Revision 5:c5482b2a4bdd Vamp.v3.ipynb

View differences:

Vamp.v3.ipynb
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{
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 "metadata": {
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  "kernelspec": {
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   "display_name": "Python 3",
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   "display_name": "Python 2",
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   "language": "python",
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   "name": "python3"
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   "name": "python2"
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  },
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  "language_info": {
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   "codemirror_mode": {
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    "name": "ipython",
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    "version": 3
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    "version": 2
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   },
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   "file_extension": ".py",
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   "mimetype": "text/x-python",
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   "name": "python",
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   "nbconvert_exporter": "python",
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   "pygments_lexer": "ipython3",
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   "version": "3.4.3"
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   "pygments_lexer": "ipython2",
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   "version": "2.7.10"
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  },
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  "name": ""
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 },
......
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       "output_type": "stream",
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       "stream": "stderr",
50 50 
       "text": [
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        "/usr/lib/python3.4/site-packages/librosa/core/audio.py:33: UserWarning: Could not import scikits.samplerate. Falling back to scipy.signal\n",
51 
        "/usr/lib/python2.7/site-packages/librosa/core/audio.py:33: UserWarning: Could not import scikits.samplerate. Falling back to scipy.signal\n",
52 52 
        "  warnings.warn('Could not import scikits.samplerate. '\n"
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       ]
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      }
......
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        " 'bbc-vamp-plugins:bbc-spectral-contrast',\n",
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        " 'bbc-vamp-plugins:bbc-spectral-flux',\n",
100 100 
        " 'bbc-vamp-plugins:bbc-speechmusic-segmenter',\n",
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        " 'cepstral-pitchtracker:cepstral-pitchtracker',\n",
101 102 
        " 'chp:constrainedharmonicpeak',\n",
102 103 
        " 'cqvamp:cqchromavamp',\n",
103 104 
        " 'cqvamp:cqvamp',\n",
......
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     "cell_type": "code",
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     "collapsed": false,
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     "input": [
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      "data, rate = librosa.load(\"data/Music/piano-scale.wav\")"
221 
      "data, rate = librosa.load(\"data/piano-scale.wav\")"
221 222 
     ],
222 223 
     "language": "python",
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     "metadata": {},
224 225 
     "outputs": [],
225 
     "prompt_number": 31
226 
     "prompt_number": 8
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    },
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    {
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     "cell_type": "code",
......
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      {
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       "metadata": {},
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       "output_type": "pyout",
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       "prompt_number": 32,
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       "prompt_number": 9,
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       "text": [
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        "22050"
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       ]
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      }
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     ],
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     "prompt_number": 32
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     "prompt_number": 9
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    },
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    {
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     "cell_type": "code",
......
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     "language": "python",
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     "metadata": {},
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     "outputs": [],
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     "prompt_number": 64
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     "prompt_number": 10
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    },
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    {
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     "cell_type": "code",
......
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     "language": "python",
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     "metadata": {},
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     "outputs": [],
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     "prompt_number": 65
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     "prompt_number": 11
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    },
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    {
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     "cell_type": "code",
......
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      {
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       "metadata": {},
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       "output_type": "pyout",
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       "prompt_number": 66,
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       "prompt_number": 12,
282 283 
       "text": [
283 284 
        "{'matrix': ( 0.092879819,\n",
284 285 
        "  array([[  0.00000000e+00,   3.95574346e-02,   1.29732716e-05,\n",
......
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       ]
609 610 
      }
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     ],
611 
     "prompt_number": 66
612 
     "prompt_number": 12
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    },
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    {
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     "cell_type": "code",
......
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     "language": "python",
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     "metadata": {},
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     "outputs": [],
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     "prompt_number": 67
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     "prompt_number": 13
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    },
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    {
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     "cell_type": "code",
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     "collapsed": false,
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     "input": [
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      "step"
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     ],
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     "language": "python",
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     "metadata": {},
633 
     "outputs": [
634 
      {
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       "metadata": {},
636 
       "output_type": "pyout",
637 
       "prompt_number": 14,
638 
       "text": [
639 
        " 0.092879819"
640 
       ]
641 
      }
642 
     ],
643 
     "prompt_number": 14
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    },
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    {
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     "cell_type": "code",
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     "collapsed": false,
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     "input": [
649 
      "float(step) * rate"
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     ],
651 
     "language": "python",
652 
     "metadata": {},
653 
     "outputs": [
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      {
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       "metadata": {},
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       "output_type": "pyout",
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       "prompt_number": 15,
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       "text": [
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        "2048.00000895"
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       ]
661 
      }
662 
     ],
663 
     "prompt_number": 15
664 
    },
665 
    {
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     "cell_type": "code",
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     "collapsed": false,
668 
     "input": [
669 
      "vamp.vampyhost.RealTime.to_frame(step, rate)"
670 
     ],
671 
     "language": "python",
672 
     "metadata": {},
673 
     "outputs": [
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      {
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       "metadata": {},
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       "output_type": "pyout",
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       "prompt_number": 16,
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       "text": [
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        "2048"
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       ]
681 
      }
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     ],
683 
     "prompt_number": 16
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    },
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    {
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     "cell_type": "code",
......
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      {
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       "metadata": {},
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       "output_type": "pyout",
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       "prompt_number": 68,
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       "prompt_number": 17,
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       "text": [
638 
        "<matplotlib.image.AxesImage at 0x7fe7fbadacc0>"
699 
        "<matplotlib.image.AxesImage at 0x7f1b8e6ca7d0>"
639 700 
       ]
640 701 
      },
641 702 
      {
......
