diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..763513e
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+.ipynb_checkpoints
diff --git a/DefensiveProgramming.ipynb b/DefensiveProgramming.ipynb
new file mode 100644
index 0000000..d3655ec
--- /dev/null
+++ b/DefensiveProgramming.ipynb
@@ -0,0 +1,143 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "   # Defensive programming (1)\n",
+    "   \n",
+    "   How much time do you spend writing software? How much time do you spend \n",
+    "   debugging that software? It turns out that it is very easy to spend lots\n",
+    "   of time fixing bugs and less time than you would like writing new software\n",
+    "   to do new science. This is a problem that is fairly well understood by\n",
+    "   the software engineering community, but many scientists don't take advantage \n",
+    "   of this knowledge. This afternoon we will take a brief look at some of the \n",
+    "   tools and technique to make your debugging less painful.\n",
+    "   \n",
+    "   We'll also think a bit about how you may know if your programmes are correct. \n",
+    "   This is a much harder but important problem. Even minor errors in research \n",
+    "   code can lead to the retraction of papers, as happened to Geoffrey Chang \n",
+    "   in 2006 (see http://dx.doi.org/10.1126/science.314.5807.1856). Chang did\n",
+    "   nothing malicious and committed no fraud, but because of a minor software \n",
+    "   error had two retract five papers just before Christmas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__NB: This notebook is designed for teaching about exceptions and error testing. It includes deliberate errors. There are probably accidental errors too.__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mean cell volume \n",
+    "First, we will look at how one programme can produce \n",
+    "the wrong answer, and how we can avoid this happening\n",
+    "when we use it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def cell_volume(X, Y, Z):\n",
+    "    # Return the volume of a unit cell \n",
+    "    # described by lattice vectors X, Y and Z\n",
+    "    # The volume is given by the determinant of\n",
+    "    # the matrix formed by sticking the three \n",
+    "    # vectors together. i.e.\n",
+    "    #\n",
+    "    #     | X[0] Y[0] Z[0] |\n",
+    "    # V = | X[1] Y[1] Z[1] |\n",
+    "    #     | X[2] Y[2] Z[2] |\n",
+    "    #\n",
+    "    # V = X[0].Y[1].Z[2] + Y[0].Z[1].X[2] \n",
+    "    #     + X[2].Y[0].Z[1] - Z[0].Y[1].X[2]\n",
+    "    #     - Y[0].X[1].Z[2] - X[0].Z[1].Y[2]\n",
+    "    \n",
+    "    volume = (X[0]*Y[1]*Z[2] + Y[0]*Z[1]*X[2] + X[2]*Y[0]*Z[1]  \n",
+    "           - Z[0]*Y[1]*X[2] - Y[0]*X[1]*Z[2] - X[0]*Z[1]*Y[2])\n",
+    "        \n",
+    "    return volume"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def mean_cell_volume(cell_list):\n",
+    "    # Return the avarage volume of a list \n",
+    "    # of unit cells. Each element of cell_list\n",
+    "    # should be a list of three lattice vectors, \n",
+    "    # each with three components. The volume of\n",
+    "    # each cell is calculated and summed before \n",
+    "    # being devided by the number of cells to give\n",
+    "    # the mean volume.\n",
+    "    \n",
+    "    num_cells = 0\n",
+    "    sum_volume = 0.0\n",
+    "    for cell in cell_list:\n",
+    "        X = cell[0]\n",
+    "        Y = cell[1]\n",
+    "        Z = cell[2]\n",
+    "        sum_volume = sum_volume + cell_volume(X, Y, Z)\n",
+    "        num_cells = num_cells + 1\n",
+    "    \n",
+    "    mean_volume = sum_volume/num_cells\n",
+    "    \n",
+    "    return mean_volume\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/DefensiveProgramming_2.ipynb b/DefensiveProgramming_2.ipynb
new file mode 100644
index 0000000..85afd4b
--- /dev/null
+++ b/DefensiveProgramming_2.ipynb
@@ -0,0 +1,82 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "   # Defensive programming (2)\n",
+    "   We have seen the basic idea that we can insert\n",
+    "   assert statments into code, to check that the \n",
+    "   results are what we expect, but how can we test\n",
+    "   software more fully? Can doing this help us \n",
+    "   avoid bugs in the first place?\n",
+    "   \n",
+    "   One possible approach is **test driven development**.\n",
+    "   Many people think this reduces the number of bugs in \n",
+    "   software as it is written, but evidence for this in the \n",
+    "   sciences is somewhat limited as it is not always easy \n",
+    "   to say what the right answer should be before writing the\n",
+    "   software. Having said that, the tests involved in test\n",
+    "   driven development are certanly useful even if some of\n",
+    "   them are written after the software.\n",
+    "   \n",
+    "   We will look at a new (and quite difficult) problem, \n",
+    "   finding the overlap between ranges of numbers. For \n",
+    "   example, these could be the dates that different \n",
+    "   sensors were running, and you need to find the \n",
+    "   date ranges where all sensors recorded data before\n",
