Rearranged np.array coverage.

I was using np.arrays in chapter 1, but didn't explain them until
chapter 6. I moved the explanation to chapter 1, and removed use
of np.array where it wasn't needed.

Also added a bit of explanation to the Gaussian text.

01_g-h_filter.ipynb

@@ -324,7 +324,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80851d0>"
+       "<matplotlib.figure.Figure at 0x7489208>"
       ]
      },
      "metadata": {},
@@ -359,7 +359,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x811ce10>"
+       "<matplotlib.figure.Figure at 0x75479e8>"
       ]
      },
      "metadata": {},
@@ -391,7 +391,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8866d30>"
+       "<matplotlib.figure.Figure at 0x7c57d30>"
       ]
      },
      "metadata": {},
@@ -427,7 +427,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0162\n"
+      "Average of measurements is 165.0539\n"
      ]
     }
    ],
@@ -458,7 +458,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0291\n"
+      "Average of measurements is 165.0597\n"
      ]
     }
    ],
@@ -491,7 +491,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x5e0b048>"
+       "<matplotlib.figure.Figure at 0x560b240>"
       ]
      },
      "metadata": {},
@@ -529,7 +529,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x5e0b470>"
+       "<matplotlib.figure.Figure at 0x560b400>"
       ]
      },
      "metadata": {},
@@ -558,7 +558,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAEtCAYAAABqG4QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8a361d0>"
+       "<matplotlib.figure.Figure at 0x560b438>"
       ]
      },
      "metadata": {},
@@ -587,9 +587,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8ab8f98>"
+       "<matplotlib.figure.Figure at 0x55fcc88>"
       ]
      },
      "metadata": {},
@@ -619,9 +619,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x89edcf8>"
+       "<matplotlib.figure.Figure at 0x7e1e320>"
       ]
      },
      "metadata": {},
@@ -656,7 +656,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80d05f8>"
+       "<matplotlib.figure.Figure at 0x7e1bfd0>"
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@@ -685,7 +685,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8895b00>"
+       "<matplotlib.figure.Figure at 0x7e1bcf8>"
       ]
      },
      "metadata": {},
@@ -727,7 +727,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x88f92e8>"
+       "<matplotlib.figure.Figure at 0x7e1b780>"
       ]
      },
      "metadata": {},
@@ -783,9 +783,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6sAAAEmCAYAAACEUwaqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x898ecf8>"
+       "<matplotlib.figure.Figure at 0x751def0>"
       ]
      },
      "metadata": {},
@@ -860,9 +860,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6sAAAEmCAYAAACEUwaqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6sAAAEmCAYAAACEUwaqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8d66160>"
+       "<matplotlib.figure.Figure at 0x8152ba8>"
       ]
      },
      "metadata": {},
@@ -902,9 +902,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6kAAAEmCAYAAACAptaXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x89c5b00>"
+       "<matplotlib.figure.Figure at 0x7cbac88>"
       ]
      },
      "metadata": {},
@@ -1005,7 +1005,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x89724a8>"
+       "<matplotlib.figure.Figure at 0x7d7bcf8>"
       ]
      },
      "metadata": {},
@@ -1053,6 +1053,300 @@
     "So, the algorithm is simple. The state is initialized with $\\mathbf{x_0}$. We then enter a loop, predicting the state for time $k$ from the values from time $k-1$. We then get the measurement $z_k$ and choose some intermediate point between the measurements and prediction, creating the estimate $\\mathbf{\\hat{x}_k}$."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NumPy arrays\n",
+    "\n",
+    "We will find NumPy's array data structure to be indispensible through the rest of the book, so let's take the time to learn about them now. I will teach you just enough to get started; refer to NumPy's documentation if you want to become an expert with them.\n",
+    "\n",
+    "`numpy.array` implements a one or more dimensional array. Its type is `numpy.ndarray`, and we will refer to this as an ndarray for short. You can construct it with any list like object. The following constructs a 1-D array from a list:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3]\n",
+      "<class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "x = np.array([1, 2, 3])\n",
+    "print(x)\n",
+    "print(type(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can create a 2D array with nested lists:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3]\n",
+      " [4 5 6]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.array([[1, 2, 3],\n",
+    "              [4, 5, 6]])\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can create arrays of 3 or more dimensions, but we have no need for that here, and so I will not elaborate.\n",
