dotsdl/SWC-OSU-2015.ipynb

## SWC-OSU-2015.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's jump in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To pull in comma-separated datasets like those for our inflammation data, we make use of a package called ``numpy``. This is the package to use for anything numerical, especially when messing with whole arrays of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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.]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.loadtxt(\"inflammation-01.csv\", delimiter=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can load one of the inflammation data sets using the ``numpy.loadtxt`` function. This function loads the data from disk, but without attaching a name to it, it isn't necessarily stored in memory. A bit about names..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The equal sign is known as the \"assignment\" operator. For example, given some weight in kg, we can attach a name to that weight like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weight_kg = 55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55\n"
     ]
    }
   ],
   "source": [
    "print weight_kg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The value ``55`` has the name ``weight_kg`` attached to it. To get the value, we need only use the name. We can use this in other expressions that may assign to other names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weight_lb = 2.2 * weight_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121.00000000000001"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_lb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``weight_lb`` is calculated from ``weight_kg``. What happens to ``weight_lb`` if we change the value attached to ``weight_kg``?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weight_kg = 66"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121.00000000000001"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_lb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nothing! That's because ``weight_lb`` only remembers the value assigned to it, not how that value was obtained. To update its value, it must be recalculated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weight_lb = 2.2 * weight_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145.20000000000002"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_lb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can assign a name to our data array, which quantifies the inflammation observed in a set of patients (rows) over succeeding days (columns), so we can begin working with it in memory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = numpy.loadtxt('inflammation-01.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "print data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arrays are rectangular datasets that consist of all a single data type (in this case, floating-point numbers). They have attributes such as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 40)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and methods to calculate quantities from their elements such as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean for all values: 6.14875\n"
     ]
    }
   ],
   "source": [
    "print \"mean for all values:\", data.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many of their methods can be fed parameters to change their behavior. For example, instead of the mean of all elements in the array, perhaps we want the mean of each row (that is, for each patient, calculated across all days):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean for each patient:\n",
      "[ 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"
     ]
    }
   ],
   "source": [
    "print \"mean for each patient:\\n\", \n",
    "print data.mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the shape of the output array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mean(axis=1).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to know the other parameters a function or method take, put a question mark at the end and run the cell. The documentation will pop up in the notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# displays the docstring\n",
    "data.mean?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get the mean across all patients for each day:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean for each day:\n",
      "[  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"
     ]
    }
   ],
   "source": [
    "print \"mean for each day:\\n\", \n",
    "print data.mean(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note*: python indices are 0-based, meaning counting goes as 0, 1, 2, 3...; this means that the first axis (rows) is axis 0, the second axis (columns) is axis 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we get at individual elements in array? We can use indices to grab them. To get the inflammation value for the fifth patient on the 10th day, we can use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[4, 9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember that python uses 0-based indices! The data for the first patient on the first day is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can select subsets of our data with slicing. To select only the first 5 rows (patients) of the array, we can do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   0.,   1.,   3.,   1.,   2.,   4.,   7.,   8.,   3.,   3.,\n",
       "          3.,  10.,   5.,   7.,   4.,   7.,   7.,  12.,  18.,   6.,  13.,\n",
       "         11.,  11.,   7.,   7.,   4.,   6.,   8.,   8.,   4.,   4.,   5.,\n",
       "          7.,   3.,   4.,   2.,   3.,   0.,   0.],\n",
       "       [  0.,   1.,   2.,   1.,   2.,   1.,   3.,   2.,   2.,   6.,  10.,\n",
       "         11.,   5.,   9.,   4.,   4.,   7.,  16.,   8.,   6.,  18.,   4.,\n",
       "         12.,   5.,  12.,   7.,  11.,   5.,  11.,   3.,   3.,   5.,   4.,\n",
       "          4.,   5.,   5.,   1.,   1.,   0.,   1.],\n",
       "       [  0.,   1.,   1.,   3.,   3.,   2.,   6.,   2.,   5.,   9.,   5.,\n",
       "          7.,   4.,   5.,   4.,  15.,   5.,  11.,   9.,  10.,  19.,  14.,\n",
       "         12.,  17.,   7.,  12.,  11.,   7.,   4.,   2.,  10.,   5.,   4.,\n",
       "          2.,   2.,   3.,   2.,   2.,   1.,   1.],\n",
       "       [  0.,   0.,   2.,   0.,   4.,   2.,   2.,   1.,   6.,   7.,  10.,\n",
       "          7.,   9.,  13.,   8.,   8.,  15.,  10.,  10.,   7.,  17.,   4.,\n",
       "          4.,   7.,   6.,  15.,   6.,   4.,   9.,  11.,   3.,   5.,   6.,\n",
       "          3.,   3.,   4.,   2.,   3.,   2.,   1.],\n",
       "       [  0.,   1.,   1.,   3.,   3.,   1.,   3.,   5.,   2.,   4.,   4.,\n",
       "          7.,   6.,   5.,   3.,  10.,   8.,  10.,   6.,  17.,   9.,  14.,\n",
       "          9.,   7.,  13.,   9.,  12.,   6.,   7.,   7.,   9.,   6.,   3.,\n",
       "          2.,   2.,   4.,   2.,   0.,   1.,   1.]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 40)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0:5].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This slice should be read as \"get the 0th element up to and not including the 5th element\". The \"not including\" is important, and the cause of much initial frustration. It does take some getting used to."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also slice the columns; to get only the data for the first five rows (first five patients) and columns 9 through 12 (10th through 13th day), we can use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  3.,   3.,   3.,  10.],\n",
       "       [  6.,  10.,  11.,   5.],\n",
       "       [  9.,   5.,   7.,   4.],\n",
       "       [  7.,  10.,   7.,   9.],\n",
       "       [  4.,   4.,   7.,   6.]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0:5, 9:13]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the output of a sliced array is another array, we can run methods on these just like before. For example, we can get the standard deviation across days for each patient for this subset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.03108891,  2.54950976,  1.92028644,  1.29903811,  1.29903811])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0:5, 9:13].std(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can treat arrays as if they are single numbers in arithmetic operations. For example, multiplying the array by 2 doubles each element of the array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "double = data * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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.]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  2., ...,  6.,  0.,  0.],\n",
       "       [ 0.,  2.,  4., ...,  2.,  0.,  2.],\n",
       "       [ 0.,  2.,  2., ...,  4.,  2.,  2.],\n",
       "       ..., \n",
       "       [ 0.,  2.,  2., ...,  2.,  2.,  2.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  4.,  0.],\n",
       "       [ 0.,  0.,  2., ...,  2.,  2.,  0.]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "double"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arithmetic operations can be applied between arrays of the same shape; these operations are performed on corresponding elements (this is also true for multiplication/division)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "triple = double + data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  3., ...,  9.,  0.,  0.],\n",
       "       [ 0.,  3.,  6., ...,  3.,  0.,  3.],\n",
       "       [ 0.,  3.,  3., ...,  6.,  3.,  3.],\n",
       "       ..., \n",
       "       [ 0.,  3.,  3., ...,  3.,  3.,  3.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  6.,  0.],\n",
       "       [ 0.,  0.,  3., ...,  3.,  3.,  0.]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "triple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This amounts to $3x_{i,j}^2$, for each element $x_{i,j}$ of the array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   0.,   3., ...,  27.,   0.,   0.],\n",
       "       [  0.,   3.,  12., ...,   3.,   0.,   3.],\n",
       "       [  0.,   3.,   3., ...,  12.,   3.,   3.],\n",
       "       ..., \n",
       "       [  0.,   3.,   3., ...,   3.,   3.,   3.],\n",
       "       [  0.,   0.,   0., ...,   0.,  12.,   0.],\n",
       "       [  0.,   0.,   3., ...,   3.,   3.,   0.]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "triple * data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interested in performing matrix operations between arrays? There are methods built into numpy for that. For example, the inner product:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5388.,  4266.,  5145., ...,  5802.,  5841.,  5220.],\n",
       "       [ 4266.,  5775.,  5463., ...,  5307.,  5895.,  5019.],\n",
       "       [ 5145.,  5463.,  7182., ...,  6444.,  6930.,  5931.],\n",
       "       ..., \n",
       "       [ 5802.,  5307.,  6444., ...,  7320.,  7122.,  6423.],\n",
       "       [ 5841.,  5895.,  6930., ...,  7122.,  8628.,  6771.],\n",
       "       [ 5220.,  5019.,  5931., ...,  6423.,  6771.,  6654.]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.inner(triple, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try viewing this array graphically. Just as ``numpy`` is the de-facto library for numerically-intensive work, ``matplotlib`` is the de-facto library for producing high-quality plots from numerical data. A saying often goes that ``matplotlib`` makes easy things easy and hard things possible when it comes to making plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can throw our data into the ``matplotlib.pyplot.imshow`` function to view it as a heatmap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa6ab0c1590>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Jj4Zf4MQlGydiY8esiakSl+Syw/zzEcWpz+JoQHIUUu3ZeB6o2iQ76ASULyn3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OlhG8g7E8TsQLuCqnbQQlC7DnoAjSVpbAvtKTw3YzRecvT0mlFyz+1dhb61ye5itBTUY5sickmt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\nYmb8bzh4HCcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6ab0e1590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can try viewing the mean inflammation value across patients for each day directly using the ``matplotlib.pyplot.plot`` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa6aafce310>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SeGBiRExItw8FXo2I7w96jmdgmpk1oO2TfSSNBu4BNgYeBW4Cdo6IGXW/mZmZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6ab091690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data.mean(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Huh...that looks a bit orderly. Almost completely linear with a positive slope to precisely day 20, then back down with about the same slope to day 40..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can try plotting the max value across all patients for each day:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa6a969c550>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TuV1c0jKV6uW9ncjt4pKWrU29vPUTufvFJa1SBH8PPI819fLeTeQj9930JC5pFd5H5b18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\nE7kktZwncklqOU/kktRy/w9gDG0lt5HRQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6a9769990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data.max(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's fishy..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The min across all patients for each day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa6a9650350>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/gFuAC8aepS6DLWk7K+pYxGBL2s4M9tDcgS2pJwZ7AdyBLakPRe3GLmLIDXgcIqmzTA7B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\nJakQ/w9laMMULb8UigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6a96bfbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data.min(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A step function, with steps that appear to go in increments of 4 or so across the whole domain of days. Weird..