heterodyne_multiple_tones_calibrated.ipynb

{
 "metadata": {
  "name": "heterodyne_multiple_tones_calibrated"
 },
 "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": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ri = roach_interface.RoachHeterodyne()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "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": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ri.set_dac_attenuator(57)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ri.set_adc_attenuator(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "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": 11
    },
    {
     "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": 12
    },
    {
     "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": 13,
       "text": [
        "1903.0"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "psd(x+1j*y,NFFT=4096,Fs=512.0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "(array([ 9.76496108,  2.9109366 ,  2.88646017, ...,  0.03529154,\n",
        "        0.05721713,  5.25963164]),\n",
        " array([-256.   , -255.875, -255.75 , ...,  255.625,  255.75 ,  255.875]))"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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mJosak9ShQ+7n8+qr/NYuo/l68Uef//qr63K8847r56qD0RT7q6+kj5mhw4oQ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\n"
      }
     ],
     "prompt_number": 14
    },
    {
     "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, 121716.58 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, 124055.36 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, 125700.07 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, 124061.19 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, 122144.94 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, 122559.71 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, 121679.94 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, 121442.11 samples per second, idle 14.50 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, 124805.63 samples per second, idle 18.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
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       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124158.92 samples per second, idle 30.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
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       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123124.94 samples per second, idle 27.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
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       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124544.15 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, 123830.48 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, 117402.56 samples per second, idle 26.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
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       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 122264.90 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, 123193.37 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, 123785.42 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, 121332.33 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, 122301.46 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, 124502.18 samples per second, idle 18.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123451.86 samples per second, idle 31.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
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       "text": [
        " \r",
        " got 1"
       ]
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      {
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       "text": [
        " \r",
        "read 2 in 0.1 seconds, 122539.61 samples per second, idle 22.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
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      {
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        " got 1"
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      {
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       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 122697.14 samples per second, idle 22.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
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       "text": [
        " \r",
        " got 1"
       ]
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      {
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       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123401.31 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, 123412.39 samples per second, idle 21.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
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       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 121575.75 samples per second, idle 31.50 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, 122598.20 samples per second, idle 23.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
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       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 122722.99 samples per second, idle 20.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        " got 1"
       ]
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      {
       "output_type": "stream",
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       "text": [
        " \r",
