TU_Lecture_Py1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this is a formatting statement for jupyter notebooks \n",
    "%matplotlib notebook "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Using Python for your science\n",
    "\n",
    "\n",
    "This is an introduction to beginning your scientific Python programming. If you havne't done so yet, please download and install the the latest versions of anaconda (www.anaconda.com/download/) and sunpy (docs.sunpy.org/en/stable/guide/installation/).\n",
    "\n",
    "Let's make sure that your instillation of anaconda is up to date. From the terminal, type:\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "conda --version"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "conda 4.5.11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To keep all of the anaconda packagesupdated, use the following command:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "conda update anaconda --all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets launch an ipython (interactive python) session"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ipython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started with Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing to note is that by itself, Python is just a platform. You can think of Python as an engine - it is very powerful but unless you connect it to a transmission, axle, and wheels, it isn't very effective at getting you down the road. \n",
    "\n",
    "Fortunatly in most cases you don't need to reinvent the wheel and you can just find some that someone else created that will fit your needs. \n",
    "\n",
    "The Anaconda package comes with literally thousands of tools to help you work with data. \n",
    "\n",
    "Let's explore some basic tools. NumPy is the fundamental package for scientific computing with Python. Let's load it in to our current session. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the syntax we are using: import \"the library\" as \"what we want to call it.\" \n",
    "Now whenever we want to call a tool in numpy, we can use `np.`\n",
    "\n",
    "Next we will want to plot our data, so lets grab some plotting tools. We will use a package called matplotlib. Matplotlib is a 2D plotting library, which produces all kinds figures. We don't need everything that matplotlib can do, so let's import just the tool that produces line plots. This tool is called pyplot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again notice the syntax we are using: `import` \"library.tool\" `as` \"what we want to call it.\"\n",
    "\n",
    "Okay, we have the tools `np` and `pl` defined. Loading in the tools and libraries you want to use is something you have to do every time you write a program or load a python session. \n",
    "\n",
    "Now lets put our tools to use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = 0.1\n",
    "Fs = 1/dt\n",
    "t=np.arange(0,Fs,dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can dynamically define variables (i.e. we don't have to specify the type) and use conventional assignments to make new variables. \n",
    "\n",
    "We also use a tool within `np` to create `t`: `np.arange`. It creates an evenly spaced array with the syntax of `output=np.arange(start,stop,step)`. \n",
    "\n",
    "So what does `t` look like? We just type the variable or use a `print` statement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n",
       "       1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5,\n",
       "       2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8,\n",
       "       3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. , 5.1,\n",
       "       5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6. , 6.1, 6.2, 6.3, 6.4,\n",
       "       6.5, 6.6, 6.7, 6.8, 6.9, 7. , 7.1, 7.2, 7.3, 7.4, 7.5, 7.6, 7.7,\n",
       "       7.8, 7.9, 8. , 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7, 8.8, 8.9, 9. ,\n",
       "       9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8, 9.9])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7\n",
      " 1.8 1.9 2.  2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.  3.1 3.2 3.3 3.4 3.5\n",
      " 3.6 3.7 3.8 3.9 4.  4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5.  5.1 5.2 5.3\n",
      " 5.4 5.5 5.6 5.7 5.8 5.9 6.  6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7.  7.1\n",
      " 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8.  8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9\n",
      " 9.  9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9]\n"
     ]
    }
   ],
   "source": [
    "print(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But what are the dimensions of `t`? How many elements are in it? \n",
    "\n",