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib can make figures that include multiple sub-figures, referred to as *axes*. We can put the three plots above side-by-side with something like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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W10PVR6xH7lzfuuQYJDPMIFnSHMCltp8EzpL0R2DeJq5J7JIVgR+mDlFjFwDf\nAT4D/CZxlqqaz/bFyrbw3g9MknQDsH+nDdu2pCE/xpQ0qd+X02xP6/SaXbAV2dKeWOvefT8BdpX4\ngM3lqcO0QtKGZGvXy3R7/vv1EMsGRmgsxCcTHYp1yf3MdpBs+xVJPwbWzL9+Hnh+do8Jg8urNIwn\nllu0rd9s8uH5bHJsgHyj5yXNCdwj6ZtkJyct0EF7syQtZfthSUszmw0xtid1cJ2uk5gH+B6wY6xh\n7D6bFyT2B6ZIvLsO/wb5G79pfV9L+m4J1+g7VOo2YF+ypUD9X6tPKfqaDTKObKNjaN8MYOXUIaqi\nlU1QF0v6jPpqv4V2LQP826b0OtMN9yey+r9bpg5SUbuQVaPZmexQli8A23bQ3h/6PX5bmvUC9FXg\nLpvLUgfpYaeRrZ3/ZOogFXQq2fKpT5Nt2Ov7FYYWa5I7FzPJ/Qx7mIik54D5yT6S7JtFLvXUnyYW\nOpfYBNjfLv2jusaT+BBwJLBa02aTO33uS1qH188+CXjF9uotPPZ0sk16i5GtP/4O8HuypS3LMZsS\ncHXrs/nu7buBD9nclDpPL5PYnGwZ2jtsXk6dZyRKPkzkr7Y3KKntWvXXVkjMDTwHvNl+tWReGCGJ\nlYBzbVZInaUMhR8mYnvB4e4TWrISsdSiKBcBT5GtJz09cZaqOZXskIZbYWSbG21vPcS3PthpqAra\nHZgaA+RK+BPZsqDtiU3h/R2QHw50MVEnuRWjgIdjgNyxmcByEnM2bRKqHa0cJjIH2Ue2Y2wfKGk5\nYCnb15SerllWJGokFyJfm/xd4DiJP0Rt29d51PYfUoeoMoklyZaj9OTR51WT9+cJwDkSp9r8J3Wm\nitiW7HVjLl7/hjcGyYOLyhYFsHle4hGyNx0zE8dJrpUScMeSddCNyU5Kei6/LV5gRibKvxXIZqrE\nX8kOHtkhdZ4Kidmn4e0P/NLmvtRBQsbm2rw/70JWFi5kr7Erebg1kaFPrEcuzgyyv8+ZiXMk18og\neT3ba0maDmD7CUlzl5yrieIgkeLtBNwgsbUdyy5yMfs0GxLLA1uTvWkN1TIR+JvE8TaPpw5TAVcC\nq5BVuQjDi5nk4txL9vd5aeogqbUySH4xLykFgKTFGeFax4EkLQz8nOxUMAM72L6qkzarTGJ+YAni\nXVmhbJ6T+BzwJ4mr7ZhFIGafhnMQcKTNo6mDhNezuVviN2QbT3dPnacC3g3cKOk+4IX8NreyCbdH\njSVOYy1TpKSvAAAb3klEQVRK30xyz2tlkHw08DtgCUmHkB3ksF+H1/0RcL7tz0iai87quNbBeODe\nWARfPJsbJL4HnC7x3jg1LWafhiLxLrLqHV9JnSUM6UDgNomjbO5PHSaxzVMHqJmYSS7OvURZRqCF\nEnAAklYGNsm/vMT2HW1fUFoImG57yHcpTStPI/FZYEubz6TO0kT5QS3nArfZTEidpxMFlIC7k+zF\noquzT3XosxJTgbNsfpI6SxiaxIHAcjbbpc4ynDo87wdT19xDyV8DngLG2DyROk/dSawH/Nhu3t6z\nwkvA5efHn277mI6SvWYM8Kikk4A1yI7e3MV2k3c0R/m3EuW747cHpkvcAvy6h2eUY/ZpEBKbAm8H\nTkidJQzrB8BdEqvZccRwaMmiZEs3n0wdpCHiQJFcKyfuXQ/sJ2mGpB9I6vSdxVzAO4Fjbb8T+Dew\nd4dtVl1s2itZvsZ0K7LSXrMkTpXYSqK0Q2+qyPbMwX6lzpWSxBzAFGBiD795qg2bZ8gqXESVi9Cq\ncWRLGmMvRjEeB+aSWCR1kNRaOUzkZOBkSW8FPgUcJmk528u3ec0HgQdtX5t/fSaDDJIlTer35TTb\n09q8XtfkJ/78gmwN8ox+v95FdqJUKJHNlcB6Em8DPk52OMHPJba3OSttusFJ2hDiFMaSfRZ4mez/\nmlAPPwF2lfiAzeWpw9SNpFFkr0VLkM2wHm/7qLSpShXl3wqUfzrbN5t8feo8KbWyca/P8mTLBt4O\n3N7uBW0/LOkBSeNt30V2mtcbNhnZntTuNVLI10QdB7wF+AbZspKxwDpkh4jERqousfkX2YvsTyQ2\nAw6XOLuKswz5m79pfV9L+m6yMA0kMQ9wMLBjFf/9w+BsXpDYD5gi8e74txuxl4DdbN8oaUHgeklT\nO9lPVHGxaa94M8j+XmOQPDuSDiPb5TgDOAM4yPZTHV53Z+BUSfOQPbG377C9KtiHbBnJ+22eA65O\nnCdkppLNpGxGdvxt6C1fBe6yuSx1kDBipwN7kr3+RJ3vEbD9MPBw/ufnJN0BvA1o6iB5LPGaW7RY\nl0xrM8n3AhuQzYzOC6wuCdt/bveitm8im2FtBInPA18D3p0PkENF5B8b/QDYgxgk9xSJN5OVq/xQ\n6ixh5GxeyY+rPio/fv7l1JnqSNJoYC2aPYgcB5yWOkTD9C0V7WmtDJJfAS4BlgVuBNYH/kZ2THXP\nk/gA2XrjjfOP+UP1nAEcIrGGzU2pw4Su2R2YGv/mtXYR2T6WHYDjE2epnXypxZlkFaQqPYEjsRCw\nIzDncPcdxDuINclFm0G2L2CvNh77PFkJudqfDTFsnWRJt5LN+v7N9pqSVgIOtV1aoem61HCUWA24\nGPi8zSWp84Sh5TNSq9p8KXWW2anLc3+gquWWWJJsH8A6NvelzhPaJ7E28HtgBZtKlQqt2vO+P0lz\nA+cBF9j+4YDvGTig303JN8dLfIKsCs3v23j4c8DBdmenAYfX5JWh9mZke9f6bAdsZKffizXI5vjv\njqTPtjJIvs722pJuBNa3/byk222v0k7glkJV+D+ePhJfBI4AdrY5I3WeMHsSC5O9M17d5sHUeYZS\nh+f+YKqWW+Jo4GWb3VJnCZ2T+DVwo12tsnBVe973kSTgFOBx22/oA1XMLfFt4O02u6TOEjojcT5w\nnM25qbMMVPhhIsADkhYBzgGmSnoSmNlmvtqTmB84CngvsInNzYkjhRbYPCVxCvAtaOvjo1ATEuOA\nrcmq8YRm2A/4m8TxNo+nDlMDGwDbADdLmp7fto/tCxNmGs444jyBpriX7N+z9lo6lvrVO2fT1m8B\nLrT9YmmhKvguF0BiJeC3wM3A122eTRwpjIDEaLJyNmPyAwsqp6rP/eFUKbfE6cCtNt9LnSUUR+LH\nwPM2u6fO0qdKz/uRqGJuiQvI1rGelzpL6IzEbmSvs99KnWWgkT73Wzlx71W2p9n+Q5kD5KqSWB64\ngmwWeZsYINePzUyyjUA7Jo4SSiLxLuD9xOE9TXQQsJ3E21MHCaWIWsfN0ZiZ5BENknvcJ4Df2vws\nCtvX2uHALvnpiKF5JgMH2vw7dZBQLJuHgR8DB6bOEoolMSewHD28lLNhZtCQGssxSG7d5kSd3dqz\nuQ54FHhP6iyhWBKbkr3Qnpg6SyjND4DNJVZPHSQUalngMZv/pg4SCjEDGC3Vf4xZ+x+gGyQWANYD\nLk2dJRTiUuADqUOE4uT/GU8B9rV5KXWeUI58L8H3oFpVLkLHxhJ1jhsjL9X4FNkpj7UWg+TWbAhc\nH+uQG2Mar6+bGOrvs8BLxPHFveCnwMpS9OEGGUusR26aRqxLjkFyaz4EVLl0ThiZvwDrSrwpdZDQ\nOYl5yGYXJ8R+geazeYGsJNwUiUpVaAhtG0fMJDdNI9YlxyC5NbEeuUHyj2zvANZNnSUU4mvAnTbT\nUgcJXXMGMA/wqdRBQiFiJrl5Yia5F0iMJasNfVPqLKFQlxNLLmovPzp1IrBP6iyhe/Ljh/cGDpHa\nOjY3VEvMJDdPzCT3iA8BF8WZ8I0zjRgkN8HuZP0z3sT2nouAB4EdUgcJHYuZ5OZpxEzyiE7c65Yq\nnQYkcQ7wG5vTUmcJxZFYGHgAWCxf41gJVXruj0SK3BJLAbcB78oPigk9RmId4BxgfIra2NFfO9fv\n/+K3xJ6C5sj/f77FZvHUWfor9cS9XpNvCNoImJo6SyiWzVPAXcA6qbOEtu0P/CIGyL3L5lqyjbi7\npM4S2jYWuDcGyI0zC5g/XxJXWzFInr13A3fZPJo6SCjFNKJeci1JrEBW9u17qbOE5PYDvi2xWOog\noS2xHrmB8jc9tV+XnGyQLGlOSdMlnZsqQwuiqkWzTSPWJdfVwcCRNo+lDhLSsrkb+DWwb+osoS2x\nHrm5ar8uOeVM8i7A7VDpj1iiPnKzXQGsny+rCTUhsTbwPuCHqbOEyjgI2FZidOogYcRiJrm5Yia5\nHZKWBT4C/ByqWQxeYkmyf9yrU2cJ5cjXJd8NrJ06S2hNfnjEFOCAFBu1QjXZPAwcAxyYOksYsZhJ\nbq6YSW7TkcCeUOmyapsBl9i8lDpIKNU0YslFnWwKLAucmDpIqJzDgc0kVk8dJIzIWGImualqP5Pc\n9SLskrYAHrE9XdKGs7nfpH5fTrM9reRoA8V65N5wObATcEiKi+d9YMMU164biTnIZpH3jTevYSCb\nZyS+BxwKfDR1njA8ibmBZYB/pM4SSlH7QXLX6yRLOgT4IvAyMC/ZaXZn2f5Sv/skqeEoMQpYn6yq\nxQ7AGjb3dztH6B6JRYGZwFurMPCqUv3SkehGbonPk+1lWD/KRYXB5PsL7gR26MYx5dFfO83BOOBi\nmzGps4TiSbwJeAZYwObl1HmgBnWSbe9re5TtMcDngEv7D5BTkDhQ4kHgemAb4FFgsxggN5/NE2Tr\npmJd8mxIminp5rwizTXdvz5vIqtosVcMkMNQbF4kO6Z8Sr5+PVRbbNprsPygrlnAqNRZ2lWFM++T\nvuBJvJds1ngj4J54Ae5Jl5PVS/5b6iAVZmBD208kuv7XgDtsLk90/VAfvybb8/Ip4KzEWcLsxaa9\n5uvbvHdf6iDtSHqYiO3LbX881fUl5gR+BEywuTsGyD1rGrEuuBVJZubyE5smAvukuH6oF5tXgL2B\nQ/I1r6G6Yia5+Wq9LrnXT9zbDngeOC1xjpDWNGB1ib2G+ohWYj6JwyQ+3N1olWHgYknXSfpKl6+9\nO/Anm5u7fN1QX1OBB8g+JQzVFTPJzVfrMnBVWG6RhMRCZGscPxYzyL3N5imJ9YCzgXdJ7NC/Bq/E\nmmRvpP4LrAVckCZpUhvYfkjS4sBUSXfavqLsi0osBXwTeFfZ1wrNYWOJCcC5Er+KmtqVFTPJzTcD\n+HTqEO3q2UEysD9wvs11qYOE9GwekHgfcCxwlcQnyTr37sBewG5k6xv/JbFUfnhBz7D9UP77o5J+\nB6xLdmLhq0oq27g/cIrNzALaCj3E5nqJPwO7At8ros0o2Vic/FO7mEluvlrPJHe9BFwryi5PI7Ei\n8FdgVZtZZV0n1E/+H/fXgUlkp/EBbNM3SJP4BXCtzdHlXL8apZn6kzQ/MKftZyUtAFwEHGD7on73\nKTy3xApkmylXsnmsyLZDb5BYHriKkp5DVeyvrahCbonFgLtsFk2ZI5RL4q1kE04LV+FT+8qXgKuI\nI4DJMUAOA9nY5jjgk8BvgQ0HzGKeDmydIltCSwJXSLqR7Jj28/oPkEt0MHBEDJBDu2zuAc4g2/gZ\nqmUcMYvcC54g29NSyzdDPTeTLPER4IfAO/KamiG0LN8t/09gPbv4kjZVmOFpR9G5JdYBzgHGx3rS\n0AmJJYHbgbWL7rPRXzvJwNbAJ2w+mzJHKJ/EDcDXbK5NnyVmkofU71jbPWOAHNqRn8p3FtlBOKEE\n+ZKXKcABMUAOnco/MTwaODB1lm6RdKKkWZJuSZ1lNmImuXfUdl1yTw2SyT4mfw74Q+ogodZ6cclF\nN20GLAOcmDpIaIzDgU0l1kgdpEtOAjZPHWIYY4nKFr2itrWSe2aQnH9MfiCwbxUWj4da+wuwiMSq\nqYM0Tf5pz2RgH5uXU+cJzWDzLFmFi0NTZ+mGvDzjk6lzDCNmkntHbWeSe6kE3JeBGTaXpQ4S6s3m\nFYlfk80m75c6T8N8DngB+F3qIKFxfgrsJrFRvA4UI18atSjtncYZNZJ7xwzgS3lFk5F62eapogO1\nqicGyRLzk9Vb/UTqLKExTgd+I7F/fDJRDIk3kVW02D7+TkPRbF6UmAhMkViv159jBdU1/zDZG9pn\n23jsE8CDbTwu1M9tZMst7mzjsQtJvMPm7+1cuNPa5j1R3UJiT2B9u76nvoRqyWdQ7gS+aHNNce2m\n33XejiJyS3wL2Mxmi4JihfA6+XKe64BDbM7svL3q9ldJo4Fzba82yPcKyS2xO7CszW6dthXCYCTO\nA35m8/ti2ovqFq+THz+9J9lMcgiFyGehYgNfQSTeQlbLdp/UWUJz2bwCTAAOyfephM7EuuJQtqTr\nmRs/SAb2IDt++vbUQULjnA58VmLO1EEaYA/gQpsql6wKDWAzFbifbJ9KI0k6HbgSGC/pAUnbl3Sp\nqFARypa0Mkaj1yRLLAF8A3hX6iyheWz+LvEQsBFwceo8dSWxFLAT8M7UWULP2Bs4V+KXTazFbbtb\nn3DFTHIo270kLGfY9Jnk7wC/GnCscAhFOhkoa5amV3wHONnm/tRBQm+wuR74M7Br6ix1lX+CthxE\nvw2lSjqTnGTjnqRRwC+AJcjO9D7e9lH9vl/EJqAVgb8CK9k81klbIQxF4q1k73TH2J3XJa3yRqDZ\naTe3xHiyj4VXtHm8+GQhDE5ieeAqOniN6LX++vo2GA1cYTOqmFQhvJHEfGQ1vxew+V/n7dVj495L\nwG62VwXWB3aStHLB15gMHBYD5FCmfGB3IbGBr10HA0fEADl0m809wBlkG0bDyI0lllqEktn8F3ic\n7BTWrksySLb9sO0b8z8/B9wBvK2o9iXeR7a+8ajh7htCAU4Edkgdom4k1gU2AH6UOkvoWQeRHXIw\nJnWQGorDQEK33EuiJRfJ1yTntRzXAq4upj0E/ACYaPN8EW2GMIxLgCUk1kgdpC7yfjoZOLCJG6dC\nPdjMAo4mGyyHkYmZ5NAtM0hUBi7pIFnSgsCZwC75jHIRtiKr2nFaQe2FMFv5OqmTiQ18I/Ehso/P\nTkgdJPS8w4EPSqyZOkjNxExy6JZkM8nJSsBJmhs4C/iV7XMG+f6kfl+2dGRmfqztocCOedH4ELrl\nZOBqiQk2L7T6oE6PzKyj/NSzycA+Ni+nzhN6m82zEgeTvXZ8OHWeGomZ5NAtM4CPpbhwquoWAk4B\nHrf9huMsO9gpvxuwSRxrG1KQuBQ4tpPjbntht7zEF4CdgXfnJxeGkJTEPGR7Y75ic2nrj2t+fx26\nDZ4EVojN8aFsEusDR9ms23lb9ahusQGwDbCRpOn5r46KReeluPYhO3I0hBROpMGneBUh/7TnYGBC\nDJBDVdi8SFblYnK+Xj7MhsQiZOOHqEoTuiHZmuQkM8nDaeddrsRPgJdsdi4pVgizJTE/8CCwhs0D\n7bXR7JkpiV2ATePTnlA1+TKga4FDW/00qOn9dejHszbwM5u1CowVwqDyN67PAsvaPNVZW/WYSS6U\nxLuAT5Cd3BVCEjb/AX4NfCl1liqSWAjYl+wTnxAqJd/HMgE4RGLu1HkqLtYjh67JP3VMsnmv9oPk\n/N3/McC+RZx4FkKHTgR2zAeE4fX2AC6wuSV1kBAGY3MxMJNYNjWcqGwRui3J8dS1HyQD2wIiqy4Q\nQmrXAecBU/N1ewGQWAr4BvFpT6i+vYHvSCyYOkiFjSUGyaG7YiZ5pCQWJivb880o+RaqIP9Y6FvA\nFcAlEosljlQV3wFOtvlH6iAhzI7NDcDlwK6ps1RYLLcI3ZZk816tB8nAAcDvba5LHSSEPvlAeQ/g\nQuAyiSUTR0pKYjzZIT+HpM4SQov2A3aVWDx1kIqK5Rah22ImeSQkVge2JtsIFEKl5APliWQnSk6T\neFviSCkdDBxuR7moUA829wKnk/Xh0E9eU3ppiE+FQlclmUmuZQm4vBzINOAMm+O6FiyENkhMJJtJ\nXSevxzqb+zarpJTEusDvyA4d+E/3k4XQHoklyA4YWdvmvsHv06z+2tpjWQH4k53mmODQm/I3Z88C\nC9q81H47vVEC7lPAwsDxqYOE0IJDyGZd9ksdpJvyN7NTgEkxQA51Y/MIcBRwUOosFRPrkUPX5RNM\nDwHLdfO6tRsk5yd2HQZ82+Z/qfOEMJx86cXXgK9LvDN1ni76ENnHsielDhJCm44ANpFYM3WQCon1\nyCGVrq9Lrt0gGdgZuM3mktRBQmiVzb/INvOdlH9s1Gh5/fIpwD42L6fOE0I7bJ4lW1N/aOosFRIz\nySGVrq9LrtUgOd9pPAHYM3WWENrwS7JlF72wGejzwH+Ac1IHCaFDPwPGS2ycOkhFxExySCVmkodx\nAHCazd9TBwlhpPotu/h/EmulzlOWfEnUQcCE/GcOobbytZATgSn5OvteFzPJIZWuzyTP1c2LdUJi\nVeAzwEqps4TQLpt/SewJnCwNX+2ipr5OtiTqz6mDhFCQ3wCLAXNDI/tsS/I3CTGTHFLp+kxybUrA\nSVwIXGDzo0SxQihE/kJzLnCZzeGv/169S0pJLATcBWxic2vqXCGUqe79deSPy8ri2by1hFghzJbE\nIsBMYOF2P6Uc6XO/FjPJEh8GxgDHps4SQqdsLLE9NLIs2h7A+TFADqGRxhFLLUIiNk9K/A94K/BY\nN65Z+UGyxNuBE4BtOykgHUKV2DyaOkPRJJYGvgHNXW8dQo8bSyy1CGnNIHsedmWQnGTjnqTNJd0p\n6W5JE4a+H28m+1j6MJup3UsYQuivxT77HeAkO46rDSGlVl9j2xAzySG1e+ni5r2uD5IlzQkcA2wO\nrAJsLWnlN96POYHTgKug2HXIkjYssr0mtV/n7E1ov4pa7bPAlpRQT7bu/6bRfrr2o7/Otr+2o6WZ\n5Do/Z8puv87ZK9J+30xyV6SYSV4XuMf2TNsvAWcA/zfI/SYDCwI7lVBGasOC22tS+2W2He3XU6t9\n9gc2j5dw/Q1LaDPa7432y2y7qlrtr+1odSZ5w4Ku18T2y2y7F9pv9kwysAzwQL+vH8xvG+gTwKdj\nHXIIybXaZ4/qTpwQwmy02l/bEWuSQ2pdnUlOsXGv1VnhLWyeKDVJCKEVLfVZu5HVOkKom5b6q8S5\nbbS9KPDPNh4XQlHuAdZu8fl7ks3ZnVys63WSJa0PTLK9ef71PsArtqf0u0/1ijeH0CVVq7safTaE\noUV/DaFeRtJnUwyS5wL+DmwC/Au4Btja9h1dDRJCaEn02RDqI/prCMXp+nIL2y9L+ibwJ2BO4ITo\nvCFUV/TZEOoj+msIxanksdQhhBBCCCGklOQwkdkpsQh6X/szJd0sabqkazps60RJsyTd0u+2RSVN\nlXSXpIskLVxw+5MkPZjnny5p8w7aHyXpMkm3SbpV0reK/Blm037HP4OkeSVdLelGSbdLOrTg7EO1\nX9jff97enHk75xaZv1vq1F/z9mrbZ+vcX/N2at9n695foV59ts79NW+rtn22Cf01b6+zPmu7Mr/I\nPhq6BxgNzA3cCKxc8DXuAxYtqK33kR3Be0u/2w4D9sr/PAGYXHD73wW+XVD+pYA18z8vSLaObeWi\nfobZtF/IzwDMn/8+F9mhM+8t+O9/sPYL+/vP2/42cCrwh6KfP2X/qlt/zdurbZ+te3/N2611n61z\nf80z1qrP1rm/5m3Vus/Wvb/mbXfUZ6s2k1xmEfT+CtmNbPsK4MkBN38cOCX/8ylk9Z6LbB+Ky/+w\n7RvzPz8H3EFWT7OQn2E27UMBP4PtvpJj85D95/8kxf79D9Y+FPT3L2lZ4CPAz/u1WVj+LqhVf4V6\n99m699e83dr22Qb0V6hZn61zf83br3WfrXN/hWL6bNUGyWUWQe9j4GJJ10n6SsFtAyxpe1b+51nA\nkiVcY2dJN0k6oaiP9ySNJntHfTUl/Az92r8qv6njn0HSHJJuzDNeZvs2Csw+RPuFZM8dCewJvNLv\ntm48f4rShP4KNeyzdeyvebt17rN176/QjD5bu/4K9eyzNe+vUECfrdoguRu7CDewvRbwYWAnSe8r\n60LO5vOL/pmOA8YAawIPAYd32qCkBYGzgF1sP9v/e0X8DHn7Z+btP0dBP4PtV2yvCSwLvF/SRkVm\nH6T9DYvKLmkL4BHb0xniXXNJz58iNaq/Qj36bF37a56vln22If0VGtZn69Bfob59tq79FYrrs1Ub\nJP8TGNXv61Fk73QLY/uh/PdHgd+RffxUpFmSlgKQtDTwSJGN237EObKPEDrKL2luss77S9vn5DcX\n9jP0a/9Xfe0X/TPYfhr4I/CuIrMP0v7aBWZ/D/BxSfcBpwMbS/plGflL1IT+CjXqs03or3mbdeuz\nTeiv0Iw+W5v+Cs3oszXsr1BQn63aIPk6YAVJoyXNA3wW+ENRjUuaX9Kb8z8vAGwG3DL7R43YH4Bt\n8z9vC5wzm/uOWP6P2ueTdJBfkoATgNtt/7Dftwr5GYZqv4ifQdJifR/DSJoP2BSYXmD2Qdvv61yd\nZAewva/tUbbHAJ8DLrX9xaLyd0kT+ivUpM/Wub/m7dS2zzakv0Iz+mwt+mveVm37bJ37KxTYZ13Q\nDsKifpF9RPN3sh24+xTc9hiy3bw3Ard22j7Zu5N/AS+SrfPanuxs+4uBu4CLgIULbH8H4BfAzcBN\n+T/ukh20/16ytTo3kj35pwObF/UzDNH+h4v4GYDVgBvytm8G9sxvLyr7UO0X9vff71of4LWdt4U9\nf7rxq079NW+ztn22zv01b78RfbbO/TXPXJs+W+f+mrdf2z7blP6at9l2n43DREIIIYQQQhigasst\nQgghhBBCSC4GySGEEEIIIQwQg+QQQgghhBAGiEFyCCGEEEIIA8QgOYQQQgghhAFikBxCCCGEEMIA\nMUgOIYQQQghhgBgkhxBCCCGEMMD/B3UGo110zZ7WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6a969ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a figure object\n",
    "fig = plt.figure(figsize=(10,3))\n",
    "\n",
    "# make an axis object for the figure, each with a different position\n",
    "# on a grid that has 1 row and 3 columns of axes\n",
    "ax1 = fig.add_subplot(1, 3, 1)\n",
    "ax2 = fig.add_subplot(1, 3, 2)\n",
    "ax3 = fig.add_subplot(1, 3, 3)\n",
    "\n",
    "# plot the means\n",
    "ax1.set_ylabel('average')\n",
    "ax1.plot(data.mean(axis=0))\n",
    "\n",
    "# plot the maxes\n",
    "ax2.set_ylabel('max')\n",
    "ax2.plot(data.max(axis=0))\n",
    "\n",
    "# plot the minses\n",
    "ax3.set_ylabel('min')\n",
    "ax3.plot(data.min(axis=0))\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the books were cooked for this data set. What about the other 11? Instead of checking them manually, we can let the computer do what it's good at: doing the same thing repetitively. We need a loop."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loops are for looping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, an aside with strings. Most programming languages generally deal in a few basic data types, including integers, floating-point numbers, and characters. A string is a sequence of characters, and we can manipulate them easily with python. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "word = 'lead'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a string ``'lead'`` that we've assigned the name ``word``. We can examine the string's type, and indeed any object in python, with the ``type`` builtin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'str'>\n"
     ]
    }
   ],
   "source": [
    "print type(word)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also access its characters (it's a sequence!) with indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'l'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'e'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And slicing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ead'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word[1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If I want to output each letter of ``word`` on a new line, I could do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l\n",
      "e\n",
      "a\n",
      "d\n"
     ]
    }
   ],
   "source": [
    "print word[0]\n",
    "print word[1]\n",
    "print word[2]\n",
    "print word[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But this doesn't scale to words of arbitrary length. Instead, let's let the computer do the boring work of iterating with a ``for`` loop:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l\n",
      "e\n",
      "a\n",
      "d\n"
     ]
    }
   ],
   "source": [
    "for letter in word:\n",
    "    print letter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This same bit of code will work for *any* ``word``, including ridiculously long ones. Like this gem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "supercallifragilisticexpialidocious\n"
     ]
    }
   ],
   "source": [
    "word = 'supercallifragilisticexpialidocious'\n",
    "print word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s\n",
      "u\n",
      "p\n",
      "e\n",
      "r\n",
      "c\n",
      "a\n",
      "l\n",
      "l\n",
      "i\n",
      "f\n",
      "r\n",
      "a\n",
      "g\n",
      "i\n",
      "l\n",
      "i\n",
      "s\n",
      "t\n",
      "i\n",
      "c\n",
      "e\n",
      "x\n",
      "p\n",
      "i\n",
      "a\n",
      "l\n",
      "i\n",
      "d\n",
      "o\n",
      "c\n",
      "i\n",
      "o\n",
      "u\n",
      "s\n"
     ]
    }
   ],
   "source": [
    "for letter in word:\n",
    "    print letter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do more with loops than iterate through the elements of a sequence. A common use-case is building a sum. For example, getting the number of letters in ``word``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35\n"
     ]
    }
   ],
   "source": [
    "counter = 0\n",
    "for letter in word:\n",
    "    counter = counter + 1\n",
    "\n",
    "print counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this very particular use, there's a python built-in, which is guaranteed to be more efficient. Use it instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(word)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It works on any sequence! For arrays it gives the length of the first axis, in this case the number of rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lists: ordered collections of other objects (even other lists)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loops work best when they have something to iterate over. In python, the ``list`` is a built-in data structure that serves as a container for a sequence of other objects. We can make an empty list with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stuff = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "print stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can append things to the end of the list, in this case a bunch of random strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stuff.append('marklar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stuff.append('chewbacca')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stuff.append('chickenfingers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['marklar', 'chewbacca', 'chickenfingers']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like with the ``word`` example, we can iterate through the elements of the list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "marklar\n",