+    "   running further analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"python-overlapping-ranges.svg\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Start off by imagining you have a working function `range_overlap` that takes\n",
+    "a list of tuples. Write some assert statments that would check if the answer from this\n",
+    "function is correct. Put these in a function. Think of different cases and \n",
+    "about edge cases (which may show a subtle bug)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def test_range_overlap():\n",
+    "    "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notes/DefensiveProgrammingNotes.ipynb b/notes/DefensiveProgrammingNotes.ipynb
new file mode 100644
index 0000000..f3fa3c7
--- /dev/null
+++ b/notes/DefensiveProgrammingNotes.ipynb
@@ -0,0 +1,575 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "   # Defensive programming (1)\n",
+    "   \n",
+    "   How much time do you spend writing software? How much time do you spend \n",
+    "   debugging that software? It turns out that it is very easy to spend lots\n",
+    "   of time fixing bugs and less time than you would like writing new software\n",
+    "   to do new science. This is a problem that is fairly well understood by\n",
+    "   the software engineering community, but many scientists don't take advantage \n",
+    "   of this knowledge. This afternoon we will take a brief look at some of the \n",
+    "   tools and technique to make your debugging less painful.\n",
+    "   \n",
+    "   We'll also think a bit about how you may know if your programmes are correct. \n",
+    "   This is a much harder but important problem. Even minor errors in research \n",
+    "   code can lead to the retraction of papers, as happened to Geoffrey Chang \n",
+    "   in 2006 (see http://dx.doi.org/10.1126/science.314.5807.1856). Chang did\n",
+    "   nothing malicious and committed no fraud, but because of a minor software \n",
+    "   error had two retract five papers just before Christmas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__NB: This notebook is designed for teaching about exceptions and error testing. It includes deliberate errors. There are probably accidental errors too.__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mean cell volume \n",
+    "First, we will look at how one programme can produce \n",
+    "the wrong answer, and how we can avoid this happening\n",
+    "when we use it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def cell_volume(X, Y, Z):\n",
+    "    # Return the volume of a unit cell \n",
+    "    # described by lattice vectors X, Y and Z\n",
+    "    # The volume is given by the determinant of\n",
+    "    # the matrix formed by sticking the three \n",
+    "    # vectors together. i.e.\n",
+    "    #\n",
+    "    #     | X[0] Y[0] Z[0] |\n",
+    "    # V = | X[1] Y[1] Z[1] |\n",
+    "    #     | X[2] Y[2] Z[2] |\n",
+    "    #\n",
+    "    # V = X[0].Y[1].Z[2] + Y[0].Z[1].X[2] \n",
+    "    #     + X[2].Y[0].Z[1] - Z[0].Y[1].X[2]\n",
+    "    #     - Y[0].X[1].Z[2] - X[0].Z[1].Y[2]\n",
+    "    \n",
+    "    volume = (X[0]*Y[1]*Z[2] + Y[0]*Z[1]*X[2] + X[2]*Y[0]*Z[1]  \n",
+    "           - Z[0]*Y[1]*X[2] - Y[0]*X[1]*Z[2] - X[0]*Z[1]*Y[2])\n",
+    "        \n",
+    "    return volume"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "240.0"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cell_volume([4.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 6.0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def mean_cell_volume(cell_list):\n",
+    "    # Return the avarage volume of a list \n",
+    "    # of unit cells. Each element of cell_list\n",
+    "    # should be a list of three lattice vectors, \n",
+    "    # each with three components. The volume of\n",
+    "    # each cell is calculated and summed before \n",
+    "    # being devided by the number of cells to give\n",
+    "    # the mean volume.\n",
+    "    \n",
+    "    num_cells = 0\n",
+    "    sum_volume = 0.0\n",
+    "    for cell in cell_list:\n",
+    "        X = cell[0]\n",
+    "        Y = cell[1]\n",
+    "        Z = cell[2]\n",
+    "        sum_volume = sum_volume + cell_volume(X, Y, Z)\n",
+    "        num_cells = num_cells + 1\n",
+    "    \n",
+    "    mean_volume = sum_volume/num_cells\n",
+    "    \n",
+    "    return mean_volume\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "240.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_cell_volume([[[4.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 6.0]],\n",
+    "                  [[10.0, 0.0, 0.0], [0.0, 4.0, 0.0], [0.0, 0.0, 6.0]],\n",
