{
 "metadata": {
  "name": "Introduction_to_nitime"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Introduction to nitime\n",
      "\n",
      "This tutorial notebook examines basic data structures and operations you can do in nitime\n",
      "\n",
      "We start by importing the timeseries module: \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import nitime.timeseries as nts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This module contains several different objects which we will demonstrate. Let's start with representations of time. \n",
      "\n",
      "## Representing time\n",
      "\n",
      "\n",
      "The simplest representation of time is a `TimeArray`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ta1 = nts.TimeArray([1,2,5], time_unit='ms')\n",
      "print ta1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "TimeArray([ 1.,  2.,  5.], time_unit='ms')\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`TimeArray` objects support indexing (notice that the representation of the singleton contains the units!):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ta1[2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "5.0 ms"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that for the purpose of unit conversions, you can initialize one `TimeArray` with another one: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ta2 = nts.TimeArray(ta1, time_unit='s')\n",
      "ta2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "TimeArray([ 0.001,  0.002,  0.005], time_unit='s')"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is because under the hood time is *always* represented in integer format in some base unit"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print ta2.__array__()\n",
      "print ta1.__array__()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1000000000 2000000000 5000000000]\n",
        "[1000000000 2000000000 5000000000]\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Don't worry - the unit we use per default is really small: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print nts.base_unit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ps\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "On the other hand, we can represent long durations without overflowing: \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We represent the longest int64 in pico-seconds and convert to days\n",
      "longest_time = nts.TimeArray(nts.TimeArray(9223372036854775807, time_unit=nts.base_unit), time_unit='D')\n",
      "print longest_time\n",
      "# That's quite a few weeks:\n",
      "in_weeks = nts.TimeArray(longest_time, time_unit='W')\n",
      "print in_weeks"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "106.75199116730064 D\n",
        "15.25028445247152 W\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A typical case of time-representation is time sampled at some fixed interval. The `UniformTime` class can be used to represent these cases: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ut1 = nts.UniformTime(duration=100, sampling_rate=100, t0=10.0, time_unit='ms')\n",
      "print ut1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[10.0 ms 20.0 ms 30.0 ms 40.0 ms 50.0 ms 60.0 ms 70.0 ms 80.0 ms 90.0 ms\n",
        " 100.0 ms]\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ut1.t0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "10.0 ms"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The sampling rate is set in units of Hz:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ut2 = nts.UniformTime(duration=100, sampling_rate=1000, time_unit='ms')\n",
      "print ut2\n",
      "ut2.sampling_rate"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0.0 ms 1.0 ms 2.0 ms 3.0 ms 4.0 ms 5.0 ms 6.0 ms 7.0 ms 8.0 ms 9.0 ms\n",
