Fixed missing import statement.

2015-07-07 14:21:59 -07:00
1 parent d44cca32ab
commit baab280e8d
@@ -433,7 +433,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7138e80>"
+       "<matplotlib.figure.Figure at 0x6c5ae80>"
      ]
     },
     "metadata": {},
@@ -474,9 +474,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8TNf7wPHPnew7SYogkiCxLy1qD6VoFaVUFK2opVR9\n7UuoiqKqaKmqKi3R/lBbaatSqvZ9rbVaa2sLIhJL9pzfH9PcGjOJJJJM8Lxfr7zMuefcc89M7sQ8\nc+59jqaUUgghhBBCCCGEsCqDtQcghBBCCCGEEEKCMyGEEEIIIYQoECQ4E0IIIYQQQogCQIIzIYQQ\nQgghhCgAJDgTQgghhBBCiAJAgjMhhBBCCCGEKAAkOBOigAsPD8dgMLB582ZrD0UIkQs2bdqEwWBg\n3Lhx1h6KEEKIAkaCMyEKOE3T9B+R+5RSNGvWDIPBgMFgICkpKdt9rFmzhueeew4PDw/c3NyoU6cO\nCxcuzIPRiseJvKdz186dO3nppZfw9PTE2dmZatWqMWPGDNLS0rLd14kTJ+jYsSNFihTBycmJ8uXL\nEx4eTkJCQh6MXAgh/qPJItRCFGzR0dFER0fj6+uLk5OTtYfz2Jk+fTrDhg3D3t6ehIQE4uPjsbe3\nz/L+s2bNon///nh7exMSEoK9vT3Lli3jwoULDBkyhClTpuTh6MWjKD4+nn/++Qdvb288PT2tPZzH\nwurVq2nfvj3Ozs6EhITg6enJDz/8wMmTJ+nQoQNLly7Ncl979uyhSZMmpKSk0KFDB3x9fdmwYQP7\n9u2jfv36bNiwIVt/I4QQIjskOBNCPLGOHTtGrVq1GDRoEIsWLeLvv//OVnB27tw5ypcvj5ubG/v3\n76dUqVIA3Lx5k1q1anH69Gl27NhBnTp18vJpCPFEi4uLo2zZsty6dYvt27fzzDPPAJCYmEiTJk3Y\nuXMnixcvJiQk5IF9paamUqVKFU6ePMnq1atp1aoVYJxh79ixIytWrGDSpEmMGDEiT5+TEOLJJZc1\nCpGBXbt2YTAYaNOmTYZtatasiY2NDefPnwcgOTmZzz77jJYtW+Ln54ejoyOenp48//zzrFmzxmIf\n/v7++uV04eHhBAYG4uDgwKBBg4D/7jnbsmWLyX6rVq2ia9euBAUF4erqiqurKzVq1MjwMp7Q0FD9\n3rXly5fz7LPP4uLigpeXF6+99hqXLl2yOL6YmBjGjBlDlSpVcHV1xcPDgypVqjBs2DBu3rxp0jY2\nNpb33nuPypUr4+Ligru7Ow0bNmT58uUZv9BWkpSUpL9+4eHh5OR7qq+//pqkpCTeeecdPTADKFSo\nEKNGjQLgiy++eOix3nuOjBo1Cn9/fxwdHQkMDGTChAkkJyeb7WMwGAgICCAuLo6BAwfi5+eHnZ0d\nM2bM0NusXLlSvxzTycmJihUrMnbsWO7cuWNxHHl5LixcuJD69evrl5H5+vrStGlT5s2bZ9LuzJkz\nvPXWWwQGBuLs7IynpycVK1akR48e/PPPP5m+jgkJCRQuXBhvb2+LrxnA0KFDMRgMJpelPsx77Ztv\nvqFWrVq4uLjw9NNPAxnfc7Z//34GDBhAtWrV8PLywsnJiaCgIAYPHkxMTIzZcRYsWKD3c+jQIV56\n6SUKFSqEi4sLjRs3ZufOnRafY2pqKl9++SUNGzakUKFCODs7U6ZMGUJDQzl27JhJ27S0NObNm0f9\n+vX186RKlSp8+OGHGb6G+W358uVcv36dTp066YEZgIODAxMmTABg9uzZWepr8+bN/PHHHwQHB+uB\nGRgvQf3oo4+A3HlPCyFEhpQQIkMVKlRQdnZ26urVq2Z1R48eVZqmqeeee07fdvnyZWVjY6OCg4NV\nr1691KhRo1RoaKjy8vJSmqapOXPmmPXj5+enNE1TL730kipevLjq0aOHGjFihPriiy+UUkqNHTtW\naZqmNm/ebLJf+fLlVaVKldQbb7yhwsLC1Ntvv63KlSunNE1TnTt3NjtOt27dlKZpqmPHjsrR0VGF\nhISo4cOHq+DgYKVpmqpQoYJKTEw02efMmTP6+GrUqKGGDBmihgwZolq3bq2cnZ3V77//rre9cOGC\nCgwMVJqmqUaNGqnBgwer3r17qxIlSihN01R4eHj2Xvw8NmLECGVvb68/Bz8/P2UwGMxeg8zUr19f\naZqmdu3aZVZ3+fJlpWmaKlWq1EOPNf130Lp1a1WiRAk1YMAANXToUFWmTBmlaZpq27at2T6apikf\nHx/1zDPPqLJly6q+ffuqIUOGqBUrViillBozZozSNE15e3urPn36qJEjR6pnnnlGaZqmqlevrm7d\numXSX16eC2FhYUrTNFW6dGnVr18/NXr0aNWjRw9Vo0YNFRwcrLe7dOmS8vT0VHZ2dqpNmzZqxIgR\natCgQeqVV15RhQoVUhs2bHjga9mnTx+laZpauXKlWV1KSooqVqyYcnNzU3fu3NG35/S91qpVK+Xk\n5KQ6deqkwsLC1JAhQ5RSSm3cuFFpmqbGjRtnst9bb72lihYtqkJCQtTQoUPV4MGD9fdn+fLlzX4n\n8+fP14/j7Oysnn/+eTVs2DDVsWNHZWNjo5ycnNTJkydN9klMTFTNmjXTz82+ffuqsLAw1blzZ1Ws\nWDE1Y8YMvW1ycrJq3bq1fvy+ffuqQYMGqaefflppmqaef/55lZKS8sDXPK916dJFaZqmlixZYlaX\nkpKinJ2dlb29vUpKSnpgX6NHj1aapqkPP/zQYn1QUJAyGAzq9OnTDz1uIYSwRIIzITIxefJkpWma\n+uSTT8zqhg0bpjRNUxEREfq2xMREdfHiRbO2sbGxqnLlysrT01PFx8eb1KV/4K1WrZqKjo422zej\n4OzMmTNmbdPS0vQPhvcHDOnbPTw81NGjR03qOnfurDRNU0uXLjXZXrduXaVpmpowYYLF53T79m29\n3LRpU2VjY2P2ASkuLk4988wzymAwmHyAz8z333+vxo4dm+Wf7AZ+W7ZsUTY2NibPKyfBmbe3tzIY\nDOrGjRsW611cXJTBYDD7nWdX+jlSrlw5dfPmTX17fHy8evbZZ5WmaWrx4sUm+2iapjRNU82bNzc7\n/s6dO5WmacrX11ddvnzZpC79PHnnnXdMtuflueDp6alKliyp7t69a9b39evX9ceffvqp0jTNJIBI\nl5SUZDKGjOzevVtpmqZefvlls7o1a9YoTdNU9+7dTbbn9L3m6upq8ZzPKDg7f/68SktLM2v/1Vdf\nWQwY0oOz+/8OKaXUnDlzlKZp6u233zbZnh4It2rVyuxcT0lJUVFRUXp5/PjxStM01b9/f5NxpaWl\nqd69eytN09TMmTPNxmvJwYMHs/WeHjt2rDp37lyW+q5Zs6bSNE0dOHDAYn2lSpWUwWBQf/zxxwP7\n6tChQ4bBu1JKvfTSS0rTNBUZGZmlsQkhRHZJcCZEJi5duqRsbW1V9erVTbanpKQoHx8fs2/YMzNt\n2jSlaZrasmWLyfb0D94//PCDxf0yCs4ysn//fqVpmho/frzJ9vQPjGPGjDHbJ/3D4rBhw/Rt+/bt\n04PGBzl8+LDSNE21b9/eYv0PP/ygNE1Tw4cPz9JzCA0N1T90ZuXHYDBkqV+ljIGEv7+/ql27tkpN\nTdW35yQ4s7OzUwaDwaSfexUvXlwZDAZ15cqVLPdpSfo58u2335rVrVu3Tmmappo1a2ayPf11OXz4\nsNk+PXv2VJqmqdmzZ5vVRUVFKScnJ+Xm5qaSk5OVUnl/Lnh5eanSpUs/8LWfOXNmhjPQ2VGxYkVl\nb2+vrl27ZrL91VdfzdX32uDBgy3ul1FwlpG0tDTl7u6umjZtarI9PThr2LCh2T7JycnK1tZW1apV\nS9+WkpKiPDw8lLOzs7pw4UKmx0xNTVXe3t6qWLFiFs/vmJgYZTAYVO3atbP0HBYsWJDt93RWfw+B\ngYGZzmbVq1cvwxnu+6XPKmY0C5v+RZalWTohhMgNtta+rFKIgszHx4dmzZoRGRnJ77//TrVq1QBY\nt24dV65cITQ0FGdnZ5N9jh07xpQpU9iyZQtXrlwxS71s6d4uTdN49tlnszW26OhopkyZws8//8yZ\nM2e4e/euSf3Fixct7lezZk2zbSVLlgQwua9l165dADRv3vyBY9m+fTtgvM8oPDzcrP7atWuAMT11\nVsyfP5/58+dnqW129e/fn6tXrxIZGYnB8OjcdqtpGo0aNTLb3rBhQwAOHTpkVufo6EiVKlXMth84\ncACAJk2amNUVKVKEKlWqsHfvXv766y8qVKiQ5+dCly5dmDlzJhUqVODVV1+lQYMG1KtXzyyTYZs2\nbRg1ahTvvPMOkZGRNG/enLp161K1atVspaXv1q0bI0eO5P/+7/8YMGAAYDz3f/jhB0qXLk1wcLBJ\n+5y+17L7nk5OTmbOnDksWbKE48ePExcXZ3JPW3be07a2thQtWtTkPf3HH38QFxdHzZo1KVGiRKZj\n+fPPP4mOjqZMmTK8//77Fts4Ojpy/PjxrDw1unXrRrdu3bLUVgghnmQSnAnxAN27dycyMpKIiAg+\n/vhjACIiIgDjjf/32rVrF02aNCEtLY2mTZvStm1b3N3dMRgMHDx4kNWrV5OYmGjxOEWLFs3ymNKz\nAZ47d47atWsTGhqKp6cntra2xMTEMGPGjAyPU6hQIbNttrbGPwWpqakmxwAe+CEOjB9eATZs2MCG\nDRssttE0LcNEE/nlxx9/5JtvvmHatGmUK1fOYhuVjcQgHh4e3Lhxg9jYWAoXLmxWHxsbq7fLDZbO\nEUdHR9zd3fVj3atIkSIW+4mNjUXTNIoVK2ax3sfHB/jvHMjrc+GTTz6hbNmyLFiwgClTpvDRRx9h\nMBho2rQpU6ZMoWrVqgCUKlWKvXv3Mm7cOCIjI1m1ahVgfF369+/PyJEjsxRwv/766Line truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7212ac8>"