      "chewbacca\n",
      "chickenfingers\n"
     ]
    }
   ],
   "source": [
    "for item in stuff:\n",
    "    print item"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And slicing works too! Slicing with negative numbers counts backwards from the end of the list. The slice ``stuff[-2:]`` could be read as \"get the 2nd-to-last element of ``stuff``, through the last element\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chewbacca\n",
      "chickenfingers\n"
     ]
    }
   ],
   "source": [
    "for item in stuff[-2:]:\n",
    "    print item"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So indexing with ``-1`` gives the last element:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'chickenfingers'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And consistently, but uselessly, slicing from and to the same index yeilds nothing. Remember, for a slice ``n:m``, it reads as \"get all elements starting with element ``n`` and up to but not including element ``m``.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff[0:0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists are mutable: that is, their elements can be changed. For example, we can replace ``'chewbacca'`` with ``'hansolo'``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'chewbacca'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stuff[1] = 'hansolo'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hansolo'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...or replace ``'hansolo'`` with our ``data`` array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stuff[1] = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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.]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['marklar', 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.]]), 'chickenfingers']\n"
     ]
    }
   ],
   "source": [
    "print stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can make another list, including ``stuff`` as an element:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "morestuff = ['logs', stuff]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['logs', ['marklar', 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.]]), 'chickenfingers']]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "morestuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists can contain lists can contain lists can contain lists...and of course any number of other python objects. Even functions can be included as elements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "morestuff.append(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['logs', ['marklar', 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.]]), 'chickenfingers'], <function len>]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "morestuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Want the third element of the second element of ``morestuff``?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'chickenfingers'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "morestuff[1][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists also have their own methods, which usually alter the list itself:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['marklar', 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.]]), 'chickenfingers']"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stuff.reverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['chickenfingers', 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.]]), 'marklar']"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``list.pop`` removes the element at the given index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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.]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff.pop(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...while ``list.remove`` removes the first instance of the given element in the list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stuff.remove('chickenfingers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now our list looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['marklar']"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing at scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We noticed what looked like fraudulent data in the first dataset we analyzed. To look at more datasets without manually running our block of plotting code on each, we'll take advantage of loops and lists."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To grab all the filenames for our inflammation data sets, we'll use the ``glob`` library. This includes a function (``glob.glob``) that will return a list of filenames matching a \"glob\" pattern, which is what we used for matching files in the bash shell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['inflammation-07.csv', 'inflammation-04.csv', 'inflammation-01.csv', 'inflammation-05.csv', 'inflammation-08.csv', 'inflammation-10.csv', 'inflammation-09.csv', 'inflammation-06.csv', 'inflammation-12.csv', 'inflammation-03.csv', 'inflammation-02.csv', 'inflammation-11.csv']\n"
     ]
    }
   ],
   "source": [
    "# this will get us all the inflammation data files; we don't care about order\n",
    "print glob.glob('inflammation*.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stealing our code block for the plots above and putting them in a loop over filenames, we can look at a whole block of figures. We can slice a subset of the filenames to limit the number we plot at one time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-07.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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J3wLvoosj0QfEI5LmB/4k6ZOkk5MW7aG9uZKWtX23pOUYZ0OM7ek9XKfvJBYC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\nL+S4tMQdRKUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6a96903d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-04.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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ndXrNLvgS8CrZ0fChu34K7CrxCZsrU4dphaQJZGvXy3R7/veNEMsGRmksxJ2J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\nkPhw3Kp9EwOXSrpB0je6fO3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s7for1LPP1ry/QgF9tmqD5G7sIlzb9njgU8BOkj5W1oWc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\nCCGEEMIAMUgOIYQQQghhgP8PHs9eW9Dkz1QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6a91f4310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-01.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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W10PVR6xH7lzfuuQYJDPMIFnSHMCltp8EzpL0R2DeJq5J7JIVgR+mDlFjFwDf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\n5uvbvHdf6iDtSHqYiO3LbX8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frXN/hWL6bNUGyWUWQe9j4GJJ10n6SsFtAyxpe1b+51nA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\nMUgOIYQQQghhgBgkhxBCCCGEMMD/B3UGo110zZ7WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6a8fd9550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-05.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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UiffU4f8gf+E3beBzSfuVcI2B00ZvIVtetjSv/l19YtHXbJDxwNmpQ9TcTGCF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\nQgghhCFikBxCCCGEEMIQ/w9eF3jqpJySQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6a8d82510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-08.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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2A9abcQ/wRTya7DjOENPSk2xmTwGfjV9OOUWR5FZ2i2ETyT2PJEeS3+HO+HpB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\nrqXg7reG98aw0vh5DD3/fw39PE713dO57PO4sdT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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6a8b294d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-10.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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mvdBhNjdLnAp8P//TlSStRFaZYRWgpxycbc9ydsD2lZKG9Xl4c2D9/ONTgMlQ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\n/wHRgciEHbeeCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6a8948d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-09.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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HgDcAv0kdpEGOA46U+FHUl57tOUljgLskfQv4NzBfB+3NkrSo7QclLcYwG2Js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\nqibVEqg3ks0O7pHi+qFebF4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6a8755d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "# get filenames\n",
    "filenames = glob.glob('inflammation*.csv')\n",
    "\n",
    "# iterate through each filename, perhaps only a slice of them\n",
    "for filename in filenames[0:7]:\n",
    "    # so we know what we're looking at\n",
    "    print filename\n",
    "    \n",
    "    # load data\n",
    "    data = numpy.loadtxt(fname=filename, delimiter=',')\n",
    "    \n",
    "    # make a figure object, with 3 axes in 1 row, 3 columns\n",
    "    fig = plt.figure(figsize=(10,3))\n",
    "\n",
    "    ax1 = fig.add_subplot(1, 3, 1)\n",
    "    ax2 = fig.add_subplot(1, 3, 2)\n",
    "    ax3 = fig.add_subplot(1, 3, 3)\n",
    "\n",
    "    # plot means\n",
    "    ax1.set_ylabel('average')\n",
    "    ax1.plot(data.mean(axis=0))\n",
    "\n",
    "    # plot maxes\n",
    "    ax2.set_ylabel('max')\n",
    "    ax2.plot(data.max(axis=0))\n",
    "\n",
    "    # plot minses\n",
    "    ax3.set_ylabel('min')\n",
    "    ax3.plot(data.min(axis=0))\n",
    "\n",
    "    # this is useful for making the plot elements not overlap\n",
    "    fig.tight_layout()\n",
    "    \n",
    "    # this will tell matplotlib to draw the figure now, instead\n",
    "    # of letting the notebook wait to draw them all at once when the cell\n",
    "    # finishes executing\n",
    "    plt.show()\n",
    "       \n",
    "print \"done\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At least one new pattern emerged: all mins were zero for one dataset! It only gets weirder..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A short aside: showing off pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't have time to cover ``pandas`` in this workshop, but it is quickly becoming the de-facto tool for working with tabular data interactively, and it adds great power to ``numpy``, which it is built on top of. Among its features are the ability to cleanly handle datasets that have missing values (non-rectangular), joining datasets along non-integer indices, grouping datasets by column values, as well as many convenience methods for quickly generating useful visualizations. There's plenty more, but you'll have to explore it on your own for now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But here's a quick demo; say we want to examine how the distribution of inflammation values of patients changes with each passing day. We can do this easily by putting this data in a ``pandas.DataFrame``, then plotting directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at what the top of our data look like as a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   0   1   2   3   4   5   6   7   8   9  ...  30  31  32  33  34  35  36  37  \\\n",
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       "2   0   1   2   2   4   1   4   2   7   5 ...   2   4   3   7   4   5   3   0   \n",
       "3   0   1   1   2   2   1   5   3   5   6 ...   3   8   2   4   6   2   2   3   \n",
       "4   0   1   2   3   3   5   3   4   4   6 ...   9   3   7   1   6   1   3   0   \n",
       "\n",
       "   38  39  \n",
       "0   2   1  \n",
       "1   2   1  \n",
       "2   2   1  \n",
       "3   0   1  \n",
       "4   1   1  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll quickly plot a histogram for each day from 1 to 21 (the rise) to see how the distributions shift with day. Cyan distributions are closer to day 0; magenta distributions are closer to day 20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6a9038d50>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sndPlOwMw01hzgGrHmoMpcGOjiUJfdzLf3RXE41UcHY6kxqLvHIdyzik/3B/E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\nAAAASmgOAAAAAEiS/n8g4SWAgIApVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6a90383d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.iloc[:, 1:21].plot(kind='hist', legend=False, xlim=(0, 20), bins=range(0, 21, 1), colormap='cool', alpha=.2, histtype='stepfilled', figsize=(13,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot day 21 through day 39 (the fall) to see how the distributions shift with day. Cyan distributions are closer to day 21; magenta distributions are closer to day 39."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6a8b3a0d0>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4eBTPV8UrmaIX08OVvV2hs0jSgepIW8UqFjoHpsOtXC5mhgc2RrGI/MC+rZ3V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\nEs0BAAAAgBqaAwAAAACSpP8PXbaWC9r54McAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6a90380d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.iloc[:, 21:40].plot(kind='hist', legend=False, xlim=(0, 20), bins=range(0, 21, 1), colormap='cool', alpha=.2, histtype='stepfilled', figsize=(13,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do the same plots as kernel density estimates quite easily, though with the dataset being discrete and rather small this might not be so useful:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6a0193610>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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37RQ3ZmEBsMbd17p7B/Bj4LU9jrkQ+IW7Pwfg7puLK/aY7lkApSKJiIiISAwUEyz82sya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\n7Z6728/tpo3E6Cx2NyJxEWrQdpKIp4B9d99z7iVGN29gQW58+eVV6L2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VT9Djx7Zi12o+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\nyn8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6a9125990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mAIOMMR8ADGCBAC4lqa8odJHzSPIpcLZYDJtxQcfaQjVKRERERKSndZWGNBO4CJgI/CLt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\nXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6a91252d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.iloc[:, 1:21].plot(kind='kde', legend=False, xlim=(0, 20), colormap='cool', figsize=(13,4))\n",
    "df.iloc[:, 21:41].plot(kind='kde', legend=False, xlim=(0, 20), colormap='cool', figsize=(13,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not pretty enough? There are tools for that. ``seaborn`` is quite nice, and ``matplotlib`` supports style presets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn.apionly as sns\n",
    "plt.style.use('ggplot')\n",
    "sns.set_style('ticks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hoZx00knbH+vr68txxx2XBx98sIPJAAAAgG41Jc4weT3r169PkgwMDOzw+MDA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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6941c64d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FAAAA9gGT4gyT1/LMM89kaGgoZ5xxxo7Hent7c+qpp+axxx6rMRkAAADQqSbF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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa693ce7110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.iloc[:, 1:21].plot(kind='hist', legend=False, xlim=(0, 20), bins=range(0, 21, 1), colormap='cool', alpha=.2, histtype='stepfilled', figsize=(13,4))\n",
    "ax.grid(False)\n",
    "sns.despine(offset=10)\n",