        " 10.0 ms 11.0 ms 12.0 ms 13.0 ms 14.0 ms 15.0 ms 16.0 ms 17.0 ms 18.0 ms\n",
        " 19.0 ms 20.0 ms 21.0 ms 22.0 ms 23.0 ms 24.0 ms 25.0 ms 26.0 ms 27.0 ms\n",
        " 28.0 ms 29.0 ms 30.0 ms 31.0 ms 32.0 ms 33.0 ms 34.0 ms 35.0 ms 36.0 ms\n",
        " 37.0 ms 38.0 ms 39.0 ms 40.0 ms 41.0 ms 42.0 ms 43.0 ms 44.0 ms 45.0 ms\n",
        " 46.0 ms 47.0 ms 48.0 ms 49.0 ms 50.0 ms 51.0 ms 52.0 ms 53.0 ms 54.0 ms\n",
        " 55.0 ms 56.0 ms 57.0 ms 58.0 ms 59.0 ms 60.0 ms 61.0 ms 62.0 ms 63.0 ms\n",
        " 64.0 ms 65.0 ms 66.0 ms 67.0 ms 68.0 ms 69.0 ms 70.0 ms 71.0 ms 72.0 ms\n",
        " 73.0 ms 74.0 ms 75.0 ms 76.0 ms 77.0 ms 78.0 ms 79.0 ms 80.0 ms 81.0 ms\n",
        " 82.0 ms 83.0 ms 84.0 ms 85.0 ms 86.0 ms 87.0 ms 88.0 ms 89.0 ms 90.0 ms\n",
        " 91.0 ms 92.0 ms 93.0 ms 94.0 ms 95.0 ms 96.0 ms 97.0 ms 98.0 ms 99.0 ms]\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "1000.0 Hz"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "But the sampling interval is set in the units of the object: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ut3 = nts.UniformTime(duration=100, sampling_interval=3.3, time_unit='ms')\n",
      "print ut3\n",
      "ut3.sampling_rate"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0.0 ms 3.3 ms 6.6 ms 9.9 ms 13.2 ms 16.5 ms 19.8 ms 23.1 ms 26.4 ms\n",
        " 29.7 ms 33.0 ms 36.3 ms 39.6 ms 42.9 ms 46.2 ms 49.5 ms 52.8 ms 56.1 ms\n",
        " 59.4 ms 62.7 ms 66.0 ms 69.3 ms 72.6 ms 75.9 ms 79.2 ms 82.5 ms 85.8 ms\n",
        " 89.1 ms 92.4 ms 95.7 ms 99.0 ms]\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "303.03030303 Hz"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Again, unit conversions can be done by reassigning into another object: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ut4 = nts.UniformTime(nts.UniformTime(duration=9223372036854775807, sampling_rate=1, time_unit='ps'), time_unit='W')\n",
      "print ut4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0.0 W 1.6534391534391535e-06 W 3.306878306878307e-06 W ...,\n",
        " 15.250281084656084 W 15.250282738095239 W 15.250284391534391 W]\n"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Representing events in time\n",
      "\n",
      "Now that we can represent time, lets consider representations of events in time. \n",
      "\n",
      "The first kind of representation is the `Events` class  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = nts.TimeArray([1, 2, 3], time_unit='ms')\n",
      "x = [1, 2, 3]\n",
      "y = ['a', 'b', 'c']\n",
      "z = [10., 20., 30.]\n",
      "i0 = [0, 0, 1]\n",
      "i1 = [0, 1, 2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# events with data\n",
      "ev1 = nts.Events(t, i=x, j=y, k=z)\n",
      "\n",
      "# events with indices\n",
      "ev2 = nts.Events(t, indices=[i0, i1])\n",
      "\n",
      "# events with indices and labels\n",
      "ev3 = nts.Events(t, labels=['trial', 'other'], indices=[i0, i1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## `Epochs`\n",
      "\n",
      "These objects can be used to represent intervals in time. For example, here is a way to represent epochs happening 300 msec after two events and lasting for 1.5 and 2 seconds, respectively:\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ep1 = nts.Epochs(t0=[0.75, 0.8], offset=0.3, duration=[1.5, 2.0], time_unit='s')\n",
      "ep1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "Epochs([(0.45 s, 1.95 s), (0.5 s, 2.5 s)], as (start,stop) tuples)"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Combining these two, we might start asking questions such as what events occurred during some interval in time. For example: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ep = nts.Epochs(start=0, stop=1.5, time_unit='ms')\n",
      "print ev1[ep]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Events:\n",
