heterodyne_multiple_tones_2.ipynb

{
 "metadata": {
  "name": "heterodyne_multiple_tones_2"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Multiple tones tutorial #2"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Setup"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's set things up as before:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "from kid_readout.utils import roach_interface"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ri = roach_interface.RoachHeterodyne()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ri.initialize()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Deprogramming\n",
        "Programming"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " iq2xpfb14mcr4_2013_Aug_02_1446.bof\n",
        "Requested sampling rate 512.0 MHz. Estimated sampling rate 512.9 MHz"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ri.set_dac_attenuator(57)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ri.set_adc_attenuator(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "freqs=ri.set_tone_freqs(np.linspace(1,220,256),nsamp=2**20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As always, take a look to make sure things seem ok:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x,y = ri.get_raw_adc()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x.ptp() # check the peak to peak values quickly, if you get > 4000, the ADC is likely overloaded"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "2034.0"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "psd(x+1j*y,NFFT=4096,Fs=512.0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(array([ 1.41668994,  0.21861778,  0.3211296 , ...,  4.10170468,\n",
        "        4.67319844,  1.93677961]),\n",
        " array([-256.   , -255.875, -255.75 , ...,  255.625,  255.75 ,  255.875]))"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEMCAYAAADXiYGSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDAqFQqGogioMCoVCoaji/wHI+0klBWDEvgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now let's do the same basic sweep we did last time, but add some synchronization to see if the phase looks better"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tones = []\n",
      "data = []\n",
      "for k in range(len(ri.tone_bins)/4):\n",
      "    ri.select_fft_bins(range(k*4,(k+1)*4))\n",
      "    ri._sync() ### This is new\n",
      "    time.sleep(0.1) #sleep for a moment for the data to become fresh. could do this smarter, but this works\n",
      "    d,addr = ri.get_data(2)\n",
      "    data.append(d)\n",
      "    tones.append(ri.tone_bins[ri.readout_selection])\n",
      "data = np.concatenate(data,axis=1)  #stack all the data into one numpy array with axes time,channel\n",
      "tones = np.concatenate(tones) #stack the tones into one long array\n",
      "davg = data.mean(0) #compute the average of the data along the time axis for convenience"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123494.45 samples per second, idle 22.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124534.22 samples per second, idle 12.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123971.22 samples per second, idle 13.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123841.64 samples per second, idle 14.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 122951.35 samples per second, idle 14.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124098.83 samples per second, idle 14.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123658.46 samples per second, idle 12.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124496.32 samples per second, idle 45.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123920.25 samples per second, idle 35.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 121930.38 samples per second, idle 16.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 125292.59 samples per second, idle 11.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124066.56 samples per second, idle 13.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124447.62 samples per second, idle 30.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124398.06 samples per second, idle 43.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 122914.40 samples per second, idle 42.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123193.37 samples per second, idle 18.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124272.08 samples per second, idle 17.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124781.15 samples per second, idle 19.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 125883.83 samples per second, idle 23.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 125850.63 samples per second, idle 30.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123380.93 samples per second, idle 15.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124348.98 samples per second, idle 45.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123025.31 samples per second, idle 19.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124400.31 samples per second, idle 21.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124661.18 samples per second, idle 16.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123892.54 samples per second, idle 18.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 125545.75 samples per second, idle 18.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124811.52 samples per second, idle 28.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124763.94 samples per second, idle 45.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123353.02 samples per second, idle 17.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 122008.32 samples per second, idle 19.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 121968.47 samples per second, idle 19.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124821.04 samples per second, idle 27.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123226.94 samples per second, idle 20.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123828.69 samples per second, idle 17.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123926.06 samples per second, idle 26.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 122754.12 samples per second, idle 42.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 122362.00 samples per second, idle 13.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123865.75 samples per second, idle 19.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123490.46 samples per second, idle 17.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123247.28 samples per second, idle 17.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 125279.34 samples per second, idle 34.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123974.35 samples per second, idle 45.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124441.76 samples per second, idle 16.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123716.34 samples per second, idle 15.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123897.90 samples per second, idle 12.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123379.16 samples per second, idle 19.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124272.08 samples per second, idle 12.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123119.65 samples per second, idle 17.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 125020.88 samples per second, idle 19.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124767.56 samples per second, idle 45.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124173.72 samples per second, idle 19.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123951.99 samples per second, idle 37.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 122554.03 samples per second, idle 28.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124560.86 samples per second, idle 21.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 125764.03 samples per second, idle 23.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124748.99 samples per second, idle 45.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124575.76 samples per second, idle 45.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 122927.16 samples per second, idle 20.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 121492.78 samples per second, idle 21.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123019.58 samples per second, idle 18.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123578.40 samples per second, idle 13.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123337.97 samples per second, idle 23.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123083.92 samples per second, idle 25.00 per read\n",
        "0\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "freqs = ri.fs*tones/ri.tone_nsamp  # convert the tone locations to frequencies"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First a sanity check, looking at the amplitude again:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(freqs,20*np.log10(np.abs(davg)))  # plot the transmission in dB power\n",
      "xlabel('MHz')\n",
      "ylabel('dB')\n",
      "grid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(freqs,np.real(davg))\n",
      "plot(freqs,np.imag(davg))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "[<matplotlib.lines.Line2D at 0x2cb60d0>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD9CAYAAABeOxsXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGUhISEhIIMlAQkJCQgL4//b3L5nakTo+AAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "That looks a lot more promising!\n",
      "\n",
      "Take a look at the phase:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(freqs,np.angle(davg))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "[<matplotlib.lines.Line2D at 0x34ae550>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD9CAYAAABHnDf0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n"
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And unwrap it:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(freqs,np.unwrap(np.angle(davg)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "[<matplotlib.lines.Line2D at 0x2aa8190>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD9CAYAAABeOxsXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To determine the cable delay, we can estimate the slope:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "unwrapped_phase = np.unwrap(np.angle(davg))\n",