+    "                  [[6.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 4.0]]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###\"Wrong\" input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-5-4eccc02f70e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m mean_cell_volume([[[4.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 6.0]],\n\u001b[1;32m      2\u001b[0m                   \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m                   [[4.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 6.0]]])\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-3-363b41d915d2>\u001b[0m in \u001b[0;36mmean_cell_volume\u001b[0;34m(cell_list)\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mZ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m         \u001b[0msum_volume\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum_volume\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcell_volume\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mZ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m         \u001b[0mnum_cells\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_cells\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-1-68194218b676>\u001b[0m in \u001b[0;36mcell_volume\u001b[0;34m(X, Y, Z)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     volume = (X[0]*Y[1]*Z[2] + Y[0]*Z[1]*X[2] + X[2]*Y[0]*Z[1]  \n\u001b[0;32m---> 17\u001b[0;31m            - Z[0]*Y[1]*X[2] - Y[0]*X[1]*Z[2] - X[0]*Z[1]*Y[2])\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mvolume\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "mean_cell_volume([[[4.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 6.0]],\n",
+    "                  [[4.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0]],\n",
+    "                  [[4.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 6.0]]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "float division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-6-dbf6a450964a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean_cell_volume\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-3-363b41d915d2>\u001b[0m in \u001b[0;36mmean_cell_volume\u001b[0;34m(cell_list)\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0mnum_cells\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_cells\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m     \u001b[0mmean_volume\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum_volume\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnum_cells\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmean_volume\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "mean_cell_volume([])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What is python telling us?\n",
+    "That something went wrong, where it went wrong, what went\n",
+    "wrong, and what the programme was doing at the time. This\n",
+    "is an exception.\n",
+    "\n",
+    "* Exception class (e.g `ZeroDivisionError`)\n",
+    "* Some further information (e.g. `float division by zero`)\n",
+    "* File (or cell) name and line number of each function in the call stack (e.g. in `mean_cell_volume` at  line `---> 19` inside cell `ipython-input-...`)\n",
+    "\n",
+    "We can create these ourselves when we run code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "Exception",
+     "evalue": "something went wrong",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-7-92c367219143>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"something went wrong\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mException\u001b[0m: something went wrong"
+     ]
+    }
+   ],
+   "source": [
+    "raise Exception(\"something went wrong\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What if we get the wrong answer?\n",
+    "This is a more difficult problem to spot - the avarage volume cannot be 0.0!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_cell_volume([[[4.0, 0.0, 0.0], [0.0, -10.0, 0.0], [0.0, 0.0, 6.0]],\n",
+    "                  [[4.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 6.0]],\n",
+    "                  [[-4.0, 0.0, 0.0], [0.0, -10.0, 0.0], [0.0, 0.0, 6.0]],\n",
+    "                  [[-4.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 6.0]]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The reason is that there is a bug in `cell_volume`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-240.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cell_volume([4.0, 0.0, 0.0], [0.0, -10.0, 0.0], [0.0, 0.0, 6.0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The volume should always be positive. We can check for this. This kind of check is known as an assertion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "The volume must be positive",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-8ba4a7a44c83>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mvolume\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcell_volume\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m10.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mvolume\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The volume must be positive\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mvolume\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAssertionError\u001b[0m: The volume must be positive"
+     ]
+    }
+   ],
+   "source": [
+    "volume = cell_volume([4.0, 0.0, 0.0], [0.0, -10.0, 0.0], [0.0, 0.0, 6.0])\n",
+    "if (volume < 0.0):\n",
+    "    raise AssertionError(\"The volume must be positive\")\n",
