DHOxSS - Using Computers to Analyse Recordings

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   "cells": [

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "# Using Vamp plugins from Python\n",

      "This notebook illustrates processing an audio file using a Vamp plugin, showing the results in a simple plot, and saving to a .csv file. This could be useful when integrating with analysis software written in Python, or for a batch process. (Note that it is also possible to run Vamp plugins in batch using the [Sonic Annotator](http://vamp-plugins.org/sonic-annotator) command-line program.)"

     ]

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "### Setup\n",

      "\n",

      "First we import some necessary modules.\n",

      "\n",

      "The `vamp` module loads and runs Vamp plugins. `librosa` is an audio analysis module from LabROSA at Columbia University. We are using it here only to load audio files, though it can also carry out some analysis functions. `matplotlib` is the usual plotting library."

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "import vamp\n",

      "import librosa\n",

      "import matplotlib.pyplot as plt\n",

      "%matplotlib inline"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [],

     "prompt_number": 1

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "### Getting started with the Vamp module\n",

      "\n",

      "List the plugins that are installed. The strings that are returned here are referred to in the module documentation as _plugin keys_ -- each one consists of the name of the library file that contains the plugin, then a colon, then the identifier for the plugin itself. So a set of plugins with the same text before the colon (such as `qm-vamp-plugins:`) were all distributed in the same plugin library file.\n",

      "\n",

      "You can find plugin descriptions and downloads at http://vamp-plugins.org/download.html."

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "vamp.list_plugins()"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 2,

       "text": [

        "['bbc-vamp-plugins:bbc-energy',\n",

        " 'bbc-vamp-plugins:bbc-intensity',\n",

        " 'bbc-vamp-plugins:bbc-peaks',\n",

        " 'bbc-vamp-plugins:bbc-rhythm',\n",

        " 'bbc-vamp-plugins:bbc-spectral-contrast',\n",

        " 'bbc-vamp-plugins:bbc-spectral-flux',\n",

        " 'bbc-vamp-plugins:bbc-speechmusic-segmenter',\n",

        " 'cepstral-pitchtracker:cepstral-pitchtracker',\n",

        " 'chp:constrainedharmonicpeak',\n",

        " 'cqvamp:cqchromavamp',\n",

        " 'cqvamp:cqvamp',\n",

        " 'cqvamp:cqvampmidi',\n",

        " 'match-vamp-plugin:match',\n",

        " 'nnls-chroma:chordino',\n",

        " 'nnls-chroma:nnls-chroma',\n",

        " 'nnls-chroma:tuning',\n",

        " 'pyin:localcandidatepyin',\n",

        " 'pyin:pyin',\n",

        " 'pyin:yin',\n",

        " 'pyin:yinfc',\n",

        " 'qm-vamp-plugins:qm-adaptivespectrogram',\n",

        " 'qm-vamp-plugins:qm-barbeattracker',\n",

        " 'qm-vamp-plugins:qm-chromagram',\n",

        " 'qm-vamp-plugins:qm-constantq',\n",

        " 'qm-vamp-plugins:qm-dwt',\n",

        " 'qm-vamp-plugins:qm-keydetector',\n",

        " 'qm-vamp-plugins:qm-mfcc',\n",

        " 'qm-vamp-plugins:qm-onsetdetector',\n",

        " 'qm-vamp-plugins:qm-segmenter',\n",

        " 'qm-vamp-plugins:qm-similarity',\n",

        " 'qm-vamp-plugins:qm-tempotracker',\n",

        " 'qm-vamp-plugins:qm-tonalchange',\n",

        " 'qm-vamp-plugins:qm-transcription',\n",

        " 'segmentino:segmentino',\n",

        " 'silvet:silvet',\n",

        " 'simple-cepstrum:simple-cepstrum',\n",

        " 'tempogram:tempogram',\n",

        " 'vamp-aubio:aubionotes',\n",

        " 'vamp-aubio:aubioonset',\n",

        " 'vamp-aubio:aubiopitch',\n",

        " 'vamp-aubio:aubiosilence',\n",

        " 'vamp-aubio:aubiotempo',\n",

        " 'vamp-example-plugins:amplitudefollower',\n",

        " 'vamp-example-plugins:fixedtempo',\n",

        " 'vamp-example-plugins:percussiononsets',\n",

        " 'vamp-example-plugins:powerspectrum',\n",

        " 'vamp-example-plugins:spectralcentroid',\n",

        " 'vamp-example-plugins:zerocrossing',\n",

        " 'vamp-libxtract:amdf',\n",

        " 'vamp-libxtract:asdf',\n",

        " 'vamp-libxtract:autocorrelation',\n",

        " 'vamp-libxtract:average_deviation',\n",

        " 'vamp-libxtract:bark_coefficients',\n",

        " 'vamp-libxtract:crest',\n",

        " 'vamp-libxtract:dct',\n",

        " 'vamp-libxtract:f0',\n",

        " 'vamp-libxtract:failsafe_f0',\n",

        " 'vamp-libxtract:flatness',\n",

        " 'vamp-libxtract:harmonic_spectrum',\n",

        " 'vamp-libxtract:highest_value',\n",

        " 'vamp-libxtract:irregularity_j',\n",

        " 'vamp-libxtract:irregularity_k',\n",

        " 'vamp-libxtract:kurtosis',\n",

        " 'vamp-libxtract:loudness',\n",

        " 'vamp-libxtract:lowest_value',\n",

        " 'vamp-libxtract:mean',\n",

        " 'vamp-libxtract:mfcc',\n",

        " 'vamp-libxtract:noisiness',\n",

        " 'vamp-libxtract:nonzero_count',\n",

        " 'vamp-libxtract:odd_even_ratio',\n",

        " 'vamp-libxtract:peak_spectrum',\n",

        " 'vamp-libxtract:rms_amplitude',\n",

        " 'vamp-libxtract:rolloff',\n",

        " 'vamp-libxtract:sharpness',\n",

        " 'vamp-libxtract:skewness',\n",

        " 'vamp-libxtract:smoothness',\n",

        " 'vamp-libxtract:spectral_centroid',\n",

        " 'vamp-libxtract:spectral_inharmonicity',\n",

        " 'vamp-libxtract:spectral_kurtosis',\n",

        " 'vamp-libxtract:spectral_skewness',\n",

        " 'vamp-libxtract:spectral_slope',\n",

        " 'vamp-libxtract:spectral_standard_deviation',\n",

        " 'vamp-libxtract:spectral_variance',\n",

        " 'vamp-libxtract:spectrum',\n",

        " 'vamp-libxtract:spread',\n",

        " 'vamp-libxtract:standard_deviation',\n",

        " 'vamp-libxtract:sum',\n",

        " 'vamp-libxtract:tonality',\n",

        " 'vamp-libxtract:tristimulus_1',\n",

        " 'vamp-libxtract:tristimulus_2',\n",

        " 'vamp-libxtract:tristimulus_3',\n",

        " 'vamp-libxtract:variance',\n",

        " 'vamp-libxtract:zcr',\n",

        " 'vamp-rubberband:rubberband',\n",

        " 'vamp-test-plugin:vamp-test-plugin',\n",

        " 'vamp-test-plugin:vamp-test-plugin-freq']"

       ]

      }

     ],

     "prompt_number": 2

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "We'll be using the `librosa` module's `load` function to load the audio file. IPython will show documentation about a function if you just give its name with a \"?\" at the end."

     ]

    },

    {

     "cell_type": "code",

     "collapsed": true,

     "input": [

      "librosa.load?"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [],

     "prompt_number": 3

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "And the main high-level Vamp module function to apply a plugin is called `collect`, because it runs a plugin and collects the results into a suitable structure rather than returning them individually."

     ]

    },

    {

     "cell_type": "code",

     "collapsed": true,

     "input": [

      "vamp.collect?"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [],

     "prompt_number": 4

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "### Extracting some chroma features\n",

      "\n",

      "Load our simplest test audio file and assign the sample data to `data` and the sampling rate to `rate`."

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "data, rate = librosa.load(\"/home/dm/data/piano-scale.wav\")"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [],

     "prompt_number": 5

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "`librosa` defaults to mixing audio down to mono and resampling it to 22050 Hz. (The original file here was 44.1kHz.)"

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "rate"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 6,

       "text": [

        "22050"

       ]

      }

     ],

     "prompt_number": 6

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "Run the `nnls-chroma` Chromagram plugin with some predefined parameters that make the output better suited to this type of sung performance."

     ]

    },

    {

     "cell_type": "code",

     "collapsed": true,

     "input": [

      "plugin_params = { \"tuningmode\": 1, \"s\": 0.9, \"chromanormalize\": 3 }\n",

      "out = vamp.collect(data, rate,\n",

      "                   \"nnls-chroma:nnls-chroma\",\n",

      "                   output = \"chroma\",\n",

      "                   parameters = plugin_params,\n",

      "                   process_timestamp_method = vamp.vampyhost.SHIFT_DATA)"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [],

     "prompt_number": 7

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "Because the output is grid-like, it comes back in a dictionary element labelled `matrix`. (Other types of features might appear in elements keyed `list` or `vector`.)"

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "out"

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     "language": "python",

     "metadata": {},

     "outputs": [

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        "            0.00000000e+00,   3.46249305e-02,   5.25626317e-02]], dtype=float32))}"

       ]

      }

     ],

     "prompt_number": 8

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "### Plotting the results\n",

      "\n",

      "The `matrix` dictionary element contains a tuple of step time (the time in seconds from one chroma feature to the next), and the chroma features themselves as a 2d NumPy array."