    "ax = df.iloc[:, 21:40].plot(kind='hist', legend=False, xlim=(0, 20), bins=range(0, 21, 1), colormap='cool', alpha=.2, histtype='stepfilled', figsize=(13,4))\n",
    "ax.grid(False)\n",
    "sns.despine(offset=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conditionals: or, what do I do now?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could look at each set of figures for each dataset manually, since this is much easier with the loop we wrote. However, what if we had hundreds of datasets, instead of twelve? That approach wouldn't scale well. Can we write some code to help identify which datasets, and how many, show what flavor of weird behavior?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use conditionals. To demonstrate how these work, we'll use absurd toy examples. Say we have some number:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num = 37"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can ask whether that number is greater than 100 with a conditional like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not greater\n"
     ]
    }
   ],
   "source": [
    "if num > 100:\n",
    "    print 'greater'\n",
    "else:\n",
    "    print 'not greater'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could add some more sophisticatioin to the conditional by checking for equality:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not greater\n"
     ]
    }
   ],
   "source": [
    "if num > 100:\n",
    "    print 'greater'\n",
    "elif num == 100:\n",
    "    print 'equal!'\n",
    "else:\n",
    "    print 'not greater'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...and we could test it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "equal!\n"
     ]
    }
   ],
   "source": [
    "if num > 100:\n",
    "    print 'greater'\n",
    "elif num == 100:\n",
    "    print 'equal!'\n",
    "else:\n",
    "    print 'not greater'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conditionals can include boolean operators such as ``and``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one part is not true\n"
     ]
    }
   ],
   "source": [
    "if (1 > 0) and (-1 > 0):\n",
    "    print 'both parts are true'\n",
    "else:\n",
    "    print 'one part is not true'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A \"truth table\" for ``and``, given two boolean values ``s1`` and ``s2``, each either ``True`` or ``False`` gives:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| s1    | s2    | s1 and s2 |\n",
    "| ----- | ----- | --------- |\n",
    "| True  | True  | True      |\n",
    "| True  | False | False     |\n",
    "| False | True  | False     |\n",
    "| False | False | False     |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's also ``or``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "at least one part is true\n"
     ]
    }
   ],
   "source": [
    "if (1 > 0) or (-1 > 0):\n",
    "    print 'at least one part is true'\n",
    "else:\n",
    "    print 'no part is true'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| s1    | s2    | s1 or s2 |\n",
    "| ----- | ----- | --------- |\n",
    "| True  | True  | True      |\n",
    "| True  | False | True      |\n",
    "| False | True  | True      |\n",
    "| False | False | False     |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And ``not``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "both parts are true\n"
     ]
    }
   ],
   "source": [
    "if (1 > 0) and not (-1 > 0):\n",
    "    print 'both parts are true'\n",
    "else:\n",
    "    print 'one part is not true'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| s1    | not s1 |\n",
    "| ----- | ------ |\n",
    "| True  | False  |\n",
    "| False | True   |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's check the data, automatically!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can try and filter the datasets according to some criteria, each matching a case of weird behavior we saw in our plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-07.csv\n",
      "suspicious looking maxima!\n",
      "inflammation-04.csv\n",
      "suspicious looking maxima!\n",
      "inflammation-01.csv\n",
      "suspicious looking maxima!\n",
      "inflammation-05.csv\n",
      "suspicious looking maxima!\n",
      "inflammation-08.csv\n",
      "minima add up to zero!\n",
      "inflammation-10.csv\n",
      "suspicious looking maxima!\n",
      "inflammation-09.csv\n",
      "suspicious looking maxima!\n",
      "inflammation-06.csv\n",
      "suspicious looking maxima!\n",
      "inflammation-12.csv\n",
      "suspicious looking maxima!\n",
      "inflammation-03.csv\n",
      "minima add up to zero!\n",
      "inflammation-02.csv\n",
      "suspicious looking maxima!\n",
      "inflammation-11.csv\n",
      "minima add up to zero!\n"
     ]
    }
   ],
   "source": [
    "filenames = glob.glob('inflammation*.csv')\n",
    "\n",
    "# we'll count the number of each weird variety as we go\n",
    "count_susp_max = 0 \n",
    "count_susp_min = 0\n",
    "count_okay = 0\n",
    "\n",
    "for filename in filenames:\n",
    "    data = numpy.loadtxt(filename, delimiter=',')\n",
    "    print filename\n",
    "\n",
    "    # check for the cases where we see min inflammation as precisely 0 on day 0\n",
    "    # and max inflammation as precisely 20 on day 20\n",
    "    if data.min(axis=0)[0] == 0 and data.max(axis=0)[20] == 20:\n",
    "        print 'suspicious looking maxima!'\n",
    "        # increment count_susp_max\n",
    "        count_susp_max += 1\n",
    "    \n",
    "    # check for cases where min is zero across all days\n",
    "    elif data.min(axis=0).sum() == 0:\n",
    "        print 'minima add up to zero!'\n",
    "        # increment count_susp_min\n",
    "        count_susp_min += 1\n",
    "        \n",
    "    # keep track of datasets that look okay, at least so far\n",
    "    else:\n",
    "        print \"seems 'okay'!\"\n",
    "        count_okay += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What did we get for each count?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_susp_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_susp_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_okay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sadly, all the data shows fishy behavior. Someone has explaining to do..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions: reusable, modular pieces of code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we had more datasets on the way, we may want to run our tests against these, too. Instead of rewriting the same block of code each time, or copying and pasting it here or there, we should put it into a self-contained function. This way we can also improve it by working on only one piece of code, not a ton of duplicates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've already been using functions. One example is the python built-in ``len``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['marklar']"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(stuff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# run this to view the docstring\n",
    "len?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's write a simple function to show the general form; the function ``smaller`` tests whether the input value is smaller than ``10``, then returns either ``True`` or ``False`` depending:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# def is followed by the name of the new function, then\n",
    "# its arguments in parentheses\n",
    "def smaller(somenumber):\n",
    "    if somenumber < 10:    # mind the indentation levels!\n",
    "        out = True\n",
    "    else:\n",
    "        out = False\n",
    "    \n",
    "    return out             # return gives the value to be output by the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smaller(157)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smaller(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smaller(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact, for such a simple function it can be condensed greatly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def smaller(somenumber):        \n",
    "    return somenumber < 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Writing functions make it easier to write *better* code as well as reusable code. *Better* in that it is easier to test, and therefore more likely to actually do what you want it to do. Functions can (and should!) be written to do individual tasks (like bash tools) so they can be used as building blocks for larger programs. As an example, let's write a function that calculates the temperature in Kelvin from a temperature in Fahrenheit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fahr_to_kelvin(temp):\n",
    "    return ((temp - 32) * (5/9)) + 273.15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's do some quick sanity tests:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "273.15"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fahr_to_kelvin(32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The freezing point of water looks good!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "273.15"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fahr_to_kelvin(212)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ummm...the boiling point of water should be closer to 373.15 K. What's up? Although languages like python have debugger facilities (see [pdb](https://docs.python.org/2/library/pdb.html), the built-in debugger), we can debug our simple function by checking the individual pieces to make sure they calculate what we think they should."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180\n"
     ]
    }
   ],
   "source": [
    "print (212 - 32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That works. The next operation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print (212 - 32) * (5/9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ah! Something funny is happening here!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "180 * (5/9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a common \"gotcha\" that has to do with how computers store numbers in memory. Arithmetic operations between integers (such as 5 and 9) always yield integers in many programming languages, including python. So when two integers are divided, everything behind the decimal point is truncated (lopped off), leaving behind only what's in front of the decimal point. In this case, just ``0``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fix this, we can convert one of the values into a floating-point number. Division between a float and an integer yields a float:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5555555555555556"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(5)/9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also we could do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5555555555555556"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5.0/9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But note, that using ``float()`` works even for variables, so it's a bit more useful in general."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fahr_to_kelvin(temp):\n",
    "    return ((temp - 32) * float(5)/9) + 273.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "273.15"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fahr_to_kelvin(32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "373.15"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fahr_to_kelvin(212)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears to work!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about a Kelvin to Celsius converter?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def kelvin_to_celsius(temp):\n",
    "    return temp - 273.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kelvin_to_celsius(273.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kelvin_to_celsius(373.15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sanity checks work out."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now write a function to convert fahrenheit to celsius temperatures by *composing* our two converters together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fahrenheit_to_celsius(temp):\n",
    "    temp = fahr_to_kelvin(temp)\n",
    "    temp = kelvin_to_celsius(temp)\n",
    "    return temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can make this simpler:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fahrenheit_to_celsius(temp):\n",
    "    return kelvin_to_celsius(fahr_to_kelvin(temp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to apply the principle of encapsulation (using functions) to our fraud-detection codes. We'll write two functions: one that generates three plots for a dataset given the filename, and another that prints the category of weirdness (or lack thereof) the dataset displays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the first function, this works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_plots(filename):\n",
    "    # so we know what we're looking at\n",
    "    print filename\n",
    "    \n",
    "    # load data\n",
    "    data = numpy.loadtxt(fname=filename, delimiter=',')\n",
    "    \n",
    "    # make a figure object, with 3 axes in 1 row, 3 columns\n",
    "    fig = plt.figure(figsize=(10,3))\n",
    "\n",
    "    ax1 = fig.add_subplot(1, 3, 1)\n",
    "    ax2 = fig.add_subplot(1, 3, 2)\n",
    "    ax3 = fig.add_subplot(1, 3, 3)\n",
    "\n",
    "    # plot means\n",
    "    ax1.set_ylabel('average')\n",
    "    ax1.plot(data.mean(axis=0))\n",
    "\n",
    "    # plot maxes\n",
    "    ax2.set_ylabel('max')\n",
    "    ax2.plot(data.max(axis=0))\n",
    "\n",
    "    # plot minses\n",
    "    ax3.set_ylabel('min')\n",
    "    ax3.plot(data.min(axis=0))\n",
    "\n",
    "    # this is useful for making the plot elements not overlap\n",
    "    fig.tight_layout()\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-02.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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LYMuk5mZJ4kDl/cS7tWuXv0zjbOa4ebasgsaWVUwGziCehHc0cYPKb9qYqW8E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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa69353e3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_plots('inflammation-02.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And for the second:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def filter_data(filename):\n",
    "    \"\"\"Apply labeling criteria for fishy datasets.\n",
    "    \n",
    "    Prints the label to standard out\n",
    "    \n",
    "    :Arguments:\n",
    "        *filename*\n",
    "            filename for a comma-separated file giving inflammation data\n",
    "            \n",
    "    :Returns:\n",
    "        *label*\n",
    "            the flavor of fishiness of this data\n",
    "    \n",
    "    \"\"\"\n",
    "    data = numpy.loadtxt(filename, delimiter=',')\n",
    "    print filename\n",
    "\n",
    "    # check for the cases where we see min inflammation as precisely 0 on day 0\n",
    "    # and max inflammation as precisely 20 on day 20\n",
    "    if data.min(axis=0)[0] == 0 and data.max(axis=0)[20] == 20:\n",
    "        out = 'suspicious looking maxima!'\n",
    "    \n",
    "    # check for cases where min is zero across all days\n",
    "    elif data.min(axis=0).sum() == 0:\n",
    "        out =  'minima add up to zero!'\n",