+    "\n",
+    "By default the arrays use the data type of the values in the list; if there are multiple types than it will choose the type that most accurately represents all the values. So, for example, if your list contains a mix of `int` and `float` the data type of the array would be of type `float`. You can override this with the `dtype` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1.  2.  3.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.array([1, 2, 3], dtype=float)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can perform matrix addition with the `+` operator, but matrix multiplication requires the `dot` method or function. The `*` operator performs element-wise multiplication, which is **not** what you want for linear algebra."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "addition:\n",
+      " [[ 2.  4.]\n",
+      " [ 6.  8.]]\n",
+      "\n",
+      "element-wise multiplication\n",
+      " [[  1.   4.]\n",
+      " [  9.  16.]]\n",
+      "\n",
+      "multiplication\n",
+      " [[  7.  10.]\n",
+      " [ 15.  22.]]\n",
+      "\n",
+      "dot is also a member of np.array\n",
+      " [[  7.  10.]\n",
+      " [ 15.  22.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.array([[1., 2.],\n",
+    "              [3., 4.]])\n",
+    "print('addition:\\n', x+x)\n",
+    "print('\\nelement-wise multiplication\\n', x*x)\n",
+    "print('\\nmultiplication\\n', np.dot(x,x))\n",
+    "print('\\ndot is also a member of np.array\\n', x.dot(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can get the transpose with `.T`, and the inverse with `numpy.linalg.inv`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "transpose\n",
+      " [[ 1.  3.]\n",
+      " [ 2.  4.]]\n",
+      "\n",
+      "inverse\n",
+      " [[-2.   1. ]\n",
+      " [ 1.5 -0.5]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('transpose\\n', x.T)\n",
+    "print('\\ninverse\\n', np.linalg.inv(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, there are helper functions like `zeros` to create a matrix of all zeros, `ones` to get all ones, and `eye` to get the identity matrix. If you want a multidimensional array, use a tuple to specify the shape."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zeros\n",
+      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
+      "\n",
+      "zeros(3x2)\n",
+      " [[ 0.  0.]\n",
+      " [ 0.  0.]\n",
+      " [ 0.  0.]]\n",
+      "\n",
+      "eye\n",
+      " [[ 1.  0.  0.]\n",
+      " [ 0.  1.  0.]\n",
+      " [ 0.  0.  1.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('zeros\\n', np.zeros(7))\n",
+    "print('\\nzeros(3x2)\\n', np.zeros((3, 2)))\n",
+    "print('\\neye\\n', np.eye(3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Excercise -  Create arrays\n",
+    "\n",
+    "I want you to create a NumPy array of 10 elements with each element containing 1/10. There are several ways to do this; try to implement as many as you can think of. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# your solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Solution\n",
+    "\n",
+    "Here are three ways to do this. The first one is the one I want you to know. I used the '/' operator to divide all of the elements of the array with 10. We will shortly use this to convert the units of an array from meters to km."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1]\n",
+      "[ 0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1]\n",
+      "[ 0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.ones(10) / 10.)\n",
+    "\n",
+    "print(np.array([.1, .1, .1, .1, .1, .1, .1, .1, .1, .1]))\n",
+    "print(np.array([.1]*10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is one I haven't covered yet. The function `numpy.asarray()` will convert its argument to an ndarray if it isn't already one. If it is, the data is unchanged. This is a handy way to write a function that can accept either Python lists or ndarrays, and it is very efficient if the type is already ndarray as nothing new is created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.1  0.2  0.3]\n",
+      "[ 0.4  0.5  0.6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def one_tenth(x):\n",
+    "    x = np.asarray(x)\n",
+    "    return x / 10\n",
+    "\n",
+    "print(one_tenth([1, 2, 3]))            # I work!\n",
+    "print(one_tenth(np.array([4, 5, 6])))  # so do I!"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1078,7 +1372,7 @@
     "        'dt' is the length of the time step \n",
     "        \"\"\"\n",
     "\n",
-    "Test it by passing in the same weight data as before, plot the results, and visually determine that it works."
+    "Return the data as a NumPy array, not a list. Test it by passing in the same weight data as before, plot the results, and visually determine that it works."
