",
    "\n",
    "    filename=namelist['equidata']['profnt_file']\n",
    "    with open(filename,'w') as of:\n",
    "        nprodt=equidt['psipro'].size\n",
    "        profiles=['psipro','tbne','tbte','tbi_provv']\n",
    "        if version=='profnt1':\n",
    "            for profile in profiles:\n",
    "                of.write('{:<10s}{:4d}\\n'.format(profile, nprodt ))\n",
    "                of.write(print_vector(5,'%16.9e',equidt[profile]))\n",
    "\n",
    "        if version=='profnt2':\n",
    "            nspec=equidt['nspec']\n",
    "            mainsp=1\n",
    "            namelist['equidata']['mainsp']=mainsp\n",
    "            kdiff_idens=equidt['kdiff_idens'] #0 #specify concentrations\n",
    "            kdiff_itemp=equidt['kdiff_itemp'] #0 #one ion temp\n",
    "            of.write('{:<10s}{:4d}{:4d}{:4d}{:4d}{:4d}\\n'.format('profnt_py',nprodt,nspec,\n",
    "                                                                 mainsp,kdiff_idens,kdiff_itemp))\n",
    "            for isp in range(nspec):\n",
    "                of.write('{:4d}{:4d}\\n'.format(int(equidt['iatm'][isp]),int(equidt['iazi'][isp])) )\n",
    "            profiles=['psipro','tbne','tbte']\n",
    "            for profile in profiles:\n",
    "                of.write('{:<10s}{:4d}\\n'.format(profile, nprodt ))\n",
    "                of.write(print_vector(5,'%16.9e',equidt[profile]))\n",
    "#write ion densities\n",
    "            for isp in range(nspec):\n",
    "#                of.write('{:<10s}\\n{:4d}\\n'.format('ion_conc',equidt['aconc'][isp]) )\n",
    "#                of.write('{:<10s}\\n{:16.9e}\\n'.format('ion_conc',equidt['tbni'][0,isp]/equidt['tbne'][0]) )\n",
    "                if kdiff_idens==0:\n",
    "                    of.write('{:<10s}\\n'.format('ni_conc'+str(isp)))\n",
    "                    of.write('%16.9e \\n' % equidt['tbni'][isp]) \n",
    "                else:\n",
    "                    of.write('{:<10s}\\n'.format('tbni'+str(isp)))\n",
    "                    of.write(print_vector(5,'%16.9e',equidt['tbni'][:,isp]))\n",
    "                if kdiff_itemp==0 and isp==0:\n",
    "                    of.write('{:<10s}\\n'.format('ion_temp') )\n",
    "                    of.write(print_vector(5,'%16.9e',equidt['tbi_provv'])) \n",
    "#write ion temperatures\n",
    "                if kdiff_itemp==1:\n",
    "                    of.write('{:<10s}\\n'.format('ion_temp'+str(isp)) )\n",
    "                    of.write(print_vector(5,'%16.9e',equidt['tbi_provv'][:,isp])) \n",
    "\n",
    "\n",
    "                \n",
    "                \n",
    "def read_equidt(filename):\n",
    "    import fortranformat as ff\n",
    "    equidt={}\n",
    "    with open(filename,'r') as of:\n",
    "        line = of.readline()\n",
    "        reader = ff.FortranRecordReader('(A10,5i4)')\n",
    "        var_name, nprodt, nspec, mainsp,kdiff_idens, kdiff_itemp = reader.read(line)\n",
    "        \n",
    "            \n",
    "        line = of.readline()\n",
    "        reader = ff.FortranRecordReader('(A10,5i4)')\n",
    "\n",
    "        if var_name=='Rfxqlo_Pro':\n",
    "        #  DMC -- define flag to indicate TRANSP format variant\n",
    "            print(' Detected: TRANSP \"Rfxqlo_Pro\" file format variant!')\n",
    "            kdiff_itemp = 1\n",
    "            kdiff_idens = 1\n",
    "            rfxqlo_pro_variant = True\n",
    "        else:\n",
    "            rfxqlo_pro_variant = False\n",
    "\n",
    "        if kdiff_itemp==0:\n",
    "            nsptmp = 1\n",
    "        else:\n",
    "            nsptmp = 10 # place holder nspec\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e05322e",
   "metadata": {},
   "source": [
    "## Process magnetic equilibrium\n",
    "There are analytic equilibria but here we will process and viz an g-eqdsk file."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d8437c3",
   "metadata": {},
   "source": [
    "### Generate toric magnetic equilibrium \n",
    "Plot equilibrium and replace limiter. Then convert to fourier representation used by TORIC with geqxpl utility.\n",
    "\n",
    "Below we see that the original first time slice limiter is wrong. Toric input routines will flag this and exit.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "78adf96b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jcwright/SPARC/TSC-V2h01\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "765db3a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "!cp /home/software/psfc/local/share/equigs_gen.ind $workdir\n",
    "equigs_file=eqdsk_file+'.equigs'\n",
    "current_dir=os.getcwd()\n",
    "os.chdir(workdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b78e6c3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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+iS+urNSpLNQHK6ktoQ6DuFBxEKWqwajtLU+Ya5XnmbRmErXthtiXB7/Mp2M/1Sp9cGpuZCurNj4cD43sip+rE9/vM15KYAxvjJqJp0241rHFpxabfX2IwpU7gyNYlRJPQY1uCUULutg6c0eXaNZmnCKv1rw0wDD3fszxn8LR4rP8nLnJrGsiHYdTr6ohrdr4NHlLE0YQmpixfppOsfb7I9/nnRHv6NTJtHQ+ltcYH2GkEglzh/cmOauAY5durIRREATeH/VvrWMrE1ZSUW9eohHgyV4DUKqa+Dre+CjzSOgwAD5L2WP2ve/wHsVYj8FszN3NwcKTJu39baOwkthxpdK4v3RLE6ZBLKSkVjuU/8VtX/DioBf12jvaqglTUaO/tLIt7oiNwMPJjq93HLvhUWZW1F242bhr/q5qqOLHhB/Nvt7PwYmp3bqz+lKC0VHG28aJucED2Jp9nhOF6Qbt2kIQBO4PvIsIh2CWpv5Cdk2eUXupICPQLobUKuPL+FuaMCpRe9n5xtA3eLzf4wbt5TIZcpmUShN9TC22C8bEkZiZx77zHevh0dxDKmdhn4e1jv3n2OcdIuBjPfuhVKn4JtH4KDC/22C62Cp4LX4T1Y2mvxAAMomUp0Pux1Ii5/MrP6BUGc8bBdrGUK00Hh64pQnTFnN7zuW1oaZLgW0t5VTXm7c6mBwbSZCHM59sPkhdg/FIrSHM7z0fqdBaEZhWdoWvjpvfeejvoGBycAQ/X0gwmjKwlsl5t9dkcmvKeStxq9mkdJY7MT9oJmnVWfyRZ7zD0t82yuT9Og1hFo1eZFZtr7XcgjoTfUwtkEklvDh1ONnF5SzZqVuKaQ58HHx0aoNf3vMB+66YP2o9Gh1HfZOS5eeNTwe9Xfx5JGwYW7PPs8pES0pbxDn3pLeiO+uyt1PeaDio52jhgZ3Mxei9Og1hLCSmyzRBHc2tbzR/tOjXzY+pcd35Yf9pTtxAjgng0b7aS+xK1XkW/PYjOy6Y13Md5OTMHUHhrEo5R3Gt8Qj0gpDBjPIKY1HSTvaayDO1QBAE5vjfSX1TPRuydxm187EONXqvTkMYiQkNlRbIpBKzCpXa4rnJQwl0d+b5ldu0mvrNxRD/IXR3797miIiFdQJPb9zGrkvmxXqe6NWfWmWjSV9GIkj4IOZOujv58Nzp9UYLxtvC18aTQW592V1wmCqlYVL2VIwz/vpmvdotAHPlNCSCoKmMMxc2lnI+f+AOVKLI/CXrySs1f2nc8mztR5mChkOEezrx9MZtHM8w3qkIEOzkwuTgCFamnKOg2vCKCdT+zJK4WXjbOPLo8V+4WK5fcKA9JnmPok5Vz96CIwZtutr1NnqPTkMYc0cYlSiarSLVFv5uCpYsmEJFTT1zv/yV9IKOVebNjpqNo2VrBruktphegbkEKJx4ZP1mnc4DfXg6ZgBNoopPzxr+D22BwtKWZf3vxVZmyYJjP5FdbTr5GWDrS5h9EHuuH73hUMJ/HWHqG5VY6qmfaWhoYMeOHaxbt45Ll/TXjXT382TFo9Oob2xizhdrOZOabfbz2cnteDxWe8n/3pG3eWF0JFYyGQ//tolyE8t9fwcF94b3Yu3l86QUmy5T8LZx4pv+s2lQKVlw7CfKGkxn4Ie7x5FbW8BVI7IhxiDcykrggrcgskD9e/0r9cilcpPXDH11KaN6BPPqXaM0x7Kzsxk7diwpKa3dgrGxsbz88svcfvvtOtNddlEZjyzfSE5xBW/OGM3EPtopAEMorikm4PMArZyXpdSS4QHjOZcBwa6uzIiJ0iq+ao9aZSOfnTuKi5UND/Xog7e9N2OCxmAntzN4zZniTB44uorezn580382Monhxr9qZQ33n3qB8V7DuC9gql4bQRDOiKLYR++5zkKYxlcbkUmMq5Mom1T0ee4LHhody6PjBgDqdtmRI0eyf/9+vdcMGTKERYsWERsbq3W8oqaOp7/fwqmr2Tw6rj/zR/czy4/6995/8/7h9814d+bDx96Hr8Z/xeQww1p4GzLP8Ur8JuZ3G8yTESON3u+dlK/Iqyvkq15v6H1PxgjTaaYkY9/KFhSUV6ISRU1TG8CmTZs0ZJFIJIwYMQK5vHWkOnjwIP369WPGjBmkprbGThxsrFi64E5u7xPO4u3HeGPtbpMqEgCvDX2NccHGVxodRU5lDlPWTmHuxrk0GYjWTvHvxVS/Xiy/cojTRRlG79dH0YP8ukJy68yvzmtBpyGMOUjLVzuqAW6tVXqffPKJ5vcnnniCvXv3kpWVxeOPP46sjWDQ2rVrCQ8P56mnnqKoSN2CaiGT8s6ssSwY048NJ5J5fPkmqgxIgLTASmbFH7P+YMXtK3C1Mdmr1yGsTFhpdPR6scc4fG0UvBa/mfomw8HLngp1p3JCWcc7KDrNlKR8VampezGEZbtO8NX2oxx57xHsrS1JTk6me3d1fMTCwoKMjAy8vb019levXuWll17it99+07qPg4MDL730Ek8++STWzV2XG04k8favewnwUPDlg5PwcdbfydAWDU0NHM8+zoGMAxRUqcssc8orOJCaTqSnOz29vYxen1qWx86rm4DWuJJUkHJg7gEG+Q3Se82R61eZf+wnno4YxYPd9NsALDzzKgG2vrwQtkDnnLEpCVEU//IP8B1wHUgycF5A3cB2FUgEYsy6rxcib6h/lE1K0RQWLF0vTv5gpebvZ599VkTdcStOnz7d4HXHjh0TBw4cqLFt+fH39xfXrVsnqlQqtd3FDHHAS4vFIS8vEY9fzjT5PIbw0tadYuh7n4oJOXkmbd85skaUvGWr+Rx4A7HLf7qIxTXFBq9ZeOwnsd/W98TyhhqDNl9eXined+JZsUnVpHMOOC0a+D/5p/RhxgHdmn/mA0tu0utq0Khs4lxaLn2D1Y1rjY2N/Phja6nBvHnzDF4bFxfHoUOH2LBhAyEhrbq+mZmZTJ8+nREjRpCQkEBcqD8/PzUThZ01C5b8zrLdJ4zKuhrCSyOH4mZny4tbd9JgQo7t5QF3MydGO+maVZHFg5sfNBhLeTx8BJXKelanGVaqCncIplJZTW6t8QLz9vin9GEmAauaCXwccBIEwfh43EHEZ+RS29BI/xC1kveOHTsoKFB/GF5eXowePdro9YIgMHnyZJKSkvj6669xcWlNwh04cICYmBgWLlyInaSJn5+aydheIXy17SgLlv7O9XLjkdn2sLey5O1xo7haVMKXh00nEb8b/xy9u2gnODdc3MDS00v12oc7ejHYPZhf0k/SYKBfKsxBLed2sVJXZdQY/imn1wdoGx/Pbj6mg7b6MB15gcMXMpBJJcR2Uxdnf//995pzc+bM0XJwjcHCwoKFCxdy5coVnnzySaTNYkYqlYqlS5fSrVs3vl+xjHdnjuGNu0eTmJnHtEU/ciCpYzU1w4IDmRoVyfJjp0nIMV7cJAgCB+/9GVfbIK3jT+98msSCRL3X3NO1H0X1VezN06/S4G3ljr3Mlku3KGHMhthGH6Yj1x26kE6vQB9sreQUFhayZcsWzTlj05EhKBQKPvvsMxITExkzZozmeFlZGY8//jiDBg0i2EbF2mfuwdPJnie+3czHm/40a+ndgn+PGoqnvR3PbdlBdYPx4msbCysOzNmCTGKlOVbfVM+M32ZQ3aBbRzPQPQhva0d+NyBOJAgCIfaBt+wIkwO07cvwbT52U5BfWsnVvGIGRwQAsGbNGpTNvkFcXByhocZT9sYQERHBjh072Lx5M8HBwZrjJ06coHfv3iz7dBHLF0xixqBoVh04y4OLf6OownjvdAvsrSz58PaxZJaU8fYu/YHFtoh0D+frCdru34WiCzy14ykdW4kg4fYu0RwvTKewTn8NTJh9ELm1BUZrZHTua7blX8NmYI6gRhxQLoqi8XG4Azh8QV3nOigsAIBVq1Zpzt13331/+f6CIHD77beTlJTEG2+8oQn8KZVKPvjgA/r17csYfwc+uHccF7KvM+M/v5CSZZ4z2c+/C48M7MfviSn8lmC6ue6hmLlMCZ+hdWzFuRWsTdJVYRjv0x0VIrty9QsoRjiovwAXKsxvt/mn9GG2AWmol9XLgUduxuu24PDFDLwU9gR5unDhwgVOn1a7P3K5nLvuuuumvY6lpSWvv/468fHxDB48WHP80qVL9O/fn3Pb1/P9Y9OQSgTmfvUr+82sFX5scBz9A7rw+o59xJvwZwBWTV6Ot32A1rH5W+eTXqpdIB7s4E5XO1eDfkyQnT+WEjlJ5eZvrvFP6cOIoig+KopikCiKPURR7JBDawyNTU2cuJzFgFB/BEHQWkpPnDgRZ2ddFYe/ivDwcA4cOMCyZcuws1MnBZVKJa+88goLZk3nvcn9CfZ04anvN7PmsPG9AUDd9vLZ5Al4OdixcN1mrpUaL+Kyk9uxdebvSIRWR76ivoKZ62fS2C7CO9IrjNPFGZQ36PZqWUhkRDh0I74DEd9bzuntKBLS86iub2BQeCAqlYqffmoVQ5wzZ87f9roSiYSHHnqIhIQEBgwYoDl+5MgRRg4ZyNQAG4ZGduW99ftZvN10/YnCxppld01GqWrigTUbTG7508urF68Ne1fr2ImcE7y6/1WtY8M8Q2kSRY5c1z/txCgiyau7bnY8ptMT5silDGQS9XL6zz//JCtLvXp3cXFh3LibmwTUh65du/Lnn3/y9ttva5bg5eXlTLtzCs5Z8UzuG843u06waOOfJknT1cWZZXdNpqCyigfWbKCq3nje6rXBzxHuPlDr2IdHPmRXamvdbg+FD05yaw4V6BeajnVWdwocLTYs9doWnZ4wxy9do4e/J/bWllrT0YwZM7Sy0n8nZDIZr7zyCkePHsXPr3UntDfffIOEtUuY2rsbPx08x3vr95skTS9fb76cOpHLhUU8vG4z9UYiwYIgsHXGGmRS7bzWnA1zNLkrqSBhkHswh65f1dtn7WrpTLh9EH8WnjSrCq9TE6aipo6U7ALiQvyoqalh3bp1mnN/53RkCLGxsZw+fZoRI0Zojm3evJlf3n6GiWEerD2SwIcbDpj8jxkaFMiHt4/l5LVsnt+8w2iNcleFL/P6vAVtyj8KqguYs3GOhiCDPbpR2lDDeQObeI3wGEBubQFJFf8Fch/GcCYtB1GEvt26sGnTJqqq1CH6kJAQ+vbt+//yTG5ubuzcuZNnn31Wc+zy5cssf+VJhnlb8cuheD7betgkaW6PDOOFkUPYfvEK/zlgvMb3rcHzcLIfpXVsV+ouPj76MQCD3bshFQT25+svTR3o0hsHmR0bc3abfH+dmzCpOchlUnr4efLzz606+nPmzOnwpp03EzKZjEWLFrFmzRqsrNSR2aKiIla8/gw95FV8v+80y3ebbpC/PzaGu3v2YNmxU2xLMaxf52lrz4yohdhaaacOXt73MieyT+Aot6aPSwB78i7oJaqlVM4d3qOIL0shudz4phqdmjDx6blE+LpTV1PNrl2tjt6sWbP+H5+qFXfffTf79u3TJDJra2tZ+8mbBDbk89X2o/x80LiunCAIvDp2ODG+Xry8bTeZJYaX27MjYnB0uhs7eas/o1Qpmbl+JuV15YzxjiC9qpiLFfpbUsZ7DcNF7sT3Gev0nm9BpyVMg1LJhezrRAd6s2XLFhob1fGHmJgYAgM7vo/034X+/ftz9OhRunbtCkBTUxNblizCozSNDzccYOOJZKPXy6VSPp08AalEwnNbdhgsp+jv5Uegkz+9/B7SOp5els6CrQsY4xWOTJCwJUt/stJSKmde4HTSq413SnRawlzMKaSxqYkofy/Wr1+vOT5tmvFNLv8/EBISwpEjRzTVf6IosmflVzjkpfDG2t3sPGdcLtXLwZ7Xx44gPiePH0/rDwRKBIGpwZFcq3Xn/l7aVXRrk9eyIWUtwz1D2ZQVT52B8s3+Lr0Y7hZn9Fk6LWGSr6mXjV2d7dixo1WbbepU/a0TN4LSihrOX87l0JlUDpy8wrH4dC6mFVBlhv5Me3h6enLgwAF6927tLDyydgU2+Rd56acdHEoxrvsyMSKUIUEBfH7wqN5NvwCmdotEBLr7zCTaI1rr3OPbH6efoyNlDbVszjIs4PhYN+Ory05b0/vvn7Zz4koW8yMcmTFDnYzr3r07588b3gXEFJ7rLXsAACAASURBVERR5GxKFruPXeJEQgb5RYZbZn3cHYkK9SEuOoABvbpiZ2PeTinl5eXcdtttHD+uLpwSBIF+sxbS5B3GsoVT6RnobfDaa6VljFu2iokRoXx4+1i9NtO3rqaotpolwwbRe3lvrX25A50CiYp4jBokbB35GHIDbTvGanpv+W2IDSExM58e/p5s3tyapb3R0UUURfafvMK3vx0lLbsYGysLYqMCmD62F/7ezjg72mAhk1Jb30hxWTXX8kq4kFbA0XNpbD+UgoVMyuDeQUwZFU3vyC5GV2iOjo5s376dkSNHcvbsWURR5NSab4iZ9RiPr9jEqifvJtBdf/7LT+HEnD49+e7EGeb1iyHM3U3H5q6QHjx7cDuVTXYsHr+YeZtaa4HSy9JxSF9DvdtoVqef4r6g/h3+rDrlCFNcWcPw177hiXH9eXHWRIqL1X3LZ86cISYmpkOvkV9UwXvf7ORU0jUCfVyYfUdfRsSFYCU3LS/SpFKRfDWPvccusePwBSqq6