novice: Copy-paste from origin/master
authorW. Trevor King <wking@tremily.us>
Tue, 11 Mar 2014 03:15:18 +0000 (20:15 -0700)
committerW. Trevor King <wking@tremily.us>
Sun, 16 Mar 2014 22:23:20 +0000 (15:23 -0700)
I couldn't find a way to merge these without clobbering a bunch of
other files, so this is just a copy-paste of the current status as of
e4b84bd (Merge pull request #359 from
swcarpentry/ahmadia/dhaine_r_ref, 2014-03-06).

75 files changed:
novice/python/.gitignore [new file with mode: 0644]
novice/python/01-numpy.ipynb [new file with mode: 0644]
novice/python/02-func.ipynb [new file with mode: 0644]
novice/python/03-loop.ipynb [new file with mode: 0644]
novice/python/04-cond.ipynb [new file with mode: 0644]
novice/python/05-defensive.ipynb [new file with mode: 0644]
novice/python/06-cmdline.ipynb [new file with mode: 0644]
novice/python/README.txt [new file with mode: 0644]
novice/python/argv-list.py [new file with mode: 0644]
novice/python/count-stdin.py [new file with mode: 0644]
novice/python/gen-inflammation.py [new file with mode: 0644]
novice/python/img/ave-inflammation-over-time.png [new file with mode: 0644]
novice/python/img/color-cube.png [new file with mode: 0644]
novice/python/img/combined-inflammation-2.png [new file with mode: 0644]
novice/python/img/combined-inflammation-per-day.png [new file with mode: 0644]
novice/python/img/initial-heat-map.png [new file with mode: 0644]
novice/python/img/loop-inflammation-01.png [new file with mode: 0644]
novice/python/img/loop-inflammation-02.png [new file with mode: 0644]
novice/python/img/loop-inflammation-03.png [new file with mode: 0644]
novice/python/img/max-inflammation-over-time.png [new file with mode: 0644]
novice/python/img/min-inflammation-per-day.png [new file with mode: 0644]
novice/python/img/python-call-stack-01.odg [new file with mode: 0644]
novice/python/img/python-call-stack-01.svg [new file with mode: 0644]
novice/python/img/python-call-stack-02.odg [new file with mode: 0644]
novice/python/img/python-call-stack-02.svg [new file with mode: 0644]
novice/python/img/python-call-stack-03.odg [new file with mode: 0644]
novice/python/img/python-call-stack-03.svg [new file with mode: 0644]
novice/python/img/python-call-stack-04.odg [new file with mode: 0644]
novice/python/img/python-call-stack-04.svg [new file with mode: 0644]
novice/python/img/python-call-stack-05.odg [new file with mode: 0644]
novice/python/img/python-call-stack-05.svg [new file with mode: 0644]
novice/python/img/python-call-stack-06.odg [new file with mode: 0644]
novice/python/img/python-call-stack-06.svg [new file with mode: 0644]
novice/python/img/python-call-stack-07.odg [new file with mode: 0644]
novice/python/img/python-call-stack-07.svg [new file with mode: 0644]
novice/python/img/python-flowchart-conditional.odg [new file with mode: 0644]
novice/python/img/python-flowchart-conditional.svg [new file with mode: 0644]
novice/python/img/python-flowchart-nested-loops.odg [new file with mode: 0644]
novice/python/img/python-flowchart-nested-loops.svg [new file with mode: 0644]
novice/python/img/python-operations-across-axes.odg [new file with mode: 0644]
novice/python/img/python-operations-across-axes.svg [new file with mode: 0644]
novice/python/img/python-overlapping-ranges.odg [new file with mode: 0644]
novice/python/img/python-overlapping-ranges.svg [new file with mode: 0644]
novice/python/img/python-sticky-note-variables-01.odg [new file with mode: 0644]
novice/python/img/python-sticky-note-variables-01.svg [new file with mode: 0644]
novice/python/img/python-sticky-note-variables-02.odg [new file with mode: 0644]
novice/python/img/python-sticky-note-variables-02.svg [new file with mode: 0644]
novice/python/img/python-sticky-note-variables-03.odg [new file with mode: 0644]
novice/python/img/python-sticky-note-variables-03.svg [new file with mode: 0644]
novice/python/index.md [new file with mode: 0644]
novice/python/inflammation-01.csv [new file with mode: 0644]
novice/python/inflammation-02.csv [new file with mode: 0644]
novice/python/inflammation-03.csv [new file with mode: 0644]
novice/python/inflammation-04.csv [new file with mode: 0644]
novice/python/inflammation-05.csv [new file with mode: 0644]
novice/python/inflammation-06.csv [new file with mode: 0644]
novice/python/inflammation-07.csv [new file with mode: 0644]
novice/python/inflammation-08.csv [new file with mode: 0644]
novice/python/inflammation-09.csv [new file with mode: 0644]
novice/python/inflammation-10.csv [new file with mode: 0644]
novice/python/inflammation-11.csv [new file with mode: 0644]
novice/python/inflammation-12.csv [new file with mode: 0644]
novice/python/readings-01.py [new file with mode: 0644]
novice/python/readings-02.py [new file with mode: 0644]
novice/python/readings-03.py [new file with mode: 0644]
novice/python/readings-04.py [new file with mode: 0644]
novice/python/readings-05.py [new file with mode: 0644]
novice/python/readings-06.py [new file with mode: 0644]
novice/python/rectangle.py [new file with mode: 0644]
novice/python/small-01.csv [new file with mode: 0644]
novice/python/small-02.csv [new file with mode: 0644]
novice/python/small-03.csv [new file with mode: 0644]
novice/python/spatial-intro.ipynb [new file with mode: 0644]
novice/python/swc_bc_coords.csv [new file with mode: 0644]
novice/python/sys-version.py [new file with mode: 0644]

diff --git a/novice/python/.gitignore b/novice/python/.gitignore
new file mode 100644 (file)
index 0000000..759975e
--- /dev/null
@@ -0,0 +1,6 @@
+01-numpy.md
+02-func.md
+03-loop.md
+04-cond.md
+05-defensive.md
+06-cmdline.md
diff --git a/novice/python/01-numpy.ipynb b/novice/python/01-numpy.ipynb
new file mode 100644 (file)
index 0000000..687d29c
--- /dev/null
@@ -0,0 +1,1534 @@
+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Analyzing Patient Data"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We are studying inflammation in patients who have been given a new treatment for arthritis,\n",
+      "and need to analyze the first dozen data sets.\n",
+      "The data sets are stored in [comma-separated values](../../gloss.html#csv) (CSV) format:\n",
+      "each row holds information for a single patient,\n",
+      "and the columns represent successive days.\n",
+      "The first few rows of our first file look like this:\n",
+      "\n",
+      "    0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0\n",
+      "    0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1\n",
+      "    0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1\n",
+      "    0,0,2,0,4,2,2,1,6,7,10,7,9,13,8,8,15,10,10,7,17,4,4,7,6,15,6,4,9,11,3,5,6,3,3,4,2,3,2,1\n",
+      "    0,1,1,3,3,1,3,5,2,4,4,7,6,5,3,10,8,10,6,17,9,14,9,7,13,9,12,6,7,7,9,6,3,2,2,4,2,0,1,1"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We want to:\n",
+      "\n",
+      "*   load that data into memory,\n",
+      "*   calculate the average inflammation per day across all patients, and\n",
+      "*   plot the result.\n",
+      "\n",
+      "To do all that, we'll have to learn a little bit about programming."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "objectives"
+      ]
+     },
+     "source": [
+      "#### Objectives\n",
+      "\n",
+      "*   Explain what a library is, and what libraries are used for.\n",
+      "*   Load a Python library and use the things it contains.\n",
+      "*   Read tabular data from a file into a program.\n",
+      "*   Assign values to variables.\n",
+      "*   Select individual values and subsections from data.\n",
+      "*   Perform operations on arrays of data.\n",
+      "*   Display simple graphs."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Loading Data"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Words are useful,\n",
+      "but what's more useful are the sentences and stories we use them to build.\n",
+      "Similarly,\n",
+      "while a lot of powerful tools are built into languages like Python,\n",
+      "even more lives in the [libraries](../../gloss.html#library) they are used to build.\n",
+      "\n",
+      "In order to load our inflammation data,\n",
+      "we need to [import](../../gloss.html#import) a library called NumPy\n",
+      "that knows how to operate on matrices:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Importing a library is like getting a piece of lab equipment out of a storage locker\n",
+      "and setting it up on the bench.\n",
+      "Once it's done,\n",
+      "we can ask the library to read our data file for us:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 2,
+       "text": [
+        "array([[ 0.,  0.,  1., ...,  3.,  0.,  0.],\n",
+        "       [ 0.,  1.,  2., ...,  1.,  0.,  1.],\n",
+        "       [ 0.,  1.,  1., ...,  2.,  1.,  1.],\n",
+        "       ..., \n",
+        "       [ 0.,  1.,  1., ...,  1.,  1.,  1.],\n",
+        "       [ 0.,  0.,  0., ...,  0.,  2.,  0.],\n",
+        "       [ 0.,  0.,  1., ...,  1.,  1.,  0.]])"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The expression `numpy.loadtxt(...)` is a [function call](../../gloss.html#function-call)\n",
+      "that asks Python to run the function `loadtxt` that belongs to the `numpy` library.\n",
+      "This [dotted notation](../../gloss.html#dotted-notation) is used everywhere in Python\n",
+      "to refer to the parts of things as `whole.part`.\n",
+      "\n",
+      "`numpy.loadtxt` has two [parameters](../../gloss.html#parameter):\n",
+      "the name of the file we want to read,\n",
+      "and the [delimiter](../../gloss.html#delimiter) that separates values on a line.\n",
+      "These both need to be character strings (or [strings](../../gloss.html#string) for short),\n",
+      "so we put them in quotes.\n",
+      "\n",
+      "When we are finished typing and press Shift+Enter,\n",
+      "the notebook runs our command.\n",
+      "Since we haven't told it to do anything else with the function's output,\n",
+      "the notebook displays it.\n",
+      "In this case,\n",
+      "that output is the data we just loaded.\n",
+      "By default,\n",
+      "only a few rows and columns are shown\n",
+      "(with `...` displayed to mark missing data).\n",
+      "To save space,\n",
+      "Python displays numbers as `1.` instead of `1.0`\n",
+      "when there's nothing interesting after the decimal point."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Our call to `numpy.loadtxt` read our file,\n",
+      "but didn't save the data in memory.\n",
+      "To do that,\n",
+      "we need to [assign](../../gloss.html#assignment) the array to a [variable](../../gloss.html#variable).\n",
+      "A variable is just a name for a value,\n",
+      "such as `x`, `current_temperature`, or `subject_id`.\n",
+      "We can create a new variable simply by assigning a value to it using `=`:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "weight_kg = 55"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Once a variable has a value, we can print it:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print weight_kg"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "55\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "and do arithmetic with it:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'weight in pounds:', 2.2 * weight_kg"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "weight in pounds: 121.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We can also change a variable's value by assigning it a new one:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "weight_kg = 57.5\n",
+      "print 'weight in kilograms is now:', weight_kg"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "weight in kilograms is now: 57.5\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "As the example above shows,\n",
+      "we can print several things at once by separating them with commas.\n",
+      "\n",
+      "If we imagine the variable as a sticky note with a name written on it,\n",
+      "assignment is like putting the sticky note on a particular value:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "<img src=\"files/img/python-sticky-note-variables-01.svg\" alt=\"Variables as Sticky Notes\" />"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "This means that assigning a value to one variable does *not* change the values of other variables.\n",
+      "For example,\n",
+      "let's store the subject's weight in pounds in a variable:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "weight_lb = 2.2 * weight_kg\n",
+      "print 'weight in kilograms:', weight_kg, 'and in pounds:', weight_lb"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "weight in kilograms: 57.5 and in pounds: 126.5\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<img src=\"files/img/python-sticky-note-variables-02.svg\" alt=\"Creating Another Variable\" />"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "and then change `weight_kg`:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "weight_kg = 100.0\n",
+      "print 'weight in kilograms is now:', weight_kg, 'and weight in pounds is still:', weight_lb"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "weight in kilograms is now: 100.0 and weight in pounds is still: 126.5\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<img src=\"files/img/python-sticky-note-variables-03.svg\" alt=\"Updating a Variable\" />"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Since `weight_lb` doesn't \"remember\" where its value came from,\n",
+      "it isn't automatically updated when `weight_kg` changes.\n",
+      "This is different from the way spreadsheets work.\n",
+      "\n",
+      "Now that we know how to assign things to variables,\n",
+      "let's re-run `numpy.loadtxt` and save its result:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "This statement doesn't produce any output because assignment doesn't display anything.\n",
+      "If we want to check that our data has been loaded,\n",
+      "we can print the variable's value:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print data"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[[ 0.  0.  1. ...,  3.  0.  0.]\n",
+        " [ 0.  1.  2. ...,  1.  0.  1.]\n",
+        " [ 0.  1.  1. ...,  2.  1.  1.]\n",
+        " ..., \n",
+        " [ 0.  1.  1. ...,  1.  1.  1.]\n",
+        " [ 0.  0.  0. ...,  0.  2.  0.]\n",
+        " [ 0.  0.  1. ...,  1.  1.  0.]]\n"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "challenges"
+      ]
+     },
+     "source": [
+      "#### Challenges\n",
+      "\n",
+      "1.  Draw diagrams showing what variables refer to what values after each statement in the following program:\n",
+      "\n",
+      "    ~~~python\n",
+      "    mass = 47.5\n",
+      "    age = 122\n",
+      "    mass = mass * 2.0\n",
+      "    age = age - 20\n",
+      "    ~~~\n",
+      "\n",
+      "1.  What does the following program print out?\n",
+      "    ~~~python\n",
+      "    first, second = 'Grace', 'Hopper'\n",
+      "    third, fourth = second, first\n",
+      "    print third, fourth\n",
+      "    ~~~"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Manipulating Data"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Now that our data is in memory,\n",
+      "we can start doing things with it.\n",
+      "First,\n",
+      "let's ask what [type](../../gloss.html#data-type) of thing `data` refers to:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print type(data)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "<type 'numpy.ndarray'>\n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The output tells us that `data` currently refers to an N-dimensional array created by the NumPy library.\n",
+      "We can see what its [shape](../../gloss.html#shape) is like this:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print data.shape"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "(60, 40)\n"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "This tells us that `data` has 60 rows and 40 columns.\n",
+      "`data.shape` is a [member](../../gloss.html#member) of `data`,\n",
+      "i.e.,\n",
+      "a value that is stored as part of a larger value.\n",
+      "We use the same dotted notation for the members of values\n",
+      "that we use for the functions in libraries\n",
+      "because they have the same part-and-whole relationship."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "If we want to get a single value from the matrix,\n",
+      "we must provide an [index](../../gloss.html#index) in square brackets,\n",
+      "just as we do in math:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'first value in data:', data[0, 0]"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "first value in data: 0.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'middle value in data:', data[30, 20]"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "middle value in data: 13.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The expression `data[30, 20]` may not surprise you,\n",
+      "but `data[0, 0]` might.\n",
+      "Programming languages like Fortran and MATLAB start counting at 1,\n",
+      "because that's what human beings have done for thousands of years.\n",
+      "Languages in the C family (including C++, Java, Perl, and Python) count from 0\n",
+      "because that's simpler for computers to do.\n",
+      "As a result,\n",
+      "if we have an M&times;N array in Python,\n",
+      "its indices go from 0 to M-1 on the first axis\n",
+      "and 0 to N-1 on the second.\n",
+      "It takes a bit of getting used to,\n",
+      "but one way to remember the rule is that\n",
+      "the index is how many steps we have to take from the start to get the item we want.\n",
+      "\n",
+      "> #### In the Corner\n",
+      ">\n",
+      "> What may also surprise you is that when Python displays an array,\n",
+      "> it shows the element with index `[0, 0]` in the upper left corner\n",
+      "> rather than the lower left.\n",
+      "> This is consistent with the way mathematicians draw matrices,\n",
+      "> but different from the Cartesian coordinates.\n",
+      "> The indices are (row, column) instead of (column, row) for the same reason."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "An index like `[30, 20]` selects a single element of an array,\n",
+      "but we can select whole sections as well.\n",
+      "For example,\n",
+      "we can select the first ten days (columns) of values\n",
+      "for the first four (rows) patients like this:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print data[0:4, 0:10]"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[[ 0.  0.  1.  3.  1.  2.  4.  7.  8.  3.]\n",
+        " [ 0.  1.  2.  1.  2.  1.  3.  2.  2.  6.]\n",
+        " [ 0.  1.  1.  3.  3.  2.  6.  2.  5.  9.]\n",
+        " [ 0.  0.  2.  0.  4.  2.  2.  1.  6.  7.]]\n"
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The [slice](../../gloss.html#slice) `0:4` means,\n",
+      "\"Start at index 0 and go up to, but not including, index 4.\"\n",
+      "Again,\n",
+      "the up-to-but-not-including takes a bit of getting used to,\n",
+      "but the rule is that the difference between the upper and lower bounds is the number of values in the slice.\n",
+      "\n",
+      "We don't have to start slices at 0:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print data[5:10, 0:10]"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[[ 0.  0.  1.  2.  2.  4.  2.  1.  6.  4.]\n",
+        " [ 0.  0.  2.  2.  4.  2.  2.  5.  5.  8.]\n",
+        " [ 0.  0.  1.  2.  3.  1.  2.  3.  5.  3.]\n",
+        " [ 0.  0.  0.  3.  1.  5.  6.  5.  5.  8.]\n",
+        " [ 0.  1.  1.  2.  1.  3.  5.  3.  5.  8.]]\n"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "and we don't have to take all the values in the slice---if we provide a [stride](../../gloss.html#stride),\n",
+      "Python takes values spaced that far apart:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print data[0:10:3, 0:10:2]"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[[ 0.  1.  1.  4.  8.]\n",
+        " [ 0.  2.  4.  2.  6.]\n",
+        " [ 0.  2.  4.  2.  5.]\n",
+        " [ 0.  1.  1.  5.  5.]]\n"
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Here,\n",
+      "we have taken rows 0, 3, 6, and 9,\n",
+      "and columns 0, 2, 4, 6, and 8.\n",
+      "(Again, we always include the lower bound,\n",
+      "but stop when we reach or cross the upper bound.)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We also don't have to include the upper and lower bound on the slice.\n",
+      "If we don't include the lower bound,\n",
+      "Python uses 0 by default;\n",
+      "if we don't include the upper,\n",
+      "the slice runs to the end of the axis,\n",
+      "and if we don't include either\n",
+      "(i.e., if we just use ':' on its own),\n",
+      "the slice includes everything:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "small = data[:3, 36:]\n",