    ]
   },
   {
@@ -1090,16 +1384,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5kAAAEPCAYAAADI9gBRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x89f2ac8>"
+       "<matplotlib.figure.Figure at 0x7dd5630>"
       ]
      },
      "metadata": {},
@@ -1125,7 +1419,7 @@
     "        x  = x_est + g * residual     \n",
     "        results.append(x)  \n",
     "\n",
-    "    return np.asarray(results)\n",
+    "    return np.array(results)\n",
     "\n",
     "book_plots.plot_track([0, 11], [160, 172], label='Actual weight')\n",
     "data = g_h_filter(data=weights, x0=160, dx=1, g=6./10, h=2./3, dt=1.)\n",
@@ -1166,7 +1460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
@@ -1184,16 +1478,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5EAAAEPCAYAAADbIUClAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5EAAAEPCAYAAADbIUClAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x89a5400>"
+       "<matplotlib.figure.Figure at 0x7d65e10>"
       ]
      },
      "metadata": {},
@@ -1226,7 +1520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
@@ -1244,16 +1538,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5kAAAEWCAYAAADsL1MCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8943a20>"
+       "<matplotlib.figure.Figure at 0x752cb00>"
       ]
      },
      "metadata": {},
@@ -1289,7 +1583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false
    },
@@ -1307,16 +1601,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAAEPCAYAAAC6OWLnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9dbd2e8>"
+       "<matplotlib.figure.Figure at 0x7519c18>"
       ]
      },
      "metadata": {},
@@ -1354,7 +1648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 35,
    "metadata": {
     "collapsed": false
    },
@@ -1372,16 +1666,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5oAAAEPCAYAAAAjwbtSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5oAAAEPCAYAAAAjwbtSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa4ed6d8>"
+       "<matplotlib.figure.Figure at 0x9316390>"
       ]
      },
      "metadata": {},
@@ -1431,7 +1725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false
    },
@@ -1449,7 +1743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 38,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1457,9 +1751,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5kAAAGNCAYAAACbjrWCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa57d5c0>"
+       "<matplotlib.figure.Figure at 0x9383128>"
       ]
      },
      "metadata": {},
@@ -1492,16 +1786,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5EAAAFSCAYAAABrMVuMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa59e828>"
+       "<matplotlib.figure.Figure at 0x9383d30>"