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "step, chroma = out[\"matrix\"]"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [],

     "prompt_number": 9

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "step"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 10,

       "text": [

        " 0.092879819"

       ]

      }

     ],

     "prompt_number": 10

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "As an aside, we may find it useful later to have the step time in samples rather than seconds. Multiplying by the sample rate gets this, with some rounding error, but the module also contains a dedicated function to get the result exactly."

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "float(step) * rate"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 11,

       "text": [

        "2048.00000895"

       ]

      }

     ],

     "prompt_number": 11

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "vamp.vampyhost.RealTime.to_frame(step, rate)"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 12,

       "text": [

        "2048"

       ]

      }

     ],

     "prompt_number": 12

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "Show the chroma plot."

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "plt.imshow(chroma.transpose(), origin=\"lower\")"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 13,

       "text": [

        "<matplotlib.image.AxesImage at 0x7f2d89c3af90>"

       ]

      },

      {

       "metadata": {},

       "output_type": "display_data",

       "png": 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TUY9QlzVMa5SuQRuMiVyxkQsMWn8v1qcXqxhUAip1NbrTNTOa8Ss8noB37d/vSnei7k7C\nUedvAe+Q0hMDQwJBYQysiyGG6yyCrA+RSd3QBLMLfBbAa8kbByyVUn8D+Cu4Jv3HwF+21ubto96R\no2k3VDjyugAjEagNDSXS5cS7Eg6SEF1CCmVDk3MrJKkMKB0cv8vS3jWgQ04sIU4sg3HGcJIxHG/o\n93OitSFaG/S6JtoYqtobGLUru4ZY2DqRikhVSqITUp1gezHZICYbRmwGMUXSp8xjqjyhLhKqPG5j\nZYWzWIzCpc913W9wq3y69xdaaF0vZJcIzzinWXhwEyCGUXdLYwlJn0tkNOSjN2AXYF+WK72LG36Z\n7LKiuz3yRyHdNu+65qHsMkDalnZvZBhMavpjS39iiXOFWoJeKtRCQWkxGCxSiw8m53UUg/KAozBE\nKiFWsSs6IRvHbIYx2TAhGySUWZ9q1RRb4TJ8bA22Yptqtz2/GAl+XFkDdeQs620bhMcLTRW2VeKs\nkpaERpbEWWIcuIbtLXU3oNzt367OdCdGwSSx7uVaZXGeGIW7jD+5v3BiTbiepSa0mmRMvX5g+43A\nWyl1H/hrwNettblS6u8B/zIu7zuQ++zmCLuWQOhSCw8EjYvR7YSW/8RuDroLxMWOIq6snFOzuwN2\nUSOyEMTNwnFiOTi55Nb9Nbfurzg8mJGelduSnJfkQtcWkNfXg4tdG6mnI8ZJxDiNGfdizKTP5a09\nrk4mXN3aYzbosZ6P2MyGbOZDqsXAYahkQ64B42cMqVtWggTmiuBKQtevCzS7AEf57y9pqIKQ4xPl\nD/tG+jxs19BdlUEi15XjdEJsA7Eww2vYxQm/rgiYdHviq0oICl0AD8cC7KY3bOdYxWBScXS/4Oh+\nydH9gt6iJHpcEj0tifIKW9bUWGoMFRYb0BpybuXHisKglaEfR/TTiH7i6qtb+07Pbu1xeWuP9VXC\n5nTI+nRCfTqm3lioC18kayMcM2IMVXglpJ1CKSAmoC0TctAW1uf3k3pr3ODS+0QnxHLVOKpO0pFD\n+qKTcnoNYF8VPwjjImLJh7q9K2DZ7b+u5xvinLTPyv8tE87ryVdJFYyBoVKqxvkQT64fIuDd5QJD\nYKxxA1+KWMhh8KEL3iK7ebzrfCW0G1hS2wS8Y9qKZzrn3CWSGjYB9okTy/7JmnsfWj74+or7dy8Y\nfJox+CxjoHN6Wc5647vKwLp06wBk/g1j33JHI604TjVHA83xUFMdT3jy4A5P39c8eTgh3uuTvJjA\niwPKFwdwPnEZj9u0awuVV/it4ovVEC4yCgdSeL838bx0jilpLKE1bQpk2OmnXZN1dwB0ecTusiMJ\naHZd3l068joi1ya90DUO3lRCvjrU4RDIu+DdNUTak9RgUnN0P+P+19e8+401o/M1SW9FUqyJz1bY\nVUWJ9QXqwOp2PSjctPEREcs40kxSxWSgGQ80T+7c4ckHd3j6MCb5YI+rJyn6B0Nqu0e2OITaQrkB\n1rgVrd0FanJPEoWpaGeJDXE6GFI4XR3wAG97NLqyASv4IJZ6RENrhFyywFJId3Tb9qbsIekjoWQy\nnIVcBJ+JwXcTPRUaJaH3FOq3tI20gbAMrydvBN7W2idKqf8U+ALXkv+TtfZ/vn7kPa7PZt0bLnE5\naEJtyGDOaBq268rAy633sGOkQBuAQjAILW85Z7iiapdo3Mw+AY6IU8vByTn3PzR88t0VH31wzniy\nZhKtGW9WDC83zJVbAzAvmwSysIT2iwX2NdxNFHcGirtjKI8P+eGDiOE3xvBNS3XUh0cTii+OWU9u\nQe+wAe6tA7N2Cm/WoDaeOpHBJtNGGMjpWtyvQ0dIn61pLKE9/xshjygKLZPnriioUFrdQdDlebvX\ntCtw9zLpgqa0vliBfxS0SRhUDK21ECC6etzNzGm3Q39ScfRuzrvfWPLxL8zYfzKll09Jz6f0fjTF\nkLcScQTiGvAmAHGIFRzGcNhTHA7hcKz44d2SvY8Skm/uUX4L+GGCsUOyxT766bFL67NLB9wSWNzJ\n+4qBIDSKWKIDGqtW2j7k+krXVjZ8T7w7KdDmomMad1P77w1xunjg6120STcAHIKtAHdoYHblZd55\nyADA7sl6l3H7evKmtMkhbpn8Q9wd/vdKqX/VWvt3WwemE7bga28Cb29h29zP4DlNBM563ivkScPG\nCmfSXeDtOkVpjR5ERANNNIiIeoq0yEiLnLTI6BU5pjaY2rpiLMaK49l2QJum37R4xEMM7+hzbseX\n3E2m3B0sSPcrkncsybspNo+opxHlLCKfRqz7MVkBmwqyErIKamMw1NuSYllby8rAsoaq6rMuErJN\nRLGEKjXUqxqzKbGZ52Rahox1bRq2bWQgjiDqQ2yAnqNUrKdWrPUAr1xtXwLeW6rYuO8Z4/lPsTAk\n26KrkDdt4BXuEbNrAhHg7qqt5Tqn/CrpDqhdawW+pMQRpInb6CtNSJVlYGr6pmBg18Q11FWEqSLq\nOsLUyuuV9Sx1qOO7sxdus+CumnNPzXlXzeknG8zQUO/1yI8PKcuKooS8tOSlpa4dTaI8TYK3vCUv\nO8LSs5AaRWIgrmFVJGwyTbGGcmGoVhV1nmPLDGs2nuve4GIQEkPqekvhPQjFeNNkvStVUybPcCII\nN4ETii7krqHBiZDyyGlv0RF62bvAU0oYJxOPVX5jV3zidb218Lgwpvc6Rkcjb