      "median_phase_diff = np.median(np.diff(unwrapped_phase))\n",
      "frequency_step_mhz = np.diff(freqs).mean() #mean not needed here, but it works\n",
      "step_radians_per_second = frequency_step_mhz * 2 * np.pi * 1e6\n",
      "radians_per_mhz = median_phase_diff/frequency_step_mhz\n",
      "print radians_per_mhz\n",
      "delay = median_phase_diff/step_radians_per_second # this should be seconds\n",
      "print \"Cable delay in seconds: \",delay"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-0.591400365034\n",
        "Cable delay in seconds:  -9.41242914415e-08\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also estimate the delay from the FFT of davg:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "txx,t = psd(davg,Fs=1/(frequency_step_mhz*1e6))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEMCAYAAAAmgtofAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n"
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t[txx.argmax()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "-9.0967465753424679e-08"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now let's try to correct the cable delay:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dcorr = davg*np.exp(-1j*radians_per_mhz*freqs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(freqs,np.angle(dcorr))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "[<matplotlib.lines.Line2D at 0x2f7ae90>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD9CAYAAAC1DKAUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHooJgIjIQzEBEBF5qP8HoMQ4nR6XjOoAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This looks more plausible, but I think the little jaggies are because we haven't sampled the phase gradient finely enough, so it may be much steeper (longer cable delay) and aliased. \n",
      "\n",
      "Let's look at the response on a very fine scale.\n",
      "\n",
      "The finest scale that makes sense is a single step in the tone frequency bins: ri.fs/nsamp. There will be nsamp/nfft tone bins across a single FFT bin, so let's do a fine scan across two FFT bins, using every other tone freq.\n",
      "\n",
      "In this case, the tones are all in a single FFT bin, so we need to reload the waveform for each measurement."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ffine = []\n",
      "dfine = []\n",
      "fftbins = []\n",
      "for k in range(64):\n",
      "    actualf = ri.set_tone_freqs(np.array([k+2**17])*512.0/2**19,nsamp=2**20)\n",
      "    fftbins.append(ri.fft_bins)\n",
      "    ffine.append(actualf)\n",
      "    ri._sync()\n",
      "    time.sleep(.1)\n",
      "    da,addr = ri.get_data(2)\n",
      "    dfine.append(da)\n",
      "dfine = np.concatenate(dfine,axis=1)\n",
      "ffine = np.concatenate(ffine)\n",
      "dfavg = dfine.mean(0)\n",
      "fftbins = np.concatenate(fftbins)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31167.13 samples per second, idle 127.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31105.93 samples per second, idle 204.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31171.52 samples per second, idle 120.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31155.52 samples per second, idle 120.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31055.72 samples per second, idle 218.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31110.92 samples per second, idle 151.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31209.88 samples per second, idle 123.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31250.70 samples per second, idle 211.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31252.86 samples per second, idle 209.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31024.43 samples per second, idle 138.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31135.98 samples per second, idle 132.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31170.33 samples per second, idle 129.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31048.87 samples per second, idle 219.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31103.34 samples per second, idle 120.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31097.94 samples per second, idle 123.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31331.40 samples per second, idle 164.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31100.98 samples per second, idle 120.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31141.40 samples per second, idle 127.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31148.88 samples per second, idle 145.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31044.64 samples per second, idle 154.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31097.32 samples per second, idle 121.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31095.91 samples per second, idle 153.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31221.05 samples per second, idle 202.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31132.54 samples per second, idle 123.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31084.57 samples per second, idle 122.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31199.08 samples per second, idle 120.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31035.72 samples per second, idle 195.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31124.14 samples per second, idle 134.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31098.16 samples per second, idle 123.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31332.94 samples per second, idle 178.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31109.37 samples per second, idle 118.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31190.64 samples per second, idle 134.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31195.85 samples per second, idle 135.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31143.91 samples per second, idle 155.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31170.07 samples per second, idle 121.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31058.42 samples per second, idle 134.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31241.89 samples per second, idle 204.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31104.64 samples per second, idle 136.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31073.84 samples per second, idle 124.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31087.89 samples per second, idle 129.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31085.53 samples per second, idle 207.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31179.21 samples per second, idle 125.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31282.45 samples per second, idle 126.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31040.40 samples per second, idle 219.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31148.43 samples per second, idle 122.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31155.29 samples per second, idle 124.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31258.01 samples per second, idle 164.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31180.31 samples per second, idle 181.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31200.13 samples per second, idle 126.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31197.50 samples per second, idle 195.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31254.76 samples per second, idle 182.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31022.44 samples per second, idle 169.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31324.69 samples per second, idle 121.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31175.90 samples per second, idle 125.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31538.49 samples per second, idle 220.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31182.69 samples per second, idle 121.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31124.84 samples per second, idle 125.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31002.48 samples per second, idle 141.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31194.07 samples per second, idle 125.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31097.35 samples per second, idle 139.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31133.02 samples per second, idle 120.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31113.28 samples per second, idle 220.50 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31170.92 samples per second, idle 126.00 per read\n",
        "0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.3 seconds, 31217.96 samples per second, idle 121.50 per read\n",
        "0\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, let's look at the magnitude of the power response on a dB scale"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(ffine,20*np.log10(np.abs(dfavg)))\n",
      "xlabel('MHz')\n",
      "ylabel('dB')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "<matplotlib.text.Text at 0x30e9490>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEKCAYAAADuEgmxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIof9f10DKFayCQxRAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's highlight which FFT bin each data point came from"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "set(fftbins)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "set([4096, 4097, 4098])"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for b in set(fftbins):\n",
      "    msk = fftbins==b\n",
      "    plot(ffine[msk],20*np.log10(np.abs(dfavg[msk])),'--.')\n",
      "xlabel('MHz')\n",
      "ylabel('dB')\n",
      "title('FFT bin response')\n",
      "grid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEYCAYAAACgDKohAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nigYhhBC9UdEghBCit/8HDQL2G+8A4h4AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What you can see here is the \"scalloping\" Daniel and others were concerned about. Fortunately, this is digitally applied and mathematically known, so we'll be able to correct for it. \n",
      "\n",
      "Now if you look back at the wide magnitude plot, you can see that the \"scruff\" has a peak to peak variation in line with what we see here, about 0.7 dB\n",
      "\n",
      "Next, let's have a look at the phase."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(ffine,np.angle(dfavg))\n",
