Python_Automation/Python_Automation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align=right><img src=keysight-logo.png align=right></div>\n",
    "<h1>Python Automation of Keysight Instruments</h1>\n",
    "*Note: This app note assumes a CPython installation on a Windows machine and assumes some prior knowledge and installation of Python.*\n",
    "<br>\n",
    "<h4>Contents</h4>\n",
    "<ul>\n",
    "<li><a href=\"#General\">General SCPI Instrumentation Control</a><br>\n",
    "<li><a href=\"#Digital_Test_Apps\">Automation of Keysight Digital Test Applications</a><br>\n",
    "<li><a href=\"#N5990A\">Automation of N5990A Automated Test Applications for Receiver Test</a>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2><a id=\"General\">General SCPI Instrumentation Control</a></h2>\n",
    "<p>\n",
    "    Requirements:<br>\n",
    "<ol>\n",
    "<li>Installation of a VISA library such as [Keysight's IO Libraries](http://www.keysight.com/find/iolibs)<br>\n",
    "<li>Installation of the [PyVISA](https://github.com/hgrecco/pyvisa) Python library<br>\n",
    "</ol>\n",
    "</p>\n",
    "<p>Example below uses [N8900A Infiniium Offline Oscilloscope software](www.keysight.com/find/N8900A):</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'KEYSIGHT TECHNOLOGIES,N8900A,No Serial,05.20.0008\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import visa # Import the instrument function from the visa module\n",
    "rm = visa.ResourceManager()\n",
    "scope = rm.open_resource(\"TCPIP0::localhost::inst0::INSTR\") # Connect to the scope using the VISA address (see below)\n",
    "scope.query(\"*IDN?\") # Query the IDN properties of the scope"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If using Keysight's IO Libraries to provide the VISA labraries you can get the VISA address from there:<br>\n",
    "<img src=\"files/IOLibs.jpg\" width=\"750\" height=\"450\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's do something more interesting...\n",
    "\n",
    "Infiniium has a nice demo wizard which we can use to load an example setup and waveform. In this example we will use the Real-Time Eye example as shown below:<br>\n",
    "<img src=\"files/RTEye_Demo_Center.jpg\" width=\"750\" height=\"450\"><br>\n",
    "How about a screenshot from the instrument to confirm that the demo example loaded?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\pyvisa\\ctwrapper\\functions.py:1569: VisaIOWarning: VI_SUCCESS_MAX_CNT (1073676294): The number of bytes transferred is equal to the requested input count. More data may be available.\n",
      "  ret = library.viRead(session, buffer, count, byref(return_count))\n"
     ]
    },
    {
     "data": {
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9kF+sdX9gI42EavsfPc/ss9/+ZsHZ59bFPvvv37mNQIAnSb/IvVKL9g0AAAAA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\nCxfaX7YN9RFyhV7o+MgvxJ500knBNl/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kwFuxYoX9UYtKwjtBgIda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0Rum077Lza3TZcP0TYZuy+2ZBmZ1rFylpNpArzG8q8S7Ap7BV6ujlJ7fQ3o\nayl5PUid87VOnQ7NL9Ou2yzVLtO2T25axu7r2V1WHms/pfNUsr1cmzxOnp/XP2nL74/7P5o6dtuT\n9ehjt68+zk/rNvx1JH3y/bRdyTwZy/PXxwX76i6bb9f5duysUx8HaVupPg7djm7LkmVzZF7yfPPz\n3HZ3efeY6DLuckmf/HJuu4yl3a7bWX/B3778upN5+X7yWOe53G1Iu9tHl3Pp/mlf/3koXY/2U+6+\nqNDJjvQJzfeFlnPXr4/dfrpe5ba78/VETk/c3HkitC6ly9TCXV4Gt61ale5LNftc7XN0j5NMyzjt\nmOs8t27uY7+fy53vtruP/deGTuvjUHvo/eP2kbH0Ce2T2999P/nvA3/avs9z89xltE2XD9E2Gbt/\nK5Jl8n8PdL5Ou+3y2E47be4+Clm3K1mnjPOPC9ryy9o++X5uux3r8t56XLoOXdal/eWxu58y3y6n\n68xz+9j2/LJ6jPU563HXx7qcztdpZffV24bL3T+fv+/uY+GuW9dv5+ce61gfFyyn7e7y8jhtOjS/\nTLtus1S7TNs+uWkZ6/FL5uf7y2Ptp3SeSraXa5PHyfPz+idt+f3hPCedbke3ZcmyOTIveb75eW67\nu7x7THQZd7mkT345t13G0m7X7azffa3o/GRefloep70n9bG0u310OZfun/b1n4fS9Wg/5e6Lcv99\nUtInNN8XWs5dvz52++l6ldvuztd/k/Xfa3eeCK1L6TK1cJfnPKfz9ZnWz+XOd9vdx/5rQ6f1cag9\n9P5x+8hY+oT2yS6v79c6+tLqLeb5hcNsMCfB2lPnrwrW3KBNAjYN/Upxr+ATi2f3TR5/ePUo20e2\n+cJFw8zsKRvaabkiUNtKqiTAC/0qrZB5cuushHpyK21oWVdPBHjuC8R9IcgfS/8PprTpC8ed545L\nvQD9ZZVuR8ahP+S6jIwJ8BpLrr6r5ocqSpHayYmb1lnrLq9Dma/1tq/NXLvS+cKe+OXadR067fdT\n/uutYDv59cu63NeeztPt6DwZ67TS5VyyTR27y/r7orRdH7vPX+fb55mfdsf6WPZDaD+d7/bXPm6b\nK1neOZ7uMvpY+0m720/7yPIyT56v9NO2pN0Zh9qFrtNuI18H4T8/mecu669P90OPfahNxn6bS+a7\nfd1pd/kQbde+bn93nj9fxrLttL4++duqdNpt13W4f4PddhVaT6ntu/1d5dr8dj3ZSpsuR/vLWPnT\nrlJtwl+vO10Pla7b76fT7nx5LPQYu/PSlKpXKbqc1jQk1O7O89tDrzeZp3SeLKd0vtvuPg6Rdnkv\n6/tZSZvukzvPHet70+8j/GWVbkfG7ntb6TIytn97nL97Ml+ntd1fj07rPO2TLJ9v07/vOl+XFfr3\nVdt0n7Rd23Q9bj8l83Q5l7a70/pY1hd6LNzlZN16fP116bTsl87TdqX75pL58rxlrNP+WPj7Jdx2\nWbeQfu6/QzKWdtkv3Y5OS7vS+cLWIX98dX3a3+2n3P0QBdvJr9/dP51vp/Pb0Xky1mmly7lkmzp2\nl/X3RWm7Pnafv863zzM/7Y71seyH0H463+2vfdw2V7K8czzdZfSx9pN2t5/2keVlnjxf6adt2u72\nD7W7few28nUQ/vOTee6y/vp0P/TYh9pk7Le5ZL7b1512l3fpfHfs93fn+fNlLNtO6+vTv/f6N8D9\nWyB0HTo/bZ2h9ZTavtvfVa7Nb/f/La7k32aX9pex8qddpdqEv153uh4qXbffT6fd+fJY6DF256Up\nVa9SdDmtaUio3Z3nt4debzJP6TxZTul8t919bP+m1ZkEbm4wt+zUgQUBnAR6q6/WGbi589NuoZWr\n9nR9EgC6wZysW2+dPW6HTZN1yD7cPq+CW3KrCfDkCjshgZ384myobyk9EeBJ0fXFoS8Cd15IqJ87\nz6UvrlAft81/YcrYnycI8LJLaqf1lNee1tX9B1wf+ycT+tjvL3Rdbh9dTvvqtKzXfSykj25PHrvr\n0/nCXbeuw29Lm9b98PddaX89cXOXDXHX764zbTl9XsnzzD9HmXbXpW3aX+f59LjJ47T9cI+d2+bO\ncxW1p9TBbdPt+Mu7z0na/Gnt4/aXsbsdXcbnt+nfr9B8/+9eaNqfp2R9aetI4/Yp1zdN2nKVrlfb\n9UQr7cTKne/3cZd1uW2l+tWqHusMKfc8VVf3x9+OTvvr1Wm/LTTtzi+l0teJPtbptOXcPu6039+f\nX649TaifO8+l7/1QH7dNH7tjf5772F2f+3dMST/3b6HOE/7fTGnTeaG/gS5/XTrt99dpt93to9P6\nPNznU2p5nyznr8OdF5K23rTldJ/d5bSfOy9tvULatE72sVMbPebaR8ZaD+2vj/3+wv33UPvocn49\nZb3uYyF93Pqn/fvqrlvX4belTet++PuutD/nOU57Sh3cNt2Ov7z7nKTNn9Y+bn8Zu9vRZXx+m7wX\nQsuE3k+haX+ekvWlrSON26dc3zRpy/nrTfv3zm13/330ufP9Pu6yLretVL9a+euUwZ3uLuWep+rq\nc/S3o9OheaX6u9Pu/FLKvf7cdnms02nLuX3cab+/P1/G9m9aHZW7qs6GbPPaCq7Sk/n+FXQuN8CT\n23PdAC/UJuHgc866S6okwBMS4smts3Llndw+K79GG+pXSk8GeP4L2Vfpi1m4fXX9PveF589z5+tj\n/SNPgJddGuCV458g+CcN/rRwXy8ufzkd+/NLtbmPdRvu/FL0RMmf785z9zvteQhdRsaiknW7j90T\nOiHbCS1fycmd0D4+aZN1uH3ksfu83Pn6WKd1nj72+4i0Y+Ry+8ljPcGtdlmXzFN+m9/u8/tV8zfV\npX+rfaG+Kq1/aL7QNnd5fewK9dXp0PLB+blB59dDNet397GZ6HPQwW9vhHLHJqlnvrZp/P7+dFo/\nnR+a9ufr2H9Pyvy0ZUWpNp/bN+29777f/XnufH0c+rvj9nHnyVj6C33st/t0/f46/X8btG9onTr2\n+2ubLqft7rZk7B9j6edOC385nXbnh7h9Q/ND7Tpdqt1/rqH+ofml+Ot0j1loWrj75PKX07E/v1Sb\n+1i34c4vhfOcVfPc5+XO18c6rfP0sd9HpB0jl9tPHnOeU9w/NF9om7u8Pnbnhfr6bW57cH5u0Pn1\nUM363X1sJvocdPDbG6HcsUnqma9tGr+/P53WT+eHpv35Otb3Wijj6Q76IxalvnPO7yPhm1495wd6\nrkoDPCFX5Mkttu5tuyVVGuAJ/dEKvZ1WAr1qrsTriQDPfUG4dH6oLaRUf3ee/1i4/yC480MI8LJL\naufW0v1HPu2xTofGbrvbpic/Ot/lLq/j0Dpcbp9SfUNtfh+dF9o/ma/9td1d3m0PTbv0xCzU7s/T\nae0favfnKfd5yGPdrt/uz3fb3Mdp20mj+6Yq6SPS/v6489KWFX5bqb5p3H3Qv4k+v38p5fpUso5S\nalm+q9sM0UEfu+M05dq7W1eed5fr5DxX93E9pe2zzK/1+bjLVfs8QttMm6f8tpBS/d15/mPh/o1w\n56cJ/Q1yp/Wx30f5y4f6ax+/ze3jC7XpPH3Opdbj9tWxezzcx/50qC1tnjtfH/t9fWnt/vMo9fx8\n0sftH3qs06Gx2+62yb+Z7r+hLnd5HYfW4XL7lOobavP76LzQ/sl87R/6d99tD027ZPm0dn+eTmv/\nULs/T7nPQx7rdv12d76O/WXdtkpJf1clfYT7eg71KbWs8NtK9U3jvw9D/P6llOtTyTpKqWX5rm4z\nRAd97I7TlGvvbl153l2uk/Nc3cf1lLbPMr/W5+MuV+3z8LcZyni6g/zi6xdLxhTcEqvhnFyVp/Pc\ngE+vutO2tCv3qgnw5BbbUusqUk2ApyTIk+++q/ZqvJ4I8Nzi16raF105+mYIIcDLLj/AC9W6XP1D\nalnGVemJjU7r2N1uaB3V8J9DaPtp0vqF5rvz9LGMldvm0zZt9/uF2ksptazOc8d+v65y1xc6/n6b\njP1+pVTSV9fp9vXnudPl6BBqUzqE2lw6hNoqpcu7Yx1C/Xw6Xwd/vtsmQ2i6O1Rbh1qU2t+Sbfl9\n8wedl/TLDzrtK9VWSjXLuX3dx75SbT6/rz+dNq9a3bEOl77fQ0Lt/nKVrEeFlvXb3em0+TotY5ff\n128P8ftVulw10rZRbltuP1eoXzlp6/HnlVPLMq7Qv5+hf1d1WsfudkPrqIb/HELbT5PWLzTfnaeP\nZazcNp+2abvfL9ReSqlldZ479vt1lbu+0PH322Ts9yulkr66TrevP8+dLkeHUJvSIdTm0iHUJkq1\nKe3jjnUI9fPpfB38+W6bDKHp7lBtHWpRan9LtuX3zR90XtIvP+i0r1RbKdUs5/Z1H/tKtfn8vv60\nCGU8vVoowJOr6iSck1+UHT58uA3o/CvtJMST78KT78Sr9Gq8ngrw9IXgDvqCCD3WQefXIvSHQual\n/QHR+WkB3gUXXJDfq1XDhRdeWNRvxYoV+dbOYZ999inqE+pXqk2G0LbkByDcwW/3XXPNNbbflClT\nitr++te/mm9/+9sF/XR44IEHivo3Iw3w3JrqtD4uxX0duNL66WN/Oq3Nby8lbTl3vp4cyTylbaF5\n0t/vo4/LCa0vTaXrLXUi6a5D+um+V7ruUtzj5rdVSpYttf+V8vfBnU5r8+frvJBSbdWSITSt49C8\n0KB9K+mnY/9xGndw5/lt7hDqo8v6be48GXS6lGpr0J01E929PuU+fx3c+Tr2uYNOu+NSffx2d9pt\n13nlBndZHUotr21pffx5bj//sQ46vxah2sq8tJr78/2++ji0vPs3OCS0nnrojnUX7KtXA3/apUOo\nrZH8Y63T+rgUf3mV1k8f+9NpbX57KWnLufPdf6+VtoXm6evU7aOPywmtL02l6y11nuCuQ/q57zG3\nXy3c4+a3VUqWLbX/lfL3wZ1Oa/Pn67yQUm3V8t/fOq3j0LzQoH0r6adj/3Ead3Dn+W3uEOqjy/pt\n7jwZdLqUamvQnTUT3b0+5T5/Hdz5Ova5g06741J9/HZ32m3XeeUGd1kdSi2vbX6fUMbTq5UK8Hxy\ntZ3+6mx7e3vSd9SoUUm73GLrr0/1ZIAng/9iCA3aJ7ScT4dQW63SAjwJ1dwQTQM9d94dd9xhPvvs\ns6I+fognwZvbz/fpp58WTPvr2XvvvYt+vVW2LYNOh1Qa4Imjjz7a9p03b15Bv2YmtQvVtBT5ox/6\nw6/z/bZQ39D8tGX9+aFpfRxSrl2F+vnzZNrlt7nTOk/nh9p1vstvS+sTGvtkvtCTXJ3n93OVatf1\nlVuHq9Qybpvb7s/XNnesj91l3LE+dud3l9Cg80Pt7jxdR2ieO/jTMmg/ty3UT4a0+TL466hk8PuH\n1qHTpdaty5Xi9/On7bxurmktQvul/Daddscy+O1pbTqE+rhjGfw+aYPbr5JlKunjDm5fXTa0fNp8\ndwgt785z6RBqawaV7Jv/+rZDide8DKH5Lh3cxzLotN9PB53vtv//7P1p2HRZVd+PKwYFBBEUkZap\nu5+nn+7n6QEaB4wYBRyiYjQOKIgYRXEARZMoIIgBQWQKo2IziYhEImpEnA1ixIBoNIpxiBqHxBd5\nE1/9rryI17/+57v7fE9/a9Xa55yqu+q+6+z7e67rc+2911p77X32rlNn3es+VRXbPGpyPaKNtmuy\nuezrfQF+Ml+UR11mm8lrfaM8a7OeMaUnmV2Uoa1EnbYpozzTU65EXc0mKyOQg6k4R2WZnkBHMn3G\nWB/VqT7KqdOSde2jJesq3xfZQXmmVxl9ZDI9YhsH7VQ3px6P6GPOEe0zH2yP+Wa/MaJdbBfZnvd0\nF7J5kahjW0scUV/T8chstMQRbWqH2s3pM8dGD7VlX8qyHM+5JkvgATxhd+HChfJkHZ7E45N2CmR8\nSg92/JVa1DOfZ53Aqx1zbPZ5ZOPxRVpL4GUg0aZJNCTV4pNy0QaJwLHkXQ31HROFJI4VOU8JPBxa\nnoQYWJwGY+NNzWUfcz30+cJ/bYxtzm/MD4h61uf0y+RE/URdZI7NGLX+c/3yGoiH6nFEm6yPHmN9\n93Ucyu8uB+ZCeKhMdbW21pWa/JDMGVNtake0xxHtM5t4jNmM6fZxbON31zkcau61IxsPMhIPyqJN\nrPOI9WgXZTgyvR6ZbO6xa79tj23GqZ0PZVGn8oyoY5vlScD95KT3qm0ZG29qLvuY66HPF/5rY2xz\nfmN+QNSzPqdfJifqJ+oic2zGqPWf65fXQDxUjyPaZH30GOu7jwM+D+F314Pz0TmpTHW1ttaVmvyQ\nzBlTbWpHtMcR65lNPMZsxnSnfWRzyHI855paAi+ChN7VV1+9uu6668oTd/whiww8iZf5ONYE3lkd\n2dy2SeDpU3B8Ki4+bYeE3Xvf+96hnSX5pogJu1oCL44VOQ8JvHjom6HW9aB8Sqf6KFd4ZDrAI9Od\nZ2KgNjtw2yFwPKTvjH35iczx62PeMbVmlGf6KKOfY2CX+fBgXeWxxKH6XY/oz8d+jn2tJf20vjdn\ncX4nHRP9dX8yf5RP6VQf5QqPTAd4ZLrzTLxn7xqLzLr37+h7V/blJzLrXH3MOqbWjPJMH2X0M8Wh\nXhfKNvMhPFhXeSxxqH7XI/pb0pHleM41cxN4GUha4OO0eOIOSbtWE3hzfOwyDvrEfnMTePzIavxY\na0zgabKNNmjrUUu64Yk6HPGpuuzju8AJvDyBpyUP7n2ER6bblZo/HpkuY9ebIPqxr5bRX2zvQuZj\nrt9sTpGo1zb7T/nImNOvps/kUcb2tmNoG/Wp/jV4RFlsb3PM9ZHpMtsxH9mhtpn/bcn6ZbJt4Z6p\nL+5loZfr3mp9TDbFXD+7+N4FnmtGPKKuZqdHpo+yKR/xUPtt+25zzPG9y/joM9WP+jm2tWPXfrXj\npP5if7bn+oVdZquyMZ+ZLDtgR2qH2mR2lEWd9lF4ZLpdqfnjkekydn0vQj/21TL6i+1dyHzM9ZvN\nKRL12p7Tv8acvjV9Jo8ytrcdQ9uoz/UT4RFlsb3NMddHpstsx3xkh9pm/rcl65fJtmXYM/G1tpe9\nXPc0299MNsVcP7v43gWea0Y8oq5mp0emj7IpH/FQ+1rfLMdzrjlJAi+Cp/TwHXh4Si/Tn0UCDxcM\nDrwgCI9aW+WxxKH6bY7og4fW5yTwsgTaNgm87GO2Y4m3mCwEnEM8nMC7/eBrhMQj6mnDMjuiLvbF\nwbrqMmjDclt2vRFpv3JtJn4oU31Wqp7EtqJ9o06hX7WPsl2Y07c2xtyxp+xUz3om0z4k2kXdaHvk\ndVY7qKvZ1PRoZ30oj8Sjps/kscShNqdBtvZRFpnSZ2ifOWOA2Ed1KlcyGzCm2we1vcvkKtMjs2HJ\nOo5MntWV2qH6zL7WVnkscah+myP64DFW1/bYMdfurI9t55mtQc2H2rKuzD22sa0dU2NHPW1YZkfU\nxb44WFddBm1Ybsuu7znar/a+Rpnqs1L1JLYV7Rt1Cv2qfZTtwpy+tTHmjj1lp3rWM5n2IVO60fbI\n66x2UFezqenRzvpQHolHTZ/JY4lDbU6DbO1r+0Sm9BnaZ84YIPZRncqVzAaM6caYuyc1u0yuMj0y\nG5as48jkWV2pHdBlOZ5zzVgCjwk5fMcduHTp0uiPVExxlgk8HPGFokS9tvVQm0yfHXNscMBuKoFX\ne/ptzkdoazb0qbLI1PfbAfges2FS7rw8gaevEb4GWGaH2vGgLIN6PaIeh8q2ZZv+2Y0nyvSXyWrU\n/FAeyxraJ8q1XrOp6cb0NXlGZjNHFv1n9cxGiXLWKecXWKtNRPWsR2qvH8p5ZLpYz9Aj0xPV81D9\nSdinL5Ct5xwZ2plM2yqnTm0oV9kc1Jf2j2UN7VOjpp/qp8zdq5pdlE+1a2R2Y+ehh7YzG5VFol7b\neqhNps+OOTY45vrTY1v7QxzbrIHaxn5ZW+ExZUd4aJ1HJsNRk+PYVgeZQlntUDselGVQr0fU41DZ\ntmzTP7tOo8xxzh1kNnNk0X9Wz2yUKGedcsc502OTuXZzydZzjgztTKZtlVOnNpSrbA7qS/vHsob2\nqVHTb9Nv7l7V7KJ8ql0jsxs7Dz20TX2W4znX1BJ4+HVZJO30u+0Ivv8OH53N+o1xVgk8fXGwrtTk\nJyHzSVl20GYsgccn4WrfYVd7uk6TapnNPhJ4c3w86lGPWv1//9//t5GUy+RLTeBxH7nPWscxpWOZ\nEXWxTRkPbUe7fRLfkNeuuURHGeu1tqI6RfWZPesMqNWO7Yzog+2sZD2TR9RO21Ge6bZBz3dfTM13\nF7Z5bY6NRz+ZzWi/mecAu138UM7+SrSJdbbHGLOZ6wPQbqpP1LEd/2AdqytT+gjton2UaXtJ1M4x\nysbgtTBXfhIyn5Rlx1wbLaeOaDfWD7oID23HEofqeVBW07GM9RpzbAhtWdZkbOuRyXBoHxKPTK91\nHFM6lhlRF9uU8dB2tNsnY+8zmY4y1mttRXWK6jN71h3nnJyp+e7CNq/NsfHoJ7MZ7TfzHGC3ix/K\n2V+JNrHO9hhjNnN9ANpN9Yk6to85zsn6HSu1c4yySJbjOddkCTw8eXfttdcOCTv+0iwTelO/Nlvj\nLBN4sTwJJ/XBN+HYxlFL4GUfY43ox2UBk2rah37YBvrkHJ7Six+DzfootacCM/7gD/5g9b//9/9e\nXb58eU32wQ9+cM1uyU/g6d5yX3mwnelVljFmk+ky2UmZeu1DnxH1sY0bowZjeqOkbIwskBvzkdkD\n2tbGzfRTfSJz7SLZ+iiQ/b/P6DY9yEntnAF0Y/pIPP+MwW7Pr8M4vs5B26qnLEP10Ta2Vaa6qXqN\nOTZgm72JTPXFHObOIyP2nfJH/ZSdktnu0n9b+1q7RuzDchu26aNjsa3lSTipj3jds61Hpo/1KGOp\nch7aVhsl6thmGeGR6TLUVuuHgkemI5k+yniojmUkk+uR2fBQWcaYTabLZCdl6rUPfUbUx3a8z7I+\n5lPJ3svHfGT2gLa1cTP9VJ/IXLtItj4KZI5zcj1lGarP6mxHe9VN1WvMsQHb7E1kqi/mMHceGbHv\nlD/qp+yUzHZuf9rNtQfRdm7f2IflNmzTh2NlOZ5zTS2Bxx+lQKKFcjyVxyfw8FHabZN4Z5HAw0Wt\nL4CxF43q1Y5y1deY0k9RS+DxByWyQ+3wkVk9soQfE3I84pN12ViqB3GcqB8DCTs9YvKO35XH413v\netea/lgZewLvtOCR6fbBnNc/OOmNWPtnvhDA0S7qx3SkJp8D+475iDrtk9WVTFYj+qr1VXm003bU\nKTVd3P+sPkX0xTrlWVsZm3MmJ3Fs1WVy1MfmMcXYfLi+asM/VDId5bEdZbweVDaHqT7Ux/XI5kC5\nlspJ1nRb4vxqY6t82/nV+o6twUmJc0Q7I9pHueprTOmnyO5R8Yj6fUC/tfnPOfddoV+OkY2jNlG3\nLbv44Ppswy59TgqPTLcPptYOenCS6zi+D2W+HOfcTvRV66vyaKftqFNqurj/WX2K6It1yrO2Mjbn\nTE7i2Kpj32gzNo8pxuYDHaHs0HFOTQ7GdID6bN2yvpRlupOs6bbE+dXGVvm286v1HVuDk5DleM41\nWQJPE3UPfvCDN+RI7qGO5B5srr766rX+Nc4qgacvAL7Itn2hAvpC37H+1Ee7rE+0rSXwzPETE3hT\nzA1A59rtg+w1GRmz0dd2Bm1wLdVQfQxSKdObNeUcI+tDXWyrjHLVq3ysTZnqlGijbbXJ6mxHWSTa\nsJ2tR03+Ife7Y21Vr+MQ3XPW58I+6i+Sjat9avMinHu003amp1zrmQ11sYy2lGVEPdY/yllXf7W9\niXK2Mx1KyDJfbEd57Ke20UahrGaj8jHUPtbVLuq1ndlOwT5aKtEu1ufKMj3QtdayxtxrLIO+0Xes\nP/XRLusTbcmYjqhN5q/mY45vMNcfgZ6M6faB+s32vDYu5TVdtMmYYzOHufHLXLt9cNLzh24M2mDP\naqhe34NVRrnacoysD3WxrTLKVa/ysTZlqlOijbbVJquzHWWRaMN2th41ueOc+TbUxTLaUpYR9acd\n5+h+U06dQjl16APUNtoolNVsVD6G2se62kW9tjPbKdhHSyXaxfpcWaYHutZakizHc64ZS+AhUTcm\nx1N4aOtTemOcZQIvvhDQJmjrxZ3p2Ef7s852lG0L3rSdwFsumsDjfm7Lrv0U9bHveUR5ZgcZmLoe\neM3wJql1lamdtsmtv35Lag8o53hqk9nvAv3Ec2JdUZsa2VwJZGTMjnLCdtTHEoEOgyvKMU5G3Pca\nmT3b1Ok4HJv1OIdsTrSLUMdzUXSMeK5ox3VAybpCO8K+0TZrZ/5UlvnB6z3KYz3rR5nqog3RNa3Z\ngKhjv7iuKot1Jc5L/UGGMusb7ShHXds11KeOPTYWfav/aM92lMc5Qa++0CbRPvpiW0sS7eK4Ucc+\n2p91tqNsW/S6Z3suc/pRp7Yk2tbsMqJt1jfuU2Rs7TKdysb67gOej5Y1WdRty679lGyO21LrF+WZ\nHWRgal+gB3ht8PXBusrUTtvEcU4+ltqyHfWxxD0QqBzjZMR9r5HZs02djsOxWY9zyOZEuwh1PBdF\nx4jnijZlWrKucL0IbDSWUHlsZ/5UlvnZZ5yDutoQXdPoR4k69ovrqrJYV2jPUv1BhjLrG+0oR13b\nNdSnjj02Fn2r/2jPdpTHOUGvvtAm0T76QjvL8ZxrTpLAYxvfkad2Nc4igacXMV84bPMNAvDmqHas\no5wCY7Gf9okvQoUvXLVxAm+5YO+4n/EmrrLMJuoiNbuszbrKlClZ7DfXXolyvt7j9ZRdX5ShxHWl\ngSuv2ezaVdAfuihnP45VfMh7BWUcn9Be/bCt46td8d0BPW2oUzugeu3PcYjacd5slz79eazJkvOh\nDCXtFegI90/blPE1kL0OKNM+GdE3Sp1jnBuBLUraZLZ6blw7lXN9UXI8Aj3KuD7RJ23VP+qqY4m+\na/T7F4E9oYw+dZy4/4R99fWg/Qh1IPomsNG5sM36YNeNRR+Us5/CPaYd6vSBOmTaX/3QjqgtS0D7\nSNShj7ZVH+s6J0VlOgf2Kcg+az/aa7+oZz3qsr1SW7a5zoB9ok+WU8TXK+XxOlagI5mewJ/aab+5\nsJ/6UnkN6lEqc2UgrklkSke4Rzo3vj5oy37Qs632KqMtUbvYZn0u2ft/ra1QF6nZZW3WVaZMyWK/\nufZKlHMNdY9AbKsMJd87UQe8ZrNrV0F/vjYU9uNYxQfuBaCXs9R+tFc/bOv4ald8d0BPG+rUDqhe\n+3MconacN9ulT38ea7LkfChDSXsFOsL90zZlfA1krwPKtE9G9I1S5xjnRmCLkjaZrZ4b107lXF+U\nHC8S1yf6hA3rKlcdS/Rdo9+/CMdmP0CfOk7cf8K++nrQfoQ6EH0T2Ohc2GZ9sOvGog/K2U/hHtMO\ndfpAHTLtr35oR9SWJaB9JOrQR9uqj3Wdk6IynQP7FGSftR/ttV/Usx51uldln80d1BJ4+H47fDwW\ndZVnCbyY6KtxFgm8jY3vX2jZCyJe1LxI8UJiv2gD9MUG6F/lrKuMdnyDR90JvOWCvcPecj/5Bp7V\nM8YCgam+BHa1IIOwHfVZXYn9ta022q7B80EZ4bWWXZOU89rhNarXserhjz4goy37Uk5bthXakTLH\nfoyaLdvsA7n6oY52YE3f+Uc76jjHYZ14vr29Qn2pi77YdzqOpX4BdGzHfVP0taAl6/DBNoGMcFyS\nzUlltEOd51Do6zw/2md6Pffiv2uzD8thjM6Oc4g66gthfMD2mk0/ltrQbvAnNmjrOKhTPtiIX8pp\nS0p/2GkptpDBjucaoY9S9mPRN1A/Qz2gtigpU736LX76eRW7ft5x/VWHOsdHHbaEMiX2YRuljgPQ\n5vx0ntpH5fSJktCW9mvt/hz0XCJcD50L4XxLf/qK/ntiX86P6wSiDYCe4wD6VznrKqMd3wMyO9pq\nO75fxLaCvoRt6jgm5kEd51Lzqf1jPYNrhvWlDPa6RqizzT5Dvd+b2Id67hHb1KkN6kpNrqjN2HqA\nuGa1dtRFeJ/I7Kb6Etjp/YZARtiO+qyuxP7aVhtt1+D5oIzw9ZBdk5TztcD95+tkQF4r9AEZbdmX\nctqyrdCOlDn2Y9Rs2WYfyNUPdbQDa/rOP9pRxzkO68Tz7e0V6ktd9MW+03Es9QugYzvum6KvBS1Z\nhw+2CWSE45JsTiqjHeo8h0Jf5/nRPtPruRf/XZt9WA5jdHacQ9RRXwjjA7bXbPqx1IZ2gz+xQVvH\nQZ3ywUb8Uk5bUvrDTkuxhQx2PNcIfZSyH4u+gfoZ6gG1RUmZ6tVv8dPPq9j1847rrzrUOT7qsCWU\nKbEP2yh1HIA256fz1D4qp0+UhLa0X2v356DnEuF66FxUZ4RaAg9P1cXvt4sJO3w/3rEn8L7yK7/y\n9hcVZd2Lhi+K4QUnOiV74bBd+okdS/qGrY7DCwxtoG3W0ccJvOWCvdP9jDf1OUz120Y/J8iETbQb\n6xfHZ/+I2oDYD22F64aS1xmuG8Lridcc2oWuTv0awU79gDJOb4M6x6Ne4biDXvxST+hL/bM/GOTi\nR/sNuo5B1rfpR0v2Qb3Ie9voe03Xo/7Zn7LavnFva/ucyRTuMdG91/3HXNDGeRRZX2bnAaAb9OzT\n63heZU3E39Bf6rBNx+j7sl2bh8K5rLVFX+j0lKMkbA/9xc+gkzrPnQx+u5JrsSbv++L+yHrNZo0w\njw1Z31fXe/CV9SWdDn34GqCPwY/YFJm0qWcflNpf7UGZj/oSOdrUl1L66RiR6H/oK31Q8jVNm2jH\nkvXBv8xB7anXPoV+jJpOoQ9AG7ZLP7FjSd+w1XH0OgbaZl37cCy14xjFVuZHmwj7sK62tTZQ3zpm\ntItroOeb2cQ1UXmBbcq6cs0PbaRUfazDRuc8yGUs6lQPeL4ZfL8GtOX7uPalbBum+m2jn7rngOx+\nNdYvjs/+EbUBsR/aCtcNJfcV+0EgK2W/b2UP+32kfo1gp35AGae3QZ3jUa9w3EEvfqkn9KX+2R8M\ncvGj/QZdxyDr2/SjJfugXuS9bfS9putR/+xPWW3fuLe1fc5kCveY6N7r/mMuaOM8iqwvs/MA0A16\n9ul1PK+yJuJv6C912KZj9H3Zrs1D4VzW2qIvdHrKURK2h/7iZ9BJnedOBr9dybVYk/d9z0OcU15H\nvT0o81FfImf/wU766RiR6H/oK31Q8jVNm2jHkvXBv8xB7dfGN3eQJfDwK7QXL14sT+FdunRpkDOB\nh8QeEhb47ju0r1y5sta/xlkl8NY2v3+BsK0vLLaLrLcb2r0O/SgrcvqCPXz0bdqyrnZs00bHdwJv\nuTCBxxt4RrzJa3uKWn8NMtSGslqwEfuoH7VRKIs6lUcbygDH0nmiDni96LVRrpX+2mN9oLNjPdUH\nNmxCf7Z5TUKmc6Jc+9EOMvovbbWTkr7Yd02f0enWfCZUdb3f0f69/2LbgfnpHrEe95EyldNWoQ8l\nrinhPFXGORbi3Eeonq+yg08yzKniY7BTecW2nGfvi31iSeiXqDzalHYYM/bboLenXbFVH8EfiP7Q\n1v0sfZJ+a3T6uOfqY1ij3p6yoU9ljGEOUqdftWFd/dB+Td/bQLY2fm9Pm1Lv7dhH/Q5zoI3oI7TP\ndIrOhf6yubGt4w7tXsf5DXL6gj189G3asq52bNNGx1e7od7JURL2pR997wDqP7NBHTLaoKQcJeWl\n3s8L9WLbt1Wvcshoq/I1dGy2g03RZTaU9SWhHnWdN1Eb9uVciZ4/UF2UaRv9CNq193cQ7wHanqLW\nX+83akOZ3ouU2Ef9qI1CWdSpPNpQBjiWzhN1oGvL/Sn71u8