0qb/DLwqbX2AkAp\n9feBPwO0wVv9h2wbNv4liP+55u9tMCPC7UOw59shodm9zoPKNcUIA2ndDunyTgoVW9IjS++Oon9b\n0T9WHEwzDqZTDqZXHEynVJmhzKHMLWVmKTGU1L4Yamxr2rD0UFwCpyj22DeWu5un3Jo+4+DFBaNJ\nRlb2WY/H5A/6bEY9ri4GXF30t3W+UBRLtqW2BZYMYzMsOUNTc1FYnmwsR1iqiyHPPj/h1Ix4toSL\n8YbFxYzNGVTnJVwsnEm/XURjocrB5A2IJ8BQw2Ds9gqxFirjVrdVxo875ZtSueI60wM517G8qtlG\nYysZrALYOW2FVLTpEmgmWnEbxLIOB1eXegj7m6CWwfs6g6AbF/myg7AjvRT2xrA/hr0RgyjjdnnB\n7WrGneqCQb4hX2sKKbWixkreB/WNuizXZHk/23D/asWdZ2tufbqmXKRc5PtcDsdc3B+zSiKqhaGc\nG8qFwdSurZUHSeWDlALkkTWMKsW4gPFaMTLw7PQWz9JDTouUF1cls2crVp9G5KcGu9q4vNRqg8sS\nyWgvxOpeu3hfQplE/v0QFEMLO/SIVXCO0DMM415iIERcT0qABvQlH7yNDbvTQeX1Grf5xJJmGW9X\nN95ksg8nZAX8CJd1/eXO86bg/TnwC0qpAa6lfhn4nWtHHf27/l4VLttBPpCL9AO8Hvj+9kn6te9E\nGwbWQnY4pDNu4puaTlKxIj2G4Qcw/lgxeQD3n2XcfXrFvadPuR8/I1tYsqV1G+fllsxaMowvjkNs\nO7MJij7K53zu15a7mym3pjMOn08ZjjJWdsR0vM/56ITzd4+5fL7H5YsJF8/3uJzsUZ5DdeExbwPG\nbMAusWqBtUvSumRUGoZYRrXFEDOzA6bLAbPnsOhv2CwhW5RUyxUs+20eprBgKl/84EhHMJo4gDmY\nuH7JrVskkdsgDVZ5w0YF+NEBb41r46IGVfn+6rrA0l+hkstgNMFnIXUlurHLlZWBJd5WSDd0rbCX\nSffcXZrmDaSXuja9fQy3jxjEM+4UMz7Ol3xSPGZvecl6qlmjWBeKTa4osN7+tD4zOXTBu5OJ5b28\n4N1pwd1nBbf2cy7rW2TFPqfDB/zw3vvMekPqs5ra1tRZhckL1BbQNihPpijfXhpDr4ZerugZRa+E\n2emIWTliepUye1yxnq7IzmuKFxlmPXdGQe3z77d8901jENoek/RduJGDWO67lsmGuiKUVthnomPQ\nVn4Jxm9oNqLb1aY3UYNCL0r0P/QG6Bz3KloxlO4EAm5vpQ+D7+9goHfIm3Lev6OU+g3g93Ct93vA\nf3ntwIEPHFkPADa8YV9bBXUfKg/cugLlB74VBREuSzo+nJ271sp1BVKxJTmC0UPY/47i6Bvw3g8z\nPppc8VH8lI+KH7PWlpWxrApYKdnCyvoCuQfvZv7XKCIUMYqIiYG764Jb05yDFwXDkcYcxswO9nl8\neJ/PJu9z8fSI88NjLsbHnA+OMYlyTMMazBSoFliuwF4BV2iTE5eGuDYkhcFmhmJpKZ4bip6liDbU\nZUFdrqjLGKqos3jF006yTzIWEg2jPTicwDu3waTeWLFtnQ9jmNsJOOi+LT1nXX+ZErd5T0mz7aDs\nJxGCd5c2C8E75LPDwFAU/LDojvClIbUg8joAHLqrck1fUXqJA++7J/D+PQZpwu3N53yyWfLzm8ec\nTJ+6NYGFYr6EJYoM2yIDdlv/zet7meH+tObOqeFWWrPuHZLZfZ4NP+R7g+9yNjjE2spRaVeSJSF8\nbZOWpkRRbE1UKSKjiEpFpBVFCcUVFD1F3q+oixX1JqPK5tgs8nplAprsJlCUOlwJHdIau0rXehcK\nLRzTXctbMEAW3okib9gNqmHbdr3C8PUuOjb01Lq0y+t6e9c9qjfRv6+SbfKrwLeAbwL/DPCngP+j\ndUS0uT4Jt8aVaj7TOCvQ9EB106pKrge+QiW4Cbhdgyil0EmNHmjiiSE9hMFeyWiUs9ffcJgsSWPr\nqGB1nfkUxy/sxjYcKXo1qA1UV7A5VcySHudmxGlvn0eHx/x4cJvLwQmXg1tcDk64GJxge7rxJAHH\ndwS76tnMURkh77cM858FYW+SHSCmJhDVkChIe2B7zsqugVJdHz9hc8vpWok8Hrx16SfdBDdwxD2u\ngi92ucnQdQyDyiGwhuAdKlGYXnadG341gEc7jpMb7l5PV244t44hTqDXg+GAKO3RQzOkZt9uOOwt\n3XqTqLFjuusmXwUASQ56BtVz63IgJj0uB3s8H7zD48EDng9OIC0hKp03tLU8ZW8DWYIdrDSUvdDl\narJQx8LVizcAlPL/KXABTNyxSvrbj12LO4+ViTeYuLdrEGS02U4z7wrkiR6IlxdOg7v2LrlJQiYg\naAftwSCKXNHK3VOr6Kag/C27C1dKdhXyf2NR1GhqT1/Jr7exa/WS3aJC+Srg/Z8B/9Ba+y8qpWJc\nyLct6y9c3Z1kuu5Jrbznq5ybXtWe7xbqJKOJJHefWhPOirs4LYupDMWlZf2ZIRpUsIDBI4V5PGT1\n+ISzi5x8btmsYVO65w6EDphs49Fl3JW/B4WiXyvmK8XZpeJJohhUPR7Pb/P4bI9HjxMuDksW52vW\nFzPKc+CihBfKbSMuqbB2RTPIZMel0I3uUg03Raa71k8gZemWl19dArED71z5or0Hq9r0CTTeEzjl\n1NYrqXGUS+n7DAPeV2mCQrvojJdc4433FPZtSM+Unc9fx/IOOVm5PqnDwNHLrLaOVZIbmGXwbAYq\nZR1POc1rfpCPSLK7HCwjllewWilWlev23LPRTqO7rvh1uSwtZyvL0dRypCxPFnf4UW/MeQpFb+W2\nyj2rfYRb6AmZQDXt3REldpB2iljpYn3KKlhZIq7bl9dltTRtgCMC08fFtvpO52QBzvaBCOIhSt1t\n8q6HLXRJSKeG2UpdPNgl3fYOV/oOXQB6SFN6uPScRLk6Vs7aC4qKDFoblDboyBBRE1ERUxNRE1OQ\nUBBjSHz8Qa5RcvG/9/dvuNyOvGm2yT7wi9bavwRgra1wZmNbNjvAG2jbth6wjWa7GtBYH7CUhpcO\n2gXcYeCr2+NutrWVorw0rD/V2EJTPgN7qVhdjDi/OOGLS025sRQbl7Bx03Ke9hwuNrcD0bRWnK80\nk0vNpNaky4TLs0Mux/tcTGIuRxWbxYbNQlEuCliscLmDNCt7raxUDJMHu6AVBnNuskbErdshZel2\n5yNyXDUplNqVKoLK90GNA24LTcaJiAFlGvCubEPTAO0MBLnOcKC84hp3Sjh4w/xv4U3lmLB+mYgL\nH4K3XFdIp8g1h647XO8X62IG0wzUDDLLJlrwvDLE5YhVeZdRNiRfQrZW5JXym5IqJKRY38i/NjIs\nLeOVYYxhVFiu0js8SSacx4oiXrpJeGZh4ftl20Zd8Jb0PkuDTpKXL4FjSdkd4FI/JQU0gI3QUWpl\naqoAxDUXvTd0AAAgAElEQVTUiY9ppVD77Xwlq0wAXFb7yvshu9AKWoqVLSZVuHZh15K3l0monz1/\nf35pf5K6bMRDXyZAT3nHWPkYrNquO1IJqLhGxxVRXBPFFQklKQUpBQklfTR9LD0q+lg0ZgvaYqH/\nRMEbx7CfKaV+DfgO8I+Av26tXbeOEsv7mgSBInGXZPn2tqbxLbdpP6GL1CUxutJ0nq0sxYXCFIri\nXLEewXqjOd8MGWw0/c2oecBG5TBoV25L266TTncPe9JGk64jelVEutRE5zGbtMcm7ZOlCZukpCrW\nVEVJVaywRdI2HGrY8vzX0qVscE+N3X8zeL8EHMXyLmr3hBCbeLc5AhP7SRQfqPQAYrtgUuO26vTA\nJ5OvkWsKXPItSEIbjEJAfJV0J6+Qiwypoy8zGYilHWbHyCAWfja8zhC8u8AdgvfGxQ+uMjYq59QY\nVmbEM3OHpDqkLhRVoagrFYQNlR/C8js35/wmZU2yMqRFTbI0ZNE+Cz1moaGIVo5my3FPGirlHoVi\nEqoozNaB5lFwE18UjWW7xAH6Pm7fmhOHUluahAa8xXCXuU9oB6WgEuNAQx2xfejq1gHyg07Z5tJ0\n8Pk211sGDTSLdTKanQS78RS4WS/CvtU4VN7DPczyNsR9B97HwB3cTrUjYKiadT8967f1sdCz6LQk\nSivitCROSvpkrTLCMqJkhGaEWyQlwK0CdHkdeVPwjoE/CfxVa+3vKqV+FfcotF9pHZXtWHR5TTmv\nF6U1SmlU5F9bg7I1GF9btwBYAi52C1YyADouVm2xU6imTZx7qQHdw6o+6AO22a9aYVLVHpstHfBK\n1x1otYZ1BOut5tIe5KHFcJPscv13