    "        \n",
    "    # keep track of datasets that look okay, at least so far\n",
    "    else:\n",
    "        out =  \"seems 'okay'!\"\n",
    "    \n",
    "    print out\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we've added what looks like documentation in triple quotes (``\"\"\"``) at the beginning of the function definition. This allows us to quickly check what our function does in the same way we can for functions like ``numpy.loadtxt``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filter_data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to just printing output to ``stdout``, the function also returns the output. This allows us to make use of it, perhaps in more code we would want to write later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-01.csv\n",
      "suspicious looking maxima!\n"
     ]
    }
   ],
   "source": [
    "value = filter_data('inflammation-01.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima!\n"
     ]
    }
   ],
   "source": [
    "print value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets use our functions to examine many files at once, as before!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['inflammation-07.csv',\n",
       " 'inflammation-04.csv',\n",
       " 'inflammation-01.csv',\n",
       " 'inflammation-05.csv',\n",
       " 'inflammation-08.csv',\n",
       " 'inflammation-10.csv',\n",
       " 'inflammation-09.csv',\n",
       " 'inflammation-06.csv',\n",
       " 'inflammation-12.csv',\n",
       " 'inflammation-03.csv',\n",
       " 'inflammation-02.csv',\n",
       " 'inflammation-11.csv']"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filenames = glob.glob('inflammation-*.csv')\n",
    "filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-07.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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8Ce8w4gKVX3Qw0yDZGNgtdYgauRA4Oni3YpYXj6UO06POIO4n+0ngFOLg49g2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\ndPjWqfOYlq0JXJs6hKnEDOB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa69361c950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-07.csv\n",
      "suspicious looking maxima!\n",
      "inflammation-04.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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IX2tkcvxyceTzPcCWIrIo4ex2075NGXIAihnVI8AE65U6qveIyG9E5EoRuVpE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa693691e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-04.csv\n",
      "suspicious looking maxima!\n",
      "inflammation-01.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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Cn3V/I4EbnHPrERb21WU7sxOBcUma/Sd2kF6TpNmbwFHAON2VZp4eJBwDfYSI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6936a8410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-01.csv\n",
      "suspicious looking maxima!\n",
      "inflammation-05.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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O1Z7NBfr6EIdyJnM6cHCWF0+nztOvsrx4MXg3FTg+eHdBlhdWVzuCqn42dYY+\nsCZWBtqu2cRx3kBrZXC8sKpuDiAiGxDrjq+vNdVgeDfw20HfLqVNxwEXBO+Oszrt+ZpXbr/4GuJR\n7R8FfgV8s9OGVXWeiIz68yoik4HJIx5uYn3yNsBriB8wTL0uJu6o8lnih7qm2VNERu6mM1NVZ3bS\nqIj8TFU/Nrz2f5h5qrpeJ+0PGJs5bp/NHNPa4PhI4HwAVX0CsO13qvFu4puDaVGWF7OCd3cQZ0V/\nnDpPj/p3+fts4H9V9Q8i0kkpylwRWVFVHxCRlYAHR3tSOSCYOfwxEVkd2KODa3dV8G4i8U7ZNNsd\npX5ZXswL3k0Bzg3enZ3lxVOpM7XpeFWdU0O708vfdyOWMC5bwzX6XvBuIeJuFTY4bs+9wHLBuyUa\n2Ccr08rg+AYR2Y94vPFLp7ipqh2lOk5lp3038PXUWRroeOKiMBscj+4aEfkJccFTLiICdLKN0QXE\nHWtmlL+f33nEnvUJ4oeLC1IHGRRZXlwdvLuWOBC0cilAVf9SfrktsCvw2IinfKe7iRprReDRLC+e\nSB2kSbK8eCF4dxewOgN8CnIrg+O3EhdDfX7E42tUH2dgrAs8nOXF3amDNNCFwLHBu7dlefGn1GF6\n0J7A24h9+1vEtQKfbuWFInIO8C5gOREJwIHEWayfisjngDnAdjVkTi54txhwKLCjlTp13X7A74N3\np2R5MXJB6CD7CPA6VX0odZCGWhObNR6vodIKGxzPj6qu3oUcg2YLbJeKcSk/1X4T2AuwwfHLHct/\nzzZNIJY7LD/WC1V1h/l8a4tKkvW2LwI3ZXlxZeoggybLi1uCd78k3kmbOtbzB4gCtpXg+Fm98fgN\nfN1xKyfkLU2cPXojcdboCGBvVX18gS80C7IFcQcQMz5nAAcF71bL8uKu1GF6jM02tSl4tzQwDdgq\ndZYBdjBwffDuW1le2FG/0QnAFSLyO+LptBAX5B2aMFOT2OB4/AZ+cNzKPscnED+9rgA8Tdx+7OQ6\nQ/Wz4N1axENUZiaO0lhZXvwb+D7wlcRRepHNNrVvb+DSLC9uSB1kUGV5cQ9wGmAH/fzHIcS+vAyw\nXPnrtUkTNYsdADJ+dzDgp+S1UnO8oaruJCLvVdXHRWRHbG/etpUr4fcgzlDtk+XFvxJHarpvAUXw\n7tBysGwim21qQ/BueeJiMJc6i2E6oMG7b2R5oanD9IAlVXWb1CEazGaOx89mjlt4zsgtjRams9Xv\niMgyInIXLxY0AAAdRklEQVSuiNwiIjeLyFs7aa/XlbPFVwAfAN6S5cVJiSM1XpYXc4j791qN4n+z\n2ab27A/8MMuLO1MHGXTlhMGxwOGps/SIm0Rk/dQhGswW5I3fncAag3y8eyszx78XkaOBJUXkPcRZ\nlss7vO43gV+p6kdFZGFiqUZfCt7tQJzNOxT4TpYXHX2wMP9lCnBD8O70LC9uTx2mR9hsU4vKUyo/\nSVxPYXrDCcBtwbs3Z3nx59RhElsZKETkTuCZ8jE7BKQFwbuliMdv3586SxNlefHv4N0TxHLaB1Ln\nSaGVmeOvE/c3fpS4GO964GvjvaCIvArYVFVPB1DV51W1L2skg3eLAP8HbJPlxbdsYFytcuHO0cS9\nj18meLdQ8O6Y4N3bu5ssKZttat2hwAlZXox6sInpviwvniT+f5k+yLNWpanAlsD/I05K7QbsnjRR\nc6wB3GnvuR25gwEurWhl5njzsl6xqprFNYB/iMgZwPrAX4A9VPXJitrvJR8A7rAZkFodD3wuePe+\nLC8uGvG96cSZwbcF7zYdkP1rbbapBcG79Ym7xuySOot5mdOJiyS3BC5JnCWZTo+hHnBWb9y52cTS\nlD+mDpJCKzPHh4jIHBE5UERWqeCaCwNvAr6rqm8CniDeHu9Hu2KnGdUqy4tnibMpxwfvFh96PHi3\nJ/GEqQ2INbeTkwTsPpttas1RwBG2mLP3ZHnxPPFgkOnlaaLGtMvqjTs30IvyWjkE5K0i8kZgJ+BP\nInI9cKqqjvcY2XuAe1R1aDb1XEYMjkVkMi8fzCwzzuslEbxbG3gDcF7qLP0uy4uLg3d/A74KHBm8\n+zix9GeTLC8eDN4dSTxOudNa+brtKSKPjHhsZjszSDbbNLbg3buIdcYfSp3FzNfPgX2Je+vbUfFt\nEpEMOIt4+M884GRVPSFtqq6ybdw6N5vBmVR6mVbKKlDVW4Cvi8i5xC20fgwsvuBXzbetB0QkiMha\nqnob8dbmTSOeM5MR+wCLyOrErdCa4svAKeXMpqnfV4Frg3f3AscAWww7IORHxENDNsny4g/JEo7t\neFWdkzpEPyvrWGcAB2R58cxYzzdpZHkxL3g3BTgpePcL+3e0bc8Be6nqdSLyCuAvInJp+V4+CCYB\nl6YO0XCzgZ1Th0hlzFtWIrKCiOwtIjcQD174CdBpecVuwNnlLPR6wJEdttdTyhO3PoEdltI1WV7M\nBr5L/DvfbviBDllePEe8jX5Aonimd3wQWIL4gcn0sCwvfkt8g/586ixNo6oPqOp15dePA7cAr0ub\nqqts5rhztiBvDLcBvwB2UdVKZt1U9XrgzVW01aM+BVxenvpkuudw4JwsL0abHTkTOCB4t3GWF9d2\nOZfpAcG7hYkfxPe2VeyNMQXIg3dnZXnxeOowTVTedd0QuCZxlLYF7xYlfphtxwRgdeJevWb87gOW\nDd4NnY7cjqebfmeulcHxtsSVw4eKyELARGB1VV211mQNVd62/TKwZ+osg6a89TrqbcMsL54N3s0g\nHvqwbVeDmV7xGeBB4Nepg5jWZHnx1+DdFcR/T+1wkDaVJRXnEneEenzY45NpxrqeK4G1af/gsZvL\nbQHNOGV58WLw7k/ECdJ2LATcS1xz1WtaXtfTyuD4ROKs20eB7xEXsRzbacI+timwCPC71EHMy5wO\n7Be82yDLi+tShzHdE7xbAjgY+NiAbOnXT/YHrg7enZjlxUOpwzSFiCxCXNj4w5EL6JuwrqecaFob\nWCXLi748C6HXZXmxebuvKc93eDx4t2gPrhVoeV1PK9vkzFPVGcTjj28lDpI/MP5sfe/LwHftDbj3\nZHnxFPFQlv1TZzFdtytQZHlxdeogpj3l6Zc/BaalztIUIjIBOA24WVVHPSSpAZYDnrWBcbOUa3zu\nBRpdXdDK4HhoH9A7gP9V1aeBV9cXqbmCdysD7yFuoWN600nAVsE7+xkeEMG7ZYgnfdrgqrkOBT4b\nvGv0G24XbUJc+7KZiMwqf22dOlSbbK/i5mr8HsmtlFVcIyI/Ia70z0VEaL/+Z1AcDZyY5cXImhbT\nI7K8eKKsYdwaOCd1HtMVXwcunM9CTdMAWV48ELw7ETiEuOe+WQBVvYrWJr96mZ1y11xDp+s1Viud\nZy/guHJP4j2JK0E/UWuqBgrebU78tH5E6ixmTBcB708dwtQvePc64EvEemPTbMcAPni3Tuogpits\ncNxcjZ85HnNwrKovqurV5de5qu6lqlp/tOYot5v5DrBnlhdPpM5jxnQRsHW5cMD0twOB07O8CKmD\nmM6UtaczsAmIQWF7FTdX4/dIbvptl16xF/GT0i9TBzFjy/LiXuIemG9PncXUJ3i3FnEB8VGps5jK\nfAd4U/DO+m7/s5nj5ur/mWOzYOUCkX2A3WyHikax0or+dzjwDdv+q39kefE0cBAwvdzqy/QvW5DX\nXLOBSU3uozY47tzxwAnl8cWmOS7EBsd9K3jngHcA30ydxVTuLOA1wDapg5h6BO8WB5YH7JTZBsry\n4mHixg2vSZ1lvGxw3IHg3XuBdYm7VJhm+SuwdHnr3fSfo4BDbQ1A/8ny4gXitnxHBe8mps5jarEa\ncHeWF8+nDmLGrdF1xzY4HqfydsExxEV47Z47bhLL8uJFIAfelzqLqVbwbgvim+tpqbOY2lxA3IPf\ndk7qT1Zv3HyNrju2wfH4bQpMBH6VOogZNyut6DPBu4WA6cD+5UlNpg+V6zumAIcG7xZLncdUzuqN\nm88GxwPqS8D3bBFeo10GuPIENdMfPlr+fm7SFKZ2WV5cCdxM/LfY9BebOW4+GxwPmuDd8sB7gTNT\nZzHjl+XFk8DviaflmYYr960+AphSls2Y/jcVmBa8Wzp1EFMpGxw3X6NPyUs2OBaRieV57xemytCB\nnYFf2DHRfcFKK/rH54A5WV5cljqI6Y4sL24ALgH2Tp3FVMoOAGk+W5A3TnsQb4k1qiyhrGn8IvC9\n1FlMJYZOy1s4dRAzfsG7JYEDiHWoZrAcCOxW3tEzDVcudp9EPKjJNFcAVixPEG6cJINjEVmFuEfl\nqUDTNol+D/BQlhd/Th3EdC7Li3uAu7HT8ppuD+CqLC/+kjqI6a4sL+4EfgjsnzqLqcTywNPlceGm\nocpt+O4h7hzUOKlmjo8jnirXxLrAXbBZ435zDrCf7ZnaTMG7ZYGvYoOjQXYE8MngXWNv45qXWL1x\n/2jsoryuD45F5H3Ag6o6ix6eNQ7e7RS8W23EY6sST906J00qU5NvAIsTb8ub5pkK/DzLi7+nDmLS\nyPLiQeBbwKGps5iOWb1x/7iDhi7KS1Fn+XZgWxHZhjggeaWInKWqOw49QUQmA5NHvK5r220F79Ym\nzm4/H7z7LjCjPGnrC8DZdupWf8ny4vng3fbAX4J312R58etEUfYUkZGLPGeq6swUYZogeJcRF8iu\nmzqLSe5Y4O/Bu/WzvLg+dRgzbjZz3D8aO3Pc9cGxqk4jHv2JiLwL+NrwgXH5nJnAzOGPicjqxLrC\nbvgi8G3gJGAGcGvwbhpxNfyWXcpguijLiweCdx8Hzg3evSXLizkJYhyvqimuOyYRmQM8BrwAPKeq\nGycN9B8HAydleXFf6iAmrSwv/h28O5J4dPg2qfOYcVsTuDJ1CFOJ2cBbU4cYj17Y57indqsI3i0F\nfAo4JcuLkOXFJ4AdgL0AzfLipqQBTW3KQwVmEAfIi6fO02PmAZNVdcNeGRiXd3jeDxydOovpGScB\nbwzevSt1EDNuNnPcPxo7c5x0cKyqV6jqtikzjGJ74I9ZXtw19ECWF1cBjnjwh+lvxxG3EDohdZAe\n1GtrBI4AjrH9xs2QLC+eIa4dmFFuCWaaxwbH/WM2MKmJfbEXZo57zS7AiSMfzPLixSwvnk6Qx3RR\neRz4zsDk4N12qfP0kHnAZSJSiMgXUocJ3r2N+IH126mzmJ7zI2AJ4IOpg5j2lHfsliNuAWYarpy4\neI74/7RRbHA8TPDOAa8FLk6dxaST5cW/iaU13w7erZI6T4/YRFU3JN492VVENk0VpJyFOAo4OMuL\np1LlML2pPDp8KnCkHe7TOGsAd2d58ULqIKYyjSytsH84/tuXiIt7rGMOuCwvrg3efQv4fvBuq/IN\nd2Cp6v3l7/8QkfOAjRm2aKbLO8xsDawAnFlT+6b5fg3sC+wInF7TNWx3mepZSUX/GRocX5M6SDts\ncFwK3i0DfAR4Q+ospmccRZwp3R04PnGWZERkSWCiqv5bRJYCtgIOGf6cbu0wUx7ffhQwrTyByZiX\nyfJiXvBuX+CnwbtzarrD0LO7yzSYDY77TyNnjq2s4j8+DVyc5cXc1EFMbygHX58mnp43yPvorgBc\nKSLXET/9X6SqlyTKsgPwFHB+ouubhsjy4mrgL8CuqbOYltkBIP3nDho4OLaZY16qYdyl/GXMS7K8\nuKOcgTo7ePfmcjX8QFHVO4ENUucI3i0KHAbsVC6cNGYs04Argnen2q4mjTAJ+H3qEKZSs4lreBrF\nZo6jd5a/W6c0ozkDuB34TvBuYuowA+yLwK1ZXlyROohphiwvbgEuBL6eOks3icjpIjJXRG5MnaVN\na2JlFf3GyiqaKHi3GLGe9BibjTKjKX8udiL+w/0TOyCk+4J3SwP7UZ6uaUwbDga+FLx7XeogXXQG\nceFqY5R3cNfABsf95h5g+XKs1RgDPzgGphM74/cT5zA9LMuLR4lvNi8AlwTvXp040qD5KvDbLC+u\nSx3ENEuWF4G4Y8WBqbN0i6peCTycOkebVgCeKLfSNH2iXLsTgNVSZ2nHQA+Og3fbAB8GvmCzxmYs\nZb3xDsCfgauCd6smjjQQgnfLE3cMOSB1FtNYRwEfDd6tlTqImS/bqaJ/Na60YmAX5AXvVgJOA7bP\n8uJfqfOYZij3O947eHcP8Ifg3UZZXjyYOlef2w/4UZYX9sZpxiXLi4eCd98ADgfs5MsaBe9eCVxL\nPKWwHUsS96c2/UeJi9ofb/N1zwLvSLGL2EAOjsu9Us8ETs7ywhbhmbZleXFc8O7txFKLs1Ln6VfB\nuzWIK53XTp3FNN43gb8H71yWF0XqMCnVfGjPWsQjg98zjtfaREN/+jpw7Dhedw6wDlDV4Ljlg3sG\ncnAM7E38lHpY6iCm0S4nvsHY4Lg+hwLftv3HTaeyvHgieHcoscRiy9R5Uqr50J5JgGZ5cVcFbZk+\nkOXF00DbPw/Bu1uIP0+/qyhKywf3DFzNcfBuRWAq8Ek7Yct06HJgs9Qh+lXwbj3iaXzjmXEwZjSn\nAasF77ZIHaROInIO8EdgLREJIrJTFy9vtcOmKslqlQdx5vijQG6fak0FbgWWCN6tnuXFnNRh+tBR\nwJFZXjyWOojpD1lePBe82x+YHrzbuFxD0HdUdYeEl1+TeDKhMZ2aDXwwxYUHbuYY2B74SeoQpvnK\nHU5m8vLaPdOh4N07iXXG30udxfSdc8vfP5o0Rf+ymWNTlWQzx0kGxyKSicjlInKTiPxNRHbvxnWD\nd6sQ33Av6cb1zECYiZVWVKo8DGA6cOAgHtdt6lXOFk8BjgjeLZI6Tx+ywbGpymANjokrWfdS1XWA\ntwK7isgbu3DdjwG/zPLi2S5cywyGy4HJ5YDOVGNb4BXAj1IHMf0py4vLgDnAzomj9JXyw8brGMfi\nK2NG8U9gkeBdVTuptCzJ4FhVH1DV68qvHwduIXaoullJhanabcAixGNPTYeCdxOBI4GpWV68kDqP\n6WtTgQODd0ulDtJHVgPuy/LiudRBTPOVpYtJZo+T1xyX28dsCFxT53WCd6sTFwpUtSWIMVZ3XL0d\ngX8Bv0odxPS3cq/jPxBPXzTVsJIKU7Ukg+Oku1WIyCuIiyP2KGeQhx6fTPUblG8H/MI+0ZoaDG3p\ndnqH7bS8QXk/Ct4tDhwCfNyOczddsj/xpMuT7KTUStjg2FRtsAbHIrII8HPgh6p6/vDv1bRB+fbA\nPh283pj5mQkcFLyb0OGgruUNyvvUrsCsLC/+mDqIGQxZXtwWvPs5scTC3h86Nwm4I3UI01dmA+t1\n+6KpdquYQNyM/WZVPb7u6wXv/gdYGbii7muZgXR7+fuaSVM0WPDuVcC+wLTUWczAORTYOXiXpQ7S\nB2zm2FTtDgao5ngT4FPAZiIyq/y1dY3X2x441xb4mDoMqzu2Ld3Gbx/i4Tw3pQ5iBkuWF/cBJwEH\nJ47SD9bEBsemWrNJMPGUpKxCVa+iuwPz7YEvd/F6ZvBcDmwOnJI6SNME71YCdiEuzDUmhaOB24J3\na2d5cXPqME1UbmdpM8emancBqwTvFs7y4vluXTT5bhV1C96tDbyauCrZmLpcDmxm+x2PywHA97O8\nuDt1EDOYsrx4BDgGOCJ1lgZbFnjRFjaaKpXnUjwAdLXsqe8Hx8BngZ+VpyIZU5c7geeBtVIHaZLg\n3euJO8kcmTqLGXjfBlzw7m2pgzSUzRqbunR9x4q+HhwH77YDPgEclzqL6W9l3fHl2H7H7TocOC7L\ni4dSBzGDLcuLp4h1x9PtDtC4WL2xqcsddLnuuG8Hx8G7zYgzAd5u15oumQlskzpEUwTvNgLeCdS+\nY40xLToTWB54b+ogDWQzx6YuNnNcheDd+sRjorfP8uL61HnMwDgPeH3wbkrqIA1xJHBYlhdPpA5i\nDEC54GcacFTwri/fH2tkg2NTFxscd6o8JjoHvpLlxeWJ45gBUi7q2Qr4f8G7L6XO08uCd5sTb5Od\nmjqLMSOcDzwJ7JA6SMPYASCmLjY47kR5kMBvgBlZXvw0dR4zeLK8uBfYEtg/eGdvrqMo6zmnA/vb\nce6m15TrB6YAhwXvFk2dp0Fs5tjUxQbHHZoGXJ3lxbdSBzGDK8uLO4CtgeOCdz51nh70EeIe6/YB\n1vSkLC+uAG4Fvpg6SxOUHyJWAkLqLKYvPQQsHLx7dbcu2DeD4+DdqsDngf1SZzEmy4u/AR8Azgje\nbZE6T68I3i1M3Et2im2vaHrcNGC/4N3SqYM0wGrAvXYnyNShvJvT1WOk+2ZwDBwGfLe8rW1Mclle\nXEOcJf1R8O5DqfP0iJ2Be4BLUwcxZkGyvLgO+C3w1dRZGsDqjU3dulpa0ReD4+DdBsB7iCccGdMz\nsry4krgt1HeDd59JnSel4N2SwEHA1HImwJhedwCwe/Bu+dRBepzVG5u62eB4HGYQt4R6LHUQY0bK\n8uIvwGbEBT67p86T0O7An7K8uDZ1EGNakeXFbOJdyRVTZ+lxdgCIqdtsungQSOMHx8G7rYA1gJNT\nZzFmfrK8uBXYFNgteLdv6jzdFrxbFtgbWxNgGibLi+OzvLghdY4eZzPHpm42c9yq4N1E4Gji4h5b\nCGB6WpYXdwHvAP6VOksC+wLnZXmhqYMYYypng2NTt64uyFu4WxeqySeBJ4gnkxnT87K8mAuckjpH\nN33oVYusSNxJZr3UWYwx1Sr3LbcFeaZudwMrB+8W6cZkaJLBsYhsDRwPTAROVdUZ7bYRvNuYuADv\nA7a4x5h6ddJn3/PKRfYATrGdZIzpjireY9uwHPB8eUKoMbXI8uLZ4N39wKp04YNY18sqRGQi8G3i\nIQlrAzuIyBvbaSN49zbgIuBzWV5cXX1KY8yQTvvskgtN2Iq4aNYYU7Mq3mPbZCUVplu6VnecouZ4\nY+B2VZ2jqs8BPyYeltCSfZZf/M3AL4Eds7y4qKaMxpj/6KjPPvT8iydnefFwbemMMcN11F/HwQbH\nplv6enC8Mv99xOQ95WMtWXeJid8DPpHlxW+qDmaMGVVHffa7/3zm+1UHMsbMV0f9dRys3th0S9cW\n5aWoOR5vffBEgAseefbgcx997nZEVq8ukjFJrVL+PjFpivnrqM/+/ZkXXysiz1SYx5iU+rq/Hrri\n4j+8cosNn271RYtMmCBzn3/xO1vYe7Kp2WErLfHYqosutNucLTbcaEHPe+JFbtjj3idPGPZQ2302\nxeD4XiAb9ueM+Mn2JSIyGZg84nWrApz76HMnACdgTP/ZT0TuHvHYTFWdmSLMMB31WeDKuoIZk1Bf\n9tcDH3h6k3Fc86jylzG1OeD+p4a+XGmMp74b2GuUx1vusxPmzevuRg8isjCgxPD3AdcCO6jqLWO8\nbjHgROAI4IUaou1JXN1bB2u7O+02te2JxMMxdlHVnpthHcA+28SfIWu7e21bfx2fpv1/bnLbTcxc\nZ9tt99muzxyr6vMi8hXgYmLg08bqtOXrnhGRu1W1ltomEXlEVedY2/W33cTMXWj77l58o4XB67MN\n/hmytrvUtvXX9jXx/3NT225i5i603VafTbLPsar+Gvh1imsbY9pnfdaY5rD+akxnGn18tDHGGGOM\nMVWywbExxhhjjDGlpg2OZ1rbfdF2Xe1a271nZgPbrqtda7t/2q6r3dRmWtt90XZd7Q5M213frcIY\nY4wxxphe1bSZY2OMMcYYY2pjg2NjjDHGGGNKSbZyGw8R2Zq4OfRE4FRVnVFh23OAx4gbnz+nqhuP\ns53TAQ88qKrrlo8tC/wEWA2YA2ynqo9U1PbBwOeBf5RPm6qqvxlH2xlwFrA88ejRk1X1hCqyL6Dt\njrOLyOLAFcBiwKLAL1V1akW559d2x7nL9icCBXCPqr6/qp+TXtGE/lq21bg+a/21rbY7zj3sGtZn\nx9/2HOw91vrs2O12nHnYNTrqr42YOS7/I78NbA2sDewgIm+s8BLzgMmqumEnb7TAGcSMw00BLlXV\ntYDfln+uqu15wDfK3BuO94cIeA7YS1XXAd4K7Fr+/VaRfX5td5xdVZ8GNlPVDYD1gM1E5B1V5F5A\n21X9ne8B3Fy2RxWZe0WD+is0s89af2297ar6K1if7YS9x1qfbaXdnumvjRgcAxsDt6vqHFV9Dvgx\n8IGKrzGh0wZU9Urg4REPbwucWX59JvDBCtuGanI/oKrXlV8/DtwCrEwF2RfQNlST/cnyy0WJMx4P\nU93f+WhtQ4e5RWQVYBvg1GFtVZK5RzSiv0Iz+6z117bahgpyW5+thL3HYn12jHahR/prUwbHKwNh\n2J/v4T//86swD7hMRAoR+UKF7QKsoKpzy6/nAitU3P5uInK9iJwmIst02piIrA5sCFxDxdmHtX11\n+VDH2UVkIRG5rsx3uareVFXu+bRdRe7jgH2AF4c9VvfPSTc1ub9Cg/qs9dcx264kN9ZnO2XvsSXr\nswtst5LMVNBfmzI4rnu/uU1UdUPgvcRbEpvWcRFVnUe1/y0nAmsAGwD3A8d20piIvAL4ObCHqv57\n+Pc6zV62fW7Z9uNUlF1VXyxvzawCvFNENqsq9yhtT+40t4i8j1jTNov5fEKu4eek2/qiv0Jv91nr\nr2O2PbmK3NZnK2HvsVifHaPdyVVkrqq/NmVwfC+QDftzRvxkWwlVvb/8/R/AecRbTFWZKyIrAojI\nSsCDVTWsqg+q6rzyf/SpdJBbRBYhdtofqOr55cOVZB/W9g+H2q4ye9neo0AObFRV7lHadhXkfjuw\nrYjcCZwDbC4iP6g6c2JN7q/QgD5r/bWltqvor2B9tmP2Hmt9toV2e6q/NmVwXAD/IyKri8iiwPbA\nBVU0LCJLisjS5ddLAVsBN1bRdukC4DPl158Bzl/Ac9tS/g8e8iHGmVtEJgCnATer6vHDvtVx9vm1\nXUV2EVlu6LaLiCwBbAnMqij3qG0Pda7x5lbVaaqaqeoawMeB36nqp6vI3EOa3F+hx/us9dfW2+60\nv4L12U7Ze6z12Vbb7aX+2pgT8kTkvfxnm5nTVPWoitpdg/hJFuLWdmePt20ROQd4F7AcsablQOCX\nwE+BVelsm5mRbR8ETCbefpgH3Al8cVhNTTttvwP4PXAD/7nVMBW4ttPs82l7GrBDp9lFZF1iYf1C\n5a8fqOoxErds6TT3/No+q9Pcw67xLmBvVd22isy9pAn9tWyvcX3W+mtbbVfWX8vrWJ9tv117j7U+\n22q7PdNfGzM4NsYYY4wxpm5NKaswxhhjjDGmdjY4NsYYY4wxpmSDY2OMMcYYY0o2ODbGGGOMMaZk\ng2NjjDHGGGNKNjg2xhhjjDGmZINjY4wxxhhjSjY4NsYYY4wxpvT/AUlNqvtwdRbsAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa693713810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-05.csv\n",
      "suspicious looking maxima!\n",