+      "print 'small is:'\n",
+      "print small"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "small is:\n",
+        "[[ 2.  3.  0.  0.]\n",
+        " [ 1.  1.  0.  1.]\n",
+        " [ 2.  2.  1.  1.]]\n"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Arrays also know how to perform common mathematical operations on their values.\n",
+      "If we want to find the average inflammation for all patients on all days,\n",
+      "for example,\n",
+      "we can just ask the array for its mean value"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print data.mean()"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "6.14875\n"
+       ]
+      }
+     ],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "`mean` is a [method](../../gloss.html#method) of the array,\n",
+      "i.e.,\n",
+      "a function that belongs to it\n",
+      "in the same way that the member `shape` does.\n",
+      "If variables are nouns, methods are verbs:\n",
+      "they are what the thing in question knows how to do.\n",
+      "This is why `data.shape` doesn't need to be called\n",
+      "(it's just a thing)\n",
+      "but `data.mean()` does\n",
+      "(it's an action).\n",
+      "It is also why we need empty parentheses for `data.mean()`:\n",
+      "even when we're not passing in any parameters,\n",
+      "parentheses are how we tell Python to go and do something for us.\n",
+      "\n",
+      "NumPy arrays have lots of useful methods:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'maximum inflammation:', data.max()\n",
+      "print 'minimum inflammation:', data.min()\n",
+      "print 'standard deviation:', data.std()"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "maximum inflammation: 20.0\n",
+        "minimum inflammation: 0.0\n",
+        "standard deviation: 4.61383319712\n"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "When analyzing data,\n",
+      "though,\n",
+      "we often want to look at partial statistics,\n",
+      "such as the maximum value per patient\n",
+      "or the average value per day.\n",
+      "One way to do this is to select the data we want to create a new temporary array,\n",
+      "then ask it to do the calculation:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "patient_0 = data[0, :] # 0 on the first axis, everything on the second\n",
+      "print 'maximum inflammation for patient 0:', patient_0.max()"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "maximum inflammation for patient 0: 18.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We don't actually need to store the row in a variable of its own.\n",
+      "Instead, we can combine the selection and the method call:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'maximum inflammation for patient 2:', data[2, :].max()"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "maximum inflammation for patient 2: 19.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 22
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "What if we need the maximum inflammation for *all* patients,\n",
+      "or the average for each day?\n",
+      "As the diagram below shows,\n",
+      "we want to perform the operation across an axis:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "<img src=\"files/img/python-operations-across-axes.svg\" alt=\"Operations Across Axes\" />"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "To support this,\n",
+      "most array methods allow us to specify the axis we want to work on.\n",
+      "If we ask for the average across axis 0,\n",
+      "we get:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print data.mean(axis=0)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[  0.           0.45         1.11666667   1.75         2.43333333   3.15\n",
+        "   3.8          3.88333333   5.23333333   5.51666667   5.95         5.9\n",
+        "   8.35         7.73333333   8.36666667   9.5          9.58333333\n",
+        "  10.63333333  11.56666667  12.35        13.25        11.96666667\n",
+        "  11.03333333  10.16666667  10.           8.66666667   9.15         7.25\n",
+        "   7.33333333   6.58333333   6.06666667   5.95         5.11666667   3.6\n",
+        "   3.3          3.56666667   2.48333333   1.5          1.13333333\n",
+        "   0.56666667]\n"
+       ]
+      }
+     ],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "As a quick check,\n",
+      "we can ask this array what its shape is:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print data.mean(axis=0).shape"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "(40,)\n"
+       ]
+      }
+     ],
+     "prompt_number": 24
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The expression `(40,)` tells us we have an N&times;1 vector,\n",
+      "so this is the average inflammation per day for all patients.\n",
+      "If we average across axis 1, we get:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print data.mean(axis=1)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[ 5.45   5.425  6.1    5.9    5.55   6.225  5.975  6.65   6.625  6.525\n",
+        "  6.775  5.8    6.225  5.75   5.225  6.3    6.55   5.7    5.85   6.55\n",
+        "  5.775  5.825  6.175  6.1    5.8    6.425  6.05   6.025  6.175  6.55\n",
+        "  6.175  6.35   6.725  6.125  7.075  5.725  5.925  6.15   6.075  5.75\n",
+        "  5.975  5.725  6.3    5.9    6.75   5.925  7.225  6.15   5.95   6.275  5.7\n",
+        "  6.1    6.825  5.975  6.725  5.7    6.25   6.4    7.05   5.9  ]\n"
+       ]
+      }
+     ],
+     "prompt_number": 25
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "which is the average inflammation per patient across all days."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "challenges"
+      ]
+     },
+     "source": [
+      "#### Challenges\n",
+      "\n",
+      "A subsection of an array is called a [slice](../../gloss.html#slice).\n",
+      "We can take slices of character strings as well:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "element = 'oxygen'\n",
+      "print 'first three characters:', element[0:3]\n",
+      "print 'last three characters:', element[3:6]"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": [
+       "challenges"
+      ]
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "first three characters: oxy\n",
+        "last three characters: gen\n"
+       ]
+      }
+     ],
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "challenges"
+      ]
+     },
+     "source": [
+      "1.  What is the value of `element[:4]`?\n",
+      "    What about `element[4:]`?\n",
+      "    Or `element[:]`?\n",
+      "\n",
+      "1.  What is `element[-1]`?\n",
+      "    What is `element[-2]`?\n",
+      "    Given those answers,\n",
+      "    explain what `element[1:-1]` does.\n",
+      "\n",
+      "1.  The expression `element[3:3]` produces an [empty string](../../gloss.html#empty-string),\n",
+      "    i.e., a string that contains no characters.\n",
+      "    If `data` holds our array of patient data,\n",
+      "    what does `data[3:3, 4:4]` produce?\n",
+      "    What about `data[3:3, :]`?"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Plotting"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The mathematician Richard Hamming once said,\n",
+      "\"The purpose of computing is insight, not numbers,\"\n",
+      "and the best way to develop insight is often to visualize data.\n",
+      "Visualization deserves an entire lecture (or course) of its own,\n",
+      "but we can explore a few features of Python's `matplotlib` here.\n",
+      "First,\n",
+      "let's tell the IPython Notebook that we want our plots displayed inline,\n",
+      "rather than in a separate viewing window:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [],
+     "prompt_number": 27
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The `%` at the start of the line signals that this is a command for the notebook,\n",
+      "rather than a statement in Python.\n",
+      "Next,\n",
+      "we will import the `pyplot` module from `matplotlib`\n",
+      "and use two of its functions to create and display a heat map of our data:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from matplotlib import pyplot\n",
+      "pyplot.imshow(data)\n",
+      "pyplot.show()"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
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WyC3m/wXq/in28dbRP0M5ONA3lL1Wo3stbzivmVAvFGwacyx0kbz1RjQh65JQj0AJgJC2+yEc4nw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\nei42+yfnPDvbO7fYOnVfvxRAqlE/AuaBqW2ft7us04JtfUUWQJBPOpsqop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+       "text": [
+        "<matplotlib.figure.Figure at 0x10600f5d0>"
+       ]
+      }
+     ],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Blue regions in this heat map are low values, while red shows high values.\n",
+      "As we can see,\n",
+      "inflammation rises and falls over a 40-day period.\n",
+      "Let's take a look at the average inflammation over time:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ave_inflammation = data.mean(axis=0)\n",
+      "pyplot.plot(ave_inflammation)\n",
+      "pyplot.show()"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
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+       "text": [
+        "<matplotlib.figure.Figure at 0x106026750>"
+       ]
+      }
+     ],
+     "prompt_number": 29
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Here,\n",
+      "we have put the average per day across all patients in the variable `ave_inflammation`,\n",
+      "then asked `pyplot` to create and display a line graph of those values.\n",
+      "The result is roughly a linear rise and fall,\n",
+      "which is suspicious:\n",
+      "based on other studies,\n",
+      "we expect a sharper rise and slower fall.\n",
+      "Let's have a look at two other statistics:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'maximum inflammation per day'\n",
+      "pyplot.plot(data.max(axis=0))\n",
+      "pyplot.show()\n",
+      "\n",
+      "print 'minimum inflammation per day'\n",
+      "pyplot.plot(data.min(axis=0))\n",
+      "pyplot.show()"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "maximum inflammation per day\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEACAYAAACuzv3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAF39JREFUeJzt3W9s1Vfhx/HPneMBpkxZBhe0+uvSUf6W9k5CeTDixa3s\ngQFKYIaKyKQTouPBdFG3GEPLA1bG2qwwH5h2mkbHcGwMiWHNwO0CoSSMCbqJTp00dqRF2kJsGYY/\nPb8Hd/euhd729t77vd/z/X7fr6QJlHJ7cnQnh3fPPd+QMcYIAOBZd7g9AABAdljIAcDjWMgBwONY\nyAHA41jIAcDjWMgBwONGXcg7Ozu1ZMkSzZ07V/PmzdPOnTslSX19faqsrFRJSYmWLl2qy5cv52Ww\nAIDbhUY7R97d3a3u7m6Vl5drYGBAX/nKV7R//3796le/0j333KMf//jH2r59uy5duqT6+vp8jhsA\n8IlRd+TTpk1TeXm5JKmgoECzZ8/W+fPndeDAAa1fv16StH79eu3fv9/5kQIARjTqjnyojo4OffWr\nX9X777+vL3/5y7p06ZIkyRiju+++O/l7AEB+pfXDzoGBAa1atUpNTU2aNGnSsD8LhUIKhUKODA4A\nMLY7x/qC69eva9WqVVq3bp2qqqokSeFwWN3d3Zo2bZq6uro0derU2/7efffdpw8//DD3IwYAHysu\nLtY///nPcf2dUXfkxhjV1NRozpw5euKJJ5KfX758uVpbWyVJra2tyQV+qA8//FDGGOs/tmzZ4voY\nGGd+Po4fN5oyxaijw2jlyi2aMcPov/91f1xenU+vjdMLYzTGZLQBHnUhP378uH7zm9/o7bffViQS\nUSQSUVtbm5566ikdOnRIJSUleuutt/TUU0+N+xsD+dTbK1VXSy0t0v/9nzR/vhSNSps2SYb7P+Fx\no6aVBx54QIODgyP+2eHDhx0ZEJBrg4PS+vXS6tXS8uWffr6pSaqokJqbpY0b3RsfkK0xG7nfRaNR\nt4eQFsaZucbG+I586FsdotGoJk6U9u6VHnggvqCXlbk3xlRsnM+ReGGcXhhjptI+fjjuFw6F5NBL\nA2lrb5dWrpROnownlZG89JJUVye9+650y6EsIO8yWTtZyOFbvb3S/fdLL7wgLVs2+tdu3CgNDMQX\ndU7Twk2ZrJ1cmgVfSnTxRx4ZexGX4r38/ffjvRzwGnbk8KXnnpNee006elSaMCG9v/PBB/Fefviw\nnb0cwUBaAZReF0+FXg63sZAj8MbTxVPZuFHq75d276aXI/9o5Ai0oefFM13EpXgvP3uWXg7vYEcO\n38iki6dCL4dbSCsIrGy6eCr0criBhRyBlIsungrny5FvNHIEznjPi48X58vhBezI4Wm57OKp0MuR\nT6QVBMqJE1JVVW67eCr0cuQLCzkCo69PikSc6eKp0MuRDzRyBIIxznbxVBK9vKUlf98TSEfg7yOH\n9zQ2Sj090r59+f2+Q+8vX7iQXg57kFbgKfns4qm89JK0dat06hS9HLlHI4evJbr4rl3DH9nmBno5\nnEIjh28N7eJuL+ISvRx2oZHDE9zq4qnQy2ET0gqsZ0MXT4VejlyjkcN33DgvPl70cuQSjRy+4tZ5\n8fGil8NtNHJYy7Yungq9HG4jrcBKNnfxVOjlyAUaOXzBC108FXo5skUjh+d5pYunQi+HG2jksEpD\ngze6eCr0criBtAJrOPHcTbfs3i3V1nJ/OcaPRg7PcvK5m26hlyMTNHJ4ktPP3XQLz/tEvrAjh+vy\n8dxNt/C8T4wXaQWe46cungrP+8R4sJDDU/zYxVOhlyNdNHJ4hl+7eCr0cjiJHTlc4ecungq9HOkg\nrcATgtDFU6GXYyws5LBekLp4KvRyjIaFHFYzJv68zZkz42klqK5elSoqpMcflzZtcns0sE0mayd3\nrSBvGhqkixe9e49Krgy9j2XRIno5sseOHHkR5C6eCr0cIyGtwEp08dTo5bgV58hhHWOkRx8Nznnx\n8eJ8OXKBRg5HeeW5m24Z2ssrKujlyAxpBY7x4nM33UIvRwKNHNbw8nM33UIvh+RQI9+wYYPC4bBK\nS0uTn6utrVVhYaEikYgikYja2trGP1r4ltefu+kWejkyNeaO/NixYyooKNC3v/1tvffee5Kkuro6\nTZo0ST/84Q9TvzA78sBqaJBefTVY96jkCvexwJEd+eLFizV58uTbPs8ijZGcOCE9+6y0Zw+LeCZm\nzpSefz7+r5n+frdHA6/I+Pjhrl27VFZWppqaGl2+fDmXY4JH9fVJa9ZILS38cDMba9dK0Wj87fvs\nl5COtH7Y2dHRoWXLliXTyn/+8x9NmTJFkvSzn/1MXV1devHFF4e/cCikLVu2JH8fjUYVjUZzOHTY\nJHGPSklJPK0gO1evxt++//3vcx+L38ViMcViseTv6+rqnDm1cutCns6f0ciDhS6ee/TyYMrbOzu7\nurqSv3799deHnWhB8NDFnUEvR7rG3JFXV1fryJEj6unpUTgcVl1dnWKxmM6cOaNQKKR7771Xv/jF\nLxQOh4e/MDvyQOC8uPM4Xx4svCEIecX94vmRuL988+b4og5/4z5y5BX3qOQH97FgLOzIkRHuUck/\n7mMJBtIK8oIu7h56uf9xHzkcxz0q7krcx9LS4vZIYBMaOcaFLu6uob184UJ6OeJIK0gbXdweL70k\nbd0qnTpFL/cbGjkck+jiu3bFjxzCffRyf6KRwxFDuziLuD3o5UigkWNMdHE70cuRQFrBqOji9qOX\n+wuNHDnFeXHvoJf7B40cOcN5cW+hlwcbjRwjoot7C7082EgruA1d3Lvo5d5HI0fW6OLeRy/3Nho5\nskIX9wd6efDQyJFEF/cHennwkFYgiS7uR/Ryb6KRIyO9vdL993OPih/Ry72HRo5xGxzkHhU/S/Ty\n5ma3RwInsSMPuOeek157TTp6VJowwe3RwAkffBDv5YcP08u9gLSCcWlvl1aupIsHAc/79A4WcqQt\n0cU5Lx4c9HJvoJEjLUO7OIt4cNDL/YsdeQDRxYOLXm4/0grGRBcHvdxuLOQYFV0cCfRye9HIkRJd\nHEPRy/2FHXlA0MVxq0QvP3RIKi93ezRIIK1gRHRxpLJ7t1RbSy+3CQs5bkMXx1jo5XahkWMYujjS\nQS/3PnbkPkYXR7o4X24P0gqS6OIYL86X24GFHJLo4sgcvdx9NHLQxZEVerk3sSP3meeek159VTp2\njC6OzNDL3UVaCbj29vhzN995hy6O7NDL3cNCHmA8dxO5Ri93Bwt5QA0OSitWSCUlUkOD26OBX1y9\nKlVUSI8/Lm3a5PZogiOTtfNOh8aCPGpslC5ejJ8ZB3Jl4kRp7954L1+0iF5uM3bkHkcXh9Po5flF\nWgkYujjyhV6eP5wjD5DEefHVq1nE4TzOl9uNRu5RjY1ST4+0b5/bI0EQDO3lFRX0ctuQVjyILg63\n0MudRyMPALo43EYvd5YjjXzDhg0Kh8MqLS1Nfq6vr0+VlZUqKSnR0qVLdfny5fGPFuNGF4cN6OX2\nGXMh/853vqO2trZhn6uvr1dlZaX+/ve/68EHH1R9fb1jA8SnEl38mWfcHgmCLNHLf/pT6U9/cns0\nkNJMKx0dHVq2bJnee+89SdKsWbN05MgRhcNhdXd3KxqN6m9/+9vwFyat5BRdHLahlzsjb8cPL1y4\noHA4LEkKh8O6cOFCJi+DNPX2StXVUksLizjssXatFI3Gmzl7NndlffwwFAoplOInHrW1tclfR6NR\nRaPRbL9d4NDFYbOmpvhxxObm+IKO8YvFYorFYlm9RsZpJRaLadq0aerq6tKSJUtIKw7huZuwHfeX\n51be0sry5cvV2toqSWptbVVVVVUmL4MxtLdLO3ZIe/awiMNeM2dKzz8ffypVf7/bowmmMXfk1dXV\nOnLkiHp6ehQOh7V161atWLFC3/jGN/Tvf/9bRUVFeuWVV/T5z39++AuzI88Kz92E13C+PDd4Q5BP\nDA7Ge/isWfG0AnhB4v7yzZvp5dlgIfcJuji8il6ePRZyH2hvl1aulE6e5KghvInz5dlhIfc4ujj8\ngl6eOe4j97DEefFHHmERh/dxH0t+sSO3BF0cfkMvzwxpxaPo4vArevn4sZB7EPeLw+82boy/UWj3\nbnp5OmjkHjO0i7OIw6+amqS//IVe7iR25C6iiyMo6OXpI614CF0cQUMvTw8LuUdwXhxBxfnysdHI\nPYDz4ggyzpc7gx15ntHFEXT08tGRVixHFwfi6OWpsZBbjC4ODEcvHxmN3FJ0ceB29PLcYUeeB3Rx\nYGT08tuRVix04oRUVUUXB1LZvVuqraWXJ7CQW4YuDqSHXv4pGrlF6OJA+ujl2WFH7hC6ODA+9PI4\n0oolOC8OZIbz5SzkVqCLA9kJei+nkbuMLg5kj14+fuzIc4guDuRGkHs5acVFdHEgt4Lay1nIXcJz\nNwFnBLGX08hdkOjiq1eziAO5Ri9Pz51uD8DrGhulnh5p3z63RwL4z8SJ0t698V5eURG8Xp4u0koW\n2tvj96i88w5dHHBSkHo5jTyP6OJAfgWll9PI84QuDuQfvTw1GnkG6OJA/tHLUyOtjBNdHHCX33s5\njdxhdHHADn7u5TRyB9HFAXvQy4ejkaeJLg7Yg14+HGklDXRxwE5+7OU0cgfQxQG7+a2Xs5Dn2OCg\ntGKFVFIiNTS4PRoAI7l6NZ5XHn9c2rTJ7dFkL5O1k0Y+isZG6eLF+B3jAOw0tJcvWhTMXs6OPAXu\nFwe8xS+9nLSSIzx3E/AmP/RyzpHnAM/dBLwrqOfL2ZHfguduAt7m9ed9klayRBcH/MHLvZyFPAt0\nccBfvNrL876QFxUV6a677tJnPvMZTZgwQSdPnsxqMG4ZHIy/2WfWrHhaAeB9ifPlmzfHF3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+       "text": [
+        "<matplotlib.figure.Figure at 0x106036bd0>"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "minimum inflammation per day\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAEACAYAAACXqUyYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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+       "text": [
+        "<matplotlib.figure.Figure at 0x106033550>"
+       ]
+      }
+     ],
+     "prompt_number": 30
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The maximum value rises and falls perfectly smoothly,\n",
+      "while the minimum seems to be a step function.\n",
+      "Neither result seems particularly likely,\n",
+      "so either there's a mistake in our calculations\n",
+      "or something is wrong with our data."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "challenges"
+      ]
+     },
+     "source": [
+      "#### Challenges\n",
+      "\n",
+      "1.  Why do all of our plots stop just short of the upper end of our graph?\n",
+      "    Why are the vertical lines in our plot of the minimum inflammation per day not vertical?\n",
+      "\n",
+      "1.  Create a plot showing the standard deviation of the inflammation data for each day across all patients."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Wrapping Up"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "It's very common to create an [alias](../../gloss.html#alias-library) for a library when importing it\n",
+      "in order to reduce the amount of typing we have to do.\n",
+      "Here are our three plots side by side using aliases for `numpy` and `pyplot`:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "from matplotlib import pyplot as plt\n",
+      "\n",
+      "data = np.loadtxt(fname='inflammation-01.csv', delimiter=',')\n",
+      "\n",
+      "plt.figure(figsize=(10.0, 3.0))\n",
+      "\n",
+      "plt.subplot(1, 3, 1)\n",
+      "plt.ylabel('average')\n",
+      "plt.plot(data.mean(0))\n",
+      "\n",
+      "plt.subplot(1, 3, 2)\n",
+      "plt.ylabel('max')\n",
+      "plt.plot(data.max(0))\n",
+      "\n",
+      "plt.subplot(1, 3, 3)\n",
+      "plt.ylabel('min')\n",
+      "plt.plot(data.min(0))\n",
+      "\n",
+      "plt.tight_layout()\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
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3vd2zZ0f5xdaNm3SncRedu9WJaApKbqTeFdB7/081yvbtm1L0aJFeeihhzAMg48++oj09HSCg4Pp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\nRf/+/XnqqacuhDLh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hmEYpmXP7fpm/vmfl5CQkL3z1sz7xxfsNF4Nw95j1s7j1TCcM2bt\nPF4Nw15j1s7j1TDsPWadMl4Nw7MxK4eJCCGEEEIIcRlLlVsIIYQQQghhBTJJFkIIIYQQ4jIySRZC\nCCGEEOIyMkkWQgghhBDiMjJJFkIIIYQQ4jIySRZCCCGEEOIyMkkWQgghhBDiMjJJFkIIIYQQ4jL/\nD8Se36QdbJ39AAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x106062fd0>"
+       ]
+      }
+     ],
+     "prompt_number": 31
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The first two lines re-load our libraries as `np` and `plt`,\n",
+      "which are the aliases most Python programmers use.\n",
+      "The call to `loadtxt` reads our data,\n",
+      "and the rest of the program tells the plotting library\n",
+      "how large we want the figure to be,\n",
+      "that we're creating three sub-plots,\n",
+      "what to draw for each one,\n",
+      "and that we want a tight layout.\n",
+      "(Perversely,\n",
+      "if we leave out that call to `plt.tight_layout()`,\n",
+      "the graphs will actually be squeezed together more closely.)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "challenges"
+      ]
+     },
+     "source": [
+      "#### Challenges\n",
+      "\n",
+      "1.  Modify the program to display the three plots on top of one another instead of side by side."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "keypoints"
+      ]
+     },
+     "source": [
+      "#### Key Points\n",
+      "\n",
+      "*   Import a library into a program using `import libraryname`.\n",
+      "*   Use the `numpy` library to work with arrays in Python.\n",
+      "*   Use `variable = value` to assign a value to a variable in order to record it in memory.\n",
+      "*   Variables are created on demand whenever a value is assigned to them.\n",
+      "*   Use `print something` to display the value of `something`.\n",
+      "*   The expression `array.shape` gives the shape of an array.\n",
+      "*   Use `array[x, y]` to select a single element from an array.\n",
+      "*   Array indices start at 0, not 1.\n",
+      "*   Use `low:high` to specify a slice that includes the indices from `low` to `high-1`.\n",
+      "*   All the indexing and slicing that works on arrays also works on strings.\n",
+      "*   Use `# some kind of explanation` to add comments to programs.\n",
+      "*   Use `array.mean()`, `array.max()`, and `array.min()` to calculate simple statistics.\n",
+      "*   Use `array.mean(axis=0)` or `array.mean(axis=1)` to calculate statistics across the specified axis.\n",
+      "*   Use the `pyplot` library from `matplotlib` for creating simple visualizations."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "#### Next Steps\n",
+      "\n",
+      "Our work so far has convinced us that something's wrong with our first data file.\n",
+      "We would like to check the other 11 the same way,\n",
+      "but typing in the same commands repeatedly is tedious and error-prone.\n",
+      "Since computers don't get bored (that we know of),\n",
+      "we should create a way to do a complete analysis with a single command,\n",
+      "and then figure out how to repeat that step once for each file.\n",
+      "These operations are the subjects of the next two lessons."