+    "print volume"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can write these more easily with the **assert** statment. It is \n",
+    "good practice to put these in your code when you write it (and you \n",
+    "know what it does, and what assumptions you have made). These act \n",
+    "as a form of documentation as well as a form of protection. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "The volume must be positive",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-11-7510ec95e9c1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mvolume\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcell_volume\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m10.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mvolume\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"The volume must be positive\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mvolume\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAssertionError\u001b[0m: The volume must be positive"
+     ]
+    }
+   ],
+   "source": [
+    "volume = cell_volume([4.0, 0.0, 0.0], [0.0, -10.0, 0.0], [0.0, 0.0, 6.0])\n",
+    "assert volume >= 0.0, \"The volume must be positive\"\n",
+    "print volume"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can think about three types of assert statment:\n",
+    "\n",
+    "* **precondition** - something that must be true at \n",
+    "the start of a function in order for it to work correctly.\n",
+    "\n",
+    "* **invariant** - something that is always true at a \n",
+    "particular point inside a piece of code.\n",
+    "\n",
+    "* **postcondition** - something that the function guarantees is true when it finishes.\n",
+    "\n",
+    "Lets think of some and add these to the functions above. My collection is inserted below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def cell_volume(X, Y, Z):\n",
+    "    # Return the volume of a unit cell \n",
+    "    # described by lattice vectors X, Y and Z\n",
+    "    # The volume is given by the determinant of\n",
+    "    # the matrix formed by sticking the three \n",
+    "    # vectors together. i.e.\n",
+    "    #\n",
+    "    #     | X[0] Y[0] Z[0] |\n",
+    "    # V = | X[1] Y[1] Z[1] |\n",
+    "    #     | X[2] Y[2] Z[2] |\n",
+    "    #\n",
+    "    # V = X[0].Y[1].Z[2] + Y[0].Z[1].X[2] \n",
+    "    #     + X[2].Y[0].Z[1] - Z[0].Y[1].X[2]\n",
+    "    #     - Y[0].X[1].Z[2] - X[0].Z[1].Y[2]\n",
+    "    \n",
+    "    assert len(X) == 3, \"X must be a three-vector\"\n",
+    "    assert len(Y) == 3, \"Y must be a three-vector\"\n",
+    "    assert len(Z) == 3, \"Z must be a three-vector\"\n",
+    "    \n",
+    "    volume = (X[0]*Y[1]*Z[2] + Y[0]*Z[1]*X[2] + X[2]*Y[0]*Z[1]  \n",
+    "           - Z[0]*Y[1]*X[2] - Y[0]*X[1]*Z[2] - X[0]*Z[1]*Y[2])\n",
+    "    \n",
+    "    assert volume >= 0.0, \"The calculated volume must be positive\"\n",
+    "    \n",
+    "    return volume"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def mean_cell_volume(cell_list):\n",
+    "    # Return the avarage volume of a list \n",
+    "    # of unit cells. Each element of cell_list\n",
+    "    # should be a list of three lattice vectors, \n",
+    "    # each with three components. The volume of\n",
+    "    # each cell is calculated and summed before \n",
+    "    # being devided by the number of cells to give\n",
+    "    # the mean volume.\n",
+    "    \n",
+    "    num_cells = 0\n",
+    "    sum_volume = 0.0\n",
+    "    for cell in cell_list:\n",
+    "        X = cell[0]\n",
+    "        Y = cell[1]\n",
+    "        Z = cell[2]\n",
+    "        sum_volume = sum_volume + cell_volume(X, Y, Z)\n",
+    "        num_cells = num_cells + 1\n",
+    "    \n",
+    "    assert num_cells >= 1, \"One or more cells must be provided\"\n",
+    "    mean_volume = sum_volume/num_cells\n",
+    "    \n",
+    "    return mean_volume"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "Z must be a three-vector",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-13-4eccc02f70e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m mean_cell_volume([[[4.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 6.0]],\n\u001b[1;32m      2\u001b[0m                   \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m                   [[4.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 6.0]]])\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-3-363b41d915d2>\u001b[0m in \u001b[0;36mmean_cell_volume\u001b[0;34m(cell_list)\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mZ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m         \u001b[0msum_volume\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum_volume\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcell_volume\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mZ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m         \u001b[0mnum_cells\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_cells\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-12-0d0fc01c2bb1>\u001b[0m in \u001b[0;36mcell_volume\u001b[0;34m(X, Y, Z)\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"X must be a three-vector\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Y must be a three-vector\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m     \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mZ\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Z must be a three-vector\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     volume = (X[0]*Y[1]*Z[2] + Y[0]*Z[1]*X[2] + X[2]*Y[0]*Z[1]  \n",