ue9hG+76fvd1IGUNpdBPAlThfsMdr6hlmcjvdQNMAT+tosDN\nDdup6xV17xNuBsiXAbfUr5KOFauUpyktSjurVNsIZSPvVASbnFq5f4O9dk2idxZVlFBUMF+jUNTK\nMtU1M9UDfYJVFmsjDBFWa2wSeJvbnPpwLOygmcralW07yHa60KS0dc8RtrMAuCCjdt9XspjlkG2W\nhpWd+wY48BYU6wdzuQdxyeSU1e+R9Srhf1e6Seb2LsaKQynjK1QZhTdqMra6pgqna8q6eMu1TfBu\nohQDUdLefrzaAe5JPCdg70I6dCB9iHte2Du057gxMDAwtDCwMDCofoHuFUS9grhXkrKmz5qBShgS\nMbGGPUr2bMGEiNgGwG3Nlop9HXlT8H4MPLbW/q7/+zfY+RzL3w5eP/Slq/xce2+wZxkeNKWXFaTL\nfFtsUVFXhqoy1FXt05Mlfq68bSrLHpxN01eNp9OLoNqPXTlw9YoRK9sUs4mwKwUr5erCuGyKsnK1\nCTUwTFML+cVdM8CXEcl9liKurAzcm4Iw3d8KJ4Dw+1LLZ11rXv4OH2rVXczigzU6otm+0y/HN4Uf\ndGGSZThiQ+tPdX5Td37LdM4jtEzY1uG9d72F9jl1TxMfapIDRXyg6SWG8SJjtFgwXmYMVgVlragq\nRVkrR+lvTQaXk223YO42gHLPbJQC0QCiA0t0YNEHlrLXY14dMK/2mVcHbPKhTwRRzR7p13SmO1HW\nwefikcq+GzXXc+RfRvd4KzzqQTx0gewIZ4hUsac4+rQ2mRKdkQlN4SadkMWypglgq9J5aZW3vmvl\nY1s+U2Vb01jfYpW3ulTu05c0g3ENkyGM34H+4W67Jayv2SSqXfJDWO27x+uttQNuyTCUnTqCJiAH\nByx461th4og6SZxzkmgybUFrah1T6R5V2SMvB6zKCfPigKgqef67f8iL3/teQxm9prxpquCpUuqR\nUuoTa+33cXnef3D9yF+66Qy0qYDQWlYM9iuO3qs4flBz8n7FeLZi9HzF+PmK0fMldlmRZ4Yit+S1\nCUIWaps9avzQMs6eZk+xLeNYkR31yN/rk73XI3/Q58wmnJt9zsweublFeZXAucaeRXCmYVlCloPK\nwGRgQs4jdE3l3kJO9XXdoa6VLVvkyhLgiOuB2tc5ZziYZQBYGi40BLnuzB+ujJXAVWenNK1diZSr\nzQrqlTt3HebwynXvygwKf79r7Qt4h6DfTd8iOE8I4CEV0ngYuq9Jb2n67yv6DxSTYcHt0wXvnE65\nfXrBATM2BWwKxaZQZHWTiy217N0nqyQTZKNdVycDRXJHkz5QJO8r1uM9nmQDnuYJZXbEZnEML6x7\nEmwNrMVDC/u3+2C7cKyE8Q9pk267idyAbCpywJ0UkNb+aWKRezBCmfoAo+yhD+LtumdDer0InaIS\nB8jbx+L5wHvtqblae2quotmVsGLLeYd1a9iE4J1DYty6/jsDuDOB/aitHruct66TH9ojMTAfw/k+\nnA/gQjfOaAje0sxyez0gVW7/rFRjdYSJoNIKqyOINSaOKeOUPC7JNgNW6zG9dU5vnaPzClXf5/Dr\nf855bbUF/iGvI18l2+SvAX9XKZXilgj95S/39W4GQlMP9nKO3yu4/62c976dc/R8zuGnUw7TKQfl\nDEPBRsHaWNaFZVOHT8VS5FhvVzpLKVKWW8AtBScajmLF6mjM6uGI1Tdd+cLuE9UxWb3HpblD/ayP\n+TyCfoQ1EegC9ArM0u0tjRRR1NCL6BJ2Xwa8u8Al+wkf0qThhRtK7TpHWHcHs5gP3UUHcu0hEELj\nCwsAhg9OGILqO/dTIu6xclYb1lnfCjdIWxthvWxC64J4eD0hAIXn2AVSHVprO/E4eNW9mPQdxeBj\nxfhbipN9ePDDkg8GUz6oH3N785zFxuddVIoFAh12CyFiHIjt7Qwwtc1X6A8i+rcj+p9E9L4dMT96\nh3R1i3KVcrk6got7jiutcEFGDO3AvKwcDD2P8P5DT0ba92UTcbe9rWuXaA/SHAa1697It5dJcW+E\n4F251zZ2/W51cwliS9TGe15rsHPc3jqx+46NHYgjC3zEO/MWuGSbXLvcruWdwNEhvDeBnzmAW6Pd\nlHeoJt2lA4mF1JfEwlkPHg2gP3AHVLTBW/Zek215UlzAMrE++wQsEX6/RgwxdRpTJilRaoiSmmhZ\nEc8q4llNPK9Qq8qnuFWwrlzbvaa8MXhba39fKfULwP8FJNba69kmN3+7U4s4ZRvs5xy9t+Hdb675\n6E9vuPPognd657xTnnN7ek5dFCwMLArnccqSECkSh5YSY7mv4F0N9zXcjjWzo33m7+8x//Y+sz9d\nEdmSvEq4qveIqzuUn45Q/RhrY9QqxlYZ1DPIZ6DFjRTglsEVAvfuMOfLRcBGzARZVjzBgXeP9rLi\nXQ+7uOYb7iglTX5il9QP3ezQhQ8BcIwj//aAEdvMArFk8INXeeubELxlo5/XkZeBT/c+u8d06Rfx\nFtyKWN1PSN6B4UeKyZ+C4+OSB/2Cr9dXfGv2hAcXn3Jl4apSXEVus0aZrte4hVvdDKQB4QOeYTSI\nGd1JGH6SMvz5hPM7mmKWczVP+Hx2BM/uO0trbuCZtNM6+CVJWRSLPOzvkEIRi/wNRCUQrR1492uf\n8KtdRkiZusnZhsvaxcT2wI1taBNpfmWhzp33ZWbO4LHhntYx1ycp34q2q5MiZfv4dOQWmr03hJ99\nB949vs4udYE8tLJjoGdc6RsXeHysoO/3YVpr1wUheMtw7+4KG6ltgNYYoNYo43G4B/St2922B2pq\n3fNELlzNvHRe/bKEZQHlHwN4e/nrwD/BocsO0UEdDrRdPk0DIMVKsTjTnH8eMzpKKE4HLB9NmJ1V\nXCzAZCWrwk1U8uzGZmA18CCs7tZmsM4zvawVy8WY5Ysxy8/GLPdHnJqIK1OzrtfU9RX28QYex3AW\nYecxbHLIF1AtnTJuV3GFNIBISAV9GfCmc7wQa2vctB8uJw4575cRe90i1rpszSkmiWh29xoEsMMM\njJBr1Y2rW1s/cJeOOrHSRmGusaKxJneBrwqOCYNuXToh5MRfdr7wvA0tYQtDNVPkTxWbHyjm54az\nzxK+eD6iNztgmb3DvIB55XeCpElIk9LGBrXNEpaku0ERMbiK6T9JGPwg5upyj9NlwmxZUSzncHbm\naJO5dTGVVtpbWLpPXpGNnuTBFd377MYLfHvIApqWcxeBHjkALleQnUGxwe2Jo8D2aZaoyb7TIQUV\nsd36wviTqwLqaQPcZk2zu6DoUTcZd5epHEpH96vKAd5ZBo82bovaWjKmPEXTHXoh25firW7ji4Uz\nBVMFK+3ZycrRfnnpPO+o9rds27cSqqDvxu0c5MNVVpy+Oe43pr5e1m5/6HXl6up1ceIrgLdS6l3g\nzwP/AfBv7j5KNCRcKRe6gLt8HNjMNZePEqLUUm40L64sk6eaydMek8sxZlGTZ243vtxef/Z3N0QW\nWeeVvjB+c8hKsbnsk302IFN9NosBz23KqSlZmDm1OcVepNjnGnsaOf5rXsImc0pimr0i2vuCi4Rg\n+vqd0XxHQDncm0Hjen+Xb9j1ZF7mNov1ltLMudI33ewEAXYBCvE25Lqsc3kl8FRL8OkKzBzs0gN4\n2EYhjXMT8IqlLKNM0SyXEtgM98sQEygkMbtZKQTXbjB5RPlCs/mBwhqNHtWkTxLKx3vMz+5wuIpZ\n5248bYz75TBRrYGbJsEr8QC+fezARpM+j0i/H5HWEavJIV9kPc42BVl2ATMFTy2cG7cybBuI7f5S\nWEKPLMxFF5F2CExDFbStUu3ujhSo1HHR+Qrq0i02KqwHEv8ghFZ8J5xcZfWkasCb0k/evmzpnHAy\nDidiib3Abn2WthFzzE92lzk8XrtJ4rksBOr52hsioVqF64zEHokVxBpi6/ageuYBPFOQV/4+lmAW\nblLStsmiCW8nDM2Ec1CYyBUDa+X231gpRxtkxj+ZRRYk/jGAN/C3gX8b5zvfICHfKFcvlEKYCxze\ntWUzi7h4bCk2ivnzmP46ojfr0Z+O6c0OIDOUJVSlS2vtZoCH8Xix9QbWW0QG+pWivEwoVUK5TCif\nJixImJuKuZ1TWTCrCDvXbiHNQuMiViWUwtHtyseGduPfRA+9TORYARrZUlWm8VBTdmnLLsDeZdFI\nBsuuzI4QvBXtTYVkIIsVmzmlq30gS9cOuM3cD9zwyemW9qAPzZfwvuTZgVI0bg2YWOB58P3uw6nl\nbyEm5VmYoU9dYjJN8UJjTUQ51VS9inKaMpvu83RqGC5HlJWLIRX19a2TmuVTrh9C/2U7hWw