calib_delay.ipynb

{
 "metadata": {
  "name": "calib_delay"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import kid_readout.utils.roach_interface"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ri = kid_readout.utils.roach_interface.RoachBaseband()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ri.initialize()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Deprogramming\n",
        "Programming"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " bb2xpfb14mcr9_2013_Dec_05_1226.bof\n",
        "Requested sampling rate 512.0 MHz. Estimated sampling rate 512.9 MHz"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ri.set_tone_freqs(np.array([101.111]),nsamp=2**18)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "array([ 101.11132812])"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d,addr = ri.get_data(2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.5 seconds, 15647.59 samples per second, idle 250.00 per read\n"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "(8192, 1)"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x=psd(d[:,0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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ZSqxRBAQEsHnzZu69915++WvsXnBwMLsdPROpGFKjEEY4cEAv9Ddjhv5gdjW5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\n"
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ri.r.write_int('loopback',1)\n",
      "ri.set_fft_gain(2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(d.real)\n",
      "xlim(0,100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "(0, 100)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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JWb3fKR1UgdYne5z5QUm5ebeb7JSUk88GuNGPVXcWPypKygCn5Sct82YjJWXV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\n"
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x,y = ri.get_raw_adc()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "(4096,)"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(x)\n",
      "xlim(0,100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "(0, 100)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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MsK1qqHUvEYw4krBK8CrsJpE9k+xiiNYTdEjwTirAz5qELgneS8tKZMNDaoLn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\n"
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x.ptp()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "3968.0"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import kid_readout.utils.sweeps\n",
      "reload(kid_readout.utils.sweeps)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "<module 'kid_readout.utils.sweeps' from '/home/gjones/workspace/kid_readout/utils/sweeps.py'>"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "swp3 = kid_readout.utils.sweeps.segmented_fine_sweep(ri,np.array([11.111]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "segment 0 log2(nsamp) 20.0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.5 seconds, 15632.72 samples per second, idle 268.50 per read\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.5 seconds, 15603.30 samples per second, idle 323.50 per read\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.5 seconds, 15634.37 samples per second, idle 247.50 per read\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
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       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(np.diff(unwrap(angle(swp3.data)))/np.diff(swp3.freqs))[2:].mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 181,
       "text": [
        "-158.36284419574466"
       ]
      }
     ],
     "prompt_number": 181
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(swp.freqs,np.angle(swp.data*np.exp(-1j*swp.freqs*_92)))\n",
      "plot(swp2.freqs,np.angle(swp2.data*np.exp(-1j*swp2.freqs*_82)))\n",
      "plot(swp3.freqs,np.angle(swp3.data*np.exp(-1j*swp3.freqs*_82)))\n",
      "ylim(-.2,0.1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 182,
       "text": [
        "(-0.20000000000000001, 0.10000000000000001)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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VLPQ6uDkaisgFTs62thtMuO62Yb4JFmY6uAHeWRA5bUS6D6uJBGVcyAMB788y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\nEelisCAiIl0MFkREpIvBgoiIdP1/ccWqovnoi9EAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 182
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot((np.diff(unwrap(angle(swp.data)))/np.diff(swp.freqs)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 91,
       "text": [
        "[<matplotlib.lines.Line2D at 0x9f31550>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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IUZ91FgnhiBHaHV/iyBESem/ipWbKhO3bKYuWuOgiuotZsAC45BISnHPPpee+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\n7BmGYcIAFnuGYZgw4P8ByWnkQVozREwAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(swp3.data.real,swp3.data.imag,'.')\n",
      "plot(swp2.data.real,swp2.data.imag,'.')\n",
      "plot(swp.data.real,swp.data.imag,'.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 96,
       "text": [
        "[<matplotlib.lines.Line2D at 0xa8da3d0>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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zsRdeeEFkZGSIrKws8Ze//CWQ1faar+1raWkR06dPd/6uNm3aFOiqe2W89n3y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\nLAYIIiKS9f8B/Jt6noOG9T0AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "abs(swp.data).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "0.10376003116199303"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(swp.freqs,swp.data.real)\n",
      "plot(swp.freqs,swp.data.imag)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 99,
       "text": [
        "[<matplotlib.lines.Line2D at 0xacd1550>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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Ye3kvJnWYJHQUUgE0KK+c0l6koe36trg19ZbGlk3lw927XO+oe/eAuiKZSWHJ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\nyvI/d18nSW9ykfIAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "blk = swp.blocks[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "blk = swp.blocks[0]\n",
      "plot(blk.data.real,blk.data.imag,'.')\n",
      "blk = swp.blocks[1]\n",
      "plot(blk.data.real,blk.data.imag,'.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 103,
       "text": [
        "[<matplotlib.lines.Line2D at 0xb0a35d0>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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HePHFF5k5c+bQJmJihtwyExGR4UXr3BlxsIwmChYRkZsXrXOnnrwXERFbKVhERMRWChYR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\nxSpoBMYAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ri.r.write_int('debug',0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 183
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d,addr = ri.get_data(2,demod=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.5 seconds, 15621.69 samples per second, idle 302.50 per read\n"
       ]
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d.real"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 106,
       "text": [
        "array([[ 0.],\n",
        "       [ 0.],\n",
        "       [ 0.],\n",
        "       ..., \n",
        "       [ 0.],\n",
        "       [ 0.],\n",
        "       [ 0.]])"
       ]
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "1/399."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 108,
       "text": [
        "0.0025062656641604009"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ri.set_tone_freqs(np.linspace(11.01,12,8),nsamp=2**18)\n",
      "ri._sync()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d,addr = ri.get_data(2)\n",
      "d = d.mean(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.1 seconds, 136870.67 samples per second, idle 26.00 per read\n"
       ]
      }
     ],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f2=ri.set_tone_freqs(np.linspace(11.01,12,8)+0.002,nsamp=2**18)\n",
      "ri._sync()\n",
      "d2,addr = ri.get_data(2)\n",
      "d2 = d2.mean(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.1 seconds, 125717.55 samples per second, idle 20.50 per read\n"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 129,
       "text": [
        "(8,)"
       ]
      }
     ],
     "prompt_number": 129
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(_109,unwrap(np.angle((d*np.exp(-1j*399.522*_109)))))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 131,
       "text": [
        "[<matplotlib.lines.Line2D at 0xd755dd0>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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xCna7HXa7XbfzubROYNOmTUhKSsLKlSsBALNnz8alS5fw/PPPl/szdevWxdmzZ28MxI3r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\nQmCC\n"
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(_109,angle(d*np.exp(-1j*399.522*_109)),'.-')\n",
      "plot(f2,angle(d2*np.exp(-1j*399.522*f2)),'x')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 136,
       "text": [
        "[<matplotlib.lines.Line2D at 0xdbc5650>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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ClawWx1t5a2M7XjofrmS1ON7KWxc3IyEzcATv53grbz1sx0stwRJNkOCtvLWw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\nRK5CYII=\n"
      }
     ],
     "prompt_number": 136
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "import kid_readout.utils.data_block"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 162
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fa = np.linspace(11.01,100,8)\n",
      "fb = np.concatenate((fa,fa+0.001,fa+0.002,fa+0.003))\n",
      "fb.sort()\n",
      "fout = ri.set_tone_freqs(fb,nsamp=2**19)\n",
      "ri._sync()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 184
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dout = np.zeros(fout.shape,dtype='complex')\n",
      "for k in range(4):\n",
      "    ri.select_fft_bins(np.arange(len(ri.fft_bins))[k::4])\n",
      "    time.sleep(0.2)\n",
      "    d,addr = ri.get_data(2)\n",
      "    dfilt = np.zeros((768,8))\n",
      "    for m in range(8):\n",
      "        dfilt[:,m] = kid_readout.utils.data_block.fftfilt.fftfilt(kid_readout.utils.data_block.lpf,d[:,m])[256:]\n",
      "    dout[k::4] = d.mean(0)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.1 seconds, 128719.98 samples per second, idle 18.50 per read\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.1 seconds, 125219.99 samples per second, idle 26.00 per read\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.1 seconds, 125296.24 samples per second, idle 18.50 per read\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.1 seconds, 125152.48 samples per second, idle 19.50 per read\n"
       ]
      }
     ],
     "prompt_number": 185
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a1 = angle(dout).reshape((-1,4))\n",
      "fout = fout.reshape((-1,4))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 187
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(np.diff(unwrap(a1,axis=1),axis=1)/np.diff(fout,axis=1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 188,
       "text": [
        "[<matplotlib.lines.Line2D at 0x9791910>,\n",
        " <matplotlib.lines.Line2D at 0x9799910>,\n",
        " <matplotlib.lines.Line2D at 0x9799950>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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TSPdPbro36A4rAyusvrGaX0HffQfs3s2+s1FR/MrSYDYGbcRIu5GoWLbiNyc2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\nlAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 188
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(dfilt.real,dfilt.imag,'.')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 170,
       "text": [
        "[<matplotlib.lines.Line2D at 0xfaaf610>,\n",
        " <matplotlib.lines.Line2D at 0xfab9610>,\n",
        " <matplotlib.lines.Line2D at 0xfab9650>,\n",
        " <matplotlib.lines.Line2D at 0xfab9690>,\n",
        " <matplotlib.lines.Line2D at 0xfab96d0>,\n",
        " <matplotlib.lines.Line2D at 0xfab9710>,\n",
        " <matplotlib.lines.Line2D at 0xfab9750>,\n",
        " <matplotlib.lines.Line2D at 0xfab9790>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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8fr8kad68eaqtre0zpqGhQaWlpUpNTVVqaqquvvpqNTQ0aNy4cSoqKpIkpaSkaMaMGfro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\n"
      }
     ],
     "prompt_number": 170
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ri.set_tone_bins?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 161
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "swp = kid_readout.utils.sweeps.fine_sweep(ri,np.array([11.01]),npoints=8,sweep_width=0.007,nsamp=2**20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "subsweep 0 of 8\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.5 seconds, 15595.43 samples per second, idle 269.50 per read\n",
        "subsweep 1 of 8\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.5 seconds, 15629.38 samples per second, idle 270.00 per read\n",
        "subsweep 2 of 8\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.5 seconds, 15625.24 samples per second, idle 306.00 per read\n",
        "subsweep 3 of 8\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.5 seconds, 15639.58 samples per second, idle 266.00 per read\n",
        "subsweep 4 of 8\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.5 seconds, 15635.86 samples per second, idle 304.00 per read\n",
        "subsweep 5 of 8\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.5 seconds, 15637.11 samples per second, idle 255.00 per read\n",
        "subsweep 6 of 8\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.5 seconds, 15623.93 samples per second, idle 316.50 per read\n",
        "subsweep 7 of 8\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "read 2 in 0.5 seconds, 15645.14 samples per second, idle 236.50 per read\n"
       ]
      }
     ],
     "prompt_number": 177
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(np.diff(unwrap(angle(swp.data)))/np.diff(swp.freqs),'.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 179,
       "text": [
        "[<matplotlib.lines.Line2D at 0xff56110>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 179
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}