    "In Python, since it is an object oriented programming language, the variable `t` is actually more than just a simple array. It is self-aware! So let's ask `t` about itself. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function ndarray.min>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.min"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Um... what just happened here? Python just said \"yep, that is a valid function of t\" but didn't actually return the value. To get the value out, we need to use a different syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also we can ask:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.9"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.95"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.332500000000001"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.var()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are all \"t.something( )\" - \"something( )\" is a function of the object `t`.\n",
    "To find out how many elements are the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see the shape of the array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to see all of the functions already available in `t`, type `t.` and then 'tab'. This will show you dynamically all of the functions that exist. Go ahead, try it!\n",
    "\n",
    "You'll note that `.size` and `.shape` don't have brackets after them, but `.min()` and `.var()` do.  This is because the size and the shape of the array are attributes of the array.  The reason `.min()` (and other methods) have brackets is that these functions have different options that you may want to use.\n",
    "\n",
    "Let's create a 2d random array\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "rand_nn = np.random.randn(t.size, t.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can find the minimum values along one dimension of the array..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.24333602, -2.02349979, -2.73366768, -2.65608884, -2.60932928,\n",
       "       -2.18201226, -3.97187883, -2.86629791, -2.24519387, -2.52948119,\n",
       "       -3.3371784 , -1.84900334, -2.10851751, -2.25138595, -2.24495985,\n",
       "       -2.13562995, -2.1507478 , -3.19312536, -2.50422909, -2.60277327,\n",
       "       -2.32061021, -2.87077529, -2.45893482, -2.76661496, -2.94232298,\n",
       "       -2.45234441, -2.52182197, -2.81033856, -1.95782275, -1.53797027,\n",
       "       -2.48181696, -2.56404482, -2.80800747, -2.19005985, -1.77584463,\n",
       "       -3.14924724, -3.10007288, -2.55095843, -2.43619021, -2.96337422,\n",
       "       -2.05119068, -2.05563276, -2.98268918, -2.62593021, -2.59999332,\n",
       "       -1.8929119 , -2.8674216 , -2.87527868, -2.05513899, -3.73574469,\n",
       "       -3.07095573, -2.20609486, -2.21556262, -2.77562846, -1.83375783,\n",
       "       -2.37725396, -2.56258267, -2.06173816, -1.90480726, -3.53714345,\n",
       "       -3.15890453, -2.57011329, -2.09043055, -2.69764676, -2.48296883,\n",
       "       -2.87478156, -3.03980389, -2.08817361, -2.60669666, -2.69503013,\n",
       "       -2.39533341, -2.33473173, -2.51610862, -2.10717168, -2.69811392,\n",
       "       -2.03678815, -2.40765596, -2.24600091, -2.92587542, -3.2802344 ,\n",
       "       -2.69261344, -2.8997628 , -2.25770742, -2.12829268, -2.76264366,\n",
       "       -2.43369478, -2.1041148 , -2.96979955, -3.65224697, -2.350073  ,\n",
       "       -2.8969977 , -2.70177732, -1.8416897 , -1.87908336, -2.44681323,\n",
       "       -2.31680036, -1.96320481, -2.50554183, -2.97613248, -3.09218866])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "rand_nn.min(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or the other one..."
   ]
  },
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   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.05119068, -2.43619021, -2.29840769, -2.28420673, -3.14924724,\n",
       "       -2.02620059, -2.12868695, -2.52182197, -2.60413809, -2.60277327,\n",
       "       -2.1507478 , -2.30682321, -2.06723645, -1.90706782, -2.06017519,\n",
       "       -1.8777104 , -2.81033856, -3.19312536, -2.55095843, -2.33136194,\n",
       "       -2.30014846, -2.15159846, -2.59999332, -3.53714345, -2.43369478,\n",
       "       -2.8969977 , -1.62780879, -2.50554183, -2.76264366, -2.24519387,\n",
       "       -2.2391551 , -3.65224697, -3.09218866, -3.15890453, -2.09484805,\n",
       "       -2.31484073, -2.86629791, -2.69811392, -1.93634766, -3.2802344 ,\n",
       "       -2.73366768, -3.10007288, -2.56404482, -2.08817361, -2.04492297,\n",
       "       -3.97187883, -2.10997231, -2.00054079, -2.69261344, -2.2880628 ,\n",
       "       -2.07287588, -1.59649192, -3.73574469, -2.52799705, -2.60385838,\n",
       "       -2.27712774, -2.12829268, -2.60932928, -2.43365371, -1.8108262 ,\n",
       "       -2.96872763, -2.12755655, -2.24495985, -2.24189612, -2.69503013,\n",
       "       -2.31680036, -2.03856347, -1.94043579, -2.76661496, -2.16545371,\n",
       "       -2.70177732, -2.13655593, -1.9761207 , -1.32417184, -2.8674216 ,\n",