f6wPYg76e6gMbNqE/22UOvUznRLn2\nox1kVf9S0tdgo/qMTrfhN1DV9X5H+/f+i20H5qd7xHrcR8pUTluFPpS4poTzVBnnWIhzH6F6vsoO\nPskwp4qPwU7lFdtynr0v9okloV+i8mhT2mHM2G+D3p52xVZ9BH8g+kNb97P0Sfqt0enjnquPYY16\ne8qGPpUxyvhSZ78oZ1390H5N39tAtjZ+b0+bUu/t2Ef9DnOgjegjtM90ICZhTUeWwANI1l1//fUF\nlSFhpyCZhx+z0L41ziqBl8k3CC8qfdHVXlTQDS/Kmg7tim/V0fZYEnhf9EVf1EVotx9///d/v/rE\nT/zE1M7cAfaON+7sRq6yKWqBQRY0RFkso40SdVkAQ1SnQU1Wz9qU1ebGtq4XynKNdOj1VOSo74t+\njKHelZwH6mvX8wh6bdOefWv9M13NVhnWgPPeEj0/lFx/onul+1eDtpkcc0Q5jNm1cY5sl/PtZZwf\n6pTPPcehD9qVfms2INhs+BCdsubjJMgYxWfXXhu/16/NKxLmOWorTN0f5/iIY4Pa+Cqv2ZBBzzWQ\ndYh21LPOvmqrrzP2Uz1lgx+RD7pENsh7v/qaXuvT66fIxil9pT99Y/8ia/0iYQ5lrE6GEnNWndrQ\nrqpDu+JbdWu2HWyXsXsbXT/I2GYf2qGEzTBOT7GFviPq2VYbtR3GCboC5aEcxqBdhTUbGUP76/7x\nvId+6CP9CPRx3qVPsFd/fE8GlBO1JfrerW2URHVz0XtEJlddlMUy2ihRN3YvU53e97J61qasNje2\n41pyP3S/i7zfv73QjzHUu5LzQB1jD+OPoHZaqjyS6Wq2yrAGnPeW6Pmh5PoT3Svdvxq0zeSYI8ph\nzK6Nc2S7nG8v4/xQp3zuOQ590K70W7MBwWbDh+iUNR8nQcYoPrv22vi9fm1ekTDPUVth6r44x0cc\nG9TGV3nNhgx6roGsQ7SjnnX2VVt9nbGf6ikb/Ih80CWyQd771df0Wp9eP0U2Tukr/dX3ZGxzHqkl\n8DKQwMPTdkjaIXmHj9lee+21qW3GUSTwKi9a6lgvNomtvqDW6GzxYt6QJ0QfvBBQn5PA+4d/+IfV\ns5/97FSnPOtZz9pIvj384Q8vsve85z2jtn/zN3+zetOb3rRmY8bB3ul+RvgaAbzJoyQMAqiLAYLq\naa+yKT1lqsv0Y8CGAQ6DHYXyMX0kjs81wjXCcri++mu01PvrZwxeaySz2TfDOP1cUW49PvuOMWYT\ndNn4XGfCPWA97lNt/wj7qx/Mg3Udp+gwD8yzY5hfX1cd6jr3wbZvn2dq63Baa7TtGGo/zLHf4zFo\nO/RRfdKfftfs5fVEm8wXZRt69hcftNP2iQn+d0LOI9OxXmwSW7TT/p3tcO1OEH3w+lcbUGz6OaEO\nm9hX6wU5BxDtVT6U6JOcK4lytCNxXLWL8jlM9d3Fd7FP5jkX7Y/6sG+djCV0lEdoB/hez3sC0HsF\ndPE+onraq2xKT5nqMv0YsOH9TuMXQvmYPhLH5xpxjbnuqOsac1/GQF8ls9k7/Ry13Hp89h1jzCbo\nsvG5zoR7wHrcp9r+EfZXP5gH6zpO0WEemGfHML++rjrUde6Dbd8+z9TW4bTWaNsx1H6YY7/HY9B2\n6KP6pD/9shzsxLbmi7INPfuLD9pp+8QE/1M4gZewTQLvpBz1E3jCxgt6pm7bF+TgS/rVEnh4Ig6J\nOx5zEnh8iu7pT3/6IEOiDsdf/dVfrdm+973vHZJ6TPJ93ud93pqNGYcJPOznsK8dvKGzzlJv+PHm\nTxnR4EGDCcpqqC+1zWREfW8DAh/0zwKgLLCNY7HNeeg1Mlwr/XVSY45Ny8xdJ31tcr2x9vp6oUzJ\n9lFhP0BfGIftMrd+7EI3F8io49zXzkFsqVvTn2dkXXZhn0ER9qTsd6Ib2HK+wz6H14CSvRbmvEZS\nmzDOXD+ZPGXL85/L0uKcKhWb6lzG6Hxpv6E+d/693TD/qO+p6af6Zezlepx7fh3pHLv+vI6h43v4\n2vt3bze0e73astR7gdZVRvQ+ovcVymqoL7XNZER9bwPuf+if3Qez+2Mci23Og+usDHtRYY5Ny8xd\nJ31tcr2x9vp6oUzJ9lFhP0BfGIftMrd+7EI3F8io49zXzkFsqVvTn2dkXXbBcU6wCePM9ZPJU7Y8\n/znscw+boZbAw/fg3XDDDeUHKgA+SoskRWY7lxYSeIdmn0/ggfgk3Vve8pbVO9/5zo0EHe2YvNMD\nciYDMa4eSA4yKchD/Wb+NKHYEti74Wbe7WV5DXVvZFrqDZ91BgKw0bbaKZmMaHBBu8yPyjQwURlt\nKKNc+8T+DHw0AIoyRfsCHUPH5Tymbgxndd0uDawl1irubQb3Sfcrk6kf1sueYcz+9T+2P5zP1B7v\nndMe75Ro4VqYdQ4j+zf1mjvPLH5tzuq6Pcm4R/Zek74GMMcwT9jgvZnvz0O910GmJd/7Aeu8J7A/\n22qnZDIS7zEsYx+V8T4FVEYbyijXPrE/7ntKJlO0L9AxdFzOI65/ZGPPTArWkq9drnHcC8J90v3K\nZOqH9bJnGLN//Y/tD+cztcd757THOyVauBZmncPI/rWwBlM4gZeQJfDwUVkk7/S77vCDFkjkIVER\n7eeylATeWbLvBB6erNOn7ZCoQwKNJWRMzjHxFtsqU19IBmayOJ4mEOEnfny3FYYn8Hrwpqrwps2S\nMgQA5c2Zb9CiU1uUDBZUTxvW1YZ9UGY2CmSURz11DFpimekjGgxpQJSRzQHnE9dF11nX3kzQrR3W\njPuG9dR9q+2hwr5RVvZCXms63lDG+ZgzYRH3x/4636ibgcUFtw3sI97H9v1edtJ93Pt7a/9+rUSb\nqOe9mSVluDeUfefei05tUcIWMtXThnW1YR+UmY0CGeVRTx3vZbHM9BHHOUdEt3ZYM+4b1lP3rbaH\nCvtGWdkLea3peEMZ52POBMc5y2dxMc5pUEvgMWmHX6G9+uqrh4Qef3EWT+hBHvuO4QTeNPtO4Ol3\n2+FpuL/9278tdSTamEiDjSbdxhJ428j49F2rT9xFYgJPiTdztCnTOttZ4EYbBgxD4AA7QXX0RaCH\nDAGIyiNxHAYxNWiD4AZ1DXYiGtRmAa7K4hiA5xjXxdQpa9TvfVnDToaSe6ZrzTap/RHC/ro3YPa+\n4PWayY0xxqwx6z31jInz0znH+aOO+wXvA9TTRu8pg52gOvoi0EMW702ROA7vZzVow/tfvCcqjnNO\nn7JG/d6XNexkKLlnutZsk2yPuB/sgzqZvS94vWZyY8xsnMBLQFINyTgFyYiHPvSh5ccqkMyjDAk9\n8OAHP7jI+WMWFy9e3PCRcZ4SeJNv6hX2ncDTJJom6rSu338H9pXAQxu+ebT65B0ZS+DtRHbjh0zl\nXR3BBEsGFkXW6ceCDNpRRzsGKvAZgxYlBjdkLFiN5RjqU9uYK8blOWfnZu6A+7q2Xv3ecm25vqyT\nWlBLW9bhl+h42XzM8eCgaHuG13eiOyuWFueYnEPv41HuV3afgEzlXR33Fpa8zxRZp+c1mZ0f7aij\nHe9b8BnvYQp1vNcRxznHBfd1bb36veXacn1ZJ7U4R+sAfomOl83HHA+Oc7ZneH0nurPAe5hQS+Ah\nMYcEHWVI2OF78CC/7rrrShtP5/GpPO1f47wk8E7ywt93Ag/wY6x46o4fZ2ViD4k2/Tgt2GcCj5yH\nRN7eE3iB+Joq7S544OuN7aLv5UMp/WgT5UO70zNAYbBCPdsa0LBONPhhOQaCp6lAV/0DzgNgXmXu\nPHczoOvENtavrFW/z2jrWm+zH+of7eE1ZIxpmuF9N9EZsyvxNcV7O19vbBd9Lx9K6UebKB/anR73\nLbR5D6Ne72m8z7FOeB/UcgzHOYdD14ltrF9Zq36f0da1dpxjzHJwAi8hS+Dx6To8bYfEBGUXLlxY\nS9jhI7Ro47vxtH+N8/QE3q4cIoHH76WLiTok1X7sx36sJNX2layrJfBI/I68ljh0Aq/GENih3Qcs\n0WaD3k4DkbWgJPFBPYIYHYdBDQMc2FGmQRDblLEOYiCV2dM/x9bxSJyruR3uCdctrq8Gs5THtvZB\nCV/0G/cB6Ng6F3M8OChaEP37bYb3sQ28j3XW7it9DBBtNujtqvejxAf1vF/ShvdNvd9RpvdHtvXe\nSRznHBbuCdctru+2cQ590W/cB6Bj61zM8eD31QXRv99GvIcJtQQenrJDck4/HnvNNdesJeyQtOBH\nbWkzhhN40xwigcekGr8Lj3Ik03DEhNq+EnioR9/x47otccgEXgwWMt2YnkFIlEdUH99IB73Iiwzt\nDgY4CgIhlNAzMGJwVCMGVKzTH+G4nMsaNfl5QtaA+xvXkHCdSfxDA8Q+8Mu9BRxjGJ94L4wxxkxQ\nvYeIbkzP+1CUR1Qf70+DXuRFhnYH7nkR3A9RQq/3yHgPVRzn7AlZA+5vXEPCdSaOc4xZBk7gJdQS\neEjc4Qm8G2+8cZAjSeEE3mGpJfCQDEPiLh5zEnn8uGwtUae/Eqvykybw0MZTf3q0+vQdOKsn8E5K\nNSAJrNl0wUoJanq5Bq+oKzEgYpDEgAlBFGGbuhr0xTE2AisHU8OaZHuRrSnQfVBb1omuNcfQPaDO\nHDcOipYN9i+S2Zll4P07HHPvS2s23T2t3Nt6Oe9zuAfynkriPZL3zXhv5f2V5Rj0xTH0Hsv5DXM9\nr/Rrku1FtqZA90FtWSe4HrnWuv/cA+rMceP31eXjPUzIEnhIRiAxB5DEu3z5ckno4Qk8TdjxiTwn\n8PbHnCfwzHGyuATeSYK/vi+DmiGYgbxHAymFgVIMpLYJaImOxeBqY67nkX4dsCdYJ64P103XNK57\nbNOefQF9xfGMMcaYo+Ek9ya5j+Ke5zjnyOjXwXGOMW3jBF5CLYGHp+yQnGMiT4H8hhtuKIk71PUp\nvTGcwJvGCbzlspcE3g4BwhBUJjoyx2YXNvxi/j0MNIu+b2twFIMlDabG0D7KMBfOIZvfOUED2Wzd\nydgfE9qHe0m/XF+Uw5qHOZjjx0FRG3gf2+Dc7OMO94s59/JD3e83/GL+PbwfFn3f1ntnvJfG+2wN\n7aMMc+EcsvmdExiPYF2ydSe7xjnD9Sj7G+dgjh/fH5eP9zAhS+ARfJT22muvLb8+iyRdltDDE3qw\nyfpHnMCbxgm85bK4J/AOwFoQ2Qc9gMEV5KXsdCgRQFHHgCoLtqCPoB99c1yONwRa5zXg6s8ba0Ow\nZnFd5641gI90Pc/rGhtjjDl3MN4odPc/xh28z0Jeyk6HEvfVeA92nLMH+vPG2hCsWVzXXeMcrreO\nZYw5fZzASxhL4GU8+MEPLj9wgSfw8NHauck74ATeNE7gLRcn8Do0yOnqQ6DZtVHiemSQNRV0RaLN\nRsDVj3feA60h6OzXBEytrwa4XFeF66p7qWOaZeKgqA28j23gfVwIGmP090W9NzrOOTxDDNKvCZha\n323jnPO+xq3g99Xl4z1M2DaBdxKcwJvmtttuK08+Zgkic9w4gTdCH2CVILRvM6AFGmRlMhD/i4r+\nKGmP9nkPbHH+MbDl+mRrGqEN1xMl/QD6H8YwxhhjzHCfdJxzWNZikH7NuT7ZmkZow/VEST/AMY4x\nx4UTeAlZAo8fncV33OFJu6jfFSfwpnnOc55T1j5LEJnj5uUvf3m6p+eRWvBDOYMmDW4JA9YIAtup\n/6AyuC3j9IEYx2W9ZXjeWAddW64X1y6D60c7ruWwjr1fbZdxWZrF4aCoDbyPbeB9XBa1mIJy3k/1\nXkwgi/dg4DhnGp431kHXluvFtcvg+tGOa6lxDWQoy/XYlWVclmZx+H11+XgPE7IEHpIR+M47fL9d\nTOBBh+/Eu3TpUvk4reqmcAJvmk/6pE9aPe95zytrq8khc9w84xnPWH3WZ31WuqfnkhDsDIGlBEMa\nfLGcCr4AbbSMaFCm82iKkTXm2k6tq8ppB7Q//elYxhhjzLlm5B5MPe+jvK/yHqv34QzaaBmBP8c5\nd6wv0DUkKqcd0P70p2MZY46D08jlfOMjP3b1o09+0Jrs3z7+E1Z/98qbCr/x3RfXdLCnLvbbBozx\nvV/88Wsy+I7jbVBL4OEHK5DAu+aaa9bkkPGXaFG/cOHCWt8xnMCbxyMe8YjVy172svKRTHP84Mk7\nJ+/qDAFXV+f1GIMmoIGVBl9KTady9anzaB4GoF2J9R5bR4X/7efaE/hJx+ngPnJfzTJZ4v3RbOJ9\nbAPv43JxnHNKOM4xW+L31eVzyD38sDt9yOoD/+bSRiLu0y5+5FoSDYk26qH763974+rmB9x10O9K\nHAf8zNOuWT32k++1JtuglsBDkg4focXHaSFDiR+vYOIOP2CBOmxgH31kOIFnjCkwaELZB2IoY5Ab\nAzAQ5bQl7K9BXssBWFw7kq2RyiiHLfVswyfq3J9s3MKYzhhjjDmv8P7Y30cd5+yO4xxjzi+nkcuJ\nT+DFxJq2kWCLT81l6BN84IWPvWqo/8VLrhQbJhCZsLvLnT900I0ylcBT2U033TQ8lceEHtpzvyfv\nLBN4Ll26PJKyC4a01MCTcg20YjAWURsGdCh1nNLu/K+N02CJ89NAleuSoTasA/Qn8AufU+O6dOnS\npUuXLvvScc7BSsc5Ll2er5L1Q5J9hBaJOk3AIYGnT+yR2kdo9eOxSABqYk6TgDq2Puk3Si2Bx6fr\nxmT4njbIkNijbAw/gWeMUTSoLfRBqAZaIAZlRPUota8GaKCM0/nX8ZcOzwsl2zx/kq0ZS4Xro77K\nOI2tmTHGGHNaOM45GTwvlGzz/Em2ZiwVxznGLI+zSuAp1COB9/7nXBo+PhufoFM0gRefrMt00fco\nWQIPT9chUYen6/hDFVmyLntSbwwn8MyueB/bYGwfGaCRGHjFAA3we02I9i8BWR+osc0gmuXi6c8p\nrlVE1whtrAd1rHO9hrVBO47X4+uxDbyPbeB9bAPvYxs4ztkz/TnFtYroGqGN9aCOda6X45zzg/dx\n+ZzGHo4l8JBgw5N2TNJp8m0s6TY3gQfwRB4+Yhu/D69KLYHH77vDx2PRxi/PIqGHknZO4Blj9oEG\nUwxCGZDGwGwM2sagjb7mBG1LQtdIqa2LtkHsX9ankbUxxhhjjgXHObuha6TU1kXbIPZ3nGPMsjhk\nLid+JFZ/nEI/QqtP2MU+mohTtkng4SO2Y742yBJ4gE/hIUFHkMC7+uqrB5trr73WH6E1p4L3sQ2y\nfUQwxYBT6wxuGXQBDc6mgB8GboAB2+A/1JcGz4tro22cP8v433vqtC/bZY1mBLa+HtvA+9gG3sc2\n8D62geOc/cHz0jiFbZw/S8c5pob3cfl4DxNqCTyAJ+xuvPHGkrxDou7SpUtruitXrhQ5Su1Xwwk8\nY8wUDLA0UMsCsRisKbEfgM8SyGrgNiOIO1Zi4A7i2jCopYx6nDfra2uTjGOMMcaY/cH7brx3ax3w\nXp4R+4HhXq6xjdYXhuMcY4xzOQljCbwxmNzDU3r4VdrMJuIEntkV72MbbL2PXcBVC9q0rtBOoY8S\nxPV+OQYCuiUEdZwn56rrwrK2HrFNe9bhE2Ucs4avxzbwPraB97ENvI9t4DhndzhPzlXXhWVtPWKb\n9qzDJ8o4Zg1fj23gfVw+3sOEXRN4u+AEnjFmFxB0MQCrBXAKgzalBG4IaCWoBRosHjUydwaiIJ5n\nth5KtC/rEscyxhhjzKnBe7rjnNvn7jjHGAOcy0kYS+DhKbuLFy8W+Gu0J8EJPLMr3sc22Gof+0CO\nQRyDMJQaoLGuwRuhTQneel+D7zjekZIF3mjH89e6rkGEtqD422EtfD22gfexDbyPbeB9bAPHOdvj\nOMccCu/j8vEeJmQJPPyAxYULF8r32/EHLFAH+NgsfokWP2AR+03hBJ4xZhsY1JXAToJcBmkx0EWJ\n70NRGdDAeCPIXSCYu54bz49wHVDqOgCcO9dgIBnDGGOMMYeFMU6JSfrYBPdo3r95f+e9HKXjHMc5\nxpwXnMtJqCXw+Au0SNqhrsk8TerhF2hvuOGGDR8ZTuCZXfE+tsGJ97EPxBi8MVhjABdl0Y4MgV02\nxhHC4J7wHFDGcx6DduX8k3Hm4uuxDbyPywb7F8nszDLw/rXBiffRcY7jHLM3vI/Lx3uYkCXw8NFZ\nJOhuueWWtSft8DFafJz28uXLa0k91LV/DSfwjDG7oEEdgjIN6oj+J1ZhIMc+JajrA+TiX+vHSDc3\nnDfnyXPh+ZDsvLWt/QB9b4xnjDHGmFPFcY7jHGPMJs7lJIwl8KYSc3hSDwm+6667LtVHnMAzu+J9\nbIOT7GMJ7rpSg7MY1LFOedbW4FADRvo/WmTeek563iTKUXK9UKb+t8DXYxt4H9vA+9gG3sc2cJxz\nAhznmD3jfVw+3sOEWgKPH51Fki7qd8UJPGPMLiDonBPYEn4/TI0Y5NH3UQa3fdAN4nmDeO6Acp4j\n+8IXZOk4xhhjjDkTeJ9Hnfd6vd/H+7zjHMc5xpwHnMtJqCXwrly5UpJ4+DGLqN8VJ/DMrngf22Av\n+9gFZwzQCIO9LMAjlDE4VI4yoO1gQFrm150z6no+8dxUrnqC/mX9xHcccw6+HtvA+9gG3sc28D62\ngeOc7XCcYw6J93H5eA8TkFTDU3YRfDQWH6PFj1Rk+l1wAs8Yc1KyII9oUEeiDWBf+qHf4juMd1pU\nx0cg2gFdPGc9zwhtyvn158lzLT7jOMYYY4w5c0os0N+39Z4PeH/P7vcK+9IP/aZxxilRHR8xSQd0\n8Zz1PCO0KefXnyfPtfiM4xhjFodzOQlZAg8/VoHkHcBTeNdff32RRbttcQLP7Ir3sQ32sY8anG0T\n6AHYIECkLdsMHs8y4MsCWwajPE8tMf/aF1oT2NIe5xb974qvxzbwPraB97ENvI9t4DinjuMcc9p4\nH5eP9zChlsC79dZb10AyD79Ke+ONN64uXbpUntCL/aZwAs8Ysy8Y8CFYY8CnZAGeylTOoG8I/M4w\nwN1AA+6u5HlkpQJbrhHXCT7WztMYY4wxR4nev1HG+zxjGY0BVKZyxARr93/GFccA5sL5dCXPIysV\n2HKNuE7wsXaexphF41xOQpbAA/gePHz/3Q033FA+Rosn8WJCDzLoYJP5iDiBZ3bF+9gGe99HCd40\nqJv6jy2gLfrC17EFfDqv7ByzcwJ6XoDntXZ+DJR3xNdjG3gf28D72AbexzZwnDMfnVd2jtk5AT0v\nwPNaOz/HOabD+7h8vIcJtQReBp66w8dp8RQensZjMg/1zD7iBJ4xZp9kQV8W7AHVsw7QlwHyUIYx\ntH0alDH74JPnB3T+em56TloH8KPncBbnY4wxxpjtcZwzfX6Oc4xpF+dyErZJ4EXwUdvrrruuJPUy\nfcQJPLMr3sc2OMQ+anAbA8BIFvgNQaQEt4P/EBSeGv24ej4o43lom6DNfkDPD/WhnY07E1+PbeB9\nbAPvYxt4H9vAcc5M+nH1fFDG89A2QZv9gJ4f6kM7G3cmvh7bwPu4fLyHCSdJ4G2LE3jGmEOiAV0W\nABINBAH7IOArZeL7NMEcEIDGeYLsfAj1PBcGs0OZjGWMWSaIbyKZnTGmHRivAL3vRzRuAOzjOMcY\nsyQc2yRkCTw8Waffb7fLD1ZkOIFndsX72AaH3EcEbgxQs2BQy0zHoLYEheJTxzg1+qBUzwfznAJ2\nZf59IMt6OsYJ8PXYBt7HNvA+toH3sQ0c58zEcY45BbyPy8d7mFBL4OkPVgD8aMXNN9+8unz5cvnY\nLGxivymcwDPG7BsEcCUADYFgFhCiHWGfIRDsfFLGALH4D+MeimGs/nyyOc85Hz2HOIYxxhhjlsEQ\nhzjOKXDueg5xDGNMGziXk5Al8MDVV19dEnVXrlwpP1KBBF5M6OEpPfygxaVLl1IfESfwzK54H9vg\n4PvYB4MoEeRpUJsRg0KggSAD5rUxToP+PHReOudaGSlzP8D8fT22gfexDbyPbeB9bAPHOTPpz0Pn\npXOulZEy9wPM39djG3gfl4/3MKGWwIs86EEPWl24cKF8pBYfrUXyjsk8/wqtMeasYPDJ/+giIIxB\nIWBgSChXWwaBlDHA1PEOCc+F8yFxzqyrjJT5yrzhk2tjjDHGmGXhOGfdtszXcY4x5wLnchLmJvAi\nSObhKTwk8/Cx2swm4gSe2RXvYxuc5j4yOEXJOqgFhbQrwW0IaE81OEwCWwXzJipfm3/vB3PW89gX\nvh7bwPvYBt7HNvA+toHjnBk4zjGnhPdx+XgPE3ZN4OE78JC8w9N3eDovs4k4gWeMORRD8CkBHcgC\nQkWDQzIEieJbx9o3Ol/MP85P2wR94nzX5skA1xhjjDGLB/f4cp93nHM7jnOMaR7nchJ2TeAB/Dot\nPkp7/fXXp/qIE3hmV7yPbXBa+8ggj4Efg0MNAlVGKAcMDOFnLWA8FN14Oj7QQDYSbdE+lXl2+Hps\nA+9jG3gf28D72AaOc0ZwnGNOGe/j8vEeJpwkgYen8PjrtJk+4gSeMea0QJAXgz/AwFDrBPagBLUd\nGtRq/RBwbJ3z2DyjjfbP/BtjjDGmHRznGGNax7mchJMm8PwjFuY08D62wWntI4K7scA2lqyTUw1s\n+7EYlNbmDbK5og179VPq2Vh7wtdjG3gf28D72AbexzZwnJMg8cnYvEE2V7Rhr35KPRtrT/h6bAPv\n4/LxHiZMJfCQpLvuuusG0FYdnr5zAs8Yc2yUILQL8GqBYhYkUk57DRCHwHaPQSPnVnx2cA6QcW7Z\nHOP5oF3mloxhjGkTxDeRzM4Y0yaMSRhLaFwAHOcYY5aOY5uEWgIPP0xx6dKl8hFZPGVH+Muz+BXa\nrN8YTuCZXfE+tsGp7mMfLDJgZDCoAW1WZ5t9Dho0dnOjf8B51OajbZboDz/0Uc45G2uP+HpsA+9j\nG3gf28D72AaOcwLd3BznmLPC+7h8vIcJtQQeEnRM2OGHKpC0A0zkbfPjFcQJPGPMaYNgj0FtLThk\nSRjQMvDc8Luv4DH44VzjfDhnwjmiLD46TiuoNcYYY8zx4DjHGNMqzuUkZAk8fDQWyTo+badyJu/w\ndB5L7TuGE3hmV7yPbXDa+zgEex0MBJUYLGawXxrgnpQ+IIVvzjWOT2JQy3NCPz3PUs/G2iO+HtvA\n+9gG3sc28D62geOcQB+XMF5BO45PHOeYfeN9XD7ew4RaAg/JOXDttdeuyZHAw3feMckHm2uuuWat\nfw0n8IwxZ0IfBMbAUQNa1mOQG4PI6HetvQXwR/86HmUMZInqOBeU9DcEuDKGMcYYY84BjnOMMQ3i\nXE5CLYHHRF1Nju/Iu3jxYkngXb58ec2uhhN4Zle8j21w6vsYAk8GgxpE1gJIwCAS9UMEjQxGdTyU\nOpdsTqzzfA4xtzF8PbaB97ENvI9t4H1sA8c568An0PFQ6lyyObHO8znE3Mbw9dgG3sfl4z1M2DWB\nxzY+ZotfolW7Gk7gGWPOghL49f9dZnDI4FFhwKjAlpRA+QT/jQYMQhmUEh2Tc4nyCPvGMYwxxhhz\nfnCcY4xpEedyEmoJPDxZxyftVB4TeFmir4YTeGZXvI9tcNr7yICWQS1KyBgYxqA2tjV4VAZ/yZhz\nGPx0dZ0PyeYB0IclA+Nyjr1f1LV9KHw9toH3sQ28j23gfWwDxzm3M/jp6jofks0DoA9L9GNJv6hr\n+1D4emwD7+PyOeQe3vej/tHqj77/htXfvfKmwr/49Huv6X/z2dcNulsffLc1HXn8p95rsHn/cy6l\nNnPAXND/qo++85occ4jzqibw8FTd2HfgoX311Vc7gWeMOWoY7A1BXx+UxgCSQIdSA0mCNmXZWHMp\n/WUeis6F7WgDeC4M1rNxjDHGGNM2GuMwNmB8oTEFgQ4l9DEOQZuybKy5lP6Oc4wxJ+SQuZwf/6YH\nD8kxJvOYqIPu7U+5es0+EpNu6m8XYv8rn3CXPCmYJfDw1N31119fEngoKdcEHmxuuOGGjV+qHcMJ\nPLMr3sc2OIt91KBWg8EYMGowGeUohwCyKxGQZmNtQ/HR+Ypjgb/7vfHAdm0OXT36PjS+HtvA+9gG\n3sc28D62geOcO3CcY84a7+PyOeQexoQZ20ic/fELL288DRfB03ea5IttEp/0g+/Xfs0DhvYLH3vV\n0F8Tds/9kvsNujWyBB5Asg6JuRtvvHFNhgQeknZI7qGO8rrrrlvrW+MsE3guXbo8n+UQ0HZtBqYI\nDhEYghg0Eg0oactgcs64tRJzUL+sKxgbOpQxsIW8+OvOg/54ftl4Ll26dOnSpct2S40DSuk4x6VL\nl42UrB8CJOqYRCNI4OnHYkn2EdptEnjxST0m5mKyEB+ZxVjxicA1agm8DDx1h6fvNImnT+hN4Sfw\nzK54H9vgLPcRwR+DUw0oGThqGYNJgD4MIBlMQhbHiWif8l/kHo5F3zqW6gjnXfp3/uaMfSh8PbaB\n97ENvI9t4H1sA8c5fZziOMccAd7H5XOae8jkWZaYyz7KumsCT5+sq+lqvgpIqiExty3XXHNNKh/D\nCTxjzFnC4JJBogaOAMEkP9ahgSXtCYPLwaeMsUEfyA52fZ2+OC7HIplsbXyZgzHGGGOM4xxjTEuc\nVi4HiTMm6WJSTZNpY3a178DbJoHH772r+SpkCTx8VBbfb3fhwoUN3UlwAs/sivexDY5mH7ugkEEi\ngkwNJLOANtYZpJZyRoAJOwJ7+ic6ZgxodXz05dhnia/HNvA+toH3sQ28j23gOMdxjjkevI/L55B7\nqB+hZVKOIGlHnX7EVRN40S76INsk8ACSd6PfwZcl8PDrsg972MOGj8riF2kvX768unjxYtFF+7k4\ngWeMOSYQJDJwBAwqVRZhUFwC2i0CTfQBsI8+Ma4CO5bQsy/7Z/6NMcYYYxTHOcaYJeNcTkItgYcf\nsMB33CGRp/CHK/DjFpcuXdrqo7RO4Jld8T62wdHsI4LSrmTgqMEl6zHg1OCSQS18INAcCzap51ix\n1DEAZITjqQ+Oe5b4emwD72MbeB/bwPvYBo5zHOeY48H7uHy8hwlZAk9BMg+JuitXrpQfsMATeTGh\nB3nWN+IEnjHmaOgCxCzgBNCNBZssGWjCTzpGB4NSHUvRcbLx4H9jzGQcY4wxxpgBxznGmIXjXE7C\nVAIvgu/Hw3fjIaHHJB6eyMtsI07gmV3xPrbBUe0jA1IJHBk8otQgEzINOBlg0pb+CmEcBrS0VTiG\nAjlt0Uadc6yNcdr4emwD72MbeB/bwPvYBo5zHOeY48H7uHy8hwnbJvAInsxD4g7M/RitE3jGmGMj\nBp4os2CTMNgE6EvQrgadnZy+1Q99a11t2C6+M7/GGGOMMSM4zjHGLBXnchJ2TeDhSbzrr7++fKQW\nv1ib2UScwDO74n1sg2PcRwSlJSDtQADJALMGA03W2S8Lahn00ob9IrUx2WfwQ9/JWKeJr8c28D62\ngfexDbyPbeA4x3GOOR68j8vHe5iwawIP4Mk7fweeMWaxdAEiAkYGkCD+hxhtwjYDTxCDzCEAhbzX\ncQzYswTqU8egHRj8dKwFt8aYcw3im0hmZ4w5x/SxA2MKwFgD9SwG0VgEMJZRn0PZ1x3nGGMOgWOb\nhCyBh4/H4jvurrvuulKPegKdvwPPnAbexzY4un3sA0YNNhlUaqAZA08GnagPAacGnvDbl7TTPuor\nA3bsC5/HFtD6emwD72MbeB/bwPvYBo5zHOeY48H7uHy8hwm1BJ7+yiwSdDfddFP5qCx+wAI2+Agt\n2k7gGWNaAMEjAkkGnAw+EWAy4IzBqAafrA9l75P9IWM/+iaUUb/mpyvjXI0xxhhjtmFvcU7ni6Xj\nHGPMIXEuJyFL4CE5d/ny5ZK0w3fcMZmXgQQf7KKPDCfwzK54H9vgmPdRg1MNMhl0AganUQ+5Qj/R\nBiX7Rh8K/ZQ59XNj/Rjw9dgG3sc28D62gfexDc5DnFP8VeIclTnOMWeN93H5eA8TsgReBN91d+nS\npdWNN95YnrbT5B2+/86/QmuMWTIMHEEMSAGDUA1GGXjSHiUDWvis+YkBLWWE/QdfXalzNcYYY4zZ\nhn3GOYxLan7Uh8pI8YN5Oc4xxkzgXE7CnAReRH+BFom9zCbDCTyzK97HNjjWfWRQizqCTQSUGphq\n4EmZ2kViwEt79RFLwP6sl/lJsKzzPEt8PbaB97ENvI9t4H1sA8c5jnPM8eB9XD7ew4RdEngASTw8\njQdQz2wiTuAZY44dBpsacKqM8mLfBZxqg2BUg1pF+ytqo4Et2xjjGIJZY4wxxiwfxBdZDBJjk2Kf\nxDmQO84xxpwGzuUknCSBh1+pxVN4+DGLzCbiBJ7ZFe9jGyxhHzWYZICZBaOwYSDKOvupjD4y1B99\nsW82t2PB12MbeB/bwPvYBt7HNjgPcU6U0UeG+mM/9s3mdiz4emwD7+Py8R4m7JrAA/i1Wn4PXqaP\nOIFnjDlm+B9gBJca2MZAlAErg1ISg1PaZbaK2rEv53LsQa4xxhhjlsE+4hyWjnOMMYfGuZyELIHH\nj8fi12XxXXcXLlzYsAFI4OHHLGCb6SNO4Jld8T62wRL2EYElgkqgASdgYBthnxjMxv4qQ7+oA9Bz\nHnFux4KvxzbwPraB97ENvI9t4DjHcY45HryPy8d7mJAl8JiY01+bxUdlb7755tXly5fLD1dcvHix\nJPigcwLPGNMCDE7HAtQY1FJOOwIfqsv6saT90DeZmzHGGGPMSXCcY4xZEs7lJGQJPHDttdeW77ZD\nkg4JOk3oKUjgwSbzEXECz+yK97ENFrGPCGy7EgGmBp5jASrltFX7qEdJvfYHkG/M5wjx9dgG3sdl\ng/2LZHZmGXj/2mAR++g4ZxJfj23gfVw+3sOEWgIvgqfy8OTdlStXynfe4Yk8cOONN/pXaI0x7YDA\nVv5LzIBTA1GUMSgFtFOibc0H+2IO5T/kYV6QZXJjjDHGmNmcMM6hLYm2NR/shzk4zjHGzMG5nIS5\nCbx94ASe2RXvYxssZR8RQCLQZICLkoFohIFpjWhT68NxSvCK4DqZ17Hg67ENvI9t4H1sA+9jGzjO\ncZxjjgfv4/LxHiY4gWeMMRW6ADMLQjWoZV1lmR3bWiKYZYmAdglBrTHGGGMawXGOMeaIcS4nYSyB\nh4/G4iOy+A48gjZ+wCKzn8IJPLMr3sc2WMI+luCSZQhsGYjGgDWTRb2iOvgswawGtEce3Pp6bAPv\nYxt4H9vA+9gGjnM2dY5zzFnhfVw+3sOEWgIP33lX+/EK/nDF3O++I07gGQV5UOgAAMH7SURBVGOO\nHgaVfYkAlwGtBqYMTqEjqtPgNcqBBrTsz/bafIwxxhhj9gXjjL50nGOMOVacy0nIEnhIzOEXaJGo\nww9VoH7hwoXy5B0TeHwiD4m+2L+GE3hmV7yPbbDIfewDT5YapGrgSqj7u9+7I4jN7GIwiwCapPM4\nInw9toH3sQ28j23gfWwDxzl34DjHnDXex+XjPUyoJfCQnGPyjnIk65DAY+IO+ptvvnmt7xhO4Blj\nlgoCTg1sa2VW1zZlDGBL2Qe2cUxjjDHGmNPAcY4x5thwLichS+AxUYcEnT5hpwk8P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OOQH9mpT5d/D89PwJ14zrgRI4zjGK93H5eA8TnMAzxhwaBFQbQVVXVzmDL8LglEFaDNqyutqj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\nyPYR1y9t+H7J9zLWixw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W+7INoOd4UUfoT/XsUy5QOT+UZJhPr482Ol+W1MXz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\nMrb2xhhjjDHGGGPONyWBhz+IX/SiF5U/ZrMkHJJtSMLhe6fQxh/P+KMUf1R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ryrrroq/lqrX2nLGoW2nQDvnLM+Hkeb1YAVFuKZN5f0CM/e8elw/ll/llzX80GXgjYLyhS8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\nANRcQ/j/9cc/bRRDiqUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image at 0x3d831b0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scope.write(\"disp:data? png\")\n",
    "data = scope.read_raw()    # Read back the raw binary data as a string\n",
    "hash_idx = data.find('#')    # Find the start of the image data\n",
    "len_datalen = int(data[hash_idx+1])    # The 1st character after '#' indicates the length of the length field\n",
    "data_start_idx = hash_idx+len_datalen+2    # The binary image data starts here\n",
    "datalen = int(data[hash_idx+2:hash_idx+len_datalen+2])    # The length of the binary data\n",
    "img_data = data[data_start_idx:data_start_idx+datalen]    # Extract just the image binary data\n",
    "\n",
