finding_faces_really.ipynb

{
 "metadata": {
  "name": "finding_faces_really"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we try the simplest possible general linear model on some example data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You are used to seeing these now:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Set defaults for the figures"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.rcParams['image.cmap'] = 'gray'\n",
      "plt.rcParams['image.interpolation'] = 'nearest'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Our faithful image library"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import nibabel as nib"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's get the data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "HOME = os.path.expanduser('~')\n",
      "DATA_PATH = os.path.join(HOME, 'data')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "img_fname = os.path.join(DATA_PATH, 'ds105', 'sub001', 'BOLD', 'task001_run001', 'bold.nii.gz')\n",
      "img = nib.load(img_fname)\n",
      "n_vols = img.shape[-1]\n",
      "n_vols"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "121"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now let's get the task.  There were blocks of different types of objects in an on-off pattern.  We'll pool the objects to get just 'on-off':"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# from sub001/model/model001/onsets/task001_run001\n",
      "block_starts_seconds = np.array([12, 48, 84, 120, 156, 192, 228, 264])\n",
      "block_length_seconds = 22\n",
      "TR = 2.5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Same in scans:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "block_starts_scans = block_starts_seconds / TR\n",
      "block_length_scans = block_length_seconds / TR\n",
      "block_starts_scans"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "array([   4.8,   19.2,   33.6,   48. ,   62.4,   76.8,   91.2,  105.6])"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Make a regressor.  Let's give the HRF a delay of 2 scans or so."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "delay = 2\n",
      "regressor = np.zeros((n_vols,))\n",
      "for start in block_starts_scans:\n",
      "    start_tr = round(start + delay)\n",
      "    end_tr = round(start + delay + block_length_scans)\n",
      "    regressor[start_tr:end_tr+1] = 1\n",
      "    print start_tr, end_tr\n",
      "plt.plot(regressor)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "7.0 16.0\n",
        "21.0 30.0\n",
        "36.0 44.0\n",
        "50.0 59.0\n",
        "64.0 73.0\n",
        "79.0 88.0\n",
        "93.0 102.0\n",
        "108.0 116.0\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[<matplotlib.lines.Line2D at 0x4022b10>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD9CAYAAAC2l2x5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKIoSULTAK4qiBJT/BaN6516uf1VnAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3ba8910>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Make the design:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = np.column_stack((regressor, np.ones((n_vols,))))\n",
      "data = img.get_data()\n",
      "slice_size = np.prod(data.shape[:-1])\n",
      "print(slice_size)\n",
      "Y = np.reshape(data, (slice_size, n_vols)).T\n",
      "Y.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "163840\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(121, 163840)"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "array([[ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.],\n",
        "       [ 0.,  1.]])"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Fit the model!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "B = np.linalg.pinv(X).dot(Y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "B.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "(2, 163840)"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Make B into an image again:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "B = np.reshape(B, (2,) + img.shape[:-1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "B.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "(2, 40, 64, 64)"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's have a look at the image:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.imshow(B[0, :, :, 40])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.image.AxesImage at 0x4534c10>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAADtCAYAAACbBE9wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3f42450>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "B_selected = B[:, 19, 12, 40]\n",
      "B_selected"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "array([  14.78175313,  540.37209302])"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Yselected = data[19, 12, 40]\n",
      "Yselected.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "(121,)"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Bselected = np.linalg.pinv(X).dot(Yselected)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Bselected"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "array([  14.78175313,  540.37209302])"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(Yselected - np.mean(Yselected))\n",
      "plt.plot(regressor * 40, 'r')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "[<matplotlib.lines.Line2D at 0x40408d0>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD9CAYAAABQvqc9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niSH+P4vs7q5h5Q2CAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x451d550>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What is the 'fitted' time course?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Yhat = X.dot(Bselected)\n",
      "plt.plot(Yhat, 'r')\n",
      "plt.plot(Yselected)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "[<matplotlib.lines.Line2D at 0x486a310>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAD9CAYAAABdoNd6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x451db90>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For convenience, let's package the model fit up into a function:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def fit_model(data, X):\n",
      "    # Shape of design\n",
      "    n_vols, n_params = X.shape\n",
      "    assert n_vols == data.shape[-1]\n",
      "    # Reshape data to be time by voxels\n",
      "    Y = np.reshape(data, (-1, n_vols)).T\n",
      "    B = np.linalg.pinv(X).dot(Y)\n",
      "    # Reshape betas to image\n",
      "    B = np.reshape(B, (n_params,) + data.shape[:-1])\n",
      "    return B"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "How would we we remove these drifts?  We need to add something to $X$."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# What should we add to X?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "B_better = fit_model(data, X_better)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "B_better_selected = B_better[:, 19, 12, 40]\n",
      "Yhat_better = X_better.dot(B_better_selected)\n",
      "plt.plot(Yhat_better, 'r')\n",
      "plt.plot(Yselected)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "[<matplotlib.lines.Line2D at 0x50e7c50>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAD9CAYAAABdoNd6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDYkSAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3229310>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "B_better.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "(5, 40, 64, 64)"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.imshow(B_better[0, :, :, 40])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "<matplotlib.image.AxesImage at 0x530e990>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAADtCAYAAACbBE9wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/8nyx0u3HXtsAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe594159150>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Can we do better than this?  Time for some hemodynamic modeling!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import nipy.modalities.fmri.hrf as hrfs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Tab completion right?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What does an HRF look like?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = np.linspace(0, 30, 100)\n",
      "plt.plot(t, hrfs.glovert(t))\n",
      "plt.xlabel('time (seconds)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "<matplotlib.text.Text at 0x6dc7510>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEMCAYAAAAmgtofAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6c24b50>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "How do we apply that to our data?  Convolution!"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}