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/novice/python/02-func.ipynb b/novice/python/02-func.ipynb
new file mode 100644 (file)
index 0000000..ba59f15
--- /dev/null
@@ -0,0 +1,1691 @@
+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Creating Functions"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "If we only had one data set to analyze,\n",
+      "it would probably be faster to load the file into a spreadsheet\n",
+      "and use that to plot some simple statistics.\n",
+      "But we have twelve files to check,\n",
+      "and may have more in future.\n",
+      "In this lesson,\n",
+      "we'll learn how to write a function\n",
+      "so that we can repeat several operations with a single command."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "objectives"
+      ]
+     },
+     "source": [
+      "#### Objectives\n",
+      "\n",
+      "*   Define a function that takes parameters.\n",
+      "*   Return a value from a function.\n",
+      "*   Test and debug a function.\n",
+      "*   Explain what a call stack is, and trace changes to the call stack as functions are called.\n",
+      "*   Set default values for function parameters.\n",
+      "*   Explain why we should divide programs into small, single-purpose functions."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Defining a Function"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Let's start by defining a function `fahr_to_kelvin` that converts temperatures from Fahrenheit to Kelvin:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def fahr_to_kelvin(temp):\n",
+      "    return ((temp - 32) * (5/9)) + 273.15"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The definition opens with the word `def`,\n",
+      "which is followed by the name of the function\n",
+      "and a parenthesized list of parameter names.\n",
+      "The [body](../../gloss.html#function-body) of the function&mdash;the\n",
+      "statements that are executed when it runs&mdash;is indented below the definition line,\n",
+      "typically by four spaces.\n",
+      "\n",
+      "When we call the function,\n",
+      "the values we pass to it are assigned to those variables\n",
+      "so that we can use them inside the function.\n",
+      "Inside the function,\n",
+      "we use a [return statement](../../gloss.html#return-statement) to send a result back to whoever asked for it.\n",
+      "\n",
+      "Let's try running our function.\n",
+      "Calling our own function is no different from calling any other function:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'freezing point of water:', fahr_to_kelvin(32)\n",
+      "print 'boiling point of water:', fahr_to_kelvin(212)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "freezing point of water: 273.15\n",
+        "boiling point of water: 273.15\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We've successfully called the function that we defined,\n",
+      "and we have access to the value that we returned.\n",
+      "Unfortunately, the value returned doesn't look right.\n",
+      "What went wrong?"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Debugging a Function"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "*Debugging* is when we fix a piece of code\n",
+      "that we know is working incorrectly.\n",
+      "In this case, we know that `fahr_to_kelvin`\n",
+      "is giving us the wrong answer,\n",
+      "so let's find out why.\n",
+      "\n",
+      "For big pieces of code,\n",
+      "there are tools called *debuggers* that aid in this process.\n",
+      "\n",
+      "We just have a short function,\n",
+      "so we'll debug by choosing some parameter value,\n",
+      "breaking our function into small parts,\n",
+      "and printing out the value of each part."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# We'll use temp = 212, the boiling point of water, which was incorrect\n",
+      "print \"212 - 32:\", 212 - 32"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "212 - 32: 180\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print \"(212 - 32) * (5/9):\", (212 - 32) * (5/9)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "(212 - 32) * (5/9): 0\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Aha! The problem comes when we multiply by `5/9`.\n",
+      "This is because `5/9` is actually 0."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "5/9"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 5,
+       "text": [
+        "0"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Computers store numbers in one of two ways:\n",
+      "as [integers](../../gloss.html#integer)\n",
+      "or as [floating-point numbers](../../gloss.html#float) (or floats).\n",
+      "The first are the numbers we usually count with;\n",
+      "the second have fractional parts.\n",
+      "Addition, subtraction and multiplication work on both as we'd expect,\n",
+      "but division works differently.\n",
+      "If we divide one integer by another,\n",
+      "we get the quotient without the remainder:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print '10/3 is:', 10/3"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "10/3 is: 3\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "If either part of the division is a float,\n",
+      "on the other hand,\n",
+      "the computer creates a floating-point answer:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print '10.0/3 is:', 10.0/3"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "10.0/3 is: 3.33333333333\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The computer does this for historical reasons:\n",
+      "integer operations were much faster on early machines,\n",
+      "and this behavior is actually useful in a lot of situations.\n",
+      "It's still confusing,\n",
+      "though,\n",
+      "so Python 3 produces a floating-point answer when dividing integers if it needs to.\n",
+      "We're still using Python 2.7 in this class,\n",
+      "though,\n",
+      "so if we want `5/9` to give us the right answer,\n",
+      "we have to write it as `5.0/9`, `5/9.0`, or some other variation."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Let's fix our `fahr_to_kelvin` function with this new knowledge."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def fahr_to_kelvin(temp):\n",
+      "    return ((temp - 32) * (5.0/9.0)) + 273.15\n",
+      "\n",
+      "print 'freezing point of water:', fahr_to_kelvin(32)\n",
+      "print 'boiling point of water:', fahr_to_kelvin(212)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "freezing point of water: 273.15\n",
+        "boiling point of water: 373.15\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "It works!"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Composing Functions"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now that we've seen how to turn Fahrenheit into Kelvin,\n",
+      "it's easy to turn Kelvin into Celsius:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def kelvin_to_celsius(temp):\n",
+      "    return temp - 273.15\n",
+      "\n",
+      "print 'absolute zero in Celsius:', kelvin_to_celsius(0.0)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "absolute zero in Celsius: -273.15\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "What about converting Fahrenheit to Celsius?\n",
+      "We could write out the formula,\n",
+      "but we don't need to.\n",
+      "Instead,\n",
+      "we can [compose](../../gloss.html#function-composition) the two functions we have already created:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def fahr_to_celsius(temp):\n",
+      "    temp_k = fahr_to_kelvin(temp)\n",
+      "    result = kelvin_to_celsius(temp_k)\n",
+      "    return result\n",
+      "\n",
+      "print 'freezing point of water in Celsius:', fahr_to_celsius(32.0)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "freezing point of water in Celsius: 0.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "This is our first taste of how larger programs are built:\n",
+      "we define basic operations,\n",
+      "then combine them in ever-large chunks to get the effect we want.\n",
+      "Real-life functions will usually be larger than the ones shown here&mdash;typically half a dozen to a few dozen lines&mdash;but\n",
+      "they shouldn't ever be much longer than that,\n",
+      "or the next person who reads it won't be able to understand what's going on."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "challenges"
+      ]
+     },
+     "source": [
+      "#### Challenges\n",
+      "\n",
+      "1.  \"Adding\" two strings produces their concatention:\n",
+      "    `'a' + 'b'` is `'ab'`.\n",
+      "    Write a function called `fence` that takes two parameters called `original` and `wrapper`\n",
+      "    and returns a new string that has the wrapper character at the beginning and end of the original:\n",
+      "\n",
+      "    ~~~python\n",
+      "    print fence('name', '*')\n",
+      "    *name*\n",
+      "    ~~~\n",
+      "\n",
+      "1.  If the variable `s` refers to a string,\n",
+      "    then `s[0]` is the string's first character\n",
+      "    and `s[-1]` is its last.\n",
+      "    Write a function called `outer`\n",
+      "    that returns a string made up of just the first and last characters of its input:\n",
+      "\n",
+      "    ~~~python\n",
+      "    print outer('helium')\n",
+      "    hm\n",
+      "    ~~~"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The Call Stack"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Let's take a closer look at what happens when we call `fahr_to_celsius(32.0)`.\n",
+      "To make things clearer,\n",
+      "we'll start by putting the initial value 32.0 in a variable\n",
+      "and store the final result in one as well:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "original = 32.0\n",
+      "final = fahr_to_celsius(original)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The diagram below shows what memory looks like after the first line has been executed:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "<img src=\"files/img/python-call-stack-01.svg\" alt=\"Call Stack (Initial State)\" />"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "When we call `fahr_to_celsius`,\n",
+      "Python *doesn't* create the variable `temp` right away.\n",
+      "Instead,\n",
+      "it creates something called a [stack frame](../../gloss.html#stack-frame)\n",
+      "to keep track of the variables defined by `fahr_to_kelvin`.\n",
+      "Initially,\n",
+      "this stack frame only holds the value of `temp`:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "<img src=\"files/img/python-call-stack-02.svg\" alt=\"Call Stack Immediately After First Function Call\" />"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "When we call `fahr_to_kelvin` inside `fahr_to_celsius`,\n",
+      "Python creates another stack frame to hold `fahr_to_kelvin`'s variables:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "<img src=\"files/img/python-call-stack-03.svg\" alt=\"Call Stack During First Nested Function Call\" />"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "It does this because there are now two variables in play called `temp`:\n",
+      "the parameter to `fahr_to_celsius`,\n",
+      "and the parameter to `fahr_to_kelvin`.\n",
+      "Having two variables with the same name in the same part of the program would be ambiguous,\n",
+      "so Python (and every other modern programming language) creates a new stack frame for each function call\n",
+      "to keep that function's variables separate from those defined by other functions.\n",
+      "\n",
+      "When the call to `fahr_to_kelvin` returns a value,\n",
+      "Python throws away `fahr_to_kelvin`'s stack frame\n",
+      "and creates a new variable in the stack frame for `fahr_to_celsius` to hold the temperature in Kelvin:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "<img src=\"files/img/python-call-stack-04.svg\" alt=\"Call Stack After Return From First Nested Function Call\" />"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "It then calls `kelvin_to_celsius`,\n",
+      "which means it creates a stack frame to hold that function's variables:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "<img src=\"files/img/python-call-stack-05.svg\" alt=\"Call Stack During Call to Second Nested Function\" />"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Once again,\n",
+      "Python throws away that stack frame when `kelvin_to_celsius` is done\n",
+      "and creates the variable `result` in the stack frame for `fahr_to_celsius`:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "<img src=\"files/img/python-call-stack-06.svg\" alt=\"Call Stack After Second Nested Function Returns\" />"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Finally,\n",
+      "when `fahr_to_celsius` is done,\n",
+      "Python throws away *its* stack frame\n",
+      "and puts its result in a new variable called `final`\n",
+      "that lives in the stack frame we started with:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "<img src=\"files/img/python-call-stack-07.svg\" alt=\"Call Stack After All Functions Have Finished\" />"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "This final stack frame is always there;\n",
+      "it holds the variables we defined outside the functions in our code.\n",
+      "What it *doesn't* hold is the variables that were in the various stack frames.\n",
+      "If we try to get the value of `temp` after our functions have finished running,\n",
+      "Python tells us that there's no such thing:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'final value of temp after all function calls:', temp"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "ename": "NameError",
+       "evalue": "name 'temp' is not defined",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-12-ffd9b4dbd5f1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0;34m'final value of temp after all function calls:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;31mNameError\u001b[0m: name 'temp' is not defined"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "final value of temp after all function calls:"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Why go to all this trouble?\n",
+      "Well,\n",
+      "here's a function called `span` that calculates the difference between\n",
+      "the mininum and maximum values in an array:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy\n",
+      "\n",
+      "def span(a):\n",
+      "    diff = a.max() - a.min()\n",
+      "    return diff\n",
+      "\n",
+      "data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')\n",
+      "print 'span of data', span(data)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " span of data 20.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Notice that `span` assigns a value to a variable called `diff`.\n",
+      "We might very well use a variable with the same name to hold data:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "diff = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')\n",
+      "print 'span of data:', span(diff)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "span of data: 20.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We don't expect `diff` to have the value 20.0 after this function call,\n",
+      "so the name `diff` cannot refer to the same thing inside `span` as it does in the main body of our program.\n",
+      "And yes,\n",
+      "we could probably choose a different name than `diff` in our main program in this case,\n",
+      "but we don't want to have to read every line of NumPy to see what variable names its functions use\n",
+      "before calling any of those functions,\n",
+      "just in case they change the values of our variables."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The big idea here is [encapsulation](../../gloss.html#encapsulation),\n",
+      "and it's the key to writing correct, comprehensible programs.\n",
+      "A function's job is to turn several operations into one\n",
+      "so that we can think about a single function call\n",
+      "instead of a dozen or a hundred statements\n",
+      "each time we want to do something.\n",
+      "That only works if functions don't interfere with each other;\n",
+      "if they do,\n",
+      "we have to pay attention to the details once again,\n",
+      "which quickly overloads our short-term memory."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "challenges"
+      ]
+     },
+     "source": [
+      "#### Challenges\n",
+      "\n",
+      "1.  We previously wrote functions called `fence` and `outer`.\n",
+      "    Draw a diagram showing how the call stack changes when we run the following:\n",
+      "    ~~~python\n",
+      "    print outer(fence('carbon', '+'))\n",
+      "    ~~~"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Testing and Documenting"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Once we start putting things in functions so that we can re-use them,\n",
+      "we need to start testing that those functions are working correctly.\n",
+      "To see how to do this,\n",
+      "let's write a function to center a dataset around a particular value:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def center(data, desired):\n",
+      "    return (data - data.mean()) + desired"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We could test this on our actual data,\n",
+      "but since we don't know what the values ought to be,\n",
+      "it will be hard to tell if the result was correct.\n",
+      "Instead,\n",
+      "let's use NumPy to create a matrix of 0's\n",
+      "and then center that around 3:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "z = numpy.zeros((2,2))\n",
+      "print center(z, 3)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[[ 3.  3.]\n",
+        " [ 3.  3.]]\n"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "That looks right,\n",
+      "so let's try `center` on our real data:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')\n",
+      "print center(data, 0)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[[-6.14875 -6.14875 -5.14875 ..., -3.14875 -6.14875 -6.14875]\n",
+        " [-6.14875 -5.14875 -4.14875 ..., -5.14875 -6.14875 -5.14875]\n",
+        " [-6.14875 -5.14875 -5.14875 ..., -4.14875 -5.14875 -5.14875]\n",
+        " ..., \n",
+        " [-6.14875 -5.14875 -5.14875 ..., -5.14875 -5.14875 -5.14875]\n",
+        " [-6.14875 -6.14875 -6.14875 ..., -6.14875 -4.14875 -6.14875]\n",
+        " [-6.14875 -6.14875 -5.14875 ..., -5.14875 -5.14875 -6.14875]]\n"
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "It's hard to tell from the default output whether the result is correct,\n",
+      "but there are a few simple tests that will reassure us:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'original min, mean, and max are:', data.min(), data.mean(), data.max()\n",
+      "centered = center(data, 0)\n",
+      "print 'min, mean, and and max of centered data are:', centered.min(), centered.mean(), centered.max()"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "original min, mean, and max are: 0.0 6.14875 20.0\n",
+        "min, mean, and and max of centered data are: -6.14875 -3.49054118942e-15 13.85125\n"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "That seems almost right:\n",
+      "the original mean was about 6.1,\n",
+      "so the lower bound from zero is how about -6.1.\n",
+      "The mean of the centered data isn't quite zero&mdash;we'll explore why not in the challenges&mdash;but it's pretty close.\n",
+      "We can even go further and check that the standard deviation hasn't changed:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'std dev before and after:', data.std(), centered.std()"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "std dev before and after: 4.61383319712 4.61383319712\n"
+       ]
+      }
+     ],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Those values look the same,\n",
+      "but we probably wouldn't notice if they were different in the sixth decimal place.\n",
+      "Let's do this instead:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'difference in standard deviations before and after:', data.std() - centered.std()"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "difference in standard deviations before and after: -3.5527136788e-15\n"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Again,\n",
+      "the difference is very small.\n",
+      "It's still possible that our function is wrong,\n",
+      "but it seems unlikely enough that we should probably get back to doing our analysis.\n",
+      "We have one more task first, though:\n",
+      "we should write some [documentation](../../gloss.html#documentation) for our function\n",
+      "to remind ourselves later what it's for and how to use it.\n",
+      "\n",
+      "The usual way to put documentation in software is to add [comments](../../gloss.html#comment) like this:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# center(data, desired): return a new array containing the original data centered around the desired value.\n",
+      "def center(data, desired):\n",
+      "    return (data - data.mean()) + desired"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [],
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "There's a better way, though.\n",
+      "If the first thing in a function is a string that isn't assigned to a variable,\n",
+      "that string is attached to the function as its documentation:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def center(data, desired):\n",
+      "    '''Return a new array containing the original data centered around the desired value.'''\n",
+      "    return (data - data.mean()) + desired"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "This is better because we can now ask Python's built-in help system to show us the documentation for the function:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "help(center)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Help on function center in module __main__:\n",
+        "\n",
+        "center(data, desired)\n",
+        "    Return a new array containing the original data centered around the desired value.\n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "A string like this is called a [docstring](../../gloss.html#docstring).\n",
+      "We don't need to use triple quotes when we write one,\n",
+      "but if we do,\n",
+      "we can break the string across multiple lines:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def center(data, desired):\n",
+      "    '''Return a new array containing the original data centered around the desired value.\n",
+      "    Example: center([1, 2, 3], 0) => [-1, 0, 1]'''\n",
+      "    return (data - data.mean()) + desired\n",
+      "\n",
+      "help(center)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Help on function center in module __main__:\n",
+        "\n",
+        "center(data, desired)\n",
+        "    Return a new array containing the original data centered around the desired value.\n",
+        "    Example: center([1, 2, 3], 0) => [-1, 0, 1]\n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 24
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "challenges"
+      ]
+     },
+     "source": [
+      "#### Challenges\n",
+      "\n",
+      "1.  Write a function called `analyze` that takes a filename as a parameter\n",
+      "    and displays the three graphs produced in the [previous lesson](01-numpy.ipynb),\n",
+      "    i.e.,\n",
+      "    `analyze('inflammation-01.csv')` should produce the graphs already shown,\n",
+      "    while `analyze('inflammation-02.csv')` should produce corresponding graphs for the second data set.\n",
+      "    Be sure to give your function a docstring.\n",
+      "\n",
+      "2.  Write a function `rescale` that takes an array as input\n",
+      "    and returns a corresponding array of values scaled to lie in the range 0.0 to 1.0.\n",
+      "    (If $L$ and $H$ are the lowest and highest values in the original array,\n",
+      "    then the replacement for a value $v$ should be $(v-L) / (H-L)$.)\n",
+      "    Be sure to give the function a docstring.\n",
+      "\n",
+      "3.  Run the commands `help(numpy.arange)` and `help(numpy.linspace)`\n",
+      "    to see how to use these functions to generate regularly-spaced values,\n",
+      "    then use those values to test your `rescale` function."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Defining Defaults"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We have passed parameters to functions in two ways:\n",
+      "directly, as in `span(data)`,\n",
+      "and by name, as in `numpy.loadtxt(fname='something.csv', delimiter=',')`.\n",
+      "In fact,\n",
+      "we can pass the filename to `loadtxt` without the `fname=`:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "numpy.loadtxt('inflammation-01.csv', delimiter=',')"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 25,
+       "text": [
+        "array([[ 0.,  0.,  1., ...,  3.,  0.,  0.],\n",
+        "       [ 0.,  1.,  2., ...,  1.,  0.,  1.],\n",
+        "       [ 0.,  1.,  1., ...,  2.,  1.,  1.],\n",
+        "       ..., \n",
+        "       [ 0.,  1.,  1., ...,  1.,  1.,  1.],\n",
+        "       [ 0.,  0.,  0., ...,  0.,  2.,  0.],\n",
+        "       [ 0.,  0.,  1., ...,  1.,  1.,  0.]])"
+       ]
+      }
+     ],
+     "prompt_number": 25
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "but we still need to say `delimiter=`:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "numpy.loadtxt('inflammation-01.csv', ',')"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "ename": "TypeError",
+       "evalue": "data type \",\" not understood",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-26-e3bc6cf4fd6a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadtxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'inflammation-01.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m','\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;32m/Users/gwilson/anaconda/lib/python2.7/site-packages/numpy/lib/npyio.pyc\u001b[0m in \u001b[0;36mloadtxt\u001b[0;34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin)\u001b[0m\n\u001b[1;32m    775\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    776\u001b[0m         \u001b[0;31m# Make sure we're dealing with a proper dtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 777\u001b[0;31m         \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    778\u001b[0m         \u001b[0mdefconv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_getconv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;31mTypeError\u001b[0m: data type \",\" not understood"
+       ]
+      }
+     ],
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "To understand what's going on,\n",
+      "and make our own functions easier to use,\n",
+      "let's re-define our `center` function like this:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def center(data, desired=0.0):\n",
+      "    '''Return a new array containing the original data centered around the desired value (0 by default).\n",
+      "    Example: center([1, 2, 3], 0) => [-1, 0, 1]'''\n",
+      "    return (data - data.mean()) + desired"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [],
+     "prompt_number": 27
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The key change is that the second parameter is now written `desired=0.0` instead of just `desired`.\n",
+      "If we call the function with two arguments,\n",
+      "it works as it did before:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "test_data = numpy.zeros((2, 2))\n",
+      "print center(test_data, 3)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[[ 3.  3.]\n",
+        " [ 3.  3.]]\n"
+       ]
+      }
+     ],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "But we can also now call it with just one parameter,\n",
+      "in which case `desired` is automatically assigned the [default value](../../gloss.html#default-parameter-value) of 0.0:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "more_data = 5 + numpy.zeros((2, 2))\n",
+      "print 'data before centering:', more_data\n",
+      "print 'centered data:', center(more_data)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "data before centering: [[ 5.  5.]\n",
+        " [ 5.  5.]]\n",
+        "centered data: [[ 0.  0.]\n",
+        " [ 0.  0.]]\n"
+       ]
+      }
+     ],
+     "prompt_number": 29
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "This is handy:\n",
+      "if we usually want a function to work one way,\n",
+      "but occasionally need it to do something else,\n",
+      "we can allow people to pass a parameter when they need to\n",
+      "but provide a default to make the normal case easier.\n",
+      "The example below shows how Python matches values to parameters:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def display(a=1, b=2, c=3):\n",
+      "    print 'a:', a, 'b:', b, 'c:', c\n",
+      "\n",
+      "print 'no parameters:'\n",
+      "display()\n",
+      "print 'one parameter:'\n",
+      "display(55)\n",
+      "print 'two parameters:'\n",
+      "display(55, 66)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "no parameters:\n",
+        "a: 1 b: 2 c: 3\n",
+        "one parameter:\n",
+        "a: 55 b: 2 c: 3\n",
+        "two parameters:\n",
+        "a: 55 b: 66 c: 3\n"
+       ]
+      }
+     ],
+     "prompt_number": 30
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "As this example shows,\n",
+      "parameters are matched up from left to right,\n",
+      "and any that haven't been given a value explicitly get their default value.\n",
+      "We can override this behavior by naming the value as we pass it in:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'only setting the value of c'\n",
+      "display(c=77)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "only setting the value of c\n",
+        "a: 1 b: 2 c: 77\n"
+       ]
+      }
+     ],
+     "prompt_number": 31
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "With that in hand,\n",
+      "let's look at the help for `numpy.loadtxt`:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "help(numpy.loadtxt)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Help on function loadtxt in module numpy.lib.npyio:\n",
+        "\n",
+        "loadtxt(fname, dtype=<type 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0)\n",
+        "    Load data from a text file.\n",
+        "    \n",
+        "    Each row in the text file must have the same number of values.\n",
+        "    \n",
+        "    Parameters\n",
+        "    ----------\n",
+        "    fname : file or str\n",
+        "        File, filename, or generator to read.  If the filename extension is\n",
+        "        ``.gz`` or ``.bz2``, the file is first decompressed. Note that\n",
+        "        generators should return byte strings for Python 3k.\n",
+        "    dtype : data-type, optional\n",
+        "        Data-type of the resulting array; default: float.  If this is a\n",
+        "        record data-type, the resulting array will be 1-dimensional, and\n",
+        "        each row will be interpreted as an element of the array.  In this\n",
+        "        case, the number of columns used must match the number of fields in\n",
+        "        the data-type.\n",
+        "    comments : str, optional\n",
+        "        The character used to indicate the start of a comment;\n",
+        "        default: '#'.\n",
+        "    delimiter : str, optional\n",
+        "        The string used to separate values.  By default, this is any\n",
+        "        whitespace.\n",
+        "    converters : dict, optional\n",
+        "        A dictionary mapping column number to a function that will convert\n",
+        "        that column to a float.  E.g., if column 0 is a date string:\n",
+        "        ``converters = {0: datestr2num}``.  Converters can also be used to\n",
+        "        provide a default value for missing data (but see also `genfromtxt`):\n",
+        "        ``converters = {3: lambda s: float(s.strip() or 0)}``.  Default: None.\n",
+        "    skiprows : int, optional\n",
+        "        Skip the first `skiprows` lines; default: 0.\n",
+        "    usecols : sequence, optional\n",
+        "        Which columns to read, with 0 being the first.  For example,\n",
+        "        ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.\n",
+        "        The default, None, results in all columns being read.\n",
+        "    unpack : bool, optional\n",
+        "        If True, the returned array is transposed, so that arguments may be\n",
+        "        unpacked using ``x, y, z = loadtxt(...)``.  When used with a record\n",
+        "        data-type, arrays are returned for each field.  Default is False.\n",
+        "    ndmin : int, optional\n",
+        "        The returned array will have at least `ndmin` dimensions.\n",
+        "        Otherwise mono-dimensional axes will be squeezed.\n",
+        "        Legal values: 0 (default), 1 or 2.\n",
+        "        .. versionadded:: 1.6.0\n",
+        "    \n",
+        "    Returns\n",
+        "    -------\n",
+        "    out : ndarray\n",
+        "        Data read from the text file.\n",
+        "    \n",
+        "    See Also\n",
+        "    --------\n",
+        "    load, fromstring, fromregex\n",
+        "    genfromtxt : Load data with missing values handled as specified.\n",
+        "    scipy.io.loadmat : reads MATLAB data files\n",
+        "    \n",
+        "    Notes\n",
+        "    -----\n",
+        "    This function aims to be a fast reader for simply formatted files.  The\n",
+        "    `genfromtxt` function provides more sophisticated handling of, e.g.,\n",
+        "    lines with missing values.\n",
+        "    \n",
+        "    Examples\n",
+        "    --------\n",
+        "    >>> from StringIO import StringIO   # StringIO behaves like a file object\n",
+        "    >>> c = StringIO(\"0 1\\n2 3\")\n",
+        "    >>> np.loadtxt(c)\n",
+        "    array([[ 0.,  1.],\n",
+        "           [ 2.,  3.]])\n",
+        "    \n",
+        "    >>> d = StringIO(\"M 21 72\\nF 35 58\")\n",
+        "    >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),\n",
+        "    ...                      'formats': ('S1', 'i4', 'f4')})\n",
+        "    array([('M', 21, 72.0), ('F', 35, 58.0)],\n",
+        "          dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')])\n",
+        "    \n",
+        "    >>> c = StringIO(\"1,0,2\\n3,0,4\")\n",
+        "    >>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)\n",
+        "    >>> x\n",
+        "    array([ 1.,  3.])\n",
+        "    >>> y\n",
+        "    array([ 2.,  4.])\n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 32
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "There's a lot of information here,\n",
+      "but the most important part is the first couple of lines:\n",
+      "\n",
+      "~~~python\n",
+      "loadtxt(fname, dtype=<type 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None,\n",
+      "        unpack=False, ndmin=0)\n",
+      "~~~\n",
+      "\n",
+      "This tells us that `loadtxt` has one parameter called `fname` that doesn't have a default value,\n",
+      "and eight others that do.\n",
+      "If we call the function like this:\n",
+      "\n",
+      "~~~python\n",
+      "numpy.loadtxt('inflammation-01.csv', ',')\n",
+      "~~~\n",
+      "\n",
+      "then the filename is assigned to `fname` (which is what we want),\n",
+      "but the delimiter string `','` is assigned to `dtype` rather than `delimiter`,\n",
+      "because `dtype` is the second parameter in the list.\n",
+      "That's why we don't have to provide `fname=` for the filename,\n",
+      "but *do* have to provide `delimiter=` for the second parameter."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "challenges"
+      ]
+     },
+     "source": [
+      "#### Challenges\n",
+      "\n",
+      "1.  Rewrite the `normalize` function so that it scales data to lie between 0.0 and 1.0 by default,\n",
+      "    but will allow the caller to specify lower and upper bounds if they want.\n",
+      "    Compare your implementation to your neighbor's:\n",
+      "    do the two functions always behave the same way?"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "keypoints"
+      ]
+     },
+     "source": [
+      "#### Key Points\n",
+      "\n",
+      "*   Define a function using `def name(...params...)`.\n",
+      "*   The body of a function must be indented.\n",
+      "*   Call a function using `name(...values...)`.\n",
+      "*   Numbers are stored as integers or floating-point numbers.\n",
+      "*   Integer division produces the whole part of the answer (not the fractional part).\n",
+      "*   Each time a function is called, a new stack frame is created on the [call stack](../../gloss.html#call-stack) to hold its parameters and local variables.\n",
+      "*   Python looks for variables in the current stack frame before looking for them at the top level.\n",
+      "*   Use `help(thing)` to view help for something.\n",
+      "*   Put docstrings in functions to provide help for that function.\n",
+      "*   Specify default values for parameters when defining a function using `name=value` in the parameter list.\n",
+      "*   Parameters can be passed by matching based on name, by position, or by omitting them (in which case the default value is used)."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "#### Next Steps\n",
+      "\n",
+      "We now have a function called `analyze` to visualize a single data set.\n",
+      "We could use it to explore all 12 of our current data sets like this:\n",
+      "\n",
+      "~~~python\n",
+      "analyze('inflammation-01.csv')\n",
+      "analyze('inflammation-02.csv')\n",
+      "...\n",
+      "analyze('inflammation-12.csv')\n",
+      "~~~\n",
+      "\n",
+      "but the chances of us typing all 12 filenames correctly aren't great,\n",
+      "and we'll be even worse off if we get another hundred files.\n",
+      "What we need is a way to tell Python to do something once for each file,\n",
+      "and that will be the subject of the next lesson."