    "from IPython.display import Image # Ipython has some nice tools to display image data.\n",
    "embed = Image(img_data)\n",
    "embed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = open('screenshot.png','wb')\n",
    "target.write(img_data)\n",
    "target.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try pulling some waveform data from the scope. First we'll just grab the first 1000 samples so that we can plot them easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x66220f0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HlhBTVWpMedtQxJXnU8pz9XQGlxJS2eYHyf5915gktT5X5ocg5dXDwb94ywYR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\nxU45qfQ0YQK7UqX7BswKw8SJnDW3EkaOzJ44K3n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      "text/plain": [
       "<matplotlib.figure.Figure at 0x65299d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# Allow plots to appear inline with the IPython notebook\n",
    "%pylab inline\n",
    "scope.read_termination = '\\n' # For ascii transfers the read is terminated with a newline character\n",
    "scope.write(\":WAV:SOUR WMEM1\")\n",
    "scope.write(\":WAV:FORMAT ASCII\")\n",
    "wfm_ascii = scope.query(\":WAV:DATA? 1, 1000\") # Get the first 1000 points of waveform data\n",
    "wfm_ascii = wfm_ascii[:-1] # Remove a trailing ','\n",
    "wfm = [float(s) for s in wfm_ascii.split(',')] # Convert the ascii list of strings to a list of floats\n",
    "x_inc = float(scope.query(\":WAV:XINC?\")) # Get the waveform's X increment\n",
    "x_or = float(scope.query(\":WAV:XOR?\")) # Get the waveform's X origin\n",
    "#mem_depth = float(scope.query(\":ACQ:POIN?\")) # Get the current memory depth\n",
    "#t = np.linspace(-mem_depth/sa_rate,mem_depth/sa_rate, len(wfm)) # Calculate the sample times of the waveform\n",
    "t = np.linspace(x_or, x_or+1001*x_inc, 1000) # Calculate the sample times of the waveform\n",
    "plt.plot(t,wfm) # Plot the waveform vs sample times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try grabbing the whole waveform. First we'll try using the method above using ascii just to see how slow the transfer is. We'll use the python time module to time the transfer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Wfm Sample Points: 320230\n",
      "ASCII Transfer Time: 0.344186125457\n",
      "Time to grab ASCII wfm & convert to array: 0.570182696058\n",
      "Check size of wfm array :320230\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x9fda5d0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vxaZe/xpExDoRMZl0jO+X9Ax96Ng3qB964bFvdVD0tm/SPyFpV+BA4NSI+GTl\nSqXrtt5Wc0N9rd6iscC2wC7AS8CPW1vOykXERqRPbKdLerNyXV94DYr130yqfwl95DWQ1CFpF2Br\n4FMRsV/N+l597OvUP4JeeuxbHRTzgW0qlrehOumaStJLxcdXgD+Sbo0tjIjNAYqXeS8Xu9fWvjWp\n9vnF+dr20jaDi/vqD2ws6bVufho9Xe+rdfbVba+bpJdVRPr/wYb35vojYl1SSPxW0q3F5j7zGlTU\nf12p/r72GkhaDNwO7E4fOvZ16v94rz32WV9g9PRE+hJnFunLmQG08MtsYAPSvT6ADYGHgf1JX46N\nLLaPovOXYwNInwBmUf5y6TFgT9K9xtovl8YW579Czi+zi/sZQucvs3u0XtKXYbNJX4RtUprvpvq3\nqJj/LvD/emv9xfGuBX5a094nXoOM+nv9awB8oNQfWB94kHQPvq8c+0b1b94bj33TT8h1DtiBpH9t\nMRM4q4V1bFt8ISYDT5dqKR7Ue4HngHsqDyhwdrHuacABFe27A08V111e0b4ecCMwg3QveEjOmn8H\nvAi8Q7oXeUKz6i2ONaM4HddN9Z9IOnFNAZ4EbiXdc+6t9e9D+s8uJwOTilNbX3kNGtR/YF94DYCP\nAn8v1j4F+H4zf1674dg3qr9XHnv/wp2ZmWVq9XcUZmbWyzkozMwsk4PCzMwyOSjMzCyTg8LMzDI5\nKMzMLJODwszMMjkozMws0/8HjjcbckYfNO4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x66083b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "num_pts = int(scope.query(\":WAV:POIN?\")) # Get the number of sample points in the waveform\n",
    "print (\"Number of Wfm Sample Points: \"+str(num_pts))\n",
    "start = time.clock() # Grab the current system time & store it\n",
    "wfm_ascii = scope.query(\":WAV:DATA?\") # Grab the whole waveform\n",
    "transfer_time = time.clock()-start # Calculate the time to transfer the waveform\n",
    "print(\"ASCII Transfer Time: \"+str(transfer_time))\n",
    "wfm_ascii = wfm_ascii[:-1] # Remove a trailing ','\n",
    "wfm = [float(s) for s in wfm_ascii.split(',')] # Convert the ascii list of strings to a list of floats\n",
    "wfm_array = np.array(wfm)\n",
    "wfm_to_array_time = time.clock()-start\n",
    "print(\"Time to grab ASCII wfm & convert to array: \"+str(wfm_to_array_time))\n",
    "print(\"Check size of wfm array :\"+str(len(wfm_array)))\n",
    "plt.plot(wfm_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can greatly simplify & reduce the transfer time by using a binary format to transfer the data & the new query_binary_values function introduced in PyVISA 1.6.<br>\n",
    "<i>[More information on reading & writing binary data with PyVISA](http://pyvisa.readthedocs.org/en/1.6/rvalues.html)</i>:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to grab binary wfm & convert to array of integers: 0.0574108663971\n",
      "Time to grab binary wfm, convert to array of floats: 0.0606590857776\n",
      "Check size of wfm array :320230\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xaf653b0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/TlBV8PvAzA4xsyVw23geyRUoom2fJX6gALd9vhNFoT1JP5vkGQD6AxhuZucm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\nFCIi4qVEISIiXkoUIiLipUQhIiJe/x8HRx3UlTRTvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6535270>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The WORD format is the most accurate for Keysight Oscilloscopes. Data is transferred as signed 16bit integers in 2 bytes.\n",
    "scope.write(\":WAV:FORMAT WORD\")\n",
    "scope.write(\":WAV:BYTEORDER MSBF\") # Set the byte order to Big-Endian (default for Infinium oscilloscopes)\n",
    "scope.read_termination = None # Since we're transferring binary data we need to remove the newline read termination\n",
    "start = time.clock()\n",
    "wfm_array = scope.query_binary_values(\":WAV:DATA?\", 'h', is_big_endian = True, container = np.array)\n",
    "wfm_to_array_ints_time = time.clock()-start\n",
    "print(\"Time to grab binary wfm & convert to array of integers: \"+str(wfm_to_array_ints_time))\n",
    "y_inc = float(scope.query(\":WAV:YINC?\"))\n",
    "y_or = float(scope.query(\":WAV:YOR?\"))\n",
    "wfm_array = wfm_array*y_inc + y_or\n",
    "wfm_to_array_floats_time = time.clock()-start\n",
    "print(\"Time to grab binary wfm, convert to array of floats: \"+str(wfm_to_array_floats_time))\n",
    "print(\"Check size of wfm array :\"+str(len(wfm_array)))\n",
    "plt.plot(wfm_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<h2><a id=\"Digital_Test_Apps\">Automation of Digital Test Applications</a></h2>\n",
    "*You can find more information about our scope apps at: [www.keysight.com/find/scope-apps](http://www.keysight.com/find/scope-apps)*<br>\n",
    "<br>Requirements:<br>\n",
    "<ol>\n",
    "    <li>Installation of [PythonNet](https://github.com/pythonnet/pythonnet)<br>\n",
    "        Installation of PythonNet essentially involves putting the appropriate version of the files **clr.pyd** & **Python.Runtime.dll** where Python can access them. However care must be taken to match the appropriate versions (32/64 bit, Python 2.x, CLR 2.0/4.0). The source code is available at the link above if you're brave and want to build it yourself however if you have the Python package manager pip installed you should be able to install the appropriate version using<br>\n",
    "        *pip install pythonnet*<br>\n",
    "        This should copy the correct version of DLL's somewhere Python can access them (ex: C:\\Python27\\DLLS\\)<br>\n",
    "        **[Note: As of September 19th 2017 only version 2.0.0.0 of pythonnet is supported. This can be installed using pip install pythonnet==2.0.0.0]**\n",
    "<br>\n",
    "    <li>Installation of the [Remote Programming Toolkit](http://www.keysight.com/find/rpi)<br>\n",
    "    * Note that with the transition from Agilent to Keysight there is a change in the Remote Toolkit naming conventions for Digital Test Apps. The user must select the appropriate version to use:<br>\n",
    "    <ul>\n",
    "        <li>For control of **both** Agilent & Keysight branded apps the user should select the files in the folder \"Agilent-Keysight Transition\"<br>\n",
    "        <li>For control only of newer Keysight branded apps the user should use the files in the folder \"Keysight Apps Only\".*\n",
    "        </ul><br>\n",
    "    The user must either copy the .config/.dll/.xml files in the Tools directory to their Python DLL's directory or temporarily modify the PATH that python searches to find them (see example below)<br>\n",
    "<br>\n",