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       "output_type": "display_data",
644 705 
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Wpys3XFvHECfQ68FwQJT26KEZUrNvNxz2lm69SdToMd11k68CgCQHPYPquXUx\nEJMel4M9ng/e4/HgAc8HJ5CWEJXOGtpqnrK3wYp2T67Z7oUupcnCPhauXrwBoJT/T4FzYOKOVdLe\nfuxa3HWsTLzBxL1dgyCjzXaques3gaYfiJUXToO79i65SUImIKgH7cEgilzSyj1TK+kmofwju4Ir\nJbsK+b+xKGo0taevwns3z7R+yW5RoXwV8P7PgP/JWvsvKqVinMu3LesvpTxNLlpcODPXylu+ypnp\nVe35bqFOMhpPcvetNeGsuIvTspjKUFxa1p8bokEFCxg8UpjHQ1aPTzi7yMnnls0aNqV770BogMmO\nA13GXW2rX9GvFfOV4uxS8SRRDKoej+e3eXy2x6PHCReHJYvzNeuLGeU5cFHCC+W2EZdQWLuiGWSy\n41JoRnephl0dOqzwHQOtLN3y8qtLIHbgnSuftLdgVZs+gcZ6Atc5tfWd1DjKpfRthgFvqzROoV10\nxkvKeOMzhW3bXbn5JsANbU5Wyid5GK3yMq2to5XkBmYZPJuBSlnHU07zmh/kI5LsLgfLiOUVrFaK\nVeWaPfdstOvRXVP8ulyWlrOV5WhqOVKWJ4s7/Kg35jyFordyW+We1d7DLfSETKCa9u6I4jtIO0m0\ndNE+ZRWsLBHX7eJ1WS1NG+CIwPRxvq2+63OyAGf7QgSxECXvVnnXwha6JKRTw2ilLh7skm59hyt9\nh84BPaRJPVx4TqJcHiun7QVJRQatDUobdGSIqImoiKmJqIkpSCiIMSTBQkEblOkHf/uG4nbkbaNN\n9oFfsNb+OQBrbYVTG9uy2QHeQFu39YBtNNvVgMZ6h6VUvDTQLuAOHV/dFvcVUynKS8P6M40tNOUz\nsJeK1cWI84sTvrzUlBtLsXEBGzct52nP4aJzOxBNa8X5SjO51ExqTbpMuDw75HK8z8Uk5nJUsVls\n2CwU5aKAxQoXO0izstfKSsUweLALWqEz5yZtRMy6HVKWbnc+IsdVk0KpXaoiqHwb1DjgttBEnIgY\nUKYB78o2NA3QjkCQcoYD5RVl3Cnh4A3jv4U3lWPC/GUiJnwI3lKukE6RMoemO1xvF+t8BtMM1Awy\nyyZa8LwyxOWIVXmXUTYkX0K2VuSV8puSKsSlWN/IvzYyLC3jlWGMYVRYrtI7PEkmnMeKIl66SXhm\nYeHbZVtHXfCW8D5Lg04Sly+OYwnZHeBCPyUENICN0FBqRWqqAMQ11In3aaVQ++18JapMAFxW+8r3\nIbvQcloPPD59AAAgAElEQVSKli0qVbh2YdeSt5dJ2D97/vn80v4kddGIhz5NgJ7yhrHyPli1XXek\nElBxjY4rorgmiisSSlIKUgoSSvpo+lh6VPRxUT+im0tE/k8UvHHs+plS6ldxW2L9A+AvW2vXraNE\n874mgaNIzCVZvr3NaWzLbdhPd01iSGJ0pWk8W1mKC4UpFMW5Yj2C9UZzvhky2Gj6m1Hzgo3KYdCu\n2Ja2XieN7l72pI0mXUf0qoh0qYnOYzZpj03aJ0sTNklJVaypipKqWGGLpK041LDl+a+FS9ngmRq9\n/2bwfgk4iuZd1O4NITbxZnMEJvaTKN5R6QHEdsGkxm3V6YFPJl8jZQpM8i1IQhuMQkB8lXQnr5CL\nDKmjN5kMRNMOo2NkEAs/G5YzBO8ucIfgvXH+g6uMjco5NYaVGfHM3CGpDqkLRVUo6koFbkMX/9FE\nYoeO1LYkZU2yMqRFTbI0ZNE+Cz1moaGIVo5my3FvGirlGYViEqoojNaB5lVwE58UjWa7xAH6Pm7f\nmhOHUluahAa8RXGXuU9oB6WgEuVAQx2xfenq1gDyg07Zpmg6+H0b6y2DBprFOhnNToJdfwrc3C/C\nttU4VN7DvczyNsR9B97HwB3cTrUjYKiadT8967f1sdCz6LQkSivitCROSvpkrTTCMqJkhGbkwduV\nVNr/9eVtwTsG/hjwF621v62U+hXcq9B+uXVUtmPR5bXOeT0prVFKoyL/2RqUrcH43LoFwOJwsVuw\nkgromFi1xU6hmjZ+7qUGdA+r+qAP2Ea/aoVJVXtstvqA73TdgVZrWEew3vZc2oM81Bhukl2m/y7z\nPazDXed3PwdS1lAK/SRAFe433LGKWpqJ3K/rYAr4aRUFZm5YT7vohm4ZbwLIlwG35K+SzsBQytOU\nFqWdVqpthLKRNyqCTU6tPL/BXiuT9DuLKkooKpivUShqZZnqmpnqgT7BKou1EYYIqzU2CazNbUx9\nOBbCMvt6KmuXtvUg2+lCE9LWvUZYzwLggozana9kMcsh2ygNKzv3DXDgLSjWD+ZyD+ISySmr3yPr\nu4S/rzSTzO1djBWDUsZX2GUUXqnJ2PY1Vbi+pqzzt1zbBO8mSjEQJfXtx6sd4N7EcwL2LqRDB9KH\nuPeFvUd7jhsDAwNDCwMLA4PqF+heQdQriHslKWv6rBmohCERE2vYo2TPFkyIiGwzcRv7GmUO5G3B\n+zHw2Fr72/7vX2fneyx/M/j80Kdu5+fad4M9y/CgSb2sIF3m22SLiroyVJWhrmofniz+c+V1U1n2\n4KqmrxpLpxdBtR+7dODyFSNWtklmE2FXClbK5YVx0RRl5XIT9sAwTC3kF3fNAG8iEvssSUxZGbg3\nOWG69wongPB8yeW3rjYvf4cvteouZvHOGh3RbN/pl+Obwg+6MMgyHLGh9qc699Sde5nOdcKNfELt\nvjtpyHXb19Q9TXyoSQ4U8YGmlxjGi4zRYsF4mTFYFZS1oqoUZa0cpb9VGVxMtt2CudsAyr2zURJE\nA4gOLNGBRR9Yyl6PeXXAvNpnXh2wyYc+EEQ1e6Rf6zPdibIOfheLVPbdqLkeI/8yusdr4VEP4qFz\nZEc4RaSKPcXRp7XJlPQZmdAUbtIJWSxrGge2Kp2VVnntu1bet+UjVbY5jfYtWnmrSeU5fUozGNcw\nGcL4Pegf7tZbwvyaTqLaKT+E1b57vd5aO+CWCEPZqSOoAnJwwILXvhUmjqiTxBkniSbTFrSm1jGV\n7lGVPfJywKqcMC8O0FXN6W//Ps9/5/dpheW+hrxtqOCpUuqRUupr1trv4+K8f/f6kb940xVoUwGh\ntqwY7FccfVBx/KDm5MOK8WzF6PmK8fMVo+dL7LIizwxFbslrE7gs1DZ61PihZbxpsqfYpnGsyI56\n5B/0yT7okT/oc2YTzs0+Z2aP3NyivErgXGPPIjjTsCwhy0FlYDIwIecRmqbybCGneoMWfE26WrZs\nkStLgCOuO2pf55rhYJYBYGm40BDkuqZbuDJWHFedndK0dilSLjcrqFfu2nUYwyvl3hUZFN6/q+0L\neIeg3w3fIrhOCOAhFdJYGLqvSW9p+h8q+g8Uk2HB7dMF751OuX16wQEzNgVsCsWmUGR1E4stuezd\nJ6skE2SjXZcnA0VyR5M+UCQfKtbjPZ5kA57mCWV2xGZxDC+sexNsDazFQgvbt/tiu3CshP4PqZNu\nvYncgGwqcsCdFJDW/m1ikXsxQpl6B6PsoQ9i7bp3Q/p+ERpFJQ6Qt5suecd77am5WntqrqLZlbBi\ny3mHeWvYhOCdQ2Lcuv47A7gzgf2o3T12GW9dIz/UR2JgPobzfTgfwIVujNEQvKWa5fF6QKrc/lmp\nxuoIE0GlFVZHEGtMHFPGKXlckm0GrNZjeuuc3jpH5QbqBxx+45/bvlbzpe+3CeSrRJv8JeBvKqVS\n4EfAn3+z07sRCE0+2Ms5/qDg/rdzPvhOztHzOYefTTlMpxyUMwwFGwVrY1kXlk0dvhVLkWO9Xuk0\npUhZbgG3FJxoOIoVq6Mxq4cjVt9y6Uu7T1THZPUel+YO9bM+5osI+hHWRKAL0CswS7e3NJKko4ZW\nRJewexPw7gKX7Cd8SBOGF24otesaYd4dzKI+dBcdSNlDIITGFhYADF+cMATVd+aneNxj5bQ2rNO+\nFW6QtjbCetmE1gXxsDwhAIXX2AVSHVprO/E4eNW9mPQ9xeBTxfjbipN9ePDDko8GUz6qH3N785zF\nxsddVIoFAh12CyGiHIju7RQwtY1X6A8i+rcj+l+L6H0nYn70HunqFuUq5XJ1BBf3HFda4ZyMGNqO\neVk5GFoe4fOHlozU78sm4m59W1cv0R6kOQxq17yRry+T4r4Iwbtyn23s2t3qpgiiS9TGW15rsHPc\n3jqxO8fGDsSRBT5inXkNXKJNrhW3q3kncHQIH0zgZw7g1mg35R12k+7SgcRC6lNi4awHjwbQH7gD\nKtrgLXuvybY8Kc5hmVgffQKWCL9fI4aYOo0pk5QoNURJTbSsiGcV8awmnldul7qNhY3Brm3g9H+1\nvDV4W2v/oVLq54H/E0istdejTW4+u5OLuM422M85+mDD+99a88mf2HDn0QXv9c55rzzn9vScuihY\nGFgUzuKUJSGSxA8tKcZyX8H7Gu5ruB1rZkf7zD/cY/6dfWZ/oiKyJXmVcFXvEVd3KD8bofox1sao\nVYytMqhnkM9AixkpwC2DKwTu3W7Ol4uAjagJsqx4ggPvHu1lxbtednHNNtyRSpr4xC6pH5rZoQkf\nAuAYR/7tASO2kQWiyeAHr/LaNyF4y0Y/ryMvA5/uc3aP6dIvYi24FbG6n5C8B8NPFJM/DsfHJQ/6\nBd+or/j27AkPLj7jysJVpbiK3GaNMl2vcQu3uhFIA8IXPMNoEDO6kzD8Wsrw5xLO72iKWc7VPOGL\n2RE8u+8G69zAM6mndXAnCVkUjTxs75BCEY38LUQlEK0dePdrH/CrXURImbrJ2YbL2kXF9sCNbWgT\nqX5loc6d9WVmTuGx4Z7WMdcnKV+LttsnRcr28enILTT7YAg/+x68f3ydXeoCeahlx0DPuNQ3zvH4\nWEHf78O01q4JQvCW4d7dFTZSWwetMUCtUcbNYW5Gt2532x6oqXXvE7lwOXPjwjrlfYblHwJ4e/nL\nwD/GocsO0UEeDrRdNk0DIMVKsTjTnH8RMzpKKE4HLB9NmJ1VXCzAZCWrAtZV8+7GZmA18CCs7lZn\nsM4yvawVy8WY5Ysxy8/HLPdHnJqIK1OzrtfU9RX28QYex3AWYecxbHLIF1AtXWfcruIKaQCRkAp6\nE/Cmc7wQa7Lnb7icOOS8u+D7siTaumzNKSqJ9OxuGQSwwwiMkGvVjalbWz9wl446sVJHYayxotEm\nd4GvCo4JnW5dOiHkxF92vfC6DS1hC0M1U+RPFZsfKObnhrPPE758PqI3O2CZvce8gHnld4KkCUiT\n1MYGtY0SlqC7QRExuIrpP0kY/CDm6nKP02XCbFlRLOdwduZok7l1PpVW2FuYwpckG5qNnuTFFd3n\n7PoLfH3IApqWcReBHjkALleQnUGxwe2Jo8D2aZaorXwbhBRUxHbrC+Mvrgqopw1wmzXN7oLSj7rB\nuLtU5VA6fb+qHJV5lsGjjduitpaIKU/RdIdeyPaleK3b+GThTMFUwUp7drJytF9eOss7qv0j2/aj\nhF3QN+N2DvLuKitG3xx3j6nPlxbWpkmvw4R6eWvwVkq9D/xp4D8A/s3dR0kPCVfKhSbgLhsHNnPN\n5aOEKLWUG82LK8vkqWbytMfkcoxZ1OSZ240vt9ff/d11kUXWWaUvjN8cslJsLvtknw/IVJ/NYsBz\nm3JqShZmTm1OsRcp9rnGnkaO/5qXsMlcJzHha4zCfcFFQjB9/Zm0OUdAOdybQeNaf5dt2LVkXmY2\ni/aW0sy50jbd6AQBdgEKsTakXNaZvOJ4qsX5dAVmDnbpATyso5DGuQl4RVOWUaZolksJbIb7ZYgK\nFJKY3agUgrIbTB5RvtBsfqCwRqNHNemThPLxHvOzOxyuYta523d9Y5p3nYRLQQS0paYSD+Db1w5s\nNOnziPT7EWkdsZoc8mXW42xTkGUXMFPw1MK5N523jtjuncIUWmRhLLqI1EOgGqqgbpVqN3ekQKWO\ni85XUJdusVFhfZy4fxFCy78TTq6yelI14E3pJ2+ftnROOBmHE7H4XmB3f5a6EXXMT3aXOTxeu0ni\nuSwE6vncKyJhtwrXGYk+EiuINcTW7UH1zAN4piCv/HMswSzcpKRtE0UTPk7omgnnoDCQKwbWyu2/\nsVKONshss6eQfTO8+Cqa918H/m2c7XyDhHyjlF4ohTAWOHxqy2YWcfHYUmwU8+cx/XVEb9ajPx3T\nmx1AZihLqEoX1tqNAA/98aLrDazXiAz0K0V5mVCqhHKZUD5NWJAwNxVzO6eyYFYRdq7dQpqFxnms\nSiiFo9sVjw3tyr+JHnqZyLECNLKlqkzjYU/Z1Vt2AfYujUYiWHZFdoTgrWhvKiQDWbTYDGwFtXdk\n6doBt5n7gRu+Od3SHvSh+hI+l7w7UJLGrQETDTwPzu++nFr+FmJS3oUZ2tQlJtMULzTWRJRTTdWr\nKKcps+k+T6eG4XJEWbnIv6K+vnVSs3zKtUNov2ynkI0mPlVElSa+1OT9IZdlylVZkJUXbhn6zMLM\nuBmi5Yjt3k0+C/KIjt8NL4uDY8S5rNmGxWnVmecs2NzRHNXSveapSl2qw/cuyl4hMnl22szgOXAF\nqqa1DfE1cAau8Rtd6YJ4eKwH76vC9bHFHAYa7Nif5i2SkCkUHWRrFslvqsmXwJXXije4fZpL/+7U\n8sJZELKyWOPyrt7RNbbD7q5xi+G2q5lVew57Qz3vbVdY/hnghbX2/1JK/eLNR3b5RgELCfjscpXu\n781cU2QJ89OYKLVEdQ9djdGVQZcGjN36NsKoohCiuldtTZgWzKXGLhX2qcakioqYylZUzKlYQ6Wa\nTQ0rxXaTJ9m7+hposuPObyrd8yUuvKbZk7hLuHW18K6Eg6Tm+lLobu+Se4eDpztZhDsGwjZiwFS4\n3QW7b0YiuH44mYfPEY60hCbCRqJsBLhls6GwX4kTNUxisViaCbax9kwOxYuYahqRfRGx1pZZlRKX\n+yRlj6g+2i7ytbY99bVhpRm510iqjUKdgrpUqM/B6IjSJpQmp7QXzkIpjdNAqrANX5ZkFYwsnOkO\n4ZT2mu5ggpY9OFr6VA3luU8rl5s9sHsOBG2fRklZ4JCtw7GHi7isIJhxk/k1kA4AeCfdR5DvGl8+\nCXgv1nAaOwvCysQ/cIeH2+PIBh5h6io6srhp6xetIFvB5gqyU6jmDrClDF22b1sfO77fDh+FW9Am\nUTe+3Nb/9gbytpr3nwT+rFLqT+OXJSmlfs1a+6+HBw34u0jHSfiElK/7v8OVcW4n47Bx49ptwBNX\nEOdgUk2VxtSTiCr1GrNNKG1KYRNMHV3f2132A9/ufCadyf8tykRLTHCRV4mAjNduVNS22gVXuzNL\nmKCD+6ZzILgWFy60pP2uvjg4Vs7r+hfkGrsoLB9HLm/q3sYI+xlRCm7DhwjL6Xn38N2CVmKown2e\nVZCLdEGpa70032sNg37BoL9m0J/R611SZz3qrE+96VFnPSwDDH0sA+x243+3R4fdLl5pJjFdQ7yJ\niLOIGI2ONPXAUvehPoSq16M0qetjJqE0aTtir8Ahuwny7iReWQfKm10gnO/4znbar2uXa4j3XEr2\nId53bdcSccpKShvQVpHTMFtsUw3rtYttLnvOSbl1mlc72lHu1+1fAp44DVz2v9EWIovqVdCrfF7j\nXgZhUMqicQG9ch2L9pGCaqugofzdlEUpC2WM3UTYTYVdr50Wu6174yiOELgjGkNMnGRhVwMXJRWk\nNC8Z5Ev65TmD8glxdYm11iUs1rZrYFtGGveCMFVNk3orKNJuh08U6/pLNvWX2xa8fB344S3B21r7\n14C/5gqp/hTwb3WBG+Bb3Eemebd05vsIbSIbrNpAG7I+H8Uw7sEohVEPiv0em8M+64MBm8M+i2jC\nvO4xN3vM6z2KTa/tBFgZtwy8LFxedXnD16ydl4pM5T62IOq3o+jk1Xu7FgWGeym1lJDusv/uHi4h\ndyuu2O7gD2ePXZEmISgYP8hiXMifD5Gy0MTZGlAy+YUafBhnLea+eB7E6xBqVIFG1vIR7DDBtzak\nc4zGMRwdvuDOrWfcvvWEk8PnbF4kZGcJm7OULE+obUpNSk0PQ4qlwHoz3157yVxNimKoNEOlGaGJ\nehH5rZTsvYTsdsLmsM+86jGvhsyqA8pyzzmb5GUXcxyfIiseS6kf23nOLu3RtZS6qll3cg2RNnZR\nFuOxS6MxRJ7b3Wp7IZXkKSm/4932FmEXUQqmPqKpFF+ClElmKmkv0eTZ0Z8IzvOXib252zeo4wp1\nXKKOK/RhRawqt1GTqoiUo07digwH67WJqExEbTWViTwIWr/pk4WVxZxazHOLee4W7jUW6hroX+9u\nYWBYGGW7bbIB7n0CLg2qktvVgtv2nDvRY4a8oDZQG0ttgnXdKui9yhs3Atp+zsSvYdty7BJWq32j\nqPe3iP+rP+K15KtGm4SPf00+5Atk4ya37qxh9W2QQF4B5aJmj2I46sPRCI5HsL4zZnZ/wuz+HrP7\nlrNkxIuqh60OWFe3KaZDeKrgma8UVUPm+bk63BtQtNg/CBHwPgKOQY+brREO/E/S90VTCz2robIp\nSs2WI5R8QxNDIxugCKhJxMUuCUEgHPwJIYBB7XtfDLoP0RhI2xqlaGBbEzhcihw+QBgAG5J43TKG\nJscuS0HRMMwuvDCODMeHZ3z04JRPP37Mw/tPmP8oZpFGzPOI+VlMRUxJRIn7bCSqZLukJgzdNAwU\nHCrNgVIcKkXaS1jeGrP4mRHLT8fM7sPz4pAoH5EXJyyy99xiGkng3mG38dZGVTYW3rZ+pU5Ck1C+\nlzroRs102yv0sPWh14e9Phz34XjggKCl8Hd9TFGjFkoWYq5R7trVxG3vwIBw1YTLJdZfwLs7wXTo\njharZVETi7pToj90KbpfkqqCRLk8VQVO/9bbvDQxZZ1S1AnGJKBAy059UY29LKi/78pn5xvsTJy9\nfmdOm7bHmNRRGCgVisVRRebIUxoDBrbkDks+5ZyvxY851E/dPF1BaS3GBpQ5/rMOksJvVuUNpBi/\noEd594wH73AG4CcM3kqpD4Bfw632t+x6EQPwkM+3bKD8czNs95+rPev/vxvD3T7cHcPdA5jfO+D8\nk2POPrWcfxoz6BlsmbIq9rko78CLfZh4UyTXzsvEwoWsld3tLa9xJW8pIXjfBX3YgPeJz4WaEdBe\n0SjLoWd1O/WFdp28usnSOAhl8IdaT5caCSM4hNoJnX/NetSt+qFjZzlEE1wwKk2hjME5aCtQQouI\nFt3WkK/F7LqL00aXUDPtcpwqOEc0+ZQ4Kjk6OOPhg2d891uP+fanX3KRaC5zxcWZ4kJpcqso0H6V\nrcbsUBDCyh4Dt1HcUXBbKfq9lKtbR1z9zCGX36s5/zQl2iiKbMRscwKrD9z7ozyd6qh3/4q5yq+8\nbWnX0teEQJW2CjfREkspDEkIna6yb4nsgjR0i1P2EjhJ4H4Mqe482i7KRap3B6daAVXPAXc68F+c\n00w4UxrQHuA0k22oBu0Q0qAZBe/7wNig7pboTwqib5bEXytIdUZfZfR9bjxoGyIMmrzuoaoepupT\nVX2sxu3UF9VEcQ3PVsAldpZhHou2vQrKFF3vbmE3Dd07Wzn2/HMf7D4DXXI7WvC16Iyfix5zRz8i\nx25fPOWXKzWh3h6LI+1T5ME7bZIso99a6d0XCOxoopvkbTXvEvgr1tr/Wyk1Bv6BUuo3rLW/Fx40\nYH3dicPNbhl8PlIwiWA/ditgdT+nGBZkk4r1Qc2gb0kLTVzEqKIHWd/t9NX3PFJUQVQ43qsVciZe\n+jD6oXv3MH+ZSO/