        "read 2 in 0.1 seconds, 125403.71 samples per second, idle 35.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
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       "text": [
        " \r",
        " got 1"
       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 125131.97 samples per second, idle 21.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
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       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123772.04 samples per second, idle 29.00 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, 124771.64 samples per second, idle 32.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
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       "text": [
        " \r",
        " got 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123164.22 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, 122066.40 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, 123461.17 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, 123028.83 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, 123421.70 samples per second, idle 24.00 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
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       "text": [
        " \r",
        " got 1"
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       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123087.89 samples per second, idle 30.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
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       "text": [
        " \r",
        " got 1"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 123468.27 samples per second, idle 21.50 per read\n",
        "0\n",
        "\r",
        " got 0"
       ]
      },
      {
       "output_type": "stream",
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       "text": [
        " \r",
        " got 1"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "read 2 in 0.1 seconds, 124621.39 samples per second, idle 34.00 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, 124648.07 samples per second, idle 36.50 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, 122609.13 samples per second, idle 31.00 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, 122811.16 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, 122669.54 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, 122474.96 samples per second, idle 33.50 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, 123780.07 samples per second, idle 14.50 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, 123318.05 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, 123215.90 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, 124748.53 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, 124914.52 samples per second, idle 26.00 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, 126102.35 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, 123697.63 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, 124096.59 samples per second, idle 29.00 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, 124343.58 samples per second, idle 34.00 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, 123492.68 samples per second, idle 24.50 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, 123865.75 samples per second, idle 26.00 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, 122910.45 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, 125298.53 samples per second, idle 37.50 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, 124527.00 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, 125781.06 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, 125068.66 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, 122554.03 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, 125085.96 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, 122855.51 samples per second, idle 25.50 per read\n",
        "0\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "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": 16
    },
    {
     "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",
      "tones2 = np.concatenate(tones) #stack the tones into one long array\n",
      "davg2 = 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, 122372.89 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, 122430.89 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, 125996.00 samples per second, idle 35.50 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, 122957.07 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, 123082.16 samples per second, idle 35.50 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, 123464.72 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, 122017.42 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, 121525.01 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, 125162.51 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, 124494.96 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, 123496.23 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, 121806.76 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, 122208.37 samples per second, idle 25.50 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, 122629.26 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, 123152.74 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, 122554.03 samples per second, idle 22.00 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, 123104.65 