      "inflammation-08.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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rvx4m7jneBOwVVMrTMmax17NVwITIfITiTqpPALsElfIewHbcybWRkz24zPHN\nkeeZdY5L1dp4UCmH2eONGEZnWcjk6gtRoqXMng0q5XCVvLUp7fOMy63AHv4uUFEXueG42oRbYroo\nW8VCYL0/1mPAvJTvuYXAhlK1dn1QKd8NvB3zHhuG0WPUzRyLyH0icq//fZ+I3CMivxaRr4tInkL4\nw8ykzLGf0PIU7sSZRp7Z5NEMbCEnVX9Si96O3VKnLBW497pnZCZ63FaxCZdxmwY7S93NZ/JEokHz\nHRt9QFApz8R9FtNsFXsxWSDWs1bUtUn48RR+3ovMHIMbyyPkE8fhHZyZ/nl8jMJUcRxOxsMvDPIk\nroxlnJ3tgI/jvMczE9oZhmF0jTy2iu8APwL+EHgTUAVquIxgKyXdBoagUt49ZVfccwwZ1govGutV\nq4DJ4rhIr2JorchjqcCXrNvKhG94kjhOuDiYC2yLrbJlvmOjGyzAjbs8tgqoX7Ei70VrKDKLHMfQ\ngDj2oj06fyBv5nhDxv5ou/X+ONcDd2PeY8Mweow84vgYVX2Xqt6iqqtU9X3A4ar6WeB5BcfX83hB\n+9ugUk46ESTNDs+alDcTGI+VTEoiKrCLzDiF4jjPZLyQ6EkxnjmGybWOozWOQyxzbHSDhcC9uIxp\n0sVuXCDWq1iRd1yG46UwW4XnUVyN+bwLLkXHcW5bRcb+aLuoiP4Ylj02DKPHyCOO9xSRnZk8/zhc\ntGNaIVH1F3Nx2aZSyr74yShrUl4eSwVMFPXPs0RtK4QZ6lyZY0/0pBifkBftEyZPxguxzHEBiMgU\nu4CIxGtODzP74sTdBpKtFUniuCVbhSeaOS7SVrERJ47z2CqgcXEctUsk7U9sV6rWfgr8BjgpZ1yG\nYRiFk0ccXwTcJCIfF5FPAjcCF4jIe4E7Co2uP9jP/16SsK/RzHEeS0XYx96+/bYcXuBmachW4dkM\nzPcT+XZl6gk/Lo7jXkbLHBfDL0XkVeETEXkf8MMuxtNrhBnNDSRbK5JsFVmZ47yZ4E10zlaxhOLE\nccO2igjXAEfkjMswDKNw8lSrOFtEbgGOx1UteI+qXisiRwH/WXB8/UB4gswrjrMyx3kqVYA70T2f\n4m/FhuJ4B43fjt0H2Jgg3C1z3B1OBr4mIucDL8f9j5Z3N6SeIhR3e9O+zHGv2Sp2oVhxvDpjf7Td\nz2PbNgCH54yrbxGR2cAf4z5j4V3XcW9RNAyjh8izQh64yXd34Ab0LiLyOlW9uriw+oqszHHShLys\nzHEjtor0T0GoAAAgAElEQVQynZnhvhBn22gkczyPZL9x2GeWOLbMcQGo6jX+bs//AA8DR6nqQ10O\nq5cIbRV70XlbRXgXKM9do2YJSyMW5TluylbhWU96lZBB4lLcZ+Y2oKi7fYZhtIG64lhEPgGc6Z/u\nAHbDVaswcezYDydQG7FVZGWOG7VVFJ05fiGuLFNecRwuBNKsOLbMcQGIyDm4qgArgRcBvxCRU1X1\n292NrGdYCPwS95nspK1is+/nKV/tpSiiq1DmoZ443grMCirlXX21mVZsFWlWlkHjhcChqrqjbkvD\nMLpKnszx23FVKT6LWxlvFHdyNRyLcSfVAxL2pU3IOyilr0ZsFXtTvE8xtFU8jpvJn4eorSJJHG9k\nItu+N05kRHmM5AsNozXKwDK/fPv3RORa4Ku45eAzEZGLgAqwXlWP8Nv2Bi7DfTesAU5U1bwXUL1I\no7aKDbgFfWaUqrXtCe0bsVUcQbHjGNosjkvV2nORBXvCO0zxahVJ34lxEQ3pkyAHjYCCJrGLyArg\nXNwE7QtU9ZyENp8DXo9LwLxDVW/J+1rDGDbyTMhbr6oP4vxkL1HVrwCvLDasvmI/XCa9HZnjhqpV\n0BlbRbPVKixz3Fsc54UxAKp6E3BkztdeDKyIbTsDuFpVD8FN7DujLVF2j9BWMeUWvy/XOI/Iha7P\n8m5g4kIvTiO2iiUUO45hwlbRrsxxvE1dW4X/Oy5k6vfCemBhuDjQAHMb8CMR+YiIfMD//E2rnYrI\ndOA83Bg9DDhJRA6NtTkeOFhVlwLvBr6Q97WGMYzkEcfPiMgLgLuAY0RkBtm3E4eNLHHc0CIg5LdV\nhH10akLefBr3Ki4geQloq3PcHV4hIleIyA9F5FoR+Qnw6zwvVNXrmFpVZCVwiX98CW6BoH4mq1rF\nHJztIZ4hzqp13IitYknOtq0QjrOGxnHShUFCm9m4jOi2+L5Y+/m46jqTSk+WqrXwdbNzxtavzAPu\nAQ7G3X09gvZU6VgO3K2qa1R1O87bfEKszc7x6i+M54vIfjlfaxhDRx5bxT8BXwLeCHwSeAfwvQJj\n6jcW4yYrzgwq5T1L1drjsHNp5Dk4S0KUeqXc8mSQNvk+9szZvllCcbyB5jLHv0nYX6+Um2WOi+EC\n4MvAm4H/AP4A+EwL/S1S1XX+8TpgUWvhdZ0sW0Va5jRrUl4jtor9fV9F0qytYjbwdIp1JGyzEFgf\nq0yTJI6TLBUhobWi6IuErqGq7yio6wNwlo2QtbiKNPXaHID77NV7rWEMHXnE8a6q+hoAEXkpsBRY\nVWhU/cV+ON9s+GVzp98+G3giYZJNy4uAlKq17UGl/AROmBdd/qlZW8VTwA0ZfYJVq+gk474s4z64\nz+gfAd8H/q3VjlV1XESmzL4XkVHcHIUoSZO0uopfnW0ObmwmVU5I+/xnTcprxFaRVA+83WwBLm/g\nOJtxF+B7kT72w7GeVIEiSRwntQsJ/+735Yyvk5wuIvG/wZiqjuV5sYj8t6r+sYjclrB7XFVf3GJ8\neStfNG1byRrLX3ne7PuCSrnZrg2jKG4vVWvx+XG5x3IecfyPuC9VVHUb8KtmohxE/Ap1e+G+8Nfi\nbo+G4jjJbwztWQQk7KdE/tukDVOq1p4JKuWn/HEaFcc7MM9xLxHewbgXeJGq/lRE0j6HeVgnIvup\n6sMispipFQjwXzhj0W0iMgKc1sJxi2ABrib3c0GlnGSraCZznPcuUNhvoRlTn9X9gwZeEo7jrAvj\nsM3T5BPHSZUqQnq5YsW5qrqmhdef7X+/F3cxlZYcaZYHmLxCawl3Pspqs8S3mZHjtZlj+c9+u+2g\nFv8+htEpco/lPOL4VhH5CHAdkS9wVf1lc7ENFPsCj5SqtWeDSjkUxyFJfmNwImV2pARSlEY8xKE4\nfqDBmBtlI64iQaN1jsdJFsebcEtfT8Myx53kJhG5DPh7oCoiAjzXQn9X4CrZnON/X956iF0jert/\nC84itXupWnvKb8sSx6+Ib2xwWfeOiOMmaEQcP8dU0dto5nhgK1ao6i/8w5XAe5h6Xvh/LR6iBiz1\nYvVB4C1MXY77CuBU4FIRORrYrKrrRGRjjtcaxtCRZ0Le0cC7cGb+b0V+DGepCCsAPMDk0kWJmeNS\ntfYc6TVA81arACcqD6QzE3nGye9VzKxzXKrWngSe9fvH/fMoW4C5QzBzvdOcjrNQ7Ap8HjcR6K15\nXigiXwd+5h5KICIn47JhrxORu4DXMJEd60fCShVhhjUu1Bq1VcwGtuZc1n0bbjwUbatolEbEcZLo\nfQyY5+dehGR5jodhIZA3A/ur6sLoT6ud+rrJpwJX4apKXaaqd4jIKSJyim/zfeBeEbkbOB/466zX\nthqTYfQ7eZaPHulAHP1K6DcGdysq6m9JqnEcEloL4uKxUVvFy+hMCajHvajPQ5j53YPkahVhnwcz\nNWscWjl2+NcXuWLYsPEZJmetpuFuk9a9la2qaZmk49oSWfeJi7bwFn84UalRW0XuEoulam08qJQ3\n03uZ4ydwt9wXkS2OD8aN1Umit1St7fDzIuYw8ZnL8hT3sq2iXSgF2eBU9Urgyti282PPT837WsMY\ndvKskLcnLit0KHAi8CngA6raa1/m3WAxE5njtUyuBZvmOYaJahNx8voUwQnLuRR/Ut1IfktF9KS4\nK+ni9lFSxLEn9B2bOG4fYdYq7YJlmIl7YeNZzCxxnJQ5bnRxnp4TxxHRnmWpCjPH47isY9r+8Htw\nX+DnKX1tAA5vOuD+4HPAj0XkR7g5GeAm5H2iizEZhpFAHlvF53BXu4twFQhmA18sMqg+ImqryOs5\nhvSFQBqxVYQl0IrOHD9KA+LYsxnnxU67rRyK43gZtxDzHbefwrJWA0DcFpDXVrFzlbzY9kbrj2+m\n92wV4OIaob44TrNLxO1jw26r+DiTbWcLGPz3bBh9SZ4JectU9WQReb2qbhWRtwG3Fx1Yn7AfTnTA\nVHFcL3OcJI4btVVAj2WOPZuZyIwkkTdzbLQPy1qlsxC3BHxI/Bb/fFwt80n46hbrcd8D0Vqxja5c\n2XOZY08ojtNKd4bid4qtIrY/pF+rVbSLWap6fLeDMAyjPnkyx/E6vbvS2iz3QSKaOd6Am0i2u3+e\nJY7Tyrk1aquggfbN0qw4TqpUEVJPHFvmuP1Y1iqdZm0VkOw7bsZW0c+Z450TGlP2h+SpczzI3C4i\nL+l2EIZh1CdP5vgnIvJpYJaI/D6uVuO1xYbVN+z0HPssUniivBcnjtNWvUrLHDdarQKKzzjdReOr\nn9XLhIXi+LqU/ZY5bj+WtUonyVZxcOR5lji+F7cw0k2RbY3aKj5K8SvkNcNmXAWeeuJ4AXUyx776\nTGIFG88GYGFQKU/LWeWjHzkAqInIfUyU+WvHIiCGYbSZPOL474AzcVmnT+FKvnyyyKD6iGjmGFw5\ntyVMiOM7k17ERBm2OM1kjotePOD7uJXUGmEz2f7W0HNtmePOcbuIvERVbXXLqaRVqwjJEse3A4fF\ntjVkqyhVa71qU9uMq2qSJY73I70UXTRzPB/YVqrWEms/l6q1bX6VtUYvLPqJM7sdgGEY+cgjjl/j\nfYnmTYzgMyHRUm4w2XecNSFvE/DShO3NeI579XZsVlWER2O/41jmuP1Y1iqdVmwVt+MWQYnSqK2i\nV9kc+x1nK26xk4dSsr1RcZxlqQgJ/+6D8LebQt7lpg3D6D55xPHHReR84CLgIlWdsrTkkLInbhGL\n6Bd5VBw34znuRVtFM/wnEF/cI0o9cZyYOQ4q5ZnA9gG+7VoklrVKwH+m5jBZAOatVgGuhFm8BNmg\nZD8zxbG3kj1GuugNbRmQXakiJPy7p9VCNgzD6Ah5FgE5WkQOBU4GbhCRVcAFqtrScrEiMh237OVa\nVX1jK311ibilApw4fr5/nGcRkJ34THSjtoonceX1eopStfaLOk3CrHJaKbctxHzOQaX8IuBWYEdQ\nKW/Cvf91wMpStZZ39b6hxbJWqSwANsYWudlpq/Djch7pY/lu4ICgUt4jstpjo9UqepV6meNwX5Y4\nDi8csipVhAxDxQrDMPqAPNUqUNU7VPXvcAsJLAQubcOxT8NlXfo1Cxi3VED+zHHSIiAzcZno7XkO\n7k/ES/s0i9pM5vg1wAW4v+tLcZ/FfZjq9zSMRkgSbVuAmb7yzBzgqbRxWarWdgC/AV4Y2Txotoqs\n+QObSRe9zdoqDMMwukpdcSwii0TkAyJyK+52+WVMrufbMCKyBDgeJ3amtdJXF4mujheS13P8AHBg\nUClHM/eNWCoAKFVrDzTSvodoxnN8DHBdqVp7qlStPVSq1lbj/J4vKChGYziYItr8BWd4iz/LUhES\nt1Y0cgeol9mMm0SXdcFeL3MciuNGbBWGYRhdJY/n+C7g28BfqepP23TcfwU+SH9PukqzVYQeu9TM\ncala2xxUygFwBHCL3zwoJ9Q8NJQ59re2j8F9ZqLcg4ljozXSRFt4i/8Z6ovjeMWKQcoc13vvecXx\nvrgqPlmYrcIwjJ4gjzheCXwA+ISI7IKbnTyiqkmlyOoiIm8A1qvqLSIymtJmFIjvmz+1ZVdJslU8\njKvVGU7yeTzj9TcAr2CyOM5bqaLfeRI4i/TMejxzfDCwHfhtrN29ONHcq5wuInFxMWb+354izQsb\n3uLfRj5xHK1YMSji+LfAz+u0uZX0FVPjmeObUtqFrGfq5EbDMIyOk0ccfwG4BPgj4D+APwA+08Ix\nfwdYKSLHA7sDc0Xky6r6trCBFw9j0ReJyAjOp9wrLMZ5DXdSqtZ2BJVyuIDAE6VqLb66YJSf4S4A\n/t0/b9hW0a/429ZZtbLjnuPQUhH3V98DvKO90bWVc1V1TbeDMDJJ88KGt/hnMKS2ilK1thb4wzpt\nPpax22wVhmH0JXkm5I2r6jnAj3GLWvwRcEKzB1TVD6tqSVUPAv4E+FFUGPcRSbYKcNaKw0nPiobc\ngLtQCBmIE2qbiGeOjyF5Nb17magOYhjNUM9WkcdzHFasmOWfD0rmuFUeA+YFlfIupC8xHcVsFYZh\n9AR5xHFoDbgHeJGqPkVyjd5m6cdqC5Atjg+jvji+E9g7qJTDk8Ew2SrqkZg5Tmj3AO5vuEdHojIG\nkXq2irriOFKxQvwmE8fs/Ls8gft75MkcW7UKwzB6gjzi+CYRuQz4IfC3IvJZ4Lk6r8mFqv5YVVe2\no68usJipnmPIKY59XdUbcb5jGCJbRQ62ArODSnmXoFJejLsYWx1v5G0rv8Wyx0bz1LNV5Mkcw2Rr\nhd0FmmAzrqb7AuCROm034OZs9GsFI8MwBoQ84vj9wL+q6l3A6bjSa39aaFQ9ji/BtjfJJ9W1wKFk\n1wYNiVor7ITq8aJ3G24VwmOAn8YWaYhi1gqjFdphqwA3KS0Ux5Y5nmAzMIIrCfd0VsNStRZ+/80u\nOijDMIws8qyQF2Y4UdUqUC06qF4iqJRnA9Niy0QvBB71tw3jrMXdXr0rR/c3AH/vH5utYjKh7zjN\nUhFi5dyMVqhnq1gP3JGjn2jFikFZProdbAaWUt9SERL+3e3vZxhG18hTrWJo8RNJqsB9uOWzQ9Is\nFeB8sDOp7zkGV9roSF/6zTLHkwl9x8cAf5nR7h4sc2w0QaTkYlJmuClbhe9zOq4+stG4OA7/7vcV\nFtEAISJ74xbmeh6wBjhRVad8XkVkBXAu7rN5gZ9kj4j8M/AG3Of1HuBkVc1z19MwBppcy0cPMafh\nROubgko5Wj0hbTIeuMwx5BDHpWptC84W8BLMcxxnC3AgLiv8y4x292KZYyNGUCkvDSrlf4j9nBxr\ntgB4JMWys57GbBV34xYAWgBs7dNl3YtgM3AI9StVhCRWrAgq5RODSnl6OwMbEM4ArlbVQ3Dzgs6I\nNxCR6cB5wArcfJiTRORQv/t/gcNV9SW4u51ndiRqw+hxTBynEFTKhwIfxpWbuxY4MbI7Sxw/6H/n\nvfr+GW5SntkqJvMY7sv85lK1lpWFM1uFkcQpwEuBpyI/nwoq5WWRNlkVFB7H3QHajxziOFKxooxZ\nAqI0a6vYSVApl3DZ0be0N7SBYCVuHQL87zcltFkO3K2qa1R1O3Apvhyrql7trZPg7mQuKThew+gL\nTBwn4CfcXQL8falauwe4EHhnpEnS6ngAlKq1p3Angjy2CpiYlGe2islswd3uy/Ibg7v9OuItMIYR\nMgr8U6la+4fwB/gn4KORNmmVKsKFajbgLrzyZI7B+Y6XY+M4ymbc37BRW0WUV+Oq0pxl2eMpLFLV\ndf7xOmBRQpsDgCDyfK3fFufPge+3NzzD6E9MUCRzJrAJON8/vwo4MKiUD/PPF5OeOQb35dOIOH4F\nZquI8xhwEHXEcalaewJ4lOQve2MICSrl+bhJsTfHdn0JeFlQKR/pn6dNxgtZD+xGfnG8GieOLXM8\nwWbc37AVW8Uo8C/ARoYweywiV4vIbQk/k8qgquo4yesG1LX4iMhHgGdU9WttCtsw+hqbkBfDnzjf\nCxwZ+gb9stCX4K6s/xaXOb4+o5v7cF/kefgNLmu8FGffMBxbgGfxlVLqEE7KC+o1NIaCVwE3xu04\npWrtqaBSPhuXPT6B+gtThPvyWqRuBz4ArGos3IEmvLBoxFZxeGzbKPCvgAKfDyrly3y5x6FAVV+X\ntk9E1onIfqr6sIgsJvki5AGgFHleYmJuDCLyDuB44LUZxxnF/R+izJ/a0jB6mtNFJJ7sGFPVsXhD\nE8dT+Tfg70rV2trY9ouB64NK+cNke47BiehcWeBStTYeVMo3AL+POwEYjseAX8ZK6KURTsr7cbEh\nGX3CKDCWsu9LwBn+IjjVVuHZgKvPuz3ncW/HVVixO0ATNCqOJ2WOvd94Li4rv5qJ7LFlOB1X4EoI\nnuN/X57QpgYsFZER3JyYtwAnwc4qFh8EXu1Xv03Ei4ex6Dbf32ktxm8YneRcVV2Tp6HZKiIElfJ+\nwItI+OItVWu/wS35XCG7lBulau2xlBrIadwAzMBOqlFux03CyYNNyjOijJIijv2cgDB7nMdWkddS\nAe5z+Axmq4gS/v0asVVEPcevBn5cqtbG/Z28j2He4yhnA68TkbuA1/jniMj+IlIFUNUdwKk4e+Bq\n4DJVDWt3fx5XzvBqEblFRP6902/AMHoRyxxPpgL8b0Z1hItwE/PqZY4b5Wf+t1Wr8JSqtW810Pwe\n3OS9SQSV8krgr4A31VudyxgMMvzGUb6EK3n1BPCDjHYbaEAce/uVYhe5UZqxVUTF8bFMvtC5Bsse\n70RVHwWOS9j+IO58Fj6/Ergyod3SQgM0jD7FMseTeQPw3Yz93wReifu7Pd7G496M89faSbU50mod\nvxPnX/xCUClP62xIRpdI9BtHiWSPD6a+raKRzDG4Ox6WOZ4g/Ps9krP9BmDfyHgdJSKOLXtsGEYn\nMHHsCSrl3XG3paZcXYeUqrVtwDeAh9pZ5N/3+xlcuSKjcaaskucXbTkWVwlkGW6ilDH4jJLuN47y\nJdzCHfdntLmHxldquwk3AcpwbADuyHvnxn8XjgOzg0r5QGBPnBUgyjW4OQm/385ADcMwQkwcT3As\ncGupWqtXZeLfgR+1++Clau1DpWptU7v7HRI2ALsFlfK8yLY3Aj8pVWsP4Arlvz+olKdYL4yBY5Qc\n4thnjw8pVWupF6Slau3aUrX21kYOXqrWzi1Va2c38ppBplStbSlVa4fVbzmJ0Fqx028c63McN/l2\nWcJrDcMwWsbE8QRvJNtSAUCpWltVqtb+ogPxGDnxJ8u4teJE4L/9/gB4M3BxUCkf0fkIjU6Q02+8\nE1viuWcJK1aMkn6hcztuKWTDMIy2Y+IY8P62en5jo7fZaa2IWCq+E+4sVWs3AqfjfOPGYFLXb2z0\nBWHFilHSxfFqptZDNgzDaAsmjh0vwZVgurPbgRhNE80cr8RZKuKTqb6Gm+wTX57WaBIRWSMit/oy\nUD/vcjij5PMbG73NeuAokv3GIXcAh9ikPMMwisDEseONwHftNmtfE52U98e4iZOT8P/fO4BDOxjX\noDMOjKrqMlVd3uVYRjFxPAhswI3hKX7jEL840DpiE3ENwzDagYljRy6/sdHT3AO8IGKpuCKlnYnj\n9tP1MnmN+o2NnmYDzjIxVqedWSsMwyiEoRfHflW8pcB13Y7FaInQVrESl3FKq0+7GpvI007GgWtE\npCYi3Zyoan7jwSFcTW+sTrvbMXFsGEYBDL04xq0idFWpWtve7UCMlvgtsD/wp/gqFSlY5ri9vFJV\nlwGvB94jIsd0KY5RzFIxKGzALRqS5jcOsYoVhmEUgi0f7SwVVsGgzylVa9uDSvkB3EIuf5rR1MRx\nG1HVh/zvDSLyP8By/F0YERnFidYo8wsK5aXAPxfUt9FZbgTel2MOyO3A+zsQz+kiEr8TNaaqYx04\ntmEYXWCoxXFQKc/Eial3djsWoy3cC9yeYakAl2HeO6iU55aqtS0dimsgEZFZwHRVfVxEZgO/B3w8\n3O/Fw1jsNSPAaQWEM0Ljq9kZPYhfiOnrOZreia9YUarWni0wpHNVdU2B/RuG0WMMu63iQGBDjlXx\njP7gB8D5WQ1K1dpzgAIv7EhEg80i4DoR+RVu2eTvqer/djoIX86rRPZS0MaAYRUrDMMoiqHOHOOy\nTWu6HIPRJkrV2r/kbBpaK7pdl7evUdX7cHaGbrMY2OiXhDaGi3BS3m+6HYhhGINDx8WxiJSAL+OW\nBx0Hvqiqn+t0HJ6DMHE8jFjFisFiBBvHw0ooji/vdiCGYQwO3bBVbAfer6qHA0fjZrh3a4LUCHZS\nHUZsUt5gMYKN42HFLnQNw2g7HRfHqvqwqv7KP96KEyr7dzoOzwh2Uh1GTBwPFiPYOB5WrNaxYRht\np6sT8vzM9WW4yTzdYAQ7qQ4jdwOloFLevduBGG1hBBvHw8od+IoV3Q7EMIzBoWviWETm4OoLn+Yz\nyN1gBDupDh1+wZd7gUO6HYvRFkawcTyUlKq1bVjFCsMw2kxXqlWIyAzgW8BXVHXKRIpOLB4QVMq7\nAQuAB9vZr9E3hNaKWws8hi0e0BlGMHE8zFjFCsMw2ko3qlVMAy4EVqvquUltOrR4wIHA2oKLxxu9\nSycm8tjiAQVjNY4NhrhihYjsDVwGPA93gXiiqk5ZBElEVgDnAtOBC1T1nNj+D+BWmFygqo8WHbdh\n9DrdsFW8Evgz4FgRucX/rOhCHCNYtmmYsUl5g8H+wCNW43iouZ3hrVhxBnC1qh4C/NA/n4SITAfO\nA1bg/k4nRStE+fKqr8OtHmoYBl3IHKvq9fTGynwjmDgeZu4Azux2EEbLjGDjeNhZDfxNt4PoEiuB\nV/vHl+DuuMYF8nLg7vAulohcCpyA+w4E+Czwd8B3Co7VMPqGXhCp3WIEO6kOMwocHFTKw75KZL8z\ngo3jYWeYK1YsUtV1/vE63JLucQ4AgsjztX4bInICsFZVi5x7YRh9xzALgxHgB90OwugOpWrtiaBS\nfgg3y/2ubsdjNM0IJo6HmlK1ti2olB/GjeWBm5QnIlcD+yXs+kj0iaqOi8h4QrukbYjIHsCHcZaK\nkGkpbUcpeJK8YXSA3JPkh10cr+lyDEZ3CX3HJo77lxHgxm4HYXSdga1YoaqvS9snIutEZD9VfVhE\nFgPrE5o9gJu0GlLCZY9fgBs/q0QEYAnwCxFZrqqT+unQJHnDKJrck+QHWhwHlfJZwG2lau1/EnaP\nYOJ42FmNE8fmtethgkp5BiClau3XCbtHgEs7G5HRg6wG3hxUyjMj29aWqrWfdSugDnEF8HbgHP87\nqWJHDVjqxeyDwFuAk1T1DiI2DBG5DzjKqlUYxuB7jt8KvCG+0WocG547GN5Z7v3ECcB3U/aNYBe5\nBnwb2A34I//zx8CVQzCn4GzgdSJyF/Aa/xwR2V9EqgCqugM4FbgKdxFxmRfGcRLtF4YxjAzsF0dQ\nKT8POAh4JmG31Tg2wInjvwqf+IumdwG3lqq167oWlRFnFBgJKuWRUrW2JtzoJ2AtwWocDz2lau0m\n4MTotqBSvhU4Evh5V4LqAD7Le1zC9geBSuT5lcCVdfqyVQYNwzPImePXAv8DHBhUyvGJAyNYtslw\n4viFQaW8a1Apvw1XweJU4PTuhmXEGMV5SV8d2x7WOH664xEZ/cAYUyeRGYZh1GWQxfFxuNtIv8DV\neYwygonjoadUrW0GHsfdanw3bnGa1wLHDmlZqJ4jqJT3xWWHP89UoTOCjWMjnTFMHBuG0QQDKY6D\nSnkazn91DW4m+ytiTUawk6rh+EfgA8AxpWrt+lK19iDOi35Ud8MyPL8LXI8by6OxfSPYODbS+Qnw\nyiHwHRuG0WYGUhzjSvps8/7EG4CjY/tHsJOqAZSqtfNK1dp3S9VadDLK1ST4+IyuMIrLAN4JzAoq\n5ZHIvhFsHBsplKq1R3BLIh/Z7VgMw+gvBlUcH4dbZx5c5vjlQaUcfa8j2EnVSOcaJhfGN7rHKDDm\nL17GmOw7HsHGsZHNGGatMAyjQQZVHL8WJ3AoVWvrgM3AIZH9I9hJ1Ujnx8DLgkp5drcDGWYifuNf\n+U1jTBY6I9g4NrIZw8SxYRgNMnDi2C8Y8LvAtZHNO33HVuPYqEepWtsK/BI4ptuxDDm/C1xfqtZ2\n+OdjmDg2GsN8x4ZhNMzAiWPgZcC9pWptQ2Rb1HdsNY6NPFyNWSu6zSiTl6zd6Tu2GsdGHsx3bBhG\nMwyiOH4tE37jkGjFihEs22TUx8Rx9xklIo5jvmOrcWzkZQyzVhiG0QCDKI6Pw/uNI6wCnh9Uynvi\nxPF9nQ7K6DtqQCmolPfrdiDDSILfOGQMv2IedpFr5GMME8eGYTTAQIljP4HqKGDS0r+lau0Z3El2\nOW5J6TUdD87oK7zPdQx3J8LoPHG/ccgYJo6NxjDfsWEYDTFQ4hg3geoXpWptW8K+0Hc8gp1UjXyY\ntaJ7jDLZbxxyJzDL71/TsWiMvsV8x4ZhNMqgieMkv3HIjZg4NhrjGuA4v+Ki0VlGSRDHEd/xH2Pj\n2Aktzi4AAApySURBVMjPGGatMAwjJ4Mmjn+PdHEcZo7NVmHk5TfAs8ALux3IMJHhNw4ZA/bExrGR\nnzFMHBuGkZOBEcdBpbwcmIfLEE+hVK09CDwJ7AM81MHQjD7FZylttbzOk+Y3Dhnzv9d0JBpjEDDf\nsWEYuRkYcQy8FzivTv3iG4D7rcax0QBXA6/vdhBDxv8Bvp+x/07gizgfqWHUxfuOVwMrux2LYRi9\nz0CIY19u6w3AhXWa3ohlm4zG+C7wkqBSPqrbgQwDQaW8DHg5cHFam1K1Nl6q1k7xVWgMIy//CJwV\nVMoDcd4zDKM4BuVL4t3AZaVqbVOddl8FzupAPMaA4CuffAI4u9uxDAlnAeeUqrUnux2IMXB8DzeH\n4IRuB2IYRm/T9+I4qJRnAn8JfL5e21K1tr5Urf2s+KiMAeNC4HlBpXxctwMZZHzWeDnOMmEYbcXP\nIfgY8FHLHhuGkUVXJieIyArgXGA6cIGqntNCd38ErC5Va7e3JTjDiFGq1rYHlfL/Bc4OKuXlpWrt\nuW7H1Au0eRwDfBTLGhvF8j2cQH4T8O3uhtI6IrI3cBnwPJxl8ERV3ZzQLnWsish7gb/GZdWrqvqh\nDoRuGD1Nx6+eRWQ6cB6wAjgMOElEDm2hy/eRI2tsGC3yTWAcV1936Gn3OPZZ45cBX2pPhIYxlQHM\nHp8BXK2qh+DKmJ4Rb5A1VkXkWNwkxRer6ouAf+lU4IbRy3Tjy2E5cLeqrlHV7cClNOkBCyrllwOL\ncNkAwygMny0+A/iHoFKe0e14eoC2jWOPZY2NTvE9YAcue9zvrAQu8Y8vIfk9ZY3VvwL+yW9HVTcU\nHK9h9AXdEMcHAEHk+Vq/rRnylG8zjLZQqtZ+CNwHvKvbsfQAbRvHljU2OsmAZY8Xqeo6/3gdLlkU\nJ2usLgV+V0RuFJExESkXF6ph9A/d8ByPN/m66QCf2G/3r1x33LKnAHbbZdrL/uORpz/9U5GRdgVn\nGFmcumC3f1s+e9f/WnPcsjeH28bhuWu37vjk1zY9E56Alvjf0zsfYcdo2zieMW3aCx559rkvfeCB\nJxch0rYADSONGXDbFw+cPe05xn+65rhl28Ltjzw7ftUZDz753/5pT4xjEbka2C9h10eiT1R1XESS\nxmXWWN0V2EtVjxaRlwHfAJ6fM7Tw77JEbNwavU3DY7kb4vgBoBR5XsJdye5EREaZutTngQBnPfzU\nK2PbV7U3PMNI57xHnoZHngZ4bWxX0ip6HxGR+2PbxlR1rIjYOky7x/FH/Y9hFM524OT7tyXtei3w\n6di2ro5jVU1doVNE1onIfqr6sIgsBtYnNMsaq2vxExNV9WYReU5E9lHVjbHjjJIyloHr8r4Xw+gy\nucdyN8RxDVgqLtv7IPAW4KRoAx/oWHSbiOzmH34KN6u2XZyOm8Xbborod9hj7Zf3Px2X1fkrVX26\njf32Er02jmG4P3P91GdR/Q7jOL4CeDtwjv99eUKbrLF6OfAa4McicggwMy6Mwc7JA9BnUf32S58N\nj+WOi2NV3SEipwJX4QK+UFXvyPG6p0XkflW9p53xiMhmVV3Tzj6L6nfYY+2z939/D59QW6bXxjHY\nZ65f+iyq3yEdx2cD3xCRd+JLuQGIyP7Al1S1UmesXgRcJCK3Ac8Ab8t7YDsn90+fRfXbL336fhsa\ny12pc6yqVwJXduPYhmG0BxvHhtFdVPVRYMriRKr6IFCJPE8cq75KxVuLjNEw+pF+n6lrGIZhGIZh\nGG3DxLFhGIZhGIZhePpNHI/1SZ9F9VtEn0X12y99FtVvEX0OCmN91G8RfRbVb7/0WVS//dLnIDHW\nJ30W1W+/9FlUv/3SZ8P9Thsfb7ZcqWEYhmEYhmEMFv2WOTYMwzAMwzCMwjBxbBiGYRiGYRierpRy\nawYRWYErDD0duEBVz2lDn2uALbgC5ttVdXkTfVyEK5mzXlWP8Nv2Bi4DnoevPamqm9vQ78eAdwEb\nfLMzVfUHDfRZAr4M7ItbUvSLqvq5VuLN6LPVWHcHfgzsBswEvqOqZ7YYa1qfLcXq+56OK7a/VlXf\n2I7PwCDSq+PY99P2sdwv47hOv03HW8Q4rtNv07FG+raxXIcixrHvdw12Tu65c/IwjuO+yBz7N3ke\nsAI4DDhJRA5tQ9fjwKiqLmv2hApc7OOKcgZwtaoeAvzQP29Hv+PAZ328yxr9sOBWTX2/qh4OHA28\nx/8dW4k3rc+WYlXVp4BjVfWlwIuBY0XkVa3EmtFnq39XgNOA1b4vWolzUOnxcQzFjOV+GcdZ/TYd\nbxHjuE6/NpYLpsBxDHZO7slz8jCO474Qx8By4G5VXeOLll8KnNCmvqe18mJVvQ7YFNu8ErjEP74E\neFOb+oUW4lXVh1X1V/7xVuAO4ABaiDejz5Zi9f094R/OxGUoNrUSa0afLcUqIkuA44ELIv20/BkY\nQHp2HEMxY7lfxnGdfluNt+3jOKPflmK1sZyLIscx2Dm5J8/JwzaO+0UcHwAEkedrmfhnt8I4cI2I\n1ETkL9rQX8giVV3nH68DFrWx7/eKyCoRuVBE5jfbiYiMAMuAm2hTvJE+b2xHrCKyi4j8ysd0rare\n3mqsKX22Guu/Ah8EnotsK/Iz0K/02ziG4v6PPTuOY/22PJaLGMcZ/bYUKzaW81DUOAY7J/fsOXnY\nxnG/iOOi6s29UlWXAa/H3Xo4pt0HUNVx2hf/F4CDgJcCDwGfaaYTEZkDfAs4TVUfj+5rNl7f5zd9\nn1vbEauqPudvtywBfldEjm011oQ+R1uJVUTegPOg3ULKlW6bPwP9TN+OY2jr/7Fnx3Gk37aN5SLG\ncUq/o63EamM5N0W+fzsn9+g5edjGcb+I4weAUuR5CXe12hKq+pD/vQH4H9ztonawTkT2AxCRxcD6\ndnSqqutVddz/Yy+giXhFZAZuEP6Xql7ejngjfX4l7LMdsYao6mNAFTiq1VgT+iy3GOvvACtF5D7g\n68BrROS/2hXngNFv4xgK+D/26jiO9dv2sVzEOI71a2O5MxQyjsHOyf1wTh6Wcdwv4rgGLBWRERGZ\nCbwFuKKVDkVklojs6R/PBn4PuK3lSB1XAG/3j98OXJ7RNjf+HxryBzQYr4hMAy4EVqvquZFdTceb\n1mcbYl0Q3koRkT2A1wG3tBhrYp/hgGkmVlX9sKqWVPUg4E+AH6nqW1uJc4Dpt3EMBfwfe3EcZ/Xb\nSrxFjOOsfm0sd4S2j2Owc3Ivn5OHcRz3zQp5IvJ6JkrHXKiq/9RifwfhrkzBlbT7ajN9isjXgVcD\nC3A+lrOA7wDfAA6k+bIx8X4/CozibjOMA/cBp0Q8NHn6fBXwE+BWJm4pnAn8vNl4U/r8MHBSi7Ee\ngTPN7+J//ktV/1lcOZZmY03r88utxBrp/9XAB1R1ZStxDjK9Oo59X20fy/0yjjP6bWksFzGO6/Rr\nY7kDtHsc+z7tnOzouXPyMI7jvhHHhmEYhmEYhlE0/WKrMAzDMAzDMIzCMXFsGIZhGIZhGB4Tx4Zh\nGIZhGIbhMXFsGIZhGIZhGB4Tx4ZhGIZhGIbhMXFsGIZhGIZhGB4Tx4ZhGIZhGIbhMXFsGIZhGIZh\nGJ7/D/1mtvzCghzeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6938a0790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-08.csv\n",