ujm78bcKXEM69sNicQwcYqKihg2bBjJyWqn19rGhvAZT+IRGMwvT8/EyVb/HlFltXWM/Po7Yv18WDJ9ks75msYGYn9Zwki/ID4bNoGFfyzkmzPfaNn4uHTHw28K20Y9g4eNbvfDf0XnY1vC7Dt/lae+28Kzg4O5b+odAHh4eJCbm4tEYr5bdjIxg1e++IOmpiYemTmEyaOikHbg+raob1Cy68gFftp6imu5pYQFevDknGH0DPM1eE1+fj79+/cnIyMDAGcXV7ynPEJszx588/CdyKT6n2Xx4eN8fvAYG++/hwhPd53zrx/dy88X4zk6YwEKS0tG/ziaPzP/1HMnAVsLG2wsbIj1iWXpxKX4Ovj+d3Q+tkVLwO5KfGtV/Lhx4zpElp2HL/Cvjzbg5mzHD+/fy9QxPW+YLACWchm3D+/BL4vm8urC2yitqGHhm2t5e8kOyg0ok3t6erJ9+3YUCnXjXUlxEWV7fuJ48hU+22p4m705fXphb2nJ10f0q2/OjYxBqVLxQ/JZLKQWrL9rPT3ce+ixFKlurKawppA/rvzBqFWjKKoxvo9kJyVMNj38PdnZZnXUkcz0riMXePPrbUSH+rDsjRl08VSYvshMSCUSxg+JZM0n85gzKZadRy5wz3MrOXpOf+1sWFgYmzZt0iRKszPSEE9vZeX+U+w3UFhub2XJ7N7R7L50ldQi3WaNQEcF4wNDWZlyjtK6WlxsXNg7Z68B0rTiUvElxv883qhNpyNMVV09F3MKCXW11VTWSaVSk20kLTiddI23luygZ5gvnzw/BVszVzcdhZWlBQtnDOb7d+/Byd6aZz7awBc/HkCp1C0SHzx4MN9++63m7wunjiJePMZrq3eRX6a/3va+vr2wlMlYflx/79FTMQOoUTbyZbxau8/N1o29c/ZyZ/idKKwUBpUwTuUa7mWCTkiY+PQ8VKJIQ06qJi/Sv39/zbBuDHmF5bz8+Rb8PBV89OxkrCzN0837K+jm786379zD1DE9Wb3tDI+/u46Sct3iqNmzZ/PUU63F3Mk713P9ciKv/rJT74ZgzrY2TO/Znc1JF8kt113+hyhcmd6tOyuTz3G1TL0ocLN1Y/1d6yl5oYT6V+ppfLWRtfcextbO/Mh4pyPMufQcpBKBaxdb4y2jRo0ycoUaSmUTr3y+FZVK5MNnJ5kdN7kZsJTLeHbeSN54bDwpqQU8+OrPZOToKlItWrSI4cOHa/7O37OWw/HJrDZQ5vlAv94IwLJj+iten+87GBsLC14+sltv0lEmkXFX14FMjzGsfdwenY8wabmE+bhz5HCrUzh0qOmNy1duPEFKaj4vPjT6pvosHcHYgeEsef1u6huUzH99DYmXtetTZDIZa9euxctL3RRaUVZK9ZGNfLb1INl6xBq9HR24MyqSdQlJ5FXoTl2u1rb8O3Yox/OyWHPJcEDz4bBhZr+HTkWYxqYmkq7l09XJkosX1ZXwcrmcfv2Mb16enl3MDxtPMGZgGCPjbrxH6WYgIsiT5W/Nwsnemiff+43TSdpSr25ubvz444+a4F9WSjzXT//JO7/t0ztKPDwwFkSRrwy03N4dGkWcVxfeO3nAoNiiQm56m+cWdCrCXMktoq5RCUWtGdXY2FiNJIc+iKLIJ9/vxdpKzlNzhhu0+yfh7e7I0tfvxtvdiWcXbeBMsjZpRo4cyQsvvKD5O+/IH+w/dood53Qr4nwcHZgZE8X6hGS9wosSQeDDQWOpb2ri1aOmC6RMoVMRJj49F4DcK60lAaamo8NnUjmTksX8uwagcLD5W5+vI3B2suWrV6bj4+7Ic4s2knxVux/prbfe0kStGxsaKDn4Ox9v2k9NvW5L7SOD4rCVy3l/70GdcwABjgqejhnAzsyr7M68sT2iWtCpCJOYmY+bgy1nTrQOv8YIo1KJLF17GD8vBZNHmNbR/6ehcLDh839Pw9nRhmc+/J2svFbpVAsLC7777juNiEBRxmUuHNzF8j26lXrONtY8NjiOQ2kZBiXrH+zRhxCFK28e30e9ka2NTeFmdT7eJgjCJUEQrgqCoLM3jSAIcwVBKBQEIb7558EbeZ3EzDzCPBxJSlK3lEqlUvr3N5xA23/yMmnZxTwwtT8ymXH1KlMQRZHMjCL27Erixx8O8fWXu/nq8118t/wAmzacISE+k9oa4w31+uCqsOPTl6YiCAL/+uh3rahwdHQ0L77Y+nEWH9/Bd1v3k12su9HW7N7RBLk48+7uP/V2GlhIpLzWbzhZleX8mGK6uc4Q/nK2WhAEKbAYGI1axuOUIAibRVFs39C7VhTFx270dUqqasguLqeHbaPG+YuIiNB0HraHKIr8uPkkfl4KRva/cUc361oxWzef488DFygqbF2J2NjIkUgk1NTWo2pSP49UKiGyhy9Dh4UxfGQkDg6Gfau26OKp4INnJvH4O+t47Ys/+M+Ld2rSFK+88grr16/nwoULNNTVknNoK59vjWHRfRO07mEhlfLKmGHMW/073504y8KBsTqvM9g3gAHefixNPMHs8GisZB2PQ92M8oZY4KooimkAgiCsQS0gpL8D/AaR3Nzc3lDYuhTt08ewIsi5C9lcSr/Oiw+OvqEcUUF+OSu+2c+B/SlIpRL6xQVz37zBhEf44O2jQC5Xf3QqlUhxUSXpaYUkJl7j+NErfPnZLpYt2cfYcVHMuGcA7u7699Fui+hQH56dN4L3l+9mxW/HWHCXukHN0tKSzz//XCM5Upx8kg07djN7aAzRAdqaTAMD/RkdEsw3R08yNToCdz1fpsd6xjFr269sSr3I3aHGUwX6cDOmJHPFgqYKgpAoCMJvgiB00XMeMCwodDFbLRGam9a6UmhbvdYe63aew9HOirGDzRNlboEoimzeeIYH7lvG0SOXmTFrAKt/e5w3353GuAk9CQh005AFQCIRcHN3IDYuiAfnD2fFD/NZuuIBRoyKZNvWeO67ZwnfLjtAXZ3pLXnuGBHFhKGRrNx4nFNJrUrdo0ePZtKk1lKGwkOb+WST/k7K50cMpqGpicWH9CcmB3j5EaJwZfVF87ZNbo9/yundAgSIohgF7AZWGjI0JCh0Iec6fq5OJMS3zr+GRpii0ioOnb7KxGHdzapraUFDvZIP3t3MF5/uJLKHL9/9uIAH5g9DoTA/TgEQ3M2DZ56fwMpfFjJ0aDirfz7KQ/OWcz7R9CYVz84biZ+XM299vYOKqlap+U8++USToKzISefg/r0cSNZNaPo7O3FXzx6sS0giR0/KQBAEpnaL5FxhHtcqO75zy80gjEmxIFEUi0VRbCmEXQEY3zJDD1LzSvBT2HD5snqEkUqlREXpX/nsOJRCk0rkjhHmD7m1tQ38+4W17N2dzLwHh/L+RzPw8DAtrWoMHh6OvPjKHXzy+T0APPPkT6z+2XhjvpWlBW88Op7Siho+XdUqMhQUFMTChQs1f5ed2s0XfxzWm2daMKAvAvDtcf07xN7m3w2A/VkdU5+Cm0OYU0A3QRACBUGQAzNQCwhp0E4A8Q6gw4rCWcVlyKuKNB92ZGSk3oCdKIpsP5RCjxBv/LzMk/poaFDy2su/kZhwjRdfvoN77h1otFquo4ju6c833z7AkGFhfLvsAO+9vYkGI1sGhnX14N47YtlxKIXjCRma4y+88IKmMa48N5NzRw6yJ1G38czLwZ4JkaH8npist6E/wFGBr50Dx3JNj3jt8ZcJI4qiEngM2ImaCL+KopgsCMJbgiDc0Wz2hCAIyYIgJABPAHM7+joqUaS2jcNrqBQzNauItOxixg40z3cRRZHPPtnOuTMZPPvCREaN6W76ohuAjY0lL782mfsfGsb+vSm88uKv1NYaXobPm9IPPy8FH3+/l/pmcnl5efHII61aTGVn97F89wm9I9Y9MdHUNDbyR4r+fukYd28Si8zbZ6ktbpag0DZRFEOaBYPebT72miiKm5t/f0kUxUhRFKNFURwuiqJ+SSQTKM1rJUxLj0977D9xGYkgMDy2m1n33LLpLLt2nGfOvMGMua3jq4aOQBAEZs0ewPMvTST+XCavvrSOegMbmsotZDwzbyQ5BWWs2dY6tTz//PNYWqoz7eU5GZw9fYoTl3W3Hozy9iTQWcHWZP0fdbiLOzlVFVQ2dKxlplNFenMzW3t3wsP1jyAHT6cSFeqDs5NpR/VaZhFLF++lb2xXZs8xLLN+szHmtihe+PftJMRn8u5bm2hq0t/NGNvDnyF9gli1+aSmhsbDw4N77rlHY1OZdJSfD+kG4gRB4LawbpzKyqFMzz5NgQ7qjH1GRccc305DGHsrSy5fav226CNMflEFV68VMrh3kM659lCpRD5ZtA1LKwuee3HiTfVZzMHI0d155PHRHD18mRXfGFbQXDhjMHV1jaza1LpMfvLJJzW/F1+MZ++Js3or84YGB6ISRY5n6I5AvvZqhz6nSjdqbAydhjCuVgKFhYUAWFtb4+/vr2PT4iAO6GW6gmzPrvMkn89mwcIROLsY3rzq78SUqX2ZNKU369aeYP8+/XHOAB8Xxg2JYMOeRIpK1eUJUVFRDBs2DACVqomS5FNsPa27jujh5YGNhQUnr+nqwrhbq0fgwhrzJGJb0GkIY9lml7OQkBC9HQInz2fi7myHv7fx1VF9fSPfrfiT0DAvxtx240nJupp6rl0t4HJiFpmX86muNL5Fnz4sfGwUEZE+fLpoG3l5+qeHuVPiUCqbtHyZ+fPna36vTU1g6yldwllIpfTw8iAhV1eZ07l5hVlSr7+jwRA6TeejpKZ16AwK0p1yRFHkXEoW/XsGmtSG2bYlnqLCSl789+0dnopy0gvZte4UJ/Ylk3lZV0jQ29+V3kNCGD65N2E9/Uw+i0wm5eXXJvPQ/Sv4+IOtLPr0Hp1n8vVwYkRcCBv3JjLvzv7YWsuZNGkSdnZ2VFVVUXE9l6TziVzNKyLYS3uPpghPd345m0CTSqWVIrGQSLGRWVBe3zGSdxrCKCtb2yn0yXlk5pZQVllLr3DDjWOgru39de0JekR1oWdMgNmvX3K9gu8+/IN9G88iSASi44IYPD4anwA3rGzl1NU0kH+thAvnMti17hRbfjxKeIw/c58bT1Q/4z6Vh6cjCx8dyScfbWPHtgTGT+ypYzNjfG/2HLvE9kPJTBvTCxsbG6ZOncrKleqgedmF0+xLStUhTIibK/XKJq6VlhPool2a6mhpRUUHV0mdhjDVxa0NVvoIk3hZXVzVI8RwMzvA4YOXKLxewRNP6W9m14cTe1P4+NnV1Nc2MvWhoUy5fwjORhKKNVV17Ntwhl+X7ueFmUsYMz2Wh1+bhLWt4cLzseOi2bk9kW+X7WfI0DDs7K20zkcEeRIa6M7GPYlMHd0TQRC45557NISpy7zAn0lpzB+tXa7atZkkGSWluoSRW3Z4hOk0PkxxQa7m9xZxnrZISc3H3tbSZHR36+ZzeHo50a9/sFE7jf2PR3hz/vd4dnHm623P8MCLE42SBcDGzoqJ9w5k2Z7nuevhEexZf4qn7vyCvGuG966WSAQefWIMFRW1rP7lmM55QRC4Y3gUqVlFXEpXJ2KHDRuGg4P6WapLizgTn0BFjTYB/BVOAGTqEYt2srKmtK5jPkynIUzh9daopL4V0qW0AkIDPYz6JPl5ZcSfy+S28VFm+S47155g8esbiB0RzqK1j+LbVVctwRisrOXMe348766aT8n1Cp6Z/hXXrhoWUO4W4snwkZFs+v00paW6q5dR/UOxkEnZeUS9IrKwsNBq4CtPT+FMmvaKSGFjja3cguwy3eWzi5UNxXWm97pui05DmOv5rYRpu28jqLcfTs0qIiRAtzG9Lf7cr/6gR442Hf5POpnGl6+up/eQUF5ePAcr6xvX/O05oBsf//ooAC/N/obrOYZ3sb/3vkHU1zeyYb1uB6KDnRX9ovzZf+KyJh0wfnxra2tVxkXOtSOMIAj4OjmSpYcwnrZ25FdXmaXP24JOQ5jaGvXQaWVlhaOjdhY5O7+URmUTwX7GR4BDhy4REuqFl5eTUbuqilo+fPpnPHyceemL2VjI/7qr59/Nk/dWzae+toE3F3xPvYH6mC5+LgwYGMKWTef0pg2GxYZQUFypmZbGjm31xWrzM4hP09Wo6+LkSGapLmF87RypUTZS1gE/ptMQpgVeXl46S9X0bLVvEOjjou8SAEpLq7mYksuAgaZzTCs/3k5JQQXPfzYLWzPLLM1BQKgXz392D2kpuSx/d7NBuylT+1BZUcvBA7p5oAG9AhEEONLc3O/j46OZopsaGzl7Ll5HODHA2YmssnLd445qJzir0vxob6cjTPvpCCCzudrez9twR+PZ0xkA9InVdZi17nUln22/HGPivQMJjfYzansjiB0ezp0PDOGPn4+ReEJ/hX90L3+8fRTs3K6reKlwsCE0wINT51sr8toWwpdcu8q1dl2Sgc7ONDY16RRUhSnUS/DUcsPOeHt0OsJ4eHjoHMvOL8XFyRYbK8N+RmJCJja2lnQL8TR6/zVf7cXSWs6sx81Tg7gR3Puv2/DwVbDkjY16E4+CIDBydCQJ8ZmUFOt2K/aO7EJKaj51DeopKy6uVfmyJi+TyzmFWvbBruqV49UibWJ42drjamXDxRLjmjBt0ekIo2//o7zCCrzdjVfHpSTnEBHpg9SAqhNAYV4ZB7clMH5WHI7OHSvL7AjUq6cJZFzK488t+ls+hgwNQxTh6BHdAqnoUB8alU1cSlOvuGJjWzsE6ovzuZqvTYxubuqp+mKBNjEEQaC3hzfnO1AX0+kIo0/Wo6CoAk9Xw7GRurpGMjOKCA83HtTbs/40qiYVE2cPMGp3MzB4fBQBIZ6s+0b/DicBgW54eDpySs+0FRmsLmBMSVX/R4eFhWnONZQVcjnnupa9naUl/gonUgq0jwP08+pCbtWtt+fjTUN7woiiSGFpFe7OhjPOGemFqFQiwd10p7O2OLDlHN1ju+LZxbDzfLMgkUiYPG8wGZfySG72r9pCEARiegcSH5+pM205O9niqrDjcqZ66lEoFLi7q0MKKmUjyZd022G7e3mQlKcbAxrorRvTMvrcHbK+BdCeMJXV9TQ0NuGiME4YAP9Aw8vu3Mwirl0pYNDfXHXXFkNv74mVjZx9G/UXa/eI6kJ1VT3XMnV9jKAuLqRltR5vO8qkp15B2Y5k3b08yK2o1NnpLVThipOldhrCGDodYdrHYEor1B+As6PhRvuc7FKkUglenobjL/FH1d/K3kP+OTkQKxtLYkdEcGxXEio9ewiER6in0EsXdcsT/L2dycor1UxnbTP4deUl5Jdqr4i6N6ttJuVrjzKCIBDjbnyqbotORxgbG21ilFWqA3qOdobjJfl5Zbh7OCCVGX67F85m4ORih4+RUejvQN9hYZQVV5GuhxTePgosLWWkpxXqnPPxcKK2vpHSCvX7bxEhAlDWVJJdok2YcA/1+7pYoHuvKDfjK8e26HSEad9aUlmtjlLaG8kEFxZW4GYiYZiWkktwd99/fJ+lHv3UcaHk07p7DUilEny7OJOVpRsn8XCxB+B6sdph9fRs/U9XVldS0K5k08HKCk97O67o0ZAJdzGeUmmLTkeYlr6cFtQ0t2rYGsn1lJbW4GxkmaxSqchOu46fCaf474C7twJHF1tSk3P1nvfyciJfTyWeS3ORe3GZOkmpRZiaCoordZOKgS4KMvRkrVsKws1BpydMbXNOxljQrqKi1qiSQllxFQ31Sjx9b/4e16YgCAJdgjzITtNd8gK4uNpTrCd452ivfj8t8iBt41NiQz3lNbr5IV8nR72Km3Kp+VIo/5Q+jKUgCGubz58QBCHgRl+rPWEaGtVNXnILw2+6proeGyNTVmmzjIepOpe/Cx4+CooM1PM6OtlQXVVPk1LbKW5RAa1u3oqn7eciiE1U1ek2ybnZ2lJSU2twky5z8JcJ00YfZhwQAcwUBCGindkDQKkoisHAp8CHHX6h5idtX/zd2PxBWhgQDGpSqmhqUhnV5K1udhztHG9eorEjcFDYUlGmvy7FppkYNbXapZQtIgN1zV2RLTvSAVRkp1HfqNuK62hthUoUqW7ouPBRC27GCKPRhxFFsQFo0Ydpi0m0Kjb8BowUOupdGijZaFmOGtpnQNmkVt42tkJqqG8epSz/fypWrWzk1NfqL3ewaB45lY3aCuIWze9H2fyFqazUdnLbx2EALJunnjoje2Gbwj+lD6Oxae7FLgf0hlMN6cMYIszNgCY0//+0E60gCIiiqDdF0PJIOqdMPKuo5wO7GW/vlnN6DenDGELLyKIv8AVoko2G2lEBTYFUoxFFhb8T9XUNyC1lepf0LXsTyNr5aJqRU6q+pn18Sp/qVl3zvSylNz6S/iP6MG1tBEGQAY6A+UUYbdD+WyhrGZoNEEIqlSCRCpppRx9s7NQOY80NNKLdDFSU1mDvpD9SXds8VVlZaftgLYoOcgv1f35bwtj7BGJloUuK8to6BNRtxzeKf0Qfpvnv+5p/nwbsE29wZ6+Gdg5bywemz8kD9XBvY21JjRGFS4WrOghWWmR+1vZmoiivDFdP/eUZ5eU1WFvLtWTSQDf+VFfXhuwSKbZ6wgyF1dW42tog+Qtz0z+lD/Mt4CIIwlXgX4DO0ttc1NZqt0VYN69+ao1oyNk7WFFRYbidwtndHqlMQkGW7t5D/wSy0wrx8nfVe66kuEpv0LG8eTTUxGPK25RZyuQ42ugmFLPLyvFy1A0d6JNpNYSbsiwQRXEbsK3dsdfa/F4HTL8Zr9WeMDbNQ3WNEXEeJydbvW0bLZDKpHj5uZCVqj949neirLiKwrwygiL0JwDz88px1zP6tER4W5Ku+W26KmQ29jjb6U5xacWl9PPX7Qy9+t9cotmeMHa26m9SpZG9pF3d7CgqNLylMEDXcG+uJuuqHPzdaMkhhfUK0DkniiJZWcX46olAFzTnkNyd1dNpQUFrFlpmY4+nk72WfXltHQWVVYS46Y5kyUXmf1E6PWEcmx3W8krDU46npxMF+RV6BQRbENbLn+s5pRQaiLj+XTj950Ws7SwJjdZVor1+vYLqqnoC9GTQ8wrLsZBJcWlW+NQaYWzt8XLWJsyF5ix1S9a6LRIK/4tLNNsHqBTNQ3JLXYw++PgqaGhQUmhklIlubp09d1i/JtzfAWVjE8d2J9F3aJje3qeWOpiQUN3yg2t5Jfh4OGqWz+nprdluuZ0TPs7avkpycx1MpId2ZrpJpSKhSLe0whA6HWFKSrQdU0c7a6RSCUVGfBS/ZocyM91wdXxgmBduXk4c3ZV0cx7UDJzcf4Hy4mqGT9Yv8Jh0Pgu5XEZQsG4WPTWrmEDf1umlZf8ogC4Bgchl2gQ8n1eAj6MDzrbavs35ogKqOpAq6HSEKS3VbjOVSARcnWwpLNW/eRRAYHNP9FUjfc2CIDB4QjRnDl6iTE92+O/Aph8O4eblRN+hYXrPnz2dQWR3H50ldVVNPTkFZXRr7vSsqakhM7O5T0kQiAzTvV9y/nUiPHTrXo7m6cqZGUOnJwyAh6s9BUWGpxt7e2u8fRRcvqi/5qQFY6b3RdnYxK5fdbeYudk4fzKVxOOpTLl/CFI9idOCgnIy0gvpE6urLXMhTe1zhAepp6oWsWsAS0cXunXRHpGq6hvILC0jUs+m6MfzsghwMN463BadjjDtpyQAbzdH8gqNt3tGRPiQnJxjtPHcv5snvQaFsPH7Q9QZWab/VTQ1qVj+7hZcPB0Zf4/+7XsOH7wEwMDBITrnEi/lIggQGawmzJkzrUXkcmcPgj2103RXCtVTcai79gpJJYqcu55LDzfzC8c6HWFahBHbwsfDiYLiSk24XB+ievpRWlLNtUzjMYdZj42itKiSTd8f+svPaghbVh3hyvlsHnhhApZW+ssu9u5OJribh94l9bmULIL93LBvDikcP9664ZiNpz8h3torodRi9Zcs2FWbSNcqyqhoqCdUYX4dc6cjTF6erkffxUuBKEJ2geElca/eAQCcPmVcX797bFf6j+nO6sV7yNdTS/tXkXYhl+8/+oPY4eEMu6OXXpvUqwVcvpTH6LG6LS81dQ0kXs6lb4/WfqK2hLH3DSTIU5tk6SWlWEgk+Dhpr5wulapHniBH8ysNOx1hcnN1/ZAW1YYWFQd98PJyws/fheNHdVtP2+PhVychkUpY9K/VOnUofwWlhZW8teB77J1sePrDuwwWnG/acAa5XKaXMKfOZ9KobKJ/tFq2rby8nORk9R6YgiChR1QvnRVSdmk5Pk4OyNplsNPL1SNPF/v/Qh9GbqlOplVWVurEYvy9nZFKBFKzjDeVDxocSkLCNcrKjGvTuvsoePztqaScyWDJmxs7JLhjCOUl1bwydzllxVW8unQuTq72eu1KiqvYves8o8Z011uHfODUVextLekZpi452rNnj+b5bDx86BUSoHNNVnkFPnpySDnVlTjILbGTHd2IHAAAIABJREFUmy+W1GkI4+nZ2nfTflqylMvw83LmcobxEPewERGomkQO7DO9mcrwSTFMXzCcbb8cY8X7W/8SaXIzi3ju7sVkp13n1aVzjcqI/LrmOE1KFXfNjNM5V9+g5NDpqwzpE6zZw3L79u2a8zZ+YfQK1M1J5RogTFFtNW7WHRMd6DSEcWnjyWdl6W7bEtrVg0vpBUb/Y7sGuRPczYPtf8SbRYB5z4/n9nsH8PuKP3n/8R81tb/mQhRF9m08yxN3fEZZcRXvrHyI3oMNd1YWFJSzaeMZRo3prtfZPXw2leraBsYMCNPcf9u21pyvfUA4fYK0k4u1jY2U1NTi46ibwCyrr+tQmyx0IsI4e7SGx9uGwVsQEeRJcVm1JilnCBNu70Xq1eukmJFoFASBhW9M4YEXJ3BkZxILbvuYPb+fpklp3K8RRZHzJ1N58Z6lLPrXL3QJ9uDLzU/RQ09MpS2WL92HgMB99w/Re37L/vO4O9vRu7t6hDp79qxmtLW0tSMiKhpPhfZU1yKG6OukO8JU1Nfj0EHCdBqdXgfX1qBTWpruSqdHN/VQfP5yrlHpj1Gju/PtsgOsW3OcyHemmXxdQRCYNn84PfoF8dWr6/nk2TX88PF2Bt0WRWSfAHwD3bC2s1QLO2eVcOFsJsd2J5GVeh0nFzsefXMK42b1N6pLA+rV24F9F7h37iC9O8Fl5ZdyIjGTB6f21+SPfv75Z815W/9whkTqEjKjRL1y9FPoOrbVjQ10tTC/iQ06EWEsHFtjCPoIE+zvho2VBecuZDN6gP5QO4C1jZxJd/bm51VHSEu9Ttcg89pEQ6P9+Hzjk5zcf4Htq4+z7ZdjbPpBN1YjkUqI7BPAnQ8OZdgdvcxS36yqquOTj7bRxc+ZmbP0a9P8uv0sMqmESSPVeyMolUpWr16tOe8Q0ovh3XUJ0xKD6eqsS4yqxgZsLTqmDtppCINN67dOH2FkUgnRYb6cSTadG5k6PZYN60/z3fIDvPPBXWY/gkQiIW5kJHEjI2mobyTjUj4F2SXU1TRgaW2Bm5cTgWFeWNmYXzPbsiNccXElXyy+T2+rS1lFDVv/TGLMwHBcm2VN9u3bpylpsHFU4BcZTa+uug7vpetF+Dg66K3j/a8mTJ28Vf/l8mW1Tm37OEbf7n4ci08n34QilYODNbNmD2DFN/s5dTKNviaEEvVBbmlBSFQXQqIM7qhsFjasP8WBfRd4YP4wwgwoZK3edob6BiX33tFXc2zFihWa3+2Co5nQO0Jvp0BCbp5G6qMtGpqaqFU24mDZsYLwTuP0FjcKGpn08vJyrYKhFsQ1B7OOxes6xe1x57S++HZx5vP/7DC69+LfiRPHr7L0670MGBTC3TP155RKyqpZt+Mco+JCCWgOUGZlZfH7779rbBzD+zApNlLn2vyKSrLLKujlq0vEFsl4Z8uOdXt2GsKU19QTGta6C1tKiu7+QAE+zni7O3L4jH4507aQy2X867nxFOSXsXTxnpv6rOYgOSmbt1/fQNeu7rz4suFteL5df4yGRiUPTm/1bRYvXkxTc1+SS9cw+sTEEOqjmw86nK4ueRgQqBv3ya9RryY9bDu2uVinIQxAl8DWqUMfYQRBYHDvIE4lXdM0qRtDVLQfd83szx9b4tm98/xNfVZjOJ+YxUvPrcHF1Z73P7pb0z/dHlevFbJxbyJTRkVrNt2orq5m+fLlGhu7yAHMGqy7XQ7ArktX8XawJ1RPHe+1iubltl3H9ubuVIRx9m71F1ryJ+0xIi6ERmUTh8wYZQDmPTCE6J5+/GfRNhITOlZMdCM4cvgyLz67GmcXOz757B4UBsQcVSqRT77fi52tJQ9Oax1dlixZoinxsHNxJ7hXLGN76pZAFFVVczgtkwkRoXpzVlfKipAIAl0dO7as7jSEEQSwcmkN3iUkJOi16x7sjaerPbuOmrfTsUwm5bW37sTLy4lXXvyVpPMd3/zbHKhUIj+tOswbr/xGQKAbn355L65u+vNJoA7SxV/M4dFZQzS9R1VVVXz4YavwhUP0EO4fGatXuWJdQhJKlYqp0bq+DUBiUQFBjs5YyQyrWujDXyKMIAjOgiDsFgThSvO/eukqCEKTIAjxzT+GRfaNwEfhiMqhNT0QHx+vJXHRAolEYMzAcE4kZGg21TSF/2vvvMOiurY2/jv0XpQmHbGADWyxC3ax90hiiYnmM6bcJKbe3BRTTDfdNKMmscTeu7GAXWyAoogKSO8gZQaG2d8fQxuZqiZXcn2fhwfOPvucfWZYZ++1V3mXo6MNnyx6BOdmdrw6fzXRUXdUVlsrsrOKeXX+Kpb/EsXAwe1Z9PU0nJ21+3Cy8kr4ZuVhurTzYXR4feWVb7/9lrw8lYPV1tmFtr0HMKFnY4+2rErB7zHn6RvgR8vmjV0MVcpqYrLS6O5xO2eCftztDPMa8KcQojXwJ9ozGiuEEKE1P2O09NGJwBbNSC+rwtdXpcDJZDKNegzAqLAOKIVgx2HNy5YmuLja8+W30wkIdGPBmxtZ/O0+ZDqyKQ1BpVzBH6uO88RjP5GQkMGLL4/gtTfGYKmDq6ZaqeT973ejVAr+/eTQuuUkPz+fTz75pK6fU9eBPBXRFysN2Qarz8WSV1bO3N4PNToHcDLzJreqKgn3Nt6ccLcC05D35Vdg3F3eTyvaerqSnFNI5y71EfYxMTEa+/q0cKZrOx82/xlrFNuSs7Mti76cxtjxXdm47jSzZ/7E/r3xjdif9KG0VMb6taeY8ej3LPnxIJ27+PPL8icZMSpUL+nib5tPcebSTV58bCBe7vXm/LfeeqsuntmmmRuhYUMY36Nx3afiChnfHz1Jb39fHtKQ5QiwKekSduYWhHn7G/W54O4Fxl0IURtrkAVoCw61quF8OSFJkk6h0sYPE+TthlIIfFrVm/21CQzAxKGhZOWVEB1jmPJbCwtLM559fhiff/UotnaWfPTBVqZFLmbpz4e4FJ9GlYaAKiEE2dnF7N8bz3vvbOLhCV/zw3f78fR04rMvHuG9hZNx15Js3xCn4lJYsv4YQ/sEMTKsXveIjY3lhx9+qDt27TOK+WMHYKbBP/VN9HFKZHJeHaTZgZlfUc6261cYFxhstP4CBlh6JUnaD2gicn2j4YEQQkiSpC1mwE8IkS5JUkvggCRJcUIIjf9JIcRPwE8Akmf9/dp7qx7BxqN+p6RLYPp3b4WnmyMrt58mrHsro+lUQ0L9+P7nJzh+7CpbN5/hj1XHWbXiGKamJrh7OOLoaIOJqUR5mbwuQxHAydmGocM7ETEyhDZtW+gZpR5p2UW8+fV2/L2a8ersIXXPK4Tgueeeq+O/cQoIZsCQYYR3aLycXM7JZeWZCzwc2lFjhiPAkvgYKqsVzOrQ1ajvoxZ6BUYIMVjbOUmSsiVJaiGEyJQkqQWgMYJJCJFe8/u6JEmHgM6AUa++u5Mdbo52VFjX7yxiY2OprKzEQkPEmKmJCY+O6sanS/8k5mIq3TsYx6kPKgW6T9829OnbhuLici6cTyXxSibZWcWUFFegVCpx9HSmU4gvvn4utGvvRWAr3XUnNaG4tIKXPtkEwEfzx6oxgi5ZsoTDhw/XPI8Jbv1G88r48EYvgFII3tl9AAcrK14I76NxnIzSEpbGn2FMYDCtnO6snsLd+pJqeV8+qvm95fYONTunciGEXJIkF6AP8Mnt/QxBJz8PEtJyaNmyJdevX6eyspKYmBh699bs4R0Z1oHlm0/y09qjdGuvv+i4Ljg62tA/LIj+WpLO7hQyeRWvfLaZjJxivvr3JHw86jeaaWlpvPTSS3XHLl3CmRoxmGDvxr6hzXGXOJuWwYcjh+JkrTnG5b2TBxHAy9363fHz3q0O8xEwRJKkq8DgmmMkSeomSVKtdywYiJEk6QJwEPhICKF5e6MHIf6epBeU0KNXvYDUvn2aYGlhxuyJvYi/msmhU/qDv/9uyCsVvLZoK3GJGbz9dIRakXYhBHPnzqWkRJWg5+jWAv+wUTw3svHLcUsm59MDR+js1YLxnW4nMFVhT/JVdt5I5NnQnvjYG2fdbYi7EhghRL4QYpAQorUQYrAQoqCmPUYIMbvm72NCiI5CiJCa37/c6Xid/FR6jE9Qve1Bl8AAjAjrQEsfF75ZeRiZhqKb/y3IKqt4/YutnIxN5vU5QxnUUz1086effmLHjh2AyuXRPGwiz40Jp7l9Y/vND8dOUVBezptDB2hklyqQlfPG0b20a+7G3BDNW21D0WQsvQDB3u6YmZpg6eFf13bkyBGNBrxamJmaMP+xgWTmlrBk/bG/4Sn1o6xczksfb+LEhRu8NnsIoweoG98uXrzI888/X3fs1T2crg/15OE+IY3ulVVyi19Pn2Nsx2A6tNC8SX3z6H6K5DIW9Y/A3MRw1m9NaFICY2VhRpCXKzdl1BnwysrKOHv2rM7rurTzYcyAjqzecYYLV/5+0qCGyCm4xVPvruH8lXTenjeiLoKuFhUVFUydOrWOs87NNwDnHsN5e8ogjdvo74+eUu2k+mkOj9h+/TLbb1zh+S59tBahUArD7UxNSmBApcdcTM2mX/96O4O+ZQnguenhtHB14J1vd+gkH/orcTEpkyfeWEl6dhGfvTyeYX2D1c4LIZg3bx7x8SrKEUsrKxzDJjNrSC/a+za2bGSW3GL9hXgmhXTA26mxXlIgK+fNY/sJcfVgbiftS9HmVM11JzWhyQlM5wBPZFUKWnWsd+kfPHhQ73W21ha8+9wo8ovK+c/X2+v4b/8OCCHYuO88Ty1Yg7m5KT8uiKRniH+jft999x3Lly+vO/YfNJH27dszb7jm2eOXEzEI4Mne3TWe//BUFCVyOZ/2G94o67EW8YXpfHrsU4M/S5MJ0axFl5Yqh5mdT71L/9ChQ1RUVDSqpXQ72gV68OrsIbz/w24W/rSX/8wdbrTNxFgUFJfz8ZJ9RMUk0Ss0gLfnRdR5nxvi8OHDanpLUO8BWLftzofThmOpgXO3oKyctefjGdMhSGOSWlxeNusS45jTsTttm6mMeDKFjAWHFnAo5RCllaWUVZWTXppHZZVu/r+GaHIC4+Jgi6+LE6nlSoKDg0lISEAmk3Ho0CEiIiL0Xj8yrD3Z+SX8vE5ltX119hCNusHdQgjBniMJfPX7IcoqKnn20TCmjuiqUUCvXr3KxIkT66LoAtq2w7TLcF4Y048gL816x+8x55EpFMzuqZkw/dOYaJwsrXm2s2p2kilkTFgzgV1JuzT2r4W/kz/JJGs93+SWJFDNMueuZ6gJSMMMQH2YNb4nj0/oyfZD8bz6+RZKDYjOMwaXrmUx7901LFi8Cy93J5YvnMYjo7ppFJbc3FwiIiLIz1cRCTg3a451/0kM6RzMtP6a2R3KKitZceY8g9sENqLwAIjLy+Jw2g2e7NQdBwtL5Aq5QcLSzLoZux/drbNPkxWY4nIZIT3qTeA7d+40OP9ZkiTmTO7Dy48P4mRsMjNf/92g9BR9iL+awaufb+aJ/6wkJaOAV2cP4ccFU2npo5m0uby8nNGjR3PtmspLYmVtjd/YJ2jpH8C7kUO1Wqb/OBtLsUzO//XSrLv8HBeDnbkF04NDEUIwe9tsvcLi5+jHzkd20tZFd5HUJrckAXX5N+ZuvtjZ2VFaWsr169dJTEykbVvDq8JOGBJKaz83FizexTPvr6N/t1bMHNeDdoGGF80sKinn4KmrbDsUT8K1LOxtrZg9qTdTI7pgqyM/SS6XM2HCBE6ePAmohDhk8hxM3f34evYY7K01X1teWcWSE2fo7e9LiFdj52ZeRRk7b1xhWnAo9haWfHzkY1bErlDr42jriY/vOJb0nY2XnSs25jY4Wzkb5DppkgLj6+KEs601lzLzGTx4MJs3bwZgx44dRgkMQMc2nqz4ZAartsewansMUTFJBPq40K9rIB3beOLv1RxnBxvMzUyQVSrIKywlNbOQhOvZnL2USnxiJkohaOnjwoszBzAyvIPOcoKgKob18MMPs2fPnrq2HpNmIXdpyeJZowhw007ws+LMefLLy3m2X2N2B4ANVy9SpVTyaFAo+67t4/U/X1c77+ngh53PJN596FF6eGpe8nShSQqMJEl09PMgNjmL0SNH1gnMxo0befHFF42+n5WFOY9P6MXDw7uw+0gC+45d5vetp6jWQQRtaiLRxt+NGeN6EN69FW383Qx6QxUKBdOnT2fLlno/bc8xkZR5dWBh5FB6ttFOBVJUIeOn46fpH+hPV5/G4ZVCCNYlxtPFzRMHcwXTN01Xq5vkbOWMb8tIfJ0CGOvT2GpsCJqkwAB08mtB1KUbDJo2GVNTU6qrqzl69Cjp6el4eRkfqwpga2PJxKGhTBwaSllFJYnJOaRnF1FYUo5CocTSwoxmjjZ4ezgR6OOKtRZ+Om2Qy+VERkayadOmuraeIydRGtCN18aHM6pbsI6r4euo45TKK3l5gGZvc3x+DleL8nm/9yBmbp5Jdlk9zayJZMKz/T9gfU42zwYPxETSrL7q0wObrMB0rHFEZpdXEx4ezp9//gmoZplnn332ru9va21B52BvNQ/y3aCiooIJEyawe3f9LqTH8LGUturFMyN686iWHVEtYjOyWHX2ApFdOjViw6zFpqSLmJuYcCN3N3uu7VE790a/N4iVQVsHd/q4aqYdEULweaJu33CT3CUBtPdVOdpiU7OYNKmetmP9+vX/rUfSioKCAoYNG6YmLP3HTKGsbX+eGNSdJ4f00Hm9rErB69v34mJrw4thmoOjFEolW69dpoNTNe9Fva12rp9vPyZ0eoLEkmymBnTXunTuzorieL5uv1yTFRgHaytaujcjNjmT8ePH15Xyi46O1ph3/d9CcnIyffr0ITq6nhok4pFZFAT04NH+nfnXqL56dZ+P/ozial4+C0cO1VpN7WhGCtlleZy88S0KZT39rLOVMysnrGR3RgIWJqYM99Kcp5QnL2BFymZCnTTH09SiyQoMqByR55MzcHV1o3+NM1IIwdq1a//LT6bCqVOn6NWrl1odgCn/9y/S3DoyoWdHXhnXONTydmyOu8Sqsxd4vEdX+gf6a+239koct0q2kFWqnoi3bOwyvB282ZdxiT5urXAw1+w+WZm6FSVK/q9lpM7nadIC0zXQi5JyOVcz85g6dWpd+++///5ffCoVfvnlF/r161c321lYWPDM2x+SYO1HRJe2vDVlkF4/VszNdN7YuZ+efj7M1xKnCyqv9PqLKykuU19Onu7+NGODxnKpOJMsWQmDW2hWqtPKs4jOPc0Ij3DcrHTH+jZpgeneSqWQnrp6kylTptQFg8fExGhNcvurIZfLmTt3LrNnz66rT+ns7Mxrny/mcLElgzq24v1HhmnkcmmIq7l5PLVuC96ODnw1fiTmptoDn76M2Upu0Sa1tk7unfhs6GcAHM5ORAL6u7fWeP2OzAOYm5gxxlNrvH8dmrTAtHB2wM/VmeOJKTg7OzNmTH1S5X9jlklLSyM8PJwff/yxrq1jx4688e0yNl0rIbxDSz6ZMULnPx8gtbCIWas3YmFqypKp43G20e6FL5GX8Xn0SwhRH3VoY27DmklrsDJTBYMfyU6ig5MXzSwbh3fKquVE58XQu3lXHC2053rXokkLDECfID9iktKQVSqYMWNGXfuKFSvqvL9/B9avX0+nTp3UaNwjIyOZ8/7X/Hr6OoM6teLzmaM0Js43RPatUh5bvYHK6mqWRU7ER0NgVENM3vAE5ZXqUYTfRHxDkIsqu6GkqoK4wnR6u2neSp8pjKeiWka4m+6dWi2avMD0DQ5AVqXg9LWbDB8+HBcXlY0iLS2NQ4cO/eXjl5SU8NhjjzF58uS6VFZTU1M+//xzOk6czc8HzzGqW7BqZtEjLEUVMh5fvZHC8gqWPDyeNlrsLbVYe3EDe6+uUWuL7BDJrNBZdccncm+gRNBHi8CczD+Po7k97Rw0L1e3o8kLTPdW3lhZmBF96Qbm5uY88sgjded+++23v3TsqKgoQkJC+PXXX+vafH192b1nH+nN2rD6yAWm9e/M+5HD9C5DcoWCp9ZtIbmwiMWTxtDJU7cD9GbxTWZtmaXW1tK5JT+M+kFt53U0JwlbMws6OTc2QFYLJReKE+ji1B5TLZbf29HkBcbS3IwerX2JungDIYTasrRhwwZKS+99dbXCwkLmzJlDWFgYycnJde3Tp0/nQPQxlsZmsT/2Ki+N7c8r48P17oaEELyxYx9n0jL4dPQwevlr9ycBKJQKxq+dQnlVPYm1mYkZqyeuxsGyPvpOCMGR7CR6ubbUmC1wozSVUkU5IU66XRIN0eQFBiCsXQAZhSUkZeXTpUsX2rVTGZ/KysrYsGHDPRtHCMEff/xBcHCwGoulk5MTf/zxBy+/9wn/t3QnSVl5fDFrNDPCDctfXnbqLFsvXuaFsN6MaKff2/72wQWcyTih1rZw4EIe8lIP9L5ckkWWrIQw98YMVQAXS1TJfR0cNZ/XhLslFJosSdJFSZKUkiRpjhVU9RsuSdIVSZKSJEnSxiFzx+jbTsWeGX3pBpIkMXPmzLpzS5cuvSdjxMfHM2zYMCIjI9VqRI8bN47Y2Fgk7yAe+2YtZiYm/P7cVAZ2bGXQfc+lZfDpgWiGtGmllc+lIQ7eOMiHRxaqtQ0LHMb83vMb9T2QeblmO61ZIK7cuo67pQvOFoZnQt7tDBMPTACitHWQJMkU+A6IANoBkZIk6bY/GwkPJ3uCvFyJuqSiW50xYwamNTpDVFQUV6/eeZpsbm4u8+bNIyQkhH379tW1e3p6snHjRn5b9QffHIzj/fUHeKi1D3/Mf0Qjo6Um3JLJmb9lFy0c7flolPYIu1rklecxYe3DCOrziNxt3fl13K+NvM9CCHanX6Rrcz9crBrz6AkhSLyVTFv7AIOetRZ3myqbIIS4oqfbQ0CSEOK6EKIS+AMVEdE9Rd/gAC4kZ1BSIcPDw4ORI0fWnVu2bJnR96usrGTRokW0bt2a77//vo5uw8TEhGeeeYaEhARadu7JlM9WsO9CIs+O6MN3c8bhZGs47+17+w6SWXKLRWNHaPUR1UIIwYhVkymSqZcw/H3877jbNc54vFycxfXSPCK8GpMOARRUFlFYVUxre3+Dnxf+Hh3GC2jo4EiradMIbYRC+tAv2J9qpeDEFVVs7hNPPFF3bvny5SgU2utBNoQQgq1bt9K+fXvmz59PcXF98dHBgwdz/vx5vvrqa9adusxj36xBCFj2zBTmDHnIqJSV/YnX2ByXwNzeDxGqIdTydjy9+y1Opx9Sa3utz2sMCRyisf+G1LM6nY1JpSoO31Z2/gY/MxggMJIk7ZckKV7Dzz2fJUBFKCSE6CaE0KoTaUJHvxbYWVlwIlElMBEREbi7q968zMxMtXBIbYiNjWXIkCGMHTuWpKSkuvbWrVuzbds29u7di19ga55dsoWvdhxlcKfWrHt5GqEailrpQmF5BW/t2k+QmytP9dVvMFtyfjvfn/pQra2nd0/eHfCuxv63qmRsSb3AMM/2OFnYaOyTVJqCqWSCn41xwWZ6BaaGlaGDhp9GXDBakA40JOT3rmm7pzAzNaFroDenk9IAMDc3V1N+f/lFe2BQTk4Oc+fOpXPnznWBWKDa/XzxxRfEx8czatQormcXEPnFKo4npvDviQP5ZMYIrcHauvDe3oMUV8j4ePQwLPTYZ/5MiWPejseBequ1g6UDqyaswtxUc8Tf6hunKK+uZEag5rhfgKulyfjbeGNpev8VpzgNtJYkKQCVoEwFHtF9yZ2hc4Anhy9ep6isAidba2bNmlXHPLlt2zZycnJwc6tPDKuoqOCbb77hgw8+qONhAZWldu7cubzzzjt1luPjV1KYv3w7luZmLH16ssGzSnZpNlEpUXXhkgnZuayLiyOsVQB/pqzhzxTt16aWZLPo+BdUK9VrVC4ZvYQAZ83KalFlOcuSjhHm3pp2TpqfsVpUc/VWMuFu2gVKG+5KYCRJGg98A7gCOyRJOi+EGCZJkiewRAgxQgihkCTpGWAPYAosFUIYzodqBNrVMDNdTs+lZxtfgoKC6NOnD0ePHkWhUPDbb7/x0ksvoVAoWL58Oe+88w7p6eqT3dChQ1m0aBHt29ev/RtPxPPeuv20dG/Ot3PG0sJZe6WUWkSnRPPK/lc4kXZC4/n1iaofYzGnyxwmt5+s9fzXCQcorZLzfDvtnueUsnRkSjlB9sbTrt6VwAghNgGbNLRnACMaHO8EDE9NvEMEuKtiOZJzCuui7x9//HGOHj0KwMKFCykqKmL16tWNai61bduWRYsWERERUbe9VSoF3+46xpL9p+gd5MdnM0diZ8Bu5sU9L/LlyS/v9cejj08fvhyu/b6n8m6wJjmG6S170sZBe7X7WoNdOwfDbEUN8Y+w9NbC1cEWc1NTsgrrl5epU6fi7+8PqEz6H3zwgZqwuLu7s3jxYuLi4hgxYkSdsMgqFby2YidL9p9iYs8OfDN7rF5hAfj65Nf3XFiszKz4cNCHHJx5EBtzzUpsgbyM185sxM+2Gc8FD9R5v7jiK3hYudLc0rg6A9CEswY0wcREwtnOmoLSev4XGxsbli9fzvDhw+tIekAV1PTiiy/ywgsvYGurHidSUFrOc0u2EpuSyfOj+jJrYDeDco7Kq8pZeJsVFqCdSycyC+3wsHdkQOsAJLTfS6ZQsPnaJRwsLInwb4OnvSeRHSNp6ax9+ahSVjM/Zh2FleWs7PcENmbaFdlKZRXxxYkMuAP9Bf5hAgNgb2XBLZl6cn1YWBjnzp1j2bJlyOVyunfvzvjx47Gxafy23swrYu6PG8kpLuXzx0YxJMQwtz/Az2d+JqdMnXl23aQtLD6US3NHJZsff5RmtppniFq8HLUbR4fW7JowkzbOusMbQLUEvnN+G6fykvmwy3itim4t4osTkSsr6ercuEaBIfjHCYyFuRmVGti6g4KC1CqBaMKN7AKe+G4dCqWSJfNeRC4+AAAS9ElEQVQmE+JvODFzZXUlnxxTZ5Od3+slNp6VkVNaxsrpU/QKS1xeNmsT43iyY3eDheXzS/vYfPM889qGMcaAbMbj+WexNrUyyuHYEE1GYBqmfOqCqYmE8g6q2GcV3mLO9xsQqCy3gR7GER9vSthExq2MumNrM2uKi0KIuZnOorEjCNET3yKE4MNTh2hmVc/pog+LrxxiWdIxIgO6M69tuN7+smo5x/PP0bNZKBYmmm042bLGBVgboskovRVVhvHSKZVCI/WoLsirFDz3y1bK5ZX8OHei0cIC8N3p79SOg5wHcTgpl1cH9WdUe/0hC0czUjiakcqzob1wsNCvXP9y9QiLrxxmvG8o/+4YYZCOdSQvhopqGQPdNRNhA5wp0F2dqMkITJGsyKB+VdVKzM2M+1hfbIvmcnoOH06LoI2n/qXgdsRmxxKdql7DOr+wHU/37cETPfTHxAgh+OzMETxt7Xk0WP+ysiHlLIsu7SfCqwMLQsdozZNuCKVQsi3jT/xtvAm21xyuCZAjS9Z5nyYjMMXyYv2dUM0WmjjhtOHc9XRWRZ/nkX6hhLU33pAF8N0p9dnFWgrkxb5jtVKh3o6o9GTO5WTyTGhPLE11P/vRnCQWXNhGH7dAFnYZZ3Bo5ZG8GNIqspjgPUzrbKQU1eTKdZieaUICs/6SYTnTFZVVWJkbxqpQrVSycMNBPJzseW6k9kQxXSiSFbEiTp2wZ1qH2fwrrLdBy4QQgi/PHsPLzoHJbXTvXG6WFfBSzHoC7d34otsULEwMezFk1XJWpmwhwNabXs21J/3ny2+iELrp25qMwCw4vKARk5ImlMoqsdND6FOLbacTuJKRy4tj+mFjaZwTrhbLzy+nvKq87tjZ0o3F417ScYU6jmakcDYng6c6PaTTEamytahemm8emoqtueFOz9Wp28irLOSJgCk6l6+MCn2hTfe5wEi3fbjHtzzOvmv7tPQGRbWSisoqgzzIVYpqfthzgg6+7hqrshoCpVDy3enFam0v9n4WMwPffICvzx3Hw8aOKW11zy5Lrh7hYlEGC0LH4G1ruIU2tugyOzIPMsy9H8F6XAFpFQlYmepOZruvBcZCcsGigfu9SlnF6NWj2Z64XWP/kgqVJdfBAIHZFpNARmEJTw3vdcdlcfYm7SWpoD7809zEnDld5hh8/fGMVE5mpTE3pIdO3SWlNJ8fE6OI8GrPUE/Do1vz5AV8kbgUL2t3ZvhP0Ns/tSwWHxvNAVe1uK8FBmHBdxE/qzXJq+WMXzOetRcbMzQUl6kExlFPmKRSKVh2IIZgbzf6Bvnf8eO9slfdDTC5/WSN4ZKaUKu7uNnYEqlndvn84j7MJVNe7TDc4GcrV1TwYcIPVAkFL7edg5Wp7peosDKToqos/G01F02vxf0tMEA3jyF8NfwrtTaFUkHkhkiWn1+u1l5YprLVOOsRmMOXrpOSW2iwj0gT1lyIJi7viFrb092fNvj6oxkpnMi6ybyQnjprL14ouMmfWZeZ3bovrlb6c59B5S/65MpPpJZnML/NbLxt9Fusr95SsXkG2mmmcq3FfS8w1/MLeK7Hc/w46kc1p51SKJm1ZZbalrawxunobKdbYFZGncPDyZ7BnQz3EzVEbmkZL+x6HxpYnzt7dKaXt2HbaCEEn8