       "       -2.87077529, -2.52948119, -3.03980389, -2.98268918, -2.24664369,\n",
       "       -3.3371784 , -1.81522994, -2.33405415, -2.96979955, -1.73791128,\n",
       "       -2.62593021, -2.87478156, -2.45234441, -2.37725396, -2.86641212,\n",
       "       -2.52044011, -2.28478272, -2.78884296, -1.94705802, -2.97613248,\n",
       "       -2.77562846, -3.07095573, -2.06173816, -2.1024369 , -2.20609486])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_nn.min(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or the whole array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-3.971878832743403, -3.971878832743403)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_nn.min(), rand_nn.min(axis=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `min( )` function with the axis option finds the minimum value along different dimensions of the array `rand_nn`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We used `axis=None`.  There is a special value in Python called `None`, which indicates **no value**.  In this case, this means that axis has no assigned value.  The axis function then resorts to a default behavior, which is to give you the minimum value over the entire array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Now let's create an array of random numbers with the same number of elements as `t`. We will use the Numpy tool `random`, but there are lots of ways to generate random numbers. \n",
    "\n",
    "To see the options of `Numpy.random` to calculate random numbers, try typing `np.random.` and then 'tab'. \n",
    "\n",
    "Let's sample from a normal distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "rand_n = np.random.randn(t.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that we can use the `t.size` attribute of `t` without having to define another variable or call a separate function.\n",
    "\n",
    "Let's plot our variables. To do this we will use the matplotlib's `plot` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t,rand_n)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib generates a plot and then in a separate command you have to display it to the screen. Yeah, it's a pain but you get use to it. \n",
    "\n",
    "Let's now generate some more complex data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "199"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = np.exp(-t/0.05)\n",
    "r_dat = np.convolve(rand_n, r)*dt\n",
    "r_dat.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The convolution returned an array that is twice the size of our other working data sets. We should trim that down. The syntax for segmenting an array uses square brackets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_dat=r_dat[:t.size]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means we want `r_dat` from the beginning element to `t.size` (which is 100). Let's create one more data set to work with using some more of NumPy's functions (`sin` and `pi`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ss = 0.1*np.sin(2*np.pi*t) + r_dat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does all of this generated data look like? Why don't we make five different plots to highlight some different plotting capabilities.\n",
    "\n",
    "First, we will define a plotting space, but instead of showing the plot this time, we are going to plot some more values. First, we will plot the magnitude spectrum of the generated data with a linear scale and a dB (logarithmic) scale. This can all be done within matplotlib. Next, We can easily show the wrapped and unwrapped phase spectrum. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `subplot`command means that we want a 3 by 2 grid of plots and we want to operate on the first sextant. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(3, 2, 1)\n",
    "plt.plot(t, ss)\n",
    "\n",
    "plt.subplot(3, 2, 3)\n",
    "plt.magnitude_spectrum(ss, Fs=Fs)\n",
    "\n",
    "plt.subplot(3, 2, 4)\n",
    "plt.magnitude_spectrum(ss, Fs=Fs, scale='dB')\n",
    "\n",
    "plt.subplot(3, 2, 5)\n",
    "plt.angle_spectrum(ss, Fs=Fs)\n",
    "\n",
    "plt.subplot(3, 2, 6)\n",
    "plt.phase_spectrum(ss, Fs=Fs)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using matplotlib, we can showcase our generated data set in several different ways. Of course this is just an example and there are many more types of plots that matplotlib can generate. To see what else is possible, visit matplotlib.org/gallery.html for examples. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Basic Programming Techniques in Python\n",
    "\n",
    "To showcase some other basic programming techniques using the skills we just learned, lets analytically calculate $\\pi$. \n",
    "\n",
    "The easiest way of doing this is using the Monty Carlo Method through finding random points inside a circle inscribed in a square. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np #always good to be explicit about our libraries used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will initialize our counting variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = 0\n",