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, 122108.04 samples per second, idle 26.00 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, 122557.97 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, 124192.57 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, 122910.89 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, 124830.57 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, 122958.83 samples per second, idle 36.00 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, 125651.81 samples per second, idle 25.00 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, 125752.06 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, 123143.47 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, 122671.29 samples per second, idle 27.00 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, 124269.83 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, 123594.85 samples per second, idle 14.50 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, 123649.11 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, 124672.94 samples per second, idle 22.00 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, 124735.40 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, 124614.16 samples per second, idle 48.00 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, 125281.17 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, 122542.67 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, 122896.38 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, 122493.30 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, 125924.42 samples per second, idle 35.50 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, 123946.62 samples per second, idle 35.50 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, 123907.29 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, 122398.18 samples per second, idle 22.00 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, 125281.17 samples per second, idle 25.00 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 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, 124959.95 samples per second, idle 14.50 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, 124464.30 samples per second, idle 41.50 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, 122327.15 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, 124311.19 samples per second, idle 44.00 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, 124430.49 samples per second, idle 36.00 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, 126435.06 samples per second, idle 40.50 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, 124193.02 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, 123994.93 samples per second, idle 46.00 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, 124967.68 samples per second, idle 40.00 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, 123968.98 samples per second, idle 41.00 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, 125352.01 samples per second, idle 27.00 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, 124294.11 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, 125072.76 samples per second, idle 37.50 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, 125131.97 samples per second, idle 32.50 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, 125363.44 samples per second, idle 32.50 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, 124883.65 samples per second, idle 40.50 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, 124874.12 samples per second, idle 33.00 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, 122842.34 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, 125283.00 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, 125118.30 samples per second, idle 37.50 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, 124477.82 samples per second, idle 38.00 per read\n",
        "0\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "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",
      "tones3 = np.concatenate(tones) #stack the tones into one long array\n",
      "davg3 = 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, 123551.74 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, 118383.48 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, 123735.05 samples per second, idle 27.00 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, 126220.94 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, 126347.19 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, 121839.59 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, 122634.95 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, 125019.06 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, 123643.33 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, 123753.77 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, 124097.03 samples per second, idle 35.50 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, 122362.00 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, 122429.58 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, 123873.34 samples per second, idle 14.50 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, 124969.50 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, 123355.24 samples per second, idle 36.00 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 33.50 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, 122440.48 samples per second, idle 44.00 