Reran cells

Forgot to rerun all cells before last check in.

04-One-Dimensional-Kalman-Filters.ipynb

@@ -450,7 +450,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1YAAAEWCAYAAABlmwYuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x48daef0>"
+       "<matplotlib.figure.Figure at 0x71faf28>"
       ]
      },
      "metadata": {},
@@ -491,9 +491,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x75e1630>"
+       "<matplotlib.figure.Figure at 0x71fa7f0>"
       ]
      },
      "metadata": {},
@@ -536,9 +536,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x48da7b8>"
+       "<matplotlib.figure.Figure at 0x4cde9e8>"
       ]
      },
      "metadata": {},
@@ -559,9 +559,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x844a9b0>"
+       "<matplotlib.figure.Figure at 0x8056ef0>"
       ]
      },
      "metadata": {},
@@ -592,7 +592,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x85be470>"
+       "<matplotlib.figure.Figure at 0x81d90b8>"
       ]
      },
      "metadata": {},
@@ -624,7 +624,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7af5f60>"
+       "<matplotlib.figure.Figure at 0x81d9438>"
       ]
      },
      "metadata": {},
@@ -660,7 +660,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x85aa7f0>"
+       "<matplotlib.figure.Figure at 0x81c3dd8>"
       ]
      },
      "metadata": {},
@@ -698,7 +698,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x48dae48>"
+       "<matplotlib.figure.Figure at 0x8824a58>"
       ]
      },
      "metadata": {},
@@ -793,7 +793,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8c305c0>"
+       "<matplotlib.figure.Figure at 0x88462e8>"
       ]
      },
      "metadata": {},
@@ -849,7 +849,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8d014e0>"
+       "<matplotlib.figure.Figure at 0x884df98>"
       ]
      },
      "metadata": {},
@@ -900,7 +900,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8d6b2e8>"
+       "<matplotlib.figure.Figure at 0x89430b8>"
       ]
      },
      "metadata": {},
@@ -991,7 +991,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8d97f60>"
+       "<matplotlib.figure.Figure at 0x89bf8d0>"
       ]
      },
      "metadata": {},
@@ -1249,7 +1249,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8dab5f8>"
+       "<matplotlib.figure.Figure at 0x9a799e8>"
       ]
      },
      "metadata": {},
@@ -1310,7 +1310,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9ec8780>"
+       "<matplotlib.figure.Figure at 0x9afe5c0>"
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@@ -1461,7 +1461,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9de05c0>"
+       "<matplotlib.figure.Figure at 0x9a4ec88>"
       ]
      },
      "metadata": {},
@@ -1576,7 +1576,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9ddbd30>"
+       "<matplotlib.figure.Figure at 0x9ab9b70>"
       ]
      },
      "metadata": {},
@@ -1634,7 +1634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -1643,7 +1643,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xb73eb70>"
+       "<matplotlib.figure.Figure at 0x83559e8>"
       ]
      },
      "metadata": {},
@@ -1664,16 +1664,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xb8d7a20>"
+       "<matplotlib.figure.Figure at 0x887c978>"
       ]
      },
      "metadata": {},
@@ -1729,16 +1729,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9f12c50>"
+       "<matplotlib.figure.Figure at 0x8846240>"
       ]
      },
      "metadata": {},
@@ -1763,7 +1763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -1772,7 +1772,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9ee4ac8>"
+       "<matplotlib.figure.Figure at 0x9af99b0>"
       ]
      },
      "metadata": {},
@@ -1806,16 +1806,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuwAAAEWCAYAAAA9wcIeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x611a9e8>"
+       "<matplotlib.figure.Figure at 0x9b0b390>"
       ]
      },
      "metadata": {},
@@ -1910,7 +1910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
@@ -1950,7 +1950,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
@@ -1973,37 +1973,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false,
     "scrolled": false
    },
    "outputs": [
     {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x85f25c0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9ec1908>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Variance converges to 0.005\n"
+     "ename": "NameError",
+     "evalue": "name 'variance' 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-29-6516ba48a651>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     12\u001b[0m                     \u001b[0mP\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m,\u001b[0m        \u001b[1;31m# initial variance\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m                                    \u001b[1;31m# large # says 'who knows?'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m                     \u001b[0mR\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvariance\u001b[0m\u001b[1;33m,\u001b[0m    \u001b[1;31m# sensor noise\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m                     Q=process_var) # error in prediction\n\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'variance' is not defined"
      ]
     }
    ],
@@ -2104,7 +2088,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -2122,22 +2106,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8d11550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "variance = 2.13**2\n",
     "process_var = 0.1**2\n",
@@ -2188,22 +2161,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAucAAADTCAYAAAAic71aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb8f5be0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "velocity = 1\n",
     "sensor_var = 300**2\n",
@@ -2246,22 +2208,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAADTCAYAAACyYMSVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8da2a90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "velocity = 1\n",
     "sensor_var = 20\n",
@@ -2309,22 +2260,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9fe24e0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "velocity = 1\n",
     "sensor_var = 30\n",
@@ -2368,22 +2308,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9fe2470>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sensor_var = 30000\n",
     "process_var = 2\n",
@@ -2414,22 +2343,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9f5d828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sensor_var = 30000\n",
     "process_var = 2\n",
@@ -2474,7 +2392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -2513,22 +2431,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9fdf4e0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from numpy.random import seed \n",
     "def plot_kalman_filter(start_pos, \n",
@@ -2590,7 +2497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -2608,22 +2515,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9f4c1d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sensor_var = 30\n",
     "process_var = 2\n",
@@ -2679,7 +2575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -2697,22 +2593,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa089390>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sensor_var = 30\n",
     "process_var = 2\n",

code/book_plots.py

@@ -22,7 +22,7 @@ import numpy as np
 
 def plot_errorbars(bars, xlims):
 
-    i = 1.0
+    i = 0.0
     for bar in bars:
         plt.errorbar([bar[0]], [i], xerr=[bar[1]], fmt='o', label=bar[2] , capthick=2, capsize=10)
         i += 0.2
@@ -40,14 +40,15 @@ def show_legend():
     plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
 
 
-def bar_plot(pos, ylim=(0,1), title=None):
+def bar_plot(pos, ylim=(0,1), x=None, title=None):
     plt.cla()
     ax = plt.gca()
-    x = np.arange(len(pos))
+    if x is None:
+        x = np.arange(len(pos))
     ax.bar(x, pos, color='#30a2da')
     if ylim:
         plt.ylim(ylim)
-    plt.xticks(x+0.4, x)
+    plt.xticks(np.asarray(x)+0.4, x)
     if title is not None:
         plt.title(title)