08aki\nqjTxpSbvD7ksU67Kgqy8cMvQZxZmxs0QrUBs99fktSCP2PjdlLg4OEaCy5ptWpxWnXnOgs0dzVEt\n3WOeqtSVWtpT4iyhz9HpM4PnwBWomtY2xNfAGbjGb3SlC+LhsR68rwqnY4s5DDTYsf+a90hCplBs\nkK1bJJ+ppl4CV94q3uD2aS79s1PLC+dByMpijau7dkfX2Q7VXeMWw21XMyu/86dxxYb3/Gp50xWW\nfwF4Ya39v5VSv3TzkV2+UcBCEj67XKX7ezPXFFnC/DQmSi1R3UNXY3Rl0KUBY7exjTCrKISo7llb\nE6YFc6mxS4V9qjGpoiKmshUVcyrWUKlmU8NKsd3kSfauvgaa7PjlLyvd70teeE2zJ3GXcOta4V0J\nB0nN9aXQXe2S3w4VqTtZyDTpd3OTjAFT4XYX7D4ZieD84WQe3kc40hKaDBvJshHgls2GQr2SIGpY\nxGOxNBNs4+2ZHIoXMdU0Ivs8Yq0tsyolLvdJyh5RfbRd5Gtte+prw0ozcq+RVBuFOgV1qVCfgdER\npU0oTU5pL5yHUppm8N6YJhEWWQUjC2e6QzilvaY7mKBlD46WPVVDee7LytVmD+yeA0HbpzFSFjhk\n63Ds4SIuKwhm3GR+DaQDAN5J9xHUu8aXLwLeizWcxs6DsDLxD9zh4fY4soFHWLqGjixu2sZFK8hW\nsLmC7BSquQNsuYYu27dtjx3vb4ePwi1ok6wbOZ3dPYe9RN7U8v4zwF9USv15/LIkpdSvW2v/9fCg\nAf8AUZyEj0j5mv87XBnndjIOOzeu3QY8cQVxDibVVGlMPYmoUm8x24TSphQ2wdTR9b3dZT/w7c5n\nokz+bzEmWmKCk7xKBGS8daOittcuuNqdWcICHdw3nQPB9bhwoSXtZ/XFwbHyvW58Qc6xi8LyeeTy\npO5tjrCfEeXCbXgT4XV63j18tqCVHKpwn2cV1CJdUOp6L837WsOgXzDorxn0Z/R6l9RZjzrrU296\n1FkPywBDH8sAu9343+3RYbeLV5pJTNcQbyLiLCJGoyNNPbDUfagPoer1KE3qdMwklCZtZ+wVOGQ3\nQd2dxCtvVW12gXC+4z3b6b+uX64h3nMl2Yd43/VdSyQoKyVtQFtFzsJssU01rNcut7nsuSDlNmhe\n7ehH+b2ufgl44ixw2f9GW4gsqldBr/J1jXsYhEEpt0TcBvdt0T5TUG0NNJT/NWVRykIZYzcRdlNh\n12tnxW7b3jiKIwTuiMYRkyBZqGrgsqSCkuYlg3xJvzxnUD4hri6x1rqCxdp2C2yvkSa8IExV06Xe\nC4q02+ETxbr+nE39xbYHL18HfnhD8LbW/i3gb7mLVH8W+Le6wA3wTe4j07xbOvN9hDaRDVZtYA1Z\nX49iGPdglMKoB8V+j81hn/XBgM1hn0U0YV73mJs95vUexabXDgKsjFsGXhaurrq84Wu2zktFpnKf\nWxD121l08ui9XYsCw72UWkZId9l/dw+XkLuVUGx38Iezx65MkxAUjB9kMS7lz6dIiSVgcROdkskv\ntODDPGtx9yXyIFGH0KIKLLJWjGCHC74lJVxgNI7h6PAFd2494/atJ5wcPmfzIiE7S9icpWR5Qm1T\nalJqehhSLAXWu/n22kPmalIUQ6UZKs0ITdSLyG+lZO8kZLcTNod95lWPeTVkVh1Qlnsu2CQPu5jj\n+BRZ8VhK+9jOfXZpj66n1DXNupNriLSxy7IYj10ZjSHy3O7W2gupJE9J+R3vtj8RqohSMPUZTaXE\nEuSaZKaS/hJLnh36RPA9f5rYu7t9gzquUMcl6rhCH1bEqnIbNamKSDnq1K3IcLBem4jKRNRWU5nI\ng6D1mz5ZWFnMqcU8t5jnbuFe46Gugf51dQsTw8Is222XDXDPE3BlUJXcrhbctufciR4z5AW1gdpY\nahOs61aB9irv3Aho+zkTv4Zty7FLWq18mXe3zfdrP+a15Ktmm4S3f03e53Nk4ya37qxh9W1QQB4B\n5bJmj2I46sPRCI5HsL4zZnZ/wuz+HrP7lrNkxIuqh60OWFe3KaZDeKrgmW8UVUPm+bk63BtQrNg/\nChHwPgKOQY+brREO/Eei+2KphZHV0NgUo2bLEUq9ocmhkQ1QBNQk42KXhCAQDv6EEMCg9toXg+5D\nNAbStkUpFtjWBQ6XIoc3ECbACliJJRlK6HLs8hQUDcPs0gvjyHB8eMYHD075+MPHPLz/hPmPYhZp\nxDyPmJ/FVMSURJS410aySrZLasLUTcNAwaHSHCjFoVKkvYTlrTGLnxmx/HjM7D48Lw6J8hF5ccIi\ne8ctppECLjd3472Nqmw8vG37SpuELqG8L23QzZrp9lcYYetDrw97fTjuw/HAAUHL4O/GmKLGLJQq\nxFyj3LmridvegQHhqglXS66/gHd3gunQHS1Wy6ImFnWnRL/vSnS/JFUFiXJ1qgqc/a23dWliyjql\nqBOMSUCBlp36ohp7WVB/312fnW+wMwn2+p05bdoeY9JGYaJUKBZHFZkjT2kMGNiSOyz5mHM+iR9z\nqJ+6ebqC0lqMDShz/GsdFIXfrMo7SDF+QY/y4RnlDgqaDvUTBm+l1HvAr+NW+1t2PYgBeMhnWzZQ\n/rkZtvvPtZ71/9+N4W4f7o7h7gHM7x1w/tExZx9bzj+OGfQMtkxZFftclHfgxT5MvCuSaxdlYuFS\n1sru9pbXuJI3lBC874I+bMD7xNdCzQhor2iM5TCyup36Qr9OHt1kaQKEMvhDq6dLjYQZHELthMG/\nZj3q1vzQsfMcogkuGZXmoozBBWgrUEKLiBXdtpCv5ey6k9NGl9Ay7XKcKviOWPIpcVRydHDGwwfP\n+M43H/Otj7/gItFc5oqLM8WF0uRWUaD9KluN2WEghI09Bm6juKPgtlL0eylXt464+plDLr9bc/5x\nSrRRFNmI2eYEVu+550d5OtVR7/4Rc5VfeduyrkXXhECVvgo30RJPKUxJCIOusm+J7II0dItT9hI4\nSeB+DKnu3NouykWat8Px4i+56jngTgf+jXOaCWdKA9oDnGWyTdWgnUIadKPgfR8YG9TdEv1RQfSN\nkviTglRn9FVG39fGg7YhwqDJ6x6q6mGqPlXVx2rcTn1RTRTX8GwFXGJnGeaxWNur4Jqi6+oWqmkY\n3tnKsaN+bB/sPgNdcjta8El0xs9Hj7mjH5Fjtw+e8suVmlRvj8WR9iXy4J02RZbRb710yc4Nh/Jr\nypta3iXwN6y1/49Sagz8I6XUb1lr/zA8aMD6ehCHm8My+HqkYBLBfuxWwOp+TjEsyCYV64OaQd+S\nFpq4iFFFD7K+2+mr73mkqIKocLxXK+VMovRh9kP318P6ZSLa6S0i1W9nafW47j2HeaFdvNpK+IUe\n7fwmOl8KWxWup4ntCubJ7OF5G+V7Q8lr07muUPtDi9K7FKqEpIbEohINSURSGZKqJqlKkrrEGscT\nGuse7vzqFpfR78pYlxwPnnPv4AUf3DnjkwcXPH2ekD5JMAcJ2TAlqiCuLZGBqA4XrVvMjqCXLKiZ\nYNkDBrpHnQ4oh0OKvZL1oaHXg2Sj0WniJrcR7QWHET77QNpF09apMBgbmqMB5aSSdgn7XPXdVdog\nypYol5Msc3FaX58bXyVdtd/qpKKJe0hwWAwf2fxK9P4V4C21xmUJpAmMeqi9GnVYo/QGrTK03hDp\nDIVuFV330VUfVQ2g7KO0grhCJTUqrogKTXw8R52UqOM5zKZ+/5SmbNk/y3bRZldU6/UGx6krFJpb\nyQUnyYLjfs5R33AIZBtLvqnJjaGqTbBGx7ZAXLb6djSKap7XEOE+THB9uc2UFfrk9RH8TTnvU+DU\nv14qpf4QuAe0wPszHuwKa9wI2vJ6VcHlBp5F7rGcq6cTptE+V8U+V9MhLxLNWZWzKGdU1TO4XMBj\n7VZIXSqX+L5ZQ7mmyckWMIxxZnHdKd1oYjgKdnGTQmrPgQjqRWMsS3AkDG4VtB/0ERpp258KreLA\nMmaMG6ndSL18MbRYQ8AX8A7/Du0FjcsUWbpUP3GRW4kPAWBbAe3gOnWB2geOE9RRgjqecDC/5Hh+\nyfF8yvH8grqoyUtLUbmnmle2m/vRBfGQ9omY2Ipb1Sl72dRtTDZXrOsDLnrHPD484UfvnlCsLOXG\nUm4M5cZiTIV78qibZLpB8SGWOZZza3iMpZcnzM/2mf9oj3k6YXqR8jQ3XOUrsvwMNjE8A57iaJMF\nsC58ap3fiOva5CnL88QDChd0GX+bsS+Rq1UKOgBz61aEuswP7YySLHM5zmQuW2SrCjawvkV2gEHY\n2DVwbp3e5vLBJe4pIbIoJ1xrUNHMXkKed34jdMYUsNTY0xQ7SqnrBK5SCg1KaYyOqXTq6BKlsEpo\nk4S87lHVKaaOQSlUpKjiCBu5bZcPpjkHwykHH58y2n/hnISZgpnCLpXbsK52MWPZar7bMq15Rk+J\n9BVanxLpz9kfFkwOlkwPDvjHB9/mh/YBxeWG8iqjuNxgisIRPdYQKYu2Fm2a+Upb/ESgwChUpaBW\nLl0wU24Vp1Y0yC6T4P9+vc92yFfmvJVSD4HvAv9n97PPea85Lqhvsrq2qlPBs8xv/V5Bpgas8yGr\nqyGrJ0NmkebKFCzrKVWt3IN3LxVcKKd3K+MGVZHTbIwUcocp7chhN1AY8hkhQIYl9scugAJM7IBZ\nHlK9pB2rEvpTsE8Yh1bAUjReviCLMca+lsETAlGrN2inZXZTC7upecpRIsZvaWev/HvBtWHZZuls\nue0goKoNHAxQ7/VRDweohwMOTjMePHvBw+dXfKi+oFiXrDJYZpZV1X7OseQxtJuiTcyOreFWecUk\nm5KuMuxcs64POO+9z6PDD/n+ux9SXlnqaU1tDVVWY33MwLY26m/aNrU1Z7iHGkywxLlmczZkkwzY\nZENWj3pclTXTcklWnbkc/yscQFz5fs59InglE5u3lLc0gyxskiCi3d4T+EEbOW+F1Nc6cjSW9q9t\n6oE7BRuBriBfwtUM1jPQZQDcXV2FneANjVobYGFdyeUDSfMMwTu0xkMXspvySwPenu2zyxh7uocx\n+9j5HvaJy4AxOqLSKYXuY5VqlcrGVMYVa2KscoSyjQxWR0Q64piCh6MZ7390ysn9x/AEF/tSYAvl\ncvT9ZRRmN0kXalqshsT6OXE8IYkm1KMB+XGP6e0Dnt+5TWkrqt6c2syoljMMa7StUcoEtXPEXDq4\nALdGVW6LawfckYtVJD6KqYIIslL8sYC3p0x+A/cghmX388940D7e113Prvt3WrkdH3slpBuospjy\nKqF8klAOYnKtyUxOZmdUNocibqzajXKrw+raFSPurAygQxpCOrR0u9HFm8BbFFY0dO5qYxq6V7Yd\n7hrzISOyy4BuoabFAYHsKnjsXwvyy6QUTn0yqXS3cA1LQdtiqpx3YgtHgWA6s6m/pla6YADmWqEO\nYtR7e+hv7qG+fcTBj1/wYFTzLT3lO5svyFXBFMtVDVd5sy41XOgfEjON9e3K0FpOqoy9LKO3zLEL\nzbo64KL3gMdH3+b71XepY4OxFTarMarCPT46zDfPWr8aUZFSk1hDamt0DtVZTLVJKF/ElIOUzNRk\nZklmKqjn7cc6ZuBTD7xZZ3CTLDTUg/RDL9ChgN9WkfOtUwU97QJYkQ+6S20j3FJv7Wrjc49XF2Ce\n456+FPbPLl/3BpGvFLYp7g3aeZEyaYtruSsdtXPeVmw9xZrbmHkMz8aYYYpRsQdu99gwNB6gXW1R\nGKu3BeX2Rzfa7b8e7WmO7+d8eG/Kd+6d8kB/4YaJAlYKM3NQkOHWP2XcDNoyohOVkEYpvTglTVLO\nR81qpxYAABS2SURBVHf54fFH/OjebX748CMuTB9Tn2GXZ5izMywzFDXYCqVqlK1dGrjQJQbXZ1WE\n0pKqGU7OYSRT0nVvSkK4Ll9leXwC/A/Af2Ot/c1dxzziB8FfD3EP4HkNqaC1xn/nllcyEOevcULZ\n3CnGDbATmuBguMxdQHxXkvaWwPO1RD48OJjczwOvz1k56TpyoUqJhTPy1zwKrjHc3yScAUKg3pWT\nG3KwOMubJW41pOyf8mUuP4bxHup2hPpghP7WMWPd53Ze8+F8zp948ZxNkXFWwnnmto+QBdPhdgY3\nEVYAPWvZK6DnN6nLrnrM8gPOond5PP4aP+ZPYgrj1rJf+YVCLHCKI2V9/VdtsIqxMHCBKy2zVHbN\n2d7wS15bmpiKAHW3P8MFUkn77b5quk3m13BWMxbWOWQLWF3C6hRqSV3qgnc3oP0y6bZ+twe+pE4I\naEtoKe9jVwmWMZIlVWv98izWXZe9fS4m6LuWvWHO/YcLvnb/nE/2Tl3XToGnYBLngK9qtxJdcs3C\n0v35noK+VvQi6Mfwo8GGR3v3uLq1xx/e+xqfmyO4egqnzyDZwymL9x+t+JKd5qtDekliHt2Mou8D\nv08TvXw9edNsEwX8V8A/sdb+6s1H/gVfd9ntULrcMrQd6RsiDV9KDG7ALmj2B+mm5YVWtyRhdxU5\n1KxwAUMI7mGB3YSR3GunY1XkwFD72o7BTnBbaRbO+tpmaXRXWIZ51dJ24X1Im8rei+IWC6iJ/fsl\nxViY5thHc8zEpWdNP1/yxaeKvWf7qOl98lXJLINp5VaDb9AUKEo0Bdrb8e0gY9j+qbFkS5iewekA\n9ouEP8j2ebRRzDYL7OYpnOGWmWfGW6HBXtJbHRIuXSxjw870yRuXbHcHYphPH9HstDik2TTq/2vv\nXGMsOa46/jt9+77v7DzW6/XueMU6mPgVJ3aMbENAOMiALSBI+ZJECor4wCdEEB8QBvHB3xASCJAQ\nX3hEKFKCRAjIESjYQCIlQkrsxCabxyZ+27ub3bseex733rmv7sOHqrpdt6ZnPTPxaHpF/6VS9+37\nOl1d9a9Tp8455QZZ93x8b4hKZimLAJWdPxua1oZrMN4w4eyziMLQ3TCUM6QtPHJUZlMJDf1Y82ae\n/vf847XgpqMbQDf7ziwfir0U2RnE3EzUgyfCaNCl2x3ywot1mtEJuq2hSUx90fxNOjK5j4aJ2ZXO\n9/dxdxGOF7FCLREz8wcu9U7ywtU23TqM0p55XhcGJrJz5Ppv6h1DFxafz9y/un7rVpwFeA9wl/f5\nz+6hTg+ueX8A+DjwLRF51l77A1X94vzH3Aq15hR2OYbD9jtB3oohKKelO7IKs6L4Ja9x+hqNM+o5\n7RcyNco3S4RNxv9d3z5tfYmiGlSq5uiS7KSxtd27/3AEBFldRcybUfx5q196QXHmBP8+9oFU0Y0h\nvL5lXm6OWL/a47VuBN1jbG7czGQ7YTCE/kQYqDNaZMXsdWT2qZl37TOkGifKeg8ud2EphdZmzKvj\nRV4bCxvjHowvwkZk8tAMxZKa8zn3ydvVnTLvGeLqK4yeCjFzXmZ+XcEdXdBWk2yx0tmMB7aOgzbh\n2YZJZKcOEHabSQ/GW8Y9cW6NY7djaJ+OAuNvaqYzbGIGddcOXBv3tfmwHedp6XlIbR24/DRu4LFC\npGA2d1BzTEM+8OU1x2H/LbpXhjwf1Rhvn+CVusBlkMug60bXmUxtXpp0PqB/N/NJJYU4FeKpmDxV\nvZO8+kaHbgrDwZaR70of3pzY9QFfefIfGMExDa45U1qbTLF0Ze9xKAf1NvkqezLOtJnX/MLRfDdC\n9yMJQ431IHAjP/a33Wqir3HlGaRh/r/Dxuum3U5Op/G4zhOaQcL7Ddz4pIEJlqkb1zSNjD1VU5Ax\nZppf9/4nJht4nFz+vTjt259RhOTtbOc+8e8DTvNmEzZH6IUe6/0t6AmbvWNc6N9Maj1NJlNhrEJC\nTEqVdHY0cvubjPn3EKUpzZ7Zt6DRh1o34q3kGOuJsJFswfQSjGIYxmYxKI29Z+O0Xvf83NTUJyXI\n2oiSBdH4cN/1w88bQal751Uy81YfQ1wuSZbtD6rzOspE3n68Tya2TD3N27WjPFm8YJ2Zdg5ZetgE\neMO0NUlAB159+cTjt2VfoL30T6fdr9v6sO6H/gbXM5fVPP8jzUSwGPW36XaHTLbrvHH1BtpxK5tQ\nbplJhFuOcBuKh5YYv4oFo/hHiRCpECXCoLfEunZ4qw+jNbsTQ29g8m7vIG9fbp+sfe3bnTtHhBbz\nfdMtEO8NP4rN+xHgL6wkf6uqf7LzUy5fdHiDsJPIQmJ3nfedgNO8XQX5I2QoS57V1Z865l13Uyjn\nEuYn4fJtzOE9tzCamg2xlzZETai0oNqyi619sm3FIHvwVe8//F7uNyBnl/eLa+Gu+ER1MPLW9RFs\njtGoB5GwrimbqXBRF4nSBdR2UJOrwhCOzmUBBN//RGfEa8hckhTpgfQxi/MCKTUSFVK2UB1hvDG8\nsoNkINM3/KMrrt6cKpwHn7yd77UfRDPLzk+2OOza3Ftk9W2LJt4YG5ozdoHOnIUxaU9dIian9buE\nVH6ehiDi0kGw5G29mHSbbEbn2lCepdh/bz+a99jWQYXZZsuzpFYeP8wReL52P0xSutspb1ytUYlO\nYDbwZY5DleyreVLuqGoFs5GCkUmnDZJB2+aSsrPUdILx0IJ5rwRXj2G95Mnva97bZO3C5QTaGw5q\n864Af4XZu/Ii8LSIPBEG6WTJd/Js2P5NhbZiF4rra8O+SSNvgSVPU8a75ssQjrmuUQKz1Kf+/DVs\nwDlTR6kYbTlq2mM98xiIrEvYbG8+OzVMm5A0IW2ZY9Q0mjd1SKpG405dp9oks7O7zRn8gA/f9OPX\nVxCeXZ1ANYZqB6oNmtGABbboSI8F2aKSTEjHZCXJ1y38GjfTzYyykoUqSScm6VRJFmJGaZPhtMFo\n2mQ4aZJO6jCqwrhqjlNsh5iQbVrsO8hPMVuUqFGn5sweLpIxbCt5A61fHKE5TRUyYnP16X8vbEt5\nbTOsHdcxXVSqI1BfQRB7KsG5j3DdJFQa3EDtNHv33F0mSkfafkCN/U1NQNetnG7aHmq//qzS9Y88\nRceeR5HxW49jiCtUqtBp9FloDOg01mnXBnYVUZCBOaqNpFGbG94kfsoSQIX9WqogHVvaMK1X6WmH\nXrpAL+0wSJowSEzpp7A9rxDkzq5qNWjUoV43R23ApAljEyjEtGJnJ9PsqFNmEcjqc9Mu9RMfg+qy\nSSxW7UBctR1IIK6YGfaLO0XLw0E17/uBF1T1FQAR+Ufg1wiCdOZTd+1mMhEyp/+qd+5rrc5+6bxD\nQqIKNfRraQP+e75JI1yEioPzvMVIX1uIMBu5Nk2p1Y3vrstlUJVs/ubmcpM6jGv2WLcao/XnnYrV\nzEaYbaTWybxbnMbqzAN5xZFL4JddrdqMXx1o12jHb3JKhqxGA1blMo1Rn0kPJlsmT9Ek2bkaEM6h\n6kEZH2sxWnWlyca0wfp2k/XBCpPtFdJeHTZjWypmRWnqBbvMFs78HWTCzueeZegu6YgsT4X125+b\ntjYwqVV94g4X7vzO6OTwXS5dO02zep4LZnI++y6JtPt8XgF2KBa+wuDPptwMISHrF0579vuS35/8\nQQIr8wboBtm0PRymQ08Z32KaY9uRCtQa0GhAo07cVlaWf8jq8ianV9Y4udBFukBXkK4gXdDERt+m\niiap1W9sVK6CBn06qkN0AqJTUDkFw8UWF6erXEqWuZQcZzC8Aboj6I5N2Q5noDnkXa/CYgeWF2Hp\nGGgTerWsjCLrgpzakpg+mjqXZNcWw0HcK9UWtBegZROLNepm67VGA+oLUJkcOnmvAq97ry8AD+z8\n2AB4HrgVdhC3gxvVnWbSZEcyHgbMGbRmC1GhBnytBbeQ0N13I+Bl4HZ2Lvj4JfQygDmydORdb0Gj\nBc2GNY2KKQ08BVltcpyKsdNux8bncxob16KkYhav0gSzU30fYy/8JmZV2g9L9gkmHPFzbPrVRZOq\ncXkJlpdp14RTUZfbom3ujC7TGawzXDObqYyGMBzN++WM5+8aZX7Cfok+q8dOMzizSP+OCYM74Mp4\niXizwWRzmc3N07DWhG5kUhmkFdMBZMRsUwCGZBsIugHWJ+Rrmd6mXHs5RjG9473295oY8nbRpy5P\nhtOQw/9x5O3I2Z+dOfL0o3qdcuDaT7jYGSoNkJH1Mxg9ybc3O5dO1x9cvhtHuq4/7eaHJ5nONNNo\nrczq8rOEitacP6P3+wKcB+6cr6eoCrUOtE0GxMpSysrqJmdPT7l99U1uPf4K8jJIC0gF2cSmg1dD\n4qjnQq8kOdYZqUN8A8Tvgvg22Lpxkc5kBZ3ErE+OQ+8MvDQAGcDa1zAc1Lcyu+cS/Gi9CksLcNNx\nOHXCzIjfrMCbtn/2xIRrug0UxM0EU2YbDOzguaAvVhvQbsCSLQsJdBqGyNe/Arf8JPwbe8JByfta\nqq2HPubhnr7GVyMMSfuLLwtktuAFTENtWHFdy/Onbq68nQ0uj8AFeAm4m6xx+iHl/o4kfj4HmNNH\nRSBuQ70NrbZ5ILNbEMNsMwuG2nUsMfvluR1IEMy+gWI07zS1Wmjfat7PASc8ufPsutc6KlTblryX\n4cZV2s0hpypVbq8MuL9ymaWNLoMUBtswWM8c7lxxOovvk7PolecZcc+isHFmwtZdsPlgjdoQJmsN\nttaWqaydhks2R0eK2RZqPDXEkYwgGkIyICM2f4bmE7R3T3NtAu+4G84Dd2DaniPvJhlxb5JF0PpK\ngf//E+ZJUjCE7btiusHBzxXc9M5dm97NQ+kzwIeZJ+F1rDM688Tthld/VuKQUy9zXSFc5A7bTkje\nvnvii8CDzD2LqAa1JWgtwbEl4htSVs68ztlbp7z31jXuPf0K0jKmbdkEuWRNdLb5J5qtekzVlB2s\nUYfqCaj+OFTfB2/92AnS0Vk2RlVeGx2H9TMgWzDYgnMvAPeREfcuyenqNVjqwKnj8K7TkLSN733k\n+iPZeC1OKHuUHPMRsEOBqsbQimEphhtjWFbjQrWk8NXzcPuH82XLwUHJ+yJ4se/m/MLOj/0nJjtJ\nCpy1JUTeCO9I3C0GJRjqcNrKbj6se0XYsF3x7Xp+Mit/1d4dYc58I4JJTtWAuGFMJ+6rrq86sX1F\nbZxzfaZI+mYA587ndqY5ICK14dh1aLWJmw3aFWGxknAi3mZl0qNfh35sxhWXHNSV0GiVYvLMLWFi\nV1vA8dqASmdEZXkMJxPa29CQmFjrSNKGrXaW5KkKJpGY7SRA1sH8cH6/gvxKCrWdvcDVq3vm4QDt\nk3ISfC+c0fg2VD/374CsPbnUBs406DeMMCLWb481TEWFJsRwVuJmBL6r40Hq5VoIzYth4IlfPzXb\nD5pQaxM1EuqdmIUlZeX4iJM3biHLEC2YLiOxmWQmqT2KKRMMZ7o78lGpGCW2tgDV41C9sc3ScEpr\nJFSHdah0jFbbnELknrH/bHMQRSaBVqsBCzav9ybz3d+tS7rqcM1oVwQz3ygy/a8ema3b2gJXvwzn\nvgwv/w/8x5/u6Wmw+128LZ4BfkJEzopIDfgI8MTOjz2EIWx3LFGiRIkSc7jlIfjlx+HOh+Ajj+/5\na6J6sJFZRB4lcxX8O1X94+D9d2rIL1GiRIn/V1DVtzUlHJi8S5QoUaLE0eGgZpMSJUqUKHGEKMm7\nRIkSJa5DHCp5i8gjInJeRJ4Xkd8/zP/aK0Tk70Xkioic866tiMhTIvIDEXlSRJaOWMYzIvIlEfmO\niHxbRD5ZUDkbIvI1EXnOyvl4EeW0MlVE5FkR+UIRZRSRV0TkW1bGrxdRRivTkoh8TkS+JyLfFZEH\niiSniNxm69CVDRH5ZJFktHL+ru0z50TkMyJS36+Mh0beXgj9Ixgv/o+JyB2H9X/7wKcwMvl4DHhK\nVd8N/Jd9fZRwe4TehXGi/S1bd4WSU1WHwAdV9R7gHuAREXmAgslp8TvAd8m8zoomowIPqeq9qnq/\nvVY0GQH+Evh3Vb0DE+l0ngLJqarft3V4L8a5ewD8S5FkFJFV4LeB+1T1bozTx0f3LaOqHkoBfgr4\novf6MeCxw/q/fcp2FjjnvT4PnLTnNwHnj1rGQN5/xeSRKaycGBfvb2BCAgslJ3AzJujgg8AXivjM\nMWG+x4NrRZNxEXgp53qh5PTk+kXgK0WTEROh/homNCIGvgD8wn5lPEyzSV4I/eoh/t+PgpOqesWe\nXwFOHqUwPoI9Qgsnp4hEIvKcledJVf06xZPzz4HfYz5/QtFkVOBJEXlGRH7TXiuajLcAV0XkUyLy\nTRH5GxFpUzw5HT5KtrNBYWRU1YvAn2EI/BKwrqpPsU8ZD5O8r0sfRDXDXiFkt3uE/jNmj9At/72i\nyKmqqRqzyc3AAyLynuD9I5VTRH4F6Krqs+wSC3fUMlp8QFXvAx7FmMl+1n+zIDLGwPuBv1bV92NC\nPeem9gWRExs8+KvAP4XvHbWMIrIMfAhjATgNdETk4/5n9iLjYZL3HkPoC4ErInITgIicwuzVdKTw\n9gj9tGZ7hBZOTgdV3QC+BPwSxZLzp4EPicjLGC3s50Xk0wWTEVX9oT1exdho76dgMmL67wVVfdq+\n/hyGzC8XTE4wg+A3bH1CseryYeBlVV1T1SnweYyZeV/1eJjkvccQ+kLgCeAT9vwTGBvzkUFk1z1C\niybnDW5FXESaGLvd9yiQnKr6h6p6RlVvwUyj/1tVf71IMopIS0QW7HkbY6s9R4FkBFDVy8DrIvJu\ne+lh4DsYm21h5LT4GPObQRapLl8FHhSRpu3rD2MW0/dXj4dsmH8U+D7wAmaPyyIsYnwWY2caY2zy\nvwGsYBa0fgA8CSwdsYw/g7HPPgc8a8sjBZTzbkye2v/FkM0f2euFktOT9+eAJ4omI8aW/Jwt33Z9\npUgyerK+D3jaPvPPYxYxCyUnJpvXG8CCd61oMj6OUXTOAf+ASX21LxnL8PgSJUqUuA5RRliWKFGi\nxHWIkrxLlChR4jpESd4lSpQocR2iJO8SJUqUuA5RkneJEiVKXIcoybtEiRIlrkOU5F2iRIkS1yFK\n8i5RokSJ6xD/B8pyYFTmNIBjAAAAAElFTkSuQmCC\n",