    "inside = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will set up a for-loop to calculate our points. Loops in Python are a bit different than other programming languages. First, Notice that in Python loops are tab delimited. Unlike Foretran or IDL there is no need for 'begin' and 'end' statements since the tabbing indicates where the loop starts and ends. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "for ii in range(10000):\n",
    "    \n",
    "    # Generate two coordinates in the interval 0-1:\n",
    "    x_coord = np.random.uniform()\n",
    "    y_coord = np.random.uniform()\n",
    "    \n",
    "    # Calculate the distance to the origin\n",
    "    r = np.sqrt(x_coord**2 + y_coord**2)\n",
    "    \n",
    "    # Count this if it is inside the circle.\n",
    "    if r< 1:\n",
    "        inside += 1\n",
    "    \n",
    "    total += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pi= 3.1514\n"
     ]
    }
   ],
   "source": [
    "print('Pi=',4.0*inside/(1.0*total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will see somethig else too: the variable we are iterating over, `ii`, is nowhere to be found in the loop. This is because the iteration of `ii` over the list that the `range( )` function produces is implicit. We can make the interation explicit using a slightly different syntax. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, `iteration` now contains the iteration number (0,1,...,9999) while `ii` contains whatever is in the list at that iteration. This could be any data type. To drive this point home, let's show a trivial example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "J\n",
      "a\n",
      "c\n",
      "k\n",
      "0 J\n",
      "1 a\n",
      "2 c\n",
      "3 k\n"
     ]
    }
   ],
   "source": [
    "for value in 'Jack':\n",
    "    print(value)\n",
    "print \n",
    "for iteration, value in enumerate('Jack'):\n",
    "    print(iteration, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few other things to notice: we are using `np.random` again, as well as `np.sqrt`. To define an exponent python uses a double astrix. We also encounter an `if` statement. For conditionals, python drops the 'then' from the statement and uses tabbing to denote the begining and ending.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Writing A Function\n",
    "\n",
    "Lets take this same python script and make it into a simple function we can iterate over. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np #always good to be explicit about our libraries used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pitest():\n",
    "    # Create the coordinate\n",
    "    xtemp = np.random.uniform()\n",
    "    ytemp = np.random.uniform()\n",
    "    \n",
    "    # Calculate the distance to the origin\n",
    "    r = np.sqrt(xtemp**2 + ytemp**2)\n",
    "\n",
    "    # Count this if it is inside the circle.\n",
    "    if r< 1:\n",
    "        return True\n",
    "  \n",
    "    return False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Python syntax for functions use the same tabbing to start and end the function that loops and conditional statemetns do. This particular function `pitest( )` does not have any variable input, but if it did, it would go in the parenthesis. Now let's put the new function in a loop and see how it works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pi= 3.1408\n"
     ]
    }
   ],
   "source": [
    "count = 0\n",
    "inside = 0\n",
    "\n",
    "for i in range(10000):\n",
    "    is_inside = pitest()\n",
    "    \n",
    "    inside += is_inside\n",
    "    count += 1.\n",
    "    \n",
    "print('Pi=',((inside/count)*4.))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python has boolean data types, `True` and `False`.  These are also understood to have the integer values 1 and 0 respectively. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running Your Own Function\n",
    "So now I have writen a script, how do I run it from the command line? Let's say that the function we wrote, __'pitest,'__ is saved with the name __'Pifunction.py'__. First, change to the directory where your program is located. "
   ]
  },
  {
   "cell_type": "raw",
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   },
   "source": [
    "cd /Users/mkirk/code/python/ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then import the name of your script. "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import Pifunction as Pifunc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then run your function like we have all along:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Pifunc.pitest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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