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, 125357.50 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, 122587.26 samples per second, idle 41.00 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, 124555.44 samples per second, idle 34.00 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, 125524.66 samples per second, idle 38.00 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, 122894.18 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, 125860.31 samples per second, idle 34.00 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, 122257.51 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, 122693.19 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, 122529.99 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, 124591.12 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, 124507.59 samples per second, idle 35.50 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, 125895.36 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, 125999.69 samples per second, idle 33.00 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, 124750.80 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, 125640.32 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, 126285.42 samples per second, idle 40.50 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, 124553.63 samples per second, idle 38.50 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, 124857.33 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, 123974.79 samples per second, idle 41.00 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, 125263.81 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, 123613.52 samples per second, idle 44.00 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, 124538.28 samples per second, idle 29.50 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, 123873.34 samples per second, idle 24.00 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, 122091.99 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, 123626.42 samples per second, idle 14.50 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, 124560.86 samples per second, idle 24.00 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, 123425.69 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, 126703.14 samples per second, idle 38.00 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, 124012.38 samples per second, idle 44.50 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, 122526.50 samples per second, idle 26.00 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, 121810.65 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, 126119.94 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, 124579.82 samples per second, idle 35.50 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, 123824.68 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, 125279.34 samples per second, idle 27.00 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, 124070.60 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, 124262.64 samples per second, idle 29.50 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, 123712.77 samples per second, idle 40.00 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, 125290.76 samples per second, idle 31.50 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, 124604.67 samples per second, idle 47.50 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, 124237.93 samples per second, idle 22.00 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, 122618.32 samples per second, idle 40.50 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, 124695.57 samples per second, idle 37.50 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, 125733.19 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, 125072.30 samples per second, idle 27.00 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, 124292.76 samples per second, idle 42.00 per read\n",
        "0\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(freqs,20*np.log10(np.abs(davg)))\n",
      "plot(freqs,20*np.log10(np.abs(davg2)))\n",
      "plot(freqs,20*np.log10(np.abs(davg3)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "[<matplotlib.lines.Line2D at 0x41042d0>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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k90QHRxOhi2Dg8oE8sO4BPj/8OU92fZKXtr/EsJXDWLp/KT+c+oGvj35NXlke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\n"
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(freqs,20*np.log10(np.abs(davg/davg2)))\n",
      "plot(freqs,np.angle(davg/davg2))\n",
      "plot(freqs,20*np.log10(np.abs(davg/davg3)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "[<matplotlib.lines.Line2D at 0x47c3b10>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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0SX5uh/706dMxbdq0fvcXFRVh1apVMBgMSE5ORlZWFnbv3u1RI5U0a5bnJR6G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\n"
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dx = davg/davg2\n",
      "d0 = davg/davg3\n",
      "plot(dx.real,dx.imag,'.-')\n",
      "plot(d0.real,d0.imag,'.')\n",
      "for k in range(0,dx.shape[0],29):\n",
      "    annotate((\"%.1f\" % freqs[k]),xy=(dx.real[k],dx.imag[k]),\n",
      "             xytext=(dx.real[k],dx.imag[k]),textcoords='data',#arrowprops=dict(width=1),\n",
      "            bbox=dict(fc='white',alpha=0.7))\n",
      "axhline(0)\n",
      "axvline(0)\n",
      "axis('equal')\n",
      "#ylim(-.7,.7)\n",
      "xlim(-.5,1.5)\n",