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/novice/python/03-loop.ipynb b/novice/python/03-loop.ipynb
new file mode 100644 (file)
index 0000000..86cbf69
--- /dev/null
@@ -0,0 +1,910 @@
+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Analyzing Multiple Data Sets"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We have created a function called `analyze` that creates graphs of the minimum, average, and maximum daily inflammation rates\n",
+      "for a single data set:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline\n",
+      "\n",
+      "import numpy as np\n",
+      "from matplotlib import pyplot as plt\n",
+      "\n",
+      "def analyze(filename):\n",
+      "    data = np.loadtxt(fname=filename, delimiter=',')\n",
+      "    \n",
+      "    plt.figure(figsize=(10.0, 3.0))\n",
+      "    \n",
+      "    plt.subplot(1, 3, 1)\n",
+      "    plt.ylabel('average')\n",
+      "    plt.plot(data.mean(0))\n",
+      "    \n",
+      "    plt.subplot(1, 3, 2)\n",
+      "    plt.ylabel('max')\n",
+      "    plt.plot(data.max(0))\n",
+      "    \n",
+      "    plt.subplot(1, 3, 3)\n",
+      "    plt.ylabel('min')\n",
+      "    plt.plot(data.min(0))\n",
+      "    \n",
+      "    plt.tight_layout()\n",
+      "    plt.show()\n",
+      "\n",
+      "analyze('inflammation-01.csv')"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
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3vd2zZ0f5xdaNm3SncRedu9WJaApKbqTeFdB7/081yvbtm1L0aJFeeihhzAMg48++oj09HSCg4Pp\n1asXn3/+eYECRkRE0KNHD+rXr09AQAC33367aavUdnC+1OJigwbB3LnwzjuqLZy/crtVffa5c3Kk\nrdDn6FFVh/ztt7qTOEPx4jB+PAwbplpzycq88CapRy6c6tXhwAH1prZ4cd1prCPPlWS3253d+u3y\nj3nSCu6qoRz8Lnf6dNi6VR3PfLHNm9Uq8z336MllFXXqqLrs227TnUQPT+/9M2fOULJkyUs+dvjw\nYSp4+dmjk8bs0KHw99/w7ru6kzhHVhY0aKAmyl276k5jHrve93bNnR9xcXDkCEyerDuJ/YSEwJdf\nwk036U7iPaa3gMvMzOTHi55/r1+/nqx/jrEp6o+Fsx7asePKlWSA8HCZIINs3vNUgwYN+P7777P/\n/+LFi7njjjs0JrKXvXtVr9CLtkQIEwQEqBKWF16QNo/Cu2QlufCkw8WV8pzlzpo1i969e5OWlgZA\nqVKlmDVrFqdOnWLEiBFeD+g027er9m8iZ+cnyb17605iTx9++CF9+vQhKiqK/fv3c+TIEdasWaM7\nlm2MGQMDBkDlyrqTOE+rVlCzpjogacAA3Wmsb/HixQwfPpzU1NTslS+Xy8WJEyc0J7O2pCTo0kV3\nCnsKDZVJ8uXyLLc47/jx47hcLkqXLu3tTI5+FFStmtqgVrOm7iTWlJCgdsJftBjqV8y495csWUL3\n7t0pVaoUa9eupVatWialy50Txuxvv6mNKzt3gg/+mfNLGzeqJ2a//656o9udN+/70NBQli9fTt26\ndU2/thPGa25q1oSVK8EH/+w5zqRJcOiQ2pPhVKZv3ANYvnw5W7du5cyZM9kfe/HFFwuezs+lpala\nqerVdSexLrcbfv1VHeRQtKg6XGXXLjVxaddONv3kpW/fvuzatYvNmzezc+dO2rdvz5NPPsmTTz6p\nO5rljRwJw4fLBNmbbr8dmjdXrS/lyOCrq1SpklcmyE527pzqECU/YwsnJMS/u0vlJM9J8mOPPcbp\n06dZvXo1jz76KJ9++imNGjXyRTbH2blTvbuV07tyV7o03HCDagW3axf88AOULKn+8XvvPejQQXdC\na7v11luZOXMmLpeLmjVr8uOPPzJ48OB8fW2fPn3473//S1BQUPaG3KNHj/LAAw/wxx9/UKNGDT75\n5BPKlCnjzW9Bi7Vr1Zsz6RPqfePGQcOG8Pjj/nWqaEHVr1+fBx54gI4dO1L8n3YDLpeL++67T3My\n6/rjD3Wg1JszAAAgAElEQVTegHRnKBw5UORK+eqTvHnzZm677TZ+/fVX0tLSaNu2Ld96sT+SUx8F\nLVwIS5ao7g0id2+/reqiGjdWv6pWhaVL4eWXVb2yk1eTdd77a9euJTAwkB49emRPkocOHUqFChUY\nOnQokyZN4tixY8RdfETkP+w8Zg0D/vUvVSfbvbvuNP7hqafUZr433tCdxDPevO979eqV/RoXmzNn\njsfXtvN4vZqvvlIlA6tW6U5iT8ePq5LQEyec+3PW9HKLa665BoBrr72W/fv3U758eQ4ePFj4hH5s\n+3a4+WbdKaxv4MArP3bvvfDSS7BsmfpvkbOdO3cycuRItmzZkl0e5XK5SMrHboymTZuSclkn+WXL\nlvH1118D0LNnT6KionKcJNvZZ5/BqVPw0EO6k/iPUaMgLAyeflr2Z+Rm7ty5uiPYjhxH7ZkyZdQq\n/KFDEBSkO4015DlJjomJ4dixYzz//PPUq1cPgEcffdTrwZxoxw7pbFFYLpdqyxUbq0ounPou11O9\ne/fmpZdeYvDgwcTHxzNnzhwyMzMLfb3U1FSCg4MBCA4OJjU11ayolpCRoTaKvv66lEH5UlCQWk0e\nPRoWLNCdxlomTZrEsGHDGDRo0BW/53K5mDZtmoZU9iDt3zx3vg2cTJKVq06Ss7KyaNGiBWXLlqVz\n587cc889nDlzxpE1ib6wYwc895zuFPbVoYNaTV66FDp10p3Gmk6fPk2rVq0wDIMbb7yR2NhYbr/9\ndsaOHevxtV0u1xWPfi8We1Fz4aioKKKiojx+TW+bOxeCg6FtW91J/M/gwerQgsREtWHXDhISEkhI\nSPDqa4SFhQFQr169q443caXdu1W9uyi883XJjRvrTmINV50kBwQE8MQTT7Dpn0PQS5YsecVpXiJ/\nsrLUxr3atXUnsa/zq8kvvqhKLgLyPArH/5QsWZLMzExq1arFm2++SZUqVTh16lShrxccHMzBgwep\nVKkSBw4cIOgqywuxNjuBIz1d3U+LF8uTCR0CA1XZxYgREB+vO03+XP7m76WXXjL9NWJiYgC45ZZb\nmDBhAikpKWRkZGT/fs+ePU1/TaeQlWTPyYEil8pzmtGqVSsWLVrkyCJ/X9q3T3VuuP563UnsLSZG\nPRZfulR3EmuaOnUqp0+fZvr06fz888988MEHzJs3r9DX69ChQ/bXz5s3j44dO5oVVbtp09RqiTTr\n0efRR1UXG9lodaWHH36Y3r17s3jxYj7//PPsXyJnhiE1yWaQDheXyrO7RWBgIOnp6RQpUiR7Fdnb\np/44ceftV1+pM+VXr9adxP4+/1wdb7tpk/NWkz2993/66adLVp8MwyAgIIBff/01z6/t1q0bX3/9\nNYcPHyY4OJiXX36Ze++9l65du7Jnz56rtoCz25g9elRtov32W9lMq9vHH8PkybB+vf3Gszfv+3/9\n61+sW7fOK9e223jNj0OH1Fg+elR3Entbs0adPPrNN7qTeEdB7/18n7jnS04cwNOnw9at8O9/605i\nf4YBDRqox7SdO+tOYy5P7/3atWvz6quvcuuttxJw0YyjRo0aJqTLnd3G7PPPw8mT8M47upOIrCxV\nRzp0KHTtqjtNwXjzvv/qq6/4+OOPadWqlel9ku02XvPjxx/hySfhp590J7G3PXugSRP19NuJTG8B\nl5WVxQcffEBycjIvvvgie/bs4eDBgzSU6vgC2bED6tTRncIZXC61gW/IELj7brj2Wt2JrKNixYp0\nkBNXrmrPHpg9Wx1DLfQLCFBP2R5/XG3ILVZMdyJrmDdvHjt27CAjI+OSN7xymEjOdu+WemQz3HAD\nHD4Mp0/DPx2A/VqeK8mPP/44AQEBrF69mu3bt3P06FFat27Nhg0bvBfKge9yW7VSnS1kF705DEP1\ntS1d2lmrgZ7e+95cfboaO43Z3r3VD4Jx43QnERdr3Ro6dsy5T7pVefO+v/nmm9m+fbtXOlzYabzm\n17hxqt/5xIm6k9hf7dqqf7wTT0U3fSX5xx9/JDExEfc/PXrKlSvHuXPnCp/QT+3YIbWPZnK51OT4\n9ttVdwKnlV0Ulqw+Xd3mzbBiheo0I6wlLg7uuQd69FCdL/xdkyZN2Lp1K7fccovuKLawe7c6OVN4\n7vzmPSdOkgsqz0ly8eLFLzmM4NChQ5f88C2M48eP069fP7Zs2YLL5WL27Nk0dnBTvrQ0OHIEqlfX\nncRZSpdWR323bw/168ONN+pOpN+GDRu8tvrkBCNHqlr20qV1JxGXu/12aN5cHezy4ou60+j3/fff\nExkZSc2aNSlRogSgVsHyswnXHyUlybHyZpE2cBfkOUkeNGgQnTp14q+//mLkyJEsWrSIcR4+p3z6\n6adp164dixYtIiMjw6M+rnawcyfUqiUnenlDw4aqjOXhhyEhAYrmeUc7m6w+5W7tWrWSvGiR7iQi\nN+PGqTE9YABUrKg7jV7xdmkebRHS/s080gbugnx1t9i2bRur/mlk2bJlS+p6sAb/999/43a7SbrK\n2xSn1UstXAhLlsAnn+hO4kxZWWoDX8OGYMLBclp5eu/XqVOH3bt3+3z1yepj1jDUo9gBA2S1yeqe\nekqVU02dqjtJ3qx+3+fGrrlzc+YMlCmjapJlMcpzS5fCrFmq3arTmF6TPGjQILp168aTTz7pUbDz\nkpOTqVixIr179+aXX36hXr16TJ06lWsd3KJg+3apR/amgACYN089rr31VrjvPv/dIS+rTzn77DP1\nA/Shh3QnEXkZNUrVQj79tHQrEPmTnKzKGWWCbA5ZSb4gz+LievXqMW7cOEJCQnjuuec87mqRkZHB\nxo0bGThwIBs3buS6664jLi7Oo2tanWza875KleCjj+DVV9V/d++uHqufPKk7mW/VqFEjx1/+LCND\n1SHHxckPUTsIClKryaNH604i7EKOozZXzZrqjUdWlu4k+uW5ktyrVy969erFkSNH+M9//sPQoUPZ\ns2cPu3btKtQLVq1alapVq9KgQQMAunTpkuMkOTY2Nvu/o6KiiIqKKtTr+dK5c2py9vvvasCe//Xz\nz6puVnjXXXepRvL79sGyZTBjBvTpAwsWgFVbByckJJCQkKA7hqPNnaveOEn7RfsYPFi1oUpMhH8a\nK4l82rt3Lz169OCvv/7C5XLRv39/nnrqKd2xvErqkc0VGKg2Nx84oNpl+rN8n7j3448/8sknn7B0\n6VLCwsI8OkP+rrvuYubMmdSuXZvY2FhOnz7NpEmTLoSyYb2UYUC/fpCaqlZAkpPVu9ukJNWY+8MP\n5dALHZYvV0dsbtig6hytzo73Plg3d3q6mmz95z+qZl3Yx1tvqZpIK1cQWfG+P3jwIAcPHiQyMpK0\ntDTq1avH0qVLL9lLZMXcnnjmGahWTR0wJczRpAlMmgRNm+pOYi7Ta5KHDh3KkiVLCAkJ4cEHH2T0\n6NGUKVPGo5DTp0/n4Ycf5uzZs4SGhjJnzhyPrmcF48fDpk3w9dfqXVijRroTCYB27dQRxAkJqr2U\n8C/TpsEdd8gE2Y4efRSmTIFVq6BlS91p7KNSpUpUqlQJgMDAQOrWrcuff/7p0YZ7q0tKAhs8bLaV\n0FD15+q0SXJB5TlJDg0NZd26dSQnJ3PmzJnsXfJ33XVXoV80IiKCnxx0wPqCBTBzJnz/vTTBt5qA\nALW68OqrMkn2N0eOqL/3777TnUQURvHiavFh2DBYv16NZVEwKSkpJCYm0sjhqzZyJLX5QkJk8x7k\nY5IcEBBAy5Yt2bdvH5GRkfzwww/ccccdrF692hf5LC8hQU3CVq+GypV1pxE5eeQRVQKzdSuEhelO\nI3xl4kTo0kWVWwh7uv9+mDxZbcLt2lV3GntJS0ujS5cuTJ06lUAbrN789ZfqUlSYKpCkJLXZTJgn\nJATefLNwC39ly6onQU6QZ03yrbfeyk8//cQdd9zBpk2b2L59OyNGjGDJkiXeC2WTeqnNm6FVK9UH\nuUUL3WnE1YwdC3/8oVb8rcwu9/7lrJZ7zx614WvzZqhSRXca4YlVq+Dxx9WbXKu1drTafX/euXPn\naN++PXfffTfPPPPMFb/vcrkYM2ZM9v+3wub4mTNVHXp0dMG/tmJFVVYnzHPggOpVXpgOF2+9pTbQ\nly1rfq6Cunxz/EsvvVSgMZvnJLl+/fps2LAhexW5ZMmShIWFsXXr1kKHzjOURf/hudj776sV5Lfe\nkhUOOzh8GG66CbZtU50OrMoO935OrJa7d2+1K9vDw0GFRbRpAx07qsNgrMRq9z2AYRj07NmT8uXL\nM2XKlBw/x4q5R45Um9tHjdKdRHjK7VZveurV053kSgW99/Os8qpWrRrHjh2jY8eOREdH06FDB7/u\nu5qeDn37woQJqsRCJsj2UKGCOkjizTd1JxHetnkzrFghK0tOEhenngalpelOYn3r1q1jwYIFrFmz\nBrfbjdvttsUhQ9LGzTmcdBhJvlvAgVq2PnHiBG3btqV48eLeC2XBd7mgTs67/36IiIB33pFNenaz\na5fqdJCSAtddpztNzqx67+fFSrljYlQ3hByeMgsbe/hhqFPHWoeMWOm+Lwgr5m7QQC1iOHyPoV8Y\nOhTKlYPhw3UnuVJB7/0CTZJ9xYoD+PffVd/AiRPVSrIdeu6KK3XurLpcmHTKuumseO/nh1Vyf/MN\n9Oyp3tCWKKE7jTBTUpJq5bdtm6pBtQKr3PcFZcXc5crBzp3qqZ+wt3ffVWcTzJihO8mVTC+3EMrS\npaq0ol8/mSDb2ZAhqvdqZqbuJMJshqHahY0dKxNkJwoJUSVTUmfuPMeOqePjy5fXnUSYISREval1\nApkk51N8vNo8IuytSRNVauGgNt3iH599BqdPq4mUcKZRo1Rfeqf8ABZKUpKqY5UFKGdwUk2yTJLz\nIS1NNbOXwyicoUULdTKicI6MDBgxQm3wkkMnnCsoCJ5+2lp1ycJzSUlyGIiTVKumWsidPas7iefk\nx0k+JCRA/fpQqpTuJMIMzZqpv1PhHHPnqsN85GmP8w0erDoLbdyoO4kwi3S2cJZixaBqVXU2gd3J\nJDkfvvwS2rbVnUKY5a671FHFGRm6kwgzpKdDbKxaRZbHtc4XGKhWkkeM0J1EmEVWkp3HKXXJMknO\nB6lHdpby5aFGDVmJcopp01Rrv4YNdScRvvLoo+oH8P/+pzuJMMPu3TJJdpqQEGfUJcskOQ+7d6ua\n5IgI3UmEmaTkwhmOHoXXXoPx43UnEb5UrJjqcjF8eOGOzRXWcn7jnnCO0FBZSfYLX34JrVvLY1yn\niYqSSbITTJwIXbpA7dq6kwhfu/9+9b+LFunNITxz9iz8+SdUr647iTCTrCT7CalHdqa77oJ166Qu\n2c727IHZs+HFF3UnEToEBMCkSTByJJw7pzuNKKw9e+CGG9TTAeEcspLsB86eVauN0dG6kwizVaig\nVi4SE3UnEYU1ZgwMGKC6Wgj/1LKl+mFsxZO9RP5IPbIznV9JttjBjgUmk+Sr+O47uPlmOSbTqaKi\npF+yXW3eDCtWwPPP604idIuLU6cspqXpTiIKQ+qRnal0aShZEg4d0p3EM9omyZmZmbjdbmJiYnRF\nyJN0tXA22bxnXyNHqhZgpUvrTiJ0c7vVAUGvv647iSgMWUl2LifUJWubJE+dOpWwsDBcFt4RJ/XI\nznbXXfDtt5CZqTuJKIi1a9VK8oABupMIqxg7FqZOhb/+0p1EFJSsJDuXE+qStUyS9+3bx4oVK+jX\nrx+GRQtWDh6ElBRo1Eh3EuEtQUFqw8imTbqTiPwyDBg6VLX/KlFCdxphFSEh8Mgj6r4Q9iIryc4l\nK8mF9OyzzzJ58mQCAqxbEv3VV2pTSNGiupMIb5JWcPby2Wdw+jQ89JDuJMJqXngBPvjA/itX/sQw\n5LQ9J3PCqXs+nwIuX76coKAg3G43CVeZncTGxmb/d1RUFFFRUV7PdjGpR/YPUVHw/vswZIie109I\nSLjqOBAXZGSoOuQpU1T7LyEuFhQETz+tjqz+4APdaUR+HD4MxYtDmTK6kwhvCA2FefN0p/CMy/Bx\nvcPIkSOZP38+RYsW5cyZM5w4cYLOnTvz/vvvXwjlcvm8DMMwYN8++P57+OEH1X/111+lwbnTpaZC\nnTrqH+siRXSn0XPvm8EXuWfOhA8/hFWr5HAfkbO0NLjpJtX5xO32/uvJePXMDz/AoEHw00+6kwhv\n2LMH7rgD9u/XneSCgt77Pp8kX+zrr7/m1Vdf5fPPP7/k474ewKNHq0nxuXPqL/SOO9Ru6YYNfRZB\naBQWBgsWwO23605inR9el6tRowbXX389RYoUoVixYqxfv/6S3/d27vR0darekiXQoIHXXkY4wNtv\nq7KcL7/0/mtZdbzmxSq5P/wQli2Djz7SnUR4Q2YmXHcdHDsG11yjO41S0Htfe8Wt7u4Wa9fC3Lmq\nLrVWLVmh8kfNmql+yVaYJFuVy+UiISGBcuXKaXn9adOgSROZIIu8PfqoKsn53/+gVSvdacTVyKY9\nZytSBG68EZKT1WKUHWmt7GvWrBnLli3T9vqZmaqGbdIk9YhOJsj+STbv5Y+ulacjR+C116Rzgcif\nYsVg/HgYPhyysnSnEVcj7d+cz+5t4Px6+8ucOXDttdCtm+4kQqeWLeHHH+GNN3I/QvPUKRg8GFau\n9G02q3C5XLRq1Yr69eszw8dnAE+cCF26qHILIfKjSxe16PHpp7qTiKuRlWTns3sbOO3lFrocPw6j\nRsF//ysryP6uQgW1gaRTJ7WBZMYM9ebpvJ9/Vi3HAgNhxw6IjtaXVZd169ZRuXJlDh06RHR0NHXq\n1KFp06Zef909e9Sb2d9+8/pLCQcJCFDHVT/2mBrXxYvrTiRyIivJzmf3lWS/nSSPHQvt20O9erqT\nCCuoUQPWrYP+/eFf/4L//Ed1NnnlFVXfOH063HOPOnzkyBEoX153Yt+qXLkyABUrVqRTp06sX7/+\nikmyN9o2jhkDAwfCPy8vRL61bKl+QM+cqe4hM0jLRvOcPq26Ct1wg+4kwptCQlRHIrvS2t0iN97e\nebt9O9x5J2zZAsHBXnsZYUOGoTaJTZigfsAWLw7z50O1aur3H3hAdT557DHvvL5Vdp1fLD09nczM\nTEqVKsWpU6do3bo1Y8aMoXXr1tmf443cmzerjVe//w7XX2/qpYWfSEyEdu3UPRQYaP71rThe88MK\nubdtg44d1dM54Vy//QZdu8LWrbqTKAW99/2yJnnwYHUogUyQxeVcLrWZ89NPVYnFqlUXJsig6tcX\nLtSXT4fU1FSaNm1KZGQkjRo1on379pdMkL1l5Ej1SybIorDcbvWm9vXXdScRl5N6ZP9Qs6bqbmHX\nTbR+t5K8YgU8+6xapZI6NVFQ//d/6tH/5s3eeUxohRWewjA79zffQM+e6qlPiRKmXVb4oaQk1Tpw\n2zZ1Kp+ZZLwW3rRpahX5rbe0xhA+UKmS2ttjhdIaWUm+isxMGDYMJk+WCbIonBIl1CPCjz/WncS5\nDEON03HjZIIsPBcSAo884l8tBPv06UNwcDDh4eG6o+Rq927ZtOcvQkPt2+HCrybJCxdCqVIQE6M7\nibAzfyy58KWlS9WmHmnNKMwyapQ63c2uP6gLqnfv3sTHx+uOcVVJSVJu4S9CQuzb4cJvJslnz6qd\n8hMmSMs34ZnmzWHvXrUZSJgrI0PtF4iLU228hDBDxYpqr8Ho0bqT+EbTpk0pW7as7hhXJSvJ/sPO\nK8l+0wJu1iz1F2VCVyrh54oWhfvvh48+8p8fur4ydy5UqQJt2uhOIpzm2WfVgTSJiWpDn/CcYaiW\nmIX5uuRktalLOF9ICCxbplr+FVTx4no3b/vFJDk9XdWjffaZ7iTCKbp1g3791GNceTJhjvR0iI1V\nParlz1SYLTBQvakdNgy++kp3Gv3M6Gs+f77qLV+Y9nrh4d5pyyesx+2G55+HOnUK/rUnT8LBg1DY\nByOe9jb3i+4Wr7wC69fDokWmXVL4OcNQ746XLoWICPOua4Vd54VhRu64ONi4ET75xKRQQlzm3DkI\nC4N//1v14PaUlcdrSkoKMTExbN68+YrfMyv3iBFqovvCCx5fSogc3X47vPce1K9vzvWku8Vljh9X\n3SzGjtWdRDiJywUPPigb+Mxy5Ai89pp/dSAQvlesGIwfD8OH27dvq5VIXbHwNt31zI6fJL/6qjp+\num5d3UmE03TrpuqSLbqQZCsTJ0KXLqpmVAhv6tJFvcn99FPdSbynW7duNGnShJ07d1KtWjXmzJnj\nldeRDhXC23R3xnB0uUVqqnq0tnEj3HijCcGEuIhhqLq6f/8bmjY155pWfnx7NZ7k/uMP9Ujtt9/U\nQS1CeNvq1aqWdutWz3rm++N4vVjZsrBrF5Qvb0IoIXLw3nuqXHbmTHOuJ+UWF3n5ZejeXSbIwjtc\nLujVC7y0SOM3xoyBgQNlgix8p0ULqFXLvB+8/ujYMVWyUq6c7iTCyfxyJXnv3r306NGDv/76C5fL\nRf/+/XnqqacuhDLhXe727Wp1b/t2eZcrvCc1Ve3Y3bNHHVTjKX9bmdq8WW2g+v13vW1+hP/ZtAnu\nvlvde4XtsuBv4/ViGzbAo4+qlnpCeEtSkjqb4I8/zLmeLVaSixUrxpQpU9iyZQs//PADb731Ftu2\nbTP1NYYPVy1HZIIsvCk4WPXelo4MhTNihPolE2Tha5GRakX59dd1J7GnpCTZtCe8r3p11QLu7Fk9\nr69lklypUiUiIyMBCAwMpG7duvz555+mXX/tWvXu9qLFaSG8pk8fmD1bdwr7+fpr2LIFBgzQnUT4\nq7FjYepU+Osv3UnsZ/du2bQnvK9oUahaFVJS9Ly+9prklJQUEhMTadSokSnXMwx47jl1/HTJkqZc\nUoiruvtutaqyfbvuJPZhGOpQh5dfhhIldKcR/iokBB55RFqEFoasJAtfCQ3VV5esdZKclpZGly5d\nmDp1KoEmHb3zySeQkaHacwnhC0WLQo8esoGvIJYsgTNn4OGHdScR/m7UKNXvXGcvVjuSlWThKyEh\n+santmOpz507R+fOnXnkkUfo2LHjFb9fmCMz/+//VH3jrFkQoH2NXPiT3r3V5oJx49SBBfnl6ZGZ\ndpSRASNHwhtvyDgV+lWsCE8/rY6s/vBD3WnsQ1aSha/oXEnW0t3CMAx69uxJ+fLlmTJlypWhCrnz\n9vXXVf/L5cvNSClEwfzrX2rDaExM4a/hD7vlZ8xQK3erVqk2ekLolpamDrJZvlz17M4vfxivOTl7\nVnXzSUsr2KKAEIWxeDHMnw9Ll3p+LVt0t1i3bh0LFixgzZo1uN1u3G438fHxHl3zyBGIi4NJk0wK\nKUQB9emjnmKI3KWnQ2ysGqcyQRZWERioVpKHD9edxB7++ENtppIJsvAFv1tJzkth3uU+/rgasNOn\neymUEHk4eRKqVVMb+CpVKtw1nL4yNXGi6jwjLfOE1Zw7p05o/fe/Ve/u/HD6eM1NfDy89hqsXGli\nKCFyceIEVKmifsZ6urhii5Vks/38s1qGf/ll3UmEPytVCu67DxYs0J3Emo4cUT9Yx4/XnUSIKxUr\nproiDR+uTpITuZN6ZOFL118P11yjp1Wj7SfJWVnwxBPqH7eyZXWnEf6uTx911nxamu4k1jNhAnTt\nCjfdpDuJEDnr0kVtJv30U91JrE06Wwhf09XhwvaT5Hnz1P/26qU1hhCA2rwXFQVt2sDff+tOYx17\n9sDcufDii7qTCJE7l0vVy7/wgr4TvuwgKUkmycK3QkL01CXbepJ87Jhq+fbmm9JKSliDywXvvKOO\nvI2OVveoUJPjgQMLX6sthK80bw61aqkuLCJnUm4hfE3X5j1bTy3HjIF774X69XUnEeKCgAD1xu3O\nO6FlSzh8WHcivTZvhi++gOef151EiPyJi1M9z0+e1J3EegxDyi2E70m5RQH98gt89JGqcxTCalwu\ntUmtbVto0QJSU3Un0mfECHV4yPXX604iRP5ERqo3uK+/rjuJ9Rw6BCVLQunSupMIfyIryQVgGDBo\nELz0EpQvrzuNEDlzuVQnh06doH171WLK33z9NWzZolo0CmEnY8fCtGn+/QY3J7KKLHSQleQCWLxY\nbYrq3193EiGuzuVSh2eUKwevvKI7jW8ZBgwbph5blyihO40QBVOzJnTvru5fcYHUIwsdbrgBjh5V\nB1L5ku0myf/3fzB0qHoMVqSI7jRC5M3lUpuA3ngDfvtNdxrfWbIEzpyBbt10JxGicF54QR2hrmMF\ny6pkJVnoEBAANWpAcrKPX9e3L+e5adPg1ltVvZgQdlG9uqqf79ULMjJ0p/G+jAxVixwXJ51nhH1V\nrAjPPKOOrBaKrCQLXXTUJdvqx9dff6kelpMn604iRMH166fKLvzh/p09Wz0ea9NGdxIhPPPss5CQ\nABs36k5iDbKSLHTRUZdsq0lybCw8/DDcfLPuJEIUnMsFM2eqUiEnl12kp6tNtZMmqe9ZCDu77jq1\nkjx8uO4k1iAryUIXHSvJRX37coW3ZQssWgTbt+tOIkThVa+uOl707g3ffw9FbTMC82/qVGjSBBo0\n0J1ECHP066dKiDIz/XsvzOnTcOQIVKmiO4nwRyEhsHKlb1/TNivJQ4aoTRTlyulOIoRnHn0UypSB\nd9/VncR8R46o/tDSv1w4SbFiqu2oP0+QQW2auvFG+XMQeug4mtoW61hffKEG58CBupMI4TmXCz74\nAEqV0p3EfBMmQNeucNNNupMIIcy2e7eUWgh9QkIgJQWysny3Idzyk+Q//oC+fWHePPVuXggnCArS\nncB8f/wBc+eq0ighhPMkJcmmPaHPtdeqp7B//glVq/rmNbWUW8THx1OnTh1uuukmJk2alOvnnTwJ\nMTGqL3J0tA8DCiEukZ8x++KL8MQTUKmSj8MJIS6R35+xBSUryUK30FDfdrjw+SQ5MzOTJ598kvj4\neLZu3crChQvZtm1bDp8HDz0EjRvD00+bmyEhIcHcCzro+nbO7oTrW1F+x2x8PDz3nPmvb/e/U7m+\nvrKpMH4AAAeKSURBVOvLeM19vBZGfleS7XzPePv6ds5uhev7ui7Z55Pk9evXU6tWLWrUqEGxYsV4\n8MEH+eyzz674vOHDIS0N3nrL/DZSuv+SrXx9O2d3wvWtKL9j9oUX4PrrzX99u/+dyvX1XV/Ga+7j\ntTDyu5Js53vG29e3c3YrXN/xK8n79++nWrVq2f+/atWq7N+//4rPW7oUFi+WOmQhdMvvmH3sMV+m\nEkLkJL/jtaCystSmqZo1Pb6UEIXm65Vkn2/cc+VzWXj5cmn3JoQV5HfMlijh5SBCiDzld7zGxBTs\nuufOqU1T111XiFBCmKRWLfjqq/zdv889B82aefiCho99//33Rps2bbL//4QJE4y4uLhLPic0NNQA\n5Jf88rtfoaGhvh6SeZIxK7/kV86/ZLzKL/llr18FHbMuwzAMfCgjI4Obb76ZVatWUaVKFRo2bMjC\nhQupW7euL2MIIfJJxqwQ9iHjVQjz+LzcomjRorz55pu0adOGzMxM+vbtK4NXCAuTMSuEfch4FcI8\nPl9JFkIIIYQQwuq0HCZyNd5qgn5ejRo1uO2223C73TRs2NCja/Xp04fg4GDCw8OzP3b06FGio6Op\nXbs2rVu35vjx46ZePzY2lqpVq+J2u3G73cTHxxf6+nv37qV58+bccsst3HrrrUybNs3U7yG365vx\nPZw5c4ZGjRoRGRlJWFgYI0aMMDV7btc3888fVE9Tt9tNzD+7EMy8f3zBTuMV7D1m7TxewRlj1u7j\nFew1Zu08XsHeY9YJ4xVMGLMe7xIwUUZGhhEaGmokJycbZ8+eNSIiIoytW7ea+ho1atQwjhw5Ysq1\nvvnmG2Pjxo3Grbfemv2x559/3pg0aZJhGIYRFxdnDBs2zNTrx8bGGq+99lrhQ1/kwIEDRmJiomEY\nhnHy5Emjdu3axtatW037HnK7vlnfw6lTpwzDMIxz584ZjRo1MtauXWvqn39O1zfzz98wDOO1114z\nHnroISMmJsYwDHPvH2+z23g1DHuPWbuPV8Ow/5i183g1DPuNWTuPV8Ow/5i1+3g1DM/HrKVWkr3Z\nBP1ihkkVJk2bNqVs2bKXfGzZsmX07NkTgJ49e7J06VJTrw/m5a9UqRKRkZEABAYGUrduXfbv32/a\n95Db9cGc7+Haa68F4OzZs2RmZlK2bFlT//xzuj6Y9+e/b98+VqxYQb9+/bKvaWZ+b7PbeAV7j1m7\nj1ew95i1+3gF+41ZO49XsP+YtfN4BXPGrKUmyd5qgn4xl8tFq1atqF+/PjNmzDD12gCpqakEBwcD\nEBwcTGpqqumvMX36dCIiIujbt69pj/dSUlJITEykUaNGXvkezl+/cePGgDnfQ1ZWFpGRkQQHB2c/\ncjIze07XNys7wLPPPsvkyZMJCLgwDH1x/5jFCeMV7Dlm7Thewd5j1u7jFZwxZu04XsGeY9bO4xXM\nGbOWmiTntwm6J9atW0diYiJffPEFb731FmvXrvXaa7lcLtO/pwEDBpCcnMymTZuoXLkyQ4YM8fia\naWlpdO7cmalTp1KqVKlLfs+M7yEtLY0uXbowdepUAgMDTfseAgIC2LRpE/v27eObb75hzZo1pma/\n/PoJCQmmZV++fDlBQUG43e5c3zV74/4xk9PGK9hjzNp1vIJ9x6wTxis4b8zaYbyCfcesXccrmDdm\nLTVJvuGGG9i7d2/2/9+7dy9Vq1Y19TUqV64MQMWKFenUqRPr16839frBwcEcPHgQgAMHDhAUFGTq\n9YOCgrL/Yvv16+dx/nPnztG5c2e6d+9Ox44dAXO/h/PXf+SRR7Kvb/b3ULp0ae655x5+/vlnr/z5\nn7/+hg0bTMv+3XffsWzZMmrWrEm3bt1YvXo13bt39/r9YyYnjFew15h1wngF+41ZJ4xXcMaYtdN4\nBWeMWbuNVzBvzFpqkly/fn1+//13UlJSOHv2LB9//DEdOnQw7frp6emcPHkSgFOnTvHVV19dsqvV\nDB06dGDevHkAzJs3L/umNcuBAwey/3vJkiUe5TcMg759+xIWFsYzzzyT/XGzvofcrm/G93D48OHs\nxzCnT59m5cqVuN1u07Lndv3zg8uT7AATJkxg7969JCcn89FHH9GiRQvmz5/v9fvHTE4Yr2CfMWvn\n8Qr2HrNOGK/gjDFrl/EK9h6zdh6vYOKYNW0LoUlWrFhh1K5d2wgNDTUmTJhg6rWTkpKMiIgIIyIi\nwrjllls8vv6DDz5oVK5c2ShWrJhRtWpVY/bs2caRI0eMli1bGjfddJMRHR1tHDt2zLTrz5o1y+je\nvbsRHh5u3Hbbbca9995rHDx4sNDXX7t2reFyuYyIiAgjMjLSiIyMNL744gvTvoecrr9ixQpTvodf\nf/3VcLvdRkREhBEeHm688sorhmEYpmXP7fpm/vmfl5CQkL3z1sz7xxfsNF4Nw95j1s7j1TCcM2bt\nPF4Nw15j1s7j1TDsPWadMl4Nw7MxK4eJCCGEEEIIcRlLlVsIIYQQQghhBTJJFkIIIYQQ4jIySRZC\nCCGEEOIyMkkWQgghhBDiMjJJFkIIIYQQ4jIySRZCCCGEEOIyMkkWQgghhBDiMjJJFkIIIYQQ4jL/\nD8Se36QdbJ39AAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x105f3e110>"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We can use it to analyze other data sets one by one:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "analyze('inflammation-02.csv')"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
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V2woHb/LLc5PP9E63OPHaV0uozXJrVtTqTh7qDBg7b2fkZGBChUq5PvczZs3UVPhEil66rNjxwJ/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\nP7YtjX37gFOngKFD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+       "text": [
+        "<matplotlib.figure.Figure at 0x105f07d10>"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "but we have a dozen data sets right now and more on the way.\n",
+      "We want to create plots for all our data sets with a single statement.\n",
+      "To do that,\n",
+      "we'll have to teach the computer how to repeat things."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "objectives"
+      ]
+     },
+     "source": [
+      "#### Objectives\n",
+      "\n",
+      "*   Explain what a for loop does.\n",
+      "*   Correctly write for loops to repeat simple calculations.\n",
+      "*   Trace changes to a loop variable as the loop runs.\n",
+      "*   Trace changes to other variables as they are updated by a for loop.\n",
+      "*   Explain what a list is.\n",
+      "*   Create and index lists of simple values.\n",
+      "*   Use a library function to get a list of filenames that match a simple wildcard pattern.\n",
+      "*   Use a for loop to process multiple files."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "For Loops"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Suppose we want to print each character in the word \"lead\" on a line of its own.\n",
+      "One way is to use four `print` statements:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def print_characters(element):\n",
+      "    print element[0]\n",
+      "    print element[1]\n",
+      "    print element[2]\n",
+      "    print element[3]\n",
+      "\n",
+      "print_characters('lead')"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "l\n",
+        "e\n",
+        "a\n",
+        "d\n"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "but that's a bad approach for two reasons:\n",
+      "\n",
+      "1.  It doesn't scale:\n",
+      "    if we want to print the characters in a string that's hundreds of letters long,\n",
+      "    we'd be better off just typing them in.\n",
+      "\n",
+      "1.  It's fragile:\n",
+      "    if we give it a longer string,\n",
+      "    it only prints part of the data,\n",
+      "    and if we give it a shorter one,\n",
+      "    it produces an error because we're asking for characters that don't exist."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print_characters('tin')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "IndexError",
+       "evalue": "string index out of range",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-13-5bc7311e0bf3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint_characters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'tin'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;32m<ipython-input-12-11460561ea56>\u001b[0m in \u001b[0;36mprint_characters\u001b[0;34m(element)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0;32mprint\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint_characters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'lead'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;31mIndexError\u001b[0m: string index out of range"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "t\n",
+        "i\n",
+        "n\n"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Here's a better approach:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def print_characters(element):\n",
+      "    for char in element:\n",
+      "        print char\n",
+      "\n",
+      "print_characters('lead')"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "This is shorter---certainly shorter than something that prints every character in a hundred-letter string---and\n",
+      "more robust as well:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print_characters('oxygen')"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The improved version of `print_characters` uses a [for loop](../../gloss.html#for-loop)\n",
+      "to repeat an operation---in this case, printing---once for each thing in a collection.\n",
+      "The general form of a loop is:\n",
+      "\n",
+      "<pre>\n",
+      "<strong>for</strong> <em>variable</em> <strong>in</strong> <em>collection</em><strong>:</strong>\n",
+      "    <em>do things with variable</em>\n",
+      "</pre>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We can call the [loop variable](../../gloss.html#loop-variable) anything we like,\n",
+      "but there must be a colon at the end of the line starting the loop,\n",
+      "and we must indent the body of the loop.\n",
+      "\n",
+      "Here's another loop that repeatedly updates a variable:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "length = 0\n",
+      "for vowel in 'aeiou':\n",
+      "    length = length + 1\n",
+      "print 'There are', length, 'vowels'"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "It's worth tracing the execution of this little program step by step.\n",
+      "Since there are five characters in `'aeiou'`,\n",
+      "the statement on line 3 will be executed five times.\n",
+      "The first time around,\n",
+      "`length` is zero (the value assigned to it on line 1)\n",
+      "and `vowel` is `'a'`.\n",
+      "The statement adds 1 to the old value of `length`,\n",
+      "producing 1,\n",
+      "and updates `length` to refer to that new value.\n",
+      "The next time around,\n",
+      "`vowel` is `'e'` and `length` is 1,\n",
+      "so `length` is updated to be 2.\n",
+      "After three more updates,\n",
+      "`length` is 5;\n",
+      "since there is nothing left in `'aeiou'` for Python to process,\n",
+      "the loop finishes\n",
+      "and the `print` statement on line 4 tells us our final answer.\n",
+      "\n",
+      "Note that a loop variable is just a variable that's being used to record progress in a loop.\n",
+      "It still exists after the loop is over,\n",
+      "and we can re-use variables previously defined as loop variables as well:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "letter = 'z'\n",
+      "for letter in 'abc':\n",
+      "    print letter\n",
+      "print 'after the loop, letter is', letter"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Note also that finding the length of a string is such a common operation\n",
+      "that Python actually has a built-in function to do it called `len`:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print len('aeiou')"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "`len` is much faster than any function we could write ourselves,\n",
+      "and much easier to read than a two-line loop;\n",
+      "it will also give us the length of many other things that we haven't met yet,\n",
+      "so we should always use it when we can."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "challenges"
+      ]
+     },
+     "source": [
+      "#### Challenges\n",
+      "\n",
+      "1.  Python has a built-in function called `range` that creates a list of numbers:\n",
+      "    `range(3)` produces `[0, 1, 2]`, `range(2, 5)` produces `[2, 3, 4]`, and `range(2, 10, 3)` produces `[2, 5, 8]`.\n",
+      "    Using `range`,\n",
+      "    write a function that prints the $N$ natural numbers:\n",
+      "    ~~~python\n",
+      "    print_N(3)\n",
+      "    1\n",
+      "    2\n",
+      "    3\n",
+      "    ~~~\n",
+      "\n",
+      "1.  Exponentiation is built into Python:\n",
+      "    ~~~python\n",
+      "    print 2**4\n",
+      "    16\n",
+      "    ~~~\n",
+      "    It also has a function called `pow` that calculates the same value.\n",
+      "    Write a function called `expo` that uses a loop to calculate the same result.\n",
+      "\n",