       "text": [

        "<matplotlib.figure.Figure at 0x7f2d89e9d410>"

       ]

      }

     ],

     "prompt_number": 13

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "### How did we know which parameters were available?\n",

      "A bit further back, we set up some plugin parameters in a Python dictionary:"

     ]

    },

    {

     "cell_type": "code",

     "collapsed": true,

     "input": [

      "plugin_params = { \"tuningmode\": 1, \"s\": 0.9, \"chromanormalize\": 3 }"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [],

     "prompt_number": 14

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "We also included `output = chroma` in the `vamp.collect` call to make sure we got the right output from the plugin. How did we know which parameters and outputs were available?\n",

      "\n",

      "Outputs are pretty simple, there's a `vamp.get_outputs_of` call that returns them directly."

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "vamp.get_outputs_of(\"nnls-chroma:nnls-chroma\")"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 15,

       "text": [

        "['logfreqspec',\n",

        " 'tunedlogfreqspec',\n",

        " 'semitonespectrum',\n",

        " 'chroma',\n",

        " 'basschroma',\n",

        " 'bothchroma']"

       ]

      }

     ],

     "prompt_number": 15

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "Querying details of a plugin's parameters is a bit more tricky with the current version of the module. You have to use a lower-level interface for this. It looks like the following."

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "plug = vamp.vampyhost.load_plugin(\"nnls-chroma:nnls-chroma\", 44100, vamp.vampyhost.ADAPT_NONE)\n",

      "plug.parameters"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 16,

       "text": [

        "[{'defaultValue': 1.0,\n",

        "  'description': 'Toggles approximate transcription (NNLS).',\n",

        "  'identifier': 'useNNLS',\n",

        "  'isQuantized': True,\n",

        "  'maxValue': 1.0,\n",

        "  'minValue': 0.0,\n",

        "  'name': 'use approximate transcription (NNLS)',\n",

        "  'quantizeStep': 1.0,\n",

        "  'unit': ''},\n",

        " {'defaultValue': 0.0,\n",

        "  'description': 'Consider the cumulative energy spectrum (from low to high frequencies). All bins below the first bin whose cumulative energy exceeds the quantile [bass noise threshold] x [total energy] will be set to 0. A threshold value of 0 means that no bins will be changed.',\n",

        "  'identifier': 'rollon',\n",

        "  'isQuantized': True,\n",

        "  'maxValue': 5.0,\n",

        "  'minValue': 0.0,\n",

        "  'name': 'bass noise threshold',\n",

        "  'quantizeStep': 0.5,\n",

        "  'unit': '%'},\n",

        " {'defaultValue': 0.0,\n",

        "  'description': 'Tuning can be performed locally or on the whole extraction segment. Local tuning is only advisable when the tuning is likely to change over the audio, for example in podcasts, or in a cappella singing.',\n",

        "  'identifier': 'tuningmode',\n",

        "  'isQuantized': True,\n",

        "  'maxValue': 1.0,\n",

        "  'minValue': 0.0,\n",

        "  'name': 'tuning mode',\n",

        "  'quantizeStep': 1.0,\n",

        "  'unit': '',\n",

        "  'valueNames': ['global tuning', 'local tuning']},\n",

        " {'defaultValue': 1.0,\n",

        "  'description': 'Spectral whitening: no whitening - 0; whitening - 1.',\n",

        "  'identifier': 'whitening',\n",

        "  'isQuantized': False,\n",

        "  'maxValue': 1.0,\n",

        "  'minValue': 0.0,\n",

        "  'name': 'spectral whitening',\n",

        "  'unit': ''},\n",

        " {'defaultValue': 0.699999988079071,\n",

        "  'description': 'Determines how individual notes in the note dictionary look: higher values mean more dominant higher harmonics.',\n",

        "  'identifier': 's',\n",

        "  'isQuantized': False,\n",

        "  'maxValue': 0.8999999761581421,\n",

        "  'minValue': 0.5,\n",

        "  'name': 'spectral shape',\n",

        "  'unit': ''},\n",

        " {'defaultValue': 0.0,\n",

        "  'description': 'How shall the chroma vector be normalized?',\n",

        "  'identifier': 'chromanormalize',\n",

        "  'isQuantized': True,\n",

        "  'maxValue': 3.0,\n",

        "  'minValue': 0.0,\n",

        "  'name': 'chroma normalization',\n",

        "  'quantizeStep': 1.0,\n",

        "  'unit': '',\n",

        "  'valueNames': ['none', 'maximum norm', 'L1 norm', 'L2 norm']}]"

       ]

      }

     ],

     "prompt_number": 16

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "From this we can work out that (for example) to set the chroma vector normalisation to \"L2 norm\" we should supply something like `parameters = { \"chromanormalize\" : 3 }` in the call to `vamp.collect`.\n",

      "\n",

      "(Strictly speaking we're supposed to call"

     ]

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "plug.unload()"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 17,

       "text": [

        "True"

       ]

      }

     ],

     "prompt_number": 17

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "after doing the above, as well; otherwise a bit of memory is wasted.)"

     ]

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "### Exporting the result to a CSV file\n",

      "This is a pretty standard use of Python's `csv` module."

     ]

    },

    {

     "cell_type": "code",

     "collapsed": true,

     "input": [

      "import csv\n",

      "out_file = open('features.csv', 'w')\n",

      "writer = csv.writer(out_file)\n",

      "writer.writerows(chroma)\n",

      "out_file.close()"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [],

     "prompt_number": 18

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "(Might be a good moment to find the `features.csv` file in the filesystem and check that it looks OK.)"

     ]

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "### Processing many files\n",

      "We define a function that exports chroma features, with the parameters used above, from an audio file given the filename. Then we can call that for a set of files. The body of the function is basically just the same code we already showed, to extract and then save the chroma features.\n",

      "\n",

      "This time we are overriding the `sr` parameter of `librosa.load` to tell it to always use the file's own sample rate (that's what `sr = None` does). We're also printing out the filename, its sample rate, and the resulting step size in audio samples between chroma features."

     ]

    },

    {

     "cell_type": "code",

     "collapsed": true,

     "input": [

      "import os, glob\n",

      "def extract_chroma(audiofile):\n",

      "    data, rate = librosa.load(audiofile, sr = None)\n",

      "    out = vamp.collect(data, rate,\n",

      "                       plugin_key = \"nnls-chroma:nnls-chroma\",\n",

      "                       output = \"chroma\",\n",

      "                       process_timestamp_method = vamp.vampyhost.SHIFT_DATA)\n",

      "    step, chroma = out[\"matrix\"]\n",

      "    print(\"File \" + audiofile +\n",

      "          \": sample rate = \" + str(rate) +\n",

      "          \", chroma step = \" + str(vamp.vampyhost.RealTime.to_frame(step, rate)))\n",

      "    out_file = open(os.path.splitext(audiofile)[0] + \"_chroma.csv\", \"w\")\n",

      "    csv.writer(out_file).writerows(chroma)\n",

      "    out_file.close()"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [],

     "prompt_number": 19

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "for file in glob.glob(\"/home/dm/data/*.wav\"):\n",

      "    extract_chroma(file)"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "output_type": "stream",

       "stream": "stdout",

       "text": [

        "File data/piano-scale.wav: sample rate = 44100, chroma step = 2048\n"

       ]

      }

     ],

     "prompt_number": 20

    },

    {

     "cell_type": "markdown",

     "metadata": {},

     "source": [

      "### Further exercises\n",

      "\n",

      "* Load the audio data and chroma .csv file generated in the last step into Sonic Visualiser, and verify that the chroma data are similar to those generated by running the plugin within Sonic Visualiser.\n",

      "* Compare the results generated by two or more different chromagram plugins, or using different sets of parameters.\n",

      "* Adapt this notebook so as to extract \"smoothed pitch curve\" features using the pYIN plugin, and figure out how to display the results using in a Matplotlib plot."

     ]

    }

   ],

   "metadata": {}

  }

 ]

}