RE42lrT2TbTjr7fnflEM4WNkwLNCz3uUqp4PMrS4grvsLTrabR2dmwJexySTSulv44W+gWrvtaYCRJIilPxZD9ZNcn+W38b5hK6juJZ3Y9w8dHVD6i/FuqyLRmdtp9Ntez81X0IH063RFlvBCCN3buo6gyQa396e5PGzxb7bhxhQu5WbzQtQ9WZtp1l4SiTI7mXGNmYC9szfTrZdWimq+uLiOmMI45AQ8bHFGXL08jvSKB9o4D9Pa9r31JlmamXM0tqDue1mkatua2PLz+YaqU9fQWr/35GqWVpbSQq6jkm2ko/lCLdcfiMDM10Vjz2RBsiU/gUNINbK2qqWjgrQjzDzPoepmiig9PRRHczJWJrXQrmL9dO46NqQUPB+iPh1cKJYuTVnA8/xyP+U9keAvDngfgXOFOTDClo+MgvX3v6xnG0sysboapxfjg8WyN3FrHfVKL96PfZ0XSIpxsrbQmvsurFGyLucTgTq1obq/bc6wJheUVLNx/mM5eLVCIcrVzzlaGbXW/v3CKtNJi3uo5UCepUK7sFjvT4xnvG6qVZqwhfk/ZzKHck0T6jGa0p/5/fC3KFcWcL9xNsGN/7My1E0rX4r4WGCszM9KLSyirYXKqxfBWw9n96G7sLNQz+o4XbOCm1VqqlZp5YQ7EXaOkXM6EO5xdPjt0hFJ5JQuGD6RYpu6qcLTSn256raiAxRdOMqZlEL09dSfcb0g5i0IoeaSl/vTZ/dlH2Zqxn+EeYUz0NnwnBXA8bx1VQk6v5lMM6n9fC4xlzfp+I7+w0bkw/zD2T9+Pk5WTWnty9SFmbp6pxiRZiy2nL9LC2Z6HWuv+Z2nChYws1p2PZ0b3zng4WqiFW9hZ2Ok11imUSuZH7cTa3Jw3e+rWFaqFkg0pZ+npEoC/nW5n6LXSVH6+voYQp2AeD5hk1K4vX55GTMFWOjkNxtXKsLre97nAqJaWGwWNBQagh3cPDs08hKuNei7zyriVTF43Gbmi3i+SU1zKiSupjOoWbHRRLaUQvL/3IK62NjzTt0cjz/ntQqsJ350/wbmcTN7tNQg3m8a5zg1xLOcaGRXFTPHXrbvIqyv5MnEpTub2vNB6VqMNgS4IoWRX5teYm1gS5jZT/wU1uK8Fpta3crNQu6c6xCOEqFlReNmrZ/BtvryZsX+MrfPz7D53BaUQeunZNWH7xctcyMhi/oC+2FlaNhIYffrLsYxUvjx3jHGBwYxrpX+buz7lLM4WNgxooTuO5o+b28mQ5fBM6xnYm+sWwttxIn89N8vjGezxJHZm+nWXWtzXAiNJEs1tbMgo0V1iLsgliI0TdmFerf7B91zbw/AVwymRl7DjzGXa+7jrrBSiCRVVVXx28AgdPNwY11H1zy6sUJ/xdM0w6aUlPHNgGwEOznzQZ6je8fLlpRzKusIYnxCd3ui08ky2ZxxgsFsfOjoaV8Eluew8h3N+I9ihHx0d9TNnNsTfxQ+TLElSnCRJ541NsneztyWntExvP3NlMwJLn8HvtuCg6NRo+i0dQFz6DSK6GPfFAiw9eYasW6W8PjisLpLP0CWprKqS2Xs3Ulmt4Kch47Cz0O8R33YzFoVQMsFXd+2BFSlbsDK14FE/41Lc8+VpbLq5kOaW3kS0+JfRlu6/nB+mAQYIIUKNTbJ3tramsFx/eGZm0S3MhRNrx28n1EPdHxKbc5Ybdovp3Fq/rtEQOaWl/Hw8hqFtW9Hdt54jzhCBUSiVPH1gG1cK8/h24BhaOekP+xRCsCn1HB2dvGjloLnWI6gU3dOFsYz1HIKDEUvRrao81qT+BxPJlEk+b2Nparxp4e/gh7krOFhZckteqbdfVqGK7619i5YcmHGAnt7qVk6ZaSYTN0Zws/impss14quo41RVVzcqN6NPhxFC8OaxfRy8eZ13ew8m3Mcw0p64onSSbuUywU/37LIpfQ82ptaMaBFu0H0ByhRFrE55g4rqW0zxfVevC0Ab/i4dRgB7JUk6I0nSk7o6NuSHyc3NxcrcDFlVla5LAEgvKMHRxgpbKwucrZ3ZO20v4f7han2uFlyl37J+JBUkab5JAyTm5LHhwkUe7RqCXzP1GUTfDPPdhZOsuhzL0yE9mBas2/vbEBtTzmFtas4ILSRAADmyfE7mn2eoRz9szAwjLypXlLA65d8UV+Uw2ecdWljfmQ8N/j5+mL5CiC6oaMueliSpv7aODflhXF1dMTMxRaFUauteh4yCErya1SfJ21vas/ORnXRwUo+QTylOof+y/lzK1U0t/8nBaOwsLXiqT2OnX6FMu9K77dplPo2JZlxgMC9301wISxPKFHJ2pMUxzLM9duZWWvvty1Z5yId7aP0K1SCrLmNN6n8orMxgks9b+NreWQJbLf4OfhiEEOk1v3NQJe/rN18aibT8YjybqbMqWJtb07bqSXzM1IfLLM2k/7L+nM3UXBvoeHIqUdeSmdv7IY207dpmmLi8bOZH7aK7uxef9B9ulEK5Iy2O8upKJvl30dqnWlRzIOc4XZw74Gqpf7enUFay/uYCcmTJTPB+gwA742s83o6/fEmSJMlWkiT72r+BoaiUZYNQrVRipqeQQ7VSSUZBCd7N1c3zmYUlJKYX8GaPL5kZom6cyq/IZ8CvAzh285hau1IIPj0QjaeDPdO7aV5OGukw1s4Uy2XM3b+Z5lbW/DB4rN7o/4YQQrAmOYY2Du6EOvto7Xe+8BJFVSUMdNMfRiGEYEfGF9wsj2e013wC7XXHuRiKu91Wj5ckKQ3ohYofZk9Nu6ckSbX0Hu7AEUmSLgCngB1CCN3VtBtAplDUuQi0Ibe4jKrq6kYCcyBOVQ96SKcglo5dyrxu89TOl8hLGPL7EPZf31/XtishkfisHP4V1lvruLcLjKOlI69E7ya7vJTvB43FxbpxEQhdOFdwk8vFWUTqqJYGcDj3FPZmtnRx1u8LO5m/gUslhwlzm0k7R8M91/rwl/PDCCGuA3dWwhSV4czGQnfaSFq+yhLs66KufB6Mv0ZL92b4u6l2Md+O+BZ7S3s+PlqfY11eVc7IVSNZN3kdI1qP4quoY7Rxbc6Y9kFax7tdhzmTm8/u5Bu8/lAYoW7G7z5+v34CB3MrRnlrD6aqqJZxujCWcNeemOvxW6WXX+ZQznKC7Psa7FQ0FPd1PAxAiUyGvaXu4KHUPNUb791AYEoqZJy9ls7MAfWZh5Ik8eGgD7G3sOc/B/9T115ZXcnYP8bSo8VgruaaMKpdEO9H3dA4VmV1JfE56ivqkvh4urm3YU4Ho0xMgCrXaH9GArNa9dZJ03GmMJ5KZRV9XXSPoVBWsj3jc+zNmxPh+dwdh6Bqw30vMEUVctq46jZO3cwrwszEBA+n+pjXowkpKJTKRtmMkiTxRv83sLWw5YU9L6idO5mpWpp+i99r1DPKlWZ83E9/0SxNWJZ0DFMTE6a11B2CeSzvLM7mjgQ7aE9zBTiet5aCynSm+r6Plalx/iUhBEkFb+vsc1/7kgAKystppqPAFEBKbiFezR3VQi4PX7yOs611Xbni2/F8z+f5efTPOsl9DIGZmQtPduxrkCX3dmRVFLMx9RxjfUJws9bOmyerlnOu6CI9mofqTEQrqcrjRP56gh36E2CnfbelDeklS8m6tUpnn/taYASqGkLN9fCqpOYV4edavxwpqpUcvZxM32B/nW/97C6z2Ra5jVbNjOd6A3C09iHQfSbPdtbOhqALPyVGI4TgyTa67TXniy5RqayiZ3PdRsBjeatRCiXhbrN09tOEvLK93Cj8iOY2w3T2u6+XJEVNRTU3O+1Tq1IpSM0tokeDoKi4lEyKy2X0b6ffJD+yzUioCmDOpk8Z2NaOQBf99g1vB2/srdvw6rFTvNZrkEFOxduRXJrH+pQzTPbrhpeN7vCIkwUXsDez1UlieKsqnwuF+whxHoqThWG5UbUoqjjO5dznsbcMoa3LZ8BirX3va4FRCkF7Dzf8nLU7DUtlclq1cFErCFFQWoGnswO9ggyLIksvKSPULZxfJ043WA9ZEheDv4MTkUG600S0Ib4oA0dza55qq3/Lq1Aq6NE8VGeAVJ48FWtTe3o0n2j0s2SVrsHa3Jf2bj9jaqJ7+ZfEHbA1/V2QJCkX0M0/YRhcgLx7cJ+mOr6xz+AnhNBYkuW+Fph7BUmSYowNq/gnjX8vn+G+Vnof4P7DA4F5AKPwvyIwP/2Pjw/36Bn+J3SYB7h3+F+ZYR7gHuGBwDyAUfjHCIwkSUslScqRJEljcJYkSeGSJBXXpLqclyTprXs8vo8kSQclSbpUk3rzLw19JEmSvq4pxxwrSZLxDp+7G//uvwMhxD/iB+gPdAHitZwPB7b/heO3ALrU/G0PJALtbuszAtgFSEBP4OTfPP5dfwf/mBlGCBEFFOjt+NeNnymEOFvz9y0ggcbVc8cCvwkVTgBOkiTdWb7HnY1/1/jHCIyB6CVJ0gVJknZJkqSbzecuIEmSP9AZOHnbKaNKMv8F48Ndfgf3tfPxHuMsKh9JqSRJI4DNwJ0n6GiBJEl2wAbgeSGE7qTwvwB6xr/r7+B/ZoYRQpQIIUpr/t4JmEuSZHwlCh2QJMkc1T9rpRBio4Yuf2lJZn3j34vv4H9GYCRJ8pBqAlwlSXoI1WfP132VUfeXgF+ABCHEIi3dtgIzanZLPYFiIUTm3zX+vfgO/jFLkiRJq1HtAlxqUl/eBswBhBA/AJOApyRJUgAVwFQh7qmZuw8wHYiTJOl8Tdu/Ad8Gz7AT1U4pCSgHjA+Nu7vx7/o7eOAaeACj8D+zJD3AvcEDgXkAo/BAYB7AKDwQmAcwCg8E5gGMwgOBeQCj8EBgHsAo/D8fpb+Mb99cfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "eq0,fig0=eqdsk.readGEQDSK(eqdsk_file,doplot=True,dolimiter=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ba584a7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig0.set_size_inches(5,10)\n",
    "ax=fig0.get_axes()[0]\n",
    "ax.grid()\n",
    "ax.plot([2.4,2.4],[-1.5,1.5],'b')\n",
    "ax.plot([1.85-0.585,1.85-0.585],[-1.5,1.5],'b')\n",
    "fig0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "85eb33f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  \n",
      "  G-EQdsk data accepted, maximum rel. GS error:   0.495642999090293     \n",
      "  \n",
      "  Bphi is counter-clockwise viewed from above (nsnccwb = 1)\n",
      "  \n",
      "  Jphi is counter-clockwise viewed from above (nsnccwi = 1)\n",
      "  \n",
      " equigs_write completion code (0=normal):           0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(str(subprocess.run([\"geqxpl @equigs_gen/g_filename=\"+eqdsk_file+\"/equigs_filename=\"+equigs_file], \n",
    "                   shell=True, stdout=subprocess.PIPE).stdout, \"utf-8\" ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ad56d6c",
   "metadata": {},
   "source": [
    "## Create output files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f9f1a184",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/jcwright/SPARC/TSC-V2h01/plasma/toric_tools.py'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toric.__file__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b85c73a5",
   "metadata": {},
   "source": [
    "Create toric object and read in equigs file created by geqxpl utility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1ba23c07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CRITICAL:  toric.ncdf  not found.\n",
      "Non-CRITICAL:  ./toric_qlde.cdf  not found.\n"
     ]
    }
   ],
   "source": [
    "case=toric.toric_analysis(mode='ICRF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7c83bf06",
   "metadata": {},
   "outputs": [],
   "source": [
    "case.idebug=False\n",
    "case.read_equigs(equigsfile=equigs_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d95830e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/jcwright/SPARC/TSC-V2h01/run6_test'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb748466",
   "metadata": {},
   "source": [
    "#### Create mappings between r/a, rhopol=root(psipol), rhotor=root(psitor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7c3d7bd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[151.]\n",
      "minor/Major radius:  0.5072690061220002 1.91278192 1.91312441 1.85060378\n"
     ]
    }
   ],
   "source": [
    "nmhd=case.equigs['nmhd']\n",
    "imom=case.equigs['imom']\n",
    "\n",
    "rmc2d0 =case.equigs['rzmcs2d'][0:nmhd]  #center of each flux surface. first term is magnetic axis.\n",
    "zmc2d0 =case.equigs['rzmcs2d'][nmhd:2*nmhd]\n",
    "\n",
    "print(case.equigs['rzmcs2d'][2*nmhd:].shape/imom/4)\n",
    "rz=case.equigs['rzmcs2d'][2*nmhd:].reshape( (nmhd,4,imom), order='F' )\n",
    "Raxis=rmc2d0[0]\n",
    "rminor= (np.sum(rz[:,0,:],1)+ rmc2d0) - Raxis\n",
    "print ('minor/Major radius: ',rminor[-1],Raxis, eq0['rmaxis'], eq0['rcentr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fb04393b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(case.equigs['srad'],np.sum(rz[:,0,:],1) + rmc2d0) \n",
    "plt.title('Reconstructed major radius from equigs file');\n",
    "plt.xlabel('srad=root(psipol)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7b27f407",
   "metadata": {},
   "outputs": [],
   "source": [
    "#map profiles vs r/a to rhopol (sqrt(Psi/Psilim))\n",
    "from   scipy.interpolate import interp1d\n",
    "rhopol = case.equigs['srad']\n",
    "rhor = rminor #TORIC fit\n",
    "rhotor = case.equigs['rhotor']\n",
    "PtoTmap = interp1d(rhopol,rhotor,kind='cubic',bounds_error=False,fill_value=0.0)\n",
    "rtoPmap = interp1d(rhor,  rhopol,kind='cubic',bounds_error=False,fill_value=1.0)\n",
    "\n",
    "Ptormap = interp1d(rhopol,  rhor,kind='cubic',bounds_error=False,fill_value=rhor[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a60be6a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yRglBRGqdkhJn4qwV/PXN+bRs0pDfH92NQzu1zHZYWaeEICK1hntwz+J7Xv+KunWMq4/pzoCurbJ+Y5rqotrcU1lEpDJ9kPcd977xFesLi7nqqO4cs09rJYI0KCGISM6btXwdf3ltHvlrNvG7I7tx0v67UbeOEkG6lBBEJGflr97EPW/M58OFq7l8cBfOOWRP6tetk+2wcpaOIYhIzvluwxbuf3sBL3/5Db/u25Hf9OtIk4b6fZsKHVQWkRphw5atPPLeQv7z4WJOPXB3hg7qQsumDbMdVk5RQhCRnFa0tYSnPl7CA+/k0b9rK646qluNvt5QZVJCEJGcVFLijJ/xDfe+8RWddmnCH47Zix677ZjtsHKahp2KSE5xd95b8B1/mTiP+vXq8Jef70efzjqprLKllRDM7GRgGHAQ0BhYCrwM3O7uq1Osoz5wMXA20APYESgEFgOvAXe7+//SiUtEao4Zy9Zy58R5rFhXyDXHdOfYnm10LkEVSbnLyMxGADeVsngxMMDdl6ZQz1jg9DJWWQocmCzBqMtIpGZZVrCJe16fzwd5q7niyK6c3bsd9TSENOMqfAzBzPoD74WzJcANwFzgWuCwsPwNdz8mST1dgAWRon8CY4H9gLuAumH5pe7+UJK6lBBEaoDvC4v5xzt5PPNpPr/q04GLBnTSENJKlIljCFdGph9z9zvCij8DlgAGHG1m+7j77DLqib/E4DXuvgF4y8wuAPYJyxukGJeI5KjibSU8/Uk+I9/+mkHdd+G1KwbQpvkO2Q6rVks1IQyKTE+NTbj7UjPLB9qHRYOBshLCLOBboG04f7eZPQ/sD+wVlm0AXkoxLhHJMe7OW3NXcsfEuezWvBH/ueAQjRyqJpImBDNrAbSIFK2IW2UF2xNC57LqcvdCMzse+BfQi+Dg8sWRVd4CfufuS0qJZQgwJFnMIlI9zVi2lttemcuajUXceGIPBnbbRQeMq5FU9hDi7y1XVMZ80xTqWwd8BRwAxB8xOhw408xmJzpA4O6jgFEQHENIYVsiUg0sX7uZe16fz9Svv+N3R3bjrN576IBxNZRKQtgYNx9/nnh0fkNZFZnZTsCHQOuw6DfAGKADwcHl7gQjmQqAv6UQm4hUY+sLi3loch5PfZLPrw5rzztXD6SpDhhXW0lTtLsXEHxBx7SJW6VtZDovSXU/Z3sy+NLdH3P3jeGB6OioorOTxSUi1de2Euepj/MZdM+7rFy/hYlX9Oeqo7srGVRzqb4777D93IH+wGgAM+sItIusNylJPbtEpuPvbt28lGkRySEfLVzNiPFzaNqwLqN/fTA9d9e/c65INSGMZHtCON/M8oA5wHWRdd6KDTk1s9HAeWH5CHcfHk5/GVm/k5mNAp4n6DL6XWTZpynGJSLVxNI1m7j91bnMWLaOPx2/Fyfs21YHjHNMOmcq3wpcX8rifIIzlZeE644mQUKw4NPxCnBcGZtaBfRx9zK7n3Rimkj1sHHLVv4x+Wue/DifC/p2ZMiATuxQv27yJ0pWZOTidu5+g5lNI7iWUS9+fC2jO9x9VQp1uJmdAvyW4DhBT4LuoSJgEfAGwbWMvkk1LhHJjpIS58Xpy7n79fn06dySiVf0p23zRtkOSypAl78WkbR9tqSAP0+YA8DNJ/Wg154tkjxDqgtd/lpEMuLbdZv5y8R5fLRwDX84tjunHrA7dXQz+xpDewgiklRh8TZGvbeQx95fxP8d2p5LBnbWBehylPYQRKRc3J3XZ6/glglz2b9dc8YP7adbV9ZgSggiktDXKzcwYvxsVqwr5O4z9+Pwzq2yHZJUMiUEEfmRDVu2cv/bC3h22lKGDu7Kr/q0p76uO1QrKCGICBB0D7385Tfc8eo8+nZpxeu/G8CuzXR/gtpECUFEmLfie24aN5sNhVt54NwD6d1h52yHJFmghCBSi63bXMx9b37F+C+/4cqjunHuIXtSV8NIay0NOxWphUpKnLGfL+Ou1+dz5N67cs0xe7FzE925tjbQsFMR+cGs5eu4cdwsShwe/VVv9m8Xf6tzqa2UEERqifWFxdz7RtA9dM0x3TmrdzudZSw/ooQgUsO5O6/M/JZbJ8xlQLdWvHnVEeoekoSUEERqsMXfbeTGcbNY+f0W7j/3QA7W6CEpgxKCSA1UWLyNf76bx+MfLObiIzpzQb+OOrlMklJCEKlhpixYxU3jZtN116ZMuLw/u++kexRIapQQRGqIld8Xcssrc5meX8CIk/fhZ3u3znZIkmN0HoJIjttW4jzx4WJGTvqacw5ux7DBXWnUQLewlMR0HoJIDTVr+Tr+9MJMGjeoy5ghh9G1dbNshyQ5TAlBJAdtKtrK395awAufL+PaY/fijIP2wEznFEjFKCGI5Jj3vlrF9S/N5KA9W/DalQNo1bRhtkOSGkIJQSRHrN6whVtfmcuni9dw66k9Gdh912yHJDWMDiqLVHPuzgufL+eOiXM57cDd+d