    "    <li>The Remote Programming Interface for the Infiniium Compliance Applications is built on a .NET API and requires a **LAN connection** to the scope.<br>\n",
    "</ol>\n",
    "\n",
    "Example using Keysight branded app:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First set up the connection to the RPI DLL via the Python for .NET module\n",
    "import clr # Import the Common Runtime Library Python module\n",
    "import sys\n",
    "sys.path.append(\"C:\\ProgramData\\Keysight\\DigitalTestApps\\Remote Toolkit\\Version 3.30\\Keysight Apps Only\\Tools\")\n",
    "clr.AddReference(\"Keysight.DigitalTestApps.Framework.Remote\") # Create a reference from CLR to the Compliance App DLL\n",
    "from Keysight.DigitalTestApps.Framework.Remote import * # Import the entire compliance app namespace from the Compliance App DLL\n",
    "scopeIpAddress = \"localhost\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're ready to start..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "scope.write(\":SYSTEM:LAUNCH 'U7231B/U7231C DDR3 Test App'\") # Launch the compliance app using the pyvisa instrument connection\n",
    "remoteAteOptions = GetRemoteAteOptions()\n",
    "remoteAteOptions.CloseStartupMessageBoxes = True # Kills any message boxes that may be launched on app startup\n",
    "remoteAteOptions.IpAddress = scopeIpAddress # Tells the Remote Interface where the scope app is running\n",
    "remoteAteOptions.ScopeAppAddressOrAlias = \"lan[localhost]:inst0\" # Tells the Remote Interface where the Digital Test App is running\n",
    "remoteAteOptions.TimeoutInSeconds = 60 # Sets the timeout for the Digital Test App to respond [s]\n",
    "remoteObj = RemoteAteUtilities.GetRemoteAteCustom(remoteAteOptions) # Connect the Remote Interface to the Digital Test App\n",
    "remoteApp = IRemoteAte(remoteObj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we must make some selections about the device we want to test.<br>\n",
    "If we want to take a look at the test ID's & test names currently available we can do that using the TestInfo type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 tjit(CC) Rising Edge Measurements\n",
      "200 tCK(avg) Rising Edge Measurements\n",
      "300 tjit(per) Rising Edge Measurements\n",
      "400 terr(2per) Rising Edge Measurements\n",
      "500 terr(3per) Rising Edge Measurements\n",
      "600 terr(4per) Rising Edge Measurements\n",
      "700 terr(5per) Rising Edge Measurements\n",
      "800 terr(6per) Rising Edge Measurements\n",
      "900 terr(7per) Rising Edge Measurements\n",
      "1000 terr(8per) Rising Edge Measurements\n",
      "1100 terr(9per) Rising Edge Measurements\n",
      "1200 terr(10per) Rising Edge Measurements\n",
      "1300 terr(11per) Rising Edge Measurements\n",
      "1400 terr(12per) Rising Edge Measurements\n",
      "2000 tCH Average High Measurements\n",
      "2050 tCL Average Low Measurements\n",
      "2100 tjit(duty-high) Jitter Average High Measurements\n",
      "2150 tjit(duty-low) Jitter Average Low Measurements\n",
      "3000 terr(nper) Rising Edge Measurements\n",
      "2200 tCH(abs) Absolute clock HIGH pulse width\n",
      "2250 tCL(abs) Absolute clock LOW pulse width\n",
      "10313 VIH.DQ(AC)\n",
      "10314 VIH.DQ(DC)\n",
      "10323 VIL.DQ(AC)\n",
      "10324 VIL.DQ(DC)\n",
      "10353 Overshoot amplitude (Clock, Data, Strobe, Mask)\n",
      "10354 Overshoot area (Clock, Data, Strobe, Mask)\n",
      "10363 Undershoot amplitude (Clock, Data, Strobe, Mask)\n",
      "10364 Undershoot area (Clock, Data, Strobe, Mask)\n",
      "11311 VOH(AC)\n",
      "11312 VOH(DC)\n",
      "11321 VOL(AC)\n",
      "11322 VOL(DC)\n",
      "11341 SRQseR\n",
      "11342 SRQseF\n",
      "10331 VSEH(Strobe)\n",
      "10332 VSEL(Strobe)\n",
      "10333 VSEH(Clock)\n",
      "10334 VSEL(Clock)\n",
      "10411 VIHdiff.CK(AC)\n",
      "10412 VILdiff.CK(AC)\n",
      "10413 VIHdiff.DQS(AC)\n",
      "10414 VILdiff.DQS(AC)\n",
      "11411 VOHdiff(AC)\n",
      "11412 VOLdiff(AC)\n",
      "11413 SRQdiffR\n",
      "11414 SRQdiffF\n",
      "30101 tHZDQ\n",
      "30102 tLZDQ\n",
      "30103 tLZDQS\n",
      "30104 tDQSQ\n",
      "30105 tQH\n",
      "30106 tDQSS\n",
      "30107 tDQSH\n",
      "30108 tDQSL\n",
      "30109 tDSS\n",
      "30110 tDSH\n",
      "30111 tWPRE\n",
      "30112 tWPST\n",
      "30113 tRPRE\n",
      "30114 tRPST\n",
      "30115 tQSH\n",
      "30116 tQSL\n",
      "30301 tDS(base)\n",
      "30302 tDH(base)\n",
      "30303 tDS-Diff(derate)\n",
      "30304 tDH-Diff(derate)\n",
      "30305 tDIPW\n",
      "30021 tDQSCK\n",
      "30022 tDVAC(Clock)\n",
      "30117 tDVAC(Strobe)\n",
      "30306 tVAC(Data)\n"
     ]
    }
   ],
   "source": [
    "remoteApp.SetConfig(\"DeviceType\", \"DDR3-1600\")\n",
    "remoteApp.SetConfig(\"TestMode\", \"Compliance\")\n",
    "remoteApp.SetConfig(\"BurstTrigMethod\", \"DQS-DQ Phase Difference\")\n",
    "remoteApp.SetConfig(\"AcLevels_DQ\", \"150\")\n",
    "remoteApp.SetConfig(\"AcLevels_CA\", \"150\")\n",
    "testsinfos = remoteApp.GetCurrentOptions(\"TestsInfo\")\n",
    "for test in testsinfos:\n",
    "    print test.ID, test.Name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's select some measurements & run some tests:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "remoteApp.SuppressMessages = True # Suppress the GUI prompts\n",
    "# Set various Configuration Parameters\n",
    "remoteApp.SelectedTests = [10313, 10314, 10323, 10324] # Select the tests to run\n",
    "remoteApp.Run() # Run the selected tests\n",
    "# Set up the project save options & save it to disk\n",
    "saveOptions = SaveProjectOptions()\n",
    "saveOptions.BaseDirectory = \"c:\\\\temp\"\n",
    "saveOptions.Name = \"Demo\"\n",
    "saveOptions.OverwriteExisting = True\n",
    "projectFullPath = remoteApp.SaveProjectCustom(saveOptions)\n",
    "# Set up the project results options and then get the results\n",
    "resultOptions = ResultOptions()\n",
    "resultOptions.TestIds = [10313, 10314, 10323, 10324]\n",
    "resultOptions.IncludeCsvData = True\n",
    "customResults = remoteApp.GetResultsCustom(resultOptions)\n",
    "results = customResults.CsvResults\n",
    "#remoteApp.Exit(True,True) # Exit the application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test ID</th>\n",
       "      <th> Test Name</th>\n",
       "      <th> Measured Item</th>\n",
       "      <th> Trial 1 Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 </th>\n",
       "      <td> 10313</td>\n",
       "      <td> VIH.DQ(AC)</td>\n",
       "      <td>             Worst VIH</td>\n",
       "      <td>    (See image)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 </th>\n",
       "      <td> 10313</td>\n",
       "      <td> VIH.DQ(AC)</td>\n",
       "      <td> Number of burst(s)...</td>\n",
       "      <td>              1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 </th>\n",
       "      <td> 10313</td>\n",
       "      <td> VIH.DQ(AC)</td>\n",
       "      <td>          Actual Value</td>\n",
       "      <td> 1.088404000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 </th>\n",
       "      <td> 10313</td>\n",
       "      <td> VIH.DQ(AC)</td>\n",
       "      <td>             NumOfMeas</td>\n",
       "      <td>          2.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 </th>\n",
       "      <td> 10313</td>\n",
       "      <td> VIH.DQ(AC)</td>\n",
       "      <td>                Margin</td>\n",
       "      <td>           17.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 </th>\n",
       "      <td> 10313</td>\n",
       "      <td> VIH.DQ(AC)</td>\n",
       "      <td>                   PUT</td>\n",
       "      <td>            DQ0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 </th>\n",
       "      <td> 10313</td>\n",
       "      <td> VIH.DQ(AC)</td>\n",
       "      <td>               PUT Src</td>\n",
       "      <td>      Channel 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 </th>\n",
       "      <td> 10313</td>\n",
       "      <td> VIH.DQ(AC)</td>\n",
       "      <td>        Supporting Pin</td>\n",
       "      <td>     DQS0,/DQS0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 </th>\n",
       "      <td> 10313</td>\n",
       "      <td> VIH.DQ(AC)</td>\n",
       "      <td>    Supporting Pin Src</td>\n",
       "      <td>      Channel 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9 </th>\n",
       "      <td> 10313</td>\n",
       "      <td> VIH.DQ(AC)</td>\n",
       "      <td> Chip Select Pin So...</td>\n",