+      "\u001b[0;31mAssertionError\u001b[0m: Z must be a three-vector"
+     ]
+    }
+   ],
+   "source": [
+    "mean_cell_volume([[[4.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 6.0]],\n",
+    "                  [[4.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0]],\n",
+    "                  [[4.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 6.0]]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "float division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-14-dbf6a450964a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean_cell_volume\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-3-363b41d915d2>\u001b[0m in \u001b[0;36mmean_cell_volume\u001b[0;34m(cell_list)\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0mnum_cells\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_cells\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m     \u001b[0mmean_volume\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum_volume\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnum_cells\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmean_volume\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "mean_cell_volume([])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "The calculated volume must be positive",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-15-6bcdb58d6c1c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcell_volume\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m10.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-12-0d0fc01c2bb1>\u001b[0m in \u001b[0;36mcell_volume\u001b[0;34m(X, Y, Z)\u001b[0m\n\u001b[1;32m     21\u001b[0m            - Z[0]*Y[1]*X[2] - Y[0]*X[1]*Z[2] - X[0]*Z[1]*Y[2])\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0;32massert\u001b[0m \u001b[0mvolume\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"The calculated volume must be positive\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mvolume\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAssertionError\u001b[0m: The calculated volume must be positive"
+     ]
+    }
+   ],
+   "source": [
+    "cell_volume([4.0, 0.0, 0.0], [0.0, -10.0, 0.0], [0.0, 0.0, 6.0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are interested - the bug is in cell_volume. This should \n",
+    "return the absolute value of the determinant. The handedness of\n",
+    "the three vectors should not matter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notes/DefensiveProgrammingNotes.pdf b/notes/DefensiveProgrammingNotes.pdf
new file mode 100644
index 0000000..68a62c1
Binary files /dev/null and b/notes/DefensiveProgrammingNotes.pdf differ
diff --git a/notes/DefensiveProgramming_2_Notes.ipynb b/notes/DefensiveProgramming_2_Notes.ipynb
new file mode 100644
index 0000000..955f6b5
--- /dev/null
+++ b/notes/DefensiveProgramming_2_Notes.ipynb
@@ -0,0 +1,307 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "   # Defensive programming (2)\n",
+    "   We have seen the basic idea that we can insert\n",
+    "   assert statments into code, to check that the \n",
+    "   results are what we expect, but how can we test\n",
+    "   software more fully? Can doing this help us \n",
+    "   avoid bugs in the first place?\n",
+    "   \n",
+    "   One possible approach is **test driven development**.\n",
+    "   Many people think this reduces the number of bugs in \n",
+    "   software as it is written, but evidence for this in the \n",
+    "   sciences is somewhat limited as it is not always easy \n",
+    "   to say what the right answer should be before writing the\n",
+    "   software. Having said that, the tests involved in test\n",
+    "   driven development are certanly useful even if some of\n",
+    "   them are written after the software.\n",
+    "   \n",
+    "   We will look at a new (and quite difficult) problem, \n",
+    "   finding the overlap between ranges of numbers. For \n",
+    "   example, these could be the dates that different \n",
+    "   sensors were running, and you need to find the \n",
+    "   date ranges where all sensors recorded data before\n",
+    "   running further analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"python-overlapping-ranges.svg\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Start off by imagining you have a working function `range_overlap` that takes\n",
+    "a list of tuples. Write some assert statments that would check if the answer from this\n",
+    "function is correct. Put these in a function. Think of different cases and \n",
+    "about edge cases (which may show a subtle bug)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def test_range_overlap():\n",
+    "    assert range_overlap([(-3.0, 5.0), (0.0, 4.5), (-1.5, 2.0)]) == (0.0, 2.0)\n",
