{
 "metadata": {
  "name": "LSL DR Spectrometer Tutorial"
 }, 
 "nbformat": 2, 
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown", 
     "source": [
      "# Basics"
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "The LSL interface for DR spectrometer data is similar to that of DRX but there are a few key differences because they data have already been Fourier transformed and averaged in time.", 
      "", 
      "First, download a snippet of DR spectrometer data:"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# This may take a bit...", 
      "import os", 
      "import urllib2", 
      "from lsl.reader import drx", 
      "", 
      "if not os.path.exists('temp.drspec'):", 
      "    fh1 = urllib2.urlopen('http://lda10g.alliance.unm.edu/tutorial/B0329+54/056770_000044687')", 
      "    fh2 = open('temp.drspec', 'wb')", 
      "    fh2.write(fh1.read(drx.FrameSize*300))", 
      "    fh1.close()", 
      "    fh2.close()", 
      "    ", 
      "print \"DR Spectrometer Size: %.1f kB\" % (os.path.getsize('temp.drspec')/1024.)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "DR Spectrometer Size: 1209.4 kB"
       ]
      }
     ], 
     "prompt_number": 1
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "The contents of the file can be found using a few of the top-level functions in the lsl.reader.drspec module:"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "from lsl.reader import drspec", 
      "", 
      "# Open the file", 
      "fh = open('temp.drspec', 'rb')", 
      "", 
      "# What is the size in bytes of each frame?", 
      "print \"Frame size:\", drspec.getFrameSize(fh), \"bytes\"", 
      "", 
      "# What is the sample rate?", 
      "print \"Sample Rate:\", drspec.getSampleRate(fh)/1e6, \"MHz\"", 
      "", 
      "# How many frequency channels are in the data?", 
      "print \"FFT length:\", drspec.getTransformSize(fh), \"channels\"", 
      "print \"-> %.3f kHz/channel\" % (drspec.getSampleRate(fh)/1e3/drspec.getTransformSize(fh),)", 
      "", 
      "# What type of data do we have?", 
      "print \"Linear (XX, YY, etc.)?\", drspec.containsLinearData(fh)", 
      "print \"Stokes (I, V, etc.)?\", drspec.containsStokesData(fh)", 
      "", 
      "# What are the exact data products in the file?", 
      "print \"Data products:\", \", \".join(drspec.getDataProducts(fh))", 
      "", 
      "# Close it out", 
      "fh.close()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Frame size: 16460 bytes", 
        "Sample Rate: 19.6 MHz", 
        "FFT length: 1024 channels", 
        "-> 19.141 kHz/channel", 
        "Linear (XX, YY, etc.)? True", 
        "Stokes (I, V, etc.)? False", 
        "Data products: XX, YY"
       ]
      }
     ], 
     "prompt_number": 2
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "To read in a single frame, use the modules read function:"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Open the file and read in a frame", 
      "fh = open('temp.drspec', 'rb')", 
      "frame = drspec.readFrame(fh)", 
      "", 
      "# Load in some basic information", 
      "beam = frame.parseID()", 
      "srate = frame.getSampleRate()", 
      "LFFT = drspec.getTransformSize(fh)", 
      "tInt = frame.header.nInts*LFFT/srate", 
      "", 
      "# Print out some basic information", 
      "print \"Beam:\", beam", 
      "print \"Timestamp:\", frame.getTime()", 
      "print \"Tuning 1:\", frame.getCentralFreq(1)/1e6, \"MHz\"", 
      "print \"Tuning 2:\", frame.getCentralFreq(2)/1e6, \"MHz\"", 
      "print \" \"", 
      "print \"Transform Length:\", LFFT", 
      "print \"Integration Time:\", tInt, \"s\"", 
      "", 
      "# Done", 
      "fh.close()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Beam: 2", 
        "Timestamp: 1398276221.03", 
        "Tuning 1: 54.4000009298 MHz", 
        "Tuning 2: 73.9999997616 MHz", 
        " ", 
        "Transform Length: 1024", 
        "Integration Time: 0.0401240816327 s"
       ]
      }
     ], 
     "prompt_number": 3
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "The time tags reported in the above example are in seconds since the UNIX epoch.  These can be converted to Python datetime instances through:"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "from datetime import datetime", 
      "", 
      "# Open the file and read in a frame", 
      "fh = open('temp.drspec', 'rb')", 
      "frame = drspec.readFrame(fh)", 
      "", 
      "# Load in some basic information", 
      "beam = frame.parseID()", 
      "srate = frame.getSampleRate()", 
