{
 "metadata": {
  "name": "what_is_an_image"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What is an image?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We get an image from our example dataset : http://practical-neuroimaging.github.com/example_data.html\n",
      "\n",
      "If we haven't got the zip archive already, we'll get it using Python"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os # The Operating System module"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zip_fname = 'two_example_images.zip'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "if not os.path.isfile(zip_fname):\n",
      "    import urllib2 # library to get URLs\n",
      "    url = 'http://nipy.bic.berkeley.edu/practical_neuroimaging/' + zip_fname\n",
      "    # Get the data from the URL address\n",
      "    fileobj = urllib2.urlopen(url)\n",
      "    data = fileobj.read()\n",
      "    # Write it to disc as the zip file\n",
      "    local_fileobj = open(zip_fname, 'wb')\n",
      "    local_fileobj.write(data)\n",
      "    local_fileobj.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We now have the file downloaded locally.  To check for the file, we can use the ``glob`` module.  This allows us to do wildcard matching to filenames.  For example, to look for files in the current directory ending with ``.zip``:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import glob\n",
      "glob.glob('*.zip')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "['two_example_images.zip']"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What does this file have inside?  We can look at it using the Python ``zipfile`` module."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import zipfile\n",
      "z_fobj = zipfile.ZipFile(zip_fname, 'r')\n",
      "z_fobj.namelist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "['ds107_sub001_highres.nii.gz', 'ds107_sub001_run01.nii.gz']"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Don't forget to tab complete on - say - ``z_fobj`` above, to see what you can do with it."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We extract the files:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z_fobj.extractall()\n",
      "z_fobj.close()\n",
      "glob.glob('*.nii.gz')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "['ds107_sub001_highres.nii.gz', 'ds107_sub001_run01.nii.gz']"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The images have ``gz`` extensions. Looks like they are gzipped. What could be better than opening the file with the ``gzip`` module?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import gzip\n",
      "gzfobj = gzip.open('ds107_sub001_highres.nii.gz', 'rb')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What type of thing is ``gzfobj``?  Try typing ``Ctrl-m b`` to insert a cell below, then type ``gzfobj.`` (remember the dot) and then tab."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's read in all the data to take a look"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "img_data = gzfobj.read()\n",
      "gzfobj.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What do we have now?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(img_data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "str"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(img_data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "25166176"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What is in here?   I do a Google search for \"Nifti header\". The second hit takes me to this page: http://nifti.nimh.nih.gov/nifti-1/documentation/nifti1fields.\n",
      "\n",
      "It reveals that that first bytes should be an integer value 348 and that it has a length of 4:\n",
      "\n",
      "``int   sizeof_hdr;    /*!< MUST be 348           */  /* int sizeof_hdr;      */   /*   0 */``\n",
      "\n",
      "Let's look at the numerical values of the first 4 bytes:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(4):\n",
      "    print ord(img_data[i])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "92\n",
        "1\n",
        "0\n",
        "0\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is 348 as a four byte integer.  Quick quiz: why? Clue follows:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "348 - 92"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "256"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "OK, so we want to put this binary form of the integer in memory, into Python as an integer object.  How to do that?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import struct"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To see what is in ``struct``, try ``Ctrl-m b`` again, and ``struct?`` to see the help.  Then check ``struct.pack?`` and ``struct.unpack?``.  Among other things, we find that ``<i`` is the code for a signed 4-byte 32-bit integer, with little-endian byte ordering."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "struct.unpack('<i', img_data[:4])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "(348,)"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Nice.  Now, what else do we need to know?  The shape of the data? We get this from this field of the NIfTI header:\n",
      "\n",
      "``short dim[8];        /*!< Data array dimensions.*/  /* short dim[8];        */   /*  40 */``\n",
      "\n",
      "The help for ``struct`` tells us we need the code ``<8h`` for 8 16-bit little-endian integers."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "struct.unpack('<8h', img_data[40:56])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "(3, 256, 256, 192, 1, 1, 1, 1)"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "OK - looks like it's 256 by 256 by 192.  What's the data type?  We want this field:\n",
      "\n",
      "``short datatype;      /*!< Defines data type!    */  /* short datatype;      */   /*  70 */``"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "struct.unpack('<h', img_data[70:72])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "(4,)"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looking at : http://nifti.nimh.nih.gov/nifti-1/documentation/nifti1fields/nifti1fields_pages/datatype.html - this seems to mean 16 bit signed integer.  Where does the image data start in this file?\n",
      "\n",
      "``float vox_offset;    /*!< Offset into .nii file */  /* float vox_offset;    */   /* 108 */``\n",
      "\n",
      "The code we need is for a 4-byte floating point value - ``<f``"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "struct.unpack('<f', img_data[108:112])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "(352.0,)"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "That's what we were expecting (well - it was what I was expecting!).\n",
      "\n",
      "So, we are expecting 256 * 256 * 192 2-byte integers, to follow the 348 bytes of the header, and the 4 extra bytes before the data starts.  So how long should the entire image file be?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we can could read the image data, veerrryyy slowly, like this:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fmt_str = '<%dh' % (256 * 256 * 192)\n",
      "img_values = struct.unpack(fmt_str, img_data[352:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(img_values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "12582912"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(img_values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "tuple"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can get the mean of all the values in the image like this:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sum(img_values) / float(len(img_values))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "32.01169069608053"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It's time to introduce ``numpy``.  Numpy is the array manipulation library for Python"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Numpy allows you to look at binary data as arrays.  For example, here's a much quicker trick to get the image data into an array:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "img_array = np.ndarray(shape=(256, 256, 192), dtype='<i2', order='F', buffer=img_data[352:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Please don't worry about the details of this command, but what we get back is - an array:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "img_array.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "(256, 256, 192)"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "img_array.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "32.011690696080528"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As we'll see soon, we can take slices out of this array to show image slices.  First we enable the plotting.  Don't worry about the details, we'll go over this again:"
     ]
    },
    {
     "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.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z_slice = img_array[:, :, 96]\n",
      "plt.imshow(z_slice)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "<matplotlib.image.AxesImage at 0x105c2a7d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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YulkYg6NVWDqSPDDohp1FaFRD1ZJWr1oEGl2NArqi5sqRlWp+i2TNSk6EaBVq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\nbS1jtXn4XhwKFhaeLgTbnCcSq5CbSYPr0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      }
     ],
     "prompt_number": 35
    }
   ],
   "metadata": {}
  }
 ]
}