      "plot(ffine,np.unwrap(np.angle(dfavg)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "[<matplotlib.lines.Line2D at 0x3e13f50>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD9CAYAAAC2l2x5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXmwd8v8nqysSNF4kAgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "unwrapped_phase_fine = np.unwrap(np.angle(dfavg))\n",
      "median_phase_diff_fine = np.median(np.diff(unwrapped_phase_fine))\n",
      "frequency_step_mhz_fine = np.diff(ffine).mean() #mean not needed here, but it works\n",
      "step_radians_per_second_fine = frequency_step_mhz_fine * 2 * np.pi * 1e6\n",
      "radians_per_mhz_fine = median_phase_diff_fine/frequency_step_mhz_fine\n",
      "print radians_per_mhz_fine\n",
      "delay_fine = median_phase_diff_fine/step_radians_per_second_fine # this should be seconds\n",
      "print \"Cable delay in seconds: \",delay_fine"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "197.006821895\n",
        "Cable delay in seconds:  3.13546095275e-05\n"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So this cable delay is much longer than what we got before. Note that the cable delay is unphysical in that 31 us would imply a length of some 31000 ft of cable! Instead, it's probably mostly composed of simple FPGA clock cycles in the processing, of the order of:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "delay_fine*ri.fs*1e6/2. # approximate number of FPGA cycles represented by the cable delay"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "8026.7800390332932"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So now let's correct the data again using this better cable delay estimate, and compare the two resulting phase plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dcorr2 = davg*np.exp(-1j*radians_per_mhz_fine*freqs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(np.unwrap(np.angle(dcorr2)),label='fine cable delay')\n",
      "plot(np.unwrap(np.angle(dcorr)),label='coarse cable delay')\n",
      "legend(loc='lower left')\n",
      "ylabel('radians')\n",
      "xlabel('MHz')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "<matplotlib.text.Text at 0x4054e10>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEKCAYAAAAMzhLIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n"
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looks much nicer. Interestingly, there is some overall slope still, so we should refine the estimate."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "txx,t = psd(dcorr2,Fs=1/(frequency_step_mhz*1e6))\n",
      "xlabel('time (s)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "<matplotlib.text.Text at 0x55d4750>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEMCAYAAAAmgtofAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXd4FNX6x7+bTiQhDZJAAokgJY2EIEiPcAFpyu8qSJdm\nQYogetWrXrh6hauAgopYALlIEREV6UWyCiI1YBKQACEVSA+QSCCBnN8fr7Mtu9nZzWyZ4XyeJ092\nZndn3ndn5rznLeccFWOMgcPhcDj3NC6OFoDD4XA4jocbAw6Hw+FwY8DhcDgcbgw4HA6HA24MOBwO\nhwNuDDgcDocDGxqDyZMnIzg4GLGxsXXeW7JkCVxcXFBWVqbZN2vWLERHR6NTp044deqUrcTicDgc\njhFsZgwmTZqE3bt319mfl5eHffv2oVWrVpp9W7ZsQW5uLs6cOYNVq1Zh0qRJthKLw+FwOEawmTHo\n1asX/P396+x/8cUX8d577+nt27lzJ8aPHw8ASEhIwJ07d5Cfn28r0TgcDodjgF1zBlu3bkVYWBji\n4uL09ufn5yM8PFyzHRYWxo0Bh8Ph2BE3e53o5s2bWLBgAfbt26fZpzsThuGsGCqVqs4xjO3jcDgc\njnnMzTxkN88gMzMT2dnZ6NixIyIjI5Gfn4/ExEQUFhYiLCwMeXl5ms/m5+cjLCzM6HEYY4r9mzdv\nnsNl4Ppx/e5F/ZSsG2Pipp+zmzGIjY1FYWEhsrKykJWVhbCwMKSkpCA4OBiDBw/G+vXrAQApKSlw\ndXVFixYt7CWa05Cdne1oEWwK10/eKFk/JesmFpsZg9GjR6N79+44f/48wsPD8eWXX+q9rxvyefzx\nx9GiRQtER0dj6tSpdT7L4XA4HNuiYmJ9CCdApVKJdnnkiFqtRlJSkqPFsBlcP3mjZP2UrBsgru3k\nxoDD4XAUjpi2k09H4USo1WpHi2BTuH7yRsn6KVk3sXBjwOFwOBweJuJwOBylw8NEHA6HwxEFNwZO\nhNLjllw/eaNk/ZSsm1i4MeBwOBwOzxlwOByO0uE5Aw6Hw+GIghsDJ0LpcUuun7xRsn5K1k0s3Bhw\nOBwOh+cMOBwOR+nwnAGHw+FwRMGNgROh9Lgl10/eKFk/JesmFm4MOBwOh8NzBhwOh6N0eM6Aw+Fw\nOKLgxsCJUHrckusnb5Ssn5J1Ews3BhwOh8PhOQMOh8NROjxnwOHYkJs3ge3bHS0FhyMN3Bg4EUqP\nWypNv19+ASZOBIQOl9L0M0TJ+ilZN7FwY8DhWElaGlBaCmRlOVoSDqfh8JwBh2MlTz0FbNgAfPUV\nMGqUo6XhcEzDcwYcjg1JSwOGDgWOH3e0JBxOw+HGwIlQetxSSfrduQOcOwdMmgQcO0b7lKSfMZSs\nn5J1Ews3BhyOFWRmAqGhQJ8+wKlTZBw4HDnDcwYcjhV8+y3lCrZuJaNw4gTQooWjpeJwjOPQnMHk\nyZMRHByM2NhYzb4XX3wRUVFRiIqKwtChQ1FaWqp5b+HChYiKikJsbCz27t1rK7E4HElITweEW9vH\nB6iocKw8HE5DsZkxmDRpEnbv3q23b9iwYUhPT8fZs2cRExOD//znPwCAkydP4rvvvkNaWhp2796N\nZ599FtXV1bYSzWlRetxSSfqlpdU1BkrSzxhK1k/JuomlXmNQVFSE5cuX48knn0TXrl3x0EMP4ckn\nn8Ty5ctRVFRU74F79eoFf39/vX0PP/wwXFzolD169MDly5cBADt27MCoUaPg6uqKFi1aIDo6GseE\nrByH44SkpQExMfTa15d7Bhz542bqjSlTpiAzMxODBg3Cc889h9DQUDDGcPXqVRw7dgwjR45EmzZt\nsHLlSqtO/Pnnn2PUX8XZly9fRt++fTXvhYWFIT8/36rjypmkpCRHi2BTlKJfVRWQlwe0bUvbPj7A\njRvA8OFJDpXL1ijl+hlDybqJxaQxeOGFFxAXF1dnf4cOHdC3b1+8+uqrSE1Nteqk77zzDjw8PDB2\n7FiLvztx4kREREQAAPz8/BAfH6+5kIKrx7f5ti23fXyS8MADwK+/ard1w0SOlo9v8221Wo01a9YA\ngKa9NAszw9q1a9mNGzf09m3bts3c1xhjjGVlZbGYmBi9fWvWrGHdunVjVVVVmn1vvfUWW7RokWZ7\nyJAh7NChQ3WOJ0JcWZOcnOxoEWyKUvRbs4axMWO0288+y9jy5crRzxRK1k/JujEmru00m0CeOXMm\nevXqhbNnz2r2vfnmm+IsjQG7d+/Ge++9hx9//BFeXl6a/YMHD8amTZtw584d5OfnIz09HV26dLHq\nHByOrdFNHgO8moijDMwag8jISKxatQojRozAN998I/rAo0ePRvfu3ZGRkYHw8HCsXr0aM2fORGVl\nJfr374+EhAQ8//zzAIDExET83//9H+Li4vDII4/gs88+g7u7u/VayRTB3VMqStHv7FkgKkq7LSSQ\nlaKfKZSsn5J1E4vZQWcJCQk4deoUSkpKMHr0aMTFxWHfvn1W5wsaAh90xnEGunQBPvoI6NqVtpcu\npZlLly1zrFwcjikkGXQWGhoKAAgKCsLu3bvh4uKC9PR0aSTk6CEkgJSKUvS7dg3w89Nu83EG8kfJ\nuonFrDHYuXOn5rWrqysWLVqE2tpamwrF4Tgz164BukNohNJSDkfOmAwTDRs2TPshAxdDpVLhxx9/\ntL10BvAwEcfRMAZ4epIn4OlJ+3bvBj74ANizx7GycTimENN2mhxnMHfuXM0Bnn76aaxcuVJzMJVK\nJa2kHI5MqKoC3Ny0hgDg1UQcZWAyTJSUlIQ+ffogKSkJjRs31rwW9nOkR+lxSyXoV16uny8AeM5A\nCShZN7Hw9Qw4HAswTB4D3DPgKAOTOYOysjIAAGMMDz/8cB3LGRAQYHPhDOE5A46j+fVX4OWXgcOH\ntftKSoB27QCdGdk5HKeiQTmDTp06aXIDjDEkJibqHfjSpUsSicnhyAfDSiJA6xkwBvB0GkeumAwT\nnT9/HllZWcjKykJ2drbmdVZWFjcENkLpcUs563frFnDmjPEwkacnGYF9+9QOkc1eyPn6mUPJuonF\npDHo3r07hg8fjk8//RTZ2dl2FInDcT7UamDyZOPGACDv4OZNu4vF4UhGvdNRZGVlYffu3dizZw/y\n8/PRs2dPDB48GH369IGnbm2dneA5A46j+OYbYPp04IUXqLz0nXf034+MBH76Cbj/fsfIx+HUR4On\no4iMjMS0adPwww8/4PDhwxg2bBj27duHXr16YciQIZIKy+E4MxUVlCjOzTXtGfCKIo6cEV1a6uHh\ngX79+mHRokU4duwYPvvsM1vKdU+i9LilnPUTpptISambQAZo5tJfflHbVSZ7I+frZw4l6yYWk8bg\nzJkzGDduHF588UXk5eVh0KBB8Pb2Rvv27fHrr78iLCzMnnJyOA5F6PWnpfGcAUeZmDQGEydORO/e\nvREaGoqHHnoIkyZNQmlpKT766CNMnz7dnjLeMyh9TnU561dRAbi6AtXVpo1BZGSS3eWyJ3K+fuZQ\nsm5iMTnO4NatW3jmmWcAAJ999hlGjhwJAOjfvz+ftZRzz3HjBtChA5CebtwY3Hcf9ww48sakZ+Dm\nprUTPj4+eu+5urraTqJ7GKXHLeWsX0UF0LEjvTZmDBo1Ak6fVttVJnsj5+tnDiXrJhaTnsG5c+cQ\n+9dCr5mZmZrXwjaHcy9RUQH07g2sX288geztDfz5p/3l4nCkwqQx+OOPP+wpBwfKj1vKWb+KCiA2\nlryCJk3qvu/tDTRpkmR3ueyJnK+fOZSsm1hMGoOIiAg7isHhODc3bgBNmwKXL9N6BoY0asRXO+PI\nG5M5g8aNG8PHx8fon6+vrz1lvGdQetxSzvpVVFDFkLe38fe9vYHz59V2lcneyPn6mUPJuonFpGdQ\nWVkJAHjjjTfQsmVLjBo1CgCwadMm5OXl2Uc6DsdJqKiggWWm8PYGbt+2nzwcjtTUOzcRACQmJuLk\nyZNm99kDPjcRx1F4ewPFxVRCaox162gt5HXr7CsXhyOGBs9NBAC1tbXYuHEj7t69i9raWnz99de8\nQebcU9y5Q71+UyEigN7j4ww4csasMdi0aRPWrFkDf39/+Pn5Yc2aNdi0aZM9ZLvnUHrcUo76lZZq\n8wX1LVzj7Q1cvqy2m1yOQI7XTyxK1k0sJnMGAm3btsWePXvsIQuH43Q88gjwj3+QMaiPRo1oARwO\nR66Y9Azmz5+PwsJCk1+8evUq5s2bZxOh7lWUXussR/2Ki4EjR8wbA29vwMMjyS4yOQo5Xj+xKFk3\nsZj0DDp37oxRo0ahuroanTp1QmhoKBhjKCgoQEpKCjw9PfHSSy/ZU1YOx+5cvw6cOFF/JRFAxqCq\nyj4ycTi2wKRnMHToUCQnJ+Prr79Gjx494ObmBnd3d/Ts2RObNm3CgQMHMHjwYJMHnjx5MoKDg/Wm\nsSgrK0P//v0RFxeHgQMH4tq1a5r3Zs2ahejoaHTq1AmnTp2SSD15ofS4pdz0Y4wGkp08KS5MVFqq\ntotcjkJu188SlKybWMzmDMLDwzVjDCxh0qRJmDlzJiZMmKDZN2/ePAwZMgSzZ8/G0qVLMW/ePCxb\ntgxbtmxBbm4uzpw5g1OnTmHSpEk4ffq0xefkcKSkshKoraU5h8SEifg4A46cEb3SmaX06tUL/gYz\neu3cuRPjx48HAIwbNw47duwAAOzYsUOzPyEhAXfu3EF+fr6tRHNalB63lJt+168DAQGAi4u4MNHd\nu0l2kctRyO36WYKSdROLzYyBMYqLixEYGAgACAoKQlFREQDg8uXLCA8P13wuLCzsnjQGHOfi+nWg\nWTMgIkJcmIiPM+DIGbNhotLSUk0DbksMB7KpTBR1T5w4UTOJnp+fH+Lj4zVWXYj7yXV76dKlitJH\n7volJ6uhUgHt2iXBx6f+z7u7A7W1S7F/fzz+9jfnkP9ev36WbAuvnUUeKfRZs2YNAAsmHWVmaNOm\nDXviiSfYjh07WG1trbmP65GVlcViYmI02/fffz8rLi5mjDFWVFTEWrduzRhjbPLkyWzz5s2az0VH\nR7P8/Pw6xxMhrqxJTk52tAg2RW767dzJ2IABjL32GmNLl5r/vLd3Mrt+3fZyOQq5XT9LULJujIlr\nO82GiTIyMvD0009j7dq1aNOmDV577TWcP39enKUxYPDgwVj31+Qt69at01QjDR48GOvXrwcApKSk\nwNXVFS1atLDqHHJGsPBKRW76Xb9Oaxe8/TYwc6b5z/v4JCk6VCS362cJStZNNJZYl59++omFhoYy\nX19f1rNnT/bLL7+Y/OyoUaNYaGgoc3d3Z2FhYWz16tWstLSU/e1vf2OxsbGsf//+rLy8XPP56dOn\ns6ioKJaQkMBOnjxptXXjcKTi008ZmzpV/OcjIxnLzLSdPByOtYhpO83OWlpSUoL169dj7dq1CA4O\nxtSpUzFs2DCkpaVh5MiRuHjxon2sFpQ/a6larVZ0D0Vu+r33HlBUBCxeLO7zkZFqbN+ehOho28rl\nKOR2/SxByboB4tpOswnk7t27Y9y4cdi6dSvCwsI0+zt16oSpU6c2XEoOx0kRwkRi8fTkFUUc+WLW\nM/jmm28wcuRIs/vsgdI9A45zMWMG0LYtMGuWuM/36QO89Rb953CcCUnWM/jvf/9bZ98777xjvVQc\njkyw1DPg8xNx5IzJMNGuXbuwc+dO5OfnY9asWRqrcvPmTZNjADgNQ+lxS7npZ6kxqKxU4+bNJJvJ\n42jkdv0sQcm6icWkMWjevDkSExOxdetWJCYmaoyBt7e3UW+Bw1EalhoDLy+eM+DIF7M5g5qaGri7\nu9tLnnrhOQOOPYmPB1avBjp1Evf5Z54BHnwQePpp28rF4VhKg6qJRowYgc2bN6OTkSdBpVIhNTW1\n4RJyOE7IhQtASorlngGfn4gjZ0wag2XLlgEAtm3bZjdh7nWUHreUi37JycA//wlUV1tmDEpK1AgJ\nSbKZXI5GLtfPGpSsm1hMVhM1b94cAM0uGh4ejoiICNy+fRspKSkIDQ21m4Acjr0pKQFKS4GKCvNT\nV+vi6cmriTjyxWzOICEhAUeOHEFRURF69uyJLl26wM3NDRs3brSXjBp4zoBjD+bMoXWPT5+2rHFf\nvBgoKBA/YpnDsReSjEAGAE9PT3z//feYMWMGXn75ZXTs2FESATkcZ6S4GJg6FbC0gtrbm+cMOPJF\n1OI2x48fx8aNGzWzjPLeuW3QnVNdichFv+JioHlzYPJky76Xl6fGn3/aRiZnQC7XzxqUrJtYzBqD\nxYsXY/78+Rg+fDiio6ORnZ2N3r1720M2DschFBcDTZta/j1vb+DGDenl4XDsgdmcgTPBcwYcW8AY\nlZMGB1P1UHg4cOgQ0KqVZcf56SdgwQL6z+E4E5LkDNLT07F48WLk5eWhtrZWc+ADBw5IIyWH42BS\nU4GuXen1+fPWewa+vtwz4NiWq1eBYcOA77+nTouUmDUGjz/+OGbPno1p06bB1dVV2rNLzNmzQGYm\n/VhyROm1zs6qX0oK8PjjVFL622+AqyuFfCzl3Dk1btxIklw+Z8FZr58UyEG3mhpq29LSgIsXHWAM\nmjRpgmnTpkl7VhugVgOPPgp07ChfY8BxDL//TvfNlSvAwYNAUJB1x7nvPu4ZcGzH779TqfNjjwGF\nhdIf32zOYP78+QgJCcFjjz0GT09Pzf6AgADppTFDfXGv0aPJtT9wAEhPt7NgHFnz8MPAq68C2dnA\n8uWAhwdw4oTlx7l5kwwJLy/l2IIvv6T2zc8PaNMGeOEF8d+VJGewZs0aqFQqvPvuu3r7s7KyxEti\nB06fBt5/H/j2W0dLwpETjGk9Ay8v6kgMHGjdsRo1oiksamoAJ5nbkaMgUlOBuDjg9m1ajlVqzJaW\nZmdnIysrq86fM3HzJpCTA3TvDpSXO1oa61F6rbMz6pefTw13SAjQoQMZB2uSxwDw889q+PrSNBZK\nxBmvn1TIQbfUVCA2lqrebBEmMmsMKioq8MYbb2DyXyNwMjMznW7yuvR0oF07quZgjM8PwxFPaip5\nBQAZgYAA640BwCuKOLaBMa1n0KyZg4zBuHHj4OPjg6NHjwIAWrRogddff116SRrA6dP0QKtUgL+/\nfL0DZ69maCjOqN8ffwDR0fRapQLat7feGCQlJaFJE5r6Wok44/WTCmfXrbCQDEJoqAM9g0uXLuGV\nV16Bh4cHAMDLywsuLqJmsbAbv/9OC5EA8jYGHPuTnQ1ERmq3+/alcJG1cM+AYwuEEJFK5UBj4OHh\ngSqduEtubq70UjQQIQEIkJsvV2Mgh7hlQ3BG/bKz9Ucav/02le5Zg1qtVrQxcMbrJxXOrltmJtC2\nLb1u1owSyFJPxmC2mmjevHno168f8vPzMWHCBCQnJ+Pzzz+XVooGkpUFtG5Nr/39gbIyx8rDkQ85\nOUBEhHTHU7Ix4DiOggIKEQE0nsXV1fL1Nswham6iwsJCHDp0CIwx9OzZEyEhIdJJYAHGamXv3NEu\nN+juDkyYAPTrBzz1lENE5MgIxuhhysuj2m0peO45Clk+95w0x+NwAODZZ4GEBO191