       ]
      },
      "metadata": {},
@@ -1537,16 +1831,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5AAAAFLCAYAAACgKmPhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4FcXixvHvOem9JxAChBJK6E1UmvTQQRBFUcCLFLni\ntVxERREL6s8u2AuiVxRUBAFBihQRVBBQ6Z1AkCSkl5N2zv7+OHAkphAgJATfjw9Pwuzs7Gzch+TN\nzM6YDMMwEBERERERETkPc2V3QERERERERKoGBUgREREREREpEwVIERERERERKRMFSBERERERESkT\nBUgREREREREpE+eKulBaWlpFXUpEREREpNz5+flVdhdEKp1GIEVERERERKRMFCBFRERERESkTCps\nCuu5NPwvxdm6dSsAbdu2reSeyJVMz4mUhZ6TypWdk0l8ygnik+OITzl+5mMcSWmnsBm2Ys85dSiD\n/PwcajYK4fje07Sofy2du3bCw80LD1dPPNy8cD/z0cPNE3dXL1ycXS6pny+/8hKfLf2AaffNZFD/\nwZfU1vksWvI1z816nIcnP1WmaxVY87HkZpOTl40lNwtLbhbZuVnk5GZhycs6U/bXMUtuFpYzdXf9\neoB8ax61G4dybE88YKJ249ASr2UymfF298HHKwBfT398PP3x9fLHxzPA/rmnP75e9s893b0xm+zj\nD5MmTyAlKxGT6a+2DAMCvEJ44/W3L/VLdsXQa1gihVVKgBQREZEr2/nCgc2wkZKReCYcniDhzMf4\n5BNkWIr/gdtkMhPsV42wgAjCAmuc+RhBaEANvNx9GDKiH4ZhYKT4Mv3+FzCde/HLoFPHzmzevImB\n/QZd1usADOo/mBUrVpT5Ws5OLvh4+uHjeeG/dDfGGAwZ0RfDMMg/7clLL75IhiWVjOw0MrJTSM9K\nJSM7lfTsFNKzU8mypJNhSSPDksbJ87RtNjvh4+lv75dfBpaCeGo2DHYcP7k/hY6dhmCzWTGbnS64\n7yJy5VOAFBERkSJ69uzNvJWvU63+XwHmxL4kXIL9eX7efSSkxJFfkFfsua7OboSeDYgBNQgLjCAs\nIIIQ/+q4OLuWeM3Rt93lGKW73OERwGQyMeW/UyvsWm/NfrvCrjX6tnGOr2XjyFal1rdaC8i0pJOe\nnVokYNo/ppKRZf+7JTeLtMwk0jKTMAcZHP3hTyIaBGEymTAMg0O7TrAp6gt+nr0QX68A/L2D8fcO\nsv/xCSr0d1+vAJydLm0UWUQqngKkiIiIOBiGwcnTx/CsZiN232nC6vk6wsGR3Sep0SCQuMQkAHw9\nAwgNrEG1c0YSwwIi8PcJckxzvBAXOkpXHioi0FXGtS7ka+nk5IyfdyB+3oHnrZtfkE9G9l8Bs6bz\nt/y0Zw0RDYI4vu80rds3x9fLj4zsVFIzk0jNTCqxLRMmfDz9zwmXQfh5/fW5v3cwft6BuDq7Af+c\nKbMiVzoFSBERkX84S24W+2J/Y/exbew5tp20Mz/0h9X1JnZvIrUbh3J832n69x1A/14Dz4TFcDzd\nvMu1HxU5Sne1u1xfSxdnFwJ9Qwj0DQGg6aR2DBnxC4ZhQIof77+5EJPJRIE1n7SsZFIzkhxBMjXz\n9DmfJ5GelXJmGm0KsQkHS7yml7sP/t5B4JdZzJTZVLp3H1Gu9ygipVOAFBER+YexjzIeZffRbew+\nto0jf+7FZrM6jvt6BtA4sjWjYloy47GnHeHgyTdfuezhTuGx/FTcdNmiU4+dnVwI8g0jyDesxHOt\nNutfI5UZhcPl2bCZlplMVk4GWTkZJUyZPc7aqE/Y+u4S/H2CCPAOJsAnhAAf+0d/72ACfILw9QrE\nSe9kipQLBUgREZF/AEtuFntjf2PP0V/to4xZyY5jZpOZeuHRNI5sTXRka2oE13EEgTtvn1Ch7yVK\n1XOxU4+dzE6O9yGp1qDYOjbDRmZ2OmlZ9mAZbl7Cln3rHFNmo1s3wNnJhUxLGpmWNE4kHC62HbPJ\njJ9XIP4+ZwNm0DkB017m5e5T6Bk/O2X2rZc/vKD7ErnaKUCKiIhchQzDIO70EXYf3caeo2dGGc/Z\nQsPXK4Do2q1pHNmGhrWalzgdtTLeS5Sq5XJOPTabzPh62bcVqRlaj6b3tHOs1kuKH/97cxkGhn0k\nM+M0yRmnSc04TUpGIimZp0k58/f07BT73zNPc+TPvcVey8XZ9a9A6R1McC0vTh3ZXe73JFLVKUCK\niIhUEStXr+CT+XOwGQWYTc7cfvMYevWIcRzPzs20v8t4dBt7jm0jPSvFccxsMlOvRhOia9tHGcOD\nI8v0A7/eS5SyqKjno7gpsyZM+HkF4ucVSO0SRjLzC/JJy0oi5Uy4tIdMe6A8Gzgtedkkpp4kMdW+\nmYnhbHB4V1yF3JdIVaIAKSIiUgUMHjaAQ3G78fT7a9uDBx6bRM3ZdfnPI+PYfWwbR//cV2iU0c8r\n0D4ttXZrGtZqgYeb10VdW+FRriQXMyru4uxCsF81gv2qlVjHkptN6tlRy0x7qHTNCCmPLotcVRQg\nRURErkBWm5XUzNMkpyeQnJ7AycSj1G0RRu3GoY46x/bEA1ks3fwpYA96Qb5hhAfXplZYfcICauLu\n6oGbqwcpGaex5Gbh5uKOm6tHmfbfO9+Ip0hluFyj4h5unni41aJ6UC1HWd9rbyU9Pb1cryNS1SlA\nioiIVIK/B8SkMx/P/knNTCo0mugWYBC7N55ajUIcK1DG7k3gmpiGjjqGYZCUHk9Sejx/HP6l1Os7\nmZ1xc/WwB8ozodLNxR13Vw9cXdw5sPMo69duoGXP2md7zItvPkV+QR79YgZeji+JSJlV5JRZESlM\nAVJEROQSlDRKd6EB8e9MmPDzDsLLzRtLbhYtb7AHxrP7MsbuTaBWozDi/7Dy36kTyc3PITfPYv/o\n+NxSTFkOOfkWrLYCsnMyyM7JKPb6m7/bw3X9Ghcqa9QlhBmv/JeNx75wbJUQ6BvqWMXybJmPpz9m\nk7lcv84iInJlUIAUERG5SCtXr+Dlt5+hQaegMyVWnnjpQT5dNYuAGm7nDYj+3kEE+oYS6BtKkG8o\ngT72z/29gzieeJgNO5Zx9NQ+AMxmJ/wD/Ph98wFqNQohdm8CoUHhPDDxPjo0631B/TYMgwJrAXn5\nFnLyLeTm5ZwJm2eCZr6Fw5ufLL7fZpNjX74TicVvmeBkdrbvyecTQmCRPflCCPQJxs3Vo8jXUtNl\nRUSufAqQIiIiFyE5PYGX3n6ahp2CC5W3janPT9/u4brw6BID4tlRu7+/h2jJzeanXav5bPVskjMS\nAfB086Zj8xg6Ne/Lzz/9QtxvM9nw1U5CfCN4YOLDFxWyTCYTLs4uuDi74OXhW2ydAJ+3gdwi5Q0j\nWvL02LdIyUgk+ex2CRmJjtUtUzJOk2lJIyktnqS0+BL74Onm7QiWxw+cZsP6DbToXvPMUSuvvDMT\nQCFSROQKowApIiJyAY6dOsDa7YvZcWATqZmngeAidSKrN+Clfy8o00I1AMnpiWz4bSmbdq4iJy8b\ngBC/6tzQagDXRHfDzcUdsIepnt17M/HfEy771hq33zyGV955lqiOAY6y/T8kc/+ER/D1CsDXK6DE\nLRPy8nMdq1kmFxMwUzISyc7NJDs3k7jTR4udLhvVMZAZr0zhQMbmv4XvMIJ8Q/Hx0jRZEZHKoAAp\nIiJyHjbDxq4jW/l+22IOxe0C7FNKvT38i63v4epVpvBoD6PfsOPAj47prvVqNKFb60E0qdO22IBU\nUfsynh35+3TBRxTY8nE2u3D/hEfKNCLo6uJGaEANQgNqFHvcMAwystMco5eHNj1SbL0Cax77Yn8r\n9piTk/OZUBlCkG8oAT7njPT6huLrFVBqwFy5egVvvPsahsmKn2+ApsyKiJSRAqSIiEgJ8gpy2bJn\nHWu3LSbhzObi7q6edGjWi84t+rOl8dYSR+lKYrNZ+ePwFtZuX8zhk3sAMJvMtGnYma6tBlIrrP55\n+1VRK0P26hFzWUKVyWTC18sfXy9/aleLItA3jOKmy0ZWa8T4gdP+WoAoI4Hk9ESS0xPItKQV2vT9\n75ycnAn0DnFMGT53KvGOrX/w3sezieoUeKZ2Lq+886zjnkVEpGQKkCIiIn+TkZ3KD78v54ffl5Nl\nse8BF+ATwg0tB3Btkx54uHkCFzZKl5tn4ec937Nu+xJOp50CwMPVk+ub9aJzi34E+PxzNywvbbps\nkzptiz0nNz/nTJiMPxMuEwutcpthSSMx7U8S0/4scu7mZcVNmQ3g9ff/j+Ba3vh5BeDrFYifdyCu\nzm7le7MiIlWcAqSIiMgZ8cknWLt9Mb/sWUeBNR+AWqH16dp6EC2jrsfJ7FTknPON0qVmJrFhxzJ+\n3PkdltwsAIJ8w7ih1QDaR3fH/W+rkf4TXcx0WTcXd6oH1aR6UM1ij+fl5xYasTw7gpmUnsBW5wPF\nnpOYepKPlr9YqMzTzRs/70B8vQLw8wq0//EOxM8r6MzHAHw9A3ByKvlHKq0wKyJXk1ID5IYNG3jx\nxRfZtm0bJ0+eZM6cOYwaNapQnf379zN16lTWrl1LXl4ejRo14tNPP6VRo0aXteMiIiLlwTAMDsbt\n4vtti9h1ZKujvGmddnRrM5h64dEXNWX0eMJh1m5fzLb9G7HZrABEVm9It1aDaF6vPeZiwug/WXlP\nl3V1caNaYE2qBRYNmDtWH6O4KbP+3sG0rH89aVnJpGUmkZaV4ljs58+k2BKvZcKEt4cvvt6B+HsF\n4ed9ZgTTK5DdO/az4OvPaNwl9Extq6bLikiVVmqAzMrKonnz5owaNYo77rijyDfQI0eO0KFDB0aP\nHs3jjz+Ov78/e/fuxdvb+7J2WkRE5FJZrQXsOLiJ77ct5njCIQBcnFxp1/gGurYaSLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa5c4828>"
+       "<matplotlib.figure.Figure at 0x99b77f0>"
       ]
      },
      "metadata": {},
@@ -1584,16 +1878,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5gAAAFLCAYAAACz/SMVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x89d9438>"
+       "<matplotlib.figure.Figure at 0x7cb1da0>"