+    "    assert range_overlap([ (2.0, 3.0), (2.0, 4.0) ]) == (2.0, 3.0)\n",
+    "    assert range_overlap([ (0.0, 1.0), (0.0, 2.0), (-1.0, 1.0) ]) == (0.0, 1.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But what if there is no overlap? What if they just touch?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def test_range_overlap_no_overlap():\n",
+    "    assert range_overlap([ (0.0, 1.0), (5.0, 6.0) ]) == None\n",
+    "    assert range_overlap([ (0.0, 1.0), (1.0, 2.0) ]) == None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What about the case of a single range?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def test_range_overlap_one_range():\n",
+    "        assert range_overlap([ (0.0, 1.0) ]) == (0.0, 1.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The write a solution - one possible one is below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def range_overlap(ranges):\n",
+    "    # Return common overlap among a set of [low, high] ranges.\n",
+    "    lowest = -1000.0\n",
+    "    highest = 1000.0\n",
+    "    for (low, high) in ranges:\n",
+    "        lowest = max(lowest, low)\n",
+    "        highest = min(highest, high)\n",
+    "    return (lowest, highest)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And test it..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "test_range_overlap()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-6-32c4d34ca9c7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_range_overlap_no_overlap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-2-0b0801057d28>\u001b[0m in \u001b[0;36mtest_range_overlap_no_overlap\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtest_range_overlap_no_overlap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m5.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAssertionError\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "test_range_overlap_no_overlap()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "test_range_overlap_one_range()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Should we add to the tests?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Can you write version with fewer bugs. My attempt is below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def pairs_overlap(rangeA, rangeB):\n",
+    "    # Check if A starts after B ends and \n",
+    "    # A ends before B starts. If both are \n",
+    "    # false, there is an overlap.\n",
+    "    # We are assuming (0.0 1.0) and\n",
+    "    # (1.0 2.0) do not overlap. If these should\n",
+    "    # overlap swap >= for > and <= for <.\n",
+    "    overlap = not ((rangeA[0] >= rangeB[1]) or\n",
+    "                   (rangeA[1] <= rangeB[0]))\n",
+    "    \n",
+    "    return overlap\n",
+    "\n",
+    "def find_overlap(rangeA, rangeB):\n",
+    "    # Return the overlap between range \n",
+    "    # A and B\n",
+    "    if pairs_overlap(rangeA, rangeB):\n",
+    "        low = max(rangeA[0], rangeB[0])\n",
+    "        high = min(rangeA[1], rangeB[1])\n",
+    "        return (low, high)\n",
+    "    else:\n",
+    "        return None\n",
+    "\n",
+    "def range_overlap(ranges):\n",
+    "    # Return common overlap among a set of \n",
+    "    # [low, high] ranges.\n",
+    "\n",
+    "    if len(ranges) == 1:\n",
+    "        # Special case of one range - \n",
+    "        # overlaps with itself\n",
+    "        return(ranges[0])\n",
+    "    elif len(ranges) == 2:\n",
+    "        # Just return from find_overlap\n",
+    "        return find_overlap(ranges[0], ranges[1])\n",
+    "    else:\n",
+    "        # Range of A, B, C is the \n",
+    "        # range of range(B,C) with\n",
+    "        # A, etc. Do this by recursion...\n",
+    "        overlap = find_overlap(ranges[-1], ranges[-2]) \n",
+    "        if overlap is not None:\n",
+    "            # Chop off the end of ranges and \n",
+    "            # replace with the overlap\n",
+    "            ranges = ranges[:-2]\n",
+    "            ranges.append(overlap)\n",
+    "            # Now run again, with the smaller list.\n",
+    "            return range_overlap(ranges)\n",
+    "        else:\n",
+    "            return None\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "test_range_overlap()\n",
+    "test_range_overlap_one_range()\n",
+    "test_range_overlap_no_overlap()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is possible to automate the process of running these tests,\n",
+    "and even plugging the tests into a version control system so \n",
+    "that you know each version passes the tests (or not). We may have\n",
+    "time to look at this tomorrow. But just having the tests isvery \n",
+    "helpful.\n",
+    "\n",
+    "But for now, lets talk about some debugging tips: http://swcarpentry.github.io/python-novice-inflammation/09-debugging.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notes/DefensiveProgramming_2_Notes.pdf b/notes/DefensiveProgramming_2_Notes.pdf
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index 0000000..d5ef77d
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