1R3WjcQL/lpHx0UFkkRy1ZvZHrX5xFwaYi/n3+Iey7R/NshyQ1mBKCSDVUvK2Ef01dxMPv5nHJwM5c0Lcj9XSmsVQyJQSRambGsrVcO3YmrZo2YNxl/dizZeNshyS1RFo/OczsZDN708zWmFmhmS0ws3vNrGW6GzazgWb2vJl9Y2ZbzGyVmU0zs7+bWf106xPJdZuKtnLLhDlcMHoaQwZ05D8XHKJkIFUq5YPKZjYCuKmUxYuBAe6+NMW67gKuKWOVZu6+IUkdOqgsNcYHed/xx7EzOah9C248sYcuTy2VpqyDyiklBDPrD7wXzpYANwBzgWuBw8LyN9z9mBTquhB4JJxdDzwAfARsAdoDA4DfuvvmJPUoIUjOW19YzJ0T5zFp3kpuPbWnrj8klS4TCWEscHo4+6i7/zYsbwcsAWKV93T32WXUU59gb2K3sGiQu09OoQ2J6lJCkJw2ef5Krn9xFv27tuJPx+9N80bqKZXKl4lhp4Mi01NjE+6+1MzyCX7ZAwwGSk0IBHsTsWSwDBhsZv8EOgD/A14Chrt7QYpxieScdZuKueWVOXy0cDV3/nxf+nfdJdshiQApHFQ2sxZAi0jRirhVovOdk1S3X2R6D+BGoDvQENgTuBz4wMwS3vXbzIaEB56nJYtbpDp6Y/YKjv7buzRuUJfXrxygZCDVSip7CE3i5ovKmG+apK74L/o5wHXALsC9wI7AXsAfw8ePuPsoYBQEXUZJtiVSbazesIXh4+cwc9laRp5zIId2SntgnkilS2XY6ca4+fgraUXnyxwZBBTGzf/e3ce5+6PAQ5Hy41OIS6Tac3cmzPiGY/8+hTY7NmTiFQOUDKTaSrqH4O4FZlbA9m6jNnGrtI1M5yWpbknc/KJSpnV+vuS81Ru2cOO4WXz1vw08/MuD6LVni+RPEsmiVE9Meycy3T82YWYdgXaRZZOS1DMV2BaZ71DKdHziEMkpr89ewbF/n8IeLRozYVg/JQPJCamOMhrJ9mGn55tZHtv7/2Peig05NbPRwHlh+Qh3Hw7g7ivM7Hng7HDZPWa2A9ASuCRS11NptkOkWli3uZgRL8/ms/wC/vGLXhzcYedshySSspQSgru/a2a3AdcT7FXcFrdKPnBhitu8HDiAYHRRT4KhplHj2H7imkjOePerVfxx7AyO6tGaiVf01yWqJeek/Il19xvC4Z7DgF5AY2Ap8DJwh7uvSrGelWZ2KPAH4OcEXUVbgVnAaGCUu5ek0QaRrNqwZSu3vzqXd+ev4p4z96dvl1bZDkmkXHSDHJEK+Gjhaq55/kv6dGrJDSf2YMcddLaxVG+6QY5IhhUWb+Ou1+bzysxvuP20fXUNIqkRlBBE0vTF0rVc9ewX7LNbc167YgAtdGVSqSGUEERStHVbCQ++k8cTHy1m+Mn7cOJ+uyV/kkgOUUIQScGS1Rv53ZgvaNKwHhOG9adN8x2yHZJIxikhiJTB3Xnus2XcOXEeQwd14fzDO1CnTsLjcSI5TwlBpBQFG4v40wszWbx6I0//9jC6t2mW7ZBEKlVa91QWqS3e+2oVx/19Cu12bsS4oX2VDKRW0B6CSERh8Tb+8to8Xp+1gnvP0klmUrsoIYiE5nzzPVeOmU7X1s149Yr+7NRYw0mldlFCkFqvpMT519RFPPRuHjecsDenHbg7ZjpwLLWPEoLUaivXF/L7Z79kU9E2xl3Wl3Y7N852SCJZo4PKUmtNnr+SE0ZO5cB2OzFmyGFKBlLraQ9Bap2irSXc/fo8Jsz4lpHnHEifzrqlpQgoIUgts/i7jQx7ejqtd2zIK5f3Z2ddh0jkB0oIUmu8OH0Zt0yYyxU/68qv+rTXgWOROEoIUuNt2LKVm8bN4sula/nvbw6lx247ZjskkWpJB5WlRpu5bB0n3T+V+nXqMH5YPyUDkTJoD0FqJPfw3ILJeQw/eR9O2l+XqhZJRglBapw1G4v4/bNfULCpmJd0boFIytRlJDXKp4vXcOLIKXRr04znLu6jZCCSBu0hSI1QUuI8/N5C/jV1IXedsR+D99I9jkXSpYQgOS/WRbRuczEvD+3Hbjs1ynZIIjlJXUaS06bFuohaN2PMRX2UDEQqQHsIkpNKSpxHpizkkSkLufP0/Tiyh7qIRCpKCUFyTsHGIn7/3JcUbCpi3NB+7K69ApGMUJeR5JTPlhRw4v1T6dSqCWOG9FEyEMmgtBKCmZ1sZm+a2RozKzSzBWZ2r5mV63KRZtbUzL42M488BpanLqnZ3J1H3lvIRU9M4+aTenDDiT1oUE+/Z0QyKeUuIzMbAdwUV9wFuAo43cwGuPvSNLd/H9A5zedILbN2UxFXP/clqzYU8eKlOtFMpLKk9BPLzPqzPRmUANcBpwEfhWUdgEfT2bCZnQRcCBSm8zypXT7PL+CEkVNp37IJz12kE81EKlOqewhXRqYfc/c7AMzsM2AJYMDRZraPu89OVpmZ7QI8Es5eC/w99ZClNohei+j20/flmH3aZDskkRov1U7YQZHpqbGJsIsoP7JscIr1jQJaA28B96f4HKkl1m0qZsgTnzH+y2946bK+SgYiVSRpQjCzFkCLSNGKuFWi80mPB5jZBcCpQAFwvrt7CnHGnjvEzKaZ2bRUnyO5ZeaydZz4wBR236kRz118uLqIRKpQKl1GTeLmi8qYb1pWRWbWEfhbOHuJuy9PYfs/cPdRBHsXmFnKiUSqP3fn6U+Wcs8b87nllJ6csF/bbIckUuukkhA2xs03LGN+Q5K67geaAU+5+5gUti21wOaibdzw0ixmLl/Lcxf3ofMuZf6uEJFKkrTLyN0LCLp3YuI7dKM/5fKSVLdH+Pfc6LkHceu8E5bvlCw2yX2LvtvIaf94nxJ3Xrqsr5KBSBalelD5nch0/9hE2AXULrJsUiaCktrhtVnfcsZDH/CLw9rz17P2p3EDXUlFJJtS/Q8cCZweTp9vZnnAHILzEWLeig05NbPRwHlh+Qh3Hx5O3w3skqD++yLTDwJfA5tTjE1yTPG2Eu56bR6vzlzBY+cfzP7ttDMoUh2klBDc/V0zuw24nmCv4ra4VfIJTjJLVs+TicrNLJoQnnf3yanEJbln5feFDH1qOo0a1GXCsH60aNIg2yGJSCjli8G4+w0EZydPAtYSjC7KI/h139vdl1RKhFJjfLRwNSfeP5W+XVrx7/MPVjIQqWYsjdMAqhUzS+cUBski9+D2lo9OWcS9Z+3PEd0S9RqKSFUwM9zdEi3TUTypVOs2FwcXplu/hXFD++py1SLVmK4fLJVmzjffc/IDU9mt+Q48e5HuXSBS3WkPQSrFc9OWcsfEedx8Ug9OOWD3bIcjIilQQpCMKizexvCXZ/Pp4jWMGXIYXVs3y3ZIIpIiJQTJmPzVm7jkyc/o0KoJ44b2o2lDfbxEcon+YyUj3przP64dO4Ohg7tw/uEdMEs4iEFEqjElBKmQrdtK+OubX/Hi9OWM+tVBHNR+52yHJCLlpIQg5bZq/RYuf3o6derA+GH9aNU0/kK4IpJLNOxUymXa4jWc/MBUendowX8uOFTJQKQG0B6CpCV2r+N/vpvHXWfsx+C9Wmc7JBHJECUESdn6wmKuHTuDpWs28+KlfXV7S5EaRl1GkpL5K9ZzygPvs1PjBjx3cR8lA5EaSHsIktRL0wY01M0AAA+6SURBVJfz5wlzuP74vfn5QXskf4KI5CQlBCnVlq3buGXCHKYu+I4nLzyUvdvumO2QRKQSKSFIQssKNnHZk5/TpvkOvDysHzvuUD/bIYlIJVNCkJ+YPH8lVz/3JRcN6MyF/TvqrGORWkIJQX6wrcQZ+fYCnvk0nwfP7cWhnVpmOyQRqUJKCALAmo1FXPHMdIq2ljB+WD92bbZDtkMSkSqmYafC5/kFnDhyCvvs1pwnLzxUyUCkltIeQi3m7jz+wWLun/Q1d/58P47qobOORWozJYRaauOWrfzxhZnkrdzAC5ceTvuWTbIdkohkmbqMaqEF/1vPyQ9MpXH9ukoGIvID7SHUMuO+WM6I8XP443F7cVbvdtkOR0SqESWEWmLL1m3cOmEu7y1YxRO/OYR9dmue7ZBEpJpRQqgFlhVs4rKnptO6WUNeHtqP5o101rGI/FRaxxDM7GQze9PM1phZoZktMLN7zSylM5jMrLmZXW5mL5jZV2a21syKzGypmT1pZgeUrxlSmsnzV3Lqgx9wfM82PPzLg5QMRKRU5u6prWg2AriplMWLgQHuvjRJHYcBH5axSjFwirtPTCEeTzX22mhbifP3txcw5tN8Rp5zoM46FhEAzAx3T3g9mpQSgpn1B94LZ0uAG4C5wLXAYWH5G+5+TJJ6DgPeB14FXgCWAgcBNwKxoS4L3b1zCjEpIZQietbx/eceqBPNROQHmUgIY4HTw9lH3f23YXk7YAkQq7ynu88uo549gJ3dfUZc+VXAvZGi1u6+MklMSggJfJ5fwNAnP+fkA3bn6qO7Ua+uRhaLyHZlJYRUDyoPikxPjU24+1Izywfah0WDgVITgrsvA5YlWDQ/bn5jinFJSGcdi0hFJU0IZtYCaBEpWhG3ygq2J4SkXT2lODsy/ba7J0wIZjYEGFLObdRYG7ds5dqxM1i4aqNONBORckulPyH+26WojPmm6QZgZlcDvwxn1wHDSlvX3Ue5e293753udmqq2FnHTRrUUzIQkQpJpcso/td6wzLmN6S6YQvuunIPcFVYtBY43t3nplpHbTf2s2Xc9upc/njsXpx1sM46FpGKSZoQ3L3AzArY3m3UJm6VtpHpvFQ2amYNgf8AZ4VFywiSwcxUnl/bbS7axs0vz2LakgKe+u2h7NVG9zoWkYpLdQjKO5Hp/rEJM+sIRH+aTkpWkZntBLzB9mQwAzhMySA1X6/cwKkPvh/cyGZoPyUDEcmYVIedHgFMDmdLCM4bmANcBxwclr/l7keF648GzgvLR7j78LB8V4Lk0iNcthS4GPg+bpMz3X1dkphq3bDTF6cv45YJc/nDMd05++B2utexiKStwsNO3f1dM7sNuJ5gr+K2uFXygQtTqKoH25MBBHsXryRYbxDbE1CtV1i8jZvHzebTxWt48sJD2but9gpEJPNSPmvJ3W8ATiPoFlpLMLooD7gP6O3uSyolwlou1kW0uXgbLw/rp2QgIpUm5WsZVTe1ocsodu+Cq4/uzv87RF1EIlJxmThTWapQYfE2RoyfzUcL1/Df3xxKj920VyAilU8JoZpZuGoDlz75OV1bN2P8sH40bai3SESqhr5tqpFYF9Hvj+7GuYfsqS4iEalSSgjVQGHxNv48YQ4ffP2dbm8pIlmjhJBleas2MPSp6XTapQnjh/Wj2Q66o5mIZIcSQpa4O89/tow7Js7TKCIRqRaUELJgfWExN7w0i7nffs/Tvz2M7m2aZTskEZHUT0yTzJixbC0n3j+Vxg3qMe6yfkoGIlJtaA+hipSUOI+9v4iHJufx51N6csJ+bZM/SUSkCikhVIHVG7Zw9XNfUrCpmJcu60u7nRtnOyQRkZ9Ql1El+yDvO04YOZXubXbkuYv7KBmISLWlPYRKsnVbCSPfXsAzny7lnjP3Z0C3XbIdkohImZQQKsHytZu58pnp7FC/LhMu78euzXbIdkgiIkkpIWTY67NXcP2LM7mwfyeG9O9EnTo6t0BEcoMSQoYUFm/jjlfn8va8lYz6VW967dki+ZNERKoRJYQM+HrleoY9/QWdWjXhlcv707yRLj8hIrlHCaEC3J2nPsnn3je+4ppjunOO7nMsIjlMCaGcCjYWce3YGSwr2MyzF/Why65Nsx2SiEiF6DyEcvjg6+84fuQU2rdszIuXHa5kICI1gvYQ0lC0tYS/vvkVL05fxt1n6NwCEalZlBBStOi7jVzxzHR2adqQVy/vT8umDbMdkohIRikhJOHuPPfZMu6cOI8rj+zKLw9rrwPHIlIjKSGUYd3mYq57cSZf/2+D7lsgIjWeDiqX4pNFazj+71No1aQB44b2VTIQkRpPewhxYhele/rTpdx5+r78bO/W2Q5JRKRKKCFELF2ziSuemU6ThvV4ZVg/dt1RF6UTkdojrS4jMzvZzN40szVmVmhmC8zsXjNrmWY97c1slJktMbMtZrbSzMaZWd/0ws+ccV8s59QH3+f4fdvy+K8PUTIQkVrH3D21Fc1GADeVsngxMMDdl6ZQTy/gLSDR1d9KgAvc/fEU6vFUYy/L+sJibho3my+XrWXkOQfSc/fmFa5TRKS6MjPcPeFQyZT2EMysP9uTQQlwHXAa8FFY1gF4NIV66gFPsT0ZvAqcDNwbiechM+uUSlwV9Xl+ASeMnBrct2BYPyUDEanVUtpDMLOxwOnh7KPu/tuwvB2wBIhlm57uPruMek4CXg5nvwfauPvmcNmbwJHhsnvc/ZokMZV7D2FbifPQ5K8Z/cFibj21J8f21A3vRaR2KGsPIdWDyoMi01NjE+6+1MzygfZh0WCg1IQQLo/5PJYMQu+zPSFE18uogo1FXPTfz6hjMH5YP9o2b1RZmxIRySlJE4KZteDH/f0r4lZZwfaE0DlJddGuoET1xCSsx8yGAEOSbKNMzXaox5kH7cHpvfagru5mJiLyg1T2EJrEzReVMZ/ssp/RutKux91HAaMg6DJKsq2E6tWtw5m925XnqSIiNVoqB5U3xs3HX9UtOr8hjboqUo+IiGRY0oTg7gVAQaSoTdwq0SOyeUmqW5ihekREJMNSPTHtnch0/9iEmXUEov0vk5LUE13ey8waR+YHpFGPiIhkWKoJYWRk+nwzu87MTgXGRMrfig05NbPRZubhY3hknYnAgnC6GfC8mZ1kZvcBR4TlhcA/022IiIhUTErDTt39XTO7DbieIIncFrdKPnBhCvVsNbNzCc5Ubg4cFz5+WAW4zN3VZSQiUsVSvpaRu99AcHbyJGAtwaigPOA+oLe7L0mxnmnAgcC/gGVAMbAaGA8c4e6PpdMAERHJjJSvZVTdZOpaRiIitUmFr2UkIiI1X07fD0H3NhYRyZyc7TKqCDOb5u69sx1HVVF7aza1t2aryvaqy0hERAAlBBERCdXWhDAq2wFUMbW3ZlN7a7Yqa2+tPIYgIiI/VVv3EEREJI4SgoiIAEoIIiISqhEJwcxONrM3zWyNmRWa2QIzu9fMWqZZT3szG2VmS8xsi5mtNLNxZta3smIvj0y018wGmNlDZjYtbGvs6rSTKzH0cqloe82suZldbmYvmNlXZrbWzIrMbKmZPWlmB1R2G9KRoff3T2Ed+Wa2MWzvCjN7w8zOs2p0Vmem/n8j9TU1s68jn2k3s4EZDrvcMvT+To5rX/zj+XIF5+45/QBGEFwlNdFjEdAuxXp6AWtKqWcbcF6225rh9v6tlDomZ7uNmW4vcFgZdTjBhRqPy3ZbM/z+Lk7S5pHZbmsm2xtX5yMJ6hqY7bZm+P2dnOT9fb5c8WX7Bargi9s/7kv7T8CpwIeR8tdTqKceMC/ynFeAk4B7ImWbgE41ob1hXdcB44AbgWerY0LI4Pt7WPj88cCvgSOBawlu1RqrJ6+mtDes67/h838ODA7b/VWkniKgcU1pb6TOk8Lnba5uCSHD7280IfRL8NirXDFm+0Wq4As8NvKiPBIpbweURJbtk+KHyIF1QKPIsjcjy+6uCe1NUO/wapoQMvX+7gHsl6D8qrgvjV1rQnvLqP+0uPa2rEntBXYBVoTPubwaJoSMtTeaEDIZY64fQxgUmZ4am3D3pQQ37YkZnKSe6PLP3X1zZP79NOqpbJlqb67ISHvdfZm7z0iwaH7c/Ma0I8ysSnl/zay+mXUDfhMp/szdV5cryszJdHtHAa0JbsB1f4Wjy7zKen8Xh8eI1pjZ22Z2ZnkDzNmEYGYtgBaRohVxq0TnOyeprlOG6qk0GW5vtVdF7T07Mv22u2ctIVRGe83sADOLdQ/NB04g6KqYQLC3kDWZbq+ZXUDQ/VIAnO/hz+jqopI/z+2B+mH9g4Fnw9sSpy1nEwLQJG6+qIz5pmnUVZF6KlMm25sLKrW9ZnY18Mtwdh0wLN06Mqwq318DGlawjorKWHvNrCPBIAmAS9x9eQVjqwyZfn9XEuwRnQccBVwAzI4sv9LMDkk3yFy+H0L8r7n4D3h0fkMadVWknsqUyfbmgkppbzjc8h6C4wcQ3A72eHefm3aEmVUZ7V1AcCCzIdARuJTg9rUnAAeb2V7uXlCOWDMhk+29H2gGPOXuYyoaWCXJ6Pvr7mfFl5nZBILbGjcLi04CPkkjxtzdQwg/yNEPc5u4VdpGpvOSVLcwQ/VUmgy3t9qrjPaaWUPgGbYng2XAAHf/sLxxZkpltNfdN7r7VHd/290fBY4AtoSLdwVOL2+8FZXh9u4R/j03OhY/bp13wvKdyhFuhVXF/6+7ryIYSRbTOt06cjYhhN6JTPePTYS7kO0iyyYlqSe6vJeZNY7MD0ijnsqWqfbmioy1N/wieAOI/bKaARzm7jMzEGemZKS9cZ/fqNgolpid0w0ww/R5plzv725mtmeC8l2AbpGib9OOMNtDsSo4jOsIfjyu9zqCA0ufRMrfjKw/OlI+PFJejx+P0X6VYHfrvkjZZqBzTWhvuGyv8LmnEvxqjq03K1Leqya0l+DX8OzIsnzgeH46drt5DWnvlQQJ7waCBPgzgvMQpkXWd4K9o5rQ3l+EbY5/RNv6QFjWsAa0dyDBnt4YgmMIg4Hzw//daP37ph1jNj8QGXqRb41746OPJUD7ZC9wuKw3QX9yonpKgAuy3dYMt3d4GfXEHqNrQnvDf6BkbXWqx1j1TLQ3/ssw0ePBbLc1k5/nUuquVu9tFX6eS4A/lCe+XO8ywt1vIBhCN4ngC72IoA/uPqC3uy9JsZ5pBAfc/kXQt1wMrCY4u/UId38s89GnL1PtzRVqb7na+w7wMPAlsArYSrCHu5Bgb/A4d78s89GnT+9vudo7jWBU0ViCgQPfE3xfLSO46sAAd7+rPPHpBjkiIgLk/kFlERHJECUEEREBlBBERCSkhCAiIoASgoiIhJQQREQEUEIQEZGQEoKIiABKCCIiEvr/ql09d+vC2VEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rminor,rtoPmap(rminor))\n",
    "plt.title(\"rhopol vs minor radius\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1fbfdc8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.02421263, 0.04810328, 0.07148202, 0.09432171,\n",
       "       0.1168191 , 0.13920484, 0.16169811, 0.18448212, 0.20774234,\n",
       "       0.23185645, 0.25711681, 0.28356376, 0.31121849, 0.34010756,\n",
       "       0.370307  , 0.40194562, 0.43512619, 0.47000388, 0.50726901])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ptormap(np.linspace(0,1,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "10c25abb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "44d261c7",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'profile_file' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-3d47c7b14d32>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprofile_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m             \u001b[0mprofiles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mEOFError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'profile_file' is not defined"
     ]
    }
   ],
   "source": [
    "with open(profile_file, 'rb') as file:\n",
    "        try:\n",
    "            profiles=pickle.load(file)\n",
    "        except EOFError:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cb63a1b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['rho_tor', 'vol_zone', 'te', 'ti', 'ne', 'Zeff', 'nD', 'nT', 'nZ', 'nZ_zave_vol', 'nHe3', 'tHe3', 'nHe4_fast', 'nHe4_thermal', 'WHe3_perp', 'WHe3_par', 'WHe4_perp', 'WHe4_par', 'FastAlpha_source', 'Pich_min', 'chi_e', 'chi_i', 'jtor', 'jtor_b', 'Bt_ext', 'Bt_plasma', 'Rmajor', 'timeExtracted', 'P_LCFS', 'W_th', 'W_tot', 'li1', 'li3', 'rho_pol', 'pol_flux', 'q', 'shat', 'rmin', 'Rmax', 'Rmin', 'Zmax', 'Zmin', 'timeLastSawtooth', 'timeRangeExtracted'])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9904eb75",
   "metadata": {},
   "source": [
    "Note the profiles['rmin'] is an average radius quantity used in transport analysis and not a true radius."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9ab96086",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['nW', 'nH', 'nbbbs', 'limitr', 'rdim', 'zdim', 'rcentr', 'rleft', 'zmid', 'rmaxis', 'zmaxis', 'simag', 'sibry', 'bcentr', 'current', 'fpol', 'pres', 'ffprim', 'pprime', 'psizr', 'qpsi', 'rbbbs', 'zbbbs', 'rlim', 'zlim', 'r', 'z', 'fluxGrid', 'iiInside'])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eq0.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ebd02b7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(profiles['rho_tor'],profiles['ne'],label='ne') #looks like 1e20 units\n",