       "      <td>            NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td> 10313</td>\n",
       "      <td> VIH.DQ(AC)</td>\n",
       "      <td> PassLimit Min (Vre...</td>\n",
       "      <td>          0.925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td> 10314</td>\n",
       "      <td> VIH.DQ(DC)</td>\n",
       "      <td>             Worst VIH</td>\n",
       "      <td>    (See image)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td> 10314</td>\n",
       "      <td> VIH.DQ(DC)</td>\n",
       "      <td> Number of burst(s)...</td>\n",
       "      <td>              1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td> 10314</td>\n",
       "      <td> VIH.DQ(DC)</td>\n",
       "      <td>          Actual Value</td>\n",
       "      <td> 1.088398000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td> 10314</td>\n",
       "      <td> VIH.DQ(DC)</td>\n",
       "      <td>             NumOfMeas</td>\n",
       "      <td>          2.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td> 10314</td>\n",
       "      <td> VIH.DQ(DC)</td>\n",
       "      <td>                Margin</td>\n",
       "      <td>           36.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td> 10314</td>\n",
       "      <td> VIH.DQ(DC)</td>\n",
       "      <td>                   PUT</td>\n",
       "      <td>            DQ0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td> 10314</td>\n",
       "      <td> VIH.DQ(DC)</td>\n",
       "      <td>               PUT Src</td>\n",
       "      <td>      Channel 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td> 10314</td>\n",
       "      <td> VIH.DQ(DC)</td>\n",
       "      <td>        Supporting Pin</td>\n",
       "      <td>     DQS0,/DQS0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td> 10314</td>\n",
       "      <td> VIH.DQ(DC)</td>\n",
       "      <td>    Supporting Pin Src</td>\n",
       "      <td>      Channel 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td> 10314</td>\n",
       "      <td> VIH.DQ(DC)</td>\n",
       "      <td> Chip Select Pin So...</td>\n",
       "      <td>            NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td> 10314</td>\n",
       "      <td> VIH.DQ(DC)</td>\n",
       "      <td> PassLimit Min (Vre...</td>\n",
       "      <td>           0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td> 10314</td>\n",
       "      <td> VIH.DQ(DC)</td>\n",
       "      <td> PassLimit Max (VDD...</td>\n",
       "      <td> 1.500000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td> 10323</td>\n",
       "      <td> VIL.DQ(AC)</td>\n",
       "      <td>             Worst VIL</td>\n",
       "      <td>    (See image)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td> 10323</td>\n",
       "      <td> VIL.DQ(AC)</td>\n",
       "      <td> Number of burst(s)...</td>\n",
       "      <td>              1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td> 10323</td>\n",
       "      <td> VIL.DQ(AC)</td>\n",
       "      <td>          Actual Value</td>\n",
       "      <td>      0.4161437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td> 10323</td>\n",
       "      <td> VIL.DQ(AC)</td>\n",
       "      <td>             NumOfMeas</td>\n",
       "      <td>          2.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td> 10323</td>\n",
       "      <td> VIL.DQ(AC)</td>\n",
       "      <td>                Margin</td>\n",
       "      <td>           27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td> 10323</td>\n",
       "      <td> VIL.DQ(AC)</td>\n",
       "      <td>                   PUT</td>\n",
       "      <td>            DQ0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td> 10323</td>\n",
       "      <td> VIL.DQ(AC)</td>\n",
       "      <td>               PUT Src</td>\n",
       "      <td>      Channel 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td> 10323</td>\n",
       "      <td> VIL.DQ(AC)</td>\n",
       "      <td>        Supporting Pin</td>\n",
       "      <td>     DQS0,/DQS0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td> 10323</td>\n",
       "      <td> VIL.DQ(AC)</td>\n",
       "      <td>    Supporting Pin Src</td>\n",
       "      <td>      Channel 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td> 10323</td>\n",
       "      <td> VIL.DQ(AC)</td>\n",
       "      <td> Chip Select Pin So...</td>\n",
       "      <td>            NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td> 10323</td>\n",
       "      <td> VIL.DQ(AC)</td>\n",
       "      <td> PassLimit Max (Vre...</td>\n",
       "      <td>          0.575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td> 10324</td>\n",
       "      <td> VIL.DQ(DC)</td>\n",
       "      <td>             Worst VIL</td>\n",
       "      <td>    (See image)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td> 10324</td>\n",
       "      <td> VIL.DQ(DC)</td>\n",
       "      <td> Number of burst(s)...</td>\n",
       "      <td>              1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td> 10324</td>\n",
       "      <td> VIL.DQ(DC)</td>\n",
       "      <td>          Actual Value</td>\n",
       "      <td>      0.4161437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td> 10324</td>\n",
       "      <td> VIL.DQ(DC)</td>\n",
       "      <td>             NumOfMeas</td>\n",
       "      <td>          2.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td> 10324</td>\n",
       "      <td> VIL.DQ(DC)</td>\n",
       "      <td>                Margin</td>\n",
       "      <td>           36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td> 10324</td>\n",
       "      <td> VIL.DQ(DC)</td>\n",
       "      <td>                   PUT</td>\n",
       "      <td>            DQ0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td> 10324</td>\n",
       "      <td> VIL.DQ(DC)</td>\n",
       "      <td>               PUT Src</td>\n",
       "      <td>      Channel 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td> 10324</td>\n",
       "      <td> VIL.DQ(DC)</td>\n",
       "      <td>        Supporting Pin</td>\n",
       "      <td>     DQS0,/DQS0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td> 10324</td>\n",
       "      <td> VIL.DQ(DC)</td>\n",
       "      <td>    Supporting Pin Src</td>\n",
       "      <td>      Channel 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td> 10324</td>\n",
       "      <td> VIL.DQ(DC)</td>\n",
       "      <td> Chip Select Pin So...</td>\n",
       "      <td>            NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td> 10324</td>\n",
       "      <td> VIL.DQ(DC)</td>\n",
       "      <td> PassLimit Max (Vre...</td>\n",
       "      <td>           0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Test ID   Test Name          Measured Item   Trial 1 Value\n",
       "0     10313  VIH.DQ(AC)              Worst VIH     (See image)\n",
       "1     10313  VIH.DQ(AC)  Number of burst(s)...               1\n",
       "2     10313  VIH.DQ(AC)           Actual Value  1.088404000000\n",
       "3     10313  VIH.DQ(AC)              NumOfMeas           2.000\n",
       "4     10313  VIH.DQ(AC)                 Margin            17.7\n",
       "5     10313  VIH.DQ(AC)                    PUT             DQ0\n",
       "6     10313  VIH.DQ(AC)                PUT Src       Channel 3\n",
       "7     10313  VIH.DQ(AC)         Supporting Pin      DQS0,/DQS0\n",
       "8     10313  VIH.DQ(AC)     Supporting Pin Src       Channel 2\n",
       "9     10313  VIH.DQ(AC)  Chip Select Pin So...             NaN\n",
       "10    10313  VIH.DQ(AC)  PassLimit Min (Vre...           0.925\n",
       "11    10314  VIH.DQ(DC)              Worst VIH     (See image)\n",
       "12    10314  VIH.DQ(DC)  Number of burst(s)...               1\n",
       "13    10314  VIH.DQ(DC)           Actual Value  1.088398000000\n",
       "14    10314  VIH.DQ(DC)              NumOfMeas           2.000\n",
       "15    10314  VIH.DQ(DC)                 Margin            36.7\n",
       "16    10314  VIH.DQ(DC)                    PUT             DQ0\n",
       "17    10314  VIH.DQ(DC)                PUT Src       Channel 3\n",
       "18    10314  VIH.DQ(DC)         Supporting Pin      DQS0,/DQS0\n",
       "19    10314  VIH.DQ(DC)     Supporting Pin Src       Channel 2\n",
       "20    10314  VIH.DQ(DC)  Chip Select Pin So...             NaN\n",
       "21    10314  VIH.DQ(DC)  PassLimit Min (Vre...            0.85\n",
       "22    10314  VIH.DQ(DC)  PassLimit Max (VDD...  1.500000000000\n",