      "LFFT = drspec.getTransformSize(fh)", 
      "tInt = frame.header.nInts*LFFT/srate", 
      "", 
      "# Print out some basic information", 
      "print \"Beam:\", beam", 
      "print \"Timestamp:\", frame.getTime(), \"(\", datetime.utcfromtimestamp(frame.getTime()), \")\"", 
      "print \"Tuning 1:\", frame.getCentralFreq(1)/1e6, \"MHz\"", 
      "print \"Tuning 2:\", frame.getCentralFreq(2)/1e6, \"MHz\"", 
      "print \" \"", 
      "print \"Transform Length:\", LFFT", 
      "print \"Integration Time:\", tInt, \"s\"", 
      "", 
      "# Done", 
      "fh.close()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Beam: 2", 
        "Timestamp: 1398276221.03 ( 2014-04-23 18:03:41.034108 )", 
        "Tuning 1: 54.4000009298 MHz", 
        "Tuning 2: 73.9999997616 MHz", 
        " ", 
        "Transform Length: 1024", 
        "Integration Time: 0.0401240816327 s"
       ]
      }
     ], 
     "prompt_number": 4
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "The data product are stored as attributes to the frame.data object.  You can either access these directly or through Python's getattr function:"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Open the file and read in a frame", 
      "fh = open('temp.drspec', 'rb')", 
      "frame = drspec.readFrame(fh)", 
      "", 
      "print \"Tuning 1 XX mean:\", frame.data.XX0.mean()", 
      "print \"Tuning 2 XX mean:\", frame.data.XX1.mean()", 
      "print \" \"", 
      "", 
      "for tuning in (1, 2):", 
      "    for prod in frame.getDataProducts():", 
      "        data = getattr(frame.data, \"%s%i\" % (prod, tuning-1), None)", 
      "        print \"Tuning %i %s standard deviation:\" % (tuning, prod), data.std()", 
      "", 
      "# Done", 
      "fh.close()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Tuning 1 XX mean: 27.6883773804", 
        "Tuning 2 XX mean: 10.2878665924", 
        " ", 
        "Tuning 1 XX standard deviation: 10.0468772781", 
        "Tuning 1 YY standard deviation: 9.77003064685", 
        "Tuning 2 XX standard deviation: 3.85698524032", 
        "Tuning 2 YY standard deviation: 4.11942237842"
       ]
      }
     ], 
     "prompt_number": 5
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "# Plotting Spectra"
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Similar to DRX data you can also plot spectra from a DR spectrometer file.  However, unlink DRX data, you do not need to make a call to SpecMaster since they data have already been transformed:"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "%matplotlib inline", 
      "import numpy", 
      "from lsl.misc.mathutil import to_dB", 
      "from matplotlib import pyplot as plt", 
      "", 
      "# Open the file", 
      "fh = open('temp.drspec', 'rb')", 
      "", 
      "# Create the array that will hold the data", 
      "products = drspec.getDataProducts(fh)", 
      "nProd = len(products)*2", 
      "nChan = drspec.getTransformSize(fh)", 
      "data = numpy.zeros((nProd, 5, nChan), dtype=numpy.float32)", 
      "", 
      "# Read in the first 5 frames", 
      "for i in xrange(5):", 
      "    try:", 
      "        frame = drspec.readFrame(fh)", 
      "    except errors.syncError:", 
      "        continue", 
      "    except errors.eofError:", 
      "        break", 
      "        ", 
      "    j = 0", 
      "    for tuning in (1, 2):", 
      "        for prod in products:", 
      "            data[j,i,:] = getattr(frame.data, \"%s%i\" % (prod, tuning-1), None)", 
      "            j += 1", 
      "fh.close()", 
      "", 
      "# Integrate across the frames", 
      "spec = data.mean(axis=1)", 
      "", 
      "# Compute the frequency bins", 
      "freq = numpy.fft.fftfreq(spec.shape[1], d=1.0/frame.getSampleRate())", 
      "freq = numpy.fft.fftshift(freq)", 
      "freq1 = freq + frame.getCentralFreq(1)", 
      "freq2 = freq + frame.getCentralFreq(2)", 
      "", 
      "# Plot", 
      "beam = frame.parseID()", 
      "", 
      "fig = plt.figure()", 
      "ax = fig.gca()", 
      "for i in xrange(spec.shape[0]):", 
      "    if i / len(products) == 0:", 
      "        freq = freq1", 
      "    else:", 
      "        freq = freq2", 
      "        ", 
      "    ax.plot(freq/1e6, to_dB(spec[i,:]), label='B%i,T1,%s' % (beam, products[i % len(products)]))", 
      "ax.set_xlabel('Frequency [MHz]')", 
      "ax.set_ylabel('PSD [arb. dB]')", 
      "ax.legend(loc=0)", 
      "plt.show()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stderr", 
       "text": [
        "ERROR: Magic function `matplotlib` not found."