    "plt.title('Densities vs rho_tor')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5daa0aee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02160237, 0.03607057, 0.05052128, 0.06487812, 0.07917478,\n",
       "       0.09347048, 0.10776363, 0.12204809, 0.13632829, 0.15060297,\n",
       "       0.1648734 , 0.17914035, 0.19340138, 0.20765023, 0.2218789 ,\n",
       "       0.2360817 , 0.25025742, 0.26440983, 0.27854628, 0.29266795,\n",
       "       0.30676159, 0.32081647, 0.33483699, 0.34883058, 0.36280089,\n",
       "       0.37674767, 0.39066773, 0.40454985, 0.41837844, 0.43213334,\n",
       "       0.44578906, 0.45931639, 0.47268601, 0.48587126, 0.49885429,\n",
       "       0.51162398, 0.52417529, 0.5365076 , 0.54862419, 0.56053118,\n",
       "       0.57223516, 0.58374485, 0.59506922, 0.60621741, 0.61719598,\n",
       "       0.62801018, 0.63866368, 0.64915968, 0.65949981, 0.6696859 ,\n",
       "       0.67971882, 0.6896002 , 0.69933131, 0.70891312, 0.71834663,\n",
       "       0.7276331 , 0.73677365, 0.74576912, 0.75462063, 0.7633289 ,\n",
       "       0.77189524, 0.78032017, 0.78860482, 0.79675012, 0.80475645,\n",
       "       0.81262528, 0.820357  , 0.82795304, 0.83541424, 0.84274147,\n",
       "       0.84993573, 0.85699807, 0.86392858, 0.87072805, 0.87739724,\n",
       "       0.88393671, 0.89034685, 0.89662839, 0.9027815 , 0.90880657,\n",
       "       0.91470409, 0.92047404, 0.92611518, 0.93162705, 0.93700766,\n",
       "       0.94225629, 0.94737223, 0.9523551 , 0.95720568, 0.96192561,\n",
       "       0.96651824, 0.97098707, 0.9753386 , 0.97958762, 0.98375987,\n",
       "       0.98785784, 0.99179819, 0.99541925, 0.9985638 ])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(profiles['rho_pol'],profiles['te'],label='te')\n",
    "plt.plot(profiles['rho_pol'],profiles['ti'],label='ti');\n",
    "plt.title('Temperatures vs rho_pol')\n",
    "plt.legend();\n",
    "profiles['rho_pol']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "839ae895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((265, 265), (265, 265))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B,GP,FRZ,Rv,Zv=eqdsk.getModB(eq0)\n",
    "Rv.shape,B.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8cd97332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dims=Rv.shape\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# Major ticks every 20, minor ticks every 5\n",
    "major_ticks = np.arange(-1, 1, 0.1)\n",
    "minor_ticks = np.arange(-1, 1, 0.05)\n",
    "\n",
    "ax.set_xticks(major_ticks)\n",
    "ax.set_xticks(minor_ticks, minor=True)\n",
    "#ax.set_yticks(major_ticks)\n",
    "#ax.set_yticks(minor_ticks, minor=True)\n",
    "\n",
    "# And a corresponding grid\n",
    "ax.grid(which='major')\n",
    "ax.grid(which='minor',alpha=0.3)\n",
    "\n",
    "plt.plot(Rv[int(dims[0]/2),:]-eq0['rmaxis'],FRZ[int(dims[0]/2),:]);\n",
    "plt.plot([-0.57,-0.57],[22,23])\n",
    "plt.plot([0.57,0.57],[22,23]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f077a1",
   "metadata": {},
   "source": [
    "### Modify input file to run `TORIC`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b543bae7",
   "metadata": {},
   "outputs": [],
   "source": [
    "toricnml=f90nml.read('/home/jcwright/etc/TORIC/torica_sparc.inp.template')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f93d698e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "&toric_mode\n",
      "    toricmode = 'toric'\n",
      "/\n",
      "\n",
      "&toricainp\n",
      "    nvrb = 3\n",
      "    nelm = 320\n",
      "    ntt = 128\n",
      "    nmod = 63\n",
      "    nptvac = -1\n",
      "    mxmvac = 15\n",
      "    freqcy = 120000000.0\n",
      "    nphi = 30\n",
      "    antlen = 27.0512957943942\n",
      "    antlc = 1.6\n",
      "    theant = 0.00481340748212431\n",
      "    iflr = 1\n",
      "    ibweld = 1\n",
      "    ibpol = 1\n",
      "    iqtor = 1\n",
      "    icoll = 0\n",
      "    enhcol = 1.0\n",
      "    imdedg = 2\n",
      "    iezvac = 1\n",
      "    icosig = 0\n",
      "    iregax = 1\n",
      "    isol = 1\n",
      "    mastch = 2\n",
      "    lenwrd = 8\n",
      "    pcblock = 4\n",
      "    bscale = 12\n",
      "    iout = 0\n",
      "    iwdisk = 0\n",
      "    idlout = 1\n",
      "    ipltht = 0\n",
      "    io_ncdf = 1\n",
      "    zeff = 1.46229248732297\n",
      "    iclres = 0\n",
      "    dnures = 2.5\n",
      "    tnures = 5.0\n",
      "    scratchpath = './ '\n",
      "    timing_on = .false.\n",
      "/\n",
      "\n",
      "&equidata\n",
      "    atm(1:10) = 1.99955210328161, 2.99426163230608, 3.97368759410027, 18.8611574140408,\n",
      "                182.174413923093, 2.9942415010902, 1.0, 2.99426163230608,\n",
      "                2.9942415010902, 3.97368759410027\n",
      "    azi(1:10) = 1.0, 1.0, 2.0, 9.0, 74.0, 2.0, 1.0, 1.0, 2.0, 2.0\n",
      "    aconc(1:10) = 0.422676777370231, 0.423182943198258, 0.00404362467411854,\n",
      "                  0.00492125525922733, 0.0, 0.0495167865541466, 0.0, 0.0,\n",
      "                  0.0, 0.00089236146100273\n",
      "    igsmhd = 1\n",
      "    intchb = 0\n",
      "    iudsym = 0\n",
      "    dist_plafars = 0.25\n",
      "    dist_plaant = 2.25\n",
      "    dist_plawall(1:4) = 7.97703388216874, 15.5273519360167, 7.94438309853072,\n",
      "                        15.4855062550096\n",
      "    rtor = 185.054908639814\n",
      "    rplasm = 52.3689345227073\n",
      "    ashift = 4.60980661239259\n",
      "    aellip = 1.0\n",
      "    sellip = 1.94253627951623\n",
      "    strian = -0.517262338839001\n",
      "    azvert = 0.0\n",
      "    strigz = 0.0\n",
      "    sp_xs1 = 0.0\n",
      "    bzero = 12.6190507930071\n",
      "    idprof = 1\n",
      "    nspec = 10\n",
      "    mainsp = 2\n",
      "    denec = 43.2856329728955\n",
      "    tempec = 21.0962123828887\n",
      "    tempic(1:10) = 19.5491739900388, 19.5491739900388, 19.5491739900388,\n",
      "                   19.5491739900388, 19.5491739900388, 149.745098336247,\n",
      "                   1168.60846761248, 363.804653716826, 292.234044359963,\n",
      "                   1026.54563910638\n",
      "    ppnei = 1.5\n",
      "    ppnee = 1.0\n",
      "    pptei = 1.5\n",
      "    pptee = 1.0\n",
      "    pptii = 1.5\n",
      "    pptie = 1.0\n",
      "    aicurr = 0.0\n",
      "    ppjte = 0.0\n",
      "    ppjti = 0.0\n",
      "    dnsepr = 9.1686403309568\n",
      "    tesepr = 0.256750917397729\n",
      "    tisepr(1:10) = 0.558695359831384, 0.558695359831384, 0.558695359831384,\n",
      "                   0.558695359831384, 0.558695359831384, 1.65353123234284,\n",
      "                   627.784402050355, 370.552475753765, 216.719102573131,\n",
      "                   718.542316411356\n",
      "    so_thickness = 0.0\n",
      "    gldn = 100000.0\n",
      "    glte = 100000.0\n",
      "    glti = 100000.0\n",
      "    inputpath = '12345P19_icrf/ '\n",
      "    equil_file = '12345P19_equigs.dat '\n",
      "    profnt_file = '12345P19_profnt.dat '\n",
      "/\n",
      "\n",
      "&gfreadinp\n",
      "    gfile = '/afs/ipp/home/m/mab/ntcc/g096161.01500 '\n",
      "    inputformat = 3\n",
      "    nmhd = 165\n",
      "    ntheta = 129\n",
      "    i2mex_remap = 1\n",
      "    i2mex_mx = 2\n",
      "    i2mex_npsi = 0\n",
      "    i2mex_kb = 0\n",
      "    i2mex_direct = 1\n",
      "    i2mex_last_norm_surface_is = 0.899999976158142\n",
      "    ic1 = 2\n",
      "    inc = -1\n",
      "    imom1 = 10\n",
      "/\n",
      "\n",
      "&tofppmodinp\n",
      "    numpsi = 0\n",
      "    power_mw = 5.5\n",
      "    path_to_t4data = ' '\n",
      "    t4data_file = 'NONE '\n",
      "    path_to_fppdata = '12345P19_icrf/ '\n",
      "    fppdata_file = '12345P19_A1_30.fppdata '\n",
      "    iwdisk = 0\n",
      "    xx_file = '12345P19_xx.dat '\n",
      "    nxx_per = 1\n",
      "/\n"
     ]
    }
   ],
   "source": [
    "print(toricnml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "01737968",
   "metadata": {
    "code_folding": [
     0,
     14
    ]
   },
   "outputs": [],
   "source": [
    "def fit_prof(r,rpro,pro):\n",
    "    \"\"\"\n",
    "    r: radius at which to evaluate the profile\n",
    "    rpro: radius at which the profile is given\n",
    "    pro: profile to map\n",
    "    \n",
    "    toric expects the radii to be root psi pol normalized\n",
    "    \"\"\"\n",
    "    fint=interp1d(rpro,pro,kind='cubic',bounds_error=False,fill_value=pro[-1])\n",
    "    pint=fint(r)\n",
    "    #print(type(fint(r)),type(pint))\n",
    "    #print('fit prof',r, rpro)\n",
    "    return pint\n",
    "\n",
    "def fit_prof_Psi(r,rpro,pro):\n",
    "    \"\"\"\n",
    "    r: radius at which to evaluate the profile\n",
    "    rpro: radius at which the profile is given\n",
    "    pro: profile to map\n",
    "    \n",
    "    toric expects the radii to be root psi pol normalized\n",
    "    \"\"\"\n",
    "    fint=interp1d(rpro,pro,kind='cubic',bounds_error=False,fill_value=(pro[0],pro[-1])  )\n",
    "    #fills with last value of off mesh, but add logic for center as well\n",
    "    \n",
    "    pint=fint(r)\n",
    "    return pint\n",
    "\n",
    "\n",
    "equidt={}\n",
    "npsipro=101\n",
    "Psipol=np.linspace(0,1,npsipro)\n",
    "\n",
    "equidt['psipro']=Psipol #defined to be root norm psipol\n",
    "#radius where we need profiles that maps to the toric psi grid\n",
    "rpsi=Ptormap(Psipol)\n",
    "\n",
    "#equidt conventions\n",
    "#            psipro: sqrt(Psipol/Psipol[a])\n",
    "#            tbne: [cm-3] on psipro mesh\n",
    "#            tbte: [keV]  on psipro mesh\n",
    "#            iatm: array of atomic masses/ (C12/12)\n",
    "#            iazi: array of atomic numbers\n",
    "#            tbni: ion densities on psipro mesh\n",
    "#            tbi_provv: ion temperatures on psipro mesh\n",
    "#            nspec: number of ion species\n",
    "\n",
    "\n",
    "#interpolate profiles to radius needed\n",
    "equidt['tbne']=fit_prof_Psi(Psipol,profiles['rho_pol'],profiles['ne'])*1e14 #fit to poloidal flux\n",
    "equidt['tbte']=fit_prof_Psi(Psipol,profiles['rho_pol'],profiles['te'])\n",
    "equidt['iatm']=iatm \n",
    "equidt['iazi']=iazi\n",
    "equidt['nspec']=nspec\n",
    "\n",
    "#advanced usage. Settings of 0,0 mean only one density and temperature profile is given.\n",
    "equidt['kdiff_itemp']=0\n",
    "equidt['kdiff_idens']=0\n",
    "#add logic for options\n",
    "\n",
    "#using concentrations:\n",
    "dt_conc=0.5*(1.-min_conc*iazi[2]/100.)\n",
    "tbni=[dt_conc,dt_conc,min_conc/100.]\n",
    "equidt['tbni']=tbni\n",
    "\n",
    "#ion temp\n",
    "tbi_provv=equidt['tbte']\n",
    "equidt['tbi_provv']=tbi_provv\n",
    "\n",
    "toricnml['equidata']['nspec']=nspec\n",
    "toricnml['equidata']['atm']=iatm\n",
    "toricnml['equidata']['azi']=iazi\n",
    "toricnml['equidata']['aconc']=equidt['tbni']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9f049bdc",
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'e', 'A': 0, 'Z': -1, 'Conc%': 100},\n",
       " {'name': 'D', 'A': 2, 'Z': 1, 'Conc%': 47.0},\n",
       " {'name': 'T', 'A': 3, 'Z': 1, 'Conc%': 47.0},\n",
       " {'name': '3-He', 'A': 3, 'Z': 2, 'Conc%': 3.0}]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plasma species in template namelist\n",
    "def get_spec_toric(toricnml):\n",
    "    \"Collect info on species for ICRF sim in TORIC in nice readable format\"\n",
    "    \n",
    "    spec_toric=list(zip(map(round,toricnml['equidata']['atm']),map(int,toricnml['equidata']['azi'])))\n",
    "    for i,s in enumerate(spec_toric):\n",
    "      name=str(elements[s[1]][s[0]])\n",
    "      spec_toric[i]={'name':name,'A':spec_toric[i][0],'Z':spec_toric[i][1],'Conc%':100*toricnml['equidata']['aconc'][i]}\n",
    "    \n",
    "    spec_toric.insert(0,{'name':'e', 'A':0, 'Z':-1 , 'Conc%': 100})\n",
    "    return spec_toric\n",
    "\n",
    "spec_toric=get_spec_toric(toricnml)\n",
    "#nspec=len(spec_toric)\n",
    "spec_toric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "25d4d826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Psipol,equidt['tbne'],'r.');\n",
    "plt.title('Electron density vs norm root pol flux')\n",
    "plt.grid();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "866b2f90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Psipol,equidt['tbte'],'r.',label='Te')\n",
    "plt.plot(Psipol,equidt['tbi_provv'],'g',label='Ti')\n",
    "plt.title('Temperatures vs norm root pol flux')\n",
    "plt.legend()\n",
    "plt.grid();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73262049",
   "metadata": {},
   "source": [
    "Note that  Conc in the aconc toric namelist is not in percent. This template is for full field D-T burn. For startup you may want to reduce alpha (4-He) concentration. Optimal 3-He concentration may be lower than 5% also.\n",
    "\n",
    "Distances are in cm in namelist.\n",
    "\n",
    "note write_profnt takes species info from namelist eq0_nml, but toric only reads from profnt for species if it exists\n",
    "\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "960d53cc",
   "metadata": {},
   "source": [
    "Using a previous Transp SPARC TORIC run as a template, we create a new input file. \n",
    "\n",
    "We keep ICRF settings on frequency, antenna, etc the same. Interested users may want to vary `nphi` for exploration of antenna phasing. \n",
    "\n",
    "`TORIC` has its own wall model with spacing for the wall and antenna specified relative to the LCF of the plasma. We give suggested values based on the actual SPARC wall and LCF for this case.\n",
    "\n",
    "In SPARC, the antenna is at an absolute position of about 2.44m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4524e751",
   "metadata": {},
   "source": [
    "distances plasma edge to wall (cm) ::   1 : outer; 2 : up; 3 : inner; 4 : down\n",
    "\n",
    "Estimate values to use from LCF and SPARC wall."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b5512d9",
   "metadata": {},
   "source": [
    "### Write equidt.data file for profiles and toric.nml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4703e193",
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import deepcopy\n",
    "eq0_nml=deepcopy(toricnml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ae2e1e3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#the values set below are determined from settings in cell 1 or the equilibrium files\n",
    "eq0_nml['toricainp']['nmod']=toric_mesh[1]/2-1\n",
    "eq0_nml['toricainp']['ntt']=toric_mesh[1]\n",
    "eq0_nml['toricainp']['nelm']=toric_mesh[0]\n",
    "eq0_nml['toricainp']['mxmvac']=min(63,eq0_nml['toricainp']['nmod'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9614db79",
   "metadata": {},
   "outputs": [],
   "source": [
    "eq0_nml['equidata']['inputpath']='./'\n",
    "eq0_nml['equidata']['nspec']=nspec\n",
    "eq0_nml['equidata']['equil_file']=eqdsk_file+'.equigs'\n",
    "eq0_nml['equidata']['profnt_file']=eqdsk_file+'.equidt'\n",
    "\n",
    "#note write_profnt takes species info from namelist eq0_nml, but toric only reads from profnt for \n",
    "#for species if it exists\n",
    "write_profnt(eq0_nml,equidt,version='profnt2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "98eb1541",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values dist_plawall: [23.964259000000034, 51.741305999999994, 4.085172000000004, 51.61501]\n"
     ]
    }
   ],
   "source": [
    "dist_plawall=(np.array(\n",
    "     [np.max(eq0['rlim'])-np.max(eq0['rbbbs']),\n",
    "      np.max(eq0['zlim'])-np.max(eq0['zbbbs']),\n",
    "      -np.min(eq0['rlim'])+np.min(eq0['rbbbs']),\n",
    "      -np.min(eq0['zlim'])+np.min(eq0['zbbbs']) ] )*100 ).tolist()\n",
    "print(\"Values dist_plawall:\", dist_plawall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "18cae903",
   "metadata": {},
   "outputs": [],
   "source": [
    "eq0_nml['equidata']['dist_plawall']= dist_plawall\n",
    "eq0_nml['equidata']['dist_plafars']= 2.0 # (np.max(eq0['rlim'])-np.max(eq0['rbbbs']))*100.-1.5 #faraday offset\n",
    "eq0_nml['equidata']['dist_plaant']= 4.0 #(np.max(eq0['rlim'])-np.max(eq0['rbbbs']))*100.-0.5  #ant offset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5e4c20a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5072690061220002,\n",
       " [23.964259000000034, 51.741305999999994, 4.085172000000004, 51.61501],\n",
       " 2.0,\n",
       " 4.0)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(rminor),eq0_nml['equidata']['dist_plawall'],eq0_nml['equidata']['dist_plafars'],eq0_nml['equidata']['dist_plaant']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b688a5",
   "metadata": {},
   "source": [
    "The namelist variable `idprof` determines whether we use doubly parabolic analytic profiles or numerical ones. Here we handle the case of numerical profiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "07c5ac9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert eq0_nml['equidata']['idprof']==1, f\"idprof must be 1 to use profiles: {eq0_nml['equidata']['idprof']}\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a3d23a3",
   "metadata": {},
   "source": [
    "Create profile inputs in the format toric uses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cf41a140",
   "metadata": {},
   "outputs": [],
   "source": [
    "eq0_nml['toricainp']['isol']=1  # 0 running diagnostic mode\n",
    "eq0_nml['toricainp']['timing_on']=True\n",
    "eq0_nml['toricainp']['nphi'] = int(kpar_ant*(eq0_nml['equidata']['dist_plaant']/100+rminor[-1]+Raxis) )\n",
    "\n",
    "eq0_nml.write('torica.inp','w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6715fdae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Toroidal mode of antenna is:  28\n"
     ]
    }
   ],
   "source": [
    "print('Toroidal mode of antenna is: ',eq0_nml['toricainp']['nphi'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb3910f2",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Execute `TORIC`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cdb0628f",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#This will run at resolution of 400x63 in under a minute if nodes are free\n",
    "#You can check that with sinfo\n",
    "#alternatively run Storic.e for serial execution in about 4 minutes\n",
    "#requires module load psfc/toric5\n",
    "!cp /home/software/psfc/local/share/toric.slurm ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ef984053",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitted batch job 45848291\n",
      "\n",
      "Job ID is:  45848291\n"
     ]
    }
   ],
   "source": [
    "jobmessage=str(subprocess.run([\"sbatch toric.slurm\"], \n",
    "                   shell=True, stdout=subprocess.PIPE).stdout, \"utf-8\" )\n",
    "print(jobmessage)\n",
    "jobid=jobmessage.split()[-1]                \n",
    "print('Job ID is: ',jobid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "98d1cbf7",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job status is\n",
      "             JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)\n",
      "          45848291 sched_mit    Toric jcwright  R       0:06      1 node550\n"
     ]
    }
   ],
   "source": [
    "print('Job status is')\n",
    "!squeue --job $jobid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d5c085ae",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profiling and runtime\n",
      "   JobName    Elapsed \n",
      "---------- ---------- \n",
      "     Toric   00:00:11 \n",
      "     batch   00:00:11 \n",
      "    extern   00:00:11 \n",
      "hydra_bst+   00:00:11 \n"
     ]
    }
   ],
   "source": [
    "print('Profiling and runtime')\n",
    "!sacct  -j $jobid -o \"JobName, Elapsed\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dc33fae",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from importlib import reload  # Python 3.4+\n",
    "\n",
    "toric = reload(toric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e82427a",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "case2=toric.toric_analysis(mode='ICRF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ee22a13",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "case2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd6544c3",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "case2.plotpower();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6b41466",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "ispec=3\n",
    "case2.plotpower(power='PwIF',species=ispec)\n",
    "plt.xlim([0,1])\n",
    "plt.title(spec_toric[ispec]['name']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31221b81",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "case2.spectrum(cx=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93cfad1a",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "case2.plot_2Dfield('Eplus',logl=-20, im=True, scaletop=0.12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90c488aa",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "case2.plot_2Dfield('Eminus',logl=-20, im=True, scaletop=0.25);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6fbd06e",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "case2.plot_2Dfield('Re2Eminus',logl=20, scaletop=0.8);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "648bfe57",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Make theta mesh from equilibrium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a053af4",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "eq=deepcopy(eq0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eab8cd01",
   "metadata": {
    "code_folding": [],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#routine for making equal arclength mesh. saved in file eqdsk_file+'.pkl'\n",
    "def mapper(eqfile,jac):\n",
    "    from scipy.signal import butter, filtfilt\n",
    "    def butter_lowpass(cutoff, fs, order=5):\n",
    "        nyq = 0.5 * fs\n",
    "        normal_cutoff = cutoff / nyq\n",
    "        b, a = butter(order, normal_cutoff, btype='low', analog=False)\n",
    "        return b, a\n",
    "\n",
    "def butter_lowpass_filtfilt(data, cutoff, fs, order=5):\n",
    "    b, a = butter_lowpass(cutoff, fs, order=order)\n",
    "    y = filtfilt(b, a, data)\n",
    "    return y\n",
    "\n",
    "def find_cut(x,y, rmaxis, zmaxis):\n",
    "  #adapted from S. Shiraiwai to find crossing going counter clockwise\n",
    "  for k in range(len(y)-1):\n",
    "       km = k-1\n",