        "\tTimeArray([ 1.], time_unit='ms')\n",
        "\t{'i': array([1]), 'k': array([ 10.]), 'j': array(['a'], \n",
        "      dtype='|S1')}\n"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## `TimeSeries` \n",
      "\n",
      "This class is the work-horse of the library"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = np.sin(np.linspace(0,10*pi, 1000))\n",
      "ts1 = nts.TimeSeries(data=data, sampling_rate=1000)\n",
      "ts1.time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "UniformTime([ 0.   ,  0.001,  0.002,  0.003,  0.004,  0.005,  0.006,  0.007,\n",
        "        0.008,  0.009,  0.01 ,  0.011,  0.012,  0.013,  0.014,  0.015,\n",
        "        0.016,  0.017,  0.018,  0.019,  0.02 ,  0.021,  0.022,  0.023,\n",
        "        0.024,  0.025,  0.026,  0.027,  0.028,  0.029,  0.03 ,  0.031,\n",
        "        0.032,  0.033,  0.034,  0.035,  0.036,  0.037,  0.038,  0.039,\n",
        "        0.04 ,  0.041,  0.042,  0.043,  0.044,  0.045,  0.046,  0.047,\n",
        "        0.048,  0.049,  0.05 ,  0.051,  0.052,  0.053,  0.054,  0.055,\n",
        "        0.056,  0.057,  0.058,  0.059,  0.06 ,  0.061,  0.062,  0.063,\n",
        "        0.064,  0.065,  0.066,  0.067,  0.068,  0.069,  0.07 ,  0.071,\n",
        "        0.072,  0.073,  0.074,  0.075,  0.076,  0.077,  0.078,  0.079,\n",
        "        0.08 ,  0.081,  0.082,  0.083,  0.084,  0.085,  0.086,  0.087,\n",
        "        0.088,  0.089,  0.09 ,  0.091,  0.092,  0.093,  0.094,  0.095,\n",
        "        0.096,  0.097,  0.098,  0.099,  0.1  ,  0.101,  0.102,  0.103,\n",
        "        0.104,  0.105,  0.106,  0.107,  0.108,  0.109,  0.11 ,  0.111,\n",
        "        0.112,  0.113,  0.114,  0.115,  0.116,  0.117,  0.118,  0.119,\n",
        "        0.12 ,  0.121,  0.122,  0.123,  0.124,  0.125,  0.126,  0.127,\n",
        "        0.128,  0.129,  0.13 ,  0.131,  0.132,  0.133,  0.134,  0.135,\n",
        "        0.136,  0.137,  0.138,  0.139,  0.14 ,  0.141,  0.142,  0.143,\n",
        "        0.144,  0.145,  0.146,  0.147,  0.148,  0.149,  0.15 ,  0.151,\n",
        "        0.152,  0.153,  0.154,  0.155,  0.156,  0.157,  0.158,  0.159,\n",
        "        0.16 ,  0.161,  0.162,  0.163,  0.164,  0.165,  0.166,  0.167,\n",
        "        0.168,  0.169,  0.17 ,  0.171,  0.172,  0.173,  0.174,  0.175,\n",
        "        0.176,  0.177,  0.178,  0.179,  0.18 ,  0.181,  0.182,  0.183,\n",
        "        0.184,  0.185,  0.186,  0.187,  0.188,  0.189,  0.19 ,  0.191,\n",
        "        0.192,  0.193,  0.194,  0.195,  0.196,  0.197,  0.198,  0.199,\n",
        "        0.2  ,  0.201,  0.202,  0.203,  0.204,  0.205,  0.206,  0.207,\n",
        "        0.208,  0.209,  0.21 ,  0.211,  0.212,  0.213,  0.214,  0.215,\n",
        "        0.216,  0.217,  0.218,  0.219,  0.22 ,  0.221,  0.222,  0.223,\n",
        "        0.224,  0.225,  0.226,  0.227,  0.228,  0.229,  0.23 ,  0.231,\n",
        "        0.232,  0.233,  0.234,  0.235,  0.236,  0.237,  0.238,  0.239,\n",
        "        0.24 ,  0.241,  0.242,  0.243,  0.244,  0.245,  0.246,  0.247,\n",
        "        0.248,  0.249,  0.25 ,  0.251,  0.252,  0.253,  0.254,  0.255,\n",
        "        0.256,  0.257,  0.258,  0.259,  0.26 ,  0.261,  0.262,  0.263,\n",
        "        0.264,  0.265,  0.266,  0.267,  0.268,  0.269,  0.27 ,  0.271,\n",
        "        0.272,  0.273,  0.274,  0.275,  0.276,  0.277,  0.278,  0.279,\n",
        "        0.28 ,  0.281,  0.282,  0.283,  0.284,  0.285,  0.286,  0.287,\n",
        "        0.288,  0.289,  0.29 ,  0.291,  0.292,  0.293,  0.294,  0.295,\n",
        "        0.296,  0.297,  0.298,  0.299,  0.3  ,  0.301,  0.302,  0.303,\n",
        "        0.304,  0.305,  0.306,  0.307,  0.308,  0.309,  0.31 ,  0.311,\n",