       ]
      },
      "metadata": {},
@@ -1633,7 +1927,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 42,
    "metadata": {
     "collapsed": false
    },
@@ -1644,15 +1938,15 @@
        "<function __main__.interactive_gh>"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5oAAAEPCAYAAAAjwbtSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4VFX6wPHvzKR3ElIJJSEJCQFCCb1IFymClSIKdpFV\nWX4rLmvBwqrYFsGy6ooiiIorCiqLoQmh955CCoQkpJDey8z8/rjJhCFt0kiA9/M8ee7Muefec64O\nkHfOOe9R6fV6PUIIIYQQQgghRDNRt3YHhBBCCCGEEELcXCTQFEIIIYQQQgjRrCTQFEIIIYQQQgjR\nrCTQFEIIIYQQQgjRrCTQFEIIIYQQQgjRrMxauwNXy8nJae0uCCGEEEII0WiOjo6t3QUh2gQZ0RRC\nCCGEEEII0awk0BRCCCGEEEII0aza1NTZq8m0A1GbI0eOABAaGtrKPRFtmXxOhCnkcyLqI5+RNmDa\nNNi4Eb7/HqZPb+3e1EiWfwlRnYxoCiGEEEKItik9Hfz9ldeRka3bFyFEg7TZEU0hhBBCCHGLu/NO\nOHBAeR0R0bp9EUI0iIxoCiGEEEKItikmpuq1jGgKcUORQFMIIYQQQrQ9OTlw5UrV+6go0Olarz9C\niAaRqbNCCCGEEKLtiY1Vjj16wOzZ0LkzaLWglnESIW4EEmgKIYQQQoi2p3LarJ8fvPBC6/ZFCNFg\n8pWQEEIIIYRoewoKwMUFunZt7Z4IIRpBRjSFEEIIIUTb8/DDyk95eWv3RAjRCDKiKYQQQggh2i4z\nGRcR4kYkgaYQQgghhBBCiGYlgaYQQgghhGj7nn8eRo2CjIzW7okQwgQSaAohhBBCiLZv507480+I\njGztngghTCCBphBCCCGEaFtSUiAiAoqLq8oCA5WjBJpC3BAk0BRCCCGEEG3L2rXQvbvx/plBQcox\nIqJ1+iSEaBAJNIUQQgghRNsSE6Mc/fyqymREU4gbigSaQgghhBCibakMNLt2RastR6/XS6ApxA1G\nNiYSQgghhBBtS2wsAEe1l/nus9lYmlkyZ8yzBKxfXzWFtrG2bQN3dwgOBrWMuQjRUuRPlxBCCCGE\naDtKSiAhAb1azfexmyktKyavKIcvt35A1oRR0KNH0+7/+OPQqxecPNk8/RVC1EgCTSGEEEII0XZk\nZ8OQIRT06EaJSmsoLiop4NutK9DpdY2/d0ICXLgAjo5KsCmEaDESaAohhBBCiLbD3R3Cw1n/1txq\np6IvnSL85ObG33vXLuU4fDhoNMrrhAR49VXQ6xt/XyFENbJGUwghhBBCtCnl2jIiL56o8dymPd8Q\n0DEET5eODb/xn38qx9tuU45lZTByJMTHg5MTLFjQqP4KIaqrd0Tz1VdfRa1WG/14eXlVq9OhQwds\nbGwYNWoU586dMzpfUlLCM888g6urK3Z2dkydOpWkpKTmfRIhhBBCCHFTiE06R3FpYY3nyrSlrPnj\nX5Rryxp+48oRzYpAU2emIf/1l5XXz/+N/f9ewsY9qzkTd1jJdCuEaDSTRjQDAwP5s/IbIEBTOdUA\nWLZsGR988AGrV68mICCA119/nXHjxhEVFYWdnR0ACxYsYNOmTXz//fc4OzuzcOFCJk+ezNGjR1FL\nti8hhBBCCHGVs/FHjN67OLqTkZOKWbmWeZ/uxzlrK1s69WbysIdMv6lWCzNnkrfzD767tJm0iK/J\nzE2jXFvG1FF+jNkZQ/cXlvHb30ay3cGKzh4BTB02B78Owc38dELcGkyK8jQaDW5uboYfFxcXAPR6\nPcuXL2fx4sXcddddBAcHs3r1avLy8li3bh0AOTk5rFq1ivfee48xY8bQp08f1qxZw6lTp9i2bVvL\nPZkQQgghhLjh6PV6zsQfNiqbOnQOfQOGUW6mwT0tH5fMQo5sW0dccoTpN9ZoOP3wFF68z4szl46T\nlpVkGBX9dXIQ57u64JhbwsOrj6DW6riYEs2K/77IZ5uWcjkjoTkfUYhbgkmBZlxcHB06dMDX15eZ\nM2cSHx8PQHx8PKmpqYwfP95Q18rKihEjRrBv3z4Ajh49SllZmVEdb29vgoKCDHWEEEIIIYSgvJyc\n1V9gERFlKNJozAjs3If7Rj2Jo60zqW7KjDm3lFzWhC2nuLTIpFsXFOfxw/ZPazyn06j5ek4omU7W\nnAn2QKdWGc6djT/C298uYN3WlWTlXWnCwwlxa6l36uygQYNYvXo1gYGBpKamsnTpUoYMGcLZs2dJ\nSUkBwN3d3egaNzc3kpOTAUhJSUGj0RhGQSu5u7uTmppaa7tHjhyp9ZwQIJ8RYRr5nAhTyOdE1Ec+\nI9eHRVISvR5+kqccrXjltdsBcLPvxJlTZwHo32UCqe478Y/NwCM1j8icVD7fsIwhfpPrvfee6I3k\nFmZVb9PMCnvLdti5OPHzR8PI0OVCZrRRHb1ex4Fz2zkcuYtpfedha+lodN7f37+xjyzETaveQHPC\nhAmG1z169GDw4MH4+PiwevVqBg4cWOt1KpWq1nNCCCGEEOLWoiopwXH/fixSU0mbPr3GOlaJiQCk\nt7c1lHm3qwrivJx8KfbvDvsu4JaWD0BM6gk8HX3wca19LeWlzGji0k8blQV3GExP76FYmFlVq5+W\ne4mjF7aTnpdoVO7l5FstyBRC1KzB25vY2NgQHBxMTEwM06ZNAyA1NRVvb29DndTUVDw8PADw8PBA\nq9WSkZFhNKqZkpLCiBEjam0nNDS0oV0Tt4jKb5XlMyLqIp8TYQr5nIj6yGekGV28CM8/DzY2dHrt\nNbCxqValdN8eAK64VgWaE2+7GxfHqtlzZffNg9Wbca8INAH2xmwiwD+AEL/B1e5ZWJzPL2s/Nirz\ncunMo3ctxExjXktnQ7lDP43TcQfZtHcNaVlJqFRqHpz0DB7O1bdVycnJqfPRhbgVNTjla3FxMRER\nEXh6euLj44OHhwdhYWFG5/fs2cOQIUMA6NevH+bm5kZ1EhMTiYyMNNQRQgghhBA3sZIS6NwZ+veH\nwkL4448aq2WeOADAFRcl0PR06WQUZAKY3zaS5F2b+fe8oYYynU7LV5vf5fj56vk/Nuz+ktBfD3PX\nz6dxS81DrVLzwPhn6wgyFSqVil5dB7F40LPMGjiHcaF31xhkCiFqVm+g+be//Y3du3cTHx/PwYMH\nuffeeykqKmLOnDmAsnXJsmXL+Pnnnzlz5gxz587F3t6eWbNmAeDo6Mijjz7KokWL2L59O8ePH+fB\nBx8kJCSEsWPHtuzTCSGEEEKI1lVcDO3bw+DBMHWqUvbf/9ZYtTTyDADpFSOawV1qGEm2s8NrxB1M\nv+M5VFQt1dLpdaz+33scP7/XUHYm7jCHInYy4FACo3bFYVdQyrj+99DRratpfd+9G83AQQx662sm\nD5pl2jVCCMCEqbNJSUnMnDmTK1eu4OrqyuDBgzlw4AAdOyrf6CxatIiioiLmz59PVlYWgwYNIiws\nDFvbqikPy5cvx8zMjOnTp1NUVMTYsWNZu3atrOMUQgghhLjZHT4M+flQUAAzZsBLL8FvvymjnJaW\nhmpanZbI9hqKAtqT6m4PQA/f/rXedkDQKAC+DVuBHj1QGWy+j16vJ7BTb37Y8Sm2+SV4peRRaq6m\ntHcvxve/3/S+e3qCSgWbNin9fvPNRvwHEOLWVG+g+d1339V7kyVLlrBkyZJaz1tYWLBixQpWrFjR\nsN4JIYQQQogb2+7dynH4cOjaFXr3hhMnYNs2mDTJUC3+ciS/jfGBMT4A2FrZ08UjoM5b1xpsbvmA\nTu5+5BRk0is2A4ALXZyZMfGvmJvVPWXWiL8/rF8PEybAW29BcDA88IDp1wtxC2vwGk0hhBBCCHET\nOHMGRo2C1atbtp3wcOVYmQTy5Zdh3bqq9xXOxh82et+9Sz/Uak29tx8QNIrZtz+HSlX1a61er+Ni\nirJFiX+MsvelfsQIOrn7Nbz/Y8fChx8qr//5Tygvb/g9hLgFSaAphBBCCNHc/vUvZU3ioUOt3ZPa\nLVgAf/6prKFsKeXlsLdizeTw4crx7rth5kywtzeqeibOeK/SYB8TMv0WFADQP3AkD443DjYr+VWM\naHad9XQDO3+Vp59Wgs3du8GswZs2CHFLkj8pQgghhBDN7Z13ICUFcnNbuyc127sXtm9XXt/fgDWL\nDXXpEtjagrs7eHnVWi09+zKpWVV7VqrVGoI696n9vnFxMHAgODtDVBQAoYG3oVKp+eaPf6HX6wxV\nVz06iKedR+EyZGhtd6ufSgXPPtv464W4BUmgKYQQQgjRnMrKlCATYNiw1u1LbV5/XTm+/DK0a1f9\nfHGxElxdlaynUXx84PJluHKlzmpn441HM/28umNtaVtLbZSgNTMTsrKgtBQsLADo1204KpWKb7Z8\ngK4i2Ox7x0O4DJZ1lUJcbxJoCiGEEEI0p4QE5dixI1hZtW5fanLgAISFKVNXFyyouc7SpUoSnB07\nwNu7ae2pVKSbl7P+5yUUFRfQs+tAhvQYh72Nk6HKmfjDhJxMRlOuI6qbK8E+tWebBZT/rj4+EBsL\nMTHQvbvhVN+AYbg4uHEwYicd2ndhSI/xTeu/EKJRJNAUQgghhGhOMTHK0a+WxDOLFin7SQ5twlTO\npkhPV0YE585Vpp5eq7yckp//i+X58+R/8DZ2H3zUpOb0ej1f/+89LqXFApCQFsOWQz/Qx38oI/1H\n42ruSEzSWf5vazQdE3N4f8HwOrc1MQgKUgLNyEijQBOgs0cAnevJWCuEaFmSDEgIIYQQojnFxyvH\nmgLN33+Hd99VtsvIyrq+/ao0ZYoSoC1eXO1UXmEOq7etYM0gBwCS//dfLmckNKm5yIQThiCzklZb\nDmvX4h7Uj9NzJqLTluOariT2wd8fVyfP+m8cGKgcIyKa1D8hRMuQQFMIIYQQojk9+Line truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8a1f668>"
+       "<matplotlib.figure.Figure at 0x751cc88>"
       ]
      },
      "metadata": {},
@@ -1705,12 +1999,12 @@