0GpHqt6O0ely3nsO40C5ebSU8oUc7vonOSd0W74aJ7XLmyezheRvlW0PJZ9Mp\nV9j7Q43SmxSqhKSGxKISDUlEUhmSqiapSpK6xBrHExrrXu786hqX0e/SWJccD55z7+AFH90542sP\nLnj6PCF9kmAOErJhSlRBXFsiA1EdLlq3mB1OL1lQM8GyBwx0jzodUA6HFHsl60NDrwfJRqPTxE1u\nI9oLDiN89IHUi6bdp0JnbKiOBpSTStopbHPVd6W0gZctUS4mWebitL4+N75Kut1+2ycVjd9DnMOi\n+MjmV9LvXwHekmtclECawKiH2qtRhzVKb9AqQ+sNkc5Q6FbSdR9d9VHVAMo+SiuIK1RSo+KKqNDE\nx3PUSYk6nsNs6vdPadKW/bNsF212RbU+b3CcukKhuZVccJIsOO7nHPUNh0C2seSbmtwYqtoEa3Rs\nC8Rlq29Ho6jmfQ0R7scEvy+4r/ct5/Ia7Re0xBuLtfYUOPWfl0qp3wPuAS3w/pwHu9waN4K2fF5V\ncLmBZ5F7Lefq6YRptM9Vsc/VdMiLRHNW5SzKGVX1DC4X8Fi7FVKXyq1Y2qyhXNPEZAsYxji1uO6k\nrjcxHAW7uEkhtedABPWiUZbFORI6twraL/oIlbTtrUKtONCMGeNGatdTLyeGGmsI+ALe4d+hvqBx\nkSJLF+onJnIr8CEAbCugHZRTF6h94DhBHSWo4wkH80uO55ccz6cczy+oi5q8tBSVe6t5ZbuxH10Q\nD2mfiImtuFWdspdN3cZkc8W6PuCid8zjwxN+9P4JxcpSbizlxlBuLMZUuDePukmm6xQfYpljObeG\nx1h6ecL8bJ/5j/aYpxOmFylPc8NVviLLz2ATwzPgKY42WQDrwofW+Y24rk2esjxPLKBwQZfxjxn7\nFLlcpaADMLduRaiL/NBOKckyF+NM5qJFtl3BBtq3yA40CCu7Bs6t67e5/HCJe0uILMoJ1xpUNLOX\nkOede4TGmAKWGnuaYkcpdZ3AVUqhQSmN0TGVTh1dohRWCW2SkNc9qjrF1DEohYoUVRxhI7ft8sE0\n52A45eDTU0b7L5yRMFMwU9ilchvW1c5nLKvOuzXTmmf0lEhfofUpkf6C/WHB5GDJ9OCAf3TwHX5o\nH1BcbiivMorLDaYoHNFjDZGyaGvRppmvtMVPBAqMQlUKauXCBTPlVnFGgTNZeWcmf/96m+2Qr8x5\nK6UeAt8D/o/ub1/wQXNckN+kdW27TgXPMr/1ewWZGrDOh6yuhqyeDJlFmitTsKynVLVyL969VHCh\nXL9bGTeoipxmY6SQO0xpew67jsKQzwgBMkyxP3YBFGBiB8zykuolbV+V0J+CfcI4tByW0uPlBFmM\nMfa5DJ4QiFqtQTsssxta2A3NU44SMX5LO3vlvwvKhmUbpbPltgOHqjZwMEB90Ec9HKAeDjg4zXjw\n7AUPn1/xsfqSYl2yymCZWVZV+z3HEsfQroo2MTu2hlvlFZNsSrrKsHPNuj7gvPchjw4/5vvvf0x5\nZamnNbU1VFmN9T4D29qov6nb1Nac4V5qMMES55rN2ZBNMmCTDVk96nFV1kzLJVl15mL8r3AAceXb\nOfeB4JVMbF5T3tIMsrBJnIh2+0yg3GCNnLVC6nMdORpL+8829cCdgo1AV5Av4WoG6xnoMgDubl+F\nneANTbc2wMK6lMsPEuYZgneojYcmZIeakUsIBW3BLmPs6R7G7GPne9gnLgLG6IhKpxS6j1WqlSob\nUxmXrImxyhHKNjJYHRHpiGMKHo5mfPjJKSf3H8MTnO9LgS2Ui9H3xSjMbpIu7GmxGhLr58TxhCSa\nUI8G5Mc9prcPeH7nNqWtqHpzajOjWs4wrNG2RikT5M4Qc+HgAtwaVbktrh1wR85XkUQetENiXPOH\nAt6eMvl13IsYlt3fP+dB+3ifdy277t9p5XZ87JWQbqDKYsqrhPJJQjmIybUmMzmZnVHZHIq40Wo3\nyq0Oq2uXjJizMoAOaQjpUNPtehdvAm/psNJD5y43pqF7ZdvhrjIfMiK7FOgWalocEMiugsf+syC/\nTErh1CeTSncL1zAVtDWmylkntnAUCKYzm/oytcIFAzDXCnUQoz7YQ39rD/WdIw5+/IIHo5pv6ynf\n3XxJrgqmWK5quMqbdanhQv+QmGm0b5eG1nJSZexlGb1ljl1o1tUBF70HPD76Dt+vvkcdG4ytsFmN\nURXu9dFhvHnWumtERUpNYg2prdE5VGcx1SahfBFTDlIyU5OZJZmpoJ63X+uYgQ898GqdwU2y0FAP\n0g69oA8F/LaKnG2dKuhp58CKvNNdchvhlnprlxsfe7y6APMc9/alsH122bo3iJxS2Ca5L2jHRcqk\nLablK0ha6SKSVynW3MbMY3g2xgxTjIo9cLvXhqHxAO1y2d9aEsrtj26023892tMc38/5+N6U7947\n5YH+0g0TBawUZuagIMOtf8q4GbRlRCcqIY1SenFKmqScj+7yw+NP+NG92/zw4SdcmD6mPsMuzzBn\nZyMazLgAABSaSURBVFhmKGqwFUrVKFu7MHChSwyuzaoIpSVUM5ycfYiu6q67eD35KsvjE+B/AP4b\na+3OPQwf8YPgr4e4F/C8hlT4pbledm55JQNx/hoXlM2dYtwAO6FxDobL3AXEdwVpbwk8n4vnw4OD\nyf088AakFXDdkAu7lMzEI1/mUVDGcH+TcAYIgXpXTG7IweI0b5a41ZCyf8qbFD+G8R7qdoT6aIT+\n9jFj3ed2XvPxfM4fefGcTZFxVsJ55raPkAXT4XYGNxFWAD1r2Sug5zepy656zPIDzqL3eTz+Oj/m\nj2EK49ayX/mFQixwHUfS+vpdbbCKsTBwgUsttVR2zdk+8Es+WxqfigB1tz3DgZq0v+6rptlkfg1n\nNWNhnUO2gNUlrE6hltClLnh3Hdovk27td1vgDfuEgLa4lvI+dpVgGSNRUrXWL49i3VXs7XsxQd+1\n7A1z7j9c8PX753xt79Q17RR4CiZxBviqdivRJdYsTN3b9xT0taIXQT+GHw02PNq7x9WtPX7v3tf5\nwhzB1VM4fQbJHq6zePvRii3Zqb46pJfE5xFGFPWAHwD/kPZCuVfL20abKOC/Av6xtfZXbj7yz/i8\ny26H0uWWoW1I3+BpeCMxuAG7oNkfpBuWF2rdEoTd7chhzwoXMITgHibYTRjJs3YaVkUODLXP7Rjs\nBLeVZuG0r22URneFZRhXLXUXPofUqey9KGaxgNpbrM8FByjTHPtojpm48KzpF0u+/Eyx92wfNb1P\nviqZZTCt3GrwDZoCRYmmQHs9vu1kDOs/NZZsCdMzOB3AfpHwu9k+jzaK2WaB3TyFM9wy88x4LTTY\nS3rbh4RLF83YsDN88sYl292BGMbTRzQ7LQ5pNo2SSVbaJ4yGiBqmTANWXb9sl1rLLqCYueXs2xWF\n/197Zxcj2XHV8d+5ffu7Z+djvbveHa/YBBPbcRzbMbINAeEgE2wJghQhkUhBEQ95QgTxgDCIB78h\nJBAgIV74iCBSEolgkC2hyAYcKShSYid2svnYxBt/7m52e73emZ7unv669/BQVX2ra3rWMxOP5o64\nf6lUPbe7p8+tW/WvU6fOORW6G4ZyhrSFR47KdCmhoR/rvJWn/z2/vhHccnQdaGffmeZDsZciu4KY\nWYl68EQY9tu02wPO/6hKPTpGuzEwiakvmp9Jhyb30SAx6UN8fx93F+F8EStUEjErf+BS9wTnrzZp\nV2GYds3zutA3kZ1DN35Trw5dWHw+c7/qxq2f+uF9wF3ejf7LDtp075r3B4FPAN8WkRfstT9W1S/N\nfsztUOucwjZ1OG2/E+StGIJyWrojqzAril/mdU5fo3FGPaf9QqZG+WaJsMv4/9e3T1tfoqgCpbKp\nXZKdNLa2e/cbjoAga6uIWTOKv271Szcozpzg38cukCq6PoA3NsyfnSFrV7u83o6gfYTO+i2MNxP6\nA+iNhb46o0VWzFlH5pyaWdc+Q6pxoqx14XIbllJodGJeGy3y+khYH3VhdBHWI5OHZiCW1JzPuU/e\nru2UWc8Q115h9FSIqfMys/sKrnZBW3WyzUpnM+7bNg76hGcbJpGtOkA4bMZdGG0Y98SZPY7t6tA+\nHQXG39QsZ+hgJnXXD1wf97X5sB/P09LnIbVt4PLTuInHCpGCOdxBTZ2GfODLa+pB7zrtKwNeiiqM\nNo/xalXgMshl0DWj64wnNi9NOhvQv535pJRCnArxREyequ4JXnuzRTuFQX/DyHelB2+N7f6Arzz5\nD4ygToNrzpTWJFMkXdl5HMpevU3+lx3p901mNb9wNt+O0P1IwlBj3QvczI/932430de45hmkYfa3\nw87rlt1OTqfxuMETmkHC+w3c+KSGCZapGtc0jYw9VVOQkV3mV73fickmHieXfy9O+/ZXFCF5uw7j\nE/8u4DRvOtAZohe6rPU2oCt0uke40LuF1HqajCfCSIWEmJQy6bQ2cvuHjPn3EKUp9a45t6DWg0o7\n4npyhLVEWE82YHIJhjEMYrMZlMbes3Far3t+zqbokxJkfUTJgmh8uO/64ee1oFS912WyAdnDEJdL\nkmXHg+qsjjKWt5/vk7EtE0/zdv1onixesI6/LJ+mh02AN01fkwS077WXTzx+X/YF2sn4dNr9mm0P\n637oH3A9dVmd53+kmQgWw94m7faA8WaVN6/eRDNuZAvKDbOIcNsR7kDx0BLjN7FgFP8oESIVokTo\nd5dY0xbXezC8Zk9i6PahO5pD3r7cPlmH1gT3PefD74/PDXZjovpJbN6PAH9tJfkHVf3zrZ9y+aLD\nG4StRBYSuxu87wSc5j3GDCR/hgxlmWd19ZeO8667JZRzCfOTcPk25vCeGxhNzYbYSxOiOpQaUG7Y\nzdYe2bFikD34svcb/ij3O5Czy/vF9XBXfKLaG3nr2hA6IzTqQiSsaUonFS7qIlG6gNoBanJVGMLR\nmSyA4Puf6JR4DZlLkiJdkB5Tb6qUCokKKRuoDjHeGF7ZQjKQ6Rt+7YprN6cKz4NP3s732g+imWbn\nJ9scdn3uOll726KJN8eG5oxtoFNnYUzaU5eIyWn9LiGVn6chiLh0ECx5Wy8m3SRb0bk+NM9S7L+3\nG817ZNugxPSw5WlSK48fZgh8vnY/SFLamylvXq1Qio4h6BYdUcm+Ok/KLU2tYA5SMDLppEbSb9pc\nUnaVmo4xHlow65Xg2jFsl3ny+5r3JpkS5VbCO8Nebd4l4G8xZ1deBJ4TkSfDIJ0s+c48G7Z/U6Gt\n2IXi+tqwb9KYt8EyT1PGu+bLEM65rlMC09Sn4e7J2ywdpWS05ahu62rmMRBZl7Dp2Xx2aZjWIalD\n2jB1VDeaN1VIykbjTt2g6pDZ2d3hDH7Ah2/68dsrCM8uj6EcQ7kF5Rr1qM8CG7Sky4JsUErGpCOy\nkszXLfwWN8vNjLKShTJJKyZplUkWYoZpncGkxnBSZzCuk46rMCzDqGzqCXZAjMkOLfYd5CeYI0rU\nqFMzZg8XyRj2lXkTrV8coTlNFTJic+3pfy/sS/P6Ztg6/sB0WnLsfQ5zXcn61fS1j3DfJFQa3ETt\nNHv33F0mSkfafkCN/Z+agK5ZOV0KhlD79VeVbnzMU3Ts6ygyfutxDHGJUhlatR4LtT6t2hrNSt/u\nIgrSN7XaSBq1ueFN4qcsAVQ4rqUM0rKlCZNqma626KYLdNMW/aQO/cSUXgqbswrB3NVVpQK1KlSr\nptYajOswMoFCTEp2dTLJap0wjUBWn5u2aZ/4CJSXTWKxcgvKZSinJmCnXIJoFETLbI+9at73A+dV\n9VUAEfkC8Bts+Vk/ddd2JhMhc5Epe699rdXZL513SEhUoYZ+I23Af883aYSbUHHwet5mpK8tRJiD\nXOumVKrGd9flMihLtn5za7lxFUYVW1etxmj9eSdiNbMh5hipNTLvFqexOvPAvOLIJfDLLpdtxq8W\nNCs047c4KQNWoz6rcpnasMe4C+MNk6donGzdDQjXUNWgjI40GK66Umd9UmNts85af4Xx5gpptwqd\n2JaS2VGaeMEu040z/wSZcPC5Zxm6Szoim6fC+v3PLVtrmNSqPnGHG3f+YHRy+C6Xrp+mWTvPBDM5\nn32XRNp9fl4BtigWvsLgr6bcCiEhGxdOe/bHkj+e/EkCK/M66DqZX3c4TYeeMr7FdI5tR0pQqUGt\nBrUqcVNZWf4xq8sdTq1c48RCG2kDbUHagrRBExt9myqapFa/sVG5ChqM6agK0TGITkLpJAwWG1yc\nrHIpWeZScpT+4CZoD6E9MmUzXIHOIe9qGRZbsLwIS0dA69CtZGUYWRfk1JbEjNHUuSS7vhhO4l4p\nN6C5AA2bWKxZhUYJmjVoLBjlap/JexV4w/v7AvDA1o/1MW4wt8IW4nZws7rTTOpsScZDnxmD1nQj\nKtSAb7ThFhK6+24EvALcztYNH7+EXgYwQ5aOvKsNqDWgXrOmUTGlhqcgq02OUzJ22s3Y+HxOYuNa\nlJTM5lWaYE6q72Hshd8E7mQ2LNknmHDGn2PTLy+aDrO8BMvLNCvCyajNbdEm740u0+qvMbhmDlMZ\nDmAwnPXLGc3eNcrsgv0SPVaPnKJ/epHeHWP6d8CV0RJxp8a4s0yncwqu1aEdmVQGackMABkyPRSA\nAdkBgm6C9Qn5Rqa3CTfejlGMW8L77f+rY8jbRZ+6PBlOQw5/x5G3I2d/debI04/qdcqB6z/hZmeo\nNEBG1s9j9CTf3uxcOt14cPluHOm68bSdH55kOtNUo7Uyq8vPEipaM/6M3v8X4Bzw3tl2ispQaUHT\nZEAsLaWsrHY4c2rC7atvcevRV5FXQBpAKkgHmw5eDYmjngu9ksyxzkgV4psgfjfEt8HG8UVa4xV0\nHLM2Pgrd0/ByH6QP176G4aCeldk9l+CfVsuwtAA3H4WTx8yK+K0SvGXHZ1dMuOZYTS1uJZgyPWBg\nC88FY7FcM0S95EoCyzVYasH1r8BtPwtfYEfYK3nfSLX10MM83FM3+GqEIWl/82WBzBa8gOmoNSuu\n63n+0s2Vt7PBzSNwAV7GuOpMM8ewVZ/0A19cs3n6qAjETag2odGEVs27BTHMNrVgqN3HEnNenjuB\nBMGcGyhG805Tq4X2rOb9InDMk3ueXfdGtUK5acl7GY6v0qwPOFkqc3upz/2lyyytt81xepvQX8sc\n7lxxOovvk7PolZcYcs+isH56zMad0HmwQmUA42s1Nq4tU7p2Ci7ZHB0p5lio0cQQRzKEaABJn4zY\n/BWaT9DePc30Cbx6O5wD7sD0PUfedTLi7pBF0PpKgf/7Y2ZJUjCE7btiusnBzxVc9167Pr2dh9Ln\ngI8yS8JrWGd0ZonbTa/+qsRhTrvMDIVwkzvsOyF5++6JPwIeZOZZRBWoLEFjCY4sEd+UsnL6Dc7c\nOuH9t17j3lOvIg1j2pYOyCVrorPdP9Fs12OipmxhjSqUj0H5p6F8N1z/qWOkwzOsD8u8PjwKa6dB\nNqC/AWfPA/eREfc2yemqFUOiJ4/Cu09B0jS+95Ebj2TztTihbC1zzEfAFgWqHEMjhqUYjsdwXOF4\nCscUnj0Hd390vmxzsFfyvghe7Lt5fWHrx/4Lk50kBc7YEmLeDO9I3G0GJRjqcNrKdj6sO0XYsV3x\n7Xp+Mit/197VMGO+EcEkp6pBXDOmE/dVN1ad2L6iNppzfapI+mYA587nTqbZIyK14dhVaDSJ6zWa\nJWGxlHAs3mRl3KVXhV5s5hWXHNSV0GiVYvLMLWFiVxvA0UqfUmtIaXkEJxKam1CTmFirSNKEjWaW\n5KkMJpGYHSRANsD8cH6/gfxGCrWdncC1q3vm4QTtk3ISfC9c0fg2VD/3b5+sP7nUBs406HeMMCLW\n748VTEOFJsRwVeJWBL6r417a5UYIzYth4InfPhU7DupQaRLVEqqtmIUlZeXokBPHN5BliBbMkJHY\nLDKT1NZiyhjDme6OfJRKRomtLED5KJSPN1kaTGgMhfKgCqUWLCRQn0DknrH/bOcgikwCrUYNFmxe\n7w6zw9/tS7rmcN1oWwQr3ygy468amaPbmgJXvgzf+jKc/yo88Rc7ehpsfxdvi+eBnxGRMyJSAX4L\neHLrxx7CELarCxQoUKDADG59CH7zcbjnIfjU4zv+mqjubWYWkUfJXAX/UVX/LHj/nZryCxQoUOD/\nFVT1bU0JeybvAgUKFChwcNir2aRAgQIFChwgCvIuUKBAgUOIfSVvEXlERM6JyEsi8kf7+Vs7hYj8\nk4hcEZGz3rUVEXlGRH4oIk+LyNIBy3haRJ4Vke+KyHdE5NM5lbMmIl8TkRetnI/nUU4rU0lEXhCR\np/Ioo4i8KiLftjJ+PY8yWpmWROSLIvJ9EfmeiDyQJzlF5Dbbhq6si8in8ySjlfMP7Jg5KyKfE5Hq\nbmXcN/L2QugfwXjxf1xE7tiv39sFPoORycdjwDOq+h7gv+3fBwl3RuidGCfa37Vtlys5VXUAfEhV\n7wHuAR4RkQfImZwWvw98j8zrLG8yKvCQqt6rqvfba3mTEeBvgP9U1TswkU7nyJGcqvoD24b3Ypy7\n+8C/50lGEVkFfg+4T1Xvwjh9fGzXMqrqvhTg54AveX8/Bjy2X7+3S9nOAGe9v88BJ+zrm4FzBy1j\nIO9/YPLI5FZOjIv3NzAhgbmSE7gFE3TwIeCpPD5zTJjv0eBa3mRcBF6ecz1XcnpyfRj4St5kxESo\nv44JjYiBp4Bf2a2M+2k2mRdCv7qPv/eT4ISqXrGvrwAnDlIYH8EZobmTU0QiEXnRyvO0qn6d/Mn5\nV8AfMps/IW8yKvC0iDwvIp+y1/Im47uAqyLyGRH5poj8vYg0yZ+cDh8DPm9f50ZGVb0I/CWGwC8B\na6r6DLuUcT/J+1D6IKqZ9nIhuz0j9N8wZ4Ru+O/lRU5VTdWYTW4BHhCR9wXvH6icIvJrQFtVX2Cb\nWLiDltHig6p6H/Aoxkz2i/6bOZExBj4A/J2qfgAT6jmztM+JnNjgwV8H/jV876BlFJFl4CMYC8Ap\noCUin/A/sxMZ95O8dxhCnwtcEZGbAUTkJOaspgOFd0boZzU7IzR3cjqo6jrwLPCr5EvOnwc+IiKv\nYLSwXxaRz+ZMRlT1x7a+irHR3k/OZMSM3wuq+pz9+4sYMr+cMznBTILfsO0J+WrLh4FXVPWaqk6A\nJzBm5l21436S9w5D6HOBJ4FP2tefxNiYDwwi254Rmjc5b3I74iJSx9jtvk+O5FTVP1HV06r6Lswy\n+n9U9bfzJKOINERkwb5uYmy1Z8mRjACqehl4Q0TeYy89DHwXY7PNjZwWHyczmUC+2vI14EERqdux\n/jBmM3137bjPhvlHgR8A5zFnXOZhE+PzGDvTCGOT/x1gBbOh9UPgaWDpgGX8BYx99kXgBVseyaGc\nd2Hy1H4LQzZ/aq/nSk5P3l8CnsybjBhb8ou2fMeNlTzJ6Ml6N/CcfeZPYDYxcyUnJpvXm8CCdy1v\nMj6OUXTOAv+MSX21KxmL8PgCBQoUOIQoIiwLFChQ4BCiIO8CBQoUOIQoyLtAgQIFDiEK8i5QoECB\nQ4iCvAsUKFDgEKIg7wIFChQ4hCjIu0CBAgUOIQryLlCgQIFDiP8DD/RVkrAIhzsAAAAASUVORK5C\nYII=\n",
645 706 
       "text": [
646 
        "<matplotlib.figure.Figure at 0x7fe7fbb4a438>"
707 
        "<matplotlib.figure.Figure at 0x7f1b8e99ee10>"
647 708 
       ]
648 709 
      }
649 710 
     ],
650 
     "prompt_number": 68
711 
     "prompt_number": 17
651 712 
    },
652 713 
    {
653 714 
     "cell_type": "code",