       ]
      }, 
      {
       "output_type": "display_data", 
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FscqDAf+Cm2ouJS8fspzIgEh8nX3ZenIrozqPwsPBo8Z9hNBCk7VB9OjRw1Dd\npDVLSRC95zyBu749u+Y+rXUooh70ekhKglatyu93i9Bz/NhfVYUBh6D3WxC+ttr9M2Zl4OXoZYJI\nhaidJlswKDo6mjfffJOEhAQyMzMNN1G9i3nn6eDbRuswRD1ZW0NQUPlyrSUlcOyoNUqVd7v9+aue\njLRaA2+klW+cFVxlf+83mq6tYlt6OgsTErhUWtpk5xACalmCCA4ONlrXevbs2SYJqiaWUoJwmRHO\nwls/ZuoQbdbNEE1Pr4f16+HFF+FcZiKtPAJJSAAGPgW9l/Jc3+d47fbXGvWcw3/9lc3p6Yb75/v0\noZWDQ6OeQ1yfLG6yvvqwhARRUFKAc4wfx8adJ/xGqY9uDpTC0BnhnXfg72nld/T/0tdqBcDa2HXh\nAnedOQPAbb/+yjdduhie2xYWxiBvb4rLytiakcHfvLxwsZbec+J/mjRB/Prrr8TFxVFY+L+pnMeN\nM/0AMEtIED9fPEH460MpWXQKG5kmqFnyDMzg0pQWLLxzEdNuerZRjtk6Npbn/v1vHt+yBQAFWNXQ\nk9DTxoYH/fy4zdOT9o6OdHZ2bpQ4hGVqsjaImJgYnnzySZ544gm+/fZbZs2axdatW+sVZHNw9I8E\nHIpaSXJoxtLPe2N9qT3fnTzeKMf7IS2NBOCxiIjy4sru3ejuuovS22+nV1ycYbvOV1T7lhUUsDQx\nkeG//kqXQ4fQxcZy89GjLExI4KOkJC6XlpJTWmr2P7iEdmpVgujSpQs//fQT3bt356effiIlJYUH\nHniAPXv2mCLGSiyhBDFt7Ud8+sP3JL37sdahCA2NfuYY33mNI/lfVWcrrqvIdesoKSjgp7Fjwel/\nkxFy6BDMn19ev/X551X2U8BvwcHMnT2bjW3bVnv8B/z8WBsaCkB6SQleNjZY/VVnVqoU1n8d61Jp\nKZ42NjL+wwLV57uzVr9xHR0dsba2xsbGhsuXL+Pr60tCQkK9gmwOTqcm4OfQWuswhMaG3ObHpv9L\nMQwsra+skhKOBAaScN99MGlS5Sd79jSaGNDrYcUKdFOm0CU+ng0TJzLf3x+dUmy55RZs9HrsS0pY\nfvfdHOjcmXUpKaxLSal0iFvd3dl7+XKVQ2/s3Jl7/1q3RSlFZmkprtbW2Op0/Cs+nlXJyewJD8fO\nyopgaUC3aLVKED179iQrK4vJkycTGRmJs7MzN998c1PHZrEScxIIdLlJ6zCExvp286Nkrw0/XzxJ\neGDHeh/nluR6AAAgAElEQVTn/XPnaJ+YSNBfDdS1Ym0NkyeX344eRbdyJSHLlsGUKTz96acQHAy/\n/MKkHTtQwIk2bej88ceVDmEsOQDc99tv1zz9jQcPAtDP3Z3vunVDp9ORWFREVmkpoU5OWEsJxCLU\nuRfT2bNnyc7OJjw8vKliqpElVDEFPR/NML/pLHvmb1qHIjSkFFjd+wD/euh25gybUO/jDNyzh4Dj\nx1k+Y0YjRgds3w79+8PMmbBlCyQloYBcR0fKrKxwLCrCrrSUUisrfm/dmhcmTaLbmTPsGDSII97G\nx3l0c3LiPW9v+tSihuEuLy/u9vLij4ICnm/ThpzSUlxtbPC3qzxRYoFej6P0yGqwRu/FdOUCPg3Z\npjFZQoJwea4jr0f8hydGd9Y6FKExXa+3oeUx1JZrT0NenZs2bWLB+fP0fboJR+UrBefPl//9558w\neHD5AI/77y9v38jJgTVrYMECAA536EDr1FT0VlZ45uTgUFJS5ZC/BgdzdNIkpvbuTWEj9dgIsLNj\nQ+fO9HWXFRrrqtETRG2m9Db1tN/mniCUUlj/y5lv70mmfx83rcMRGntw1mHW5UxCvVv/3kzeO3Zw\nws4O3zvuaMTIGmj/fliypDxx7NwJvr4QGgr5+eUN59XIdnLCLT8fgAw3NxyLinh/61bedXfndEFB\nnULwsrGhj5sb/Tw86OHiwp1XrE8z2Nsba52OTVeMFWnuGj1BWFtb43Rljwkj3NzcSExMrNNJASZM\nmMD27dvx9fU1LDmamZnJ6NGjOXfuHMHBwXz22Wd4eFQeaGbuCSKzIJMWL4dw8anL+PtrHY3Q2q9/\nZBK21pvzT1+glUdgnff/Mz+fbrGxXI6MRGcp69ZWTGR14QKsXVs+A+Kjj5Yv2PHNN+UN6889V3W/\nSZMo7NsXm+BgKC7GZv589N9+C717k9utG5+MGsXj9Qgn0tWVvu7utLK352Z3d75IS2NBQgLzQkLw\ntLXFycqKQHt7bve8vheOsqiR1Pv27cPFxYVx48YZEsSsWbNo0aIFs2bNYv78+WRlZfH6669XDtjM\nE8TRC7/S4/XR6N/6DavGGUArLJhS4PTYABbc+zRP3DGszvt/fPIkOzZu5LMXX+S6mzdeKfjjDxg9\nGupYC5Hj6EiKpyeuoaF8OWAA0W+9RevUVIqnTuX7nj3ZFxrKsuJi0q99KIO2Dg4cjYzE/TodwGRR\nCQIgPj6ewYMHGxJEx44d+e677/Dz8yM5OZmoqCh+//33SvuYe4JY/cN/mbLqTQo/2K11KMJM3PDE\nU/TtHMKqx+o+onrmli14xcXx/PPPN0FkZkQpePfd8iT4888wdWr5gh1t28KpU+VVWK++Wt6wnpdX\n+8MCKZ6enGzdml9CQrBt1Yod7dpxISSEe774gv/cfTdxf3XZvVKIgwNlwLkrZo4AGNGiBX/z8qK/\nhwdtHR2xBosZE9Jk4yBMJSUlBT8/PwD8/PxIuapfdoWYmBjD31FRUURFRZkgutr5/eJFXAnQOgxh\nRtr7hPDLhfh67Xu+oIDuHs1gPi+dDh6vpgKp9V9jijZsgPj48nVlR46EAwfKFybv0QPs7MpvcXFQ\nXFw+De/eveg+/xz/rCz8s7Lo/9NPAEy94tBzP/6YUisrim1teWfvXmb+1T5y9qrEUOGL9HS+SK9c\nLtkWFkZmSQku1tYczc2lt6srg1u0aMjVaBSxsbHExsY26BhmVYLw9PQkKyvL8LyXl1eVacXNvQRx\n/zuvcvy3XH5/u3Fn8RSWa/a6Tbx/8GNSl2yp8743b9jAfKXod//9TRBZM6EUnDsHly+DuzssXVpe\nIhk+HBYvhm3b4Kp21DwHBxJbtKDExoYbEhNxKClhd48e6K2sGPjGG7U67ROBgcxu0wZfO/NY37zJ\nShA///wzv//+OzqdjtDQULo0Uc+Aiqolf39/kpKS8LWURrkrJF5Kxs+5ndZhCDPStU0w2YfrNzX+\nWQcHQqS3Q8PodOUDAyssXPi/v999t/xWUADPPFPesL53L865uXTw9oa/Sh0A0X8tmqYGDABgy803\n0/30ab697TbWR0ez84YbKp12WWIiy/5KPHd6erKiY0cO5eRgr9PRydmZ1vb2Zl89VWOCuHz5MkOH\nDuX8+fOEh4ejlOKXX36hdevWbNmyBTe3xu3GOWTIEFatWsU//vEPVq1axbBhdW/U01pqfgo9PG7R\nOgxhRnq1b0uR05/oy/RYW9V+wFdObi7Z9vYEdO3ahNEJABwd4f33q39ery9PNFZWhs4CQ/fvB2Dc\nhg2M27DBsGmRrS0TZs1i/vvv02rjRgB2ZWUR9OOPRg+9oVMnerm5meW0JDX2s/nnP/9JZGQkZ86c\nYdOmTWzevJlTp07Rs2dPXnzxxQadeMyYMdx8882cPHmSVq1asXLlSp577jl2795Nhw4d+Oabb3jO\nWFc4M5dVkkKIj5/WYQgz0trXHV1uIAfj46698RVO/for7TMysHJ0bKLIRK1ZW2PollixtKBSkJwM\nb79dPvbjwAF45x3sZ85k3auvEpSejhowgNMPPEC306d58513GL53LwDTr0goo+PiCDlwAF1sLAey\ns9GbURV6jW0QoaGh/Pzzz9ja2lZ6vKSkhLCwsCo9jEzB3NsgnGZ15N3+mxh/T6jWoQgz4jp5KPNG\nPcKT0bUvFa/dvp3tCQl8+uijTRiZaFLZ2bB7N9x7b5Wn8hwc2Ni/P49U80P4i86dGW6kh1V9Nfp6\nEHZ2dlWSA4CtrS329vZ1i66ZKLJJIbSN5bWdiKbladWa3xLP1Wmfk4WF3Fhc3EQRCZNwcyvvcaUU\nlJWV964CuOkmnIODefi//0UNGEDZgAGM2LuXWRs2MO/DDwEY8dtvJBYVaRj8NdogioqKOHr0aJXp\nipVSFGkcuDkqKi2mzCaX0ODre0SmqLuWTm04nVa3hurzJSVEmXkjpqgDnQ7s7cuTxZXi4tC9/DL/\neeklw0NPfvEF459/njHOznwXGalZY3aNCcLf35/p06cbfc6UE/RZij+S0tEVtMDVRYZQi8rae7Xn\nQE5snfZJLCoiyAJ78ok66tQJPv20/JabC05OuMTEsHL+fPoFBLA8MJBJAdqMraoxQTR0kEVzcyox\nFdsS+UCLqjoFhLDtj7qVIC44OBAUEtJEEQmz5OJS/u/cubgNGMCi6dP5R1CQZgmixp+6hw4dIikp\nyXB/1apVDBkyhKeeeqrKADYBf6ak4lgmCUJUFd42kHyrpGtv+BelFBfc3QkMCmrCqIRZGzCAXr//\nzhEHB1I0aouqMUFMmTLF0Bi9d+9ennvuOcaPH4+bmxtTpkwxSYCW5Fx6Kq5WjdfrQFw/urbzolSX\nT0FJ7aa0zs7LQ1dWhpsMkmvWXNetA+Bfp09rcv4aE0RZWRleXl4AbNiwgalTpzJy5EheeeUVTmsU\nsDlLvJyMp420zYiqAgJ0kNuS+IzalSJOJSTQLi0NmRK4mRs+nPcXLuSXixc1OX2N7z69Xk/JXytF\n7dmzhwF/DTEHKC0tbdrILFBSTjI+TvKLT1RlbQ32xQEc/6N2H/QLaWm0/mviONG8RYeHc16jsV81\nNlKPGTOG/v3706JFC5ycnOjXrx8Ap0+frrKQj4CMomQiPbVZq1uYPzddAL+dvwi1mIklJSsLPzMe\nECpMJzg0lDy9ntTiYpNP/FdjgnjxxRe57bbbSE5O5s4778Tqr+KuUoqlS5eaJEBLklWaRCtPKUEI\n43zsAzidXLsSREpuLn7X6cI1om50I0dyy+LFfOPvz/0RESY9d41VTAUFBRw4cIA9e/awdu1aQ7VS\nhw4d6N69u0kCtCR5Ko1gH+nFJIwLdAvgfFbtEkRqcTF+MluBAHBwYNCPP/LfjAyTn7rGBDF+/HiO\nHDlC165d2bFjR7WD5kS5IusMQvy8tQ5DmKngFgEk5SfUatsUwNfZuWkDEhajY6dO/KHB7BU1lmFP\nnDhhWMxn4sSJ9OzZ0yRBWSKlFKW2GdwQIAlCGBfRuj2f/nGmVtumWFvj5+raxBEJSxEcGkr9VhRp\nmBpLEDZX1IHaSH1ojXKL8kFZ0cpfpmYWxnW7IZB869p1c01xcMDvry7mQrQKDSXd1pZ8vd6k563x\nW//nn3/G9YpfMQUFBYb7Op2O7Ozspo3OgvyZnIGu0BszWV1QmKEuwb6UOaZSqi/Dxrrm8Q0pLi74\nNeJUz8KyWXfuzA0bN3I6N5dwd3eTnbfGBKE3cbaqEBwcjJubG9bW1tja2nLw4EFN4qiLP5IysCuV\n6iVRPVcne3QlLsQnZ9EusPr3SqFeT6GNDR5+svCU+IuzMzempXEyIcF8EoRWdDodsbGxhlHcliA+\nJQOHMkkQoma2Rf6cuJBcY4JIzcjA9/JldLKSnLjCjcXFnExKgi5dTHZOsx3Hb86rxhlzITMDZytJ\nEKJmjnp/Ticl17hNSloavnl5JopIWIob7ew4mZNj0nOabQnijjvuwNramqlTpzJ58uRKz8fExBj+\njoqKIioqyrQBGnExKwN3O0kQomZu1n6cTU2pcZuUzEz8ZEEucZUbPT2py/Dk2NjYBi/ZYJYJ4ocf\nfqBly5akpaURHR1Nx44dDdN8QOUEYS7ScjPwcrCcKjGhDW97f85n1lyCSM3JwU/mOhNXad+mDWfq\n0C589Y/nOXPm1PmcZlnFVLFanY+PD8OHD7eIRur0/AxaOEsJQtTM38WfpJyaE0RaXh4+stSouIpX\nhw4U63TkmrDzkNkliPz8fHL+qmfLy8tj165dhIWFaRzVtV0uycDfXRKEqFkrD3/SC2tOEOlFRbSQ\ncUfiKrpWrQhKT+eCCafcMLsEkZKSQr9+/ejWrRu9e/dm0KBB3HnnnVqHdU25+gwCPSVBiJq18w0i\nq6zm6TbS9XpaODiYKCJhMaysCMrL48Kff5rslGb3MyUkJITjx49rHUadFZBBK29JEKJmnVu1Js/m\nfI3bpAMtZB4mYUSrsjLOp9TcyaExmV0JwlIV2aTR1l9GvoqadQ0OpMQhscZu3OnW1rQw4WAoYTla\nK0VCQe2WrW0MkiAagVKgt0+nfaAkCFGzID8nKLMmLTu32m3S7ezwsaBBosJ02tjact6EPdwkQTSC\nrOwisCnE38NN61CEmdPpwLrIh7j4tGq3SXdyooXMwySMaO3kxDlra5OdTxJEIziVmIZVYQt00jVR\n1IKD3pdTialGnyvR68l1cMBdEoQwoo2PD+dNOCOoJIhG8GdyOnal8oEWteOq8+dMsvGurhnZ2Xjl\n5GAlvZiEEa3atCHB1dVkUxFJgmgE59LScFKSIETteNn5cy7DeIJIz8ighczDJKrhFBCAU2Eh6SUl\nJjmfJIhGcD4jDRerFlqHISyEn1NLLmZXU4K4dAnvwkITRyQshrMz/pmZpORW38mhMUmCaATn09Px\ncZYShKidQA9/UvONryyXmZuLl4l+HQoLpNPhn5tLsolGU0uCaASpuWn4uUiCELUT0qIlWaXGSxCZ\neXl4lZWZOCJhSfwLCyVBWJLMojT83aSKSdROO39/cjGeILIKC/E0cTzCsvjZ2JBiotHUkiAaQXZp\nGkFeUoIQtdOptT9FtsarmLKKivA0YT93YXn8ra1JNtF6IZIgGkE+6YT4SoIQtdO5jT9ljilcvly1\nq2JmaSle9vYaRCUshb+9PckmGk0tCaIRlM/DJFVMonYcbR2wLnPi6ImsKs9llZXhKWMgRA38nZ1J\nMtGgXEkQDVRaCnr7NNq1lBKEqD2nspYcPlm1milTp8NLZnIVNWgZGMhFE1VDSoJooLPxenDMwtdV\npvoWtedl588vZ6s2VGdZW+PpJnN6ieoF+viQ6OpqknNJgmigYyfTsS31xMbK7JbWEGYswK0lp5IS\nqzyeaW+Pp4eHBhEJS+Hp60uxlZVJlh41ywTx1Vdf0bFjR9q3b8/8+fO1DqdGx+LjcVfBWochLEyo\nX1vOZVddGSzL0REvT+noKqqn8/bGLyuLtPz8Jj+X2SUIvV7PE088wVdffUVcXByffvopJ06c0Dqs\nav2WGI+ffbDWYQgL06NtW9L1f3LlmLgypbjs6IiHrEwoamJlhU9uLqnp6U1/qiY/Qx0dPHiQdu3a\nERwcjK2tLffffz9btmzROqxqnUmL54YWwVqHISxMmxa+WHkkcv6K1Ucv5+fjUliItTRSi2vwLSwk\nLatqL7jGZnYV54mJibRq1cpwPygoiP/7v/+rtE1MTIzh76ioKKKiokwUXVWJ+fGMbN1Fs/MLyxTo\nFkhx4Df8+CMEB5c/lpmZiVdeXvmqQkLUwKe0lNTLl2vcJjY2ltjY2Aadx+wSRG0W3bkyQWjp0iXI\nsYonst0grUMRFibcLxxbnT0Ll2UzZkx5r6WsS5fwMuF6w8Jy+ZaVkXaNaeGv/vE8Z86cOp/H7KqY\nAgMDSUhIMNxPSEggKChIw4iq9+ij4NAynnbewVqHIiyMTqfD3d6dIwWfM2FC+WOZ2dl4FhdrG5iw\nCL5WVqSaYFp4s0sQkZGRnD59mvj4eIqLi9mwYQNDhgzROqwqlIINWzMpcP6dNh5ttA5HWCBPJ3ec\nBr7MmTPl9zPz8vAy4YL0wnL52NmRaoL3itklCBsbG5YtW8Zdd91Fp06dGD16NKGhoVqHVYVOB5/u\n3wuAi52LxtEIS7T+3vW0a+XG3vK3EVkFBTLVt6gVX0dH0kyw7KjZtUEADBw4kIEDB2odxjVtPLWG\n5/o+p3UYwkL5OPnwc8rPzNs3jxf6vUBmURGe0kAtasEnOJjUixeb/DxmV4KwFEop9GV6nrtFEoSo\nnyC3IEaGjuTQxUMAZJWW4mUlH0lxbb4+PqQ5Ojb5ecyyBGEJdDodm+/frHUYwoLpdDo+H/W54X5m\nWRmhdnYaRiQshY+3N6muriilatXzs77k54oQZiJTKTxlLQhRC47e3tiVlJDTxA3VkiCEMBNZVlZ4\