+       "<matplotlib.figure.Figure at 0x6ce98d0>"
      ]
     },
     "metadata": {},
@@ -530,9 +530,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7138898>"
+       "<matplotlib.figure.Figure at 0x6c5a978>"
      ]
     },
     "metadata": {},
@@ -553,9 +553,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x793f9e8>"
+       "<matplotlib.figure.Figure at 0x7485748>"
      ]
     },
     "metadata": {},
@@ -586,7 +586,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c154a8>"
+       "<matplotlib.figure.Figure at 0x7734f98>"
      ]
     },
     "metadata": {},
@@ -619,7 +619,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c15358>"
+       "<matplotlib.figure.Figure at 0x773e4a8>"
      ]
     },
     "metadata": {},
@@ -655,7 +655,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7abe0>"
+       "<matplotlib.figure.Figure at 0x7a6c518>"
      ]
     },
     "metadata": {},
@@ -692,7 +692,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7138eb8>"
+       "<matplotlib.figure.Figure at 0x75e7c50>"
      ]
     },
     "metadata": {},
@@ -781,7 +781,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b47c88>"
+       "<matplotlib.figure.Figure at 0x76a2a90>"
      ]
     },
     "metadata": {},
@@ -842,7 +842,7 @@
     "data": {
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      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b42860>"
+       "<matplotlib.figure.Figure at 0x76a2c18>"
      ]
     },
     "metadata": {},
@@ -894,7 +894,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d1eba8>"
+       "<matplotlib.figure.Figure at 0x7883898>"
      ]
     },
     "metadata": {},
@@ -1168,7 +1168,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d25208>"
+       "<matplotlib.figure.Figure at 0x798ac50>"
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     "metadata": {},
@@ -1229,7 +1229,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b4b400>"
+       "<matplotlib.figure.Figure at 0x7947ef0>"
      ]
     },
     "metadata": {},
@@ -1347,7 +1347,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e83e10>"
+       "<matplotlib.figure.Figure at 0x79f4c88>"
      ]
     },
     "metadata": {},
@@ -1539,7 +1539,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7eb8c18>"
+       "<matplotlib.figure.Figure at 0x7a21d68>"
      ]
     },
     "metadata": {},
@@ -1569,7 +1569,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b4f9b0>"
+       "<matplotlib.figure.Figure at 0x786fb00>"
      ]
     },