      "minima add up to zero!\n"
     ]
    }
   ],
   "source": [
    "for f in filenames[:5]:\n",
    "    make_plots(f)\n",
    "    filter_data(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our ``for`` loop went from being long and complicated to simple and easy-to-understand. This is part of the power of functions. Because the human brain can only keep about $7 \\pm 2$ concepts in working memory at any given time, functions allow us to abstract and build more complicated code without making it more difficult to understand. Instead of having to worry about *how* ``filter_data`` does its thing, we need only know its inputs and its outputs to wield it effectively. It can be re-used without copying and pasting, and we can maintain it and improve it easily since it can be defined in a single place."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we may want to use these functions later in other notebooks/code, we can put their definitions in a file and import it. Opening a text editor and copying the function definitions (and the ``import``s they need) to a file\n",
    "``datastuff.py``, that looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import numpy\r\n",
      "import matplotlib.pyplot as plt\r\n",
      "\r\n",
      "def filter_data(filename):\r\n",
      "    data = numpy.loadtxt(filename, delimiter=',')\r\n",
      "    print filename\r\n",
      "\r\n",
      "    # check for the cases where we see min inflammation as precisely 0 on day 0\r\n",
      "    # and max inflammation as precisely 20 on day 20\r\n",
      "    if data.min(axis=0)[0] == 0 and data.max(axis=0)[20] == 20:\r\n",
      "        out = 'suspicious looking maxima!'\r\n",
      "    \r\n",
      "    # check for cases where min is zero across all days\r\n",
      "    elif data.min(axis=0).sum() == 0:\r\n",
      "        out =  'minima add up to zero!'\r\n",
      "        \r\n",
      "    # keep track of datasets that look okay, at least so far\r\n",
      "    else:\r\n",
      "        out =  \"seems 'okay'!\"\r\n",
      "    \r\n",
      "    print out\r\n",
      "    return out\r\n",
      "\r\n",
      "def make_plots(filename):\r\n",
      "    # so we know what we're looking at\r\n",
      "    print filename\r\n",
      "    \r\n",
      "    # load data\r\n",
      "    data = numpy.loadtxt(fname=filename, delimiter=',')\r\n",
      "    \r\n",
      "    # make a figure object, with 3 axes in 1 row, 3 columns\r\n",
      "    fig = plt.figure(figsize=(10,3))\r\n",
      "\r\n",
      "    ax1 = fig.add_subplot(1, 3, 1)\r\n",
      "    ax2 = fig.add_subplot(1, 3, 2)\r\n",
      "    ax3 = fig.add_subplot(1, 3, 3)\r\n",
      "\r\n",
      "    # plot means\r\n",
      "    ax1.set_ylabel('average')\r\n",
      "    ax1.plot(data.mean(axis=0))\r\n",
      "\r\n",
      "    # plot maxes\r\n",
      "    ax2.set_ylabel('max')\r\n",
      "    ax2.plot(data.max(axis=0))\r\n",
      "\r\n",
      "    # plot minses\r\n",
      "    ax3.set_ylabel('min')\r\n",
      "    ax3.plot(data.min(axis=0))\r\n",
      "\r\n",
      "    # this is useful for making the plot elements not overlap\r\n",
      "    fig.tight_layout()\r\n",
      "    \r\n",
      "    plt.show()\r\n"
     ]
    }
   ],
   "source": [
    "%cat datastuff.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then import this as a module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datastuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And use the functions defined inside it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-01.csv\n",
      "suspicious looking maxima!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'suspicious looking maxima!'"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datastuff.filter_data('inflammation-01.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-01.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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Cn3V/I4EbnHPrERb21WU7sxOBcUma/Sd2kF6TpNmbwFHAON2VZp4eJBwDfYSI\nHJz/+lHsUBWj9cbN6zsIpNYamTl+Mz/y+TFgSxG5nnB2u2qfAHXbx3i+8pnhrw583FtzHHCst+Za\nnT2eq0+JyEhgCcKH36GE40Dnu/uHc+7W/LCd/nYCtsq/vohwOE9PD5C9NZ8GhgO7xs7Swy4DDgW+\nQgN3NWpqGcJAZd3890PQ2uNm6cxx8yYDn4kdIrZGBscnA78gnIR3PGGByh9LzFQnOnPcnN8BxxBq\nFfVDxeDOI+wn+1Xg54TBx2lttLeSc66v9mwG0NOHOOQzmWOBY5M0mxU7T69K0my2t2YUMN5b84ck\nzbSudgDn3LdjZ+gB6wB/iR2iYiYTxnm11sjgeEHn3DYAIrIxYeux+0tNVQP5KtoV0e3KGpa/oY4h\nzB7/SWePBzUn337xw4Sj2ncmfJA4s92GnXNzRGTQP3MRGUFYj9Dfsu1eM4IdCfXtF8cOUgN/Juyo\n8m3Ch7qqOVBEBtbfT3TOTWynURH5rXPuayLy4CDfnuOc+3g77deMzhw3T/c6prHB8UnA1QDOuVcB\n3X6nGOsBTyRp9k7sIBVzJTp7PC+v5P+dDHzMOfdXEflQG+3NEJGVnXPTRWQV4LnBnpQPCCb2fywv\n0TigjWt3lLdmKOFO2Wjtl+VL0myOt2YkcIW35tIkzV6PnalJ451zU0tod2z+3/0IJYzLlXCNnpef\nI7A2Ojhu1tPA8t6axSrYJwvTyIK8B/IFAZ8RkU/0/So9We/TkooW5Fvy9NUe62KeD7pTRH5DWDx3\niIicDrSzjdEfCDvWkP/36jbzdbP/JXy4+EPsIHWRpNkdwF2EgaACnHN351/uRDho69gBv1RjVgb+\nk6TZq7GDVEk+MfAkYa1KbTUyc7wZ4aSt7w54fO3i49SKDo5b1zd7vAPwp8hZus2BhO3vFgR+TFgr\n8K1GXigiEwiL75YXEQ8cTZjFulxEvkO+lVsJmaPz1iwCjAF213KdjjsCuMVb84skzV6MHaaLfBVY\n1Tn3QuwgFbUOOmvcqr7SikdiB4mlkeOj1+pAjjoS4PrYIapoQO3xdTqYeZ/TgH2BvlMXhxDKHVac\n3wudc7vN5VvbFpKsu+0NPJSk2a2xg9RNkmaPeGt+DxzGewdOqTB5olsJtk7rjVtX+7rjRk7IW4ow\ne7Q+YdboROBg59zMkrP1OgHOih2iwq4gzDh9E/hV5CzdRGebmuStWQoYDXwudpYaOxa431vz4yTN\n9Kjf4CykJ3KOAAAd7klEQVRgkoj8hXA6LYQFeWMiZqoSHRy3rvaD40Zqjs8ifHpdCZhF2D/13DJD\n9bq8VnY9tKyiZXnt8e7A6d6adWLn6SI629S8g4EbkjR7IHaQukrSbBpwPqFcSgXHEfryssDy+a8V\noiaqFj0ApHVPEMpSaquRmuPhzrk9ReTzzrmZIrI78FDZwXrcKsDrSZr1/DG8ZUrS7H5vzfHABG/N\nFvnJW3Wns01N8NasSFgMZmJnUYwFnLfm9CTNdOIAFnfO6YmgrdOZ49bpzHEDzxm4pdGCtLf6HRFZ\nVkSuEJFHRORhEdmsnfYqSBfjFefHhO3FdPAX6GxTc44ELknSbErsIHWXpNm/CTXzJ8TO0iUeEpGN\nYoeoMF2Q17opwNp13hGqkZnjW0TkFGBxEdmeMMtyc5vXPRO41jm3s4gsSCjVqBMdHBck3yt1T+A+\nb82NSZrdGDtTZDrb1CBvzTDgG4T1FKo7nAU85q357yTN/h47TGSrAZmITAHeyB/TQ0Aa4K1ZgnD8\n9rOxs1RRkmaveGteJZTTTo+dJ4ZGZo4PA2YSZqNOJJyOd0irFxSRZYAtnXMXADjn3nbO1a1G8iPo\n4LgwSZo9T9iD9yJvTd1nSXW2qXFjgLOSNBv0YBPVeUmavUb4exlb51mr3ChgO+D7hEmp/YD9oyaq\njrWBKfnaFNWaJ6hxaUUjM8fb5PWKRd22Xht4XkQuBDYC7gYOcM69VlD7VSCEQxpUQZI0u9Fb8yvg\nOm/Nz4FrkjSr4ydenW1qgLdmI8IWdfvEzqI+4ALCIsntqPF2l+0eQ11zWm/cvsmE0pTbYweJoZHB\n8XEicg7hH6wLnHPTCrjmJ4AfOuf+LiLjgZGEAwd6jrdmXWANwqewafnpM1pWUY4jgXuALwOneGse\nIZzodnaSZq/M85W9Q/eJbczJwIk1+rmojCTN3vbWHEGYPb5RZ/9UC7TeuH21XpTXyCEgm4nI+sCe\nwN9E5H7gPOdcq8fITgOmOef66smuIAyO3yUiI4ARA163bIvXi8Zbsx5wC/A44SjGFbw1nrBbhS4A\nKliSZm8DlwOXe2sWJvwMHQosx4CfsS51oIgM3MFkYjMzSDrbNH/emq0IdcZfiZ1FzdWVwOGEvfUv\ni5ylckQkAS4mHP4zBzjXOVenffV1G7f2TeaD47DaaGTmGOfcI8BhInIFYXeAy4BFW7mgc266iHgR\nWc859xjh1uZDA54zkXCq17tEZC3ggFauGUNe+3otcESSZufnjy1KGCQvmqTZWxHj9bx8W7frvTX/\nBO7y1pyQpFm3H1wz3jk3NXaIXpbXsY4DjkrS7I35PV/FkS+0HQmc4635nW7T2LS3gIOcc/eJyJLA\n3SJyQ/5eXgfDgBtih6i4ycBesUPE0sgJeSsRTiHbI3/+eYBt87r7AZeKyMKET3d7ttleV8kHwVcD\nl/cNjAGSNJsFPBotWA0laTbZWzOR0MnrNHOiBvdlYDHg17GDqHlL0uwmb81k4LvAz2LnqRLn3HTy\nXQby8wkeAVYF6jQ41pnj9uiCvPl4DPgdsI9z7q9FXNQ5dz/w30W01W28NQsAFwGeUAOr4jsNuNRb\n89O85lvVkLdmQeAk4GCtY62MkUDqrbm4And+ulJ+13U4cGfkKE3Ly+MWa/JlQwh3aLV0sT3PAMt5\na/pOR27GrKrfmWtkcLwTYeXwGBFZABgKrOWcW6PUZNV1ImHHgG31Dbg7JGn2N2/NdEKN6RWx86ho\n9iAcGPOn2EFUY5I0u8dbMwk4ED0cpGl5ScUVhB2hZvZ7fATVWNdzK/BRmj947OF8W0DVoiTNZntr\n/kaYIG3GAsDThC1ru03D63oaGRyfTZgJ3Rn4OWGAcVq7CXtN/gn3FODzwOZ5CYXqHqcR9ufWwXEN\neWsWA44Fvpak2ZzIcVRzjgTu8NacnaTZC7HDVIWILERY2HjJwAX0VVjXk68P+CiwepJmdTsLoSsk\nabZNs6/x1iwEzPTWLNyFawUaXtfTyCEgc5xz44BJhHrZnYEvtZ6t93hr1iR8wl0b2CxJs39FjqQ+\n6GpgRW/Np2MHUVHsC2RJmt0RO4hqTpJm/yTsQjM6dpaqEJEhwPnAw8658bHztGh54E0dGFdLvtnA\n04QtbCurkcFx3z6gTwAfc87NAj5UXqRq8dZ8AbiL8I/3l5M0ezFyJDWIvNZ4PKFESNWIt2ZZwkmf\nOriqrjHAt701lX7D7aDNCQvptxaRe/NfO8QO1STdq7i6Kr9HciNlFXeKyG+Ao4BURITm6396krfm\ncMKM1FeSNKvlKTIVcyFwjLdmnSTNdCVzfRxGODGxLiv1e06SZtO9NWcDx9FjuxuVwTl3G41NfnUz\nPeWuuvpO16usRjrPQcAZ+Z7EBxJWgv5vqakqIF/5fjiwpQ6MqyFf7X4u4edY1YC3ZlXgB4R6Y1Vt\npwLWW7NB7CCqI3RwXF2Vnzme7+DYOTfbOXdH/nXqnDvIOadHH4et6HySZk/GDqKa8lPgW/mHG9X7\njgYuSNLMxw6i2pPXno4j7Aikep/uVVxdld8jueq3XWLaHrg+dgjVnCTNniYcYb5x7CyqXPnx7TsD\nJ8fOogrzU+ATurC2FnTmuLp6f+ZYzdX2wJ9jh1AtmQRsFTuEKt0JwOm6/VfvyLfIPAYYm2/1pXqX\nLsirrsnAsCr3UR0ct8Bb8yFgA+C22FlUS3Rw3OO8NQbYAjgzdhZVuIuBDwM7xg6iyuGtWRRYkXCX\nT1VMvmvXbEI/rSQdHLdmW+A2PeijsiYBW3prhsYOokpzMjAmSbNXYwdRxcq3ZRwNnKx9uGetCTyV\npNnbsYOollW67lgHx63RkooKS9JsBjAd+HjsLKp43pptCW+u58fOokrzB8Ie/LXfOalHab1x9VW6\n7lgHx03Ka2h0cFx9WlrRg7w1CwBjgSPzk5pUD8qPAB8JjPHWLBI7jyqc1htXnw6Oa2Z9Qi2NbmdX\nbTo47k075/+9ImoKVbokzW4FHibsY616i84cV58Ojmtme+DP+cyFqq5JwGfymUbVA7w1CxH2wB2Z\npJme4lkPo4DR3pqlYgdRhdLBcfVV+pS8aAMDERman/d+TawMLdKSih6QpNkzwAvAx2JnUYX5DjA1\nSbMbYwdRnZGk2QOE/eYPjp1FFUoPAKk+XZDXogMIt8QqMwPrrVkM2By4KXYWVQgtregR3prFgaMI\ndaiqXo4G9vPWrBg7iGpfvq5nGDAldhbVFg+s7K1ZOHaQVkQZHIvI6oQ9Ks8DqrRJ9JbAA0mavRQ7\niCqEDo57xwGE7RXvjh1EdVaSZlOAS4AjY2dRhVgRmJUfF64qKt+Gbxph56DKiTVzfAZwKGFhW5Vo\nSUVv6as7rtIHNDWAt2Y54Efo4KjOTgS+4a2p7G1c9S6tN+4dlV2U1/HBsYh8AXjOOXcv1Zo1Bvgc\nOjjuGUmaeWAmYQcSVV2jgCuTNHs8dhAVR5JmzwE/BsbEzqLapvXGveMJKroob8EI1/w0sJOI7Ags\nCiwtIhc753bve4KIjABGDHjdsh1LOAhvzWrAqkAWM4cq3CTCz9rDkXMAHCgiA0t2JjrnJsYIUwXe\nmgTYC9gwdhYV3WnA496ajZI0uz92GNUynTnuHZWdOe744Ng5N5pw9CcishVwSP+Bcf6cicDE/o+J\nyFqEusKOyG/Vbk4YzH8K2AQ4Lz+6VPWOiYT6959FzgEw3jk3NXaIwYjIVOBl4B3gLefcplEDvedY\n4Jx89xFVY0maveKtOYlwdPiOsfOolq0D3Bo7hCrEZGCz2CFa0Q17vHbdbhXempWAR4EfAm8Q/rFN\nkjQ7KGowVYZJwFZadzxfc4ARzrnh3TIw9tZ8FPgicErsLKprnAOs763RhbbVpTPHvUNnjlvhnJtE\nGJx0mxOAi5M0OyR2EFWuJM2memveANZDTz2cn277AHEicKruHqP6JGn2hrfmKGCct+ZTelhTJeng\nuHdMBoZ5a4ZUrS92w8xxV/HWDAe+ABwfO4vqmEnA1+f1BG/Nkt6a5TuUpxvNAW4UkUxEvhc7jLfm\nU4ABfhI7i+o6vwYWA74cO4hqjrdmUWB5whZgquLyiYu3CH+nlaKD437yW+tnAsfoHou1ciLwLW/N\n+PwI4vfJB2IPEPZSravNnXPDgc8D+4rIlrGC5P30ZODYJM1ej5VDdaf86PBRwEnemqh3R1XT1gae\n0rU9PaWSpRX6D8f77QwsDZwfO4jqnCTNnLdmU8KM0/Xeml2SNHs+f2M9AtgHOAg421uzQpJmz8fM\nG4Nz7tn8v8+LyFXApvRbNNPhHWZ2AFYCLiqpfVV9fwIOB3YHLijpGrq7TPG0pKL39A2O74wdpBk6\nOM7lR0OfCuyhn1rrJ0mzF701feU0f/fW7E94c30V+ESSZs94a75I+AB1dsSoHSciiwNDnXOviMgS\nhP2+j+v/nE7tMOOtWYAwazw6P4FJqQ9I0myOt+Zw4HJvzYSS7jB07e4yFaaD495TyZljLat4z8FA\nlqRZNy4QVB2QpNk7SZqNBg4hHG1+JbBDv23CJgC7xcoX0UrArSJyH+HT/x+dc9dHyrIb8DpwdaTr\nq4pI0uwO4G5g39hZVMP0AJDe8wQVHBzrzDHvHvBxIPDfsbOo+JI0u8Jbc+Ugq2uvB37prVk9SbPa\nLBhxzk0BNo6dw1uzMGFmf8+qrXxW0YwGJnlrztNdTSphGHBL7BCqUJOBb8YO0SydOQ7GAL9I0mxK\n7CCqOww2+ErS7A3gKmCXzidSwN7Ao3p3RzUqSbNHgGuAw2Jn6SQRuUBEZojIg7GzNGkdtKyi12hZ\nRRV5awTYCRgXO4uqhMuoZ2lFVN6apQiLI0fHzqIq51jgB96aVWMH6aALCQtXKyPfhWZtdHDca6YB\nK3prFokdpBm1HxwTZo1P01tuqkE3A4m3Zt3YQWrmR8BNSZrdFzuIqpYkzTxhx4qjY2fpFOfcrcCL\nsXM0aSXg1STNXokdRBUnXzjtgTVjZ2lGrQfH+YEfWwI/jp1FVUO+k8lvgV1jZ6kLb82KwP7AUbGz\nqMo6GdjZW7Ne7CBqrnSnit5VudKKui/IOx44OUmzV2MHUZUyATiXcMy4Kt8RwK+TNNM3TtWSJM1e\n8NacTuizumagRN6apYG7CKcUNmNxwv7Uqvc44FJvzcwmX/cmsEWSZjNKyDRPtR0ce2s2BzYEvho7\ni6qcO4ClvDUbJmlWtQUvleKtWZuw0vmjsbOoyjsTeNxbY5I0y2KHiankQ3vWIxwZvH0Lr32uoAyq\nuxwGnNbC6yYAGwBFDY4bPrinloPjvPD/JMLxs2/EzqOqJUmz2d6a3xBKK3RwXK4xwE9izByo3pKk\n2avemjGEEovtYueJqeRDe4YBLkmzJwtoS/WAJM1mAU3/PHhrHiH8PP2loCgNH9xT15rj7QjF/7+K\nHURV1gRg1/yDliqBt+bjhNP4WplxUGow5wNremu2jR2kTCIyAbgdWE9EvIjs2cHLa+2wKkq0WuXa\nzRzng5kTgaP1+FnVhvsItw43A/4WOUuvOhk4KUmzl2MHUb0hSbO3vDVHAmO9NZsmaTY7dqYyOOdi\nbje5DuFkQqXaNRn4cowL13HmeGdgKHBF7CCquvJDQi4Cvh05Sk/y1nyGUGf889hZVM/p+7d/56gp\nepfOHKuiRJs5jjI4FpFERG4WkYdE5B8isn8nrpsfP3sycGivzhiojroY+Jq3ZvHYQXpJfndnLOHu\njq4JUIXK/+0fCZzorVkodp4epINjVZR6DY4Jt6MPcs5tQLgtva+IrN+B6/4AeDxJs5s6cC3V45I0\ne5qwc8X/xM7SY3YClgR+HTuI6k1Jmt0ITAX2ihylp+QfNlalhcVXSg3iX8BC3pqidlJpWJTBsXNu\nunPuvvzrmcAjhA5VGm/NMoT9Ug8r8zqqdi5A32AL460ZSthJZlR+4IpSZRkFHO2tWSJ2kB6yJvBM\nkmZvxQ6iqi8vX4wyexy95jjfPmY4cGfJlzocSHVfWlWwa4AN8/14Vft2B/4NXBs7iOpt+V7HfyWc\nvqiKoSUVqmhRBsdRd6sQkSUJiyMOyGeQ+x4fQYEblHtrVgf2BjZqtQ2lBpOk2RvemgnAHsCxbTbX\n8AblvchbsyhwHLBrPmOgVNmOBP7qrTknSbN/xw7TA3RwrIpWr8GxiCwEXAlc4py7uv/3StigfAxw\nTpJm01p8vVLzcgFwtbdmTJsLPRveoLxH7Qvcm6TZ7bGDqHpI0uwxb82VhBKLQ2Pn6QHDgCdih1A9\nZTLw8U5fNNZuFUMIm7E/7JwbX+a18oMELDCuzOuo+krS7D7gRWDr2FmqKl8TcDgwOnYWVTtjgL28\nNUnsID1AZ45V0Z6gRjXHmwPfBLYWkXvzXzsUfZF8S6jTgBOSNPtP0e0r1Y8uzGvPoYQ1AQ/FDqLq\nJUmzZ4BzaL8sSoUDQHRwrIo0mfBz1VFRyiqcc7fRmYH5V4BV0IMEVPl+DRzvrVk2SbOBdcNqHrw1\nqwD7EBbmKhXDKcBj3pqPJmn2cOwwVZRPRunMsSrak8Dq3poFO3mqcfTdKsqSH8xwOrCfbiujypak\n2QvADcCusbNU0FHAL5M0eyp2EFVP+QfaU4ETY2epsOWA2bqwURUpSbM3gelAR8ueenZwTNjP+K4k\nzW6OHUTVxrnASG/NGrGDVIW3Zl1gF8LexkrF9BPAeGs+FTtIRemssSpLx3es6MnBcb7n7H7AIbGz\nqPpI0uwG4ExgkrdmrchxquIE4Ix85l2paJI0e51Qdzw2LxFQzdF6Y1WWJ+hw3XFPDo4J5RSn621a\n1WlJmp1BWAQ6yVvT8UUEVeKt2QT4DFDqjjVKNeEiYEXg87GDVJDOHKuy6Mxxu7w12wMbEgYoSnVc\nkmY/IZQJ3OytWS92ni52EnB8kmavxg6iFEC+4Gc0cLK3pufeH0umg2NVFh0ct8NbszDhtvZBSZrN\nip1H1VeSZn1bQ/1FZ5A/yFuzDeE22Xmxsyg1wNXAa8BusYNUjB4Aosqig+M2/T9gCvDH2EGUStLs\nAkKJzy91Fuo9eT3nWOBI3UlGdZv86PKRhK0ZF46dp0J05liVRQfHrfLWLEe4HXZw/o+bUt2gr572\nh1FTdJevEvZYvzx2EKUGk6TZJOBRYO/YWaog/xCxCuBjZ1E96QVgQW/Nhzp1wZ4ZHANHA1foBu6q\nmyRpNptwct7RWl4B3poFCXvJjsz/bJTqVqOBI7w1S8UOUgFrAk/rnSBVhnzCs6PHSPfE4Dhf9PRN\n9PhP1YWSNHscOBk4T8sr2AuYRjgwRamulaTZfcBNwI9iZ6kArTdWZetoaUWvvFGPA05N0uy52EGU\nmovxwKLAD2IHiSU/tfIYYJSWPqmKOArY31uzYuwgXU7rjVXZdHDcDG/NCGBjwi4VSnWlJM3eIcya\njqnxASH7A39L0uyu2EGUakSSZpOB44GVY2fpcnoAiCrbZDp4EEilB8f5LerTCPWLunWb6mpJmj0C\n/B81/CCXL5g9GDgidhalmpGk2fgkzR6InaPL6cyxKltHZ44X7NSFSvJN4E101buqjv8DTOwQERwO\nXJWkmYsdRClVOB0cq7J1dEFeZQfH+cr/U4Avaf2iqor8BK47YufopK8ss9DKwHeBj8fOopQqVr5v\nuS7IU2V7CljNW7NQJ3ZFiTI4FpEdCAuUhgLnOefGNfN6b80ywDXAcUma3VlCRKVUP+302e2XXugA\n4BdJmj1dVj6l1HvafY9t0vLA20mavVTiNVTNJWn2prfmWWANOvBBrOM1xyIyFPgJsAPwUWA3EVm/\n0dcvN3TIUOAy4C9Jmp1dTkqlVJ92++ziCwz5HGFHGaVUydrtry3QkgrVKR2rO46xIG9T4J/OuanO\nubcIA90vNfri41dZbDRhxvvAkvIppd6vrT77wtuzz03S7MXS0iml+murv7ZAB8eqU3p6cLwa7z9i\nclr+WEOWWGDICGCXvHZTKVW+tvrsz/71xi+LDqSUmqu2+msLtN5YdUrHFuXFqDludfHcUIDzXph1\nxG2vvrMMIssUmEmpmFbP/zs0aoq5a6vPPv7G7BVE5I0C8ygVU0/31zErL3rJrdsOb3hr1IWGDJEZ\nb8/+6bYia7V4XaUacvwqi728xsIL7Dd12+GbzOt5r87mgQOefu2sfg813WdjDI6fBpJ+v08In2zf\nJSIjgBEDXrcGwG2vvjOhxGxKxXSEiDw14LGJzrmJMcL001afBW4tK5hSEfVkfz16+qzNW7jmyfkv\npUpz1LOv9325ynye+lngoEEeb7jPDpkzp7O7oInIgoAjhH8GuAvYzTn3yHxetwhwNnAi8E4J0Q4k\nrO4tg7bdmXar2vZQwuEY+zjnum6GtYZ9too/Q9p259rW/tqaqv09V7ntKmYus+2m+2zHZ46dc2+L\nyA+BPxMCnz+/Tpu/7g0Reco5V0ptk4i85Jybqm2X33YVM3eg7ae68Y0W6tdnK/wzpG13qG3tr82r\n4t9zVduuYuYOtN1Un42yz7Fz7k/An2JcWynVPO2zSlWH9lel2hNjtwqllFJKKaW6kg6OlVJKKaWU\nylVtcDxR2+6JtstqV9vuPhMr2HZZ7WrbvdN2We3GNlHb7om2y2q3Nm13fLcKpZRSSimlulXVZo6V\nUkoppZQqjQ6OlVJKKaWUykXZyq0VIrIDYXPoocB5zrlxBbY9FXiZsPH5W865TVts5wLAAs855zbM\nH1sO+A2wJjAV2MU591JBbR8LfBd4Pn/aKOfcdS20nQAXAysSjh491zl3VhHZ59F229lFZFFgErAI\nsDDwe+fcqIJyz63ttnPn7Q8FMmCac+6LRf2cdIsq9Ne8rcr1We2vTbXddu5+19A+23rbU9H3WO2z\n82+37cz9rtFWf63EzHH+P/kTYAfgo8BuIrJ+gZeYA4xwzg1v540WuJCQsb+RwA3OufWAm/LfF9X2\nHOD0PPfwVn+IgLeAg5xzGwCbAfvmf75FZJ9b221nd87NArZ2zm0MfBzYWkS2KCL3PNou6s/8AODh\nvD2KyNwtKtRfoZp9Vvtr420X1V9B+2w79D1W+2wj7XZNf63E4BjYFPinc26qc+4t4DLgSwVfY0i7\nDTjnbgVeHPDwTsBF+dcXAV8usG0oJvd059x9+dczgUeA1Sgg+zzahmKyv5Z/uTBhxuNFivszH6xt\naDO3iKwO7Aic16+tQjJ3iUr0V6hmn9X+2lTbUEBu7bOF0PdYtM/Op13okv5alcHxaoDv9/tpvPeX\nX4Q5wI0ikonI9wpsF2Al59yM/OsZwEoFt7+fiNwvIueLyLLtNiYiawHDgTspOHu/tu/IH2o7u4gs\nICL35fluds49VFTuubRdRO4zgEOB2f0eK/vnpJOq3F+hQn1W++t82y4kN9pn26XvsTnts/Nst5DM\nFNBfqzI4Lnu/uc2dc8OBzxNuSWxZxkWcc3Mo9v/lbGBtYGPgWeC0dhoTkSWBK4EDnHOv9P9eu9nz\ntq/I255JQdmdc7PzWzOrA58Rka2Lyj1I2yPazS0iXyDUtN3LXD4hl/Bz0mk90V+hu/us9tf5tj2i\niNzaZwuh77Fon51PuyOKyFxUf63K4PhpIOn3+4TwybYQzrln8/8+D1xFuMVUlBkisjKAiKwCPFdU\nw86555xzc/K/6PNoI7eILETotL9yzl2dP1xI9n5tX9LXdpHZ8/b+A6TAJkXlHqRtU0DuTwM7icgU\nYAKwjYj8qujMkVW5v0IF+qz214baLqK/gvbZtul7rPbZBtrtqv5alcFxBvyXiKwlIgsDXwf+UETD\nIrK4iCyVf70E8DngwSLazv0B2CP/eg/g6nk8tyn5X3Cfr9BibhEZApwPPOycG9/vW21nn1vbRWQX\nkeX7bruIyGLAdsC9BeUetO2+ztVqbufcaOdc4pxbG9gV+Itz7ltFZO4iVe6v0OV9Vvtr4223219B\n+2y79D1W+2yj7XZTf63MCXki8nne22bmfOfcyQW1uzbhkyyEre0ubbVtEZkAbAUsT6hpORr4PXA5\nsAbtbTMzsO1jgBGE2w9zgCnA3v1qapppewvgFuAB3rvVMAq4q93sc2l7NLBbu9lFZENCYf0C+a9f\nOedOlbBlS7u559b2xe3m7neNrYCDnXM7FZG5m1Shv+btVa7Pan9tqu3C+mt+He2zzber77HaZxtt\nt2v6a2UGx0oppZRSSpWtKmUVSimllFJKlU4Hx0oppZRSSuV0cKyUUkoppVROB8dKKaWUUkrldHCs\nlFJKKaVUTgfHSimllFJK5XRwrJRSSimlVE4Hx0oppZRSSuX+P7Cg1sEOnH31AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa693635810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datastuff.make_plots('inflammation-01.