     "    def measure_position(pos):\n",
     "        return pos + random.randn()*500\n",
     "        \n",
-    "Let's put that in a cell and plot the results of 100 seconds of simulation."
+    "Let's put that in a cell and plot the results of 100 seconds of simulation. I will use NumPy's `asarray` function to convert the data into an NumPy array. This will allow me to divide all of the elements of the array at once by using the '/' operator."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 43,
    "metadata": {
     "collapsed": false
    },
@@ -1719,7 +2013,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwMAAAE6CAYAAABKyI5vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x88e9be0>"
+       "<matplotlib.figure.Figure at 0x7d7ab00>"
       ]
      },
      "metadata": {},
@@ -1746,7 +2040,7 @@
     "pos, vel = 23*1000, 15\n",
     "zs = gen_train_data(pos, vel, 100)\n",
     "\n",
-    "plt.plot(zs/1000) # convert to km\n",
+    "plt.plot(zs / 1000.)  # convert to km\n",
     "plt.xlabel('time(sec)'); plt.ylabel('km')\n",
     "plt.title('Train Position')\n",
     "plt.show()"
@@ -1763,16 +2057,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 44,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa5189b0>"
+       "<matplotlib.figure.Figure at 0x9994828>"
       ]
      },
      "metadata": {},
@@ -1782,7 +2076,7 @@
    "source": [
     "zs = gen_train_data(23000, 15, 100)\n",
     "data = g_h_filter(data=zs, x0=23000, dx=15., dt=1., g=.01, h=0.0001)\n",
-    "plot_g_h_results(zs/1000, data/1000, 'g=0.01, h=0.0001', z_label='Measurements')"
+    "plot_g_h_results(zs, data/1000., 'g=0.01, h=0.0001', z_label='Measurements')"
    ]
   },
   {
@@ -1794,16 +2088,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 45,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8a62f98>"
+       "<matplotlib.figure.Figure at 0x9994ac8>"
       ]
      },
      "metadata": {},
@@ -1827,16 +2121,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 46,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8aa0a58>"
+       "<matplotlib.figure.Figure at 0x9994c88>"
       ]
      },
      "metadata": {},
@@ -1860,7 +2154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 47,
    "metadata": {
     "collapsed": false
    },
@@ -1877,16 +2171,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 48,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8da9d30>"
+       "<matplotlib.figure.Figure at 0x9994c18>"
       ]
      },
      "metadata": {},
@@ -1908,16 +2202,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 49,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9dc8400>"
+       "<matplotlib.figure.Figure at 0x7d69dd8>"
       ]
      },
      "metadata": {},