......
658 719 
     "language": "python",
659 720 
     "metadata": {},
660 721 
     "outputs": [],
661 
     "prompt_number": 69
722 
     "prompt_number": 18
662 723 
    },
663 724 
    {
664 725 
     "cell_type": "code",
......
669 730 
     "language": "python",
670 731 
     "metadata": {},
671 732 
     "outputs": [],
672 
     "prompt_number": 70
733 
     "prompt_number": 19
673 734 
    },
674 735 
    {
675 736 
     "cell_type": "code",
......
680 741 
     "language": "python",
681 742 
     "metadata": {},
682 743 
     "outputs": [],
683 
     "prompt_number": 71
744 
     "prompt_number": 20
684 745 
    },
685 746 
    {
686 747 
     "cell_type": "code",
......
691 752 
     "language": "python",
692 753 
     "metadata": {},
693 754 
     "outputs": [],
694 
     "prompt_number": 72
755 
     "prompt_number": 21
695 756 
    },
696 757 
    {
697 758 
     "cell_type": "code",
......
702 763 
     "language": "python",
703 764 
     "metadata": {},
704 765 
     "outputs": [],
705 
     "prompt_number": 73
766 
     "prompt_number": 22
706 767 
    },
707 768 
    {
708 769 
     "cell_type": "code",
......
713 774 
     "language": "python",
714 775 
     "metadata": {},
715 776 
     "outputs": [],
716 
     "prompt_number": 74
777 
     "prompt_number": 23
717 778 
    },
718 779 
    {
719 780 
     "cell_type": "code",
720 781 
     "collapsed": true,
721 782 
     "input": [
722 783 
      "def extract_chroma(audiofile):\n",
723 
      "    data, rate = librosa.load(audiofile)\n",
784 
      "    data, rate = librosa.load(audiofile, sr = None)\n",
724 785 
      "    out = vamp.collect(data, rate,\n",
725 786 
      "                       plugin_key = \"nnls-chroma:nnls-chroma\",\n",
726 787 
      "                       output = \"chroma\",\n",
727 788 
      "                       process_timestamp_method = vamp.vampyhost.SHIFT_DATA)\n",
728 789 
      "    step, chroma = out[\"matrix\"]\n",
790 
      "    print(\"File \" + audiofile +\n",
791 
      "          \": sample rate = \" + str(rate) +\n",
792 
      "          \", chroma step = \" + str(vamp.vampyhost.RealTime.to_frame(step, rate)))\n",
729 793 
      "    out_file = open(os.path.splitext(audiofile)[0] + \"_chroma.csv\", \"w\")\n",
730 794 
      "    csv.writer(out_file).writerows(chroma)\n",
731 795 
      "    out_file.close()"
......
733 797 
     "language": "python",
734 798 
     "metadata": {},
735 799 
     "outputs": [],
736 
     "prompt_number": 75
800 
     "prompt_number": 24
737 801 
    },
738 802 
    {
739 803 
     "cell_type": "code",
740 804 
     "collapsed": false,
741 805 
     "input": [
742 
      "for file in glob.glob(\"data/Music/*.wav\"):\n",
806 
      "for file in glob.glob(\"data/Music/*.flac\"):\n",
743 807 
      "    extract_chroma(file)"
744 808 
     ],
745 809 
     "language": "python",
746 810 
     "metadata": {},
747 
     "outputs": [],
748 
     "prompt_number": 76
811 
     "outputs": [
812 
      {
813 
       "output_type": "stream",
814 
       "stream": "stdout",
815 
       "text": [
816 
        "File data/Music/07 - King Henry.flac: sample rate = 44100, chroma step = 2048\n"
817 
       ]
818 
      }
819 
     ],
820 
     "prompt_number": 25
749 821 
    },
750 822 
    {
751 823 
     "cell_type": "code",

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