     "metadata": {},
@@ -1687,14 +1687,14 @@
   "outputs": [],
   "source": [
    "def volt(voltage, temp_variance):\n",
-    "    return (random.randn() * temp_variance) + voltage"
+    "    return (randn() * temp_variance) + voltage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "We generate white noise with a given variance using the equation `random.randn() * variance`. The specification gives us the standard deviation of the noise, not the variance, but recall that variance is just the square of the standard deviation. Hence we raise 2.13 to the second power.\n",
+    "We generate white noise with a given variance using the equation `randn() * variance`. The specification gives us the standard deviation of the noise, not the variance, but recall that variance is just the square of the standard deviation. Hence we raise 2.13 to the second power.\n",
    "\n",
    "> **Sidebar**: spec sheets are just what they sound like - specifications. Any individual sensor will exhibit different performance based on normal manufacturing variations. Numbers given are often maximums - the spec is a guarantee that the performance will be at least that good. So, our sensor might have standard deviation of 1.8. If you buy an expensive piece of equipment it often comes with a sheet of paper displaying the test results of your specific item; this is usually very trustworthy. On the other hand, if this is a cheap sensor it is likely it received little to no testing prior to being sold. Manufacturers typically test a small subset of their output to verify that everything falls within the desired performance range. If you have a critical application you will need to read the specification sheet carefully to figure out exactly what they mean by their ranges. Do they guarantee their number is a maximum, or is it, say, the $3\\sigma$ error rate? Is every item tested? Is the variance normal, or some other distribution. Finally, manufacturing is not perfect. Your part might be defective and not match the performance on the sheet.\n",
    "\n",
@@ -1723,16 +1723,30 @@
   },
   "outputs": [
    {
-     "ename": "NameError",
-     "evalue": "name 'random' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-24-3913a849411a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mN\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m50\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mzs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mvolt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mactual_voltage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvariance\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[0mps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mestimates\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m<ipython-input-24-3913a849411a>\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mN\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m50\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mzs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mvolt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mactual_voltage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvariance\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[0mps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mestimates\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m<ipython-input-23-78e329cd0e61>\u001b[0m in \u001b[0;36mvolt\u001b[1;34m(voltage, temp_variance)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mvolt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvoltage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtemp_variance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mtemp_variance\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mvoltage\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m: name 'random' is not defined"
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x783e8d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x770fe10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variance converges to 0.858\n"
     ]
    }
   ],
@@ -1831,7 +1845,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
@@ -1849,11 +1863,22 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x77c8208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "variance = 2.13**2\n",
    "actual_voltage = 16.3\n",
@@ -1904,11 +1929,32 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x79cbfd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAEjCAYAAADqsm07AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7b41f60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "sensor_variance = 30\n",
    "movement_sensor = 30\n",
@@ -1928,7 +1974,7 @@
    "    ps.append(pos[0])\n",
    "    vs.append(pos[1])\n",
    "\n",
-    "    pos = predict(pos[0], pos[1], movement + random.randn(), movement_variance)\n",
+    "    pos = predict(pos[0], pos[1], movement + randn(), movement_variance)\n",
    "\n",
    "bp.plot_filter(ps, vars=vs)\n",
    "bp.plot_measurements(zs)\n",
@@ -1975,11 +2021,22 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAucAAAEWCAYAAADFITnpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x81de208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "sensor_variance = 30000\n",