+      "1.  Python's strings have methods, just like NumPy's arrays.\n",
+      "    One of these is called `reverse`:\n",
+      "    ~~~python\n",
+      "    print 'Newton'.reverse()\n",
+      "    notweN\n",
+      "    ~~~\n",
+      "    Write a function called `rev` that does the same thing:\n",
+      "    ~~~python\n",
+      "    print rev('Newton')\n",
+      "    notweN\n",
+      "    ~~~\n",
+      "    As always, be sure to include a docstring."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Lists"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Just as a `for` loop is a way to do operations many times,\n",
+      "a list is a way to store many values.\n",
+      "Unlike NumPy arrays,\n",
+      "there are built into the language.\n",
+      "We create a list by putting values inside square brackets:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "odds = [1, 3, 5, 7]\n",
+      "print 'odds are:', odds"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We select individual elements from lists by indexing them:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'first and last:', odds[0], odds[-1]"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "and if we loop over a list,\n",
+      "the loop variable is assigned elements one at a time:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for number in odds:\n",
+      "    print number"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "There is one important difference between lists and strings:\n",
+      "we can change the values in a list,\n",
+      "but we cannot change the characters in a string.\n",
+      "For example:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "names = ['Newton', 'Darwing', 'Turing'] # typo in Darwin's name\n",
+      "print 'names is originally:', names\n",
+      "names[1] = 'Darwin' # correct the name\n",
+      "print 'final value of names:', names"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "works, but:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "name = 'Bell'\n",
+      "name[0] = 'b'"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "does not.\n",
+      "\n",
+      "> #### Ch-Ch-Ch-Changes\n",
+      ">\n",
+      "> Data that can be changed is called [mutable](../../gloss.html#mutable),\n",
+      "> while data that cannot be is called [immutable](../../gloss.html#immutable).\n",
+      "> Like strings,\n",
+      "> numbers are immutable:\n",
+      "> there's no way to make the number 0 have the value 1 or vice versa\n",
+      "> (at least, not in Python&mdash;there actually *are* languages that will let people do this,\n",
+      "> with predictably confusing results).\n",
+      "> Lists and arrays,\n",
+      "> on the other hand,\n",
+      "> are mutable:\n",
+      "> both can be modified after they have been created.\n",
+      ">\n",
+      "> Programs that modify data in place can be harder to understand than ones that don't\n",
+      "> because readers may have to mentally sum up many lines of code\n",
+      "> in order to figure out what the value of something actually is.\n",
+      "> On the other hand,\n",
+      "> programs that modify data in place instead of creating copies that are almost identical to the original\n",
+      "> every time they want to make a small change\n",
+      "> are much more efficient.\n",
+      "\n",
+      "There are many ways to change the contents of in lists besides assigning to elements:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "odds.append(11)\n",
+      "print 'odds after adding a value:', odds"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "del odds[0]\n",
+      "print 'odds after removing the first element:', odds"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "odds.reverse()\n",
+      "print 'odds after reversing:', odds"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "challenges"
+      ]
+     },
+     "source": [
+      "#### Challenges\n",
+      "\n",
+      "1.  Write a function called `total` that calculates the sum of the values in a list.\n",
+      "    (Python has a built-in function called `sum` that does this for you.\n",
+      "    Please don't use it for this exercise.)"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Processing Multiple Files"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We now have almost everything we need to process all our data files.\n",
+      "The only thing that's missing is a library with a rather unpleasant name:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import glob"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The `glob` library contains a single function, also called `glob`,\n",
+      "that finds files whose names match a pattern.\n",
+      "We provide those patterns as strings:\n",
+      "the character `*` matches zero or more characters,\n",
+      "while `?` matches any one character.\n",
+      "We can use this to get the names of all the IPython Notebooks we have created so far:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print glob.glob('*.ipynb')"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "['01-numpy.ipynb', '02-func.ipynb', '03-loop.ipynb', '04-cond.ipynb', '05-defensive.ipynb', '06-cmdline.ipynb', 'spatial-intro.ipynb']\n"
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "or to get the names of all our CSV data files:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print glob.glob('*.csv')"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "['inflammation-01.csv', 'inflammation-02.csv', 'inflammation-03.csv', 'inflammation-04.csv', 'inflammation-05.csv', 'inflammation-06.csv', 'inflammation-07.csv', 'inflammation-08.csv', 'inflammation-09.csv', 'inflammation-10.csv', 'inflammation-11.csv', 'inflammation-12.csv', 'small-01.csv', 'small-02.csv', 'small-03.csv', 'swc_bc_coords.csv']\n"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "As these examples show,\n",
+      "`glob.glob`'s result is a list of strings,\n",
+      "which means we can loop over it\n",
+      "to do something with each filename in turn.\n",
+      "In our case,\n",
+      "the \"something\" we want is our `analyze` function.\n",
+      "Let's test it by analyzing the first three files in the list:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "filenames = glob.glob('*.csv')\n",
+      "filenames = filenames[0:3]\n",
+      "for f in filenames:\n",
+      "    print f\n",
+      "    analyze(f)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "inflammation-01.csv\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
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3vd2zZ0f5xdaNm3SncRedu9WJaApKbqTeFdB7/081yvbtm1L0aJFeeihhzAMg48++oj09HSCg4Pp\n1asXn3/+eYECRkRE0KNHD+rXr09AQAC33367aavUdnC+1OJigwbB3LnwzjuqLZy/crtVffa5c3Kk\nrdDn6FFVh/ztt7qTOEPx4jB+PAwbplpzycq88CapRy6c6tXhwAH1prZ4cd1prCPPlWS3253d+u3y\nj3nSCu6qoRz8Lnf6dNi6VR3PfLHNm9Uq8z336MllFXXqqLrs227TnUQPT+/9M2fOULJkyUs+dvjw\nYSp4+dmjk8bs0KHw99/w7ru6kzhHVhY0aKAmyl276k5jHrve93bNnR9xcXDkCEyerDuJ/YSEwJdf\nwk036U7iPaa3gMvMzOTHi55/r1+/nqx/jrEp6o+Fsx7asePKlWSA8HCZIINs3vNUgwYN+P7777P/\n/+LFi7njjjs0JrKXvXtVr9CLtkQIEwQEqBKWF16QNo/Cu2QlufCkw8WV8pzlzpo1i969e5OWlgZA\nqVKlmDVrFqdOnWLEiBFeD+g027er9m8iZ+cnyb17605iTx9++CF9+vQhKiqK/fv3c+TIEdasWaM7\nlm2MGQMDBkDlyrqTOE+rVlCzpjogacAA3Wmsb/HixQwfPpzU1NTslS+Xy8WJEyc0J7O2pCTo0kV3\nCnsKDZVJ8uXyLLc47/jx47hcLkqXLu3tTI5+FFStmtqgVrOm7iTWlJCgdsJftBjqV8y495csWUL3\n7t0pVaoUa9eupVatWialy50Txuxvv6mNKzt3gg/+mfNLGzeqJ2a//656o9udN+/70NBQli9fTt26\ndU2/thPGa25q1oSVK8EH/+w5zqRJcOiQ2pPhVKZv3ANYvnw5W7du5cyZM9kfe/HFFwuezs+lpala\nqerVdSexLrcbfv1VHeRQtKg6XGXXLjVxaddONv3kpW/fvuzatYvNmzezc+dO2rdvz5NPPsmTTz6p\nO5rljRwJw4fLBNmbbr8dmjdXrS/lyOCrq1SpklcmyE527pzqECU/YwsnJMS/u0vlJM9J8mOPPcbp\n06dZvXo1jz76KJ9++imNGjXyRTbH2blTvbuV07tyV7o03HCDagW3axf88AOULKn+8XvvPejQQXdC\na7v11luZOXMmLpeLmjVr8uOPPzJ48OB8fW2fPn3473//S1BQUPaG3KNHj/LAAw/wxx9/UKNGDT75\n5BPKlCnjzW9Bi7Vr1Zsz6RPqfePGQcOG8Pjj/nWqaEHVr1+fBx54gI4dO1L8n3YDLpeL++67T3My\n6/rjD3Wg1JszAAAgAElEQVTegHRnKBw5UORK+eqTvHnzZm677TZ+/fVX0tLSaNu2Ld96sT+SUx8F\nLVwIS5ao7g0id2+/reqiGjdWv6pWhaVL4eWXVb2yk1eTdd77a9euJTAwkB49emRPkocOHUqFChUY\nOnQokyZN4tixY8RdfETkP+w8Zg0D/vUvVSfbvbvuNP7hqafUZr433tCdxDPevO979eqV/RoXmzNn\njsfXtvN4vZqvvlIlA6tW6U5iT8ePq5LQEyec+3PW9HKLa665BoBrr72W/fv3U758eQ4ePFj4hH5s\n+3a4+WbdKaxv4MArP3bvvfDSS7BsmfpvkbOdO3cycuRItmzZkl0e5XK5SMrHboymTZuSclkn+WXL\nlvH1118D0LNnT6KionKcJNvZZ5/BqVPw0EO6k/iPUaMgLAyeflr2Z+Rm7ty5uiPYjhxH7ZkyZdQq\n/KFDEBSkO4015DlJjomJ4dixYzz//PPUq1cPgEcffdTrwZxoxw7pbFFYLpdqyxUbq0ounPou11O9\ne/fmpZdeYvDgwcTHxzNnzhwyMzMLfb3U1FSCg4MBCA4OJjU11ayolpCRoTaKvv66lEH5UlCQWk0e\nPRoWLNCdxlomTZrEsGHDGDRo0BW/53K5mDZtmoZU9iDt3zx3vg2cTJKVq06Ss7KyaNGiBWXLlqVz\n587cc889nDlzxpE1ib6wYwc895zuFPbVoYNaTV66FDp10p3Gmk6fPk2rVq0wDIMbb7yR2NhYbr/9\ndsaOHevxtV0u1xWPfi8We1Fz4aioKKKiojx+TW+bOxeCg6FtW91J/M/gwerQgsREtWHXDhISEkhI\nSPDqa4SFhQFQr169q443caXdu1W9uyi883XJjRvrTmINV50kBwQE8MQTT7Dpn0PQS5YsecVpXiJ/\nsrLUxr3atXUnsa/zq8kvvqhKLgLyPArH/5QsWZLMzExq1arFm2++SZUqVTh16lShrxccHMzBgwep\nVKkSBw4cIOgqywuxNjuBIz1d3U+LF8uTCR0CA1XZxYgREB+vO03+XP7m76WXXjL9NWJiYgC45ZZb\nmDBhAikpKWRkZGT/fs+ePU1/TaeQlWTPyYEil8pzmtGqVSsWLVrkyCJ/X9q3T3VuuP563UnsLSZG\nPRZfulR3EmuaOnUqp0+fZvr06fz888988MEHzJs3r9DX69ChQ/bXz5s3j44dO5oVVbtp09RqiTTr\n0efRR1UXG9lodaWHH36Y3r17s3jxYj7//PPsXyJnhiE1yWaQDheXyrO7RWBgIOnp6RQpUiR7Fdnb\np/44ceftV1+pM+VXr9adxP4+/1wdb7tpk/NWkz2993/66adLVp8MwyAgIIBff/01z6/t1q0bX3/9\nNYcPHyY4OJiXX36Ze++9l65du7Jnz56rtoCz25g9elRtov32W9lMq9vHH8PkybB+vf3Gszfv+3/9\n61+sW7fOK9e223jNj0OH1Fg+elR3Entbs0adPPrNN7qTeEdB7/18n7jnS04cwNOnw9at8O9/605i\nf4YBDRqox7SdO+tOYy5P7/3atWvz6quvcuuttxJw0YyjRo0aJqTLnd3G7PPPw8mT8M47upOIrCxV\nRzp0KHTtqjtNwXjzvv/qq6/4+OOPadWqlel9ku02XvPjxx/hySfhp590J7G3PXugSRP19NuJTG8B\nl5WVxQcffEBycjIvvvgie/bs4eDBgzSU6vgC2bED6tTRncIZXC61gW/IELj7brj2Wt2JrKNixYp0\nkBNXrmrPHpg9Wx1DLfQLCFBP2R5/XG3ILVZMdyJrmDdvHjt27CAjI+OSN7xymEjOdu+WemQz3HAD\nHD4Mp0/DPx2A/VqeK8mPP/44AQEBrF69mu3bt3P06FFat27Nhg0bvBfKge9yW7VSnS1kF705DEP1\ntS1d2lmrgZ7e+95cfboaO43Z3r3VD4Jx43QnERdr3Ro6dsy5T7pVefO+v/nmm9m+fbtXOlzYabzm\n17hxqt/5xIm6k9hf7dqqf7wTT0U3fSX5xx9/JDExEfc/PXrKlSvHuXPnCp/QT+3YIbWPZnK51OT4\n9ttVdwKnlV0Ulqw+Xd3mzbBiheo0I6wlLg7uuQd69FCdL/xdkyZN2Lp1K7fccovuKLawe7c6OVN4\n7vzmPSdOkgsqz0ly8eLFLzmM4NChQ5f88C2M48eP069fP7Zs2YLL5WL27Nk0dnBTvrQ0OHIEqlfX\nncRZSpdWR323bw/168ONN+pOpN+GDRu8tvrkBCNHqlr20qV1JxGXu/12aN5cHezy4ou60+j3/fff\nExkZSc2aNSlRogSgVsHyswnXHyUlybHyZpE2cBfkOUkeNGgQnTp14q+//mLkyJEsWrSIcR4+p3z6\n6adp164dixYtIiMjw6M+rnawcyfUqiUnenlDw4aqjOXhhyEhAYrmeUc7m6w+5W7tWrWSvGiR7iQi\nN+PGqTE9YABUrKg7jV7xdmkebRHS/s080gbugnx1t9i2bRur/mlk2bJlS+p6sAb/999/43a7SbrK\n2xSn1UstXAhLlsAnn+hO4kxZWWoDX8OGYMLBclp5eu/XqVOH3bt3+3z1yepj1jDUo9gBA2S1yeqe\nekqVU02dqjtJ3qx+3+fGrrlzc+YMlCmjapJlMcpzS5fCrFmq3arTmF6TPGjQILp168aTTz7pUbDz\nkpOTqVixIr179+aXX36hXr16TJ06lWsd3KJg+3apR/amgACYN089rr31VrjvPv/dIS+rTzn77DP1\nA/Shh3QnEXkZNUrVQj79tHQrEPmTnKzKGWWCbA5ZSb4gz+LievXqMW7cOEJCQnjuuec87mqRkZHB\nxo0bGThwIBs3buS6664jLi7Oo2tanWza875KleCjj+DVV9V/d++uHqufPKk7mW/VqFEjx1/+LCND\n1SHHxckPUTsIClKryaNH604i7EKOozZXzZrqjUdWlu4k+uW5ktyrVy969erFkSNH+M9//sPQoUPZ\ns2cPu3btKtQLVq1alapVq9KgQQMAunTpkuMkOTY2Nvu/o6KiiIqKKtTr+dK5c2py9vvvasCe//Xz\nz6puVnjXXXepRvL79sGyZTBjBvTpAwsWgFVbByckJJCQkKA7hqPNnaveOEn7RfsYPFi1oUpMhH8a\nK4l82rt3Lz169OCvv/7C5XLRv39/nnrqKd2xvErqkc0VGKg2Nx84oNpl+rN8n7j3448/8sknn7B0\n6VLCwsI8OkP+rrvuYubMmdSuXZvY2FhOnz7NpEmTLoSyYb2UYUC/fpCaqlZAkpPVu9ukJNWY+8MP\n5dALHZYvV0dsbtig6hytzo73Plg3d3q6mmz95z+qZl3Yx1tvqZpIK1cQWfG+P3jwIAcPHiQyMpK0\ntDTq1avH0qVLL9lLZMXcnnjmGahWTR0wJczRpAlMmgRNm+pOYi7Ta5KHDh3KkiVLCAkJ4cEHH2T0\n6NGUKVPGo5DTp0/n4Ycf5uzZs4SGhjJnzhyPrmcF48fDpk3w9dfqXVijRroTCYB27dQRxAkJqr2U\n8C/TpsEdd8gE2Y4efRSmTIFVq6BlS91p7KNSpUpUqlQJgMDAQOrWrcuff/7p0YZ7q0tKAhs8bLaV\n0FD15+q0SXJB5TlJDg0NZd26dSQnJ3PmzJnsXfJ33XVXoV80IiKCnxx0wPqCBTBzJnz/vTTBt5qA\nALW68OqrMkn2N0eOqL/3777TnUQURvHiavFh2DBYv16NZVEwKSkpJCYm0sjhqzZyJLX5QkJk8x7k\nY5IcEBBAy5Yt2bdvH5GRkfzwww/ccccdrF692hf5LC8hQU3CVq+GypV1pxE5eeQRVQKzdSuEhelO\nI3xl4kTo0kWVWwh7uv9+mDxZbcLt2lV3GntJS0ujS5cuTJ06lUAbrN789ZfqUlSYKpCkJLXZTJgn\nJATefLNwC39ly6onQU6QZ03yrbfeyk8//cQdd9zBpk2b2L59OyNGjGDJkiXeC2WTeqnNm6FVK9UH\nuUUL3WnE1YwdC3/8oVb8rcwu9/7lrJZ7zx614WvzZqhSRXca4YlVq+Dxx9WbXKu1drTafX/euXPn\naN++PXfffTfPPPPMFb/vcrkYM2ZM9v+3wub4mTNVHXp0dMG/tmJFVVYnzHPggOpVXpgOF2+9pTbQ\nly1rfq6Cunxz/EsvvVSgMZvnJLl+/fps2LAhexW5ZMmShIWFsXXr1kKHzjOURf/hudj776sV5Lfe\nkhUOOzh8GG66CbZtU50OrMoO935OrJa7d2+1K9vDw0GFRbRpAx07qsNgrMRq9z2AYRj07NmT8uXL\nM2XKlBw/x4q5R45Um9tHjdKdRHjK7VZveurV053kSgW99/Os8qpWrRrHjh2jY8eOREdH06FDB7/u\nu5qeDn37woQJqsRCJsj2UKGCOkjizTd1JxHetnkzrFghK0tOEhenngalpelOYn3r1q1jwYIFrFmz\nBrfbjdvttsUhQ9LGzTmcdBhJvlvAgVq2PnHiBG3btqV48eLeC2XBd7mgTs67/36IiIB33pFNenaz\na5fqdJCSAtddpztNzqx67+fFSrljYlQ3hByeMgsbe/hhqFPHWoeMWOm+Lwgr5m7QQC1iOHyPoV8Y\nOhTKlYPhw3UnuVJB7/0CTZJ9xYoD+PffVd/AiRPVSrIdeu6KK3XurLpcmHTKuumseO/nh1Vyf/MN\n9Oyp3tCWKKE7jTBTUpJq5bdtm6pBtQKr3PcFZcXc5crBzp3qqZ+wt3ffVWcTzJihO8mVTC+3EMrS\npaq0ol8/mSDb2ZAhqvdqZqbuJMJshqHahY0dKxNkJwoJUSVTUmfuPMeOqePjy5fXnUSYISREval1\nApkk51N8vNo8IuytSRNVauGgNt3iH599BqdPq4mUcKZRo1Rfeqf8ABZKUpKqY5UFKGdwUk2yTJLz\nIS1NNbOXwyicoUULdTKicI6MDBgxQm3wkkMnnCsoCJ5+2lp1ycJzSUlyGIiTVKumWsidPas7iefk\nx0k+JCRA/fpQqpTuJMIMzZqpv1PhHHPnqsN85GmP8w0erDoLbdyoO4kwi3S2cJZixaBqVXU2gd3J\nJDkfvvwS2rbVnUKY5a671FHFGRm6kwgzpKdDbKxaRZbHtc4XGKhWkkeM0J1EmEVWkp3HKXXJMknO\nB6lHdpby5aFGDVmJcopp01Rrv4YNdScRvvLoo+oH8P/+pzuJMMPu3TJJdpqQEGfUJcskOQ+7d6ua\n5IgI3UmEmaTkwhmOHoXXXoPx43UnEb5UrJjqcjF8eOGOzRXWcn7jnnCO0FBZSfYLX34JrVvLY1yn\niYqSSbITTJwIXbpA7dq6kwhfu/9+9b+LFunNITxz9iz8+SdUr647iTCTrCT7CalHdqa77oJ166Qu\n2c727IHZs+HFF3UnEToEBMCkSTByJJw7pzuNKKw9e+CGG9TTAeEcspLsB86eVauN0dG6kwizVaig\nVi4SE3UnEYU1ZgwMGKC6Wgj/1LKl+mFsxZO9RP5IPbIznV9JttjBjgUmk+Sr+O47uPlmOSbTqaKi\npF+yXW3eDCtWwPPP604idIuLU6cspqXpTiIKQ+qRnal0aShZEg4d0p3EM9omyZmZmbjdbmJiYnRF\nyJN0tXA22bxnXyNHqhZgpUvrTiJ0c7vVAUGvv647iSgMWUl2LifUJWubJE+dOpWwsDBcFt4RJ/XI\nznbXXfDtt5CZqTuJKIi1a9VK8oABupMIqxg7FqZOhb/+0p1EFJSsJDuXE+qStUyS9+3bx4oVK+jX\nrx+GRQtWDh6ElBRo1Eh3EuEtQUFqw8imTbqTiPwyDBg6VLX/KlFCdxphFSEh8Mgj6r4Q9iIryc4l\nK8mF9OyzzzJ58mQCAqxbEv3VV2pTSNGiupMIb5JWcPby2Wdw+jQ89JDuJMJqXngBPvjA/itX/sQw\n5LQ9J3PCqXs+nwIuX76coKAg3G43CVeZncTGxmb/d1RUFFFRUV7PdjGpR/YPUVHw/vswZIie109I\nSLjqOBAXZGSoOuQpU1T7LyEuFhQETz+tjqz+4APdaUR+HD4MxYtDmTK6kwhvCA2FefN0p/CMy/Bx\nvcPIkSOZP38+RYsW5cyZM5w4cYLOnTvz/vvvXwjlcvm8DMMwYN8++P57+OEH1X/111+lwbnTpaZC\nnTrqH+siRXSn0XPvm8EXuWfOhA8/hFWr5HAfkbO0NLjpJtX5xO32/uvJePXMDz/AoEHw00+6kwhv\n2LMH7rgD9u/XneSCgt77Pp8kX+zrr7/m1Vdf5fPPP7/k474ewKNHq0nxuXPqL/SOO9Ru6YYNfRZB\naBQWBgsWwO23605inR9el6tRowbXX389RYoUoVixYqxfv/6S3/d27vR0darekiXQoIHXXkY4wNtv\nq7KcL7/0/mtZdbzmxSq5P/wQli2Djz7SnUR4Q2YmXHcdHDsG11yjO41S0Htfe8Wt7u4Wa9fC3Lmq\nLrVWLVmh8kfNmql+yVaYJFuVy+UiISGBcuXKaXn9adOgSROZIIu8PfqoKsn53/+gVSvdacTVyKY9\nZytSBG68EZKT1WKUHWmt7GvWrBnLli3T9vqZmaqGbdIk9YhOJsj+STbv5Y+ulacjR+C116Rzgcif\nYsVg/HgYPhyysnSnEVcj7d+cz+5t4Px6+8ucOXDttdCtm+4kQqeWLeHHH+GNN3I/QvPUKRg8GFau\n9G02q3C5XLRq1Yr69eszw8dnAE+cCF26qHILIfKjSxe16PHpp7qTiKuRlWTns3sbOO3lFrocPw6j\nRsF//ysryP6uQgW1gaRTJ7WBZMYM9ebpvJ9/Vi3HAgNhxw6IjtaXVZd169ZRuXJlDh06RHR0NHXq\n1KFp06Zef909e9Sb2d9+8/pLCQcJCFDHVT/2mBrXxYvrTiRyIivJzmf3lWS/nSSPHQvt20O9erqT\nCCuoUQPWrYP+/eFf/4L//Ed1NnnlFVXfOH063HOPOnzkyBEoX153Yt+qXLkyABUrVqRTp06sX7/+\nikmyN9o2jhkDAwfCPy8vRL61bKl+QM+cqe4hM0jLRvOcPq26Ct1wg+4kwptCQlRHIrvS2t0iN97e\nebt9O9x5J2zZAsHBXnsZYUOGoTaJTZigfsAWLw7z50O1aur3H3hAdT557DHvvL5Vdp1fLD09nczM\nTEqVKsWpU6do3bo1Y8aMoXXr1tmf443cmzerjVe//w7XX2/qpYWfSEyEdu3UPRQYaP71rThe88MK\nubdtg44d1dM54Vy//QZdu8LWrbqTKAW99/2yJnnwYHUogUyQxeVcLrWZ89NPVYnFqlUXJsig6tcX\nLtSXT4fU1FSaNm1KZGQkjRo1on379pdMkL1l5Ej1SybIorDcbvWm9vXXdScRl5N6ZP9Qs6bqbmHX\nTbR+t5K8YgU8+6xapZI6NVFQ//d/6tH/5s3eeUxohRWewjA79zffQM+e6qlPiRKmXVb4oaQk1Tpw\n2zZ1Kp+ZZLwW3rRpahX5rbe0xhA+UKmS2ttjhdIaWUm+isxMGDYMJk+WCbIonBIl1CPCjz/WncS5\nDEON03HjZIIsPBcSAo884l8tBPv06UNwcDDh4eG6o+Rq927ZtOcvQkPt2+HCrybJCxdCqVIQE6M7\nibAzfyy58KWlS9WmHmnNKMwyapQ63c2uP6gLqnfv3sTHx+uOcVVJSVJu4S9CQuzb4cJvJslnz6qd\n8hMmSMs34ZnmzWHvXrUZSJgrI0PtF4iLU228hDBDxYpqr8Ho0bqT+EbTpk0pW7as7hhXJSvJ/sPO\nK8l+0wJu1iz1F2VCVyrh54oWhfvvh48+8p8fur4ydy5UqQJt2uhOIpzm2WfVgTSJiWpDn/CcYaiW\nmIX5uuRktalLOF9ICCxbplr+FVTx4no3b/vFJDk9XdWjffaZ7iTCKbp1g3791GNceTJhjvR0iI1V\nParlz1SYLTBQvakdNgy++kp3Gv3M6Gs+f77qLV+Y9nrh4d5pyyesx+2G55+HOnUK/rUnT8LBg1DY\nByOe9jb3i+4Wr7wC69fDokWmXVL4OcNQ746XLoWICPOua4Vd54VhRu64ONi4ET75xKRQQlzm3DkI\nC4N//1v14PaUlcdrSkoKMTExbN68+YrfMyv3iBFqovvCCx5fSogc3X47vPce1K9vzvWku8Vljh9X\n3SzGjtWdRDiJywUPPigb+Mxy5Ai89pp/dSAQvlesGIwfD8OH27dvq5VIXbHwNt31zI6fJL/6qjp+\num5d3UmE03TrpuqSLbqQZCsTJ0KXLqpmVAhv6tJFvcn99FPdSbynW7duNGnShJ07d1KtWjXmzJnj\nldeRDhXC23R3xnB0uUVqqnq0tnEj3HijCcGEuIhhqLq6f/8bmjY155pWfnx7NZ7k/uMP9Ujtt9/U\nQS1CeNvq1aqWdutWz3rm++N4vVjZsrBrF5Qvb0IoIXLw3nuqXHbmTHOuJ+UWF3n5ZejeXSbIwjtc\nLujVC7y0SOM3xoyBgQNlgix8p0ULqFXLvB+8/ujYMVWyUq6c7iTCyfxyJXnv3r306NGDv/76C5fL\nRf/+/XnqqacuhDLhXe727Wp1b/t2eZcrvCc1Ve3Y3bNHHVTjKX9bmdq8WW2g+v13vW1+hP/ZtAnu\nvlvde4XtsuBv4/ViGzbAo4+qlnpCeEtSkjqb4I8/zLmeLVaSixUrxpQpU9iyZQs//PADb731Ftu2\nbTP1NYYPVy1HZIIsvCk4WPXelo4MhTNihPolE2Tha5GRakX59dd1J7GnpCTZtCe8r3p11QLu7Fk9\nr69lklypUiUiIyMBCAwMpG7duvz555+mXX/tWvXu9qLFaSG8pk8fmD1bdwr7+fpr2LIFBgzQnUT4\nq7FjYepU+Osv3UnsZ/du2bQnvK9oUahaFVJS9Ly+9prklJQUEhMTadSokSnXMwx47jl1/HTJkqZc\nUoiruvtutaqyfbvuJPZhGOpQh5dfhhIldKcR/iokBB55RFqEFoasJAtfCQ3VV5esdZKclpZGly5d\nmDp1KoEmHb3zySeQkaHacwnhC0WLQo8esoGvIJYsgTNn4OGHdScR/m7UKNXvXGcvVjuSlWThKyEh\n+santmOpz507R+fOnXnkkUfo2LHjFb9fmCMz/+//VH3jrFkQoH2NXPiT3r3V5oJx49SBBfnl6ZGZ\ndpSRASNHwhtvyDgV+lWsCE8/rY6s/vBD3WnsQ1aSha/oXEnW0t3CMAx69uxJ+fLlmTJlypWhCrnz\n9vXXVf/L5cvNSClEwfzrX2rDaExM4a/hD7vlZ8xQK3erVqk2ekLolpamDrJZvlz17M4vfxivOTl7\nVnXzSUsr2KKAEIWxeDHMnw9Ll3p+LVt0t1i3bh0LFixgzZo1uN1u3G438fHxHl3zyBGIi4NJk0wK\nKUQB9emjnmKI3KWnQ2ysGqcyQRZWERioVpKHD9edxB7++ENtppIJsvAFv1tJzkth3uU+/rgasNOn\neymUEHk4eRKqVVMb+CpVKtw1nL4yNXGi6jwjLfOE1Zw7p05o/fe/Ve/u/HD6eM1NfDy89hqsXGli\nKCFyceIEVKmifsZ6urhii5Vks/38s1qGf/ll3UmEPytVCu67DxYs0J3Emo4cUT9Yx4/XnUSIKxUr\nproiDR+uTpITuZN6ZOFL118P11yjp1Wj7SfJWVnwxBPqH7eyZXWnEf6uTx911nxamu4k1jNhAnTt\nCjfdpDuJEDnr0kVtJv30U91JrE06Wwhf09XhwvaT5Hnz1P/26qU1hhCA2rwXFQVt2sDff+tOYx17\n9sDcufDii7qTCJE7l0vVy7/wgr4TvuwgKUkmycK3QkL01CXbepJ87Jhq+fbmm9JKSliDywXvvKOO\nvI2OVveoUJPjgQMLX6sthK80bw61aqkuLCJnUm4hfE3X5j1bTy3HjIF774X69XUnEeKCgAD1xu3O\nO6FlSzh8WHcivTZvhi++gOef151EiPyJi1M9z0+e1J3EegxDyi2E70m5RQH98gt89JGqcxTCalwu\ntUmtbVto0QJSU3Un0mfECHV4yPXX604iRP5ERqo3uK+/rjuJ9Rw6BCVLQunSupMIfyIryQVgGDBo\nELz0EpQvrzuNEDlzuVQnh06doH171WLK33z9NWzZolo0CmEnY8fCtGn+/QY3J7KKLHSQleQCWLxY\nbYrq3193EiGuzuVSh2eUKwevvKI7jW8ZBgwbph5blyihO40QBVOzJnTvru5fcYHUIwsdbrgBjh5V\nB1L5ku0myf/3fzB0qHoMVqSI7jRC5M3lUpuA3ngDfvtNdxrfWbIEzpyBbt10JxGicF54QR2hrmMF\ny6pkJVnoEBAANWpAcrKPX9e3L+e5adPg1ltVvZgQdlG9uqqf79ULMjJ0p/G+jAxVixwXJ51nhH1V\nrAjPPKOOrBaKrCQLXXTUJdvqx9dff6kelpMn604iRMH166fKLvzh/p09Wz0ea9NGdxIhPPPss5CQ\nABs36k5iDbKSLHTRUZdsq0lybCw8/DDcfLPuJEIUnMsFM2eqUiEnl12kp6tNtZMmqe9ZCDu77jq1\nkjx8uO4k1iAryUIXHSvJRX37coW3ZQssWgTbt+tOIkThVa+uOl707g3ffw9FbTMC82/qVGjSBBo0\n0J1ECHP066dKiDIz/XsvzOnTcOQIVKmiO4nwRyEhsHKlb1/TNivJQ4aoTRTlyulOIoRnHn0UypSB\nd9/VncR8R46o/tDSv1w4SbFiqu2oP0+QQW2auvFG+XMQeug4mtoW61hffKEG58CBupMI4TmXCz74\nAEqV0p3EfBMmQNeucNNNupMIIcy2e7eUWgh9QkIgJQWysny3Idzyk+Q//oC+fWHePPVuXggnCArS\nncB8f/wBc+eq0ighhPMkJcmmPaHPtdeqp7B//glVq/rmNbWUW8THx1OnTh1uuukmJk2alOvnnTwJ\nMTGqL3J0tA8DCiEukZ8x++KL8MQTUKmSj8MJIS6R35+xBSUryUK30FDfdrjw+SQ5MzOTJ598kvj4\neLZu3crChQvZtm1bDp8HDz0EjRvD00+bmyEhIcHcCzro+nbO7oTrW1F+x2x8PDz3nPmvb/e/U7m+\nvrKpMH4AAAeKSURBVOvLeM19vBZGfleS7XzPePv6ds5uhev7ui7Z55Pk9evXU6tWLWrUqEGxYsV4\n8MEH+eyzz674vOHDIS0N3nrL/DZSuv+SrXx9O2d3wvWtKL9j9oUX4PrrzX99u/+dyvX1XV/Ga+7j\ntTDyu5Js53vG29e3c3YrXN/xK8n79++nWrVq2f+/atWq7N+//4rPW7oUFi+WOmQhdMvvmH3sMV+m\nEkLkJL/jtaCystSmqZo1Pb6UEIXm65Vkn2/cc+VzWXj5cmn3JoQV5HfMlijh5SBCiDzld7zGxBTs\nuufOqU1T111XiFBCmKRWLfjqq/zdv889B82aefiCho99//33Rps2bbL//4QJE4y4uLhLPic0NNQA\n5Jf88rtfoaGhvh6SeZIxK7/kV86/ZLzKL/llr18FHbMuwzAMfCgjI4Obb76ZVatWUaVKFRo2bMjC\nhQupW7euL2MIIfJJxqwQ9iHjVQjz+LzcomjRorz55pu0adOGzMxM+vbtK4NXCAuTMSuEfch4FcI8\nPl9JFkIIIYQQwuq0HCZyNd5qgn5ejRo1uO2223C73TRs2NCja/Xp04fg4GDCw8OzP3b06FGio6Op\nXbs2rVu35vjx46ZePzY2lqpVq+J2u3G73cTHxxf6+nv37qV58+bccsst3HrrrUybNs3U7yG365vx\nPZw5c4ZGjRoRGRlJWFgYI0aMMDV7btc3888fVE9Tt9tNzD+7EMy8f3zBTuMV7D1m7TxewRlj1u7j\nFew1Zu08XsHeY9YJ4xVMGLMe7xIwUUZGhhEaGmokJycbZ8+eNSIiIoytW7ea+ho1atQwjhw5Ysq1\nvvnmG2Pjxo3Grbfemv2x559/3pg0aZJhGIYRFxdnDBs2zNTrx8bGGq+99lrhQ1/kwIEDRmJiomEY\nhnHy5Emjdu3axtatW037HnK7vlnfw6lTpwzDMIxz584ZjRo1MtauXWvqn39O1zfzz98wDOO1114z\nHnroISMmJsYwDHPvH2+z23g1DHuPWbuPV8Ow/5i183g1DPuNWTuPV8Ow/5i1+3g1DM/HrKVWkr3Z\nBP1ihkkVJk2bNqVs2bKXfGzZsmX07NkTgJ49e7J06VJTrw/m5a9UqRKRkZEABAYGUrduXfbv32/a\n95Db9cGc7+Haa68F4OzZs2RmZlK2bFlT//xzuj6Y9+e/b98+VqxYQb9+/bKvaWZ+b7PbeAV7j1m7\nj1ew95i1+3gF+41ZO49XsP+YtfN4BXPGrKUmyd5qgn4xl8tFq1atqF+/PjNmzDD12gCpqakEBwcD\nEBwcTGpqqumvMX36dCIiIujbt69pj/dSUlJITEykUaNGXvkezl+/cePGgDnfQ1ZWFpGRkQQHB2c/\ncjIze07XNys7wLPPPsvkyZMJCLgwDH1x/5jFCeMV7Dlm7Thewd5j1u7jFZwxZu04XsGeY9bO4xXM\nGbOWmiTntwm6J9atW0diYiJffPEFb731FmvXrvXaa7lcLtO/pwEDBpCcnMymTZuoXLkyQ4YM8fia\naWlpdO7cmalTp1KqVKlLfs+M7yEtLY0uXbowdepUAgMDTfseAgIC2LRpE/v27eObb75hzZo1pma/\n/PoJCQmmZV++fDlBQUG43e5c3zV74/4xk9PGK9hjzNp1vIJ9x6wTxis4b8zaYbyCfcesXccrmDdm\nLTVJvuGGG9i7d2/2/9+7dy9Vq1Y19TUqV64MQMWKFenUqRPr16839frBwcEcPHgQgAMHDhAUFGTq\n9YOCgrL/Yvv16+dx/nPnztG5c2e6d+9Ox44dAXO/h/PXf+SRR7Kvb/b3ULp0ae655x5+/vlnr/z5\nn7/+hg0bTMv+3XffsWzZMmrWrEm3bt1YvXo13bt39/r9YyYnjFew15h1wngF+41ZJ4xXcMaYtdN4\nBWeMWbuNVzBvzFpqkly/fn1+//13UlJSOHv2LB9//DEdOnQw7frp6emcPHkSgFOnTvHVV19dsqvV\nDB06dGDevHkAzJs3L/umNcuBAwey/3vJkiUe5TcMg759+xIWFsYzzzyT/XGzvofcrm/G93D48OHs\nxzCnT59m5cqVuN1u07Lndv3zg8uT7AATJkxg7969JCcn89FHH9GiRQvmz5/v9fvHTE4Yr2CfMWvn\n8Qr2HrNOGK/gjDFrl/EK9h6zdh6vYOKYNW0LoUlWrFhh1K5d2wgNDTUmTJhg6rWTkpKMiIgIIyIi\nwrjllls8vv6DDz5oVK5c2ShWrJhRtWpVY/bs2caRI0eMli1bGjfddJMRHR1tHDt2zLTrz5o1y+je\nvbsRHh5u3Hbbbca9995rHDx4sNDXX7t2reFyuYyIiAgjMjLSiIyMNL744gvTvoecrr9ixQpTvodf\nf/3VcLvdRkREhBEeHm688sorhmEYpmXP7fpm/vmfl5CQkL3z1sz7xxfsNF4Nw95j1s7j1TCcM2bt\nPF4Nw15j1s7j1TDsPWadMl4Nw7MxK4eJCCGEEEIIcRlLlVsIIYQQQghhBTJJFkIIIYQQ4jIySRZC\nCCGEEOIyMkkWQgghhBDiMjJJFkIIIYQQ4jIySRZCCCGEEOIyMkkWQgghhBDiMjJJFkIIIYQQ4jL/\nD8Se36QdbJ39AAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x105f4a750>"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "inflammation-02.csv\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
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V2woHb/LLc5PP9E63OPHaV0uozXJrVtTqTh7qDBg7b2fkZGBChUq5PvczZs3UVPhEil66rNjxwJ/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\nY4w9hgfJjDHGGGOMPeb/AeKLvR1eWwu1AAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x105f05610>"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "inflammation-03.csv\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
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wPr1Mn6Fb0k9ctFcfz0cPKhe1AbS2QQFKXAl2e12Z7d+y/k5b1rB5RvKwa9yc3rtNfj+e/jkE91J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\noky5nmGoX3rPPGPK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+       "text": [
+        "<matplotlib.figure.Figure at 0x106a0ab10>"
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Sure enough,\n",
+      "the maxima of these data sets show exactly the same ramp as the first,\n",
+      "and their minima show the same staircase structure."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "challenges"
+      ]
+     },
+     "source": [
+      "#### Challenges\n",
+      "\n",
+      "1.  Write a function called `analyze_all` that takes a filename pattern as its sole argument\n",
+      "    and runs `analyze` for each file whose name matches the pattern."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "#### Key Points\n",
+      "\n",
+      "*   Use `for variable in collection` to process the elements of a collection one at a time.\n",
+      "*   The body of a for loop must be indented.\n",
+      "*   Use `len(thing)` to determine the length of something that contains other values.\n",
+      "*   `[value1, value2, value3, ...]` creates a list.\n",
+      "*   Lists are indexed and sliced in the same way as strings and arrays.\n",
+      "*   Lists are mutable (i.e., their values can be changed in place).\n",
+      "*   Strings are immutable (i.e., the characters in them cannot be changed).\n",
+      "*   Use `glob.glob(pattern)` to create a list of files whose names match a pattern.\n",
+      "*   Use `*` in a pattern to match zero or more characters, and `?` to match any single character."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "#### Next Steps\n",
+      "\n",
+      "We have now solved our original problem:\n",
+      "we can analyze any number of data files with a single command.\n",
+      "More importantly,\n",
+      "we have met two of the most important ideas in programming:\n",
+      "\n",
+      "1.  Use functions to make code easier to re-use and easier to understand.\n",
+      "1.  Use lists and arrays to store related values, and loops to repeat operations on them.\n",
+      "\n",
+      "We have one more big idea to introduce,\n",
+      "and then we will be able to go back and create a heat map\n",
+      "like the one we initially used to display our first data set."