      "grid(True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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z9ixFWFy3joJyMRxUrCaUD+AGAFisAIwF88sA4AW0cWmDy4su17lscsfq7pye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\nQw9rz1/M0wAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 114
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First a sanity check, looking at the amplitude again:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(freqs,dx.real)\n",
      "plot(freqs,dx.imag)\n",
      "len(freqs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 119,
       "text": [
        "256"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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r7wVAJBJhQfsFWBa8DEVcEb87Z3jzvgDweW593/yTlpWGY3HHMK55+WaXLYmW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\n/wBo3iXrRfdmzAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import kid_readout.utils.fit_resonator\n",
      "reload(kid_readout.utils.fit_resonator)\n",
      "res = kid_readout.utils.fit_resonator.fit_res(freqs[:160],dx[:160])\n",
      "print np.pi/(2*res.params['beta'].value)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "initial guess of f0 32.7763671875\n",
        "[[Variables]]\n",
        "     alpha:     0.056708 +/- 0.006935 (12.23%) initial =  0.100000\n",
        "     beta:      0.027404 +/- 0.000154 (0.56%) initial =  0.047925\n",
        "     l0:        0.713038 +/- 0.011040 (1.55%) initial =  0.000000\n",
        "     phi0:     -0.247468 +/- 0.008886 (3.59%) initial =  0.000000\n",
        "[[Correlations]] (unreported correlations are <  0.100)\n",
        "    C(beta, l0)                  = -0.780 \n",
        "    C(beta, phi0)                =  0.371 \n",
        "    C(alpha, phi0)               = -0.138 \n",
        "57.3205064411\n"
       ]
      }
     ],
     "prompt_number": 205
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(freqs,dx.real)\n",
      "plot(freqs[:160],dx.real[:160],lw=2)\n",
      "plot(freqs,kid_readout.utils.fit_resonator.res_s21(res.params,freqs).real)\n",
      "np.pi/4."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 209,
       "text": [
        "0.78539816339744828"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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J+HuoRyTs2aPyks2BTsfZcbHclA0f7bLtOvH79skGH40IAXv3XlZSUmGPmN3g\nj545Kocb5eVw6JC5L68JKQUy/h4/IEJ+8ZTBm4U+c+5nylEd+04nUlxerLUck7Fvn/xKNZKbC87O\n0LevZpoUloHZDb6w+hgGhFzVaiej+P15sjxDRJ8QNYI3I+7TbuXGXAecDAa+yfpWazkm4wqD37tX\nptQo7B6zGnw3Bx/qqCK/LN+uwjTHTh8HIMi1L2RnqwYf5sLTk4qB/ozMgw92rdBajUk4dUr+GB4w\noMnGvXsvS4hX2CtmNfh+7oMAOHT6kFzVmpIi61XbOPkV2QAE5VyQqWsuLhorsh/cZ8zm5mOO7C7Z\nSEphitZyjE5yshy9N9afATmkVwavwMwGPzZQTvp8tf9b6NRJpnFt2GBOCWanrLqMC6IEJ9zwSD+m\nvnhmptO0W7m70BOABWv/pLEa45OcfFk05uIEqwrRKDCzwf9qxP0AfJn+hVxCPmkSbNpkTglm53ip\nDM/4uAxAt2+fir+bm5Ej8S+qoHeFG4kFa8k7l6e1IqNyRfz9+HGZgtu7t2aaFJaDWQ0+qncUgZ2H\nUiFKZW7yjTfCTz+ZU4LZyS6V4ZkBPQaqCVYtcHHBYdx4Hi8NB2Dt4XUaCzIuV4zgVfxd0QSzZ9E8\nM/4x+f+1L1IdHARlZZCTY24ZZiO7YQQ/zL2vnBELDtZYkR0yeTK3n3IF4Ktk2+lHcPq0LMzarMGH\nCs8omnBdg09ISCAiIoLQ0FCWLFly1X1WrFhBdHQ0kZGR3H333S2e7+GYB+nXKYSTNcf4z5535Sje\nhsM0mSflCH7MGUdZnkBVkDQ/kyczaG82CNhxchMVtRVaKzIKycnykXJo+i1WI3hFE1o0+OrqaubP\nn09CQgKpqamsXLmSlJTmmQgHDhzgtddeY9u2baSmpvLWW2+1eEFnR2dev+WvALy98zOZTWMHBh9y\n4oIKz2hFUBCOLq5MLw+njip+Om4bYcEr4u8Gw1ViNgp7pkWDT0pKIiwsDF9fX5ycnJg3bx7r1jWP\nYX700Uc88cQTdGmordKrV6/rXnTqoKk405kj5fsoHBEq4/BCdOBjWC4nzkmD9z1UoAxeK3Q6mDyZ\nx8v9AFiZahthmiu8/Ngx6NEDvLw006SwLFo0eL1ej7+/f+NrPz8/9Hp9s30OHTrE/v37GT58OMOG\nDWP16uv3wuzk3IlxvlMA+F/VfnB1hczM9ui3aAzCwKkaGYPvmnZI/XTWksmTuSGrDIDVWWsRNjCg\nuOoKVvUGj9OXAAAgAElEQVSMKZrg1NKbumarJ66OwWDgxIkTJCUlkZeXxw033MDo0aOvOpJftGhR\n45+H+Q1hI7B832oevximCQ1t8wewZArOF1BPDYHlHjhUVMLAgVpLsl9uvJFuv/wlPmP6cIp8Dpw6\nwNDe1tsysbQUiopg8OAmG5XB2wSJiYkkJiYa5VwtGryfnx95eZfyhvPy8pqN6AH8/f0ZPXo0jo6O\nBAQEEBoayuHDh4lvVt5O0tTgSypKePnll9ld9DMVY5fS+Zu18JvfdPDjWBYXc+BvKvGE4f0vW26o\nMCs9e6KLiOC3hp78waGQ7zLXWrXBp6RAVNRlc/Z798KLL2qmSWEcxo8fz/jx4xtfL168uN3najFE\nExsbS3p6Ovn5+dTW1rJixQqmTp3abJ/p06c3/mtTUlJCZmYmgc3ytq6OZ2dPInuOwqCrZWOAQbax\nq69v9wexRC7mwI877azi75bA5MncXijTJb9Ose44/BXxd4NBun6zmI3C3mnR4N3c3Fi2bBlTpkwh\nKiqKOXPmEBMTw8KFC1nTUChs9uzZeHh4EBYWxujRo/nHP/6BVysnee4ZfisAH+RslqVNk5M7+HEs\ni6OnpcFHF1Yqg7cEJk1iwP4cnHAls2y3VXd6uliDppHDh+XkaiuSHBT2g06YabZJp9NdMbF19MxR\ngt4MopOuB+eL78HR1w+ee84ccszC7E/v49vs5VS+4Y7bwUOqPrfWVFeDhwc3/eUGNpZt4MOZH/JA\n9ANaq2oXERHw6adNOj8uXy6rs379taa6FMbnat7ZWsy+krUpg3oNwt81lEpxlvRwb5vLhz9Skk3/\nc6BzdVPmbgm4usKwYfzGUc5MWuuq1ro6OHoUhgxpslFNsCqugqYGD3BH1EwAPnLPh1275CjLRtCX\nHye6EAxDI7WWorjI2LGMzZWT3Zv1P8qid1bGsWNyvNC5c5ONyuAVV0Fzg58bIePwn5/4AREaCjt3\naqzIOFTUVnDOUEhUkQOu0ao3psUwdiw9dh8goFME1VxgS84WrRW1mYyMyzKK6+th/341waq4As0N\nfoTvCLo79KakLoeiuHCbCdOcOHsCgJjiTjioDk6Ww8iRkJzMncE3A7Ayzfq6imVmXmbwWVlySN+9\nu2aaFJaJ5gbvoHPgliG3APC9f53NGPzFFMnIIqFa9FkS7u4QGsp9tXLR2cr0bzAIg8ai2sYVI/j9\n+5vMtioUl9Dc4AHuHCrDNK/VpsKBA1BZqbGijpNdmo1bLfQ9W33ZbJhCc8aOJSSzhF6O/pyp17M7\nf7fWitrEFQaflqYGEYqrYhEGP3HgRJzpTHrtflkjfu9erSV1mMyT2YSUwDk/L9WD1dIYMwbd1q3c\nETEHgC/2r9RYUOupr4dDhy5rK6AMXnENLMLg3ZzcGouPZQb1hO3bNVbUcTJPHifiFFQGD9JaiuJy\nRo+GnTu5M0xmcK3N2KixoNaTkwOentC1a5ONaWmymbtCcRkWYfAA9zasav1vlzOwY4fGajrO8dJs\nwovAMcp6653YLB4e0L8/w07Jxz+v8hD1Busok3FFeObcOThzBgYM0EyTwnKxGIOfPng6OuHAp90z\nMOzYbtX14YUQFFZnE1EE3YeP1lqO4mqMHYt7UjI9HPpSRxU556yjbeQVBp+eDmFhl7V1UigkFvNU\neHb2JKrnaPTd66h0dZS1NayUovIiaqkg8pQO92FXVtVUWABjxsCWLQzqEQJAVkmWxoJah5pgVbQF\nizF4uFR8bG+/zlYdh88uzaZnJXSt1UG/flrLUVyNMWNg2zai+8rZyoxi62g