{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from pyannote.core import notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SlidingWindowFeature (`pyannote.core.feature.SlidingWindowFeature`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyannote.core import SlidingWindowFeature, SlidingWindow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SlidingWindowFeature** are used to manage feature vectors extracted on a sliding window (e.g. MFCC in audio processing)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# one 4-dimensional feature vector extracted every 100ms from a 200ms window\n",
    "frame = SlidingWindow(start=0.0, step=0.100, duration=0.200)\n",
    "\n",
    "# random for illustration purposes\n",
    "data = np.random.randn(100, 4)\n",
    "\n",
    "features = SlidingWindowFeature(data, frame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cropping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may use `crop` to extract a temporal subset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method crop in module pyannote.core.feature:\n",
      "\n",
      "crop(self, focus, mode=u'loose', fixed=None) method of pyannote.core.feature.SlidingWindowFeature instance\n",
      "    Extract frames as numpy array\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    focus : Segment or Timeline\n",
      "    mode : {'loose', 'strict', 'center', 'fixed'}, optional\n",
      "        In 'strict' mode, only frames fully included in focus coverage are\n",
      "        returned. In 'loose' mode, any intersecting frames are returned. In\n",
      "        'center' mode, first and last frames are chosen to be the positions\n",
      "        whose centers are the closest to the focus start and end times.\n",
      "        Defaults to 'loose'.\n",
      "    fixed : float, optional\n",
      "        When provided and mode is 'center', override focus duration to make\n",
      "        sure two `focus` with the same duration always result in the same\n",
      "        (fixed) number of frames being selected.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    data : numpy array\n",
      "        (nSamples, nFeatures) numpy array\n",
      "    \n",
      "    See also\n",
      "    --------\n",
      "    SlidingWindow.crop\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(features.crop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.170939  ,  0.7268368 , -1.11357192,  0.9102115 ],\n",
       "       [ 0.75526988,  0.13631738, -1.12959046,  0.57945519],\n",
       "       [-1.63332126, -1.05239927, -2.80404685, -3.19025518],\n",
       "       [-1.2622323 ,  0.80263563,  0.8912935 ,  0.85251067],\n",
       "       [-0.58865284, -1.05005899,  0.5468816 ,  1.03976293],\n",
       "       [ 2.99740924,  1.544513  , -2.18700361, -0.19051365],\n",
       "       [-1.16020413,  2.75010658, -1.63448261,  0.96354481],\n",
       "       [-1.09404058, -0.56915077, -0.73265453, -0.5412315 ],\n",
       "       [-3.83225883,  2.48916336, -0.475149  , -0.56418448],\n",
       "       [ 0.01583975, -1.41733235, -0.47454703, -1.18346613],\n",
       "       [ 0.4350982 , -0.61821957,  0.0458578 , -0.66109202],\n",
       "       [ 1.39603516,  1.00657646,  1.67931705,  0.01362444],\n",
       "       [ 0.95027176, -0.02961472, -0.71587331, -0.04884979]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyannote.core import Segment\n",
    "features.crop(Segment(2, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Need help?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can always try the following...  \n",
    "Who knows? It might give you the information you are looking for!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class SlidingWindowFeature in module pyannote.core.feature:\n",
      "\n",
      "class SlidingWindowFeature(__builtin__.object)\n",
      " |  Periodic feature vectors\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  data : (nSamples, nFeatures) numpy array\n",
      " |  \n",
      " |  sliding_window : SlidingWindow\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __getitem__(self, i)\n",
      " |      Get ith feature vector\n",
      " |  \n",
      " |  __init__(self, data, sliding_window)\n",
      " |  \n",
      " |  crop(self, focus, mode=u'loose', fixed=None)\n",
      " |      Extract frames as numpy array\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      focus : Segment or Timeline\n",
      " |      mode : {'loose', 'strict', 'center', 'fixed'}, optional\n",
      " |          In 'strict' mode, only frames fully included in focus coverage are\n",
      " |          returned. In 'loose' mode, any intersecting frames are returned. In\n",
      " |          'center' mode, first and last frames are chosen to be the positions\n",
      " |          whose centers are the closest to the focus start and end times.\n",
      " |          Defaults to 'loose'.\n",
      " |      fixed : float, optional\n",
      " |          When provided and mode is 'center', override focus duration to make\n",
      " |          sure two `focus` with the same duration always result in the same\n",
      " |          (fixed) number of frames being selected.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      data : numpy array\n",
      " |          (nSamples, nFeatures) numpy array\n",
      " |      \n",
      " |      See also\n",
      " |      --------\n",
      " |      SlidingWindow.crop\n",
      " |  \n",
      " |  getDimension(self)\n",
      " |      Dimension of feature vectors\n",
      " |  \n",
      " |  getExtent(self)\n",
      " |  \n",
      " |  getNumber(self)\n",
      " |      Number of feature vectors\n",
      " |  \n",
      " |  iterfeatures(self, window=False)\n",
      " |      Feature vector iterator\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      window : bool, optional\n",
      " |          When True, yield both feature vector and corresponding window.\n",
      " |          Default is to only yield feature vector\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(SlidingWindowFeature)"
   ]
  }
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