        "        0.312,  0.313,  0.314,  0.315,  0.316,  0.317,  0.318,  0.319,\n",
        "        0.32 ,  0.321,  0.322,  0.323,  0.324,  0.325,  0.326,  0.327,\n",
        "        0.328,  0.329,  0.33 ,  0.331,  0.332,  0.333,  0.334,  0.335,\n",
        "        0.336,  0.337,  0.338,  0.339,  0.34 ,  0.341,  0.342,  0.343,\n",
        "        0.344,  0.345,  0.346,  0.347,  0.348,  0.349,  0.35 ,  0.351,\n",
        "        0.352,  0.353,  0.354,  0.355,  0.356,  0.357,  0.358,  0.359,\n",
        "        0.36 ,  0.361,  0.362,  0.363,  0.364,  0.365,  0.366,  0.367,\n",
        "        0.368,  0.369,  0.37 ,  0.371,  0.372,  0.373,  0.374,  0.375,\n",
        "        0.376,  0.377,  0.378,  0.379,  0.38 ,  0.381,  0.382,  0.383,\n",
        "        0.384,  0.385,  0.386,  0.387,  0.388,  0.389,  0.39 ,  0.391,\n",
        "        0.392,  0.393,  0.394,  0.395,  0.396,  0.397,  0.398,  0.399,\n",
        "        0.4  ,  0.401,  0.402,  0.403,  0.404,  0.405,  0.406,  0.407,\n",
        "        0.408,  0.409,  0.41 ,  0.411,  0.412,  0.413,  0.414,  0.415,\n",
        "        0.416,  0.417,  0.418,  0.419,  0.42 ,  0.421,  0.422,  0.423,\n",
        "        0.424,  0.425,  0.426,  0.427,  0.428,  0.429,  0.43 ,  0.431,\n",
        "        0.432,  0.433,  0.434,  0.435,  0.436,  0.437,  0.438,  0.439,\n",
        "        0.44 ,  0.441,  0.442,  0.443,  0.444,  0.445,  0.446,  0.447,\n",
        "        0.448,  0.449,  0.45 ,  0.451,  0.452,  0.453,  0.454,  0.455,\n",
        "        0.456,  0.457,  0.458,  0.459,  0.46 ,  0.461,  0.462,  0.463,\n",
        "        0.464,  0.465,  0.466,  0.467,  0.468,  0.469,  0.47 ,  0.471,\n",
        "        0.472,  0.473,  0.474,  0.475,  0.476,  0.477,  0.478,  0.479,\n",
        "        0.48 ,  0.481,  0.482,  0.483,  0.484,  0.485,  0.486,  0.487,\n",
        "        0.488,  0.489,  0.49 ,  0.491,  0.492,  0.493,  0.494,  0.495,\n",
        "        0.496,  0.497,  0.498,  0.499,  0.5  ,  0.501,  0.502,  0.503,\n",
        "        0.504,  0.505,  0.506,  0.507,  0.508,  0.509,  0.51 ,  0.511,\n",
        "        0.512,  0.513,  0.514,  0.515,  0.516,  0.517,  0.518,  0.519,\n",
        "        0.52 ,  0.521,  0.522,  0.523,  0.524,  0.525,  0.526,  0.527,\n",
        "        0.528,  0.529,  0.53 ,  0.531,  0.532,  0.533,  0.534,  0.535,\n",
        "        0.536,  0.537,  0.538,  0.539,  0.54 ,  0.541,  0.542,  0.543,\n",
        "        0.544,  0.545,  0.546,  0.547,  0.548,  0.549,  0.55 ,  0.551,\n",
        "        0.552,  0.553,  0.554,  0.555,  0.556,  0.557,  0.558,  0.559,\n",
        "        0.56 ,  0.561,  0.562,  0.563,  0.564,  0.565,  0.566,  0.567,\n",
        "        0.568,  0.569,  0.57 ,  0.571,  0.572,  0.573,  0.574,  0.575,\n",
        "        0.576,  0.577,  0.578,  0.579,  0.58 ,  0.581,  0.582,  0.583,\n",
        "        0.584,  0.585,  0.586,  0.587,  0.588,  0.589,  0.59 ,  0.591,\n",
        "        0.592,  0.593,  0.594,  0.595,  0.596,  0.597,  0.598,  0.599,\n",
        "        0.6  ,  0.601,  0.602,  0.603,  0.604,  0.605,  0.606,  0.607,\n",
        "        0.608,  0.609,  0.61 ,  0.611,  0.612,  0.613,  0.614,  0.615,\n",
        "        0.616,  0.617,  0.618,  0.619,  0.62 ,  0.621,  0.622,  0.623,\n",
        "        0.624,  0.625,  0.626,  0.627,  0.628,  0.629,  0.63 ,  0.631,\n",
        "        0.632,  0.633,  0.634,  0.635,  0.636,  0.637,  0.638,  0.639,\n",
        "        0.64 ,  0.641,  0.642,  0.643,  0.644,  0.645,  0.646,  0.647,\n",
        "        0.648,  0.649,  0.65 ,  0.651,  0.652,  0.653,  0.654,  0.655,\n",
        "        0.656,  0.657,  0.658,  0.659,  0.66 ,  0.661,  0.662,  0.663,\n",
        "        0.664,  0.665,  0.666,  0.667,  0.668,  0.669,  0.67 ,  0.671,\n",
        "        0.672,  0.673,  0.674,  0.675,  0.676,  0.677,  0.678,  0.679,\n",