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/novice/python/04-cond.ipynb b/novice/python/04-cond.ipynb
new file mode 100644 (file)
index 0000000..c247d96
--- /dev/null
@@ -0,0 +1,1413 @@
+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Making Choices"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Our previous lessons have shown us how to manipulate data,\n",
+      "define our own functions,\n",
+      "and repeat things.\n",
+      "However,\n",
+      "the programs we have written so far always do the same things,\n",
+      "regardless of what data they're given.\n",
+      "We want programs to make choices based on the values they are manipulating.\n",
+      "To help us see what decisions they're making,\n",
+      "we'll start by looking at how computers manipulate images."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "objectives"
+      ]
+     },
+     "source": [
+      "#### Objectives\n",
+      "\n",
+      "*   Create a simple \"image\" made out of colored blocks.\n",
+      "*   Explain how the RGB model represents colors.\n",
+      "*   Explain the similarities and differences between tuples and lists.\n",
+      "*   Write conditional statements including `if`, `elif`, and `else` branches.\n",
+      "*   Correctly evaluate expressions containing `and` and `or`.\n",
+      "*   Correctly write and interpret code containing nested loops and conditionals.\n",
+      "*   Explain the advantages of putting frequently-modified code in a function."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Image Grids"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Let's start by creating some simple heat maps of our own\n",
+      "using a library called `ipythonblocks`.\n",
+      "The first step is to create our own \"image\":"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from ipythonblocks import ImageGrid"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Unlike the `import` statements we have seen earlier,\n",
+      "this one doesn't load the entire `ipythonblocks` library.\n",
+      "Instead,\n",
+      "it just loads `ImageGrid` from that library,\n",
+      "since that's the only thing we need (for now).\n",
+      "\n",
+      "Once we have `ImageGrid` loaded,\n",
+      "we can use it to create a very simple grid of colored cells:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "grid = ImageGrid(5, 3)\n",
+      "grid.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blocks84e827e4-60f2-4e82-b41a-c95955a972aa td {border: 1px solid white;}</style><table id=\"blocks84e827e4-60f2-4e82-b41a-c95955a972aa\" class=\"blockgrid\"><tbody><tr><td title=\"Index: [0, 2]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [1, 2]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [2, 2]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [3, 2]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [4, 2]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [0, 1]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [1, 1]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [2, 1]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [3, 1]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [4, 1]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [0, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [1, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [2, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [3, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [4, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td></tr></tbody></table>"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.HTML at 0x102376190>"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Just like a NumPy array,\n",
+      "an `ImageGrid` has some properties that hold information about it:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'grid width:', grid.width\n",
+      "print 'grid height:', grid.height\n",
+      "print 'grid lines on:', grid.lines_on"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "grid width: 5\n",
+        "grid height: 3\n",
+        "grid lines on: True\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The obvious thing to do with a grid like this is color in its cells,\n",
+      "but in order to do that,\n",
+      "we need to know how computers represent color.\n",
+      "The most common schemes are [RGB](../../gloss.html#rgb),\n",
+      "which is short for \"red, green, blue\".\n",
+      "RGB is an [additive color model](../../gloss.html#additive-color-model):\n",
+      "every shade is some combination of red, green, and blue intensities.\n",
+      "We can think of these three values as being the axes in a cube:\n",
+      "\n",
+      "<img src=\"files/img/color-cube.png\" alt=\"RGB Color Cube\" />\n",
+      "\n",
+      "An RGB color is an example of a multi-part value:\n",
+      "like a Cartesian coordinate,\n",
+      "it is one thing with several parts.\n",
+      "We can represent such a value in Python using a [tuple](../../gloss.html#tuple),\n",
+      "which we write using parentheses instead of the square brackets used for a list:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "position = (12.3, 45.6)\n",
+      "print 'position is:', position\n",
+      "color = (10, 20, 30)\n",
+      "print 'color is:', color"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "position is: (12.3, 45.6)\n",
+        "color is: (10, 20, 30)\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We can select elements from tuples using indexing,\n",
+      "just as we do with lists and arrays:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'first element of color is:', color[0]"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "first element of color is: 10\n"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Unlike lists and arrays,\n",
+      "though,\n",
+      "tuples cannot be changed after they are created&mdash;in technical terms,\n",
+      "they are [immutable](../../gloss.html#immutable):"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "color[0] = 40\n",
+      "print 'first element of color after change:', color[0]"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "ename": "TypeError",
+       "evalue": "'tuple' object does not support item assignment",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-11-9c3dd30a4e52>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcolor\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m40\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'first element of color after change:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "If a tuple represents an RGB color,\n",
+      "its red, green, and blue components can take on values between 0 and 255.\n",
+      "The upper bound may seem odd,\n",
+      "but it's the largest number that can be represented in an 8-bit byte\n",
+      "(i.e., 2<sup>8</sup>-1).\n",
+      "This makes it easy for computers to manipulate colors,\n",
+      "while providing fine enough gradations to fool most human eyes,\n",
+      "most of the time.\n",
+      "\n",
+      "Let's see what a few RGB colors actually look like:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "row = ImageGrid(8, 1)\n",
+      "row[0, 0] = (0, 0, 0)   # no color => black\n",
+      "row[1, 0] = (255, 255, 255) # all colors => white\n",
+      "row[2, 0] = (255, 0, 0) # all red\n",
+      "row[3, 0] = (0, 255, 0) # all green\n",
+      "row[4, 0] = (0, 0, 255) # all blue\n",
+      "row[5, 0] = (255, 255, 0) # red and green\n",
+      "row[6, 0] = (255, 0, 255) # red and blue\n",
+      "row[7, 0] = (0, 255, 255) # green and blue\n",
+      "row.show()"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "html": [
+        "<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blocks9d1f4bb1-553c-4074-aec0-dc48fde66e06 td {border: 1px solid white;}</style><table id=\"blocks9d1f4bb1-553c-4074-aec0-dc48fde66e06\" class=\"blockgrid\"><tbody><tr><td title=\"Index: [0, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [1, 0]&#10;Color: (255, 255, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 255, 255);\"></td><td title=\"Index: [2, 0]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [3, 0]&#10;Color: (0, 255, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 255, 0);\"></td><td title=\"Index: [4, 0]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [5, 0]&#10;Color: (255, 255, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 255, 0);\"></td><td title=\"Index: [6, 0]&#10;Color: (255, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 255);\"></td><td title=\"Index: [7, 0]&#10;Color: (0, 255, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 255, 255);\"></td></tr></tbody></table>"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.HTML at 0x10340e910>"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Simple color values like `(0,255,0)` are easy enough to decipher with a bit of practice,\n",
+      "but what color is `(214,90,127)`?\n",
+      "To help us,\n",
+      "`ipythonblocks` provides a function called `show_color`:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from ipythonblocks import show_color\n",
+      "show_color(214, 90, 127)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"height: 60px; min-width: 200px; background-color: rgb(214, 90, 127)\"></div>"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.HTML at 0x10340a590>"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "It also provides a table of standard colors:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from ipythonblocks import colors\n",
+      "c = ImageGrid(3, 2)\n",
+      "c[0, 0] = colors['Fuchsia']\n",
+      "c[0, 1] = colors['Salmon']\n",
+      "c[1, 0] = colors['Orchid']\n",
+      "c[1, 1] = colors['Lavender']\n",
+      "c[2, 0] = colors['LimeGreen']\n",
+      "c[2, 1] = colors['HotPink']\n",
+      "c.show()"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "html": [
+        "<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blocksb8c954b3-f908-4ab1-8013-65bf49ec1b2f td {border: 1px solid white;}</style><table id=\"blocksb8c954b3-f908-4ab1-8013-65bf49ec1b2f\" class=\"blockgrid\"><tbody><tr><td title=\"Index: [0, 1]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [1, 1]&#10;Color: (230, 230, 250)\" style=\"width: 20px; height: 20px;background-color: rgb(230, 230, 250);\"></td><td title=\"Index: [2, 1]&#10;Color: (255, 105, 180)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 105, 180);\"></td></tr><tr><td title=\"Index: [0, 0]&#10;Color: (255, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 255);\"></td><td title=\"Index: [1, 0]&#10;Color: (218, 112, 214)\" style=\"width: 20px; height: 20px;background-color: rgb(218, 112, 214);\"></td><td title=\"Index: [2, 0]&#10;Color: (50, 205, 50)\" style=\"width: 20px; height: 20px;background-color: rgb(50, 205, 50);\"></td></tr></tbody></table>"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.HTML at 0x10340e350>"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "challenges"
+      ]
+     },
+     "source": [
+      "#### Challenges\n",
+      "\n",
+      "1.  Fill in the `____` in the code below to create a bar that changes color from dark blue to black.\n",
+      "\n",
+      "    ~~~python\n",
+      "    bar = ImageGrid(10, 1)\n",
+      "    for x in range(10):\n",
+      "        bar[x, 0] = (0, 0, ____)\n",
+      "    bar.show()\n",
+      "    ~~~\n",
+      "\n",
+      "2.  Why do computers use red, green, and blue as their primary colors?"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Conditionals"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The other thing we need in order to create a heat map of our own\n",
+      "is a way to pick a color based on a data value.\n",
+      "The tool Python gives us for doing this is called a [conditional statement](../../gloss.html#conditional-statement),\n",
+      "and looks like this:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "num = 37\n",
+      "if num > 100:\n",
+      "    print 'greater'\n",
+      "else:\n",
+      "    print 'not greater'\n",
+      "print 'done'"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "not greater\n",
+        "done\n"
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The second line of this code uses the keyword `if` to tell Python that we want to make a choice.\n",
+      "If the test that follows it is true,\n",
+      "the body of the `if`\n",
+      "(i.e., the lines indented underneath it) are executed.\n",
+      "If the test is false,\n",
+      "the body of the `else` is executed instead.\n",
+      "Only one or the other is ever executed:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "<img src=\"files/img/python-flowchart-conditional.svg\" alt=\"Executing a Conditional\" />"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Conditional statements don't have to include an `else`.\n",
+      "If there isn't one,\n",
+      "Python simply does nothing if the test is false:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "num = 53\n",
+      "print 'before conditional...'\n",
+      "if num > 100:\n",
+      "    print '53 is greater than 100'\n",
+      "print '...after conditional'"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "before conditional...\n",
+        "...after conditional\n"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We can also chain several tests together using `elif`,\n",
+      "which is short for \"else if\".\n",
+      "This makes it simple to write a function that returns the sign of a number:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def sign(num):\n",
+      "    if num > 0:\n",
+      "        return 1\n",
+      "    elif num == 0:\n",
+      "        return 0\n",
+      "    else:\n",
+      "        return -1\n",
+      "\n",
+      "print 'sign of -3:', sign(-3)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "sign of -3: -1\n"
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "One important thing to notice the code above is that we use a double equals sign `==` to test for equality\n",
+      "rather than a single equals sign\n",
+      "because the latter is used to mean assignment.\n",
+      "This convention was inherited from C,\n",
+      "and while many other programming languages work the same way,\n",
+      "it does take a bit of getting used to...\n",
+      "\n",
+      "We can also combine tests using `and` and `or`.\n",
+      "`and` is only true if both parts are true:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "if (1 > 0) and (-1 > 0):\n",
+      "    print 'both parts are true'\n",
+      "else:\n",
+      "    print 'one part is not true'"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "one part is not true\n"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "while `or` is true if either part is true:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "if (1 < 0) or ('left' < 'right'):\n",
+      "    print 'at least one test is true'"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "at least one test is true\n"
+       ]
+      }
+     ],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "In this case,\n",
+      "\"either\" means \"either or both\", not \"either one or the other but not both\"."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "challenges"
+      ]
+     },
+     "source": [
+      "#### Challenges\n",
+      "\n",
+      "1.  `True` and `False` aren't the only values in Python that are true and false.\n",
+      "    In fact, *any* value can be used in an `if` or `elif`.\n",
+      "    After reading and running the code below,\n",
+      "    explain what the rule is for which values are considered true and which are considered false.\n",
+      "    (Note that if the body of a conditional is a single statement, we can write it on the same line as the `if`.)\n",
+      "    \n",
+      "    ~~~python\n",
+      "    if '': print 'empty string is true'\n",
+      "    if 'word': print 'word is true'\n",
+      "    if []: print 'empty list is true'\n",
+      "    if [1, 2, 3]: print 'non-empty list is true'\n",
+      "    if 0: print 'zero is true'\n",
+      "    if 1: print 'one is true'\n",
+      "    ~~~\n",
+      "\n",
+      "2.  Write a function called `near` that returns `True` if its first parameter is within 10% of its second\n",
+      "    and `False` otherwise.\n",
+      "    Compare your implementation with your partner's:\n",
+      "    do you return the same answer for all possible pairs of numbers?"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Nesting"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Another thing to realize is that `if` statements can be combined with loops\n",
+      "just as easily as they can be combined with functions.\n",
+      "For example,\n",
+      "if we want to sum the positive numbers in a list,\n",
+      "we can write this:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "numbers = [-5, 3, 2, -1, 9, 6]\n",
+      "total = 0\n",
+      "for n in numbers:\n",
+      "    if n >= 0:\n",
+      "        total = total + n\n",
+      "print 'sum of positive values:', total"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "sum of positive values: 20\n"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We could equally well calculate the positive and negative sums in a single loop:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pos_total = 0\n",
+      "neg_total = 0\n",
+      "for n in numbers:\n",
+      "    if n >= 0:\n",
+      "        pos_total = pos_total + n\n",
+      "    else:\n",
+      "        neg_total = neg_total + n\n",
+      "print 'negative and positive sums are:', neg_total, pos_total"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "negative and positive sums are: -6 20\n"
+       ]
+      }
+     ],
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We can even put one loop inside another:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for consonant in 'bcd':\n",
+      "    for vowel in 'ae':\n",
+      "        print consonant + vowel"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "ba\n",
+        "be\n",
+        "ca\n",
+        "ce\n",
+        "da\n",
+        "de\n"
+       ]
+      }
+     ],
+     "prompt_number": 22
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "As the diagram below shows,\n",
+      "the [inner loop](../../gloss.html#inner-loop) runs from start to finish\n",
+      "each time the [outer loop](../../gloss.html#outer-loop) runs once:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "<img src=\"files/img/python-flowchart-nested-loops.svg\" alt=\"Execution of Nested Loops\" />"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "We can combine nesting and conditionals to create patterns in an image:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "square = ImageGrid(5, 5)\n",
+      "for x in range(square.width):\n",
+      "    for y in range(square.height):\n",
+      "        if x < y:\n",
+      "            square[x, y] = colors['Fuchsia']\n",
+      "        elif x == y:\n",
+      "            square[x, y] = colors['Olive']\n",
+      "        else:\n",
+      "            square[x, y] = colors['SlateGray']\n",
+      "square.show()"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "html": [
+        "<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blocksa391625e-64d4-47e1-a54a-2953c1aea09c td {border: 1px solid white;}</style><table id=\"blocksa391625e-64d4-47e1-a54a-2953c1aea09c\" class=\"blockgrid\"><tbody><tr><td title=\"Index: [0, 4]&#10;Color: (255, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 255);\"></td><td title=\"Index: [1, 4]&#10;Color: (255, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 255);\"></td><td title=\"Index: [2, 4]&#10;Color: (255, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 255);\"></td><td title=\"Index: [3, 4]&#10;Color: (255, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 255);\"></td><td title=\"Index: [4, 4]&#10;Color: (128, 128, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(128, 128, 0);\"></td></tr><tr><td title=\"Index: [0, 3]&#10;Color: (255, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 255);\"></td><td title=\"Index: [1, 3]&#10;Color: (255, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 255);\"></td><td title=\"Index: [2, 3]&#10;Color: (255, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 255);\"></td><td title=\"Index: [3, 3]&#10;Color: (128, 128, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(128, 128, 0);\"></td><td title=\"Index: [4, 3]&#10;Color: (112, 128, 144)\" style=\"width: 20px; height: 20px;background-color: rgb(112, 128, 144);\"></td></tr><tr><td title=\"Index: [0, 2]&#10;Color: (255, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 255);\"></td><td title=\"Index: [1, 2]&#10;Color: (255, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 255);\"></td><td title=\"Index: [2, 2]&#10;Color: (128, 128, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(128, 128, 0);\"></td><td title=\"Index: [3, 2]&#10;Color: (112, 128, 144)\" style=\"width: 20px; height: 20px;background-color: rgb(112, 128, 144);\"></td><td title=\"Index: [4, 2]&#10;Color: (112, 128, 144)\" style=\"width: 20px; height: 20px;background-color: rgb(112, 128, 144);\"></td></tr><tr><td title=\"Index: [0, 1]&#10;Color: (255, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 255);\"></td><td title=\"Index: [1, 1]&#10;Color: (128, 128, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(128, 128, 0);\"></td><td title=\"Index: [2, 1]&#10;Color: (112, 128, 144)\" style=\"width: 20px; height: 20px;background-color: rgb(112, 128, 144);\"></td><td title=\"Index: [3, 1]&#10;Color: (112, 128, 144)\" style=\"width: 20px; height: 20px;background-color: rgb(112, 128, 144);\"></td><td title=\"Index: [4, 1]&#10;Color: (112, 128, 144)\" style=\"width: 20px; height: 20px;background-color: rgb(112, 128, 144);\"></td></tr><tr><td title=\"Index: [0, 0]&#10;Color: (128, 128, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(128, 128, 0);\"></td><td title=\"Index: [1, 0]&#10;Color: (112, 128, 144)\" style=\"width: 20px; height: 20px;background-color: rgb(112, 128, 144);\"></td><td title=\"Index: [2, 0]&#10;Color: (112, 128, 144)\" style=\"width: 20px; height: 20px;background-color: rgb(112, 128, 144);\"></td><td title=\"Index: [3, 0]&#10;Color: (112, 128, 144)\" style=\"width: 20px; height: 20px;background-color: rgb(112, 128, 144);\"></td><td title=\"Index: [4, 0]&#10;Color: (112, 128, 144)\" style=\"width: 20px; height: 20px;background-color: rgb(112, 128, 144);\"></td></tr></tbody></table>"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.HTML at 0x10344e950>"
+       ]
+      }
+     ],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "This is our first hand-made data visualization:\n",
+      "the colors show where `x` is less than, equal to, or greater than `y`."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": [
+       "challenges"
+      ]
+     },
+     "source": [
+      "#### Challenges\n",
+      "\n",
+      "1.  Will changing the nesting of the loops in the code above&mdash;i.e.,\n",
+      "    wrapping the Y-axis loop around the X-axis loop&mdash;change the final image?\n",
+      "    Why or why not?\n",
+      "\n",
+      "2.  Python (and most other languages in the C family) provides [in-place operators](../../gloss.html#in-place-operator)\n",
+      "    that work like this:\n",
+      "    \n",
+      "    ~~~python\n",
+      "    x = 1  # original value\n",
+      "    x += 1 # add one to x, assigning result back to x\n",
+      "    x *= 3 # multiply x by 3\n",
+      "    print x\n",
+      "    6\n",
+      "    ~~~\n",
+      "    \n",
+      "    Rewrite the code that sums the positive and negative numbers in a list\n",
+      "    using in-place operators.\n",
+      "    Do you think the result is more or less readable than the original?"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "Creating a Heat Map"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The last step is to turn our data into something we can see.\n",
+      "As in previous lessons,\n",
+      "the first step is to get the data into memory:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "data = np.loadtxt(fname='inflammation-01.csv', delimiter=',')\n",
+      "print 'data shape:', data.shape"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "data shape: (60, 40)\n"
+       ]
+      }
+     ],
+     "prompt_number": 24
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "The second is to create an image grid that is the same size as the data:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "width, height = data.shape\n",
+      "heatmap = ImageGrid(width, height)"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [],
+     "prompt_number": 25
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "cell_tags": []
+     },
+     "source": [
+      "(The first line of the code above takes advantage of a neat trick:\n",
+      "we can unpack the values in a tuple by assigning it to\n",
+      "as many variables as it has entries.)\n",
+      "\n",
+      "The third step is to decide *how* we are going to color the cells in the heat map.\n",
+      "To keep things simple,\n",
+      "we will use red, green, and blue as our colors,\n",
+      "and compare data values to the data set's mean.\n",
+      "Here's the code:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for x in range(width):\n",
+      "    for y in range(height):\n",
+      "        if data[x, y] < data.mean():\n",
+      "            heatmap[x, y] = colors['Red']\n",
+      "        elif data[x, y] == data.mean():\n",
+      "            heatmap[x, y] = colors['Green']\n",
+      "        else:\n",
+      "            heatmap[x, y] = colors['Blue']\n",
+      "heatmap.show()"
+     ],
+     "language": "python",
+     "metadata": {
+      "cell_tags": []
+     },
+     "outputs": [
+      {
+       "html": [
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gb(255, 0, 0);\"></td><td title=\"Index: [37, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [38, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [39, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [40, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [41, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [42, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [43, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [44, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [45, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [46, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [47, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [48, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [49, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [50, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [51, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [52, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [53, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [54, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [55, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [56, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [57, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [58, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [59, 39]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td></tr><tr><td title=\"Index: [0, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [1, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [2, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [3, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [4, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [5, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [6, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [7, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [8, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [9, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [10, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [11, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [12, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [13, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [14, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [15, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [16, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [17, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [18, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [19, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [20, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [21, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [22, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [23, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [24, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [25, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [26, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [27, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [28, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [29, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [30, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [31, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [32, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [33, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [34, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [35, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [36, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [37, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [38, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [39, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [40, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [41, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [42, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [43, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [44, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [45, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [46, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [47, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [48, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [49, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [50, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [51, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [52, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [53, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [54, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [55, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [56, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [57, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [58, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [59, 38]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td></tr><tr><td title=\"Index: [0, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [1, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [2, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [3, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [4, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [5, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [6, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [7, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [8, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [9, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [10, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [11, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [12, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [13, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [14, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [15, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [16, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [17, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [18, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [19, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [20, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [21, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [22, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [23, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [24, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [25, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [26, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [27, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [28, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [29, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [30, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [31, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [32, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [33, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [34, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [35, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [36, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [37, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [38, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [39, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [40, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [41, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [42, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [43, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [44, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [45, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [46, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [47, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [48, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [49, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [50, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [51, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [52, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [53, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [54, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [55, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [56, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [57, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [58, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [59, 37]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td></tr><tr><td title=\"Index: [0, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [1, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [2, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [3, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [4, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [5, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [6, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [7, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [8, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [9, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [10, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [11, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [12, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [13, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [14, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [15, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [16, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [17, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [18, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [19, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [20, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [21, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [22, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [23, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [24, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [25, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [26, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [27, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [28, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [29, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [30, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [31, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [32, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [33, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [34, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [35, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [36, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [37, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [38, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [39, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [40, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [41, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [42, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [43, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [44, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [45, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [46, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [47, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [48, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [49, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [50, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [51, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [52, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [53, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [54, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [55, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [56, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [57, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [58, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [59, 36]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td></tr><tr><td title=\"Index: [0, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [1, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [2, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [3, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [4, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [5, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [6, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [7, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [8, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [9, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [10, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [11, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [12, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [13, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [14, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [15, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [16, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [17, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [18, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [19, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [20, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [21, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [22, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [23, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [24, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [25, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [26, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [27, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [28, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [29, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [30, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [31, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [32, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [33, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [34, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [35, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [36, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [37, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [38, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [39, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [40, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [41, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [42, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [43, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [44, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [45, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [46, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [47, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [48, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [49, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [50, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [51, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [52, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [53, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [54, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [55, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [56, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [57, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [58, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [59, 35]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td></tr><tr><td title=\"Index: [0, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [1, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [2, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [3, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [4, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [5, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [6, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [7, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [8, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [9, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [10, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [11, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [12, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [13, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [14, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [15, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [16, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [17, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [18, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [19, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [20, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [21, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [22, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [23, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [24, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [25, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [26, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [27, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [28, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [29, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [30, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [31, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [32, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [33, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [34, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [35, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [36, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [37, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [38, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [39, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [40, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [41, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [42, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [43, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [44, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [45, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [46, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [47, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [48, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [49, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [50, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [51, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [52, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [53, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [54, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [55, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [56, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [57, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [58, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [59, 34]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td></tr><tr><td title=\"Index: [0, 33]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [1, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [2, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [3, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [4, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [5, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [6, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [7, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [8, 33]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [9, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [10, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [11, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [12, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [13, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [14, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [15, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [16, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [17, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [18, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [19, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [20, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [21, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [22, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [23, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [24, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [25, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [26, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [27, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [28, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [29, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [30, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [31, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [32, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [33, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [34, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [35, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [36, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [37, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [38, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [39, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [40, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [41, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [42, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [43, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [44, 33]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [45, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [46, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [47, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [48, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [49, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [50, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [51, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [52, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [53, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [54, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [55, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [56, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [57, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [58, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [59, 33]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td></tr><tr><td title=\"Index: [0, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [1, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [2, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [3, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [4, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [5, 32]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [6, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [7, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [8, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [9, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [10, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [11, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [12, 32]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [13, 32]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [14, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [15, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [16, 32]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [17, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [18, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [19, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [20, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [21, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [22, 32]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [23, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [24, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [25, 32]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [26, 32]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [27, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [28, 32]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [29, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [30, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [31, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [32, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [33, 32]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [34, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [35, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [36, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [37, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [38, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [39, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [40, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [41, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [42, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [43, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [44, 32]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [45, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [46, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [47, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [48, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [49, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [50, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [51, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [52, 32]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [53, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [54, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [55, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [56, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [57, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [58, 32]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [59, 32]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td></tr><tr><td title=\"Index: [0, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [1, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [2, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [3, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [4, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [5, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [6, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [7, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [8, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [9, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [10, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [11, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [12, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [13, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [14, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [15, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [16, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [17, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [18, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [19, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [20, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [21, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [22, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [23, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [24, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [25, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [26, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [27, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [28, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [29, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [30, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [31, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [32, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [33, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [34, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [35, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [36, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [37, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [38, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [39, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [40, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [41, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [42, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [43, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [44, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [45, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [46, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [47, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [48, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [49, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [50, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [51, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [52, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [53, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [54, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [55, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [56, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [57, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [58, 31]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [59, 31]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td></tr><tr><td title=\"Index: [0, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [1, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [2, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [3, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [4, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [5, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [6, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [7, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [8, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [9, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [10, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [11, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [12, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [13, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [14, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [15, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [16, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [17, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [18, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [19, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [20, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [21, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [22, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [23, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [24, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [25, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [26, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [27, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [28, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [29, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [30, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [31, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [32, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [33, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [34, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [35, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [36, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [37, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [38, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [39, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [40, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [41, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [42, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [43, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [44, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [45, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [46, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [47, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [48, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [49, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [50, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [51, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [52, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [53, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [54, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [55, 30]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [56, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [57, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [58, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [59, 30]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td></tr><tr><td title=\"Index: [0, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [1, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [2, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [3, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [4, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [5, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [6, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [7, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [8, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [9, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [10, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [11, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [12, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [13, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [14, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [15, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [16, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [17, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [18, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [19, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [20, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [21, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [22, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [23, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [24, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [25, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [26, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [27, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [28, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [29, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [30, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [31, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [32, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [33, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [34, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [35, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [36, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [37, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [38, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [39, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [40, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [41, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [42, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [43, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [44, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [45, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [46, 29]&#10;Color: (0, 0, 255)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 255);\"></td><td title=\"Index: [47, 29]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td&