    "       if y[km] < zmaxis and y[k] > zmaxis:\n",
    "         return k\n",
    "  return -1\n",
    "\n",
    "\n",
    "import scipy.integrate as integrate\n",
    "import scipy.fftpack as sft\n",
    "import scipy.interpolate as interpolate\n",
    "\n",
    "eqfileobj=eqdsk_file+'.pkl' #mapping stored in pkl file\n",
    "mu0=4.*np.pi*1.e-7\n",
    "R=eq.get('r')\n",
    "Z=eq.get('z')\n",
    "B,grad_psi,fRZ,RR,ZZ=eqdsk.getModB(eq)\n",
    "psi=eq.get('psizr').T\n",
    "curtor=[]\n",
    "area=[]\n",
    "\n",
    "\n",
    "#Parameters\n",
    "mapzmaxis=0.0\n",
    "ifrhopol=True #use root psipol mesh instead of psipol (eg for torlh)\n",
    "jac='straight' #'eqarc'\n",
    "\n",
    "# get flux surface on fine rectangular mesh\n",
    "newR=np.arange(min(R),max(R),0.01)\n",
    "newZ=np.arange(min(Z),max(Z),0.01)\n",
    "newZ=newZ-eq['zmaxis']  #axis needs to be at z=0 for mapping\n",
    "\n",
    "RR_finer,ZZ_finer=np.mgrid [min(R):max(R):200j, min(Z):max(Z):200j ] #200x200 2D coordinate meshes\n",
    "\n",
    "spline_psi = interpolate.RectBivariateSpline(R,Z,psi.T,bbox=[np.min(R),np.max(R),np.min(Z),np.max(Z)],kx=5,ky=5)\n",
    "psi_int=spline_psi.ev(RR_finer,ZZ_finer)\n",
    "psi_int_r=spline_psi.ev(RR_finer,ZZ_finer,dx=1)\n",
    "psi_int_z=spline_psi.ev(RR_finer,ZZ_finer,dy=1)\n",
    "grad_psi=np.sqrt(psi_int_z**2+psi_int_r**2)\n",
    "\n",
    "#check for save\n",
    "import pickle\n",
    "\n",
    "if True:\n",
    "  #generated mapped mesh size:\n",
    "  npsi=128\n",
    "  ntheta=128\n",
    "\n",
    "  #we will have eq_theta at each filtered_cx,cy\n",
    "  #like polar contour needed to be converted to regular mesh\n",
    "\n",
    "#get psi mesh for surfaces for theta within LCF\n",
    "#LCS is at psi=0\n",
    "#drop first point, magnetic axis. We add this one manually since it cannot\n",
    "#be contoured.\n",
    "\n",
    "  #psimesh=eq['fluxGrid'][1:] #poloidal flux grid, created by readGEQDSK from eqdsk but not in eqdsk file\n",
    "#resize psi to the number of desired psi levels, psimesh is already uniform\n",
    "#initial psimesh is [-psimin,0].\n",
    "#the following is only necessary if psimesh is not uniform which it should be for an eqdsk file.\n",
    "\n",
    "  nidx=100\n",
    "#reference psi\n",
    "  fity=np.linspace(eq['simag'],eq['sibry'],nidx)\n",
    "\n",
    "  if ifrhopol:\n",
    "    rhopol = np.linspace(np.sqrt(np.abs(eq['simag'])),np.sqrt(np.abs(eq['sibry'])),nidx)*np.sign(fity)\n",
    "    fity = rhopol**2*np.sign(fity) #reference psipol consistent with uniform rhopol\n",
    "    #rhopol is linear so just declare it\n",
    "    rhopol = np.linspace(0,1,npsi) \n",
    "    #rhopol = (rhopol-np.min(rhopol))/(np.max(rhopol)-np.min(rhopol)) #normalize\n",
    "    eq['rhopolmap']=rhopol  #sqrt norm rho pol for map size npsi, linear spaced\n",
    "\n",
    "  fitx = np.linspace(0,nidx,nidx)\n",
    "#get psimesh\n",
    "  fitxvals = np.linspace(0, nidx, npsi)\n",
    "  psimesh = np.interp(fitxvals, fitx, fity) #this just stretches fity to size of npsi\n",
    "\n",
    "  rmaxis = eq['rmaxis']\n",
    "  zmaxis = mapzmaxis #eq['zmaxis']\n",
    "  eq['psipolmap'] = psimesh\n",
    "\n",
    "  c_pprime = np.interp( psimesh, eq['fluxGrid'], eq['pprime'] )\n",
    "  c_ffprime = np.interp( psimesh, eq['fluxGrid'], eq['ffprim'] )\n",
    "\n",
    "#Extract contours and values for flux coordinate system.\n",
    "#contours go counter-clockwise, which we want\n",
    "#contours don't necessarily start at y=0., so rebase\n",
    "  fig = plt.figure()\n",
    "  ax = fig.add_subplot(111)\n",
    "  ax.set_aspect('equal')\n",
    "  fig.set_figheight(6)\n",
    "  psi_cs=plt.contour(RR_finer,ZZ_finer,psi_int,levels=psimesh)\n",
    "  #psi_cs=plt.contour(Rv,Zv,psi,levels=psimesh)\n",
    "\n",
    "#Get Psi contours, careful to exclude field coils\n",
    "  psixy=[]\n",
    "  for p in psi_cs.collections:\n",
    "      for pp in p.get_paths():\n",
    "        v = pp.vertices\n",
    "        x = v[:,0]\n",
    "        y = v[:,1]\n",
    "        if np.abs(np.average(y))<0.20*np.max(eq['z']): #only keep core plasma contours\n",
    "          psixy.append( (x,y) )\n",
    "\n",
    "#Define uniform theta mesh\n",
    "  uni_theta=np.linspace(0,2.0*np.pi,ntheta,endpoint=False)\n",
    "\n",
    "#Set up X(psi,theta) Y(psi,theta) and initialize with magnetic axis point\n",
    "  eq_x=[]\n",
    "  eq_y=[]\n",
    "\n",
    "  #is there any reason for two meshes here?\n",
    "  points = np.array( (RR_finer.flatten(), ZZ_finer.flatten()) ).T\n",
    "  #points = np.array( (Rv.flatten(), Zv.flatten()) ).T\n",
    "  gpsivalues = grad_psi.flatten()\n",
    "  Bpoints = np.array( (RR.flatten(), ZZ.flatten()) ).T\n",
    "  Bvalues   = B.flatten()\n",
    "\n",
    "  from scipy.interpolate import griddata\n",
    "  #print('points',points.shape,gpsivalues.shape)\n",
    "  c_idx=-1\n",
    "  minmodes = 8\n",
    "  maxmodes = 20\n",
    "  for cx,cy in psixy:\n",
    "      c_idx+=1\n",
    "      print('cx',c_idx, len(cx),len(psixy)) #just to see progress\n",
    "      #for each surface, low pass filter to central 8+ DC Fourier modes\n",
    "      #remove last element for fft since it is equal to the first element\n",
    "      #size of (cx,cy) is  variable\n",
    "\n",
    "      #shift to midplane as first element\n",
    "      idx=find_cut(cx,cy,rmaxis,zmaxis)\n",
    "      if (idx>=0):\n",
    "          #now project to centers\n",
    "          #this assumes that values stradle midplane which seems to be the \n",
    "          #case for python contour, but should be made more robust.\n",
    "          cx=0.5*(np.roll(cx,-idx)+np.roll(cx,-idx+1))\n",
    "          cy=0.5*(np.roll(cy,-idx)+np.roll(cy,-idx+1))\n",
    "\n",
    "      #print('0',cy[0],cy[-1],cx[0],cx[-1],np.average(cy))\n",
    "\n",
    "      #filter out high freq noise, esp needed near axis\n",
    "      nmodes=max( minmodes,int(len(cx)/np.float(maxmodes) ))\n",
    "      fftx=sft.fft(cx)\n",
    "      fftx[int(nmodes/2)+1:-int(nmodes/2)]=0\n",
    "      filtered_cx=sft.ifft(fftx).real\n",
    "        \n",
    "      ffty=sft.fft(cy)\n",
    "      ffty[int(nmodes/2)+1:-int(nmodes/2)]=0\n",
    "      \n",
    "      #restore value for idx=0 for Y. \n",
    "      ffttotal=np.sum(ffty) #want to restore to total before filter.\n",
    "      ffty[int(nmodes/2)+1]=-ffttotal/2.\n",
    "      ffty[-int(nmodes/2)-1]=-ffttotal/2.\n",
    "\n",
    "      filtered_cy=sft.ifft(ffty).real\n",
    "\n",
    "    #interpolate |grad psi| and B onto this surface\n",
    "    #this significantly slows down this routine. I think this can be moved outside of the \n",
    "    #loop if cx,cy are saved.\n",
    "\n",
    "      print('1',filtered_cy[1],filtered_cy[0],filtered_cy[-1],filtered_cx[0])\n",
    "\n",
    "      c_gradpsi = griddata( points, gpsivalues, (filtered_cx,filtered_cy), method='cubic' )\n",
    "      c_B       = griddata( Bpoints,    Bvalues, (filtered_cx,filtered_cy), method='cubic' )\n",
    "\n",
    "    #derivative from fft needs factor of 2pi\n",
    "      df_dx=sft.diff(filtered_cx)*np.pi*2.0/len(filtered_cx)\n",
    "      df_dy=sft.diff(filtered_cy)*np.pi*2.0/len(filtered_cy)\n",
    "      dl=np.sqrt(df_dx**2+df_dy**2) #These two steps could be done with FFT too.\n",
    "\n",
    "      #flux surface integrals here\n",
    "      c_curtor = -integrate.simps(\n",
    "          dl*filtered_cx/c_gradpsi * ( c_pprime[c_idx] + c_ffprime[c_idx]/filtered_cx**2/mu0 ) \n",
    "      )\n",
    "      c_area = integrate.simps(\n",
    "          dl/c_gradpsi\n",
    "      )\n",
    "\n",
    "      #if straight field line, multiply dl by 1/R*|gradpsi|\n",
    "      if jac=='straight':\n",
    "        dtheta=dl/np.abs(c_gradpsi*filtered_cx)\n",
    "      else: #jac='eqarc'\n",
    "        dtheta=dl\n",
    "      L=integrate.cumtrapz(dtheta,initial=0)/len(dtheta) \n",
    "      \n",
    "      #now put each on same theta mesh, Jacobian selection\n",
    "      this_theta=L/np.max(L)*2.0*np.pi\n",
    "      t_map=interpolate.interp1d(this_theta,filtered_cx,kind='cubic')\n",
    "      eq_x.append(t_map(uni_theta))\n",
    "      t_map=interpolate.interp1d(this_theta,filtered_cy,kind='cubic')\n",
    "      eq_y.append(t_map(uni_theta))\n",
    "      curtor.append (c_curtor)\n",
    "      area.append( c_area )\n",
    "\n",
    "  #add origin term\n",
    "  area.insert(0,0.)          #area of origin is 0. \n",
    "  curtor.insert(0,curtor[0]) #current density maximum at origin\n",
    "  curtor=np.array(curtor)\n",
    "  area=np.array(area)\n",
    "  Xmap=np.real(np.vstack(eq_x))\n",
    "  Zmap=np.real(np.vstack(eq_y))\n",
    "  eq['darea_dpsi']=area\n",
    "  eq['dI_dpsi']=curtor\n",
    "  \n",
    "  #update npsi to # of actual contour surfaces\n",
    "  npsi=len(psixy)+1\n",
    "  #add origin\n",
    "  NXmap=np.zeros([npsi,ntheta])\n",
    "  NZmap=np.zeros([npsi,ntheta])\n",
    "  NXmap[0,:]=eq['rmaxis'] #origin for all theta\n",
    "  NZmap[0,:]=mapzmaxis #eq['zmaxis']\n",
    "  NXmap[1:,:]=Xmap\n",
    "  NZmap[1:,:]=Zmap\n",
    "\n",
    "  #print(\"Error of axis is\",100.*np.average(np.abs(Zmap[0,:]-mapzmaxis))/mapzmaxis )\n",
    "\n",
    "  del(Xmap)\n",
    "  del(Zmap)\n",
    "  Xmap=NXmap\n",
    "  Zmap=NZmap\n",
    "\n",
    "  #smooth radial dependence with lowpass filter #this seems to introduce wiggles to theta lines\n",
    "#  for ith in range(Zmap.shape[1]):\n",
    "#    theZ=Zmap[:,ith]\n",
    "#    theX=Xmap[:,ith]#\n",
    "\n",
    "#    smoothZ = butter_lowpass_filtfilt(theZ,6,npsi)\n",
    "#    smoothX = butter_lowpass_filtfilt(theX,6,npsi)\n",
    "#    Zmap[:,ith]=smoothZ\n",
    "#    Xmap[:,ith]=smoothX\n",
    "  \n",
    "  print('shapes of mapped arrays: ',Xmap.shape,Zmap.shape)\n",
    "  #save this in pickle file\n",
    "  eq['xmap']=Xmap\n",
    "  eq['zmap']=Zmap\n",
    "  \n",
    "\n",
    "  import pickle  \n",
    "  with open(eqfileobj, \"wb\")  as file_pi:\n",
    "    pickle.dump([Xmap,Zmap,psixy,curtor,eq], file_pi);\n",
    "\n",
    "else:\n",
    "    try:\n",
    "      Xmap,Zmap,psixy,curtor,eq = pickle.load(f)\n",
    "      print('loaded previous mapping')\n",
    "    finally:\n",
    "        f.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c49fc816",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "eq.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a05215c",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Plot coordinate mesh\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_aspect('equal')\n",
    "fig.set_figheight(20)\n",
    "\n",
    "#Theta lines\n",
    "for i in np.arange(0,len(Xmap[0,:]),5):\n",
    "    plt.plot(Xmap[:,i],Zmap[:,i])\n",
    "    \n",
    "#Psi surface   \n",
    "for i in np.arange(0,len(Xmap[:,0]),10):\n",
    "    plt.plot(Xmap[i,:],Zmap[i,:])\n",
    "plt.plot(Xmap[-1,:],Zmap[-1,:]);\n",
    "\n",
    "plt.title('Surfaces of constant theta and psi (every 10th)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b413bec1",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "Xmap.shape, Zmap.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61a35a24",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "!ls *.pkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33ccbccb",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  }
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