bo1sHs38MAD\n4r7f4HEGNTU12LVrF86dOwcAiIqKQpC1wzNtRFEREBioreuWc5iIY1/KywEXF+kMAcA9A45tKCig\nXIGAkDcQawzEYDJncPnyZcTGxmL58uUoLy9HWVkZPvroI8TGxuLKlSvSSdBALl8GWrTQbss5TOTs\nccuG4mz6ZWdLGyLiOQP54uy6FRTQWBgBWySRTXoG//znP/HSSy9h6tSpevtXr16N1157Df/73/+s\nPum8efOwceNGuLi4ICYmBmvXrkVhYSHGjBmDyspKREdH46uvvoK7iGGcly/TQiQC/v40HTGHY46c\nHMunqTaHr69tRody7m0MjYEtxhqY9AxOnTpVxxAAwKRJk3Dq1CmrT3jx4kV89dVXSE9Px7lz5+Dq\n6oqNGzdi1qxZeOWVV5CWloaQkBB8/PHHoo5nzDOQa5jI2WudG4qz6Se1Z5CUlARfXz7OQI44s26M\nUcOvGyZq0kT6ke4mjYGbm3GnQaVSieqxmyIgIADu7u74888/cefOHdy8eRMtW7bEkSNHMHz4cAA0\n0G3Hjh2ijmdoDAIC5Bsm4tgXW3gGTZooN0zEcQzXrwOenvrTqttivW2TYaJr167hu+++08tACxnp\n8gZ0vQMCAjB37ly0bNkSjRo1wsCBAxETE6OXmG7RogXy8/NFHe/yZaBPH+22nD0DOcyp3hCcTb/M\nTEDKFVwpZ5CkWGPgbNdPSpxZN8MQEUDlpXbLGfTu3dvkHER9dFtfC8nMzMTSpUuRnZ2NJk2aYMSI\nEdi3b5/o70+cOBERf/n2fn5+SE+Px5gxSQDogubkAOXl2m1A6wI6+/bp06edSh4l68cYcPCgGhMm\nAIB0+nl5QbPAjaN/byVfv3tpG0hCcLD++97ewIULaqjVxr+vVquxZs0aANC0l2ZhdmbDhg1sypQp\nmu21a9eyKVOmsKCgIM2+Y8eOsX79+tX5rjFx27dnLC1Nu11QwFjTptLKzFEeFy4wFh4u/XHPnWOs\nbVvj75WVMVZbK/05Ocpm40bGRozQ37d6NWNPPSX+GGKaertPMtSmTRscOXIEVVVVYIxh//79aN++\nPR566CH88MMPAIB169Zh8ODBoo535UrdBPK1a9IP1eYoi8OHacpzqfHzM56zqq0FOnUCnGzCX44M\nMBUm+vNPac9jd2Pw4IMP4oknnkBcXBzat2+P27dvY8aMGfjwww/x7rvvIjY2FoWFhZg5c6bZY1VW\n0kIiuoOGPDxoAJrUP5Q90LqFysSZ9LOFMVCr1ZpBj7W1+u8dOEDVSz/+KO057YkzXT+pcWbdCgvr\nGgO7JpBtyfz58zF//ny9fZGRkfjtt98sOo5KBXz+Of3XRXggGzduoKAcxXL4MGCkcrrBuLsDPj7k\nnequDLt6NTBrFrB5M3mthvcsh2OK/HxamlUXWxgDk3MTbdmyxeR8FiqVCn//+9+llUQEYubXAGgB\niK++0s5kyuHs309hmoAAKtULC6Nwji2Wp3zgAWDHDu0sk1VVVCOelQV06wZs2kTzzHA45rh7l8Lg\nhw7RuscCR48CM2cCx45p9y1YQJ2M116re5wGzU20bds2qOrpvjjCGIhFzuWlHOmpqQFGjgQGDQLW\nr6cHKTHRdusUBwUBJSVaY3DhAhmfwECSYe9ebgw44vj1VwoR6RoCwLhncPFiw0KfJo2BUJYkR+Q6\nWZ3aiWudpcBR+iUn00jjI0eAXbvIGNgieSzoJxgDgfPnaVlWgB7qv+Z9lB1Kvj+dVbctW4DHH6+7\n35gxuHQJGDvW+nOZzRnU1tbi+++/R0ZGBu7cuaPZ/69//cv6s9oYOU9Wx5GeLVuA0aMpfPPmm3R/\nvPCC7c7XtKm+McjI0BqD4GDgl19sd26Ocrhzh+7dvXvrvmesmujSJeD++60/n9lqosmTJ2Pr1q34\n5JNPwBjDN998g5ycHOvPaAfkGiZyxp6JlDhCv9pa4IcfqHf16KNUgZacDDz0kPTnEvQz9AwyMvRX\nqbLFkoX2QMn3pzPqtmULrVsQFVX3PUPPoLqa7qvwcOvPZ9YYHDlyBGvXrkVgYCDmzZuH48eP4+LF\ni9af0Q7I1RhwpCc/H3Bzox6Tiwvw8svkIdhyWY76wkTBwXxWU0793LxJU6W8/z4wZ47xzwjGQMgJ\n5+RQotnElHKiMGsMfP9aV83NzQ0FBQVQqVRO7xnIdbI6Z651lgJH6Hfxon7ybfJkwFZiCPrpGgPG\n9MNEcvYMlHx/OpNu77xDBQZVVcCwYcY/4+ZGf7dv03ZDQ0SACGMwZMgQ3LhxA3PnzkVcXBwiIiIw\nevTohp3VxnDPgCOQmaldHxug0jvdqYBtgWAMbt0Crl6lcwYG0nv+/hTrFR5iDqe6Wr+oYPt2YM8e\nIDWV1jo2hW6oyObGoLa2Fv3794evry/GjBmDS5cu4dy5c1i4cGHDzmpj5GoMnDFuKSWO0C8zs25Z\nnq0wzBnMmAHExJBXIFRpu7hQgrm42D4ySYmS709H6rZ5M/6aMJHW4758GejSxfz3dI1BVpaNjYGL\niwtmzZql2W7cuDH8pFww1kbINUzEkZ6LF/U9A3sQFESN/b59FPedN0//fTmHijjSk5xMjTkA7NwJ\nPPJI/R6BgG5F0aVLQGRkw+QwGyZKSkrC999/L2rkr7MgV8/AmeKWtsAR+tnTM9DNGWRl0WC3p56i\nh1sXuSaRlXx/OlK35GTyJCsrKTw0aJC479k1TAQAn376KR5//HF4eHjAx8cHPj4+mqSysyJXY8CR\nFsYc4xn4+dG5k5KMz0Fki8XMOfIkN5eWr2zXjiYyPH0aePBBcd+V2hiYLUSqrKxs2BkcgJ8fzT9T\nW0sxWlNs2ULZeg8P+8lWH0qOyQL216+4mK6tvSKbgn4uLpQwNpxcTECuYSIl35+O0i05mToNlZVA\nejpNVy228yKEicrLqfOhOzGiNZj1DPr16ydqnzPh5kY/1I0bZHXnzQP+/nf9QRr5+TRfTWam4+Tk\n2BbDslJ78re/AQMHGn9PrmEiZ2LPHqBXL6rYcgZu37ZOFrWaOg0REZQvaNtWXL4A0HoGQr6goTPh\nmjQGVVVVKC0tRXFxMcrKyjR/eXl5Tj/OANBOSTFpEpCWRmVaaWna91etIs+hqspxMhpiq7hlTQ2V\nrzkae8dljx4F4uPtdz5d/TZsAFq2NP45uXoGzpIz2LqVxosUFVHP2hJqa4Gvv667+FVDdXv3XeD1\n1y3/nuAZREbSvFkxMeK/q2sMGhoiAuoxBp999hk6d+6MjIwMJCYmav4GDhyIadOmNfzMNiYgAJg+\nneJwGzYAPXuSQQBozo+VKynR5yw9C1vyr38Zn9bWWfj9d+Ctt6Q3zDt3AiIXzLMrSswZ3Lhhn+NX\nVFDJ7saNtB7F1q2WHSc/n+apWrpUWvn++AM4eLDu/vrqbrKyyKNo356MQUkJEB0t/pxCmEiKslIA\n5hfG/PDDD8UvtGljRIirYdw4xiZOZKy4mLaXLGFsxgx6ff48YxERjPXty9j+/dbLs2cPY6+/bv33\n7UFtLWO+hpxPAAAgAElEQVStWzMWG+toSfS5fp2xu3cZS0lhLDCQsebNGfvpJ+mOf+MGYz4+jFVU\nSHdMqcjIYCwy0tFSSMcvvzDWrBljlZW2Of6JE4w1acJYVRVjb76pXfs3I4Ox0FC6j8Ry8CBj999P\n66Rv2yadjJ07M+bmxtjNm9p9X33F2LPPmv7OqlWMjRpFr48fZwxg7McfxZ9z5kzGli6lcyxfXv9n\nxbSdZnMGtbW1uH79umb7+vXr+PjjjyUwQ7blq6+AL7/UzkHTsaPWM7h2jRJ8Xl4N8wy2b6cBI87M\n2bMUIsrNdZ6BTowB/fpRDf6GDcBzz1H+5sQJ6c7x0080GZ0zrnbXujUlCh1Zm3H1KvDRR9Ic69Ah\n6tUuXy7N8Qz54gsqCDl0iJ636dNpf9u2gK8vVeCIJTeXqnV+/BF49lng228bLh9jtGZFy5b69/D2\n7RSOvnzZ+PeSk7VFBsIYAUs9A92cQUMxawxWr16NJk2aaLabNGmClStXNvzMdiYujowBY3RjNWkC\nNGpkXWhCcP0OH6ZJyOob4JadLX7+elvEZH/4ARg+nJJtjg75CvqdOEE38LJlwDffAE8+CXTuDBw/\nLt25kpOB/v2lO54YxF4/V1cKDZw9a1t56uPIEWDJEsu+Y0q/o0dpavDFi6XPTf35J90jzzwDfPgh\nhYsSE7Xvt2tHk7SJJTeXGu2HHgI++IA6jEBd3W7coByAmM6i8PwPHQoIK/cyRmGjgQNNG8mzZ7WL\nHAUEUKFLRIR4XeyWMxCoNri6jDHckmGgvWlT8gTy88kz8POzzjPIz6dZL48dozhht276S88ZsnQp\nVZY4akT03r3AkCHUAzlwwDEyGLJiBfCPf1Clz333UdLswQel9QwyM7WTwzkjMTFUSghQw1Fba9/z\n5+ZSIypU2KWk0NKglsIYGZZJk8jIlZbqv79/PxUwWMu2bdRwT51Kr4cN0y8XDwqqe876EIwBQM/l\nwYN154mqrAR69wb++1/tyOD6EKrWunWjDiJAv21tLbBwIRkzY9y6RR1SgCqB5s+vvxTeEG9vasvy\n84FWrcR/zxRmT923b1+MGjUKP/30E/bv349Ro0ahb9++DT+zAxC8g/o8g127TLvvd+8CY8aQEXnq\nKTpenz7UMzJFWhotWzd7tvH39+3TPiy6tc4nT9JfQ7l4EejQAejb1/LKC6lJSkpCTQ255pMmUc/0\nvffoQWjThuqlpQplZWdb1suSAktq1XWNwcqVNLW2Ib/9Rr1gWxiK3Fz6n5FB/z/6yHSjJWBMP+E4\nLVuSYdd9do4dI+/s55+tl/PoUXrGEhOpQ/foo/rvBwZabwwCAmitgF9/1dft6FFqaHv2pHCeIRs3\nAt9/r90WjMGDDwKnTtG+Q4fo+2Fh+tOZ63LrFuDpKV52Q+67jzz/zp2pTWooZo3BsmXL0LVrV3zw\nwQdYunQpunfvjo+kCjbamfBw4MoVrTEw5hm8+KLpmzcjg26mn38ma9y9O9C1K/WMTJGWRq7oDz/U\n7SF99BEwYACwdi31TnRvvC++AMaNIwNkLbdu0Y3YogUZruJi0t+RnD1L8jRrRgvUDx1K+11c6IGX\nwjtgjIyBFL0lW6FrDC5e1DbKAlevAiNG0Htnz1IOZMQIMp5SkJtLDdG5c/R77d5NlTqWcvgw9dxV\nKsrPCHPl1NYCs2aRd3bokPVynjxJ94WLC702nKqhIcYAoOdvzx79z5w/T9cnNNS4MVi1ikpbhfcE\nYyBUBN24QTr36EHtTEWF9jneu5e8HICe+YY04t7e1A499ZT1x9DFrDFwc3PD1KlT8e6772L79u14\n4YUX4Cp2VISTIcwmee2acc+AMbpZLl0y/v3qagovBQZSvHHsWJpd0FQDVlhIZawxMdRLFXoNCxdS\nr2TxYmD1amDRImr4O3dWQ1hZNCeHEk/r1pnW5513gPHjTQ+cy86mG9/VlR6mpCTbewdr1tRdjg8A\nduwA3npLjePHTQ+379PH+BJ/llJaSiOPdVJddsGSnI+uMbhyhWarFKiupob/6adpsOTBg1QeHBoK\nfPaZ9nOHD9Pvag25uZRH+uMP8pYLCsyXhxrqt3UrNfjCjJuNG2s9A7Wa7oNFi4yXXIqhtpaSw506\n0XZ4eN2BVYYLCZnD0BgMHEjGQFc3Yf2JkBAyyoYyHT9OK+e9+SbtE4yBiwt5GmfOkLfRsyft8/HR\n/rYFBVojcvt2wzwDb28yJiNHWn8MXcwag82bNyMhIQFDhgwBAKSnp2teyw2hF3H9uvGcQXm5NiFj\njDt3tCsJTZ1KN2loKD28xnon6elAbCzdwL1709q3ixZRpdPJk9RYT5xIFRF5eWQghMY/Oxt44w0K\nIZhi/37t54xhWH9sj7zB3Lnaqi1dtmwh9/r4cXJrjfHEE1QtUl9YZPVqCi/V1xt0RIjIUsLDqeEs\nLyejL4RbAPoN/f2psenVi8I3Z89SDPvqVW0Pfv16qqzRWZpcNHl51BCeO0deQUyM5Z7Bu+8Cn39O\nBgvQn0Vz/Xq6t3v2pHCRNXmD8+fpma1vmgUxnsHChaTbjRv0W/n7a9/r2pU6Xro5PWGZUmOeQUYG\nGaCpU2l8DKCfwI2JIeOXna0d8Kg7V1pVlbbNaagxiI6mZ1+qTo9ZYzB//nycOHEC/n/9gjExMcjT\n7cbICKEXoRsm0vUMhAfSVE9b1xgIqFR041y4UPfzaWlkDAAyBhs20AO0d692+LhKBXz3HT2QH3+c\nhHfe0Xoojzyi30gYkp1N3ompqQ0MS85snTcoKaGHyphr/ccfQEZGEnbsMO0ZREXRg/P997Tc32OP\n1e2ZrVoF/O9/wCuvmJbDUcbAkpyBSkUNyKVL5Blcu0YN1tq1dC989RX1Knv3pl724MHUExR6ngAZ\niGvXyNCKhTFqhEpLyVM8cYK8jQkTzBsDQ/3Ky/WT9IJncOsWXcNRo+h6RkRYVv4pIISI6sOcMbh6\nFfjnP+n8eXnkFeh6F25u9Fz8+WcSpk2jfIGwTGlISN17+ehRMiChodp788oVCn0C9Lx//jl9Rmgr\n/PzoOgHU3ggJ64Yagw4drBv1bApRYSLDNQzuWNMVcQJ0PQMhTKTrGeTmUsKnPs/A3b3u/rZt6QYy\nRNcY9OpFYaK5c+kcuoSF0Q3TqxfdNKmpZKjataMbzlhP+c4dugnj4+s+DEePAlOm1PUMOnQgzyc7\n27h+AH2nd2/renJC3NvwAWKMjMGjj9J7HTuaPsbIkfTn4kKNnaGsZWVUiSSU8BlDDp4BoG8MgoPp\n/nvxRUqwC49c69bU8Ai979hY7bQqgrfw8stkIE2NdmVMG6bo3ZsMTfPmdD9kZ1NRxGOPWe4ZlJXp\n97KFBPKePXRfCg1k9+71Xy9TpKSYNwbmwkTbt9P/CxfIAzC2YPyAAcCCBcCnnwIff0yeWmSk8TDR\nsWMUGg4JoTBwbS19pnlzej8mhjqTPXtqv2PMM6itpYiCs0ySCYgwBlFRUVi/fj3u3LmDrKwsvPzy\ny3hQ7ByrToZhzsDQM8jLo95SVpbxB6umxviC06aMwcWL9B5AD/Tnn5te4BoAfv5ZjU6dyFOIiKBe\ng5+f8Z7/lSukT/PmdR+GgwepcTh6VN8zUKkoVFSfd/CPf1As2jCctGFD/UPrAdPGoKCAfrfevUk/\nb2/Tx5g1i0JJS5aQboZlf0KPNidH29tKTtZP0DsqeWzpOJHWrSnUcPcuNSJqNXVQdI2lSkWN62OP\n0bZQEVdaSo3Ks8+St7RoEdXhG2PzZjICGRmU2Jw/nxrF++6jRvrttymubcwY1NZqE6y6+jFW1xgI\nCeSsLJJTIDbWujEVZ8+an6vHnGewdSuFUy5coON16FD3MxQuU2PRImDTJrp33N2Nh4mOHydj4OlJ\n+l64QPezUCIqyGvKGNy6RX+CIbCklNTWmBXliy++wMmTJ8EYw7Bhw1BbW4sVK1bYQzbJMcwZGPMM\noqLoITEW6jAWJgKoB2/MGFy7pv+wPP20+eoBXWMAUO/K2AjGnBz6jGDgGAOmTSODlpZGN+ihQ3UH\no9RnDI4epZ7P/Pn6IzNraigcZW4+nXPnSF7Dz/3xBz2EnTqZryxp0kSbMDTM6TBGD5VQiSQMUvvg\nA2DmTErCA/LyDA4epN+sZUu67sb6WbGx2vtO8AyE31SlovLNbduodysUKehy+jQZHSHZW1amTaJ2\n7UrH8PU1bgzOnaNcjiF//kmNme79LHgGQmdLICpKnDGortbvnF28SGN66iMggO4JY95zZSXl6WbO\npEb79GnjExdGRFCV1ty59PsLHTjDMJHg4UZF0XZoKIWyBK9A2PfYY1RhJWAYJrp1q+EhIltg1hg0\nbtwY77//PpKTk6FWq7FkyRJ419e1E8G1a9cwYsQIdOzYER06dMCRI0dQVlaG/v37Iy4uDgMHDsQ1\n4deTEDE5g5Ytte67wPz5lAw2ZQwEz2DFCv36Y+E8YklKSkJiIp1L6NmGhVH5mCFCg+ftTb2Lmzep\nAd+zhxqLl16izxkOU+/bl3r9xnr5u3dTyGD8eNJj4EBKYAuXwtx03xkZVBFkaEiFhispKckit9jT\nU98zqKiga+bhQQ+bML6joIDKd/fupV5paqr9F7QBLJ8T//77yfg2b0733c8/m1/YJDaW9Pv9d22j\nBNB1nj6dcg6GnDlD110oaxw5su594e1Nv7VhBPiPP6hRra7W18/QKwC0OQOhsyUg5DnMeZbvvUdh\nMoA6ILm55qdZcHMjr8ZYc/HNN3Q/du5cvzEAgJdfToJKBTz/PIWNAOo8XrumHVV99SoZPEG30FAK\nZekaA5WKvFTdaVAMw0S3b8vUGPz6669o164dOnfujMTERLRv3x6HhWF2VvL000/j73//O37//Xec\nOXMGUVFRmDdvHoYMGYLU1FQMGjQI8wwXjpUAPz+KnZaWGs8ZCAmm1q21xqCigpK+Fy+aNgYPPEAN\n4T/+QQkd4aa31BgA2l6xGM9AMBiBgfSZkhJq6M+do17gvHl1H9jWremG1fVk9u2jG/7IEeoptmpF\nozPLy0kvqYyBpRgag7IybWVJ167aOHRBAV2DUaNozqP4eMvmeHEUrVvT/de8OYVt7t41bwyCg6l+\n/Y036v6mw4aRh2DY6J45Q/fwtGn03Y8+ontVF2GcgOGAS2EqFcNQTHl53SofIUxk6BkEB1PP3dyA\nwu3btWXaOTn0u4hpMA1DRc89R/p++inp/MADZAwyM83fh2PHkicBUEl206baMO2FC/qeijHPwBiK\n8QymTJmCL7/8Ejk5OcjJycGXX36JyZMnW33C0tJSnD59GqNHjyYBXFzg6+uLnTt3Yvz48QCAcePG\nYYe1BdT14OqqXQXNlGcQHk69EWEY+tatdPHu3DFtDHx8qNGdMYPO8dNP9EBWVJD7LRa1Wo3ISJJN\nrGcAkMeTlkY95u+/p5vUz488GsO6bJWK4sx9+lBYorycKlW2bqVeateu9Lmvv6b5YMrKtDfyxYv0\nX4gX61JTQzIZG7UpGANLY+qGxqC0lB58gOLqQm+zsJAanGnT6HquWNHwhT6swVL9WrYkr04IEwGm\ny251WbOG7iuh4yAQH0/66xr6mzepoxAfD3zyibbu3cen7nGN5Q3++IP+l5To62fMMxDCRIadIJWK\nvAPhWGPG1K2+KymhUNLZs/ScXbggfmEiQ2OQlkbjMoqKyLv19SXd2rY13QCbuna6eYPz57UhJIDC\nSIaegTGMJZBv3ZJm1LCUmDUGvr6+6N69u2a7W7duehPXWcqFCxfQtGlTjBw5EjExMZgwYQIqKipQ\nXFyMwL+e9KCgIBTZaCmowEDyCDw89D2DO3foordooX8DbNhALnRNjelqIoAStq+/Tq76F1/QQ+Hl\nZdx41IdKpY1dAuI8g6AgChskJtJDKFQwmeKNN0ivuXOpJ+nlpU0ihoZqPyfEY4UbOTOTDFPfvpTE\n1aWggH7bVq2ocdbtnerGWS2hPs9AGE1eWkrX0cuLkpb5+doqFmfHw4OMQPPmNHFdnz7ilugMDKTG\n0nDBQZWKRnRv26bdd+4c9WbF3Ie6g6N0v+/tbZlnYBgmArR5g7w8Gm9i6GXu20f5rLAwOqeYfIGA\nYUVRYSGFDT/5RLtq2AMPWLfQkW7ewJhncOOGZcZAMASy9Ay6d++O559/Hmq1Gmq1GjNmzED37t2R\nkpKClJQUi09YW1uL48eP4+WXX0Z6ejoCAgLw9ttvi/7+xIkTMX/+fMyfPx9Lly7Vs+iCjPVtu7ur\nNb2WM2fUKCqi91NTgdBQNX79VY3gYLoBDhxQY/9+NQYMIEPw++9qlJYaP37//sCJE2rcvatGdjY9\nEJ6e5uUx7JGo1Wq8+SbdYGo1nU/wDNRqkueTT6jxLyyk7wcGkvweHmq0b6/WVDTUd76+fenzc+eq\n8Z//UK/n/vv1P5+To8alS2pcu0bypKSoMX26GmFh9FDrHq+yEnB1VeP4cTXc3OghUavV2LFDjRs3\ntCV9lvweJSVqpKdrtw8dUuPOHdr29AR8fNT48ks1QkK03z94UPzxpd62VD+1Wo2AADVatKDfZ/58\n8efz8DB9PCGJrFar8e23ao0hNicPY2r8/LN2+8ABNc6cUeOhh7TGQPh8WRlw+7b+97Oy1MjOVmvC\nRLrHj4oC9u5V47//pe3ycv339+yh+695czVOnxa8UHG/h+AZCNuFhTQ5o7e39vPR0UCTJqaPl5SU\nZPT4rq5qTZTg8GE1amq075eXqwGoNcbAlHxCmEitViM/X42aGvIQqqttdz+q1WpMnDhR016KwtyC\nB3369GFJSUmaP8NtS8nNzWWtWrXSbB88eJD179+ftW7dmhX/tRJNUVERa926dZ3vihDXLI8+yli7\ndvQ6NZWxmBh6/cEHjD33nCATY927M3b5MmPBwbRIzqpVjP3vf4yNH1//8c+fp8UzzpxhrH37BovL\nzpzRyssYY9u30/ZHH2kX9Zg+nRZLmTWLsUuXGCsoEHfsL75gzNWVsZISxkaOpGPqcvo0LYrz2Wf0\nuwUE0GIiGRmMubszduuW9rPHjzOWmEiv27Rh7Nw5ev3bb9r9ljJ7Ni1KJPDJJ/qLhTz0EGNvvcVY\nnz7WHd8ZyM6mRVukYtcuxgYM0G6/+ir9RmLo25exvXu12zk5dL2nTqV7QJd332Vs7lz9fXv3Mtav\nHy2mdOGC/nupqYwFBdF9GhlZdzGWBx+ke2XBAjruoEHiF3rRvU/+/JMxDw9a1EmXmzcZq6kRdzxd\nli9nbMoUeh0Vxdjvv2vfS06mBWl++63+Yxw+zFjXrvQ6KYm+s38/3b/2QkzbadYzUKvVSE5O1vwZ\nbltKeHg4goKCcP6vwOb+/fvRoUMHDBo0COv+moth3bp1GGyj9QoDA7UurG7O4JdfqBYb0C5LePky\nhRzc3bVhInPutjClrjXJY8NeJqDNGQhhl+RkKqGcMUNboxwYSDmOFi0o3xEcLO58Y8dSxUVgIFWh\nGK5mKri3165RrLS6mkIDbdtSeaduJK+yUltBoeta6yaPjelXH4alpbo5A4BCLMeOidfX1liqH0Bh\nNSljx8ZCJubCGAK6OYP0dBrr0b499HreApYkkAEKXX77LV2rxx+vm3MSKvkSEmjhmePHrcsZCPkj\nw5xRo0b1P7umrl3HjtqxIJcu6cskeKSWJpABIXJQ//fsjUljsHXrVuTqzIXwxhtvoEOHDhgwYICm\nIbeWVatWYezYsYiOjsaxY8cwf/58/Pvf/8aOHTsQFxeHXbt24a233mrQOUwRFKS9UYWcQW0tGYNe\nvWi/ECbSNQb1JZB1adKEHoiSEmnmDPH1JfmEOV90V0fS1QmwPFbeqJF2ZKunpza+KhAQQA9teTkZ\nhtatgf/7P3pP+I0EKispgQhoR2cC1lcSCTKZyhkA1JAePap9KDlU/aJbtVNRYTxZbAxdYzB5Mt3D\nc+caH9hleC0Auv4VFaY7Qn36UAVYcLA2hg7QM1heTtexZ09a7GjZMjJEYhBW/AK0xkAq4uIo15Ga\nSs+XblV9aCgZHXP3n2ECGaDfyNkSyCabttdffx0n/qrz+v7777Fp0yZ8/fXXOHXqFJ555hmrekEC\nHTt2xHEjy1rt27fP6mOKJTBQe6MKPc8//qB9wjQRPj7UAJ8/TzeAmxt5BiqVeWPg4kIXPyvLcmNg\nqk5deBhraiiJZVh+aK0xMMd999E5Cwsppj1nDj3QgH6DD5CxMuUZCMVnltbhe3rSQyNQVqafHG/V\niho+ZzEGlupnC4KC6DdhjO5XSyraBGOQk0MJ3l9/pY5QYSFVbiUlJeHSJZopVegg6NK4MZ3b3b3+\naRb8/fUHoeXl0bPn4kLHsCCFCEC74hdgvTEwde18fOj+WrCAVgzUpUkT8mDq0xXQegaMaT1dWXkG\nrq6u8PrLdG3btg1TpkxBYmIipk6dimJnWUzXCuLjaTg5oJ3COjVVfw4UlYpuKKFszBLPAKAHMjNT\nutkEBWPwyy+0mpLhzSeETqQ2BiqV1rD5+dG86UI5qxBKE9ANE+l6DVJ7BrphIqGaylmMgTNw3310\n3YTG8cYN8Z6BMAr5u+9oFK1QOafrGajVwL//TV6DMc+gqMj8fS9UqQkYTittKbpT0UvtGQAUKvr2\nW5pW3BBzcycB1Ol0cSEZq6poW1bG4Pbt26ioqABjDMnJyegjdAkBqBxRxC0RAwdqR+cKnkFBgX5J\nJaA1BrqeQX2lpboEBVF80ZIxBoDpuKXwMKan160tF84H2KakMiCAdDHsBZoLExUUUEOel6cdDWyp\nN2lsnIFuAyQ0IHLOGdgCwTsArAsTbdlCcX0B3ZxBZia9PnLEuGcAmC+PFcImd+9SPky3TNoapPAM\n6rt2HTvSvSZ0Iq1B0Lmqil7LyhjMnDkT8fHx6NSpEyIjI9GtWzcAtJ5BoG73TMa4utJffn7dGygk\nhEIyuglkUxPVGRIYKL1nUFZm+kZv1ow+08BZQozi708NuuEDbugZGIaJCgtpHEBIiDgDagwxOQPh\nfBwtTZtqk8iWhokKCmh+I92VbXXXGb50ie63qqq6nkGjRuSVmLvv/f3pWh44QOdpqGcghTGoj7//\nnWaHbUgfWAgVVVVpB746W87ApDGYPn069u/fj88++0wvlh8YGIivvvrKLsLZAy8vGjlreAMJ25Ym\nkAF6eKTMGQQE0MNYVGT8Rg8Lk2a9ZFPnZqyuMTDMGRirJsrP15+u29KYupdX/cagSRPy6JxlkJkz\n5AwAfc/AkjCRjw8VKMTEaGfhBLSeQVJSEjIzaXp0oK4xcHGhhlmsZ3DpEnW49u1zfJiovmsXGwv8\nNWGC1Qg637qlNQay8QwAIDIyEl26dNFb5jI0NBQtG3LlnIxGjchNrc8Y6IaJxBqDW7ekzxkUFVGv\nzBi2mrJZeOCNeQbmwkSGxsBSPD21CbfCQmrYDBug8+edJ0zkLAiegTAliiXGIDub8lK6+PtTr/bu\nXWiMwf33G/c4GjcW5xmUl9O53NxoynRHh4lsjb+/1kD7+MjQGNwLmPIMQkLIUPj5WTbOANAmOaUY\nZyAcr7TUMTe6EBc2ljMwFSYSxiAIVSICDckZzJwJzJ5dN3muOzuko3G2nMHt29RbN1ftIiAYDUNj\nIMwMummTGtXVZAgyM43PxX/ffebv+8aNSbbz57WlzQ31DGyZM5ACPz+a9VSYOoUbAyekUSNqaI15\nBi1aaMtJLQ0TAdJ5BkK9f32ega0ICKCb1jC+acwzEBpmDw/qNaamNtwzuH2byveOH6dZWDnmETwD\nSydKNGUMAOqQnD2rnfXWFI0bmw8TCVVqp06Rl9GyZcNzBkKYyBHPiBj8/SmHJltjcPfuXURZM8OY\njBAaOcMbKDaWZvME9D0DsdVEgOXVRPWNMygqIlfd3rl7f3/jD7e/P/XGhIdQN0wEkGd14oR+PN+a\ncQa3b1OPKjbW+RJuhjhbzsCSfAFA91Z4uPGlIfv2BTZvTjK7ToSYMBGgLVmOiqIwrW6OwlKEMJEQ\nFrOmE2bra+fnR8bAy0s7fsbZ7ud6jYGrqyvatWuHy8amzVQIXl7a6ax1aduWRkEC2gSyJdVEgLQ5\ng4wM6qUbjhK2NQEBxo2BiwvNkBkfT7LphokAMgYZGdJ4BhUVzhUOcnZ0PQNLjEG7dtRbN9bzX7CA\nPGhzxkB38Zf68PfXLi3ZUIQEclUVHdPa6jVb4u+vHya6cUNmngEAlJSUoF27dujbty+GDRuGYcOG\n4dFHH7WHbHahUSPzMUZrEsiAtDkDY3kNe2DKGABUj96tG1WD6IaJAG25pxQ5A8NjOyvOljOwNEwE\nmPY8g4KAN99U468lR0zi4yPOGAQEUGhIis6N4BlY6gnpYu+cQVWV8xkDs02bML20SqUC+2u2NDkP\nOjPEy8t8nbqlCWRb5AwAx8RCu3enhUKMoVJRT7GgoG6YSJgsrCE9P6G0tLLS+of8XkSYn6ghjaMx\nOnY0v1bGggXirrm/v3TrVHt40PN57Zrz3ieCZ3D//doohOyMQVJSEi5cuIBLly5h4MCBqKqqQk1N\njT1kswuNGpm/gSxNIPv50ZB9Sy+2qbilUMnjCM/Az0+bOzFGSAiNRjUWJjIccGZNzuDWLfl4Bs6S\nM2jenOLTloaJzCFGv3btxB3L379heQJdVCryDoqKrNfX1tfO35/ki4rSGgNZ5QwA4MMPP8SoUaPw\n/PPPAwAKCgoUFSby8jLfyFrqGahUpnvT1uDmRo2yM1ZJCKvCGQsTNSRfAPCcgbU0aULhl+xsy8NE\n9iIqyvx6z5bQqBGVlTqrZyCEzho10nYSnc0zMGsMVqxYgcOHD8P3r7sqMjIS5bqzTMkcMTkD3RHI\ntkxO1Re3DAhwTmMQEkLur2GY6MEHgZEj9T/Lcwb2IyKC1gKWsnGUUr/nn6e1uKWioZ6Bra+d4N0L\nOQPA+YyB2X6uh4cHPHWkrq2tRXV1tU2Fsid/+5vxUjpdLE0g24LAQOccWRkaSnPLqFT6g5s6dLB+\nttGKHAwAABINSURBVFIBuRkDZ6JVK5rYUOwCMXLH25s8A2f1hHQ9A2c1BmY9g169euGdd97BzZs3\nkZycjDFjxthsFTJHMHo0LahRH5ZOVGct9cUtW7c2X9bnCJo1owFxYhpra8cZyMUYOEvOACDP4I8/\npG0cnUk/Qxo1cu6cgY8PlWN7eck4Z7Bs2TL4+PjggQcewJIlS9CjRw8sWrTIHrI5DZYmkG3Bxo3a\nhWWcCXd3qp7SDRFJhYcH/eZSV8XcC7RqRZ2Xe+V3EzwDZ9XXxYW8A1nnDNRqNZ5++mls374d27dv\nx8yZM+FibFISBWNpAtlanCnmbAkhIeJ67pbqJ4SeSkvl4Rk40/UTyjadNWcgNQ1NINtDN8EYyDZM\n9L///Q8dO3ZE165d8fLLL2Pbtm2KSiCLwV7GQK6Ehtqusfb0pNG0cjAGzoQwC6izxtClxtk9A0Bb\nTuusxsBs07Z27VoAwJUrV/Dtt99i+vTpuHLlCu7cuWNz4ZwFe4WJnDkmWx8hIfrrDpjCGv08PeXj\nGTjT9bOFZ+BM+hnSUGNgD938/PRzBrIzBl999RUOHTqE1NRUNG3aFDNmzEBPcxlXhWHpRHX3GqGh\n2pWwpEZIIsvBGDgTAQGUx3HmnrKUNGpEAx+dWV9Dz0B2CeTZs2fj1KlTeOaZZ7Bs2TL84x//QPfu\n3e0hm9MgeAa2riZy5phsfdgqZwBoe0/O/JALONP1U6mAyZOlXfTImfQzRFjy1dqwmD10a9eOBmI6\nawLZbNNWUlKCM2fO4ODBg3j99ddx8eJFtG3bFuvWrbOHfE4BzxnUT//+tltpzdOTfnOxC7RwtHz4\noaMlsB/C1BbO3Gn4z3/of1oa/ZedMaioqEBubi5ycnKQnZ2Na9eu8WoiG+HMMdn6iI6mP3NYo5+X\nF3kdcpgbUa7XTyzOrJ/gGThzzkBAtjmDnj17okePHujVqxdmzJiBsIZOOCNDnGGcwb2KpyfPF3DM\n01BjYE+8vGjuKGdrS8x28VNTU7FixQoMGDAAjaSaZlBm8HEG0mBtzkAuxoBfP8fR0DCRPXUTVjtz\nNswag5SUFERFRaFTp05ISEhAdHQ0UlJS7CGb06A7NxGvJrIvnp7y6O1xHIucPAM/P2D6dEdLURcV\nE1asMUFiYiI++OAD9O7dGwBw8OBBzJ49GydPnrSLgLroLrBjT65dowRpYCDw009AZKTdRbhneewx\nmsL6wAFHS8JxZtavByZMoA6bHPJL9kZM22k26FFdXa0xBABNXKekxW3EYK+J6jh18fSkhc45nPrw\n9iavgBsC6zEbJmrevDkWLlyI7OxsZGVlYeHChQiVYBXru3fvIiEhAcOGDQMAZGVloVu3boiNjcWo\nUaOcyuDYK4HszDFZKbBGP6GaSA7w6+c4BGNgLc6sm70waww2bNiArKwsDBkyBEOHDkVOTg42bNjQ\n4BMvW7YMUVFRmvWUZ82ahVdeeQVpaWkICQnBxx9/3OBzSAX3DByHnBLIHMchZvlaTv2YzBlUVFRg\n+fLluHTpEqKjozFt2jR4SDTyJz8/HxMnTsTrr7+O999/Hz/88ANCQkJQXFwMADhx4gReffVV7N+/\nX19YB+UMAJqC9r77aCEXYdUiju2ZMYMGnL3/vqMl4TgzeXnAkiXA0qWOlsQ5EdN2mvQMxo0bh/T0\ndHTq1AnJycmYNWuWZILNmTMHixYt0gxeKyoqQlBQkOb9Fi1aID8/X7LzSYG7Oy3OzquJ7AuvJuKI\nITycG4KGYjLokZGRgXPnzgEApk6divj4eElOuH37djRr1gwJCQmaOJ0lvf2JEyci4q8pGf38/BAf\nH68ZPSgczxbb7u7An3+qcfgwMGCAbc63dOlSu+njiG1r9AsOBrp0cQ75+fVTrn7Ca2eRRwp91qxZ\nAwCa9tIszATx8fH1blvLa6+9xsLCwlhERAQLCQlh3t7ebNy4cSwoKEjzmWPHjrF+/frV+W494tqc\nJk0YAxirqbHdOZKTk213cCeA6ydvlKyfknVjTFzbaTJn4OrqCm9hJAeAqqoqzQhklUqFGzduiLM2\n9fDzzz9j8eLF2LZtG4YNG4YpU6Zg+PDheOGFF9CqVSu8+OKLep93ZM6gaVNaZKW2lpevcTgcedGg\ncQZ3796VXCBjCNVEH374IcaMGYM333wT0dHRWLx4sV3OLxZ3d5pPhBsCDoejRMyOQHYmHOkZtGwJ\nFBVREtlWqNVqTfxPiXD95I2S9VOybkADq4k4+ri780oiDoejXLhnIJJ27cgzKC93yOk5HA7Harhn\nICHu7nz0MYfDUS7cGIjEHsZAt9ZZiXD95I2S9VOybmLhxkAkbm7cM+BwOMqF5wxE0r07cPUqkJXl\nkNNzOByO1fCcgYRwz4DD4SgZbgxEYo/SUqXHLbl+8kbJ+ilZN7FwYyASXk3E4XCUDM8ZiGToUODK\nFSAlxSGn53A4HKvhOQMJ4Z4Bh8NRMtwYiMQeCWSlxy25fvJGyfopWTexcGMgEu4ZcDgcJcNzBiJ5\n6ikgPx/46SeHnJ7D4XCshucMJISPM+BwOEqGGwOR8LmJGg7XT94oWT8l6yYWbgxEwj0DDoejZHjO\nQCRz5gC5ucCWLQ45PYfD4VgNzxlICK8m4nA4SoYbA5G4ufG5iRoK10/eKFk/JesmFm4MRMI9Aw6H\no2R4zkAkb78N5OQAK1c65PQcDodjNTxnICHcM+BwOEqGGwOR8HEGDYfrJ2+UrJ+SdRML7+uKJCEB\nCA11tBQcDodjG3jOgMPhcBQOzxlwOBwORxTcGDgRSo9bcv3kjZL1U7JuYrG7McjLy0Pv3r0RGxuL\ndu3a4b333gMAlJWVoX///oiLi8PAgQNx7do1e4vmcE6fPu1oEWwK10/eKFk/JesmFrsbAw8PD3zy\nySdIS0vDyZMnsXLlSvz++++YN28ehgwZgtTUVAwaNAjz5s2zt2gOR+kGkOsnb5Ssn5J1E4vdjUFw\ncDBiYmIAAI0bN0ZcXBwuX76MnTt3Yvz48QCAcePGYceOHfYWjcPhcO5ZHJozyM7OxvHjx9GzZ08U\nFxcjMDAQABAUFISioiJHiuYQsrOzHS2CTeH6yRsl66dk3cTisNLSyspKJCUl4Y033sDw4cPh6+uL\nGzduaN433AaoPIrD4XA4lmOuqXfIoLOamho8/vjjGDt2LIYPHw4AaNq0KUpKShAUFITi4mI0a9as\nzvf4GAMOh8OxDXYPEzHGMGXKFERFRWHOnDma/YMHD8a6desAAOvWrcPgwYPtLRqHw+Hcs9g9THTo\n0CH07t0bcXFxmrDPwoUL0aVLFzz55JMoLCxESEgIvvnmG/j5+dlTNA6Hw7l3YTLjww8/ZHFxcSwm\nJoa99NJLjhbHJixevJipVCpWWlrqaFEkZc6cOaxDhw6sQ4cObMiQIaykpMTRIknCrl27WExMDOvQ\noQP773//62hxJCU3N5f16tWLxcTEsLZt27J3333X0SJJzp07d1h8fDwbOnSoo0WRnPLycvbEE0+w\nuLg41r59e3b48GGTn5WVMdi+fTsbMmQIq6mpYYwxxTQmuuTm5rKBAweyiIgIxRmDAwcOsLt37zLG\nGHvllVfY7NmzHSxRw7l16xaLiIhg+fn5rKamhnXu3JmlpKQ4WizJKCgoYGlpaYwxxioqKtgDDzzA\nTp8+7WCppGXJkiVszJgxbNiwYY4WRXKeeOIJtmHDBsYYY3fv3mXXr183+VlZTUexcuVKvPLKK3D7\nay5poRRVSbz44ouaUdlK4+GHH4aLC91yPXr0wOXLlx0sUcM5evQooqOj0aJFC7i5ueHJJ59U1BgZ\nY+OCrly54mCppCM/Px87d+7E1KlTFVegUlpaitOnT2P06NEAABcXF/j6+pr8vKyMwblz57Bnzx7E\nx8ejW7duOHz4sKNFkpStW7ciLCwMcXFxjhbF5nz++ed47LHHHC1Gg8nPz0d4eLhmOywsDPn5+Q6U\nyHbojgtSCnPmzMGiRYs0nRQlceHCBTRt2hQjR45ETEwMJkyYgMrKSpOfd7r1DPr374+CgoI6+995\n5x3U1taioqICp0+fxvHjx/H4448jJydHVuMP6tNv4cKF2Lt3r2afHHsqpvRbsGABhg0bBoB09fDw\nwNixY+0tnuTI6d5rCJWVlRgxYgSWLVsGHx8fR4sjCdu3b0ezZs2QkJCgyInqamtrcfz4cSxbtgwP\nPvggZs+ejbfffhvvvvuu8S/YJ3IlDf369WNqtVqz3bp1a3b16lUHSiQdaWlprFmzZiwiIoJFREQw\nNzc31qpVK1ZYWOho0SRlzZo1rFu3bqyqqsrRokjCL7/8woYMGaLZfu+999h//vMfB0okPdXV1WzA\ngAHs/fffd7QokvLaa6+xsLAwFhERwUJCQpi3tzcbP368o8WSjNzcXNaqVSvN9sGDB9mAAQNMfl5W\nxuD9999n//rXvxhjjGVkZLDQ0FBNQlJpKDGBvGvXLhYVFcWKi4sdLYpkVFVVsVatWrH8/HxWXV3N\nOnfuzE6ePOlosSSjtraWjR8/XhHJ/vpQq9WKrCZKTExkGRkZjDHG5s2bx1544QWTn3W6MFF9zJgx\nA5MnT9YktNasWaPIWB+gzPDDzJkzUV1djf79+wMAunXrhk8++cTBUjUMLy8vrFixAgMHDkRtbS3G\njx+PTp06OVosyfj111+xbt06xMXFISEhAQCNC3rkkUccLJn0KPGZW7VqFcaOHYubN2+iVatWWL9+\nvcnPymrZSw6Hw+HYBmV2qzkcDodjEdwYcDgcDocbAw6Hw3EkkydPRnBwMGJjYxt8rOTkZCQkJGj+\nGjVqhB9//FHUd3nOgMPhcBzIwYMH0bhxY0yYMAFpaWmSHbe8vBxt2rTB5cuX4eXlZfbz3DPg3LNc\nv34dK1as0GxfuXIFI0aMsMm5tm/fjvnz55t8PzU1FVOmTLHJuTnOTa9eveDv76+3LyMjAw8//DA6\nduyIrl274syZMxYfd/PmzRg8eLAoQwBAXoPOOBwpycrKYjExMXY5V1JSEisoKKj3M3369FHcIEOO\nOAzvxe7du7MLFy4wxhg7cuQI69Gjh8XHfPjhh9mOHTtEf15W4ww4HCl59dVXkZmZiYSEBAwYMADP\nP/88hg4dirS0NKxZswY//PADbt++jXPnzmHu3Lm4desWvv76a9TW1mLv3r0ICgpCRkYGnnvuOZSV\nlcHLywurV69GdHS03nny8vJQXV2N4OBgAMDGjRuxYMECuLm5oXHjxjh48CAAYNCgQdi8eTOmT59u\n99+C4zyUlJQgJSVFz0utqqoCAHz33XeYN29ene+EhYVh165dmu2rV68iPT0dAwcOFH9ii80Nh6MQ\nsrOz9Xpjur2zL7/8krVp04ZVVVWx4uJi5uvry1auXMkYo3UZFi1axBgT14PbuHEjmzFjhmY7KiqK\nFRUVMcYYq6ys1Ow/cOAAGzlypMRacuSA7r1XVFTEwsPDG3S8pUuXsmeffdai7/CcAeeehZmpnXj4\n4Yfh5eWFoKAg+Pn5aZZijY2NRV5eHkpLSzU9uISEBDz33HMoKSmpc5zc3FyEhoZqtnv37o1x48bh\n888/1/T4ACA0NBTZ2dnSKMeRLU2bNkXTpk2xfft2AHSfWpoz2Lhxo2bqarHwMBGHYwJPT0/NaxcX\nF822i4sLamtrwRhD06ZNcerUKbPH0jU8K1aswLFjx7Br1y4kJibi1KlTCAgIAGNMkVMicOpn9OjR\n+Pnnn1FSUoLw8HC89dZb2LRpE55++mn885//xN27dzFy5Mg64UdTZGdn4/Lly+jTp49FcnBjwLln\nadSoEW7evGnx94SGPSgoSNODGzp0KBhjOHv2bJ2HtlWrVjh06JBmOzs7G126dEGXLl2wY8cOZGdn\nIyAgAFevXkWrVq0aphRHdmzcuNHo/uTkZKuOFxERgby8PIu/x8NEnHuW4OBgxMfHIyoqCq+88gpU\nKpWmZ677WtjWfS1sb9q0CUuWLEFcXBxiYmLw7bff1jlPjx49kJKSotmeM2cOOnbsiNjYWDz00EOa\nie2OHTuG3r1720RXDsccfNAZh2MH+vbti/Xr1+vlDgxJSkrCN998g2bNmtlRMg6H4J4Bh2MHXnrp\nJXz66acm309NTUWbNm24IeA4DO4ZcDgcDod7BhwOh8PhxoDD4XA44MaAw+FwOODGgMPhcDjgxoDD\n4XA44MaAw+FwOAD+HxY5s5ikIu11AAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t[txx.argmax()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "-9.096746575342362e-09"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tweak = t[txx.argmax()]*(2*np.pi*1e6)\n",