nOTlpHYawBHZ2+GRnk9rEo6klQQhhJjJtbGSqb1Frvnl5TT4fkyQIIcxElr29zOQqas23qIi0zMwm\nPYckCCHMRKajI14tZOlaUTs+ej2pOTlNeg5JEEKYgUK9nlIrK5wkQYha8oUmXxNCEoQQZiArOxuv\nnBx00kgtasnHxobUJp67SxKEEGYgMz1dZnIVdeJrb09qEy87KglCCDOQlZUlM7mKOvFxdSWtic8h\nCUIIM5CZnY1XSYnWYQgL4uvlRapN006GIQlCCDOQmZuLpwlm5xTXD18/P9IcHJr0HJIghDADWQUF\neMlMrqIOWgQFkebigmrCHxaSIIQwA5nFxXg1cXWBuL7Yu7vjXFjIpSacbkMShBBmIKukRCbqE3Wj\n0+GTl9ek60KYVYKIiYkhKCiIiIgIIiIi+Oqrr7QOSQiTyAS8TDC/v7i++BYVkZqa2mTHN6syrU6n\nY9q0aUybNk3rUIQwqUydDi8XWbpW1I1vWRkpzamKqSkbXIQwV1k2Nni6u2sdhrAwrZXifG5ukx3f\nrEoQAEuXLmX16tVERkaycOFCPDw8qmwTExNj+DsqKoqoqCjTBShEE8iUmVxFPQTb2nK2qMjoc7Gx\nscTGxjbo+Dpl4p/s0dHRJCcnV3n81VdfpU+fPvj4+AAwe/ZskpKSWL58eaXtdDqdlDLE9aWsDO8v\nv+T3fv3wkSQh6mDT5s2szMpi6yOPXHPb+nx3mrwEsXv37lptN2nSJAYPHtzE0QihvbwLF8h3cMDb\nSGlZiJq08fLiXBPO4WVWbRBJSUmGvzdt2kRYWJiG0QhhGufOnKF1djZWMlBO1FFwy5bEu7s3Wa2K\nWbVB/OMf/+D48ePodDpCQkJ4//33tQ5JiCZ3NjGREGtrrcMQFsgzKAh16hSX0tLw9PVt9OObVYJY\nvXq11iEIYXJ/ZmUR4u+vdRjCAukcHbnh4kVOHTxI70GDGv34ZlXFJERz9EdxMe1dXbUOQ1ionra2\nHE5JaZJjS4IQQmOn7Oxo17Kl1mEIC9XT1pavExMpKi1t9GNLghBCQ/rUVPZ16ECPdu20DkVYqIG3\n386mW2/loZUrG/3YZtUGIURzk+3pyajgYAJlmg1RT0H+/nx09iw3du3a6Mc2+UC5hpKBckIIUXf1\n+e6UKiYhhBBGSYIQQghhlCQIIYQQRkmCEEIIYZQkCCGEEEZJghBCCGGUJAghhBBGSYIQQghhlCQI\nIYQQRkmCEEIIYZQkiEbS0MXBm4LEVDsSU+2ZY1wSU9PRJEFs3LiRzp07Y21tzdGjRys999prr9G+\nfXs6duzIrl27tAivXszxDSEx1Y7EVHvmGJfE1HQ0mc01LCyMTZs2MXXq1EqPx8XFsWHDBuLi4khM\nTOSOO+7g1KlTWFlJQUcIIUxNk2/ejh070qFDhyqPb9myhTFjxmBra0twcDDt2rXj4MGDGkQohBAC\npaGoqCh15MgRw/0nnnhCrV271nB/4sSJ6vPPP6+0DyA3uclNbnKrx62umqyKKTo6muTk5CqPz5s3\nj8GDB9f6ODqdrtJ9JWtBCCGESTRZgti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      }
     ], 
     "prompt_number": 6
    }
   ]
  }
 ]
}