4owxe0RYsyuBnh0iD\n/8bjFIbt2zRW036yS7OJOAW5vj1VDXhLxdcXevRgfG0PAJJzLd/ghZCLnEJCmmxUE6yKFrAogw/s\nFYi/WyiJ/lVUbrbeBU/HzhwnogjODfbTWoqiJcaOZdiRCwCkFli+weflQbdusu0qIB0/PV2N4BXX\nxKIMHmBe1K2ke4NDQSGcPq21nHZxsECO4A0RIdffWaEdY8bQLy0XgGPnD1r8itaMjMtG7wUF4OwM\n3t6aaVJYNhZn8HPDb6XeAZL6OiKsNF3ycLHMoHEdqqr7WTRjx9Jpx256OfhRxTkOFh3UWlGLqPi7\noq1YnMHH+sbS3aEPm/2qKNr4rdZy2kVu2THCi8BzxHitpShaYuBAEILbOskqjFtytmosqGWuavAq\n/q5oAYszeAedAzOH3ML2flCRaH1x+Oq6arqe1nPeBfwCVB14i0ang9GjmVcu29wlZFihwasRvKIF\nLM7gAeYNnUmSL3hn5UFtrdZy2kTuuVwiiiCrdyecHZ21lqO4HnFxDM2VTT+267e2uzWaqRHiGouc\nlMErWsAiDX7iwIlUuXXhaE8Dp7YmaC2nTWSXyvh7vr+n1lIUrSEujl6pR+is60VpfX5jHX9Lo7BQ\n1qzzvPhY1dXJOvBhYZrqUlg2Fmnwbk5ujPebwg5/OLzmY63ltImLOfAXhqjaIFZBTAy6jAxG9RoJ\nwNZcywzTXDF6P3pUNvno0kUzTQrLxyINHuSq1u39oGazdS14OtSQQeMyTP10tgo6dYLgYO53lv8g\n/5hlJQavJlgVrcBiDX560HR2+jkQcqSIc5VntZbTag7lHCXwDHQbqvqwWg1xcYwudALg52wrMXgV\nf1e0Aos1eI/OHnQfOBodsGXLcq3ltJ7DmZzoAQG9g6+/r8IyiIvDPzMfZzpTUHOIovIirRVdgcqg\nUbQHizV4gLsbwjSHv/lCaymtQgiBd14uaT4woIeKwVsNcXE47N5DZC9Z+XNbruWFBZXBK9qDRRv8\n7JBb2eEPXZL3UVtv+emSpVWlBJ+qJNPbBc/OKovGahg8GM6eZW4fueBp45EtGgtqTnExGAzg49Ow\nobwc8vNhkOoWpmgZizb4wF6BHAscQHxeDZtzNmst57o0pkj69UGnqkhaDw4OMGIEU0u7A7DhkGXF\n4S+O3hsfqYwM+Y+Ss1pnoWgZizZ4gNCpdzDoDHy3879aS7kuF1MkK4YM1lqKoq3ExRGSfR4H4cSx\n8v2UVZdpragRNcGqaC8Wb/Czo+eQ3AcKNn5jsasML6LPzcCjEnpFqPQ1qyMuDpe9yQR1jUHoDOzM\n26m1okZU/F3RXize4If3Hc4+/y5EnCjmwKkDWstpkQt7U8jwgsG9B2otRdFWRoyAPXu4cWAcACmF\n+zUWdAll8Ir2YvEG76BzgBtGEa+HVQe/01pOi7ikHybNGwb2VAZvdXh5gYcHY+t6ApCcZzk9WtUi\nJ0V7sXiDB4iY9Qvi9PDVXssuH+ydU0CajzJ4qyUujqE5svBYeqFlGPyZMzJpxte3YUNxMVRVgZ/q\nFqa4PlZh8KNjb+Osmw4H/X5yz+VqLeeq1BnqGHSyjDRvCOgRoLUcRXuIi6NfZgEAJ8qzLGLOJzPz\nsgyaixOsKktL0QqswuDdnNzICfIlXg/fZa3WWs5VyTubS8QpyOvTGzcnN63lKNpDXBydktPopOtB\npThrEStaVfxd0RGswuAB3MeNJS4fPt9rmQafn7mbGkdw81GLT6yWoUPRHT5MsHMQAJkl2jfivqIP\nq4q/K9qA1Rh80LT7iNPDnuJEzlWd01rOFVxI3kmaDwR5qfi71eLmBuHhzKiUq5CzSrSPw6sRvKIj\nXNfgExISiIiIIDQ0lCVLllxzv1WrVuHg4EBycrJRBV6k+8jxhJ52wLWuloSjltcEpP5AGmneEN5X\nGbxVExfH+NMyvp1aYGEGbzDAwYNqBK9oNS0afHV1NfPnzychIYHU1FRWrlxJSkrKFfudP3+eN954\ng/j4eJMJxc2N0oF9iSmEdZk/me467cQtM5s0HxjkoQzeqomLY8gxWZ46ReNUybIymUXTv3/Dhpwc\n6NEDevbUVJfCemjR4JOSkggLC8PX1xcnJyfmzZvHunXrrtjvxRdf5LnnnsPV1dWkmQcO8fHE6+Gn\no5ZVDAqgz4lilQNvC8TF4Z1+HICjZ7U1+MxMCA6WpXIAFZ5RtJkWDV6v1+Pv79/42s/PD71e32yf\n5ORk8vPzmTZtGoBJi2x53ngLI/WO5NdkcerCKZNdp83U1RFQXMFBZfDWT2AgTlU1+J13oqQuh4ra\nCs2kqAVOio7i1NKb1zNrg8HAggUL+OSTTxq3tTSCX7RoUeOfx48fz/jx41unsgGnkTcwusAJqGdL\nzhZuD7u9TcebigsHUyjsCrXObvR27621HEVH0OnQjRjBlJNpfNBVz6GSQ0T3idZEylUNfvp0TbQo\nzEdiYiKJiYlGOVeLBu/n50deXl7j67y8vGYj+vPnz3Pw4MFGoz558iQzZ85kzZo1xMTEXHG+pgbf\nLgID6WpwpG8Z/HBos8UYfElSIune4OMyQJUJtgXi4pi46ygfBMlMGi0N/le/arIhPR2ee04TLQrz\ncfngd/Hixe0+V4shmtjYWNLT08nPz6e2tpYVK1YwderUxve7d+9OcXExx48f5/jx48THx1/T3I2C\nTkfVsEji8mHDEcupD1+Vsruhi5MKz9gEcXHEFsiSBRlF2sXhm43ga2rg2DEZlFcoWkmLBu/m5say\nZcuYMmUKUVFRzJkzh5iYGBYuXMiaNWvMpbEZ3cbexEi9I7lV6ZRUlGii4XIcD2aS5g2hfZTB2wQj\nRtDvWBEOBthzQpvFTuXlcPIkDLjY+TErCwICZK6+QtFKWgzRAEydOrXZqB2u/ZPh559/No6qFnC6\nYTQTPu8KnGVb7jZmBc8y+TWvR/fDeaRFwWOqTLBt0KsXht4+hBbnkOaWqomErCzZtMnp4jdUZdAo\n2oHVrGRtZMQIIvIrcDTAj4ctIExz4QLdzlzgaC/VaNuWcB45mni9AwXVhymvKTf79dUKVoUxsD6D\n79GDur69CSuC9ZkbtVYDBw9yxMuZegeVImlLOMaP5MaT3UEnSC9KN/v1VZs+hTGwPoMH3EaNY7Te\nhZyqdPLL8jXVYkhNJcWrFoABPdUI3maIi2NkoUz51aKT2MUywY2oEbyiHVilwTvGj2TGGR8Avte4\nLk158i5SfQx0dfDG3cVdUy0KIxIZSd+SctyrISnH/AbfbAR/7hycPt1kxlWhaB1WafDExxNXKEfN\nq/Zra/B1qftJ8wa/zio8Y1O4uFAREsTwAkjKMW9/1qoqyMuDQRcrT6enS7d3sM6vq0I7rPOJiYig\nR1EZ3apgs34DdYY6zaS4ZR4hzQcCPdToytZwuWE0I/LhaFkqBmEw23UPH4aBA8HZuWGDCs8o2ol1\nGryTEw4xw7i5yJdKcY4kfZI2Ok6dQtTVUegO4b5qBG9rdB49gdH5rlRzgeOlx8123atm0ERGmu36\nCtvBOg0eIC6Ou6tk2YS1WRqFadLSyPbtBjoI8lQGb3PExREvW7SadaL1CoNPTVUGr2gX1mvw8fGM\nLJQ/m/+Xqp3Bp3rI2jMqRdIGCQigEw74lkFygUYGL4QK0SjajfUafFwcXmnZOAkXDpfv5XTFafNr\nSE1ll4csJ6sM3gbR6TgXFUycHrYfNd9Ea7M+rHl50LkzeHqa7foK28F6Dd7PD52LC5ProwDYlrvN\n7BIM+1NI8i7DEWd8u/qa/foK0+M8chRx+ZBeYp4RfE0NZGfLMgWACs8oOoT1GjxAfDy/qPcDNKgu\nWVcHhw6R7g2ezv1xdHA07/UVZsFj/DTi9DpK6nLMUrLg6FHo169JTbHUVBWeUbQb6zb4uDhGNkyC\n/ZBlZoM/dIgKn15UuEBANxWesVUc40cyrBAcDXD0zFGTX09l0CiMiXUbfHw8vhl5OAhnjpXv51zV\nOfNdOzWVwoHeAASrKpK2S48enO7ZmdBiOHLmiMkvpzJoFMbEug1+2DAcD2YQ2WUYQmcwbxw+NZXM\n3vJ3tKoDb9ucDOtHnB6OnDbzCL66WgbkVZMPRTuxboPv0gUGD+ZBlyEAbDxqxjDNgQPs6lYPQKDK\noLFpqhu6iKXqzTyCz8yEwEBwdTX5dRW2iXUbPEBcHJNKOgHwfaYZDT41lcRuZwFVRdLWcR01ljg9\nZJwyrcHX1cGRI00G7GqCVdFBbMLgBx05jU44cvj8Ps5Xnzf9NU+fRpw/z95OsiG5yoG3bXxGTmJg\nKZwuOWzS62RnQ58+Mu0dUBOsig5j/QYfH4/z7r0M7hqD0NWzPW+76a+ZmkpNWDC1uircdZ70cOth\n+msqNMPfM5ADvXUE6U9xoeaCya6jJlgVxsb6DX7IEDhzhnl94wBYn2WGLk+pqZQM6gOAf5dB19lZ\nYe04OjiS2d+DuHzYf9J0K1qvavAqRKPoANZv8A4OMGIEc8p7A7AmY4Ppr3ngANl+XQEI9g4y/fUU\nmlMzfChxethywnSZWs0MvrgYKivB399k11PYPtZv8ABxcYRlX8CZTpyoTOXkhZOmvV5qKvs8ZTu3\nKH9l8PZA38nTGZEPCRlmMviL8XedzmTXU9g+tmHw8fE47dlLrNc4ADZmmzBMU1cHGRlscJKLqoZ4\nKoO3B6JHzsalHgqObDVJ84/6esjKalJkTFWQVBgB2zD4ESNg927mRE4E4Ns0E4ZpjhwBX1/SK04A\nENRLGbw90K9Hf5L93IjSl3Gw6KDRz5+TIwtGdu3asEFNsCqMgG0YvJcXeHoyU0iz3Zi9ASGEaa6V\nmoqIjKSgWq5qDPJQBm8vnAoNJC4fUk6mGP3czUoEgzJ4hVGwDYMHiI9n0OESujv04ZyhkIPFxh9l\nAXDgAGWD+1NHFV0dvOnm2s0011FYHGJkNHF6SCvMNPq5MzIgLKzhRX293BAebvTrKOwL2zH4uDh0\nu3czaeBNAPx41ERhmtRUcgJk3ns/lSJpV3QdPYGYQkg5bvzBQ7MJ1mPHwMenSbxGoWgftmPw8fGw\naxezIyYDsCLle9Nc58AB0nvL2xbio8Iz9kRQYCz53UBkGL/5RzODV/nvCiNhOwYfFQVHj3Jz79Ho\nhAN7ihMpqy4z7jXOnIFz59jjKGvQRPkpg7cnBnsMZndfGHQij+q6aqOdVwhZV6xZBo2KvyuMgO0Y\nvKsrREbikXGc8B43YNDVsuGYkcM0DalrBwrkBOtglSJpV7g6uZLZ34vYAmHU2vB5eTIa07NnwwY1\nwaowErZj8ABxcZCUxF0xMwD4ev8a457/wAGIjGz8cqsUSfvjTHiIrCxZnGG0c6oSBQpTYZMGf2vI\nLQB8f3Qd9YZ6450/NRVDZCQnq48BMKiXmmS1N3qMHMHAUjhy3Hg1aZoZ/JkzskxBkBo8KDqObRl8\nw0RriEcw3s4DuSBK2J2/23jnP3CAooE+1FFNNwcfurqqLAd7I6xfBAd6w/ktO412zmYpknv2wLBh\n4KiauCs6jm0ZfEAA1NWhy89ndpgM0/zvoJHCNA25yVm9nQDo765GWPZIiGcISb7QM814ufAHDzYZ\nwSclyV+iCoURsC2D1+kaR/FzI2SY5r8HjGTw6eng709WbQGgUiTtlWDPYJL8YMiJYuoMdR0+nxCX\nhWh275alNxQKI3Bdg09ISCAiIoLQ0FCWLFlyxfuvvPIKYWFhhIeHM3bsWI4fP24Soa2mIQ4/tv9Y\nXOlKTlU6J86e6Ph5d+6EkSM5eFJOsKoUSfukq2tXDvv3YUS+geNnsjt8voICcHMDDw+k26sRvMKI\ntGjw1dXVzJ8/n4SEBFJTU1m5ciUpKc3rcMTHx5OcnEx6ejp33303CxYsMKng69IwgndxdGGC/xQA\n1hxa2/HzNhj8xcbLKkXSfnEZEI6jAU6kbunwuZqN3k+cABcX8PXt8HkVCriOwSclJREWFoavry9O\nTk7MmzePdevWNdtnzJgxuDZ0fR81ahT5+fmmU9saYmNh/36oreWuYTJM88U+I4RpGgz+WKlKkbR3\nYvqHsjkAajb+2OFzNTN4NXpXGJkWDV6v1+PfpKOMn58fer3+mvu/88473HrrrcZT1x66dYP+/SE1\nlamDpoLQsacosWPNuEtK4NQp6oOHcLJG/ixXKZL2S7RvGD8NgE5b9nb4XPv3N0l5V/F3hZFxaulN\nXRu6yXz++eckJyezefPma+6zaNGixj+PHz+e8ePHt/r8bSI+HpKS8Bo2jPDuI0kv28GG7A3MCZnT\nvvPt2gWxseSVF1BPDd0d+tDFpYtxNSushqG9h/JqALy4NU/GzTvQdWnvXnjiiYYXSUnw178aRaPC\neklMTCQxMdEo52rR4P38/MjLy2t8nZeX12xEf5GNGzfy0ksvsWXLFpydna95vqYGb1Li4mD7dvj1\nr7kz5hb+mLiDr/evab/BN4RnjpyW4ZmArio8Y89E+ERwpJcDuvo6Kg8dpFNw+8r6lpfLwpEREUBt\nrVwpPWyYccUqrI7LB7+LFy9u97laDNHExsaSnp5Ofn4+tbW1rFixgqlTpzbbJyUlhccee4w1a9bg\n6enZbiFGpWGiFeDWEJkP//3Rde1vtbZrF4wcSXpDJ5+IvkOMIlNhnbg5ueHfOZSfB0Dh6i/afZ79\n+2XJdxcXZJ2jgAAZYlQojESLBu/m5sayZcuYMmUKUVFRzJkzh5iYGBYuXMjatTIz5fe//z3l5eXM\nnTuX6OhoZs2aZRbhLRIWJvPPzpwhzCsML6cAzhuK27eqtb5eri6Mj+enQ0kAjA2MNbJghbUxrO9Q\nfhoA9T+1v//vnj0wfHjDCxV/V5iAFkM0AFOnTr1i1N70J8OGDSbsf9peHB1lNs2OHehmzGBW6Aze\nS32Lbw6uId4vvm3nSk+Hvn2hVy+S8qXBx/mpTAd7Z1RgNP8ZsJzXPklvdxx+71648caGFyqDRmEC\nbGsla1OmTIGEBADmRl5c1dqOfPiG+HtReRHFdcdxoQthXmHXP05h08T0ieZ4DyinHrKy2nWOvXvl\nOASQI3hl8AojY7sGP20arFsHQjCu/zhcced4ZSo5Z3Padp4Gg0/Sy9F7aI/hODqoQlD2ztDeQ0EH\nGwLqqf9pU5uPLysDvb6hyUdZGeTkqB6sCqNjuwYfHg51dZCVhauTK+P8ZCu/NYfbOIpvMPjtOdLg\nJwxWoywF9OzUE0+nAH4aUM+FH9q+kG7fPtmEzMkJOZQfOhRayEBTKNqD7Rq8TidH8evXAzSuav1y\nXxsMvmGBE6GhjROsowOUwSskUd7R/BwArtt2gqFtGVrNwjOJiTBqlLHlKRQ2bPAA06c3Gvy0oGkg\ndCQV/cSFmgutO75hgZPBQUfaGZmBE+erDF4hGTMoGn13KHNzlJPxbWDv3iYZND/8IOeMFAojY9sG\nf+ONcvKqrAzvLt6EdoujnprW92rduhVGjeJQySGqKKOnox++3VQhKIVkmG80ANsCOsFPP7Xp2N27\nG0bwp0/LjttqBK8wAbZt8O7uMHIkbJS5ynfGyDDNitZm06xdC9OmsUsvF03F9lGjd8UlontLg/+6\n/znE2taH/goK4Px5GDwY2LQJxo6VTeMVCiNj2wYPzcI0F1e1rj/SilWt2dkyBh8by+ZjMv4+cYgy\neMUl+nbti7vOizWBFYg9u+Xz0goa5u1l6rwKzyhMiO0b/MWJViGI8I7Aw6kfZYZT7Mnf0/Jx69bJ\nfxwcHNiaLQ0+3l8ZvOISOp2OsF7RVDpD4chw+O67Vh23Y4c0eISQBj95smmFKuwW2zf4oCDo0gUO\nHECn0zErRIZpvs24zk/qtWthxgwqais4XpGGTjgyrI8qBKVozqhAGabZOqwPrFrVqmN27IAbbkDG\n3p2cGmI1CoXxsX2Dh0uLnoC5kTJM8/X+FnKXz5+X38KbbmJfwT6Erp6AzuGqRLDiCkb4S4P/yKsC\ntm2Ds2db3L+qClJTGyZYL4ZnOlBuWKFoCfsw+JkzYeVKEILxAeNxoQvHKw+Qey736vtv2CB/Q3ft\nyq6GFaxjBrSxho3CLhjaeygAey6kwYQJsKblRU/79snVq126AN9+C5fVeVIojIl9GPyECXI5+O7d\nuDm5MdZXxjzXHl539f3XroVbZChnU5Y0+PGDVPxdcSVBHkG40IXS+nzKZkyWA4kWaAzPHD4sa9hM\nm2YeoQq7xD4M3sEB5s+HpUsBuDNGhmmW77nKpFhZmZwsmzWLqroqtubLOiMj/UeaTa7CenDQObBo\nxJt4JKzFafZcGabJuXa9o0aDf/99uP/+hmLwCoVpsA+DB3jgAVi9GkpKmDnkFhyFCzuLfuR46fHm\n+73/vsxq8PdnZcYqKkQpg92HEewZrI1uhcXz3M0P0KN4Oof1PvDww/D661fdT4gGgx9eA598IvdV\nKEyI/Ri8h4eMxX/0EV5dvLh10B2gE/xr+9JL+9TVwRtvwIIFAPwz8V0Anh77iBaKFVaCTgczZjTM\n4//2t/Dpp3KF6mVkZckBu3/ydzIQr7JnFCbGfgwe4Ne/hmXLoKaG5yY8CcC7+96l4HyBfH/VKujf\nH2JjOVRyiJTSLTjTmbsj79JQtMIamD5dTt3Qty/Mnt0YDmzKt9/KMYbu/ffgETVoUJge+zL4ESNk\nO78//YlY31gm+M6gijLu+epXCIMBXnutcfS+dNf7AMwdcifdXFWfTEXLjB0LGRlQXAw8+yy89RZc\naF7U7n//gweCtskd57SzAbxC0QZ0QghhlgvpdJjpUi1TXCxrb3/yCfkjQgj6VxiV4hxbxUOMXpUE\n+/dzsrKYoH9GcEGUsOuhXapFn6JVzJkDs2bBL36BnPNxdJRzOkBuLsRF11DQdzi6F/8Id9yhrViF\n1dAR77SvETyAl5ec4Lr/fnzP1LJs5r+56RgMeeVDlv9hJjsKkoj49wguiBJCusUywlc1Qla0jhkz\nGsI0AP/+N2zZAi+9BELwxpIqEnreiS54CNx+u6Y6FfaD/Y3gL/LGG7B4MaJPH0oKj3P7rZVsDrj0\ndmTPkWx46Fu8u3hrpVBhZRQXQ3AwpKRAv35Afj7cdht1eQWcLayky6yb6PTVxyo1UtEmOuKd9mvw\nIL+RhYXUDQzg29wf+dvGN0kp3cLswHv44s73cXNy01qhwsp44QUoLIQPP2zYYDDwf7cdxdvPhWfe\nDOAhCXkAAAZ2SURBVNBSmsJKUQZvRM5WnaWHWw+tZSislHPn5BTPzJnw6qtyweqECXDkCHTvrrU6\nhTWiDF6hsCBKS2WYfcAAWXvm/vtlerxC0R6UwSsUFsbZs7Je3fTp8MorqmCkov0og1coLBCDQZZB\nUig6gkqTVCgsEGXuCq1Rj6BCoVDYKMrgFQqFwkZRBq9QKBQ2ijJ4hUKhsFGUwSsUCoWNogxeoVAo\nbBRl8AqFQmGjKINXKBQKG0UZvEKhUNgo1zX4hIQEIiIiCA0NZcmSJVe8X11dzbx584iIiGDUqFHk\n5OSYRKgtkZiYqLUEi0Hdi0uoe3EJdS+MQ4sGX11dzfz580lISCA1NZWVK1eSkpLSbJ+33nqLPn36\nkJaWxu9+9zuefPJJkwq2BdTDewl1Ly6h7sUl1L0wDi0afFJSEmFhYfj6+uLk5MS8efNYt25ds33W\nr1/PfffdB8DMmTPZsWOHKiqmUCgUFkCLBq/X6/H392987efnh16vv+Y+Dg4OeHh4UFRUZAKpCoVC\noWgLTi29qTNyEWtjn8+aWbx4sdYSLAZ1Ly6h7sUl1L3oOC0avJ+fH3l5eY2v8/Lymo3oL+6Tm5uL\nt7c3BoOB06dP4+XldcW5VNhGoVAozEuLIZrY2FjS09PJz8+ntraWFStWMHXq1Gb7TJs2jeXLlwPw\n3XffMXLkSBxUIWyFQqHQnBZH8G5ubixbtowpU6ZgMBi47777iImJYeHChQwfPpxbbrmFJ554gvvu\nu4+IiAi6du3KF198YS7tCoVCoWgJYWK+//57ER4eLkJCQsQ//vEPU1/O4ujfv7+IiIgQQ4cOFbGx\nsUIIIU6fPi0mTZokIiIixOTJk0VpaanGKk3DAw88ILy9vUV4eHjjtpY++29+8xsRGhoqoqOjRXJy\nshaSTcbV7sXChQuFr6+vGDp0qBg6dKhYv35943t/+9vfREhIiAgPDxc//PCDFpJNQm5urhgzZowI\nDw8XgwcPFkuWLBFC2Odzca17YcznwqQGX1VVJQICAoRerxe1tbVi+PDhNvUX1BoCAgLE6dOnm217\n4oknxOuvvy6EEOL1118XTz75pBbSTM6WLVtEcnJyM1O71mdfuXKluPXWW4UQQiQnJ4uoqCjzCzYh\nV7sXixYtEq+99toV++7du1cMHz5c1NXVCb1eLwICAkR1dbU55ZqMkydPirS0NCGEEOfPnxdBQUFi\n//79dvlcXOteGPO5MGmwvDV59PaAuGyCuenagXvvvddm78mYMWPo2bNns23X+uzr1q1r3B4dHU1d\nXd0VKbnWzNXuBVw9+WDdunXceeedODo64uvrS1hYGLt37zaHTJPj4+NDeHg4AO7u7kRGRpKfn2+X\nz8W17gUY77kwqcG3Jo/e1tHpdNx0001ERkby1ltvAVBcXIyHhwcAnp6edrVu4FqfPT8/3y6flf/8\n5z+EhIRw7733cubMGUDeCz8/v8Z9bPVenDhxgj179jB69Gi7fy4u3osxY8YAxnsuTGrwKu8ddu3a\nRXJyMps2beKjjz5i48aNWkuyWC4ftdj68/P4449z7NgxMjIyCAwMtKsyHxcuXGDu3Lm88cYbdOvW\nrcV9bf25uHDhArfffjtvvPEGXbt2NepzYVKDb00eva3j7e0NgJeXF3PnzmXPnj14eXlRUlICyBHt\nxX3sgWt99sufFb1e32y0Yot4enqi0+nQ6XQ8+uij7NmzB7j6vbCl701tbS233XYb99xzD7NmzQLs\n97m4eC/uvvvuxnthzOfCpAbfmjx6W6aiooKKigoAysvLSUhIICwsrNnageXLlzNt2jQtZZqVa332\nadOm8fnnnwOQnJzcGGe0ZZqG5latWkVYWBgg78XXX3/dGG9OT09nxIgRWsk0KkIIHnroIUJDQ3n6\n6acbt9vjc3Gte2HU58K488JXsn79ehEWFiZCQkLE3/72N1NfzqLIzs4WkZGRIioqSgQFBYkXX3xR\nCNE8Jeymm26y2TTJO++8U/Tp00c4OzsLPz8/8eGHH7b42R9//PHGdLh9+/ZpqNz4XH4vPvjgA3Hv\nvfeKyMhIERwcLKZMmSL0en3j/i+99JIICQkRYWFhIiEhQUPlxmXr1q1Cp9OJqKioxjTA77//3i6f\ni6vdi/Xr1xv1udAJoWoIKBQKhS2iagooFAqFjaIMXqFQKGwUZfAKhUJhoyiDVygUChtFGbxCoVDY\nKMrgFQqFwkb5f2Wzj2RkCMmFAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 209