    "movement_variance = 2\n",
@@ -2030,11 +2087,22 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x81fb320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "sensor_variance = 30\n",
    "movement_variance = 2\n",
@@ -2085,11 +2153,22 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAucAAAEPCAYAAADh+Gq6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4FNXXx7+z6Y0QAkmkJTSB0KQJSpHeiwhKEZSuEKn6\n4g9FAUGQKhKKdBAITZGiiKFEihQTQJDeQgmQQEI6KZvd+/5xM9vL7GaXTTmf58mT3Zm7M3dm7tz5\nzrnnniMwxhgIgiAIgiAIgnA4MkdXgCAIgiAIgiAIDolzgiAIgiAIgigkkDgnCIIgCIIgiEICiXOC\nIAiCIAiCKCSQOCcIgiAIgiCIQoKzoytgLampqY6uAkEQBEEQBEFYja+vr94yspwTBEEQBEEQRCGB\nxDlBEARBEARBFBKKrFuLJjv+KoXRvQVHV4MohMTExAAAmjRp4uCaEIUZaieEFKidEFKgdkKYw5xr\ndrGwnEeedXQNCIIgCIIgCKLgFAtxfigayMphjq4GQRAEQRAEQRSIYiHO018A+086uhYEQRAEQRAE\nUTCKhTgHgK1/OroGBEEQBEEQBFEwio04j4wG0jLJtYUgCIIgCIIouhSLaC0AkJML/PY3MKiTo2tC\nEARBEIQlMMYgl8uhVCodXZUCExwcDADIzs52cE0IRyCTyeDi4gJBsD6KYLER5wDwSxSJc4IgCIIo\nSjDGkJ2dDVdX1wKLmsKAu7u7o6tAOAjGGJRKJbKzs+Hu7m51Wy42bi0A8McZcm0hCIIgiKKEXC6H\nq6srnJycirwwJ0o2giDAyckJrq6ukMvlVm+nWIjzBtX5/+xcYNdRx9aFIAiCIAjpKJVKyGTFQo4Q\nBADu2lIQF61icTcM6ar+vP2w4+pBEARBEITlkMWcKE4UtD0XC3Hev73687EL5NpCEARBEARBFE2K\nhTivUE5Aw1f55zwFcPCMY+tDEARBEARBENZQLMQ5APRsqf68+aDj6kEQBEEQBEEQ1lJsxPkHXdSf\n/zgDPH5Gri0EQRAEQTiGjRs3QiaTQSaT4eTJkwbLVK9eHTKZDG3btn3JtSM0OXXqFGbOnInU1FRH\nVwVAMRLnVSsIaNOQf1Yqgc1/OrY+BEEQBEEQHh4eiIiI0Ft+5swZ3L17t0DxsAnbQOLcjgzrof68\n4TceDJ4gCIIgCMJRdO3aFbt27UJeXp7W8oiICNSqVQvVqlVzUM1sQ2ZmpqOrYDMKi24sVuK8bxvA\nx5N/vvkQOPWfQ6tDEARBEEQJZ+DAgXj+/Dn+/FM9pK9QKLBz5068//77euUZYwgPD0e9evXg4eGB\nwMBAjBw5EklJSVrl9u3bh549e6JSpUpwd3dHSEgIpkyZgpycHK1yCQkJGDlypKpcUFAQunXrhqtX\nr6rKyGQyzJw5U68uISEhGDZsmOq76KoTFRWF8ePHIzAwED4+Pqr10dHR6NatG0qXLg1PT0+0atUK\nf/31l9Y2Z8yYAZlMhuvXr2Pw4MEoXbo0ypUrhy+//BIA8PDhQ/Tu3Ru+vr4ICgrCwoUL9eqVk5OD\nmTNnokaNGnB3d0fFihUxefJkZGVlaZWTyWQYM2YM9uzZg7p168Ld3R1169bVuhYzZszAlClTAABV\nqlRRuSIdP34cAHD+/Hl069YNAQEB8PDwQEhICD744ANkZ2fr1ctWONttyw7A013AgI4Ma/by7+t/\nB1rUd2ydCIIgCIIouVSsWBGtWrVCREQEunfvDgA4fPgwnj59ioEDB2Lbtm1a5ceMGYP169dj6NCh\nGD9+PB48eIDw8HD8888/iI6OhpubGwAulD08PDBhwgT4+vri9OnT+P777/Hw4UOtbfbr1w+XL1/G\nuHHjUKVKFTx9+hTHjx/HrVu3EBoaqipnyLVGEASDy8eNG4cyZcrgq6++UrmCHDt2DJ07d0ajRo0w\nffp0ODs7Y/PmzejUqRMOHTqEt956S2sbAwcORO3atTFv3jz8/vvvmDt3Lnx9fbF27Vp06NAB8+fP\nx5YtWzBlyhQ0btxY5ZfPGEOfPn1w/PhxjB49GqGhobh69SpWrFiBK1euaAlvADh9+jT279+PsWPH\nwtvbG0uXLkXfvn3x4MEDlClTBn379sWtW7ewbds2LFmyBGXLlgUA1K5dG8+ePUPHjh0REBCAzz//\nHH5+fnjw4AH279+PFy9ewN3dXVojsBRmI+RyOZs6dSqrUqUKc3d3Z1WqVGHTpk1jeXl5WuWmT5/O\nypcvzzw8PFibNm3YlStXtNZnZ2ezTz75hJUtW5Z5eXmxXr16sbi4OL39paSkqP40OXNZyYQ3+Z93\neyVLz1Ta6hCJIkh0dDSLjo52dDWIQg61E0IK1E7sQ1ZWlqOrYBc2bNjABEFgZ8+eZatWrWJeXl7s\nxYsXjDHGhgwZwt544w3GGGN16tRhbdu2ZYwx9vfffzNBENiWLVu0tnXy5EkmCAJbvXq1apm4LU3m\nzJnDZDIZe/jwIWOMseTkZCYIAlu0aJHJugqCwGbOnKm3PCQkhA0bNkzvmJo3b84UCoVquVKpZDVr\n1mQdO3bU+n1ubi6rU6cOe/PNN1XLpk+fzgRBYCNHjlQtUygUrFKlSkwQBDZnzhzV8pSUFObp6ckG\nDx6sWrZ161Ymk8nY8ePHtfa1detWJggCi4yM1DouNzc3dufOHdWyS5cuMUEQ2LJly1TLFixYwARB\nYPfv39fa5p49e5ggCOzcuXMGzpppTLVrYxpWxGZuLXPmzMGqVasQHh6OGzdu4IcffsCKFSswd+5c\nVZl58+Zh8eLFWLZsGaKjoxEQEICOHTsiIyNDVWbixInYvXs3tm/fjhMnTiAtLQ09evSQnAb19VCg\ndgj/nJkFHIq21RESBEEQBEFYzrvvvgu5XI49e/YgKysLe/bsMejSsnPnTnh7e6NTp05ITExU/dWs\nWRMBAQGIiopSlfXw8AAAKJVKpKamIjExES1atABjDBcuXFCVcXV1RVRUFJKTk212PKNGjYJMppaQ\nFy9exM2bNzFw4ECteqempqJDhw44e/asnhvIyJEjVZ9lMhkaN24MQRAwYsQI1XJfX1/UrFkTsbGx\nWufo1VdfRWhoqNa+WrduDUEQtM4RALRt2xZVq1ZVfa9Xrx5KlSqltU1jlC5dGgCwf/9+vTkD9sRm\n4jw6Ohq9evVC9+7dUblyZfTs2RM9evTA2bNnAfBhiCVLlmDq1Kno06cP6tSpg02bNiE9PV01izk1\nNRXr16/HwoUL0b59ezRs2BCbN2/GpUuXcPjwYUn1EAQBfTRGTvb/basjJAiCIAjC4cyYAQiC/t+M\nGbYpbwf8/PzQuXNnbNmyBfv27UNWVhb69++vV+7mzZvIyMhAYGAgAgICtP6ePn2KZ8+eqcpevnwZ\n3bp1g4+PD/z8/BAQEIA2bdoAgMrVxM3NDfPmzcPBgwcRGBiIVq1aYe7cuYiLiyvQ8ehOYr158yYA\nYMSIEXr1Xrp0KRhjej7zlStX1vru6+sLFxcXBAQEaC0vVaqU1ovFzZs3cePGDZQrV05rP+L2NM+R\nof0A/HpIeVl566230K9fP8ycORP+/v7o1asX1q5dixcvXpj9bUGwmc95165dMW/ePNy4cQM1a9bE\n1atXERUVhS+++AIAEBsbi4SEBHTq1En1G3d3d7Ru3RqnTp3C6NGjce7cOcjlcq0yFStWRO3atXHq\n1Cmt5abo8SYwZxP//PNRYN4YhnJ+FKaIIAiCIIo8M2ZYJqwtLW8nBg0ahA8++ABpaWno2LGjyrdZ\nE6VSCX9/f+zYscPgNvz8/ABw8d22bVv4+Phgzpw5qF69Ojw8PBAXF4ehQ4dqeRtMmDABvXv3xt69\ne3Ho0CHMmjULc+bMwW+//abnB66LMWuxaLXXrDfAPSQaN25s8De6x+vk5KRXxlhISaYRRUWpVKJO\nnTr44YcfDJYtX7682f3obtMUO3fuRHR0NH777TccOnQIo0ePxty5c3HmzBmUK1dO0jYsxWbifOzY\nsYiLi0Pt2rXh7OyMvLw8TJs2DR9//DEAID4+HgAQGBio9buAgAA8fvxYVcbJyQn+/v5aZQIDA5GQ\nkCC5Lq+HAnWqAFdigYwsYO5mYPH4ghwdQRAEQRCE9fTu3Rtubm44deoUNm3aZLBMtWrVcPjwYTRr\n1gxeXl5GtxUVFYWkpCTs3r0brVq1Ui0/dOiQwfIhISGYMGECJkyYgEePHuG1117Dt99+qxLnfn5+\nSElJ0fpNbm4unjx5IunYREu6t7c32rVrJ+k31lK9enWcO3fOpvsxF2e+adOmaNq0KWbOnImDBw+i\nW7duWLNmjcoAbWtsJs6XLl2KDRs2YPv27ahTpw4uXLiACRMmICQkBMOHDzf524IG34+JidFb9mE7\nX0xZVx0AsOIXJZqFXEO18vYLe0MUbgy1EYLQhdoJIQVqJ7YlODjYflEvChEeHh5YuXIl7t69i7ff\nfttgmQEDBmDlypX45ptvMG/ePK11CoUC6enpKF26tMoarGkhVyqVWLx4sdZvxNCCmpbuChUqoFy5\ncloJd6pVq4Zjx45p/Xb16tWS5/s1adIE1atXx+LFizFkyBB4e3trrX/27JkkK7MUPdi/f38cOHAA\nK1euxJgxY7TW5eTkQC6X6+3fHOKL0PPnz7XcYFJSUuDr66tVr4YNecZLcwmL0tPTcfnyZYPratSo\nYfK3NhPn3377LaZNm4b33nsPAFCnTh3cv38fc+fOxfDhwxEUFASAx9usWLGi6ncJCQmqdUFBQVAo\nFEhKStKynsfHx6N169YW1eeteqmoG5yBy/e9kZsnw9dbqmDL/10DJeEiCIIgCMIRDB482OBy0cWi\nVatWCAsLw4IFC3Dp0iV06tQJbm5uuH37Nn755RfMmjULH3zwAVq2bAl/f398+OGHGLine truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x81fb240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "sensor_variance = 30000\n",