        "        0.68 ,  0.681,  0.682,  0.683,  0.684,  0.685,  0.686,  0.687,\n",
        "        0.688,  0.689,  0.69 ,  0.691,  0.692,  0.693,  0.694,  0.695,\n",
        "        0.696,  0.697,  0.698,  0.699,  0.7  ,  0.701,  0.702,  0.703,\n",
        "        0.704,  0.705,  0.706,  0.707,  0.708,  0.709,  0.71 ,  0.711,\n",
        "        0.712,  0.713,  0.714,  0.715,  0.716,  0.717,  0.718,  0.719,\n",
        "        0.72 ,  0.721,  0.722,  0.723,  0.724,  0.725,  0.726,  0.727,\n",
        "        0.728,  0.729,  0.73 ,  0.731,  0.732,  0.733,  0.734,  0.735,\n",
        "        0.736,  0.737,  0.738,  0.739,  0.74 ,  0.741,  0.742,  0.743,\n",
        "        0.744,  0.745,  0.746,  0.747,  0.748,  0.749,  0.75 ,  0.751,\n",
        "        0.752,  0.753,  0.754,  0.755,  0.756,  0.757,  0.758,  0.759,\n",
        "        0.76 ,  0.761,  0.762,  0.763,  0.764,  0.765,  0.766,  0.767,\n",
        "        0.768,  0.769,  0.77 ,  0.771,  0.772,  0.773,  0.774,  0.775,\n",
        "        0.776,  0.777,  0.778,  0.779,  0.78 ,  0.781,  0.782,  0.783,\n",
        "        0.784,  0.785,  0.786,  0.787,  0.788,  0.789,  0.79 ,  0.791,\n",
        "        0.792,  0.793,  0.794,  0.795,  0.796,  0.797,  0.798,  0.799,\n",
        "        0.8  ,  0.801,  0.802,  0.803,  0.804,  0.805,  0.806,  0.807,\n",
        "        0.808,  0.809,  0.81 ,  0.811,  0.812,  0.813,  0.814,  0.815,\n",
        "        0.816,  0.817,  0.818,  0.819,  0.82 ,  0.821,  0.822,  0.823,\n",
        "        0.824,  0.825,  0.826,  0.827,  0.828,  0.829,  0.83 ,  0.831,\n",
        "        0.832,  0.833,  0.834,  0.835,  0.836,  0.837,  0.838,  0.839,\n",
        "        0.84 ,  0.841,  0.842,  0.843,  0.844,  0.845,  0.846,  0.847,\n",
        "        0.848,  0.849,  0.85 ,  0.851,  0.852,  0.853,  0.854,  0.855,\n",
        "        0.856,  0.857,  0.858,  0.859,  0.86 ,  0.861,  0.862,  0.863,\n",
        "        0.864,  0.865,  0.866,  0.867,  0.868,  0.869,  0.87 ,  0.871,\n",
        "        0.872,  0.873,  0.874,  0.875,  0.876,  0.877,  0.878,  0.879,\n",
        "        0.88 ,  0.881,  0.882,  0.883,  0.884,  0.885,  0.886,  0.887,\n",
        "        0.888,  0.889,  0.89 ,  0.891,  0.892,  0.893,  0.894,  0.895,\n",
        "        0.896,  0.897,  0.898,  0.899,  0.9  ,  0.901,  0.902,  0.903,\n",
        "        0.904,  0.905,  0.906,  0.907,  0.908,  0.909,  0.91 ,  0.911,\n",
        "        0.912,  0.913,  0.914,  0.915,  0.916,  0.917,  0.918,  0.919,\n",
        "        0.92 ,  0.921,  0.922,  0.923,  0.924,  0.925,  0.926,  0.927,\n",
        "        0.928,  0.929,  0.93 ,  0.931,  0.932,  0.933,  0.934,  0.935,\n",
        "        0.936,  0.937,  0.938,  0.939,  0.94 ,  0.941,  0.942,  0.943,\n",
        "        0.944,  0.945,  0.946,  0.947,  0.948,  0.949,  0.95 ,  0.951,\n",
        "        0.952,  0.953,  0.954,  0.955,  0.956,  0.957,  0.958,  0.959,\n",
        "        0.96 ,  0.961,  0.962,  0.963,  0.964,  0.965,  0.966,  0.967,\n",
        "        0.968,  0.969,  0.97 ,  0.971,  0.972,  0.973,  0.974,  0.975,\n",
        "        0.976,  0.977,  0.978,  0.979,  0.98 ,  0.981,  0.982,  0.983,\n",
        "        0.984,  0.985,  0.986,  0.987,  0.988,  0.989,  0.99 ,  0.991,\n",
        "        0.992,  0.993,  0.994,  0.995,  0.996,  0.997,  0.998,  0.999], time_unit='s')"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import nitime.viz as viz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = viz.plot_tseries(ts1)\n",