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(freqs,dx.imag)\n",
      "plot(freqs,kid_readout.utils.fit_resonator.res_s21(res.params,freqs).imag)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 208,
       "text": [
        "[<matplotlib.lines.Line2D at 0xa292810>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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dXNkZv1PvcQnliQRQQ7FZsXg5eXP7lurOBCpT8OKL8g27q1flnx9o8wCFxYUc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\nKZEABEEQzNT/Ax2jqP8GnthLAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 208
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(freqs,np.abs(d0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 116,
       "text": [
        "[<matplotlib.lines.Line2D at 0x6125890>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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CKTQ0kDsID5dnjInptQN1CqNG0WuIa0Wno3xFZSVt2Qo4Fz6SJJ6S6rd4Kq9g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\n"
      }
     ],
     "prompt_number": 116
    },
    {
     "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": 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TAY8PMFu0aeN8IsjOpnErHx85bfJUXboAhw6p3QptqDERnDhxAjk5OQCAnTt3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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": 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ymCvNMZIeQdM0YRNV/JNBW28znh/nZ/f/LNPFac6cEfbSyAg89hisilJH6LrOfHme4fSw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\n9a4M4u0tJmVgN65UL5n2mai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W65Z7GUhrtyuA7BQzUNpRuJGBXOXbTeyqMgDn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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": 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pEwYHB9Hf349bb73VuJx0cOW9lLlzVc66NAgFuEuFrBRkcp0t09mplHDWjlFW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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": 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Zs6p76oCSm5trdwheQ9fiNF2L03QtPKPaySAqKoo1a9Zw0003Vfr8pEmTSEtL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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": 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U19ejf//+iI6OBmMMpaWlOHToEAIDA/Hcc8+5sqwcjglFRbR+gZI1CoKDgYoK55eJw/FE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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": 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//XXcuHEDM2fOrPZ7fH19kVfFTurutCn8/Plyo+8339Q6EiJSW2Qk8MMPwKef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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": 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D0bpfg53gnscd/RosGubComEgJc0JWrDZgK1b+eb2NFrfvdpsvIM1GxYNA4mM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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": 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CIysrK3z11Vfw8fHBtGnTUFjY+NrGs2fPxpIlS2CtgLnzpVK3L/rkk8Lyr7a2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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": 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sWAGjR0OjRnD0KFSqZHQqxyDjzBhWFbxbLqcs4eHhd35tMpkwmUzWHPY+5y+n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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": 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tG3DpEp859+23gR49gGXLgDZtGh8PUT/R0dHSVUrloUGzx8pb3759sXLlSnTq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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": 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u77xDBQZVVcCwYcY/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": 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lI6lYyToNInvatBEtjpdeAhYuBPLyRHXchg3FByRyjXvvFbv5efiQrUdSfdIA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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": 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kGRjOK2jUiEZ+HI7A6dNA06aUYbZ7N02G3LOn5t+4xU2k1WrRr18/BAYGYuzY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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": 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FrotPPgE+/lgUejR1NGI0UlJSsGvXLvzyyy95/p8zLo7s7Gz4+fnB3t4enTp1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\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": 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ydfEl2D2YL/d8yatRrxrXmAXUqyfOOAgPF0NFoXc/Dlovh3IO4efiZ/QpiL//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\nPOHh4fj5+TF8+HC97mXqZBARYZq2akq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      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}