      "print tweak #radians/MHz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-0.0571565444253\n"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dcorr3 = davg*np.exp(-1j*(radians_per_mhz_fine+tweak)*freqs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(freqs,np.unwrap(np.angle(dcorr3)))\n",
      "xlabel('MHz')\n",
      "ylabel('radians')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "<matplotlib.text.Text at 0x643ec50>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEKCAYAAADuEgmxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n"
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This looks about as good as we can guess. Note that the system will have some phase response other than cable delay (the slope around 200 MHz is likely from the low pass filters).\n",
      "\n",
      "We can see the phase of the two resonances clearly.\n",
      "\n",
      "Another way to see that we can't reliably say what the cable delay is any more accurately, take a look at the width of the peak in the delay spectrum:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "txx,t = psd(dcorr3,Fs=1/(frequency_step_mhz*1e6))\n",
      "xlabel('time (s)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "<matplotlib.text.Text at 0x6b978d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEMCAYAAAAmgtofAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t[txx.argmax()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 73,
       "text": [
        "0.0"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now let's plot the amplitude and phase together"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subplot(2,1,1)\n",
      "plot(freqs,20*np.log10(np.abs(dcorr3)))\n",
      "ylabel('dB')\n",
      "grid()\n",
      "title('System Response')\n",
      "subplot(2,1,2)\n",
      "plot(freqs,np.unwrap(np.angle(dcorr3)))\n",
      "ylabel('radians')\n",
      "xlabel('MHz')\n",
      "grid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEXCAYAAABRWhj0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXdYVFfzx7+LgoqA2ChCFGxIBxG7uPaCGluisaImMdYo\neROTGE3s0dcajYn5vdHYEktiDYolurE3EGOJJiKWBRQBgyIqbX5/nLBLr7t7t8zneXj07N49d3a4\n3LlnzhQZEREYhmEYphSYSS0AwzAMYziw0WAYhmFKDRsNhmEYptSw0WAYhmFKDRsNhmEYptSw0WAY\nhmFKDRsNRi84duwYWrRogerVq8PW1hatW7fGpUuXKjSni4sLjh07piEJi+fu3bswMzODtbU1qlev\njnr16uHTTz8FR7QzxkZlqQVgmOTkZLz++uvYsGEDBg0ahMzMTJw+fRpVq1at0LwymUznN+2UlBSY\nmZkhIiICHTt2RIsWLTBw4ECdysAw2oRXGozk3Lx5ExYWFhg8eDBkMhnMzc0hl8vh5eWF9PR01K5d\nG9euXVMdn5CQgOrVqyMpKQnx8fHo0aMHrK2tUbNmTbRv3x5EhJEjR+L+/fvo27cvrK2tsXTpUgBi\nRePv7w8bGxs0a9YM4eHhqnnlcjlmzZqF9u3bw9raGv369UNiYiKGDx+OGjVqwNvbG3fu3CnVdwoI\nCICnpydu376tem316tVwcXGBjY0NOnbsiOjoaABAVlYWJk6ciFq1asHGxgaenp64fv06ACAkJATv\nvfceevToARsbG7Rq1SrPnL/99hu8vLxgbW0Nb2/vPCsruVyO2bNno0OHDrCyskJQUBAeP34MAEhN\nTcWQIUNQo0YN1KhRAwEBAUhISAAAJCUl4a233kKtWrVQp04dfPDBB8jOzi7T75QxYohhJObJkydU\no0YNGjNmDIWHh1NiYmKe9ydOnEgzZsxQjVeuXEn9+vUjIqLQ0FCaMGECZWZmUnZ2Np07d051nIuL\nC/3222+q8e3bt8nW1paOHj1KREQKhYJq1KhBcXFxRETUsWNHatq0KT148IBSUlLI29ubGjduTCdP\nnqSsrCwKCQmhYcOGFfodYmJiSCaTUWZmJhERnT17lqpXr04KhYKIiLZu3UpNmjShO3fuEBHRokWL\nyM/Pj4iI9u3bRwEBAZSSkqKS8+HDh0RENHr0aKpRowZdvHiRsrKy6KOPPqKAgAAiIoqPjydra2va\nuXMnERHt2rWLbGxsVJ/t2LEjNW7cmO7du0cvXrwguVxOoaGhRET01VdfUd++fenFixdERHT16lV6\n+vQpERF169aNJk2aRK9evaLk5GRq1aoVrVixouRfJGMSsNFg9IKrV6/SiBEjyMnJiczMzKhXr14U\nHx9PRETnzp2j+vXrq44NCAhQ3Shnz55N/fv3p+jo6AJz5jcaX3zxBY0cOTLPMT169KB169YREZFc\nLqeFCxeq3vvwww+pd+/eqnFYWBh5enoWKn+O0bC1taVq1aqRTCajpUuXqt6Xy+X0/fffq8ZZWVlk\naWlJt27domPHjlHTpk3p/PnzlJWVlWfekJAQGjVqlGqclpZGFhYW9Pfff9N3331H7du3z3N8UFAQ\nffvtt6pzLliwQPXe2rVrqUuXLkREtH79emrbti1dvXo1z+fv3r1LVapUURkTIqIff/yR2rRpU+j3\nZkwPdk8xeoGXlxc2b94MpVKJW7duITExEZMmTQIAtGrVCtWqVYNCocDNmzcRHR2Nfv36AQD+85//\noH79+ujatStcXFywYMGCIs+hVCqxc+dO1KxZU/Vz+vRpJCcnq46xt7dX/d/CwgJ2dnZ5xq9evSr2\neyQlJSE1NRUrVqzAypUr8fTpU9W533//fdV5a9euDQBITExEp06d8N577+G9996DnZ0dxo4di5SU\nFNWcTk5Oqv9Xq1YNtWrVwqNHj5CQkIDXXnstz/nr16+vcjMBgIODQ57P5sg/cuRIdOnSBW+++SYc\nHR0RGhqK9PR0KJVKZGRkwNHRUSXre++9l0cexrRho8HoHY0bN8aYMWNUfn0AGD16NLZs2YLNmzfj\njTfegIWFBQDA2toaq1atwp07d3Dw4EF89dVXOHz4MACxEZ4bR0dHjB07Fk+ePFH9PHv2DB9//HGh\ncuT/fGkxMzPD+++/D1dXV6xYsUJ17g0bNuQ59/Pnz9G2bVsAwPTp0xEZGYmbN2/i7t27+PLLLwEA\nRITY2FjV3C9evEBycjIcHBxgb2+P+/fv5zn3/fv38xi+oqhcuTLmzp2LGzdu4MKFCzh06BA2bNgA\nR0dHWFlZITk5WSVnSkpKnt8FY9qw0WAk56+//sLXX3+tekJ+8OABfvrpJwQGBqqOGTFiBHbt2oWt\nW7di1KhRqtcPHTqEu3fvAgCsrKxQqVIl1c2+Vq1aiImJUR07cuRI7N69G8ePHwcRISMjA6dPn0Zc\nXJzqGMoVbUUVjLyaMWMGVq9ejbS0NLz77rtYuHChahM7NTUVe/bsAQBERkYiMjIS2dnZqFatGqpU\nqQIzM/Wf5r59+xAREYGsrCzMmTMHXl5eaNSoEYKDg/HHH39g165dAIA9e/bg8uXLqlVYcd/h999/\nx59//gkAqF69OszNzWFmZoaGDRsiMDAQn376KZ4/fw4AuHfvHk6fPl0hXTDGAxsNRnKsrKzw22+/\nwcfHB9WrV0dAQAAaNWqE1atXq4557bXX0Lx5c5iZmaF9+/aq169fv46goCBUr14dgYGBGDduHLp1\n6wYA+PDDDzFr1izY2tpi+fLlaNKkCX766Sd8+umnqFGjBhwcHDB//vw8kUG5VxcymazAaqO41Uf+\n94KDg+Hg4ID//e9/GDFiBN5991306tULNjY2cHNzUxmNJ0+eYNSoUbCxsYGTkxNsbGzw0UcfqeYc\nOnQoPvnkE9SsWRPHjh3Dtm3bAIjVyy+//ILZs2fDysoKs2bNwu7du/O4pIr6PrGxsejXrx+srKzQ\npEkTtGnTBiEhIQCAnTt3Ii4uDg0aNICNjQ369u1bYEXDmC4yqujjFMPoiHHjxsHJyQlz586VWhSd\nMWbMGDg7O2PevHlSi8IwADi5jzEQ4uLisHv3bkREREgtik7hZzpG32D3FKP3zJo1C+7u7ggNDYWr\nq6vU4uiUwlxkDCMl7J5iGIZhSo3k7qmcsgqVKlWCubk5Lly4gOTkZAwZMgSPHj2Co6Mjtm/fDltb\nW6lFZRiGMXkkX2m4uroiIiICtWrVUr02ZcoUNGrUCNOmTcPKlSsRExODVatWSSglwzAMA+iJ0bh0\n6ZIqQxYAGjVqhAsXLqB27dpITExE69at8xRpYxiGYaRB8o1wmUyGbt26wcfHB2vWrAEAPH78WGVE\n6tSpk6csAsMwDCMdku9pnDt3DnZ2dnj8+DF69uyJZs2alfqzHFXCMAxTPsrrZJJ8pZFTEK5u3boY\nPHgwLl68iLp16yIxMRGAWHXkLhqXHxKVek3+Z/To0ZLLoC8/rAvWBeui+J+KIKnRSEtLQ1paGgDg\n+fPnCA8Ph6enJ3r37o0tW7YAALZs2YLevXtLKaZB4OLiIrUIegPrQg3rQg3rQjNI6p569OgR+vfv\nD5lMhrS0NAwdOhT9+vVD+/btMWTIEKxfvx4ODg7YsWOHlGIaLM+eAdbW5fvsixfA8+fA/PnA++8D\nH38MeHgAbm7A7t3AmjXAW28B27YBly8DXl6ApSXw5AnAf5sMY7xIajRcXV1x5cqVAq/XqlULR44c\nkUAiwyUnj+XGDSAhAUhKAkaPBo4eBT76SPz/1CkgJgZwcgLi4wEfH2EYEhPF8UlJgJ0dkJUFnDkj\njEBQkDjO01O8lpYG1K0LyOXAo0fAxInA/v3A4MEAERAZKcYffQRs2QLIZEAFW32XWxcM6yI3rAvN\nIPlGOKMZoqP9cPUqMGUKcPEiYGUFvPEG0K4dMGoUsGAB0LYtEBoKPH4M1KsHXL8O2NgAdeoAtWuL\nn/v3hWE4fFgYEScnsbJo00YYiSdPhBEYP14YET8/YMYMYO1aoHJloEEDoH17YSzGjwcOHAA+/xzw\n9hZyyuXa14Wfn5/2T2IgsC7UsC40g+R5GhVBJpNVeFPHkCECoqIAZ2dxs7axEauD778Hzp4FPvkE\n+PVXIDhYHFupkuZliI4W7qgdO4Dq1cVKZelSYNkyICBAGJQlS8S5s7OBvn2B2Fhg6lTg/HnA1xfo\n2VMYmZcvgVevgGvXgE6dgL17xXtpaYC5OVCjhublZxhTpCL3TjYaBszJk8J91KkTUL8+4OAgbtRv\nvCG1ZIKsLGEsLl0SLq2EBCA8XKxo1q4FunQRq52LF4VBAYTxcHISq5zMTGHwIiIAR0ext7JpE7Bz\npziOYZjywUbDxNixA1i1SmxyN2wI/PADsHSpAhMnyqUWrVwkJwvXlpWVMBQ5hsbNDWjRAujcGbhy\nRezX2NsDY8cCW7eKlYiTE1ClCpCr0R0UCgXkuvCDGQCsCzWsCzUVuXfynoYBsnYtkJEhntB/+QVY\ntEhEMBkqucqO4d/W32jVSvwbFSVcbvfvq/dbevcGunYFpk0D/v4bePddYNIksaHv7Kx7+RnGlOCV\nhgGxd6+Iepo1C3jwQDyh16sntVS65949sXnfpIlYidy8CfTqJfTzxx/Avn0iHPjJE2GQzCRPYWUY\n/YLdU4WQkSH83n//LSKBpk4V41evgDlzRATQ0KFAo0ba2SDWNBkZYrPbygro109sNps6KSli89/b\nG1AqgcBA4K+/hLHo1Ak4fhz44APx/t27IrLs/n3xO8/Zb2EYU4SNRiG88YbIGXjxQrg33NyEqyM9\nXYSh2tkBR46IMNKWLUUSW6tWIkpHH9m9G1i+HDhxQkRC5X96NmV/7S+/iETGjh2Fjrp0UWD7djkG\nDwYmTBAPC3Z2YiP++XOge3exIR8cLFYidnbiAeKff4ReW7c2no12U74u8sO6UGPwexpZWVlo0aIF\nnJ2dsX//foSEhODEiROo8W+M5caNG+Hj41Pq+c6cEeGc//uf8JF7eQHr1onQz2rVAFdX9U0hNVXc\ndMaNA+LiRIZzcLA2vmX5yMoSG7/HjgELFwq5jeWGpikGDVL/f/VqQKEAfvpJjG1txU/jxmIV4uoq\nroX584UhrlJFbLAPGCBcXg8fijBgHx8R0VWzJtCjh0hu5NUJw+jJSmP58uWIiIjAs2fPsG/fPowZ\nMwZ9+/bFwIEDi/1cYdby4UMRbfPJJ8DIkWWT49w5kUfQurVwa+jDQ8nevcDcuSLvISiI/fPa5sUL\nYVRSUoSuHz8WBqhxY+HaOnZMuDcXLABee00cw78TxtAw6JWGUqnEgQMHMHPmTCxfvlz1enm/0MCB\nwJAhZTcYgDAWZ88KX/iwYSLMs27dcomhMVauBD78UD8MmClQrZqIysrN9OniWrh4UeyPuLkBY8aI\nQISBA4ULLDJSbMYzjLEj+TPS9OnT8d///hdm+R7XZs6cCXd3d0yePBmvXr0q1VzPnok/7k8/Lb88\njRsD77wjXEIBAcDmzeWfqyJkZYls6tjYvO6XolAoFFqXyVDQtC5cXYH+/cWKb+tWkWyYlSX2xtau\nFeG+w4aJjPbkZI2eusLwdaGGdaEZJF1p/Prrr7Czs4O/v3+eX+jixYthZ2eH9PR0TJgwAfPmzcP8\n+fMLnSMkJERV8jgpyRYNGvjB3FwOQH2R5Gx+lWU8fz7g5KTAxIlAnz5y1KxZsfnKOv75Z2DXLgWW\nLoVGvo8pjXPQ9PwnTypQty5gYSHHyZPi/cuXgdOn5fDyAt56S4H9+4HLl+Vo2hQ4c0YBmUxafURF\nRUn++9CXcVRUlF7Jo8uxQqHADz/8AKDiJeIl3dP49NNPsXnzZlSuXBkvX77E06dPMWjQIGzatEl1\nzJkzZzBnzhwcOnSowOfz++VWrABu3wa+/lpzMo4eLdwRFVm9lIcBA4DXXwdCQnR7XqZs3LolorJi\nY0VuSM+eYrP85k3hVhw/XmoJGaYgRhFy+/vvv2Pp0qXYv38/EhISYGdnByLChx9+iOzs7Dz7HTnk\n/+IjRoh6RmPGaE6u69eFG8LeXkRk6aK6ckqKqCV1755uzsdUnPR0EcLdoYPIp2nbVpSh//ZbkZA5\nZIjUEjKMmooYDcn3NHIgIlXP76FDh8LPzw/NmjWDUqnEF198Uao5Ll0SGcKaxNNT+KmbNxfVY3XB\nxo0iOa0sBiO/a8aUkUIXFhYiVNvGRqwy9u8XK44+fURS4YsXOhcJAF8XuWFdaAbJo6dykMvlKl/c\nsWPHyvz5p09FaQ13dw0LBhFSGRoqGg117ix6SGgrVyImBpg3D/j9d+3Mz2gfe3vx76pVYsP8q6+A\nb74Rqw0nJ2llY5iKojfuqfKQe4n1++8iN+PMGe2dLzRUxOwvXQoMH66dc4wZI6J1Zs/WzvyM7jl5\nUkTAvXwpQnMbN5ZaIu3z8qXoteLpKb6zv7+IPPPwEJWZ4+PFv6dPi1VYzl4QoxuMYk+jPMhkMpw/\nT/j+e6BpU7EH8NVX2j3n+vXAoUPA9u2an/vlS9E34vp10yxEaOx8+aXIA/rpJ1HaxpiIjxf9XObN\nE/8mJAD//a/IMxo7Vqyyjh0TK/QqVcQe0PPnolpDWppo5BUaKkr9/POP6BTZqhU33tIWRrGnUV5u\n3AA2bBC9sAMCtH++nj1FAcTMTM3PffCgcH2Vx2Cwv1aNvupi2jThRq1bV1yvukCburh7V1ROuHFD\nXZ5l5UoRafjVV2KPZ+xY0e/lzh3RM/6nn0R5H6VSFBM9e1YkTeZEKEZGioenhQuFKy8gQKxGNIG+\nXheGht7saZSXx49FBdjwcN1Ufq1XT/yBbNggMoA12b9h2zYRtskYJ1WrimoDO3YAM2eKSD9DrCN2\n5oxYWXz9tQhx/+474X6bNElcv/XqiVX/+vWicOiAASJ0PT8ODuJfCwtRFy4/GRmijM6gQaJGXPfu\nIrLQ2VkYqEuXRD96RrcYvHvqww8Jhw+Lp5anT3XjF/3mG/FE5eEhit5pgtRU8WR1545oh8oYL9nZ\noijilCmigZQh8OqVaH6VlCRcT40bi7+1vXvF38MHHwh31Ouvi2CUrCzRjVETEIly97/8Itx6y5eL\nhltxceLvxdFRc+cyFUx6TyMkhNCihShvrcve2Eql2NxLSNDM0+KPP4qNwrCwis/F6D/Xr4vimB99\nBLz3ntTSFM2VKyLow8kJqF4d+M9/hCtJygebu3eFZ+HPP4URO3pURKplZAjXX48e0slmKFSogR0Z\nMAAoOJho3z5pzu/iQnTzpmbm6tuXaNOm8n/++PHjmhHECDAUXdy4QVS3LlFKivbOUV5dKJVE335L\n1KQJ0fz5RBMnEiUmala2inL7NpFMRvTee0S1ahFVqULUvbt4Lyur4PGGcl3ogorc+g1+I/zxY+kq\n0bZvD5w6VfF5MjKEr7tPn4rPxRgO7u7iqVjfujDevSuubYVCuNBmzhT7F/rmNm3USLilv/lG7Ikc\nOCA21n/8UVQjzonQYjSMBo1XucnMzCQ/Pz/q06cPERHduXOHWrduTV5eXjRkyBBKT08v9HMAyNVV\nPHFIwbffiiebc+cqNs/580Q+PpqRiTEs7t0jcnAgOnxYakkE0dFEDRoQrV4ttSTlo3t3oqpVhRdg\nxAjx/yNHiLKzxQ8jqMitXy9WGqtWrYKHh4eqjMjUqVMxY8YMXL16FQ4ODlizZk2Rn5VypTFggIgA\n6dJFPNWUl5MnRc0ixvSoX1+EoY4eXbFrSBPcvi3K18yYAUyeLK0s5WXwYNEXZ/du4NdfRdHId98F\nmjUDFi0SxxjuLq6eoEHjVS4ePHhAXbp0oWPHjlGfPn0oMzOT6tSpo3r/4sWL1KVLl0I/C4AsLKR/\ngvD2FquF8vL660