    "movement_variance = 2\n",
@@ -2127,11 +2206,22 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9346390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "sensor_variance = 30000\n",
    "movement_variance = 2\n",
@@ -2185,11 +2275,22 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_kalman_filter>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
   "source": [
    "from IPython.html.widgets import interact, interactive, fixed\n",
    "import IPython.html.widgets as widgets\n",
@@ -2225,24 +2326,42 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9352470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_kalman_filter>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
   "source": [
+    "from numpy.random import seed \n",
    "def plot_kalman_filter(start_pos, \n",
    "                       sensor_noise, \n",
    "                       movement,\n",
    "                       movement_noise):\n",
    "    n = 20\n",
-    "    zs = []\n",
-    "    ps = []\n",
-    "    \n",
-    "\n",
-    "\n",
+    "    zs, ps = [], []   \n",
    "    dog = DogSimulation(start_pos, velocity=movement, measurement_variance=sensor_noise)\n",
-    "    random.seed(303)\n",
+    "    seed(303)\n",
    "    pos = (0., 1000.) # mean and variance\n",
    "    dog.movement_noise = movement_noise\n",
    "\n",
@@ -2295,7 +2414,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
@@ -2313,11 +2432,22 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x934f438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "sensor_variance = 30\n",
    "movement_variance = 2\n",
@@ -2374,7 +2504,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
@@ -2392,11 +2522,22 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x935a9e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "sensor_variance = 30\n",
    "movement_variance = 2\n",
@@ -2407,7 +2548,7 @@
    "for i in range(100):\n",
    "    pos = predict(pos[0], pos[1], movement, movement_variance)\n",
    "\n",
-    "    Z = math.sin(i/3.)*2 + random.randn()*1.2\n",
+    "    Z = math.sin(i/3.)*2 + randn()*1.2\n",
    "    zs.append(Z)\n",
    "    \n",
    "    pos = update(pos[0], pos[1], Z, sensor_variance)\n",
@@ -2472,7 +2613,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.4.1"
+   "version": "3.4.3"
  }
 },
 "nbformat": 4,