       "23    10323  VIL.DQ(AC)              Worst VIL     (See image)\n",
       "24    10323  VIL.DQ(AC)  Number of burst(s)...               1\n",
       "25    10323  VIL.DQ(AC)           Actual Value       0.4161437\n",
       "26    10323  VIL.DQ(AC)              NumOfMeas           2.000\n",
       "27    10323  VIL.DQ(AC)                 Margin            27.6\n",
       "28    10323  VIL.DQ(AC)                    PUT             DQ0\n",
       "29    10323  VIL.DQ(AC)                PUT Src       Channel 3\n",
       "30    10323  VIL.DQ(AC)         Supporting Pin      DQS0,/DQS0\n",
       "31    10323  VIL.DQ(AC)     Supporting Pin Src       Channel 2\n",
       "32    10323  VIL.DQ(AC)  Chip Select Pin So...             NaN\n",
       "33    10323  VIL.DQ(AC)  PassLimit Max (Vre...           0.575\n",
       "34    10324  VIL.DQ(DC)              Worst VIL     (See image)\n",
       "35    10324  VIL.DQ(DC)  Number of burst(s)...               1\n",
       "36    10324  VIL.DQ(DC)           Actual Value       0.4161437\n",
       "37    10324  VIL.DQ(DC)              NumOfMeas           2.000\n",
       "38    10324  VIL.DQ(DC)                 Margin            36.0\n",
       "39    10324  VIL.DQ(DC)                    PUT             DQ0\n",
       "40    10324  VIL.DQ(DC)                PUT Src       Channel 3\n",
       "41    10324  VIL.DQ(DC)         Supporting Pin      DQS0,/DQS0\n",
       "42    10324  VIL.DQ(DC)     Supporting Pin Src       Channel 2\n",
       "43    10324  VIL.DQ(DC)  Chip Select Pin So...             NaN\n",
       "44    10324  VIL.DQ(DC)  PassLimit Max (Vre...            0.65"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# An example of how to manipulate and display results using the Pandas library\n",
    "import pandas as pd\n",
    "from StringIO import StringIO\n",
    "df_data = pd.read_csv(StringIO(results), sep=',', header=0, quotechar = '\"')\n",
    "pd.set_option('display.max_colwidth', 22)\n",
    "df_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<h2><a id=\"N5990A\">Automation of N5990A Automated Test Applications for Receiver Test</a></h2>\n",
    "*More information about [N5990A solutions](http://www.keysight.com/en/pd-734301-pn-N5990A)*<br>\n",
    "<br>\n",
    "Requirements:<br>\n",
    "<ol>\n",
    "    <li>Installation of PythonNet (see above)\n",
    "<br>\n",
    "    <li>The Valiframe dll’s that are needed are typically installed in the following directory: C:\\Program Files (x86)\\BitifEye\\ValiFrame\\PCI-Express3\n",
    "You can copy over the ones you need to a directory that Python looks in but the easiest way to reference them from your Python script is to add this directory to the sys.path variable which tells the Python interpreter where to look for modules and dll’s.<br>\n",
    "</ol>\n",
    "\n",
    "Example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys \n",
    "sys.path.append('C:\\\\Program Files (x86)\\\\BitifEye\\\\ValiFrame\\\\PCI-Express3') # Add the location of the Valiframe dll's to the system path\n",
    "import clr # Import the Common Runtime Library Python module\n",
    "clr.AddReference(\"ValiFrameRemote\") # Create a reference from CLR to the Valiframe App DLL\n",
    "from BitifEye.ValiFrame.ValiFrameRemote import * # Import the entire valiframe app namespace from the DLL\n",
    "my_vf_pcie = ValiFrameRemote() # Creates an instance of the ValiFrameRemote class\n",
    "my_vf_pcie.InitApplication(\"PciExpress3\") # Initialize the application\n",
    "my_vf_pcie.LoadProject(\"my_pcie3_proj.vfp\")\n",
    "#my_vf_pcie.ConfigureApplication() # This method creates a GUI prompt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a couple of interesting things to be aware of when using .NET dll's in Python via the pythonnet module.<br>\n",
    "<ol>\n",
    "    <li>A .NET method may require the user to pass the variable name it wishes to modify as an argument. Python does not support \"pass by reference\" but the variable name must exist in python before it can be handed to the .NET method. The .NET method will simply point to the new instance.<br>\n",
    "        The example below shows how to handle this.\n",
    "    <li> .NET attributes can often return .NET collections such as Arrays. The object returned by python.net is indexed and iterable in Python but it is not a native Python collection object like a list.<br>\n",
    "        The example below shows a simple way to convert Arrays of Strings and Int32s to lists of strings & ints.\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300101 Random Jitter Calibration\n",
      "300103 De-Emphasis Calibration\n",
      "300104 Eye Height Calibration\n",
      "301101 Random Jitter Calibration\n",
      "301103 De-Emphasis Calibration\n",
      "301104 Eye Height Calibration\n",
      "302120 Equalization Preset Calibration\n",
      "302121 Equalization Custom Preset Calibration\n",
      "302122 Random Jitter Calibration\n",
      "302123 Sinusoidal Jitter Calibration\n",
      "302125 DM Sinusoidal Interference Calibration\n",
      "302126 Eye Height and Width Calibration\n",
      "302127 Compliance Eye Calibration\n",
      "310101 Compliance Test\n",
      "311101 Compliance Test\n",
      "312120 Preset Compliance Test\n",
      "312121 Compliance Test\n",
      "320101 Unit Interval \n",
      "320102 Template Tests \n",
      "320103 Median to Max Jitter \n",
      "320104 Eye-Width \n",
      "320105 Peak Differential Output Voltage (Transition)\n",
      "320106 Peak Differential Output Voltage (NonTransition)\n",
      "321140 Unit Interval \n",
      "321141 Template Tests -3.5dB \n",
      "321142 Peak Differential Output Voltage -3.5dB \n",
      "321143 Eye-Width -3.5dB with crosstalk \n",
      "321144 RMS Random Jitter -3.5dB with crosstalk \n",
      "321145 Maximum Deterministic Jitter -3.5dB with crosstalk \n",
      "321146 Total Jitter at BER-12 -3.5dB with crosstalk \n",
      "321147 Eye-Width -3.5dB without crosstalk \n",
      "321148 RMS Random Jitter -3.5dB without crosstalk \n",
      "321149 Maximum Deterministic Jitter -3.5dB without crosstalk \n",
      "321150 Total Jitter at BER-12 -3.5dB without crosstalk \n",
      "322160 Unit Interval \n",
      "322161 Template Tests \n",
      "322162 Eye-Width \n",
      "322163 Peak Differential Output Voltage \n"
     ]
    }
   ],
   "source": [
    "procedureIDs = procedureNames = [] # The variable names must be assigned to something but are overwritten in the next call\n",
    "_, procedureIDs, procedureNames = my_vf_pcie.GetProcedures(procedureIDs, procedureNames)\n",
    "procedureIDs = [int(id) for id in procedureIDs] # Convert to a python list of integers\n",
    "procedureNames = [str(name) for name in procedureNames] # Convert to a python list of strings\n",
    "procs = zip(procedureIDs, procedureNames) # Zip the id's & names together. A dictionary may also be a useful way to store these.\n",
    "for proc_id,proc_name in procs:\n",
    "    print proc_id, proc_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Valiframe often uses dialog windows to give the user information or prompt the user to make a change to the setup.\n",
    "There are different ways to handle this in an automation environment. Below is a basic way to suppress all dialog windows but there is a more complete example of [how to handle messaging and events in Valiframe](N5990A_Python_Automation.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_vf_pcie.DialogPopUp += DialogShowEventHandler(VFDialogInformation()) # Register the Dialog event handler (suppresses all Dialogs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True,\n",
       " u'<?xml version=\"1.0\" encoding=\"utf-16\"?>\\r\\n<TestResults>\\r\\n  <Summary>\\r\\n    <ProcedureName>Eye-Width </ProcedureName>\\r\\n    <ProcedureID>320104</ProcedureID>\\r\\n    <Result>Incomplete</Result>\\r\\n    <DateTime>11/13/2013 12:32:21 PM</DateTime>\\r\\n  </Summary>\\r\\n</TestResults>')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eyewidth_result = str()\n",
    "my_vf_pcie.RunProcedure(320104, eyewidth_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2014 Keysight Technologies Inc. All rights reserved.\n",
    "\n",
    "You have a royalty-free right to use, modify, reproduce and distribute this content (and/or any modified version) in any way you find useful, provided that you agree that Keysight has no warranty, obligations or liability."
   ]
  }
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