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that if we make changes to the file and want to see those reflected in our current notebook session, we can use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'datastuff' from 'datastuff.pyc'>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reload(datastuff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Errors and exceptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encountering errors is part of coding. If you are coding, you will hit errors. The important thing to remember is that errors that are loud are the best kind, because they usually give hints as to what the problem is. In python, errors are known as ``exceptions``, and there are a few common varieties. Familiarity with these varieties helps to more quickly identify the root of the problem, which means we can more quickly fix it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import some example code; we need to first add the location of the module we want to import to the list of places python looks for modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('scripts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can import it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import errors_01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One example is the ``IndexError``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-179-44b1b870e6a6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0merrors_01\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfavorite_ice_cream\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/home/alter/Dropbox/SWC-2015.05.27/osu-data/python/scripts/errors_01.pyc\u001b[0m in \u001b[0;36mfavorite_ice_cream\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[1;34m\"strawberry\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     ]\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[1;32mprint\u001b[0m \u001b[0mice_creams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "errors_01.favorite_ice_cream()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's dissect this. The first line shows where the error originates in *our* code. In this case it's the only line in the cell. The next set of lines shows where the error originates at the source: that is, inside the definition of ``errors_01.favorite_ice_cream()`` in line 7 of the module ``errors_01``. The last line gives us a hint as to the nature of the ``IndexError``: we appear to be using an index for an element of a list that doesn't exist."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see this type of error for lists of our own:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = ['marklar', 'lolcats']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-181-016a87a854bc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "a[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another common type of exception is the ``SyntaxError``. This is the equivalent of using poor grammar, and the python interpreter is telling us it doesn't understand what we are saying:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-182-3804679f8f3a>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-182-3804679f8f3a>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    for item in a\u001b[0m\n\u001b[1;37m                 ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "for item in a\n",
    "    print item"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's also the ``IOError``. \"IO\" means \"input/output\", which commonly means reading and writing to disk. It commonly shows up when we try to read a file that doesn't exist:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: 'myfile.txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-184-f6e1ac4aee96>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfile_handle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'myfile.txt'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIOError\u001b[0m: [Errno 2] No such file or directory: 'myfile.txt'"
     ]
    }
   ],
   "source": [
    "file_handle = open('myfile.txt', 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or if we try to read a file that we've opened exclusively for writing, in which case python is telling us that our code is probably doing something we don't want it to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_handle = open('scripts/myfile.txt', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "File not open for reading",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-186-056c6cd01625>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfile_handle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIOError\u001b[0m: File not open for reading"
     ]
    }
   ],
   "source": [
    "file_handle.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are other types of exceptions, but these are the most common. You need not know all of them, but having some knowledge of the basic varieties is valuable for understanding what's going wroing, and then how to fix it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defensive programming: using assertions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Say we write a function that does something a bit obtuse (heh): Given the coordinates of the lower-left and upper-right corners of a rectangle, it normalizes the rectangle so that its lower-left coordinate is at the origin (0, 0), and its longest side is 1.0 units long:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def normalize_rectangle(rect):\n",
    "    \"\"\"Normalizes a rectangle so that it is at the origin, \n",
    "    and 1.0 units long on its longest axis.\n",
    "    \n",
    "    :Arguments:\n",
    "        *rect*\n",
    "            a tuple of size four, giving (x0, y0, x1, y1), the (x,y)\n",
    "            positions of the lower-left and upper-right corner\n",
    "            \n",
    "    :Returns:\n",
    "        *newrect*\n",
    "            a tuple of size four, giving the new coordinates of our rectangle's\n",
    "            vertices\n",
    "    \n",
    "    \"\"\"\n",
    "    x0, y0, x1, y1 = rect\n",
    "    \n",
    "    assert x0 < x1, 'Invalid x coordinates'\n",
    "    assert y0 < y1, 'Invalid y coordinates'\n",
    "    \n",
    "    dx = x1 - x0\n",
    "    dy = y1 - y0\n",
    "    \n",
    "    if dx > dy:\n",
    "        scaled = float(dx) / dy\n",
    "        upper_x, upper_y = 1.0, scaled\n",
    "    else:\n",
    "        scaled = float(dx) / dy\n",
    "        upper_x, upper_y = scaled, 1.0\n",
    "        \n",
    "    assert 0 < upper_x <= 1.0, 'Calculated upper X coordinate invalid'\n",
    "    assert 0 < upper_y <= 1.0, 'Calculated upper Y coordinate invalid'\n",
    "        \n",
    "    return (0, 0, upper_x, upper_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that we added a nice documentation string at the top that explicitly states the functions inputs (arguments) and outputs (returns), so when we use it we don't need to mind its details anymore. Also, at the beginning of the function we have ``assert`` statements that check the validity of the inputs, while at the end of the function we have ``assert`` statements that check the validity of the outputs. \n",
    "\n",
    "These statements codify what our documentation says about what the inputs should mean and what the output should look like, guarding against cases where the function is given inputs it wasn't designed to handle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test the function!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, 0.5, 1.0)"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize_rectangle((3, 4, 4, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good. First point is at the origin; longest side is 1.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, 0.5, 1.0)"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize_rectangle((0.0, 1.0, 2.0, 5.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Calculated upper Y coordinate invalid",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-194-a41732464e3f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnormalize_rectangle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-188-57d2a27bd6aa>\u001b[0m in \u001b[0;36mnormalize_rectangle\u001b[1;34m(rect)\u001b[0m\n\u001b[0;32m     31\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mupper_x\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Calculated upper X coordinate invalid'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m     \u001b[1;32massert\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mupper_y\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Calculated upper Y coordinate invalid'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     34\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupper_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupper_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAssertionError\u001b[0m: Calculated upper Y coordinate invalid"
     ]
    }
   ],
   "source": [
    "normalize_rectangle((0.0, 0.0, 5.0, 1.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh...what happened here? The inputs should be fine, but an assertion caught something strange in the output. Looking closely at the code, we spot a mistake in line 26:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def normalize_rectangle(rect):\n",
    "    \"\"\"Normalizes a rectangle so that it is at the origin, \n",
    "    and 1.0 units long on its longest axis.\n",
    "    \n",
    "    :Arguments:\n",
    "        *rect*\n",
    "            a tuple of size four, giving (x0, y0, x1, y1), the (x,y)\n",
    "            positions of the lower-left and upper-right corner\n",
    "            \n",
    "    :Returns:\n",
    "        *newrect*\n",
    "            a tuple of size four, giving the new coordinates of our rectangle's\n",
    "            vertices\n",
    "    \n",
    "    \"\"\"\n",
    "    x0, y0, x1, y1 = rect\n",
    "    \n",
    "    assert x0 < x1, 'Invalid x coordinates'\n",
    "    assert y0 < y1, 'Invalid y coordinates'\n",
    "    \n",
    "    dx = x1 - x0\n",
    "    dy = y1 - y0\n",
    "    \n",
    "    if dx > dy:\n",
    "        scaled = float(dy) / dx\n",
    "        upper_x, upper_y = 1.0, scaled\n",
    "    else:\n",
    "        scaled = float(dx) / dy\n",
    "        upper_x, upper_y = scaled, 1.0\n",
    "        \n",
    "    assert 0 < upper_x <= 1.0, 'Calculated upper X coordinate invalid'\n",
    "    assert 0 < upper_y <= 1.0, 'Calculated upper Y coordinate invalid'\n",
    "        \n",
    "    return (0, 0, upper_x, upper_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, 1.0, 0.2)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize_rectangle((0.0, 0.0, 5.0, 1.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, 0.6666666666666666, 1.0)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize_rectangle((2, 3, 4, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we were to try feeding in only three values instead of four?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "need more than 3 values to unpack",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-223-c5d8e9954719>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnormalize_rectangle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-222-d4ce9e9a4cfa>\u001b[0m in \u001b[0;36mnormalize_rectangle\u001b[1;34m(rect)\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m     \"\"\"\n\u001b[1;32m---> 16\u001b[1;33m     \u001b[0mx0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrect\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[0mx0\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mx1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Invalid x coordinates'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: need more than 3 values to unpack"
     ]
    }
   ],
   "source": [
    "normalize_rectangle((2, 4, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a rather unhelpful error message. We can also add an assertion that checks that the input is long enough, and gives a message indicating what the problem is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def normalize_rectangle(rect):\n",
    "    \"\"\"Normalizes a rectangle so that it is at the origin, \n",
    "    and 1.0 units long on its longest axis.\n",
    "    \n",
    "    :Arguments:\n",
    "        *rect*\n",
    "            a tuple of size four, giving (x0, y0, x1, y1), the (x,y)\n",
    "            positions of the lower-left and upper-right corner\n",
    "            \n",
    "    :Returns:\n",
    "        *newrect*\n",
    "            a tuple of size four, giving the new coordinates of our rectangle's\n",
    "            vertices\n",
    "    \n",
    "    \"\"\"\n",
    "    assert len(rect) == 4, \"Rectangle must have 4 elements\"\n",
    "\n",
    "    x0, y0, x1, y1 = rect\n",
    "    \n",
    "    assert x0 < x1, 'Invalid x coordinates'\n",
    "    assert y0 < y1, 'Invalid y coordinates'\n",
    "    \n",
    "    dx = x1 - x0\n",
    "    dy = y1 - y0\n",
    "    \n",
    "    if dx > dy:\n",
    "        scaled = float(dy) / dx\n",
    "        upper_x, upper_y = 1.0, scaled\n",
    "    else:\n",
    "        scaled = float(dx) / dy\n",
    "        upper_x, upper_y = scaled, 1.0\n",
    "        \n",
    "    assert 0 < upper_x <= 1.0, 'Calculated upper X coordinate invalid'\n",
    "    assert 0 < upper_y <= 1.0, 'Calculated upper Y coordinate invalid'\n",
    "        \n",
    "    return (0, 0, upper_x, upper_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Rectangle must have 4 elements",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-226-c5d8e9954719>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnormalize_rectangle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-225-e9732fa1861f>\u001b[0m in \u001b[0;36mnormalize_rectangle\u001b[1;34m(rect)\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m     \"\"\"\n\u001b[1;32m---> 16\u001b[1;33m     \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrect\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Rectangle must have 4 elements\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m     \u001b[0mx0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrect\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAssertionError\u001b[0m: Rectangle must have 4 elements"
     ]
    }
   ],
   "source": [
    "normalize_rectangle((2, 4, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now when we use this function six months later, we won't have to scratch our heads too long when this sort of thing happens! *Defensive programming* in this way makes code more robust, easier to debug, and easier to maintain. This is especially important for scientific code, since incorrect results can occur silently and end up getting published, perhaps only getting uncovered years later. Much embarrassment and misinformation can be avoided by being skeptical of our code, using tools such as ``assert`` statements to make sure it keeps doing what we think it should."
   ]
  }
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