TbtlVMBvbXqjFEXXTuTLR1q+bnLa0u/vqLyNmZaN06zcugS7KziXIcExkZ4t/Z\ns8X3srE5ToMHE7VqpT7GVKnIrV/yQLWcJkxPnz4FACQkJKBOnTqq952cnKBUKov8fOXKIZgzxwUA\nYGtrCz8/P53Xq2/TRo6zZ4G0tLJ/PjsbOHVKjrVr9av+viGPc9AXeUozfv99YMYMBerV020/jSdP\ngG++kePSJeDttxVo2hQApNeHJsanTonxnDlifORIFF69AmrWlCMkBLCxUWDwYKBLF/2QV5tjhQb7\naUi60ti/fz9NnDiRiMQTUZ8+fSg2NpaaNWumOiYuLo7c3NwK/TwA8vPTiajFsmED0dCh5fvs9etE\nDRtqVBzGAMnMJHJ1rfj+WFmZMoVo3Dixt2IqxMcTvf8+UcuWRHPnSi2NNFTk1i/pSuPMmTPYt28f\nDhw4oGrCNGPGDCQmJqqOUSqVcC4m7VrqHt6AqJNTyurtBeD9DAYQiXKTJ4vyG1u3av98KSmiAsHW\nrSLfwc5O++fUFxwcRHJubKxopbB3r6iZxT3eS4ekG+ELFy7EgwcPEBMTg23btqFz587YvHkzWrdu\njT179gAAtmzZgt69exc5hz4YjaZNRYJfeYoYaspo5HfNmDKGqouxY0X9sbg4zc1ZlC5mzxZGY9Mm\n0zEY+XXh5AT89Zfo/R4SAuzcKYlYBodeRE/lkBM99dVXX2Hx4sXw9vbGo0ePMGXKlCI/ow9GQyYT\nTytTpohy0GWBVxpMDra2onbTt99q9zwZGSJi6/vvRVFBU8baWpRHOXIEeP99US+LKR6DLyPyzz+k\nN+WTy9pu9t49oGVL4OFDwyxcx2iemzcBuVxcG1WqaOcc+/eLZLiTJ7Uzv6Hy119At24i5HjiRKml\n0S4mXRpdXwwGIMo4R0SU/vicVQYbDCaHZs1Ej4n9+7Uz/9dfC1fM++9rZ35DpmlT0cxt3jzR+4Mp\nHIM3GvpE8+blMxqawFD9+NrA0HXRr58oyKcJcusiPR2YNUv0pxg8WDPzGxKluS5cXETfjxEjxEY5\nUxA2GhrE3x/444/SN2ji/QymMHr2FEZD047j334T9a6aNdPsvMZGly4iku3NN8X+D5MXg9/T0Dfx\n3dyAn38WBdOKIzFRFFxLTubeyExeiMS1sW+fcFVpirFjxXU5fbrm5jRWsrNFb5DGjYEVK6SWRvOY\n9J6GvtGyZekq3546JfI72GAw+ZHJ1KsNTUAkwkp//VU8PTMlY2YmwpH37uVQ3Pyw0dAwgwaJ+PeS\n0LRrytD9+JrEGHTRq5fI2agoCoVCVT48KkrkJpgqZb0uatYUBmPiRFHMkRFozGhER0erMrnPnj2L\nJUuWYNu2bcjOztbUKQyCXr2Aa9eA+/eLP473M5ji6NQJuHBBtAGuKEeOiOuyXr2Kz2VqBAQAEyaI\nzoCMQCN7Gp9++im2/5sO/dZbb+Ho0aOQy+U4f/483Nzc8G0R2UovX75Ehw4dkJmZiefPnyM4OBgr\nVqxASEgITpw4gRr/xtNu3LgRPj4+BYXXwz0NQJRidnMTfZMLIzVVlDJITASqVtWtbIzh0KULMG2a\naAtbEfr3F26pYcM0I5epce+eMB5KpfH8vVbk3qmR2lM7duzAjRs3kJaWhvr16+Phw4eoXr06MjMz\n4e7uXuTnqlatihMnTqBatWrIzMxE+/btcfz4cchkMixduhQDBw7UhHg6JyhI+I+L4tw5wM/PeC5A\nRjvk7GtUxGhkZooOfN99pzGxTI4GDURk5K5dbHgBDbmnrK2tUaVKFdSsWRONGzdG9erVAQCVK1eG\nlZVVsZ+tVq0aACA9PR1ZWVmw+7cQjj6uIEqLtzdw9WrR71+4IDbBNYkx+PE1hbHoomdPsa9RkT+F\nd95RwNvbdOpLFUdFrotPPgE+/lgUejR1NGI0UlJSsGvXLvzyyy95/p8zLo7s7Gz4+fnB3t4enTp1\ngqenJwBg5syZcHd3x+TJk/Hq1StNiKkzmjUD7twBXr4s/P2bN0W8PMMUh5cX8OpV+TdhDx0CDh8W\nIeBMxejcWewLlbeatTGhkT2NkJAQVbHBwtiwYUOJc6SkpKBHjx748ssv4eHhATs7O6Snp2PChAlw\ndHTE/PnzC3xGJpNh9OjRqqYiUjVhKmzs7Q1MnapAkyYF358xQ47ly4GMDOnk47FhjJcsAXr2lGPq\n1LJ//q23FKhWDVi/Xn++jyGPt25VYOpU4PFjOczMpJenLGNFviZMc+bMKbc3RyNGY9myZcW+/0FR\nO8L5mDdvHszNzfHxxx+rXjtz5gzmzJmDQ4cOFTheXzfCAeH77NkTGDUq7+tEIpTv9m0gV4NChimU\nn38W1WjLE37bqZNwqfTooXm5TBVPT/H7aN1aakkqhuTJfc+ePUNqaioiIiLw7bffIi4uDnFxcVi3\nbh0uX75c5OeSkpLw7NkzAMCLFy9w5MgReHt7IyEhAYDY19i1a5fKZWVI+PiIkiL5SUgQCX2aNhg5\nTxWMcemiWzdRK+r587J9LitL1EF79UqhFbkMEU1cF6+/LhL+TBmNRE998a+jTy6X48qVK7C0tAQg\nVg7FNVCKi4vDqFGjQER4+fIlhg0bhuDgYHTu3BnJycl48eIF/P398Z0Bhn74+IjuYPm5dYtr/zCl\np0YN0V3u2LGyRVH9+Sdgbw/Y2GhPNlPkzTeB7t3FT6dOUksjDRqtPdW4cWP8+eefMDc3ByAiojw8\nPHBbS+mU+uyeevAACAwUvTJy83//B5w9y81emNKzbBnw999la860bp1IIN2yRXtymSq//QYMHAg8\nemS4YfOS52nkMGzYMAQEBGDgwIEgIuzZswfDhw/X5CkMBmdnEfmSkJA33PHWLZH4xzClpXdv8VNa\nbt4U7Vx37NCeTKZMly6Ah4dwG3bpIrU0ukejtafmzp2LdevWwdLSEtbW1li3bh3mzJmjyVMYDDKZ\ncFHlz9fQltEwJj9+RTE2Xbi5iYePp09Ld/zcucB//gN07Gh8uqgImtRFt27A0aMam86g0OhKAwDa\ntGmDNprOXDNQvL3FZnjupxFeaTBlxcxMPNlev166pNAbN4DQUO3LZcp07Sp0vGiR1JLoHu6noUW+\n+06UDMnZv0hPFxuTT58CFhbSysYYFmPGAG3bAu+8U/xx2dmAtTUQH8+b4NokPV3Uj1u5Ehg50vBa\nNksecssjtkfEAAAgAElEQVQUTv6w2+ho4LXX2GAwZcfLS6w0SkKpFBFXbDC0i4WFqB78xRem56Zi\no6FFvLxE6GNWlhhr0zXFvms1xqgLT09Rcr8k8l9jxqiL8qJpXQQEACEhwniYEmw0tIiVlVjC5kQc\n834GU168vMpnNBjt0rmzyKExJdhoaJncLiptJvbl1JthjFMXTk6iAObjx8Ufl99oGKMuyos2dNGy\nJfDXX8CTJxqfWm+R1Gi8fPkSgYGB8Pf3R9OmTTH93473MTExaNOmDby9vTF06FBkZGRIKWaFyG00\nIiJERBXDlBWZrHT7Gn/+ySsNXWJhISLaCimNZ7RIajRymjBdvnwZN27cwNmzZ3H8+HFMnToVM2bM\nwNWrV+Hg4IA1a9ZIKWaFyOmtER0tMkgDA7VzHvZdqzFWXZTkokpJAS5eFFFWORirLsqDtnTx0Uci\n/DYuTivT6x2Su6cKa8J07tw59O/fHwAwYsQIhIWFSSlihfDxASIjge3bRdvNSpWklogxVDw9i19p\n7N0LyOWAra3ORGIg8rBGjgQWLJBaEt0gudHI34SpZs2aqJOrBKyTkxOUSqWEElaMxo1Fa9fPPhP1\narQF+67VGKsuSlppbN8ODBmS9zVj1UV50KYu3nwTOH5ca9PrFRrPCC8rZmZmiIqKUjVh8vPzK9Pn\nQ0JC9LIJU+7xnj1yhIUBlSsroFBILw+PDXOckqJAVBRAJIdMlvd9IuC33xSYOBEA9ENeUxr7+QH3\n7yuwezcwYID08uQfK/I1YaoIepURPm/ePMhkMqxatQqP/w0TuXjxIj755BMcLSSDRt8zwnWJQqFQ\nXSymjjHrwt5euDudnPK+/uiRWInkj64yZl2UFW3rok8fYPRo4I03tHYKjWGwGeGFNWHy8/ND69at\nsWfPHgDAli1biu3JwTCmRFEuqpgYoIIPkEwFkctNI2dD0pXG1atXCzRhmj17NmJiYjBs2DCkpqbC\n09MTmzdvVvXoyA2vNBhTY+pUoEEDIH8H5W3bgF9+AXbulEYuBrh/X2SJHz8ujLs+ozf9NMqKt7d3\noe1gXV1dcfbsWQkkYhj9xstLFMHMz927gKurzsVhclG/vqh6O26c+B0ZWhHD0iJ59BSjGXI2vRjj\n1kVZ3VPGrIuyogtdjBsHbN5svAYDYKPBMAaFp6fol5Gdnff1u3d5T0MfkMmApk2llkK76FX0VFnh\nPQ3GFKlfH1AogIYN1a81bQrs2SOaNTFMSRhs9BTDMGUnf5n07GyxCcsrDUYXsNEwEth3rcbYdZF/\nX+POHaBOHcDSsuCxxq6LssC60AxsNBjGwMhf7fbkSaB9e+nkYUwL3tNgGAMjIgIYOxa4ckWMx4wR\nfR0mTJBWLsZwqMi9k40GwxgYaWlA7drA06eAubnYEP/1V94EZ0qPwW6EP3jwAEFBQfD29oabmxuW\nLFkCAPjiiy/g7OwMf39/+Pv7Izw8XEoxDQL216oxdl1YWoraU7dvAw8eAM+eAe7uhR9r7LooC6wL\nzSBpRriFhQXWrl0LLy8vpKamonnz5ujRowdkMhlCQ0MRGhoqpXgMo7fkbIanpwNBQcadTMboF5Ia\nDXt7e9jb2wMArKys4OPjg9jYWABgt1MZ4UqmakxBF/7+olRFaqowGkVhCrooLawLzaA30VN3797F\nxYsX0aFDBwDA119/DXd3d4wYMQLJyckSS8cw+sXAgaI4oUIBdOwotTSMKaEXG+Gpqano1KkTZs6c\nif79+yMxMRG1a9cGIPY3oqOjsWXLlgKfk8lkGD16tN43YdJVk5Uc9EEeKcc5r+mLPNoYEwGurgok\nJgIpKXJUqlT48VFRUZg2bZrk8urDeOXKlSZ9f8jdhGnOnDmGGz2VkZGBPn36oGfPnpg+fXqB9+Pi\n4tCpUyfcunWrwHscPaVGwc12VJiKLhYsAC5cEL3Bi8JUdFEaWBdqDDbklogwevRo1K5dGytWrFC9\nnpCQADs7OwDA6tWrcfz4cezatavA59loMKZMZqbY07C1lVoSxtAwWKNx6tQpBAUFwcfHB7J/wz8W\nLlyIH3/8EX/88QfS09PRoEEDfP/993DK398SbDQYhmHKg8EajYrCRkMNL73VsC7UsC7UsC7UGGxy\nH8MwDGNY8EqDYRjGxOCVBsMwDKMT2GgYCblzFEwd1oUa1oUa1oVmYKPBMAzDlBre02AYhjExeE+D\nYRiG0QlsNIwE9teqYV2oYV2oYV1oBr1swpScnIxu3brBx8cHPXr0wD///COlmAZBVFSU1CLoDawL\nNawLNawLzSCp0chpwnT16lVERETgf//7H65cuYLPP/8cwcHB+OOPP9CrVy98/vnnUoppELBhVcO6\nUMO6UMO60AySGg17e3t4eXkByNuE6cCBAxg5ciQAYMSIEQgLC5NSTIZhGOZf9GZPI6cJU/v27fH4\n8WNVP406deogISFBYun0n7t370otgt7AulDDulDDutAMehFym5qaCrlcjs8++wz9+/eHjY0Nnj59\nqno//zgHGTdGZhiGKRflvfVL2iMcEE2YBg0ahOHDh6N///4AgLp16yIxMRF16tTB48ePVb018qMH\n9o5hGMakkNQ9RUQYN24cPDw88nTt6927t6q965YtW9C7d2+pRGQYhmFyoXdNmBYtWoSWLVtiyJAh\nePToERwcHLBjxw7YcnsyhmEY6SED5ODBg+Tl5UXu7u705ZdfSi2OzmnQoAF5e3uTn58fBQYGEhFR\nUlISde3alby9val79+705MkTiaXUDmPGjCE7Ozvy8vJSvVbcd58yZQp5eHiQv78/RUZGSiGyVihM\nD59//jk5OTmRn58f+fn50YEDB1TvLVy4kNzd3cnLy4sOHTokhcha4/79+9ShQwfy8vKipk2b0uLF\ni4nINK+LonShyWvD4IzGy5cvycXFhZRKJWVkZFCLFi2M6pdeGlxcXCgpKSnPa5MnT6YVK1YQEdGK\nFSto6tSpUoimdU6cOEGRkZF5bpZFffeff/6ZXn/9dSIiioyMJF9fX90LrCUK08MXX3xBy5YtK3Ds\npUuXqEWLFpSZmUlKpZJcXFzo1atXuhRXqzx8+JCuXr1KRETPnj2jJk2aUFRUlEleF0XpQpPXht6E\n3JaW8+fPw9PTE05OTqhcuTKGDBliknkclM+raCq5LR06dEDNmjXzvFbUdw8LC1O97u/vj8zMTCiV\nSt0KrCUK0wNQeHBIWFgYhg4dikqVKsHJyQmenp64cOGCLsTUCWXN9zLm66IoXQCauzYMzmgolUq8\n9tprqrGzs7PR/MJLi0wmU5VZWbNmDQCYdG5LUd89NjbW5K6Vr7/+Gu7u7hgxYgSSk5MBCD04Ozur\njjFmPZQm38tUroscXXTo0AGA5q4NgzManJsBnDt3DpGRkfjtt9+wYcMGHD16VGqR9Jb8T1fGfP1M\nmjQJ0dHRuHHjBho1aoSpU6dKLZJOSU1NxeDBg7Fq1SrY2NgUe6yxXxepqal44403sGrVKlhbW2v0\n2jA4o+Hs7IwHDx6oxg8ePMjz1GAK5OSt1K1bF4MHD8bFixdVuS0Ais1tMUaK+u75rxWlUpnnqcrY\nqFOnDmQyGWQyGcaPH4+LFy8CKFwPxvY3U1y+F2Ba10WOLoYNG6bShSavDYMzGoGBgbh27RpiY2OR\nkZGBHTt2oFevXlKLpTPS0tKQlpYGAHj+/DnCw8Ph6elp0rktRX333r17Y+vWrQCAyMhIld/WWMnt\nkvzll1/g6ekJQOhh+/btKt/9tWvX0LJlS6nE1DhUxnwvY74uitKFRq8Nze7d64YDBw6Qp6cnubu7\n08KFC6UWR6fcuXOHfHx8yNfXl5o0aUKzZs0iorzhhd26dTPakNuhQ4eSo6MjmZubk7OzM61fv77Y\n7z5p0iRVaGVERISEkmuW/Hr4/vvvacSIEeTj40PNmjWjHj16kFKpVB2/YMECcnd3J09PTwoPD5dQ\ncs1z8uRJkslk5OvrqwopPXjwoEleF4Xp4sCBAxq9NvSi9hTDMAxjGBice4phGIaRDjYaDMMwTKlh\no8EwDMOUGp0ajdL2/q5UqRL8/f3h7++vChljGIZhpEenG+FTpkxBo0aNMG3aNKxcuRIxMTFYtWpV\ngeOsra3x7NkzXYnFMAzDlBKdGo1GjRrhwoULqF27NhITE9G6dWvcvn27wHFsNBiGYfQTnbqnSlsf\n6eXLl2jRogWaN2+OHTt26FJEhmEYphg03u61W7duePjwYYHXFyxYUOo5YmNjYWdnh5iYGHTu3Bm+\nvr5wc3MrcJyx1YthGIbRFeV1MmncaBw5cqTI90rb+zvndVdXV3Tv3h2RkZGFGg2A+4TnEBISgh9+\n+EFqMfQC1oUa1oUa1oWaijxw69Q9VZr6SCkpKcjIyAAAJCUl4ffff1fVSWEYhmGkRadGY86cOQgL\nC4OPjw8OHjyIuXPnAgAiIiLwzjvvAACuX7+O5s2bw9fXF+3atcPUqVPh4+OjSzENEhcXF6lF0BtY\nF2pYF2pYF5rBoGtPyWQydk/9i0KhgFwul1oMvYB1oYZ1oYZ1oaYi907OCGcYhmFKDRsNhmEYptSw\ne4phGMbEYPcUwzAMoxN0ajR27twJT09PVKpUCZGRkUUeFx4eDm9vb3h4eGDx4sU6lNBwUSgUUoug\nN7Au1LAu1LAuNIPGk/uKw9vbG7t378b48eOLPObVq1eYMGECTp06BXt7e7Rp0wbdu3eHv7+/DiVl\nGMPlwQNg9WogMhKIjwcsLAAzM+CNN4CxY4EicmoZplTo1Gg0a9asxGPOnz8PT09PVaP3IUOGICws\njI1GCXAooRpT1sWzZ0CnTkCfPsBHHwHVq8tRrRqQmgps2gS4uQHLlgnjYWqY8nWhSXRqNEqDUqnE\na6+9pho7OzvzspJhSsn06UBQELByZcH3goKAGTMAuRy4fFkYlqAgoFo1nYvJGDA6K1i4cOFC9O3b\nt8TPl7UmSkhIiCrT09bWFn5+fqonihxjYwrj3IZVH+SRcpzzmr7Io6vx/PkKHDgA3Lqlfj8qKgrT\npk3Lc/zp03Js3Qp8+KEC9+8DM2bIMWUKcOmSfn0fTY9Xrlxp0veHnLpbFc6MJwmQy+UUERFR6Hsn\nTpyg4OBg1XjJkiU0f/78Qo+VSHy95Pjx41KLoDeYoi7S04kcHYlOncr7ekm6uHGDaOhQIjs7osWL\niVJTtSej1JjidVEUFbl3ShZyS0XECAcGBuLatWuIjY1FRkYGduzYgV69eulYOsMj5+mCMU1dHDsG\nNGgAtGuX9/WSdOHuDvz0k/j8pUtAo0ZizyMtTXuySoUpXhfaQKdGY/fu3Xjttddw7tw5BAcHq4xB\nXFwcgoODAQBVq1bFN998gx49esDX1xcDBw5E8+bNdSkmwxgc27cDb75Z/s97egI7dgCHDwNnzgCN\nG4uNc4bJD2eEGwkKLsamwtR0kZEBODgAUVFArhgSAOXXRWSkCNF9800RhVWzpmZklRJTuy6KgzPC\nGcaEuXZNGI38BqMiNG8OnDoFKJVAw4bA+PHiPAzDKw2GMXC+/x74/XftuZMePgT+7/+Ab74RYbrL\nlwNWVto5F6MbeKXBMCZMZCSgzdxXBwdg1izg5k0gMxPw8wOOHgX4ec00YaNhJOTOUTB1TE0XkZHC\nnVQYmtSFjQ2wfj2wdCkwaRLQujUQFqax6bWOqV0X2kIvCxa6uLjAx8cH/v7+aNmypQ4lZBjDIisL\nuHpVPP3riv79gRs3RHZ5aCgwdCiQlKS78zPSotM9jZs3b8LMzAzjx4/HsmXLigyldXV1RUREBGrV\nqlXsfLynwZg6N24A/foBt29Lc/4XL4DPPhO5Ht98A7z+ujRyMGXDYPY0mjVrhqZNm5bqWDYGDFMy\nt28DpfyT0grVqolkwO3bgQ8+AEaOBJ48kU4eRvvo5Z6GTCZDt27d4OPjgzVr1kgtjkHA/lo1pqSL\nBw+KD7XVlS46dACuXAFsbQFvb+DQIZ2ctkyY0nWhTfSuYCEAnDt3DnZ2dnj8+DF69uyJZs2aoWvX\nroUeywULeZx/nIO+yKPN8enTgIdH0e9HRUXpTJ6LFxUYNAgYMECOESOArl0VGDYM6NlTOv3kHkdF\nRUl6finHCg0WLJQkT6NTp07F7mnkZtGiRQCATz75pMB7vKfBmDrDhwM9egCjRkktSV5iY4H33wfO\nnwfmzxduKzO99GuYJgazp5GbogROS0tD2r/V0p4/f47w8HB4enrqUjSGMRhKck9JhZMT8PPPop7V\n2rVA587AX39JLRWjCfSuYOHDhw/Rpk0b+Pn5wd/fHx07dkS/fv10KaZBkt81Y8qYki70ZU+jKNq0\nEQUQBwwA2rYF5s4FXr6URhapdWEs6LRz34ABAzBgwIACr9erVw9h/2YJNWzYEFeuXNGlWAxjkGRn\nA3FxgLOz1JIUT6VKwlXVvz8weTLQpYtICrS1lVoypjxw7SmJIQLK2KyQYQAA8fGAry+QkCC1JKUn\nO1sYkNOnRYRV3bpSS2SaGOSeBiNKMlhaCv9v69aiFPU33wBPn0otGWMI6Ot+RnGYmQFffQX06gU0\naSJ6lF+/LrVUTFlgoyER+/cDn38OREQAZ8+KyqEDBogOas2aAVu3lq0gHPtr1ZiKLkpjNPRRFzIZ\nsGABcOuWiPqSy0VGubbRR10YIiUaje3btyM1NRUAMHfuXPTt2xcXLlwo18lCQ0Ph4eEBDw8P9OnT\nB0lFFKwJDw+Ht7c3PDw8sHjx4nKdS595+hSYOBHYsgXw8ADq1xebhMOGATt3Art2iaJwQUFAeDhX\nE2UKxxBXGrmxtwfefhs4ckSUIvniC77WDYKSmoh7eXkREdHvv/9Ocrmc9u/fT4GBgeVqSH7s2DHK\nysoiIqIZM2bQtGnTChzz8uVLcnFxIaVSSRkZGdSiRQuKjIwsdL5SiK+XLFxINHJk8cdkZBBt3kzk\n7U3k50cUHa0b2RjDITSUaPFiqaXQDA8fims9NJQoO1tqaYyfitw7S1xpmP2bkXPgwAG8/fbb6NOn\nDzIzM8tloDp16qSar127doiNjS1wzPnz5+Hp6QknJydUrlwZQ4YMUUVWGQt795acjFW5MjBihCjN\nMGYM0KkT8N13wD//6EZGRv958ECsUo0Be3tAoRDdAkeOBP51bjB6SIlGw8nJCRMnTsTOnTsRHByM\n9PT0chuN3Hz33Xd4vZCSmEqlEq/lWnM7OztDqVRW+Hz6wqNHwpcbFFS642UyYOpUkSB19Kio63Pg\nQMHj2F+rxlR0Yah7GkVRq5bY07OwAFq0AP74Q7PzG5Iu9JkS8zS2bduG8PBwTJ8+Hba2tnj48CGW\nLl1a5PGlqT21YMECWFhYYPjw4QWOk5Ux/tTQak8dPAh06yaHhUXZPh8cDFSvrkCbNsDkyXK0agUE\nByvg7Kxf308fxjnoizzaGt++rYB4nir6eF3WntLUeP16OTZvBoKCFBg3Dli6VA6ZjGtPVWSs0HXt\nqVevXuHx48fIzs5WvVa/nOvijRs3Yt26dTh27BiqVq1a4P2TJ09i8eLF+PXXXwEA//3vf5Geno6Z\nM2cWFN4A8zSGDxclFcaNK/8cqanAypXAqlVA376iFaerq+ZkZPSfjAygenUgLU24Mo2RW7eAN98U\n0YTffQfUqCG1RMaDVvM0li5dCkdHR3Tv3h3BwcGqn/IQHh6OJUuWYN++fYUaDAAIDAzEtWvXEBsb\ni4yMDOzYsUNVbsQYiIgAAgMrNoeVlYg2+ftv4Z4IDATGjwfu39eMjIz+ExcH2NkZr8EAADc34Nw5\noHZt0c72zz+llogBUPIWev369SkxMbHcO+25ady4MdWvX5/8/PzIz8+PJkyYQEREsbGx1Lt3b9Vx\nBw4cIE9PT3J3d6eFCxcWOV8pxNcrnj4lsrQUkVGaJDGRaNiw41SrFtGkSURKpWbnNzSOHz8utQha\n5+RJotatSz7OWHTx/fdEzs5EV6+Wfw5j0YUmqMi9s8TnlCZNmqBmzZoaMVB///13oa/nrj0FAL16\n9TKq1UUOUVFiI1vTT4e1awPvvAOsWAEsWSLOMXq06OHs4KDZczH6gaHnaJSVsWNFUEjnzsCgQSKr\n3NxcaqlMkxL3NMaOHYu///4bvXv3hoWFhfiQTIbQ0FCdCFgchransXKlcCl9/bV2zxMfD3z5JbBh\nA+DnB/TsKX78/LingbGwZImIxFu2TGpJdMuzZ8DQoaKG1c6dwlXLlJ2K3DtLfOatX78+6tevj/T0\ndKSnp4OIyhzhxAgiIkTJBG3j6Cg2yRctAk6cEFnlw4aJ3s3du4vqogkJwJAh4t8aNUT9K2dnoF49\noIjtJkaPuHdP+PxNDWtrkec0YQLQsaOolsurad3CVW51SMuW4mbepo3m51YoFKpQu6KIiREG5OlT\nEXmzdy/QoIEYx8aKn/h48Yfp5KT+cXbOO3ZyEjH1mnp2SE8Hrl0TRvXJE6BVK7G5X7Vq+VZG+XVB\nBGRmGpc7o2dPYMoUoKSYlNJcF4YIkegIuGEDcPBg6QyoseqiPGh1pREfH4+FCxfi1q1byMjIUJ3w\n2LFj5TqhpklKEj59QyA2VtreB66u4gkth8mTCx6TnQ08fqw2Ijk/Z84ASqV6/OpVQUOS38A4Oqpv\n1PfuiT2d9HTx2adPRbZ7RARw4wbQsCEQECBWQR99BFy+LG70Li6i/HdAgKgInJAgKqS2ayduHLGx\nYg4AqFMHiI4W8p86JQzjtWvCUD56JIycpWXBn+rVC3/d0hLw8gK6dhVGUp+IjgYaNZJaCumQyUSo\nubOzWHH88ou4JhjtU+JKIygoCKNGjcLSpUuxbt06bNq0CbVr18aSJUvKfLLQ0FCEh4cDEM2WNm7c\niNqF3PFdXFxgY2ODSpUqwdzcvMgCiTKZDFOnElatKrMoOiczE6hWTcTVG8MTb2qqCPvMbUhyfnJe\nS0gQBt3BQWzctm0rVg8WFuJG7e0tjIGvrxjnhkgYsOhoYUAiIoDnz8V8YWGideirV6L8hK+vODYx\nEWjaVJy7RQuhazc3dRnujAzxWs7P8+fFj1NTgUuXgJMnRWHJHj3ET8uW0oa6ZmYKfaWksCsREH05\nRo4E1q0TlaKZkqmQl6ak8KqcgoU5/xIRtWzZslyhWqUpWEhE5OLiQklJSSXOB4DefLNcouic2Fgi\ne3uppdAtGRki/PfCBaInTzQ7d3IyUXq6ZucsipcviY4eJfrwQyIfH6KaNYkGDSL67juie/d0I0Nu\n7twR4aeMmogIonr1iFavlloSw6AUt/4iKdFjbGlpCQCoXbs2Dhw4gMjIyELLhJSG0hQszGXMSjXn\no0flEkXnxMYKl422yF9CQx+oXFl858BAzbf2rFmz6BWbpnVRpYpoUbpkiXCpXb8O9OsnCuy1aAG4\nuwPTpgG//67R0xbJnTuld03p43WhDZo3Fy7JNWtEqHmu4hUqTEUX2qZEozFz5kw8ffoUy5cvx/z5\n8/H2229j5cqVFT5xUQULAbF06tatG3x8fLBmzZpi5ymn/dI52jYajO5wdBRVirduFdffli0iOzsk\nBHjvPREK+uyZ9s5v6vsZReHqKtrInjologW5Uq52KNEz269fPwBA8+bNcebMmRInrGjBQgA4d+4c\n7Ozs8PjxY/Ts2RPNmjVD165dCz02JiYEX3zhAkC/CxbGxgIymQIKhXbmzylKpi/f19TGAQGAt7cC\nW7cCGzbIMWUKMHCgAq+/DvToodnzRUfL0agRF3Asanz0qByTJwNeXgqsWAEMGCDPowOp5ZNirNBF\nwcLFixdjxowZmDJlSsEPyWT46quvynXCkgoW5mfRokUAgE8++aRQOczNCU+f6v+G4MyZQsZZs6SW\nhNEFV6+Kdr4XLgCffCIymqtV08zcgwaJQn5DhmhmPmNl9mwRjnv8OCcB5kcrBQs9PDwAAAEBAYX+\nlIfSFCxMS0tDWloaAOD58+cIDw+Hp6dnkXPa2YkoHX3HFPc0pEIfdOHtLdr27tkDHD4sIrzi4zUz\n95UrgI9P6Y7VB11IxZw5ogrCoEEi1NuUdaFJinRP5biSQkJCNHayKVOmID09Hd26dQMAtGnTBmvX\nrkVcXBzeeecdhIWF4eHDhxgwYABkMhnS0tIwdOhQlYusMBwchF9Z3zuY8Z6GadKihUiinDdPNN6a\nNk3UCbOwKN98KSniem/aVLNyGiMyGfDNN8DgwcBbb+XNUWLKT5HuqRyjARRcyshkMuzbt0/70pWA\nTCZD796E994TfSX0GQ8PYMcOkSzGmB5EIp9g+XKRA7JypTAoZeXECZH8eO6c5mU0Vl69AgYOFC6q\nrVuNu5x8adGKe+qDDz7ABx98gIYNG8LKygrvvvsu3nnnHdjY2KBhw4blFlbT5Kw09BkikfAmZTY4\nIy0ymSj9ER4OvPGGePodMkRkxpeFqCjA3187MhorVaqIjPF//hHVn7OypJbIsCnSaORE5Jw7dw4/\n/vgj+vbti379+mHr1q04e/asLmUsFnt7/c/V+OcfcdPQdK5Cbthfq0afdWFmBrz/PnDzpsg1CQws\nWy/sy5fLZjT0WRe6pGpVIDRUgUePCi+fw5SeEvM0kpOTcffuXdX43r17SE5O1qZMZcIQVhr37+v/\nngujW6pWBb79VkTTdekiSmBkZhZ9PJFIXNu/X5QxYcpOlSrqwAQ98K4bLiWljO/Zs4ccHBwoKCiI\ngoKCyMHBgfbu3VvuFHRNAoC2bSMaMEBqSYpn716i4GCppWD0levXidq3J2rcmGjDhsLLoxw8SNSo\nEdGpUzoXz+g4cYLI0ZHo8WOpJZGOUtz6i6RUpdFfvHiBq1evwszMDF5eXqXKryiMzz77DPv370dW\nVhZq1aqFH374odD9kY0bN6oKIs6YMQOjRo0qdD6ZTIbYWIKXl6iUqq919VevFv2N166VWhJGn/n9\nd2DuXFHC/uOPRVazlZVYgTRvLkJIuSCfZvjPf0Tl5R07NFfi35CoyEZ4qYxGREQEbt68iczMTFUD\npl5kCxcAABEoSURBVKJu5MWRmpoKq3+zbFavXo1Lly5h48aNeY6Jj49Hhw4dEBUVBQDw8/PD6dOn\nYW9vX1D4f7/4tGlirIHqJlrhww9F2e4ZM7R3jtyZrqaOoevizBlgwQKRlNamjUgKNDMTobtlvcEZ\nui40SW5dvHwpDPGsWSIc19TQSvRUDh9//DH+85//YPr06VAoFPjoo4/KHW5rlSstMzU1FY6OjgWO\nOXLkCHr16gUrKytYWVmhZ8+eOHLkSLHzfvIJsH27iEzRR+7dE82OGKY0tG0ryr+npIjMbw8P4Oef\nTfOJWFtUrQps2iTyZuLipJbGsCgxYnnXrl34888/ERAQgA0bNiAxMbHImlGlYebMmdi8eTMsLS1x\nrpBg89jYWDjnik11dnaGUqksdk57e/FH1b8/cOuW/jXM0cVGOD9NqjEWXZibA+PHV2wOY9GFJsiv\nixYtRMLfuHHAgQNslEtLiUajRo0aqFSpEogIqampqF27NqKjo4s8vqSChQsWLMCCBQvw5ZdfYvr0\n6diwYUOFvkBISIiqAFeDBrb49FM/fPutHID+FAy7d0+OBg30Rx4e85jHYty+PfDrr3L83/8BTZtK\nL4+2xgoNFiwsdgs9KyuLxowZQykpKbR8+XJq1KgR+fr60ogRI8q9857DvXv3yM3NrcDrmzZtokmT\nJqnGEydOpC1bthQ6R37xL1wgql9fNP/RF168ILKwIPq395TWOH78uHZPYECwLtSwLtQUpYtr14hq\n1yaKjtatPFJSwq2/WIrd05DJZIiIiICNjQ2mT5+OsLAwbNy4EZs3by6XgYqJiVH9f+/evfD29i5w\nTNeuXREeHo5nz57h2bNnCA8PL7Isen4CA0V9J32KwVYqhUxmJe4eMQwjBZ6eIlptzJjCmzcx+SjJ\nqowePZouXbpUbquUmwEDBpCPjw+5u7tT7969KS4ujoiILl26RG+//bbquPXr15O7uzu5u7vTDz/8\nUOR8hYm/bRtRUJBGxNUIR48SyeVSS8EwTHFkZhJ16EC0bJnUkuiGUtz6i6TEkFs3Nzfcvn0bDRo0\nQPXq1QGIFcgfZal9oCUKCxvLyBAdvH79VZRFlpoNGwCFAsgXWcwwjJ5x5w7QqpUoCunuLrU02kWr\nIbeHDh1CdHQ0jh07hv3792P//v16UeG2KMzNgYkTgXL2iNI4ugq3zdn0YlgXuWFdqClJFw0bAvPn\ni1a+GRm6kckQKdFouLi4FPqjz7z7LrB7N/D4sdSScI4GwxgS774L1K4N/NswlCmEUmWE6yvFLbHe\neQewthb9C6SkSxexyfZv3ymGYfSc2FhRSfjgQaCcTUr1Hq26pwyVRYtEXZkSksm1Dq80GMawcHIC\nVqwQvTfS06WWRv/QqdH47LPP4OvrCy8vLwQFBeHOnTuFHlepUiX4+/vD398f/fv3L9e56tQRm88h\nIdK5qbKzRcjta69p/1zsu1bDulDDulBTFl0MGyaqOHz9tfbkMVR0ajQ+/vhjXLlyBdeuXcMbb7yB\nOXPmFHqcpaUlLl++jMuXL2PPnj3lPl+XLsDIkUBwsKjlo+uOXY8eATVqiIJzDMMYDjKZcG0vXAgk\nJkotjX6hU6NRmoKFmmb+fFFfZs4coFEj4bbS1crj0iWgWTPdnCundADDusgN60JNWXXRrJlYcXz+\nuXbkMVR0vhGev2ChbSE9UM3NzeHr64vs7Gx8/PHHePPNNwudSyaTYfTo0apoLltbW/j5+RVZi2Xd\nOgX27AHOnZNj7Figc2cFqlfXXu2XDh0UaNUKWLpUO/PzmMc81u746VPg7bflOHwY+Ocf6eUp71iR\nr/bUnDlzyr0RXv60wCLo2rUreXl5FfjZt29fnuMWLVpEISEhhc7x6NEjIiK6c+cOubi40M2bNws9\nrrziP3xIFBJCVK8e0datRNnZ5ZqmWOLjiWxtiZ4+1fzchcE1htSwLtSwLtSUVxdbtxK5uhpXp7+K\n3PpLrHJbVkrqfZHDsGHD0L1790Lfs7OzAwC4urqie/fuiIyMhJubm8ZktLcXmdqnTwNTp4reyytW\niGxQTbFpk+iyZm2tuTkZhtE9w4YBkZHAe++JFgymjk7dUzExMXB1dQUgOvedOHECO3fuzHNMSkoK\nLC0tYW5ujqSkJLRr1w47duyAj49PgfkqEmucQ3a2iLKaPRtwdATGjgWGDgUK8ZqVGiLhD12/HmjX\nrkLiMQyjB7x4AXh5iWiqnj2llqbiGEyexgcffABfX194eHggPDwcX/1b6+PSpUt45513AADXr19H\n8+bN4evri3bt2mHq1KmFGgxNYWYmqlvGxIjN8t9+A1xcgOHDgaNHy1f18vRpMW/bthoXl2EYCahW\nTXgkJk8WrWJNGaPNCK8IiYnAjz8KF9aTJyLJZ+RIEX1VUnevmBiR/f3pp2LVoisUCoVqA8zUYV2o\nYV2o0YQuBg0CfHwMP6LKYFYahkKdOmKv4/JlUcPqyROgfXvAxkYsT4lEzserV+rPZGSIENsOHYDQ\nUN0aDIZhdMPKlcDq1cDt21JLIh280igDt28DQ4aInt/p6cJQODoCyclAaipQty6wbBnw1ls6E4lh\nGB3z3/8C4eGi/YKhJu5W5N7JRqMcxMcDFhbiglEqRVXMmjW5Ox/DmAIZGaJ8+h9/iD1QBwepJSo7\nBueeWrZsGczMzJCcnFzo+xs3boSnpyc8PT2xadMmHUtXMo6OwlBYWgJNm4r/S20wchJ5GNZFblgX\najSlC3Nzsec5cCAweLDpFTXU+a3uwYMHOHLkCBoUUfo1Pj4e8+bNw/nz53H+/HnMnTsXjx490rGU\nDMMwRSOTiWjLWrWA6dOllka36NxohIaGYsmSJUW+f+TIEfTq1QtWVlawsrJCz549S50waMpwhIwa\n1oUa1oUaTevCzAzYvFm4qLZv1+jUeo3GM8KLY+/evXB2di427yI2NhbOzs6qsbOzM5RKpS7EYxiG\nKRM1agDffy+CXxo1AhISgN69pZZKu2jcaHTr1g0PHz4s8PqCBQuwaNEiHD58WPWaJjaxQ0JCSl2w\n0JjHuf21+iCPlOOc1/RFHinHUVFRmDZtmt7II+V45cqVWrs/tGoFyOUKVKkCfP21HEOHSv99898f\nchcsrAg6i566du0aunTpAktLSwCAUqmEk5MTLly4oKo1BQCbN2/G+fPnsWbNGgDApEmT0LZtWwwf\nPryg8BJFT+kjCk7iUsG6UMO6UKNNXTx7JjbE4+KABQuAn34qORFYSgwy5NbV1RURERGoVatWntfj\n4+PRoUMHXL58GQDg5+eHM2fOwN7evsAcbDQYhmHKjsGF3AJC6BwiIiJUtaccHR0xc+ZMtGrVCq1a\ntcLs2bMLNRgMwzCM7uHkPiOB3RBqWBdqWBdqWBdqDHKlwTAMwxgevNJgGIYxMXilwTAMw+gENhpG\nQu4cBVOHdaGGdaGGdaEZ9LJgYaVKleDv7w9/f3/0799fx9IZJlFRUVKLoDewLtSwLtSwLjSDTsuI\nACUXLAQAS0tLVZ4GUzr++ecfqUXQG1gXalgXalgXmkHvChYyDMMw+otOjUZpChYCwMuXL9GiRQs0\nb94cO3bs0JF0hs3du3elFkFvYF2oYV2oYV1oBo2H3BZXsHDhwoU4fPgwbGxs4OrqikuXLqF27doF\njk1ISICdnR1iYmLQuXNnhIeHw83NraDw+lzchWEYRo/R+9pTpS1YmJ/x48dDLpfjLW68zTAMIzl6\nV7AwJSUFlpaWMDc3R1JSEtq1a4cdO3aU6NJiGIZhtI/eFSy8fv06mjdvDl9fX7Rr1w5Tp05lg8Ew\nDKMvkAFy8OBB8vLyInd3d/ryyy+lFkfnNGjQgLy9vcnPz48CAwOJiCgpKYm6du1K3t7e1L17d3ry\n5InEUmqHMWPGkJ2dHXl5ealeK+67T5kyhTw8PMjf358iIyOlEFkrFKaHzz//nJycnMjPz4/8/Pzo\nwIEDqvcWLlxI7u7u5OXlRYcOHZJCZK1x//596tChA3l5eVHTpk1p8eLFRGSa10VRutDktWFwRuPl\ny5fk4uJCSqWSMjIyqEWLFkb1Sy8NLi4ulJSUlOe1yZMn04oVK4iIaMWKFTR16lQpRNM6J06coMjI\nyDw3y6K++88//0yvv/46ERFFRkaSr6+v7gXWEoXp4YsvvqBly5YVOPbSpUvUokULyszMJKVSSS4u\nLvTq1StdiqtVHj58SFevXiUiomfPnlGTJk0oKirKJK+LonShyWvD4MqInD9/Hp6ennByckLlypUx\nZMgQhIWFSS2WzqF8W1EHDhzAyJEjAQAjRowwWp106NABNWvWzPNaUd89LCxM9bq/vz8yMzONpt98\nYXoACo+ICQsLw9ChQ1GpUiU4OTnh/9u7l1D43jAO4N+jv5RbKZPk2JAYh2EW2FAsJKOxQcZlxUIa\nlGJpycJulKVbkUtZEZNLuWxkmFm47IjMbJDVmIh6/4tfTr/xR0PHzH9mvp/dmXOmnveZZ3pOZ973\nHUVRcHh4GIwwgyItLQ0FBQUAgMTERBgMBng8nqisi89yAWhXG2HXNNxuNzIzM9VjWZYj5gMPlCRJ\nqK6uhsFgUP8W9+7uTp2+nJqaitvb21CGGFSfjd3j8URdrYyPj0Ov16O9vV3dpsfj8UCWZfWaSM7D\n1dUVHA4HysvLo74u3nJRUVEBQLvaCLumwbUZwMHBAZxOJ7a3tzE1NYWtra1Qh/S/9f7uKpLrx2q1\n4uLiAufn58jOzkZfX1+oQwoqr9eLxsZG2Gw2JCcnf3ltpNeF1+tFU1MTbDYbkpKSNK2NsGsasizj\n5uZGPb65ufG7a4gGb+tadDodGhsb4XA4oNPpcH9/D+DPnfdXa18izWdjf18rbrfb764q0qSmpkKS\nJEiShK6uLjgcDgAf5yHSvjMvLy9oaGhAW1ubuslptNbFWy5aW1vVXGhZG2HXNEpKSnB6egqPx4OX\nlxcsLS2htrY21GEFjc/ng8/nAwA8Pj7CbrdDURSYTCbMzs4CAGZnZ2EymUIZZlB9NnaTyYS5uTkA\ngNPpVJ/bRqq/H0kuLy9DURQAf/KwuLioPrs/PT1FaWlpqMLUnBACnZ2dyM/PR39/v/p6NNbFZ7nQ\ntDa0/e0+ONbW1oSiKEKv14uRkZFQhxNUl5eXwmAwiKKiIpGTkyOGhoaEEP7TC6urqyN2yq3FYhHp\n6ekiNjZWyLIsJicnvxy71WpVp1YeHx+HMHJtvc/DxMSEaG9vFwaDQeTl5YmamhrhdrvV64eHh4Ve\nrxeKogi73R7CyLW3v78vJEkSRUVF6pTS9fX1qKyLj3KxtramaW2E9d+9EhFRcIXd4ykiIgodNg0i\nIgoYmwYREQWMTYOIiALGpkH0DTExMeoWFADw+voKnU4Hs9kMAJienkZvb6/feyorK3F8fBzUOIl+\nC5sG0TckJCTg7OwMT09PAIDNzU3IsqyuKP5oZfHboiqiSMCmQfRNJpNJ3fxufn4eLS0t6rYUn81g\nF0JgZWUFRqMRRqMRubm5yMrKClrMRFph0yD6pubmZiwsLOD5+RknJycoKyvzO7+4uKg2B6PRiKOj\nI0iSBLPZDJfLBZfLheLiYgwODoZoBEQ/90+oAyAKN4WFhbi6usL8/Dzq6ur+c95isWBsbEw9rqqq\n8js/OjqK+Ph4dHd3/3qsRFpj0yD6gfr6egwMDGB3dxd3d3d+577aZGFrawvLy8vY29v77RCJfgWb\nBtEPdHR0ICUlBYqiYGdnJ6D3XF9fw2q1YmNjA3Fxcb8bINEvYdMg+oa3WVAZGRno6elRX/t79tRH\nM6WEEJiZmcHDw4O6XXVGRgZWV1eDFDmRNrhhIRERBYyzp4iIKGBsGkREFDA2DSIiChibBhERBYxN\ng4iIAsamQUREAfsXyomVLmrGZbQAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(freqs,dcorr3.real)\n",
      "plot(freqs,dcorr3.imag)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "[<matplotlib.lines.Line2D at 0x8285a50>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD9CAYAAABeOxsXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngDHGGHgyYIwxBp4MGGOMAfgFbRrUSc6WFEcAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}