      "fig.set_size_inches([10,10])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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9aJqzIUk7Li9mo1qVM+GpVBh7WZEUezRdxVFluqTM1gCTOE6f9j0KPUxOAmNj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\nPtL1Y6erPtI7XTbzJk2XZ6QnDhbtxZF+bAQTf30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ZWfv3onblwwmPXlzGHjtd5ePyyA92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\naEYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c0c9d50>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here's what it looks like with mult-channel data: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = ts1.data[np.newaxis] + np.random.randn(10, 1000) * 0.1\n",
      "ts2 = nts.TimeSeries(data, sampling_rate=10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`TimeSeries` instances have some other useful attributes that you can look at:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ts2.duration"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "100.0 s"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ts2.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "(10, 1000)"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To get the data out for a particular time-point, you can use the at method: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ts2.at(nts.TimeArray(0.06,time_unit='s'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "array([-0.15152709, -0.05623551,  0.07572541, -0.06196148, -0.05499754,\n",
        "       -0.04667306,  0.12209412, -0.11817527, -0.01104071,  0.04953479])"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The same thing happens if you index into the object:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ts2[(nts.TimeArray(0.06,time_unit='s'))]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "array([-0.15152709, -0.05623551,  0.07572541, -0.06196148, -0.05499754,\n",
        "       -0.04667306,  0.12209412, -0.11817527, -0.01104071,  0.04953479])"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Analysis\n",
      "\n",
      "Most analysis will rely on `analyzer` objects. These typically take as their input a `TimeSeries` object and additional parameters. For example, let's perform a spectral analysis of the `TimeSeries` object we just created. We start by importing the analysis module: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import nitime.analysis as nta"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We initialize a `SpectralAnalyzer` object, with the time-series as input"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sa1 = nta.SpectralAnalyzer(ts2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This analyzer has several attributes. For example, we can get the spectrum for every channel calculated using a windowed psd method : "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, psd = sa1.psd\n",
      "psd.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "(10, 33)"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1)\n",
      "ax.plot(f, psd[0])\n",
      "fig.set_size_inches([8,6])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAeEAAAFtCAYAAAAwONKtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIoQBoBAhDACFCGEAKEQIA0Ah/w9NeQexfBCc8gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c0ebc90>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or using a multi-taper (slepian) windows: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, per = sa1.spectrum_multi_taper\n",
      "per.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "(10, 501)"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1)\n",
      "ax.plot(f, per[0])\n",
      "fig.set_size_inches([8,6])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10c582d90>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or the pair-wise cross-spectrum between every pair of channels in the time-series"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, csd = sa1.cpsd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "csd.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "(10, 10, 33)"
       ]
      }
     ],
     "prompt_number": 56
    }
   ],
   "metadata": {}
  }
 ]
}